Handbook of Ethological Methods

Handbook of ethological methods SECOND EDITION PHILIP. N. LEHNER Depdrtmenl of Biology, Colorado Stote Universiry h-eo...

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Handbook of ethological methods SECOND EDITION

PHILIP. N. LEHNER Depdrtmenl of Biology, Colorado Stote Universiry

h-eol

F,.

J,uu Psigtilw,tZ -(z

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C.m,rnnrDGE UNIVERSITY PRESS

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Contents

pagexw

Prefoce 'l

lt

IT r,,'l

I

INTRODUCTION

l.l What

I

is ethology?

2

1.2

Why study animal behavior?

2

1.3

What to study?

3

l.j.l 1.j.2 l.j.j

4

l*vels

oJ behovior

Areasofstudy Categorics

of

4 questiore

E

1.4 Scientific method

l0

t.s

ll

Ethological approach

l3

1.6 Descriptive versus experimental research

I

GETTING STARTED

t7

2 A CONCEPTUAL MODEL OF ANIMAL BEHAYIOR

19

2.1 Continuous stream of behavior

l9

z.z

Predisposition to behave: dilfering contributions

20

2.2.1 2.2.2

m

Genotype, envircnmenl (experience) ond htemction

Anotomyond physiology

2.3 A model for

21

a behavioral act

24

2.3.1

The

2.3.2

Stimuli

24

2.3.3 2.3.1

Behavior

26

Ethologicol model ol innate behavior

26

2.i.5

Associotive learning paradigms

28

2.3.6 2.3.7

Feedbock

37

Feedlorword

37

mimol

24

2,4 Application of the model

38

2.1,1 Cancuptuallzlng the oreos ol study 2,1,2 OrynkW thc typcs of questioa

2,1,t 2,11

Focuhgrutorch Dowhpht an .xpanM ot mon locutd

,,1t Dlqfit st tnlr,lI, tfu

38 39 39

nobl

4t

ltchst h otlmb

4

I' CONTENTS

CONTENTS

4

2.5 Summary

3 CHOICE OF SUBJECTS

6.5.2 6.5,3

xi

Experimmtal designs

129

Rondom, haphazord and oppoilunistic samples

t4

47

6.6 Relative efficiency of experimental designs

147

3.1 Species-oriented research

47

6.7 Determination of

t48

3.2 Concept-oriented research

50

3-2.1

August Krogh principle

5l

3.2.2

Suitobility verus owilobility

53

sample size

7 EXPERIMENTAL RESEARCH z.l Thevaryingvariables 7.1.1 Noturol variation

4 RECONNAISSANCE OBSERVATION 4.1 How to observe 4.1.

I

4.1.2 4.1.3 4.1.4 4.2.1 4.2.2

Walching versus obreming

54 58

Fieldnotes

6t

Equipment

65

describe behavior

Empirical versuslunctionoldescriplions Cotalog, repertoire md elhogrom

4.3 Information 4.3.1 4.3.2 4.3.3

54

An exercbe in obserting

-- 4.2 How to '

54

resources

7.

z.r

8l

86-

Otherresearchers

97

Films ond videotapes

98

5 DELINEATION OF RESEARCH 5.1 Conceptualizingtheproblem

100

Whal are the questions?

100

5.L2

Statingobjectives

102

5.1.3

Research hypotheses

t02

t57 167

Field to laboratory: a continuum

177

COLLECTING THE DATA

Research desigrr and data collection Scales

of measurement

8.3 Sampling methods

-8.3.1 8.3.2 8,3.3

t00

5.1.1

5.2 Using the model for a behavioral act

t

8.2

155

167

8 DATA COLLECTION METHODS

93

l5l

7.2.1 In thefield 7.2.2 In the laborotory

II

8l

94

Artificial manipulation

7.2 Further examples of experimental manipulation

8.

Literature

1,2

150

Adlibit':um sampling Continuow recording sampling methods

E,5.2

Intro-obrner

183 183

€,

tc{ 197

206

2lo

8.5 Reliability

I

183

201

8.4 Observereffects E,5.

l8l

r89

Focal-animal (pair, group) versus all-animal samplhg

E,3.4 Time sampling 8.3.5 Summory

t03

174

212 or sef-reliability

Inter-observer reliability

219 214

6

DESIGN OF RESEARCH

105

ldentification and naming of individuals

221

6.1

Description versus experimentation

,105

E.6.1 Noturolmarks

221

6.2

Variables

107

8.6.2

6.3

Behavior units

109

E.6.3 Assigunent of

6.3.1 6.3.2 6.3.3 6.3.4

il0

6.3.5

Classifcation of behavior unils Spotial and lemporal aspecls

l14

Descriptions and definitions of behavior unils

il5

Number of behavioral units to meosure

t23 t24

Volidity

6.4 Rescarchcr-statistician

6.' Barlc oxpcrimcntd doltnl

6t,t l,fi',t r,rryt attffiitttltttr tfia*d

IN t26 _ 126

t,6

9 9,1

9,2

Copture and marking numbers and natnes

223 210

DATA.COLLECTION EQUIPMENT

233

Notobook and pcncil

235

Drb hnnr

236

9,2.1 Clnrclabtkt ol hhavlor unltt

236

11,1 Cohtmtai,ovt

217

tl *q )Al lrilrnlrlry*r,' '

238 238

CONTENTS

9.3 Clocks and counters 9.4 Calculators

CONTENTS

9.5 Strip-chart event recorders

250 253

254

9.8 Microcomputers

256

Datacollection Dala storage and manipulution

9.9 Audio-taperecorders

I 2 9.9 3 9.lo

I

266

267

Dato collection on uudio-tape recordcrs

267

9.9

Recording unimol sounds

269

Playback of sounds

2'13

347 348

Hypothesis testing

I 5 Selecting the alpha level ll 6 Poweroiatest I

282

I1

Computer analysis of recorded sounds

283

276

Still photogruphy

285 293

Filmanalysis

296

9.14 Metronomes

308

307

Determining geographic location

309

l5.l 9 l5 2

Global Positioning System and Argos Satellite System

3t0

Biotelemetry

313

IO SELECTED EXAMPLES OF DATA COLLECTION

AND DESCRIPTION

319

Individualbehavior

319

l0 I

1

Terreslriql tetrupod loc.omolion

10.2 Social behavior 10.2

l0 l0

I

Disploys

358

7.1 7.2

Sample disttibutions

358

Sumple mean, mode und median

358

7.3

Skevness

359

7.4

Location

359

7.5

7.6 I I 7.7 I I 7.8

284

299

9

I

275

Motion-picture photogruphy

352

Variubilily

361

Standard deviation

361

Sample mean confdence interval

362

CoeJfcient

of voriation

12 SELECTION OF A I

353

1.7 Sample statistics

276

l.l 2 3

351

I

Equipment

I

349

1.4 Type I and type II errors

I

9.12 Videotape recording and analysis 9.13 Stopwatches

lo.l

I

366

STATISTICAL TEST

I I Parametric versus nonparametric statistical 12 I I Paramelrictesls l2 I 2 Datu lranslbrmalions 12 I 3 Nonpurumelrictests

tests

369

377 379

lr I Completely randomized design l-1 I I Ttro indcpcndent sumples l -1 I 2 Thrta or ttrorc indepcnclent sumples I Randomized block, matched pairs or repeated l-l ) I Ttn rtlutcd or nul

For example, the genotype provides the 'innate template' in the young Swamp sparrow's brain that allows it to filter out all but swamp sparrow syllables from the

(G+E+D+(A+P)

irr

Behavior

Behavior

-+ ,,\, -a 't-a-

it hears in the environment (Marler and Peters,l9lT). The adult male swamp sparrow songs it hears provide the syllables to create the 'acquired template'which will be the basis for comparison when the young swamp sparrow begins singing its subsong, listening to itself, and improving it vocal output to create its crystallized songs

-s

-'

------

\\\r.

-)

Consequences

'-)

Feedforward

, (Contingencies)

,ttstl

Negative feedback

I ig.

2.2 The model for

a behavioral act.

primary song. 2.3.2a Endogenous stimuli

2.3

A MODEL FOR A BEHAVIORAL ACT

I rttlogenous stimuli arise from the animal's internal environment. An assumption of rlrc model is that all behavior is the result

of exogenous and/or endogenous stimula-

rron. Th&t is, we operationally define behavior as that which an animal does as the

2.3.t The animal The basis lor the animalin the model is the formula for it's predisposition to behave described above. That is, all the important components that predispose an animal to

perform specific behaviors under a given set of environmental conditions are provided by Gu, EB. IB, A,and P. Also incorporated into the animal portion of the model are endogenous stimuli (discussed below). Other parts of the model (Figure 2.2) are e-\ogenous stintuli, hehavior, ('onsequen('es, contingencies, feedbac'k and./bedforward, all of which are discussed below

rr'srrlt

of stimulation. We observe behavior being emitted, but we assume that all

1,,'lravior is elicited. Like Kuo, the Model assumes that'there is no such thing as ,lrorrtaneous behavior'(behavior thut is not stimulated) (quoted

in Marler

and

I;rntilton, 1966:605). However, there are behaviors that occur for which we cannot ,t('rcrmine the source of stimulation, and these are often called'spontaneous' I

1,,'lurviors (e.g. Hinde, 1966).

Although behavior does not occur spontaneously, it may be elicited by endogerr.rrs Stirnuli that arise spontaneously. . . . every cell in the nervous system is not just sitting there waiting to be told what to do. It's doing it the whole darn time. If there's input to the nervous system, fine. It will react to it. But the nervous system is

2.3.2 Stimuli

Stimuli are changes in the environment (internal or external) to which an animal normally responds. However, the environmental context in which the stimulus occurs will determine whether it is effective in eliciting a response. Therefore, an effective stimulus is a change in the environment which elicits a response at that point in the animal's stream of behavior when the environmental context (internal

L)r ('xiu.nple, Bekoff (1978a) found neural pattern generators in embryonic chicks rlr.rl slintulatc coordinateci limb movements and develop in the absence of any pat-

and external) is appropriate. Stimuli that are effective in one context may be filtered

I, rnr'(l sct'ri()ry inptrt.

(centrally or peripherally) in another context. For example, cat lirocl kibblcs will normally stimulate feeding only when the cat is hungry (interrral cttvirot.ttt'tcttt ) itrttl not involved in a higher priority activity (e.g. chasing a r.nousc extct'ttitl ctlvittrtl-

r)(,r

ment).

Stirrrrrli havc httll't trt'tit'tttirlrrrl ltttrl ttr,qtrtti:ttliorrrrl litttcliotts. only ittitittl,lrrrtl

olirll lrr'llrviot'rlit't't'lly,

'lltltl

is. tltev ttol

lrtrt lltt'Y ltlso lttr ililrtlt';tnr,l ttt,rittl,ttrr lt.'lt:n'

i0t lttttl ltrt'tlttltrttt';tll ;tllllllill l() l)ilt ltt ttl;tl lrt'll;tVtt,ts

prirnarily a device for generating action spontaneously IGraham Hoyle quoted in Allport, l986J

llrcrc rrrc llso bchirviors that occur in the absence of the exogenous stimuli that nrirlly clicit llrcnr (c.g. hirrls'nest building'without materials, cats'prey catching'

rtlrorrl grrt'v. tlogs 'btrryirrg'

'covering' food in a concrete floor). Lorenz ,.,1,1;rrrrt'tl llrt'ot't'rrrrerrr,'c ol'llrcsc'vircrrtrttr'behitviors its an excessive buildup of r( tr()n spt't'rlrt't'rrt'r;,y'1ullt'lt lirrcctl oPctt (ltc vltlvc itt lris Psychohytlraulic Model; l ,rrt'1t,,. l()\O) llorvt'r't'r.;tn()llrt'r Possilrilitv is lllrt v:rctrutu bclutvior is clicitctl irntl ,,r rt'nlr'rl 1,1 trrr,rl,nt;u \'('\ol'('n()lr\ slttttttlt't tt';tlt'rl lrv tlrt'ltttitttltl (ttlsrl sttl'llestetl by 'r

lrttl


A CONCEPTUAL MODEL OF BEHAVIOR.

Lorenz,l98l); lbr example, we refer to'hallucinogenic'play in cats, implying that

Releaser(s)

+

the'prey'is in the cat's'mind's eye'. Physiological needs are signaled through endogenous stimuli that elicit behaviors designed to meet those needs. For example, low blood sugar leads to the 'state

Key

Positive feedback

stimuli

v

of hunger'which stimulates the anirnal to seek, find and ingest food. There also appears to be a psychological need to perform a normal range of behaviors. Various terms have been applied to this psychological need, including 'ethological need' (Hughes and Duncan, 1988), 'drive' and 'instinct'. For example, Garcia et al. (1973:3) state that 'drive is the psychological concomitant of physiological need', and Dawkins (1986:64) states that 'instinct . . . refers to the inner drive

Animal lnnate releasing mechanism

--?-+

Behavior

+

Modal action pattern

F,...-

-) .r'

t-'-----

'... ,r,

Consequences (Contingencies)

-)

Feedforward

ootat'/-

Negative feedback

or motivational force that leads an animal or person to behave in a certain way'. The

model acknowledges that behaviors can be elicited by endogenous stimuli regardless

i\r

e--tt------isr\

I ig.

2.3 Where the ethologists' model of innate behavior fits into the model lor

of how you choose to perceive and label the basis for those stimuli.

a

behavioral act.

rrrtl physiology, and behavior. This hypothetical construct is diagrammed 2.3.2b Exogenous

stimuli

l,

as

rllttws'

Exogenous Stimuli come from the external environment (biotic and abiotic), take many forms (e.g. light, sound, odor) and arrive via the animal's various sensory receptors (e.g. eyes, ears, nose). The animal's anatomy and physiology are responsi-

Releaser(s)*key stimuli---innate releasing mechanism (IRM) J

modal action pattern (MAP)

ble lor receiving, processing and responding to exogenous stimuli, as well as gener-

ating behaviors which produce exogenous stimuli. Several terms have been applied to exogenous stimuli depending on the role they play in different behavioral para-

digms. Sign ,stirttuli, relea,sers, unconditioned ,stimuli, neutral stirnuli, conditioned stintuli, and disc'riminative stimuli are all discussed in the contexts of the ethological model of innate behavior and the learning paradigms below.

rilr'r)r irs .fi.rcd uction potterns. However. it is now recognized that experience also t,l;rvs un important role. For example, Gerard Baerends, who began his ethological

2.3.3 Behavior

Behavior results when an effective stimulus is received or generated by the animal. As shown in the model, when one behavior is elicited, an ongoing behavior may be

inhibited. This is required

A morphological structure and/or movement (releaser) emit one or more tey 'rrrttuli. The key stimuli operate on the innate releusing nrcchanism (at present, an ,rrritlcntified part of the animal's anatomy and physiology) which 'releases' a modal 'r, titttt pattern, a relatively stereotyped behavior pattern. Since these modal action l,.rllcrns are very similar between individuals of the same species, ethologists 1,,'licved that they are primarily under genetic control and originally referred to

if

the the two behaviors are mutually exclusive; that is,

they cannot occur at the same time (e.g. sitting and walking). A behavior may also

stop because it is no longer stimulated. For example. sleepingis inhibitcrl when an animal is ingesting food; ingestion will cease when the animal is satiated untl cirting is no longer stimulated by the sight and smell

of food.

r,.r'iu'ch six decades ago. recently concluded from his many years of studying 1,, ring gulls that'The infbrmation encoded in the lP.Mlinnate releasing mecha,,irrf iulrl the acquired inlormationfleurning]were found to work in combination.' r rt,rlrcs tninc, I9u5:37). llistoricirlly, cthologists and psychologists have been branded as having diametrr,;rlly o1-r1-rosctl vicwpoints on the relative contribution of the genotype and learnrrrj' lo bclurvior'. llowcvet. cthologists and psychologists are increasingly borrowing

lntl llrcorics. and overlap between ethology and learning tlrcotv ('rn nr()re relrtlily ltc lirrrrttl in thc litcr:rture. For example, adjunctive behavlrr )nr ('ir('l) olltcr''s rcsciu'ch

2.3.4 Ethological model of innatc bchavior

'l'lrcclltolog,tcltl tnotlel ol ittn;tlt'llt'lutviot llrt'lorv. ('.1'. l.ort'nz. l()liI)ist';rsrlv rrrtor l)()t;rl('(l tnl() lltt'tttorlr'l (l'llrut(' .' l)sttttt'rl rtrrolvt'r t'\,)1,('n()l\ slnnrrlr,;ur:rl()nlv

,,,r, r,l lt';ttttttrl, lltr'orrrl\'iu('r't't'y sitttilru'tlvltirtnicirlly irncl tirnctionally to what l,,rrt'lrt't'lt krrorvrr rrr llrt't'llrolol,it';rl litenrlrrrc lrs tlisPlirccntcnl irclivitios'(Davey, l't1.'t1 5t1,


A CONCEPTUAL MODEL OF BEHAVIOR

UCS/CS or SD

2.3.5 Associative learning paradigms

Positive feedback

\

Learning can be defined as the 'adaptive'modification of behavior as the result of experience (e.g. Lorenz, 1965). Natural selection has shaped various types of

k-"Animal

learning in animals (Staddon, 1983), but each type of learning incurs selective costs (in terms of fitness), as well as selective benefits (Johnston, 1981). Also, the

--?--> Behavior

+

(G+E+D+(A+P)

elicits

UCS

ta,

l'ig.

act.

The paradigm begins with a behavior being emitted and does not consider ,trrtruli that might elicit that behavior. However, the paradigm does include tliscrirurtrtrtit,c

stimuli. Figure 2.4 illustrates how both classical and instrumental condition-

lit into the model.

An unconditioned stimulus (UCS) elicits an unconditioned response (UCR). When a neutral stimulus (NS) is paired with the UCS, over time it becomes a conditioned stimulus (CS) capable of, by itself, eliciting a conditioned response (CR), a reasonable facsimile of the UCR. Note the similarity between classical conditioning and the ethologists'model of innate behavior (discussed above). The UCS is essentially the same as the etholois essentially the

ethologists'MAP(FAP).

) conditioning

The instrumental conditioning paradigm (below) states that a behuvirtr is emitted ancl

followed by consequences (So*, So ) that either maintain, increase or decrease the probability of that behavior occuring again (see Consequences scction bclow). is

Discriminative stimuli 15t), ttr SI) Ilcltlrvior' (r'rrriltt'tl)

i

v

e

st

imu

I

i

discriminative stimuli (S"*) predict that a specific behavior will be followed l,r l.rositive consequences, and Negative discriminative stimuli (So ) predict that a l'( )sitive

elicits

23.5b Instrumental ( operant

Feedforward

2.4 Where classical and instrumental conditioning fit into the model for a behavioral

D is c r iminat

UCR

UCR

--)

UCR

CS--.--..----.------*CR

gtst's releaser, and the

Consequences (Contingencies)

---------t'

elicits

NS+UCS \\ '..

,

Negative feedback

rrrs

.---.------------------.-*

+

-\trs

2.3.5a Classical conditioning

The classicul conditioning paradigm (below) describes how neutral stimuli become conditioned through association, thus gaining the ability to elicit specific behaviors.

IJCR

\/

benefits of a specific type of learning may be limited to the environmental contexts

in which it evolved (McNamara and Houston, 1980). Possible routes of evolution of the primary associative learning paradigms, clossic'al and instrumental c'onditioning, are discussed by Weisman and Dodd (1980) and Skinner (1988), respectively. Both of these learning paradigms are incorporated directly into the model.

Behavior

)

( tlnsetlrtcrtt't's

'Pr'cific behavior will be lollowed by aversive consequences (see discussion below rlrottt specific types of consequences). Since exogenous stimuli in the model include

l,,,th conditioned and discriminative stimuli as types of exogenous stimuli, rrrtcrcsting to note how Davey summarizes their similarity in function. . . . Pavlovian CSs or instrumental discriminative stimuli (SDs) elicit motivational state appropriate to the reinforcer and . . . this

it

is

a

motivational state in some way mediates the emission of the instrumental response. IDavey, l9B9:210J

llrt'rrbility to usc thc cnvirc>nmental context (including

SDs) to predict the consebcltrvior is a marjor benefit of learning. Tarpy presents a psychologist's t','rspcclivc without using the term discriminative stimuli.

,lu('nce s ol'

l,c:tt'ttittg is I'rrttrlunrcntally a process whereby the animal comes to crpcel rt lirlrrt'c cvcrtt birscd upon the patterns of stimuli in the ('n\'u()nntt. rrt or rr1'rtlll its own hcltitviol ITurpv, 1982:lB l9J

I tkr'ttlrt'. I r)l('tll l)l('s('ttls lttt r.'lltolollist's ltcrsPective rlrt prctlictirrg tlrc tltttcrllt.tc ol' rr rllrorrl rrrrrrll lltt'l('lln (lt\( tlnlnt;tltrr.slirnttlt.

l', lr,r\ lot. itl\o

A CONCEPTUAL MODEL OF BEHAVIOR


Reinforcing stimuli

Presented

Omitted

domestic chicks, during the process of socializatron, received 'self-reinforcement' from social experience during the early sensitive period. Positive reinforcing stimuli that meet these ethological needs are said to have an underlying 'hedonic value'

Positive (So*)

Positive reinforcement

Extinction

(Tarpy, 1982); that is, they possess a sub.jective quality of a positive affective state (Toates, 1988). Whether this is pleasure as we know it, or what Lorenz calls 'feeling

Aversive (So*)

Punishment

N egative

Table 2.1. During or immediately.follow,ing the behavior the rein/brcing stimulus is:

reinforcement

. . . a bird that 'wants'to carry out the beautiful motor pattern of nest building . . . learns to recognize the situation in which performing the

good'and'satisfaction'(Nisbett, 1977:138, 289), does not affect the operant conditioning paradigm or the model. Even reinforcers that meet basic physiological needs might be considered hedonistic.

I have see

nest-building movements gives the maximum satisfaction.

it.

ILorenz, 1981.291]

' Consequences

'

stimuli(So ) are stimuli which the animal perceives as 'bad'(e.g. pain). That is, under those conditions the animal will perform

Aversive reinforcing

s.

Positive reinforcemenr results when the consequence of performing a behavior is receipt of SR*. The probability of the behavior occurring again is maintained or increased.The SR*s which result in positive reinforcement are received under different contingenc'ies, discussed below.

behavior. Positive reinfrtrcing stimuli (So*) are often called rewards. They are reinforcing stimuli which the animal perceives as'good'. That is, in the appropriate contexts, the animal will perform the behaviors necessary to receive those SR*s. The SR*s are received by the animal under different contingencres of behavior called

'

Extinction ( omission ) can only occur after an animal has been positively reinforced for a behavior.

If that same behavior now

does not result in the

animal receiving the SR+, omission is the consequence and the probability

of the behavior occurring again deueases until it is extinguished. Extinction is not simply a dissipation of the response, but is an active

schedules of reinfbrcement (discussed below).

An SR* may be a single stimulus (e.g. food item) or the opportunity to perform a chain of behaviors (e.g. search, stalk, capture, kill, consume). Lorenz (1965) considered the consummatory act in a chain of behaviors as a reinforcer for antecedent

learning process (Davey, 1989).

'

Punishmen r results when the consequence

of performing

a behavior is

receipt of SR . The animal initially escapes the SR , and the probability the behavior occurring again decreases.

behaviors (appetitive behaviors). Also, behaviors may be organized in a 'preference hierarchy'(Premack, 1965) so that an SR* could be the opportunity to perform a

'

preferred behavior.

of

Negutive reinfbrcement can only occur after an animal has been punished a behavior. Ii under the same conditions in which the original behavior resulted in punishment, a different behavior results in avoiding the

lor

Although we often think of SR*s as meeting obvious basic physiological needs (e.g. food and water), ethological needs (discussed earlier) may stimulate an animal

Sr{ . then the probability of that behavior is maintained or increased.

to perform behaviors in order to obtain subjective rewards.

Anin-rals nt:ry pcrlrlrrn belrirviors llurl rrt'c pt'irttrtt'ily irttt;rtt' (r'1,, t'otttlsltip lttttl rrrirtirrg) ltccirrrsc tlrcy tc('('iv('rttlrt'tcttt. sttlrir't'tivr'tt'wttttls. lirr t'x;ttttplt'. irt Si1'rrrrrnrl's 1lt)t)1..)0i',i ) r'it.rr'. '1ll;rV rs tls ou'n t('\\'intl' (',rlllts (l()(r.)1 tlott'tl llt:tt

[Bolles, |9BS.450J

behaviors necessary to avoid those SR

Proximate consequences The fourproximate ('onsequences, in the matrix shown in Table 2.1. are the immediate result of a behavior. They are determined by the type of' reinforcing stimulus and whether the reinforcing stimulus is presented or omitted. Reinlorcing stimuli and consequences are defined by their effect on the animal's

. . . the opportunity to manipulate, to explore, or to merely observe is labeled a reward, reflecting the assumption that if learning hits ttccurrcd there must have been some reward, even though it citnttot bc crtrpirically specified. f (itrrusy'is not anthropomorphic, but rather refers to iunc-

Push

Sandbathing Ventrttm rub

Pat

Diggirtg ForePaw m()vemcllts

Ytwtr

Kick hirck Tttrtl lttttl lltrslr (lill'cllltw's ;ttttl lrtt'lrsl 'l'ttrtt lttttl lltrslr (ttosc)

Slr:r kc

M

Stretch

if not impossible, to do so (Crocker, l98l). It can be argued that

directly or indirectly; therefore, researchers sometirnes unconsciously slip into its trse (Kennedy, 1992). As Rioch (1967) has remarked, we are both limited and

Chopping with incisors

Rolling over the back Writhing

of how strongly one might attempt to avoid anthropomorphisrn, it is

cannot have knowledge of anything which we have not ourselves experienced either

Sifting

Licking

Side rub

very diflficult,

and c'ac'hing

Nibble

Wiping with the fbrePaws Nibbling the toenails

l,

Regardless

Holding with the forePaws Gat he'r ing t'bodstulf.s

lr

Sourc'e: From Eisenberg (1967).

Swallowing

Mouth Hauling in

Mouthing the fur

Upright Testing the air

QuadruPedal saltation BiPedal walk

Diagonal coordination Fore and hind limb alteration

Elongate, investigatory

Biting

Manipulatin with forePaws Drinking (laPPing) Gnawing (with incisors) Chewing (with molars)

Climbing

Gathering

Defecation

Diagonal

JumPing

Isolated animol exp loring

Stripping

Ingestiort

Bipedal saltation

Nest building

oltlittl'

)

lrolurll! rlctcrrrtinerl conccpts. In tliis regard. anthropomorphism might be useful as r rrtctlrphot' lot'tlcscrihing wlrir( urt irnintirl does (Kennedy. 1992; Ristau, 1986) and rrlrrrl ils'cnl()liorr;rl'rurtl ttroliv:rtiorrirl slirtcs ul)pcar ttr bc (c.g. I'car). witliout irlplyrttl llt;tl \()nrr' l('\r'l ol t'orrsciorrs tlro111,,l11 is irrvolvctl. l'or exarrtple, tlrc 1'lht'itsc'tltc ,ltt1l 7r/1r1s',' lltc sr';t l)r()\ trlt's lts rr itlr ;r r tstt;tl itnrr;,,.' ;ur;rlo1'ous lrl ;1 1;g,',t.t"t''s 1'llrlw ;,rtslttltl';t\l(lt'lltr",,,rl \\"rlr't (l')S I l('/)ttolt". llt.tl '11llrtttktttt':tl16ttt lpltpttttttili,.'t

I

R

86

HOW TO DESCRIBE BEHAVIOR

ECON NAI SSAN CE OBSERVATION

drawn from human interaclor manipulation in animal "ornpgnir:ation, analogies i,clude 'cJeceit" 'selfishness" and tions tencl to dominate'; commonly used terms 'these familiar words tnake visu,spite'. ( 1983: 167) concludes that the use of

I

mates the complete repertoire. The size of the repertoire will,

we should provide technical definialization of technical discussions easier', but misleading inf-erences' tions of the terms in order to 'guard against lir-re'l The saf-est approach is to avoid Where and how cloes the beginner draw the ttse only empirical descriptions' You using terms that could be misir-rterpreted anwed that the average

territory size of black-cappec'l chickadees (Prrru.r utricupillu.r)

r,'ariecl cluring

six stages of the breeding season: prenesting, nest building, egg ltrying. incubation, nestling. and fledgling.

ttt-t1lt>rtittlt thing seents to t-ne is not to miss the natural experiments anci yct to know when it becomes necessary to continue by planned tests.

ITinbargcn, 1955;259J

The use of natural variation has limitations which are both clualitativc arrrl cprirrr-

titative. Waiting lor the proper conditions to arise' ancl atternpting to gltlre r ir sulii-

cient number

of

observations st'rt'netinrcs clrivcs

thc cthologist to

rrrtilicirrl

manipulation:

''\ttolltt't t'tPt'tilttt'ltlltl lt1'rlttrxtclt trl thc sttrrlv tll'cirrrse

Systcntittic erltlrlitrrtirln ol'srrt'lr rtrrtrulrl t'xlx'riln('llls llr:rl r\. \\\l('ntitlt( r'()t)tl)irtisort ol tltt'stltt;rli()n\ \\'l)it'lt rlo trrrrl lltost'r\lrr, lr,l,) n()l tt'lt';rrt';t t'tt't'tt t('Sl)()ll\('

i.t.z Artilicial ntanipulation

t;ttt lrt'ltltllrtsl

ll\ l'()(,rl tlr ;rl,ttltt,',1 r'\lrt'l ttttt'ttl'.

lltt'

arrcl etlect

o{'behavior is to

l:tkt't'ottllol ol lllt'v'ltt'iltllles:tttrl tnlrrriprrllrtr'llrcnr irr thc Iiclrl rlr the labrt*tt.ry.

.\lllt()ttl'lt llrr'rrrlilill)ltlitltr)lt t\;illtlt(.t;tl. t.rcrY lrllt.nrgrl slrorrlrl lte rrrlrtlc t() lll)l)t.()xittt;tlr'lltt'rr,tr ttrtrl.,lrtrttrlt;tttrl llrt,n,tlut,tl, 1r,rrr1,,..,,r., t 1o,.1.11 :rr Por:iltlt,.

THE VARYING VARIABLES

EXPERIMENTAL RESEARCH

t58

7.t.2t Elimination, disruption and manipulation When manipulating the animal or exogenous stimuli to answer questions about 'how'an animalperlorms a behavior there are dilferent levels of intervention which lead to differing degrees of validity of results. For example. in determining the exogenous stimuli and corresponding sensory systems (or endogenous stimuli and corresponding hormonal/neuronal systems) involved in a behavior you can eliminilte, di.>'rupt or nrunipulate variables. These interventions (all of which are usually referred to as 'manipulations') can be made on the stimuli or the animal's anatomy

(e.g. sensory system). Elimination, disruption and manipulation represent decreased levels of perturbation, respectively; in general, manipulation provides more rigorous and valid results than does elimination or disruption (see exan-rples of bird orientation/rnigration studies later in this chapter). When stimuli are manipulated (e.g. von Frisch changing the plane of polarized light received by a dancing p. 155), you can make predictions about the resultant behavior (e.g. orientation of the bee's dance will track with the changing plane of polarization); that is. you can invoke research and statistical hypotheses with higher resolution and greater bee,

159

Since the pigeons homed successfully, you might harve concluded that they don't use the sun as a compass and therelore hypothesized that they use the earth's magnetic field. If you then attached bar magnets to their backs (to tli.srupt the magnetic field

around them), but tested them on a sunny day, they would still have homed, but this tirne they would have been using the sun as a compass. Further, unless you recognized that you should have been controlling more than one of the variables at a time, you might conclude that the pigeons use neither the sun nor geomagnetic field as cornpass cues. Even if you recognized your design error and proceeded to disrupt

their orientation by applying magnets and testing the pigeons on overcast days. you would not have as conclusive results as you could have obtained by monipulatirzg the variables and predicting the changes in orientation (see experiments clescribed in section 7.3).

When you eliminate or disrupt an animal's sensory system you also run the risk systems which could be important

of affecting other anatomical and physiological

for the behavior(s) you are measuring. The lollowing story illustrates how attempts

to eliminate a sensory system can have additional elfects on the animal's behavior and the researcher's ability to interpret results:

statistical power (Chapter ll). With elimination ancl disruption you are only attempting to eliminate or disrupt the behavior being studied (often in an unpredictable manner). For example, in attempting to locate the circaclian pacemaker it was known that surgical ablation (elimination) of the suprachiasmatic nucleus (SCN) in the brains of mammals eliminated overt behavioral rhythmicity; those

A zoology student had succeeded in training cockroaches, ernd he proudly displayed the results of his long efforts to his professor. He had his cockroaches fall in, and he gave them the command: 'Forward, march!'the cockroaches marched lorward. 'Column left!' the student commanded, and all the cockroaches turned left. The professor was about to congratulate the student on this remarkable accomplishment, but the student interrupted him. 'Wait!' lre said. 'l still have to show you the most important thing.' The student picked up a cockroach from the last row, pulled off its legs, and put it back in its place. Once again he commanded: 'Forward, march!'

experiments provided sorne evidence lor the SCN being the pacemaker. However, conclusive evidence was provided when Ralph et al. (1990) conducted transplanta-

tion experiments with normal hamsters and a mutant strain with a short circadian period. They demonstrated that srnall neural grafts fl'om the SCN of donor hamsters restored circadian rhythms to arhythmic hamsters whose own SCN had been ablated; the restored rhythms always exhibited the period of the donor genotype, normal or mutant (short). If you eliminate or disrupt stimuli in order to determine their role in a eliciting or

The cockroaches marched as before. except, of course. lor the one without legs. 'Column left.'Again, all the cockroaches turned on command. except lor the one that lay where it had been placed. The prof-essor looked inquiringly at the student.

orienting a behavior, the behavior may come under the control of other stintuli anrl sensory systems. Thus, when the behavior does not disappear, or is not clisruptccl.

Thc studcnt said proudly, 'This experiment proves conclusively that cockroaches hear with their legs.' I Eigen and Winkler, l98 I :298 -299

you could draw incorrect conclusions. Indeed. this is what occurrecl in c:rrly cxpcriments designed to determine the environmental cues used for oricrrlulion by liu'rrg-

]

important questions: l. Was the manipulation appropri-

ing bees and migrating birds (Gould, 1982). For exarnple, if you wcrc an ctlrologrst

I Itis lrrlc grvcs risc to three

in the early part of the century and were interestecl in thc cnvironn.rcrrlirl errcs llt;rt homing pigeons use to orient back to the hornc lol't. you rrrighl lr:rvc rlcsiltn('(l('\lx'l iments to elimirtatc rlr rlisrrrgrt polcrrlilrl crrcs. ll'yott ltypolltr'sizr'rl tlrtl pi1't'ons usr' thc strn its:l c()n)l)ltss rilttl tesletl llrt'nr ott rl!'r'rt'lrsl tl:rVs (ltt t'littttttrtlr'lltt'srilr ;rs rr r'ttc). tltt'lli,'t'otrs rvrlrrlrl slrll lrlrvt'll()nr('(l ltstttl'lltt't'lttllr': ttt;r1'trr'ltr ltr'lrl:tr llrr't ttt'

rrlt'lo obllrin vllitl rcsults'/ If so,2. Was this severe a manipulation necessary to iurs\\'('r llre tescrrtclr tlrrcslirln'l Il'so.3. Wts the answer to the research question rrorllr tturkinl llris s('verc lt tnlutipttllttiort'l Sincc thc rttrswcr tt> questions I and 2 is 'No', lrllr

(':ur t ottt lrrrlt' tlr;rl llrt'slrttlt'ttl u'lrs t'illtcr rr rtlrivc or sittlistic rescarcher. If \\r';'11t' llrr'rlrrrlr'nl llrt'ltt'ttt'ltl ,rl ,r',.,unutr1' lltt'1 \\('r('{rnlV tt:rivt'tttttl ittscttsillvc. wc


EXI'ER I M ENTAL RESEARCH

160

l6l

should recommend that they answer those questions befbre making any manipula-

tion in their next experinrent. Manipulation of variables (versus elimination or disruption) is the method being employed when the researcher uses rnoclels and dummies. or conditioning (all are discussed below).

7.t.2h Models and dummies

ll[ot{cl,t constructeci to rnimic animals. or parts of animals. and tlumnzre.s (stutled of animals) have a long history ot' use in ethology. Dun-rmies were used by Allen (1934) in his study of the courtship of rufled grouse (Bortu,su wnbellu:; L.), in Chapman's (1935) study of courtship in Gould's manakin (Murtucus vetellinus

skins

t,ircllinu,t), and by Lack ( 1943) in his study of aggression in robins (Eritltucu.s rubeczrla: also see Table 8.3). Models and dummies have the advanta-ee

of allowing the

(e.g. visr-ral. auditory, chemical. tactile) in a systematic

experimenter to vary stimuli way in order to measure the effect

of qualitative and quantitative

ditferences.

Tinbergen was an early and exemplary proponent of the use of models (Dawkins cl u1.,1992).

As a typical example. Tinbergen and Perdeck (1950) presented models of an aclult herring gull's head to herring gr"rll chicks. They fbund that the color of the spot on the bill (qualitative property)of the nrodel had an eff-ect on the number of pecks given by the chicks (Figure 7.3A). Tinbergen and Kuenen (1939) used simple moclels to demonstrate that the gaping response of nestling blackbirds (Turtlu.s nterulu nterula) and thruslrcs(Turclu.s ericetonnn ericelrtruni) is oriented by the relative size of the parent's head to their bocly (Figure 7.38).

Moller (1987) stuclied the role of badge size (extent of dark coloratiou on the throat and breast) on status signaling in house sparrows (Pusscr tlonrcstit'u,s) by placing stulfed male house sparrows (dummies) near nests. Stout ancl Brass (1969) placed pairs of dummies, or wooden-block models witli tiltable boclies and adjustable stuffed heads (Figure 7.4).

in glaucous-wingecl gull territories: tlicy

demonstrated that the head and neck are the parts of the bocly that relcusc territol'ial aggression displays in this species. Some researchers have incorporated movement ztnd/ot' ttclors lttttl stlttrttl irtto their models and dummies. For example. Esch (1967) used et wootlcn. tt.totot'-tlt'ivctt model in his research on communication ol'firod source locittion in ltortcybces. I lte rnodelwas the approximate size ot'the honcybccs bcing sturlicrl. btrt it rlitln't t lost'll resemble them physically: this probablv ltirtl littlc cl'll'ct sirtcc lltc cxlrerittt('lrls \\'('r(' carrieclttut in a tlark lrivc.-l-lrc rttorlcltlirl Ilrr,c tltc itle lrtit'rtl.,tlot rtl lltt'lttrr"s tltlrtlr ititnls itnrl ltcrlirnut'(l ;t 'n()nltitl'n'lr1'1'1r.' rl;rrrt',.'. lrttl no lrt't's lt'll llrc lttrt'l,r:t';lr'lt lol

litotl itr llrc tltrr'( ll()n ptrrl l;lttttt'rl lrt lltt'ntotlr'l r tlrtttt t' l ',,1t ,,,tt, ltl,l,',1 llr,rl ',r)lll('

lir

71

A. A cultlbtxrrrl n.roclelol a herring gull head being presented to a chick (adapted l'r'onr'l'inbcrgcn l9(r0b by Lori Miyasato). B. Presentation of models of the l)iucnl\'lrcrrtl. both'rrnrl tail to study the rc-lationships that orient nestlings' lrrpirrl' r'esl)()nsc (ltlirp(ctl ll'ont Tirthcrgcn 1972 by Lclri Miyasato).



(Chocton aurign)

validity of his

use

to a cleaner (Labroides pltthrirophugus), he demonstrated of

the

a cleaner model through three indicators: pose duration, pose-

to-inspect ratio, and approach behavior of the host fish to both live cleaners and his models.

Not only must the

Lrse

of rnodels and dummies be carefully planned. but

the

results of such experiments must be carefully interpreted. As an example, in another

of Tinbergen and Perdeck's (1950) experiments on the begging response in neonatal herring-gull chicks, they changed the position of the red spot from the

aspect

model's bill to its forehead. The ohicks delivered significantly n"rore pecks to the model with the spot on the bill than they did to the model with the spot on the forehead. They concluded that it was the position

of the red spot on the head that

caused the decrease in the chicks'responses. Hailman (1969) re-investigated this

phenomenon by placing the rnodels at different distances from the pivot point of the rod holding the model. Further, he adjusted the height of the chick so that it was always at eye levelwith the red spot. He had created three models: a 'normal model'

with the spot on the bill, a model with the spot on the forehead and the pivot point the same ('slow model'), and a model with the spot as on the bill-spot model ('fast model'). The fast forehead-spot model received more pecks than the slow lorehead Fig.

7.4 Models and a durnrny (2d;

used by Stout and Brass ( 1969)

in their study ol

glaucous-winged gulls. la, upright, threat-postured body 1b, trumpetingpostured body; lc, choking-postured body; 2a, basic wooden model;2b, upright threat posture; 2c, model without wings: 2d, dummy showing upright threat posture with wings.

thing more than the dance was necessary to elicit foraging. More recent research used a motor-driven model bee which not only danced, but also vibrated artificial wings and exLlded sugar-water samples; this dummy bee was much more successful

in recruiting foragers (Mollett .1990). Hunsaker (1962) used a head-bobbing machine to move the model heacls ol' lizards (Sc'eloperu^r sp.) in dilferent species-typical patterns. He firuncl thiit l'emalcs selected those rnodels which head-bobbeci in the pattent typical ol' thcir own spccics. Jarvi and Bakken (1984) used three dummy great tits to str-rdy thc f'unction ol'tltc

variation in the breast stripe. Their dummies could be turned 360'. by radio cont rol. to keep them always oriented in the direction from which tlre livc hirrls irpprolclrctl.

Models shor-rld contain the important f-eatures ol-thc livc irnirrurl. irntl tlrev should be used in a normal context (see Cr,rrio 197.5 lirr an cxccllcrrt cxlrrrple ol extensive and proper use ol'modcls). Irt olltcr wortls.';rrr rrrrtlerlying rrssrrrrrptiorr ol the ntcthrttl is thlrt rcsll()ltsc to

1l19

tttotlcl tlcPcttrls ()ll r))u('lr lltt's;rtttt't';ruslrl slslt'nr

ilst'csll()llsclolltctt;ttttltlsttrrrtrltts'(l.ost'\'. l()77.).).1; llrrlor lrrn;rlt'lr titlttistlttt'lVr:tlirl;rlr'tl

llrrs;rssrrrrrP

llttttr'\('r lnl,r:t'\''st'\[t'1g1111'1ll:.onllrt't,",1)on'.('ol lro',l lr',lr

model, although f-ewer than the 'normal model,'revealing the elfect of speed of the red spot on the chicks'responses. Therefore, Tinbergen and Perdeck (1950) were correct in concluding that position of the spot is important; but I{ailman demonstrated that speed of the spot is also a contributing factor.

Models and dummies shor.rld be used with appropriate caution. They may be either too simple with the inrportant stirnuli absent, or too complex with extrarleous stirnuli confounding the experiment. As with any tool, however, in the hands of a skilled researcher. models and dummies can be an important means of manipula-

tion in the field.

7.t.2c Instrumental and classical conditioning

An iniportant technique fbr manipulating variables in the laboratory is through the ruse ol' instrurnental and classical conditioning. Conditioning is a powerful method lirr studyrng 'cirus;ution' ancl answering 'how' questions; the basic paradigms for irrstrtrrncntuI irntl classicaIconclitioning were discussed in Chapter 2.

('orrtlitronrng is thc busis lirr many psychophysicalstudies designed to determine

'lrrrw' rr spccics tliscrirninirtcs bctween varic'rus stimuli. For example, May et al. ( f ()lili ) strrtlit'rl lrorv .lrrprrrrcsc lnirca(l ucs ( lllttcuut.f ir.st'ulu) discriminate between diflr.'tt'rr( ( ()() \ot;rliz;tliotts ltv ttsutp, itt.ttt tttttt'ttlttl t'otttliliottitt,q lct trairr inclividr"ral lnir( ir(lu('s lo tlrst rrrnrr;rlt' \nroollt t';tr lr'' l;;1'11' ;trttl 'slt)orltlr llrtc ltiglt'ctlo sttttnds. Ilrt'rrrrrt rr,lu('\ \\'('rt'lrrrrrt'rl lo nr;rk,'lt,rtt,lt

o111:tr'l

tt'tllt;t tnt'lrtlt'Vlnttlct itt I'csPottsc

164

F.X

t'hlL l M

E

NTA L

R

THE, VARYING VARIABLES

ESE,ARCH

r65

to one type of vocalization and release contact in response to the other vocalization.

First, generalization tests showed that the macaqLles responded appropriately to both natural and computer-synthesized coo soutrds. Then acoustic f'eatures were systematically removed from the computer-synthesized sounds to determine the minirnal elements necessary lbr the macaques to recognize them as distinct coo sounds. Pietrewicz and Kamil (1977\ studied the ability of blue jays (Ct'rzrnc'ittcr t'risttrttt) to detect cryptic moths by instruntcntully t'onditiorting them to respond diff'erentially to the presence and absence of moths in projected images (slides). If the projected slide contained a nroth. l0 pecks on the stimulus key resulted in the blue jay being positively reinfbrced with half a mealworm. The jays were able to detect

the moths, but their ability was allected by the background upon which the motl-t was placed and the moth's body orientation. In a later study, Pietrewicz and Kamil (1919) used the same in.slrruncntul cotrtlitioning procedure to stucly search irnage formation in blue jays.

Often questions about 'how' an animal uses environmental

cr-res

begins with

studies of what a species'can'perceive (Miller 1985); that is, what stimuli they are capable of perceiving and responding to. For example. Lehner ancl Dennis (1971) hypothesized that waterfowlrnight use atmospheric pressure changes as a cue lor orientation during migration. They used instrumcntul t'onditiortirtg to train mallarcl

Fig.

ducks. in a barometric pressure chamber, to peck one microswitch when the pressure increased and another microswitch when the pressure decrensed. They then exposed

7.5 Coyote in tcst chambcr uscd by Horn and Lehner (1975) to dctermine the coyote's scotopic (clirrk aclaptcd) light scnsitivity. A stinrulus patch is at the coyote's eye level at the ccnter ol the right wall; it is not illuminated in this photo. Two loot treadles Are on the floor, separated by a plexiglas partition.

the ducks to sequentially smaller changes in pressure and demonstrated that the ducks could perceive atmospheric pressure changes as small as 0.4 psi. Kreithen and

at the coyote's eye level. They were then instrumentally conditioned to step on a foot

Keeton (l9l4a) used r'lrr.r'.ricul t'ontlitioninglt-t test the capabilities ol- homing pigeons to detect atmospheric pressure changes. The procedure was to place the pigeons indi-

treadle to their right when the light was on. and a treadle to their leli wlien it was ofl. Once they consistently perlbrn-red this discriminzrtion task, then intensity of tl"re

vidually in an airtight cl-ramber. change the pressure over a 5 second irrterval(neutral stimulus), hold the pressure steady lbr the llext 5 seconds, and then deliver electric

light stirnulus rvas pr"rt under the control of'tl-re coyotes. When they stepped on the right lbot treadle (indicating they could perceive the light stimulus), the light auto-

shock (r,rnconclitionecl stimulurs) to the pigeon. causing the heart rate to incrcusc (unconditioned response). After a f-ew presentations, the pressure change becullc it

ruatically decreased in intensity: conversely. stepping on the left treadle (indicating they cor-rld not perceive the light) ar.rtomatically increased the light intensity. The

conditioned stirnulus that caused the heart rzlte to increase (conditionctl t'cspottsc) without the electrical shock being adrninistered. Then, the pigcons'pcrccption ol'rlil-

irtterrsity ol-the light stin-rr.rlus was continuously recclrded resulting in a graph of the coyotcs'psychophysicaI threshold lbr vision at nigl"rt.

ferent amounts of pressure change was determinecl by observitrg chungcs irt tltcir' heart rate. They determined that the homing pigeon is ahle to tlclcct ltttttospltct'it' pressure changes of l0 mm of H,O. or lower. Kreithcn iurcl Kccton ( 197-lh) ttsctl lltt'

rrg

Ittstt'untcrttal contlitioning has sl

bc-en an irnportant technique in studies of foragnrtcgics. As ittt cxanrple. Har ct a/. ( 1990) irt.strLunentully t'ontlitioneclcaged gray

i;rvs ( /'r'r'i lr tt't'tr.:' r'trnttrlt'l,rn') to

As part of thcir rcscarch oll coyotc 1'rrctlrrliort. llont lrrtl l t'lrtt't (lt)i.l)rr;tttlr'tl t1r clctcnlinc tltc lrlu'csl ctt'u ilontnt'rtl,rl lilltt lt'rt'ls llt;tl t'ovolt's. tt lrtt lt lttttrl ;)ttnt:ll

altcrnate hclps on two perches in order to receive fbod l'lrc lirritgc corrltl in two'lirod patches', each of which had two perches irrvs ;rellcts. ;ttttl :t pt'llr'l tlispt'nscr (liigtrrc 7.(r). Thc tirod pellets were delivered on variable ratio ',t'ltr'tlttlr's (\11(. sr'r'('1t;tptct l) irr botlr 1'rirlcltcs. l}trth VR scherlules hac'l the salne ttt(';rtt(r'l' ntt';tll ()l ,l01lr'tt'lt ltops Vl{.10). l)ul ()nt' plrlclt lrirtll high vitriirnccabclut

ilyirl rrip.lrt.erlttltl Pt'tt'rrvr' ('rr\'oli's\\('rr'n)(lr\rrlrt,rlll ll;rtttr'rllo',l,ttttl ttt rt,l.ttL 1,"'l t'l11rtnlrr'r (l'i1'rrrt' / r)iilr(l lltr't';r rlrrrrrtlrt', lr1'lrl Irrllt't lt'rl ()tr .tn ill).t(ltrt' 1rl.r',1t, ,lt'1.

lltt'tttr',ttt:tttrl lltt'o(lrt't ;t lr)\\ \ittl;lltr'r' Ilrr't'r;1 rtt lltt' lrry'lr r,u r,ln( (' lor rtl P;rlt lr

same r'1a.ssit'ul t'ontlitiottittg procedure to detcrnritrc thc

ability ol. ltotttiltlt pigcotts lo

detect polarized light. a cue usecl by bces itt ot'ictttlttiott.

1;tf

: t'l)psr'ttl lilt'ltt]c

1t;ClcpClfliif

lly

ill EXAMPLES OF EXPERIMENTAL MN NII'IIT-ATION


166

161

7.2 F'URTHER EXAMPLES Otr EXPERIMENTAL

MANIPULATION 7.2.1 In the field

Many experiments arise from descriptive studies in the field and progress through mensurative experiments to artificial manipulation of the animal and/or its environment.

7.2.ta Manipulation of the animal

Manipulation of the animal involves altering the anatomy and/or physiology of the animal (A and P in the model in Chapter 2). For example, the role of sensory receptors and physiological state can be studied by manipulation of the animal per se. Layne (1961) studied the role of vision in diurnal orientation of bats (Myotis' uus'troriparius) by releasing normal, earplugged, and blinded bats (two types of sensory elimination) at various distances from the home cave. None of the eye-covered bats homed, suggesting that vision is an important in homing behavior. Ehrenfeld and Carr ( 1967) measured the role of vision in the sea-finding behavior of lemale green turtles (Chelonia m1,das) by blindfolding them or fitting them with spectacles containing dillerent filters (elimination and disruption of the visual sense). Blindfolded

Perch InPut lines Fig.

jay the instrumental conditioning apparatus used to study gray to perches attached two of consisted each patches loraging strategies. The two and an were reached' pellets clispensed which through hole a microswitches, operated automatic pellet dispenser. A microcomputer recorcled perch hops and

7.6 Diagram of

turtles and those wearing red, blue, and 0.4 neutral density filters had significantly reduced orientation scores.

ai'' the feeclers. as well as controlling lights ancl backgound noise (from Ha et Press' 1990). Copyrighted by Academic

Morphological changes are occasionally made on animals in the field, and the effect on the animal's ability to obtain and/or retain a mate, social status or a terri-

tory is then measured. In these studies, it is the change in behavior of other individLrals that engage in interactions with the altered individual which is usually being

opporLaboratory research using classical or operant conditioning provides the resultatnt animal's the measLlre and tunity to manipulate variables very precisely how tcl tritnsbehavior very accurately. The drawback is that we don't always know its normal crlvilate these laboratory results into what the animal actually does in whethcr tlrirt ronment. We only determine what the animal'can'do; we are not sllre

rneasured; but an ellect can also often be found by observing the altered individual.

As an example, Bouissou (1912) showed that dehorning and reduced weight tlecreased the ability of domestic cattle to obtain and maintain high social rank in

the herd. Harris sparrows (Zonotrichia quereula) signal their dominance status by variations in the amount of black leathering on their crowns and throat. Rohwer (l9l1l rankecl individuals into 14 'studliness' categories (Figure 7.7) and then

is'how'they normallY do it. If you are interested in more detailed inlormation ttn spccilic cotltlitiottittpr otl t'esertt't'lt methods, you should consult the primary literature tbr papcrs rcpot'tittg otl lcrtrtlitll' similar to what you are planning. Also, there atre scveralgoocl tcxt books l()S I (c'8 1)ilvcv' Itlctltotlology basic the and experimental psychology that present

:

Iverson and Lattal, l99l ).

rrltcrctl thc unrount of black feathering on selected individuals to determine the elll'ct orr thcir status. Subordinates dyed to mimic the highest ranking birds were still pcrsccrrlctl l'ry lcgitimate'studlies,'and bleached birds eventually exerted their rrornurllv lrrglr-rrrrking tlorninrtnce. The data suggested that 'cheating' (i.e. lowerrrrrkinllrrtlslreirruelcv:rlcrl instatrrssinrplybyhavingaclarkercrownandthroat)is '.,,r'rrll\ t onlr,rllctl Molle r'(l()li7) rrsctl sirrrilru' ttltrtipulittions itnd demonstrated a ',1.rltrr rtltr;rltttr'' lttttt'liott lirt lrlttl,'t'stzt'(tllttk r'olot;tlirlrt olt llttrxtl irtttl brcitst)in ft,,tt',,'',1);tt t(ltr'. ( /ilr \t r rlrtrttr'\//{ r/\)

I

hXAMPLES OF EXPERIMENTAL Mn Nll'trt-ATION

169

The role of the red epaulets of male red-winged blackbirds (Agcluius phoeniceus) was studied by D. G. Smith (1912) by dying the epaulets black on selected

rr-

males. He lbund that the epaulets were important in maintenance

o.

against rival males, but they had little eff-ect on the males'ability to obtain mates. N.

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0.)

f-

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territorial

of territories

G. Smith (1967) changed the eye-ring color of one member of mated pairs of sympatric glaucous gulls(Lurus hyperborea^r), Kumlien's gulls (L. gluuc'oirlc.r) and herring

o E

o

gulls. In all cases where the female's eye-ring color had been changed the pair broke

&

up. but alterir-rg the male's eye-ring appeared to have rro ef-fect on the pair's behavior. o

It

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o.

(o

(f)

L L

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important in all research where animals are manipulated and the elfects are studied in interactions with other individuals to observe the ellects on the manipulated animal, as well as on others responding to it. This is true in both intra- and interspecific studies, such as the effects of altered rnales on selection by females and is

altered prey on selection by preclators.

1o

7.2.th Manipulation of the envintnment

s !

Altering the biotic or abiotic environment (see section 2.3.2b) in order to study its resultant eftect on behavior ranges from gross-perturbation experin-rents to subtle

O

changes in one clr a lew stimuli.

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Stewart and Aldrich ( l9-51 ) were able to get an indication of the extent of the surplus'floating'population of unmated rnale birds the spruce fir tbrests by drastically reducing (by shooting) a large number of territorial holders on a 4O-acre tract. During nine days in June. they removed 148 territorial males, reducing the population to 19"/,, ctl the original. They continued to shoot birds as they moved into the area. and by July 8 they had collected a total of 455 individuals. This is a rather drastic perturbation experinrent, and as they adrnit 'the breedin-u territories were completely disrupted durring the period when the original occupants were being rernoved and at the serme time new adult males were constantly invading the area'. On a smaller scale, Krebs ( 197 I ) shot six pairs of great tits occupying territories and observed thitt residents expandecl their territories ancl fbur new pairs took up occupancy. In contrasl to these major manipulations. Tinbergen was prone to concen-

lrate on srrbtlc environrncntal changes in order to study ef-fects without greatly tlisturbing llrc nornralactivities of the animals. 'l lrc trick is. to insert experiments now ancl then in the normal lif-e of the :rrrirrurl so tlrat this normal lif.e is in no way interrupted; howeverexciting tlrr'rrstrlt rrl'l lcst nriry bc lirr us. it must be a nratter of daily routine to llrr';rrrirrrrrl. A rrurrt u'lro lrrcks thc lccliltg lirr this kind of work will nt('\ llltlrlV t'onttttit ollt'ttst's ittst ;ts s()ltlc l)c()lllc citttttttt help kicking and rl;rrn,rr'urt'tlt'lrt;rlt'llrtntlur('urir t()r)nl \\illtottl e\en ll()ticirrg it. f l'tttl't t..t"'tr. /(i-5-i /-i'\/

I

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I

EXAMPLES OF EXPERIMENTAL MAN II'III-ATION


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importance of the various configurations in releasing egg retrieval was l. largerlsmaller;2. speckled>not speckled; 3. greenlblue, red>grey; and 4. shape, other than roundness, was relatively unimportant. The titation method used by Baerends and Kruijt is worthy of careful consideration lor other studies using models. This method allowed them to rank the models on a relative basis between and within the four categories of features. Our experiments with the size series showed position preference to be a quantitative phenomenon. A first choice for the smaller egg in the preferred position can always be overcome by increasing the size of the model in the non-preferred position. With our series of models gradually increasing in size it was possible to identify stepwise, in successive tests with the same bird. the minimum size of a model required to overcome position preference, when in competition with a dummy of a smaller size in the preferred position. Thus, through this 'titration,'a model was lound the value of which. in combination with that of the non-preferred site, could just outweigh the combined values of the smaller model and the preferred site. Empirically it turned out that the birds were acting in accordance with the ratios between the surlaces of the maximal projections (maximal shadows when turned around in a beam of parallel light) of the models. Different pairs of models, matching each other with respect to other parameters tries (e.g., volume), or equal with regard to the dillerence instead of the ratio in the parameters used, proved to be unequal in counteracting position preference. The ratio between sites olten remained constant for a couple of hours, and within that period the relative value of dummies with any kind of stimulus combination could be measured and expressed with reference to the standard size series. IBaerand.s ancl Kruijt, I973:30J

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Results

of observer effects encountered in ethological research (drawing by Dan

Thonrpson).

Error oJ apprehending is due to the physicar arrangement of the animar and/or the it difficurt to observe the behavior. For example, in a study of mockingbird behaviot Breitwisch (l9gg) stated his avoidance observer making

of error of

hending as follows:

appre-

surements of the location of the bull's-eye. Rifleman lt's measurements (shot holes

I did not record wing-flashing, a dispray sometimes given in the of predators and other contexts . . . because

in target) were both reliable (precise) and valid (accurate). Rifleman B's measurements were reliable. but inaccurate. Several potential observer erro[s, which may have severe effects on the results

ethological studies, have been discussed by Rosenthal (1976) and are depicted in Figure 8.5. Observer ef/bct is due to the visual presence of the observer, or other

stimuli (e.g. odor) lrom the observeq and results in a change in the animal's behavior. In psychology and sociology, this change in the subject's behavior is known as the How,thorn e.f.fect (Martin and Bateson, 1986). An indirect observer eflect was measured by Gotmark and Ahlund (1984). They determined wlrether observers attracted predators (crows and gulls) to the nests of common eic'lers (a result that would affect both the reproduction and behavior of the eiders); they tbund that human disturbance did increase loraging ef-fort and suscess lor gr-rlls, but n

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recording social interactions of

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focal individual tested in a four-nonkey group

Digit position

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Role of focal individual

Focal individual behavior

Interactor behavior

S

\

rrnsocial

Initiate with physical contact Inttiate - no physical contact Reciprocate - with physical contact Reciprocate - no physical contact Ignore - with physical contact Ignore - no contact

ID-direction

Passive

Passive

Nonsocial

Explore

Explore

Monkey

Withdraw

Withdraw

Monkey 2

Disturbance-fear

Disturbance-fear

Ro

ck-hu ddle

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sel

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Monkey 3 f-cl a sp

Monkey 4

Stereotypy

Stereotypy

Self

Play

Play

Toy

Sex

Sex

Ladder-shelf

Threat-aggression

Threat-aggression

Window

No response2

The four monkeys in the group are arbitmrily labeled from I to 4 as subject ID (SID) codes. : The no response category for the third-digit interactor behavior Code 9 rcfers to social interactions initiated by the focal subject that produce no change in behavior of the potential interactor from that occurring befor€ the initiation. S,x?,ce. Frcm Sacketr et al.(1973).

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On Mother

On Mother

Missed Total on & offRI (mothermoves)R2 (mother - rejects passively) ,i13 (mother pushes, etc,) F.i

9.1

I

Total )60 cms only

Total types

ol commercially available detectors which have been used to of animals (Fenton et al., 1973). These devices have

cletcct thc ultrasonic sounds

bccn uscrl in rcscarch on bats (Fenton, 1970; Kunzand Brock, 1975) and insects Scc Sirlcs antl Pye (19741ftir a review

(Klcirr. l()55).

( )ttc rtllr':rsor)ie tlctcclor is rrurrrtrlhe ttrrctl by [{olgates of Totton, Southampton, I lrrilr'tl Kinl'1l1v1,t ll trst's lr t'lr1l;rt'tlln('('nli('r'o;lltonc cirpirblc rll'rcsponcling to frc-

(lu('n(r('\lrclrvt't'rr l0;rnrl lXOLllz:r',rrt'll;rst'lt't'ltorut'lrrrrirrl', lolrnril lltcirrprrrl ll:utrl

i

ANALYSIS OF ANIMAL SOUNDS

DATA-COLLECTION EQU IPM ENT

width. Another that uses a crystal microphone adjusted for maximum sensitivity at 40kHzis manufactured by Alton Electronics Co., Gainesville, Florida. Paige et al' (1985) provide a schematic diagram for constructing an inexpensive, hand-held

(Figure 9.208). This provides only a relative measurements and says nothing about

ultrasonic detector.

versus frequency is the section display (Figure 9.20C). This display samples the

the actual intensity of the sound.

Another feature of the Sona-Graph that provides a relative measure of intensity recording at six or fewer predetermined points and presents relative amplitude as a

9.IO ANALYSIS OF

ANIMAL SOUNDS

available Several types of sound spectrographs and computer/software systems are below. described briefly are tools for the analysis of animal sounds. Some of these

down to 2.5 ms.

e.lo.l EquiPment e.to.ta Kay Sona-GraPh Model7029A for conThe Kay Sona-Graph Model 1029|(Figure 9.19A) is an electronic device an from input sound records It display. visual a to verting tape recorded sound reproduced is then sound recorded audio-tape recorder onto a metallic drum. The burns a by a stylus which scans the various frequencies across time and electrically on the vertical axis sheet of paper to produce a 'picture'of the sound with frequency

of representing frequency on a on a linear scale, although reproduced is usually

axis. You have the option

and time on the horizontal linear or logarithmic scale;

it

Marshall (1917) argued for use of the log scale. The frequency range and duration spectrogram produced depends on the speed at which you set the

of the sound

a sound metallic recording drum to spin while recording from the tape' For example' of duration have a will axis vertical on the spectrogram which displays 20-2000 Hz several produce It will 9.6 s, and an 80-8000 Hz display will have a duration of 2.4 s. mechanitypes of display, all of which are useful in the analysis of vocalizations or

cal sounds (e.g. grasshopper calls)' 9'204' The norntal clispla-v of the Sona-Graph is the sonagram shown in Figure (pitch) reprcis is represented on the horizontal axis (l s/mark), frequency

Time

sented on the vertical axis

horizontal mark at each frequency, inverted from the normal sonagraph (frequency increasing from the top to the bottom of the paper). Note that the section through the bark shows a much wider range of frequencies than that through the howl. Marler and Isaac (1960) describe a device for modifying the sound spectrograph to make frequency-versus-amplitude sections serially through a syllable at intervals

(1

kHzlmark), and amplitude (intensity)

is represetrtetl hy

l4'5 crtt x the blackness of the mark. Sound spectrograms are produced on paper 9'20' Figure in 32.4 cm.only a portion of which is shown From the sonagrams above we can see that the coyote's howl bcgittl with thrcc bcirtg clcirt'ly bursts of energy over several frequencies, with none ol'the fi'eqtrencics llcgrttt 111 11 1'r-'l:ldefined;these are essentially introductory'barks'. The howl portiotl t'cttt;rirtctl lirt tively low frequeng:y and then rose to approximzrtcly l.(r kllz. wltcl'c it o11'sllrrrPly. tltrrppctl approximately two secgncls, at which point thc l'r'ctltrcrtcy lrlltt'kttt'ss)' tt't' Sincc thc dillcrcnce bctwccn intcrrsitics is one ol'rle1',11'1'(rt'l;rtivc

ci,l .sc lltc r,plrlrrrr t'rli:ylttt'lrl rlt'littt'lrlr';tlt';ts ()l ('(lllill llll('ll\llV lll \('\('ll l'l:l(lillt(tll\

Sections are useful for determining the relative amplitude of frequencies in a particular syllable or sonagram. However, they cannot be used to make absolute measurements (e.g. number of decibels) without considerable difficulty, and they should not be used for comparisons between sonagrams since they are affected by the investigator's choice of settings on the sound spectrograph. Vocalizatktn terntinologv has been rather inconsistently applied, with few authors using similar terms. Kroodsma (1977) used the terms in Figure 9.2lAto

detail song development in the song sparrow. These terms are similar to those used by Rice and Thompson (1968) for indigo bunting vocalizations. Although not

totally satisfactory (Kroodsma, pers. commun.), these terms are applicable to vocalizations of numerous other species and are uselul in sonagram analysis. Temporal patterns are extremely important in insect sounds. Bentley and Hoy (1972) developed the terminology in Figure 9.218 for their study of the genetic control of cricket

(Te

leogryllus gryl lus) song patterns.

The Kay Sona-Graph Model 1029Ahas been used for more than two decades in ethology, but it is no longer being manufactured (although limited parts are available; Kay Elemetrics Corp.

-

address below). Although more sophisticated equip-

ment is now being marketed (see below), used 7029A machines may still be available, and they are satislactory for analyzing sounds in many studies (e.g. Miller l994,Payne and Payne 1993).

e.tl.th Kay

DSP Sona-Graph Model550A

The Kay DSP Sona-Graph (Figure 9.198) is a workstation that combines a real-time souncl spectrograph, a computer-based data-acquisition system and a dual channel

I;li'l'rrnlrlyzcr'. Sounrl input is stored digitally for display and analysis, and it can be tlowrrlo:rtlctl lo lrnollrcr corrrl'rtr(cr lirr storlgc tlr analysis by other computer prof,,liuns (st't'lrt'lorv). l( is l ln('nrr tlrivr'rr svst('nl llrlr( tlisplrrys rlscilltlgrtnrs (wave lirttttr). ('(rnt(!ut l)o\\'('t sPt'r'lttun',,ur(1 .'1x'r'lt{r|riun\ rrrr lltc vitletl lll()l)il()t'llLtt citn



Fig.9.19B Kay Elemetrics DSP Sona-Graph Model 5500 interface with a microcomputer.

then be printed. It has a history of use in ethological studies (e.g. Brenowitz and Rose, 1994) and is available with several hardware and software options. The newer

CFL 4300

Model

is a completely computer-based system

that may replace the DSP 5500 for animal sound analysis (Kay Elemetrics Corp., l2 Maple Ave. Pine Brook, NJ 07058).

9.I0.tc Uniscan II and Ubiquitous Spectrum Analyzer Two sound-analysis machines that are no longer being manufactured, but may still be available for use, are the Uniscan II and the Ubiquitous Spectrum Analyzer. Both can be used for analyzing animal sounds. The Uniscan II (Multigon Industries Inc.) system includes a keyboard, processor, monitor and printer. It produces a real-time display of a sound spectrogram to

the monitor or printer in several selectable frequency ranges. Any 1.6 4(XX) Fig.9.19A Kay Elemetrics 70294 Sona-Graph and LJher

lutlio-trtpc rccoltlcl

second

scglttcttt can be liozen on the display for measurements of frequency and duration. I Ihitltritous is the trade narre for the Federal Scientific Spectrum Analyzer.It is a

t'cltl-lirnc. titnc-corttprcssion scirnrrirrg unalyzer which can be used for analysis of rttrtttutl \'()ellli/iltirtns willr llre ;rtltlrtiorr ol'rr tlispllry syslcll (tlrtpkins rl u1..1974). A tltl'tl;rlsvstt'rtt is rrst'tl lo sPt't'tl rrp tlrc rrl'rrrl. iul(l itrr;rrr;rlop,s\slct)l swccl)s tltc tirttc-

11

DATA-COLT-ECTION EQU IPM ENT


A

Note comPlex

Trill

Phrase

Phrase

'r_ I x53-

-r+.

t I

IJ

I *{

281

Trill

Note complex

Phrase

Phrase

'+

{ l'-Syllable

'n'*,fu Ini

tr!!'fufi'

I

l'* Syllable 1.0 s

B

kJc ffi

Et-

F .;.

E iFr

F * r

k

Fig.

-

Pulse

9.21 Terminology used in a study of song development in the song sparrow (from Kroodsma, l9T7): B. Diagram of structural components and terminology of Telegryllu,s songs: upper line: T. ot'eonit'u.y; lower line: t'ommodus.lnterchirp interval : interval between onset of A-pulses. Intrachirp interval : interval between onset of B-pulses. Intertrill interval-interval between onset of last Bpulse in one trill and the first pulse of the next trill (from Bentley and Hoy,1972).

F

I

D

frr-*

l

compressed signal through a filter. Spectrograms can be displayed on a storage

s

oscilloscope or photographed for permanent copy. Narrow and wide bandwidth analyses are possible, and section displays can be made at intervals as short as 3.125 ms.

Fig.

'l display of the same howl; C. Section display abovc thc norttutl tlrspliry. itrte i' marked on the horizontal axis in or-rc-sccoutl itttcrvltls. lrrctlttcttcy is tttlttkctl ol' the vertical irxis ip onc kllZ irrtcrvirls. (Scc tcrl lirt'irrt erlllirlt:tlttttt ol llrt'l!pt's,,1

One advantage of this system is the speed at which spectrograms can be produced. Hopkins et al. (1974) report that a 2.4-second-long spectrogram take approxittrrrtcly 1.3 minutes to analyze on the Kay SonagraphT02gA and only 9.6 sccotttls ott tho I Ibiquitous. Another advantage is the relative ease with which scctiott tlisplays c:rn bc proclucecl. Spectrograms produced by the Ubiquitous are gt;rittir't tluttt lltosc ptrrtltrcctl hy tlre Sonirgnrph: however, this apparently does not

sottttrl s;lccl rrlgt'rtltts.

;rlli't'l utlcr

9.20 A. Normal display sound spectrogram (sonagram) o[ a coyotc howl:

)

lJ.

('otttottt

grtr'l;rl iort ( I loPk ins,'1 ,r/

.

l

()7.1 )


DATA-COLLECTION EQUIPM ENT

283

redraw the trace and compare lor its accuracy in representing the original spectro-

g.t0.td Desktop computers

gram.

Desktop computers, commonly the IBM-PC (and compatibles) and the Apple Macintosh, are frequently used with specially designed software to store and

Duration measurements are generally made from wide band pass spectrograms. However, the mark intensity can affect the measurements if they are either under- or

analyze animal sounds digitally.

over-burned.

Digitizing tape recordings of animal sounds is accomplished with an analog-todigital (A/D) converter, often a circuit board inserted into the computer where the

All the measurements described above (and many more) can be made more quickly and accurately with a desktop computer and special software.

sound will be stored. For example, Drosophila courtship songs were digitized using

a Campbridge Electronics Design 1401 (Ritchie and Kyriacou,

1994) and a

Canopus Sound Master (Tomaru and Oguma, 1994), and Gerhardt et al. (1994) used a Soundfx interface board to digitize tree frog calls.

9.10.3 Computer analysis

of recorded sounds

A specialized field of data analysis

has developed around the use

ers in bioacoustics. Software currently exists to permit 9.10.2 Analysis

vals between them; and 3. relative intensities

of portions of the sound. They

can

also be used to compare components of the sounds between and within individual animals. Hall-Craggs (1979) provides several useful suggestions for basic sound spectrogram analysis, and Thompson (1979) offers suggestions for preparing sound spectrograms for publication. The techniques described below involve using hand-

operated mechanical devices (e.g. rulers, calipers, computer stylus) and human judgement. Although time consuming and generally less accurate than computer analysis, they may be suitable for some studies. Frequencies are measured from either a narrow band.filter display on a normal spectrogram or from a section display. Transparent overlay grids are useful in the

making of these measurements. Frequency measurement is more accurate when lower-frequency spreads are used for display (e.9.20-2000 versus. 160-16000 Hz). Contour displays are often useful to determine more accurately the dominant frequencies when large areas are burned. Horii (1914) described a method for producing digital sound spectrograms with simultaneous plotting of intensity and

fundamental frequency. A digitizer system (X. Y cursor, Teletype and computer) was used by Field (1976) to analyze sound spectrograms of wolf vocalizations. Thc cursor is moved along a selected frequency band (e.g., dominant frequency). Thc X. axes

of the cursor's plane of movement

input of animal sounds from it is digitized and stored,

a tape recorder or microphone to a microcomputer where

of sound spectrograms

Sound spectrograms (hardcopy) are generally used to measure: 1. frequencies (Hz), both dominant frequencies and harmonics; 2. durations of sounds and time inter-

Y

of microcomput-

represent time and treclucncy. rcspcc-

tively. The operator depresses a button at predetermined points along tltc tt'ltcc, ittttl the X, Y coordinates are transmitted to the compute r lilr storugc ittttl pt'itllctl rtttl otl

using hardware such as the Unisonic (for IBM-PC and compatibiles), MacRecorder digitizer, or various analog-to-digital interface boards (see section 9.10.1d); the maximum length of sound that can be digitized and stored at one time is limited to

the computer's available random access memory (RAM). Once the sound is digitally stored, the soltware can quickly produce spectrographs of frequency, time and intensity, and oscillographs of time, amplitude and frequency. These spectrographs and oscillographs can then be printed out or analyzed further. Some software can make matches between sound segments (e.g. MATCH; Payne and Payne, 1993) and produce three-dimensional visualizations of sound measurements.

Another important aspect of this bioacoustical software is the ability to manipulate sounds which have been digitized and stored in the microcomputer. Portions of the sound can be deleted, duplicated, moved, reversed or frequency-altered at the touch of a key. The modified sound can then be played directly to an animal or fed back into a tape recorder with a built-in (or peripheral) digital-to-analog converter (e.g.

Allan and Simmons, 1994; Randall, 1994). Sounds can be created from scratch

using these programs, or even more easily and inexpensively, using any of the large number of music programs on the market. Davis ( 1986) describes the Personal Acoustics Lab (PAL), which is a microcomputer based system lor digital signal acquisition, analysis and synthesis. Some of the commercially available sound analysis soltware packages are listed below:

'

('unur1, (Apple Macintosh)

Ilioacoustics Research Program

('orncll Labr>ratory of Ornithology

the teletype. Field (1976) usctl an ovcrlayirrg gritl to locitlc coot'tlitutlc s:tttlplt' poitlls

l5() Sirpsrrckcr Woocls I{rl.

evcry 0.0-5 sccrlntl. Thc grcirlcr tlrc vlrt'ilrliott itt l'tetlttcttt'y ol lltt'sotttltl, lltt'tttolt' ol'lcrt llrc r'orlrrlirurlt's slrorrltl llt'slrrrrlllt'rl I ltt't'.r.rttltnltlt'st'lr t'ltll lltt'tt lrt'ttst'rl l,r

Itlurcrr. NY l-1ti50

' i'lttt

,\1tt'r't

lt I

rtlt

(A;rplt' M;lt tttlpslt

)

DATA-COLLECTION EQUI

PM

PHOTOGRAPHY

ENT

GW Instruments P.O. Box 2145 264 Msgr

record environmentalconditions, lens used, film type, shutter speed, lens opening

(/

stop), filters, and any exposure compensations made. This log may be kept as part

of

your field notebook.

O'Brien HwY #8

The techniques of good photography are beyond the scope of this book; com-

Cambridge, MA 02141

plete and useful discussions can be found in Blaker (1976) and Anonymous (1970).

STGNAL (IBM-PC)

Engineering Design

e.ll.l

43 Newton St.

Belmont. MA 02178 SountlEdit v.2.0.3 (Apple Macintosh; can edit frequencies only from 0-11

KHz; not designed for computer analyses) Farallon ComPuting

The most useful still camera for the ethologist is the 35-mm single lens reflex (SLR)

camera. A distinct advantage of the SLR camera is that the image you see in the viewfinder is the same (93-100'2, accuracy) as the image that is recorded on the film. The SLR camera is also compact, lightweight and versatile. It accepts

2150 Kittredge St.

film types

Berkeley, CA94704 SoundEdit Pro (APPle Macintosh)

(see

a

large variety

of

following sections), although the most commonly used is color-slide

film. Ideally, an ethologist entering the field in an unfamiliar area should be prepared with two camerasloadedwithdifferent types of filmdependingupon the proposed use

MacroMind ParacomP, Inc.

of the photos or slides. Typically, a black-and-white negative film for black-and-white prints is kept in one camera and a color slide film for presentations is loaded in the

600 Townsend St. San Francisco,

Still photography

CA 94103

Although most sound-analysis programs will perform the functions described in greater previous paragraphs, some are easier to use, have faster sampling rates and dynamic ranges, and have additional graphic and analysis capabilities;for example, more Weary and Weisman (1993) state that MacSpeech Lab and SIGNAL are ,sophisticated'software packages than SoundEdit v.2.0.3. You should obtain additional information from researchers who have used the software, as well as the dis-

tributors of the software. before choosing a software package to use or purchase, available sometimes demonstration programs (shortened. simplified versions) are

for you to trY.

other. Color prints can also be made from the slides, if necessary. It is helpful if both

cameraswill accept the same lenses

so

that theycan be easilychanged orinterchanged.

There are a large number of makes and models of 35-mm SLR on the market today, most of which have their own group of ardent supporters. Nikon and Leica

quality and versatility; however, Leica Minolta, Canon, Pentax and Olympus are other camera manufacturers to consider seriously, each having within their systems the necessary equipment lor simple to complex photography. These camera brands will have a complete line of accessories to cover your photographic needs, including a wide variety of lenses, motor drives (automatic film advance), and flashes. The following features are excellent camera systems known for their

is very expensive.

should be considered necessities in any camera you use or purchase: 9.I

I

PHOTOGRAPHY research. Pictures shotrlcl

Ethologists should make a photographic record of their are ncccsbe sharp, well composed and suitable for reproduction if needed. Prints orttl prclirr useful (slides) very are sary for publication, while color transparencies sentations.

photos should depict the: l. study site; 2. animals studied; 3. cqtriprttctrt ittttl methodolo gyl4.results of data analysis (tables and figures). ancl 5. yottr itttct'Pt'ctrthe tion of the results (e.g. models; Chapter l8). As photcls arc takctt it log sltottltl lrtkt'tt wrts thc wlry Plloto kept of photo number, {ate. time. location. suhicct nr:tttcr. (i.e. what yoll were trying trl tlcpict)irntl whirl itt pitt'licttlltl yott sllottlrl ttttlt'u'ltt'tl lllilV you sec t5c tprnsl-lltrcllcv tlr'pl'irrl. ln;rtltlitiott. lo itttPtovt'l'ttltttt'Pltolos, \'()ll

t Maximum z Automatic

shutter speed of at least l/l000th second. and manualexposure settings with a maximum lens opening

of

at least./ 1.9; that is, the/'stops to go as low asfl.9.

r Through-the-lens light meter. .t Black camera body to reduce reflections

and glare directed to the animal.

Tlrc lirllowing are optional features to be considered: Irlcctnrnic cable release - enables remote firing of the camera (connects to I

ltc rttrtl ot' tl rivc).

l)epllr-ol-licltl prcvicw Ptr.'r it'rv

grcrrrrits y()u

lo stop thc lcns down rnanually to

tlt'Ptlr-rrl lit'ltl rvtllt lt I'tvt'tt / slolt.

286

PHOTOGRAPHY

DATA-COLLECTION EQUIPM ENT

Interchangeable finder screens - allows replacement of a split- image focusing Screen with a clear matt Screen for easier focusing. is, Data back - provides on frame information when photo is taken; that provided (information settings exposure number, date, time, and

frame

depends upon capability of different backs)' rubber Water resistence or waterproof - some cameras are sealed with

for a gaskets that resist leakage to moderate depths underwater. A rating camera if the and rain heavy in depth of only 3 meters will be worthwhile is dropped in a stream.

l5 years ago' Computer Today, cameras have advanced far beyond the cameras of functions includchips, instead of manual mechanisms, control most of the camera's

OM-3, Canon Fing fbcus, exposure and flash photography. However, the Olympus therefore l. and the pentax K- 1000, have mechanically controlled shutter speeds and does however K-1000, Pentax The rely on batteries only to run the exposure meter. design years of not accept a motor drive or data back. Electronic cameras, through Their ability to and testing, have reached a high level of reliability and performance. auto-focus, and self-adjust the exposure in difhcult and contrasty lighting conditions, However, since research. in tool imprint data on a photo make them a very valuable that recommended is it electronic (automatic) cameras rely on batteries to function, batteries be replaced yearly and spare batteries be kept on hand.

All of these exposure metering systems will, under normal lighting conditions,

give

you the correct exposure. However, when selecting a camera for use or purchase determine whether that camera has the system that best meets your needs. Lenses play an

important role in the quality of your photographs. The quality of

a lens varies optically and in durability. A very expensive camera will take poorquality photos if a poor-quality lens is used. The 'standard' Iens that is most often supplied with a 35 mm camera is a 50 mm focal length lens. It is considered to have a normal perspective (angle-of-view). Any lens that has a focal length longer than 50 mm is a'telephoto'lens, while a lens with a shorter focal length is a'wide angle'lens.

Wide angle lenses are used where a wider perspective is desired (e.g. habitat photos, photos in tight quarters), and telephoto lens are used to magnify subjects that are

far away. The magnification of an object is directionally proportional to the focal length of the lens; that is, a lens with twice the focal length will double the magnification (e.9. a 100 mm lens produces twice the magnification of a 50 mm lens). Zoom lenses have variable focal lengths built into them, such as a 28 80mm zoom lens. Their advantage is that you can carry one or two zoom lenses rather than several fixed focal length lenses. The disadvantage of zoom lenses is that their quality varies greatly and is often not as good as a fixed focal length lens. Commonly used zoom lenses are 28-80 mm and 80-200 mm.

An accessory which many ethologists find useful is a motor-drive unit which automatically advances the film after each shot is taken. A motor drive comes builtin to some cameras. The speed of film advancement ranges from 1.5 to 5 frames-per-

in The majority of auto-focus cameras have the ability to self-focus accurately available focusing manual standard near dark conditions; they will usually have the choices liom fgll also. Exposure metering systems in cameras give you a variety of electronmanual exposures to fully automatic exposures controlled by the camera's and systems metering exposure of types several ics. The following list describes

to maintain continual observation of the animal(s) through the viewfinder without moving it to advance the fllm manually. You can then concentrate on the animal's behavior and photograph carefully selected behavior units, especially sequences, for

defines their [unction:

later analysis or presentations.

Standard program - the camera sets both shutter speed and lens aperture with a bias of hand-held shutter speed of l/125 s or above. Z Wide program - the camera sets both shutter speed and lens aperture with a bias of smaller aperture over shutter speed lor greater depth-ol'-

I

:

field. Tele program

-

the camera sets both shutter speed and lens apcrturc

witlt

bias towards liigher shutter speeds to freeze action' tllc Shutter-priority auto - you set the shutter speed and the calllcril scts lens aperture.

Aperature-priority

auto

you set thc lcns rlpcrttll'c (/ slpp) lirl. tlcpllt-ol'

second. Besides allowing you to take photos very rapidly, a motor drive allows you

Automatic film advances are necessary when cameras are left set up in the field and are triggered by an animal's activity. For example, Savidge and Seibert (1988) used an infrared device to trigger a camera that photographed predators when they visited artifical nests. Electronic.flashes are helpful when additional illumination is necessary and the subject is within range of the flash output. For nocturnal animals, this may be the

only means to photograph them in their natural habitat; however, flashes are likely to alter their behavior. Photographing small animals (e.g. field mice) by natural light otiert procluccs unsatisfactory photos. The combination of a slower film for quality rrnrl rt snritll apcrturc lor clepth-of-field lorces you to use a slow shutter speed and a

field ancl the cuptcrit sclccts tltc c6r't'cct slttttlct'spcctl. Mtrlrrirlcxp()sr1'c y6rr scl lrollt llte sltttllt't slleetl ;ttttl lltt'l('lls;llx'lllll('

tripotl lirt'strppot'l. A llirsh will allow thc usc ol'a small aperture lorgreaterdepthol-licltl. lrctlt't tlclrril on low lilllrt srrlrjet'ts. rrtrtl tlrc rrbility to ll'cczc nrovcn-rcnt with a

witlr tlte p'ttttl;tllt'e ol tlrt'lrrrrll ttt lt1'ltl lll('l('l

Ittl'11r.',

slttttlct sPt't'tl.'l

tV

lr, lr\('il ll;r'-lr llt;rl

ts

lltt's;rntt'lllrrttl

lrs

llrt'(',lln)cl:t so lllrl il

i.J

oo oo

-.1

?1?#g

o

1

g

i EEi Ei EE1BE1E EE1FEi ?11z1it?i11*i g

iI i;

g

E

EE

i

E EEEEE

g

i I E+e Er I E

zt;iica+ +il

--

g

sI

q

EEE

rn

o .l

i

EE

a

i

i

'

Table 9.8. Selccred,(o d1k black-and+,hitu rtlm for use in 35 mm still camerus Definition Speed

ISO

r-\t

\x 100

T:.-\ Pan

T-\IAX

400

of

Sharpness

enlargement

Very high

Very high

Very high

High

For prints. A good all-around film that combines reasonable speed with high definition qualites

Extremely

Extremely high

125

Very fine

High Very

32

Degree

power

fine

P.::t.tt trnr iC-X

P.-:-\ Pan

Resolving

Graininess

allowed Suggested uses

Very high

High

Sharper than PLUS-X

Medium

Very high

Moderate

For prints. Its major quality is high speed which can be pushed to ASA 800 in some cameras. It can be used in very low light (e.g. forest) or to stop

Fine

Medium

Very high

Moderate

Sharper than TRI-X Can be pushed up ro 1600 ISO

Very fine

400

motion (e.g. running antelope)

i-l15 Recording 1000 tEstar-Ah Base)

Contrast

Coarse

Low

Very high

Low

For prints. This is a poor-quality film which has only its speed to recommend it. It should be used only when very low light or high speed call lor it

Fine

High

Very high

Very high

For copying printed materials (e.g. photos, charts, tables, drawings, etc.). Useful in preparing visual aids for presentations and field trips

3200

Medium

Medium

High

Low to Moderate

multi-

coarse

Has an ISO Range of 1000 6400. Allows photography in very low light at high speeds with good results

64

Copy Film 5069

T-MAX

P3200

it will

fine Medium

100

to high

Hi_eh

For prints. With a special reversing process produce slides. It shoudl be used when rhe emphasis is on very-high-quality prints for publication or enlargement

400

speed

rn ^.)

=,=liTEE,txlEi'gE66EEEIliil1tllatzitz

F:.:l

z = z rn

z -t

Table 9.9. Selected color-reversalfilmfor use in 35 mm still cameras Definition

Daylight ISO

Graininess

power

Sharpness

of enlargementallowed Suggesteduses

25

Extremely

High

High

Slides2

Resolving

speed

Film Kt-rdachrome

25

Type of picture and degree

Has high color quality and a wide exposure

latitude. It should be used under most daylight

fine

conditions when sufficient light is available and fast

High

motion does not need to be stopped Kodiichrome

64

Extremely

64

High

High

Slides2

Combines good definition with relatively high speed.

It

does not have the color

High

so

it should be

fine

F..,:-ichrome

100

Extremely

100

Medium

Medium

Slides

Should be used as a substitute for Kodachrome-64

Moderate

when you expect to do your own processing

fine

! r::.-hrtrme 200

Extremely

200r

Medium

Medium

For use in dim light, shade, or to stop rapid

Slides

movement; also with telephotos lacking large lens

fine

I - :chr..nre -i0

Extremely

50

High

High

fine

Extremely

High

High

fine

l-..

;;:

ISO

be pushed to -100

1 _-:-:-=,.:.. ::-r:.;:n

openings, in order to increase depth

Slides2

Same as Kodachrome 25.

High

Excellent color rendition

Slides2

Good definition with higher speeds. Excellent blues

High

and greens. Can be pushed to ISO 200 with good results

uith special processing.

11s.. be t-rbtained throu-sh an

tili g

additional process.

{Ei q 3q= E€ g;rE x; a1"; ?

5

+

. Ft (D

=

€

*'= E Z3Ei i c-g g

?

f 3{

= 1q3

=

?'

a:

3

c

E ='4 e';as.*1 rESg

(J

e,J

X

+

a

(D

o-

E

::r-lidId

3 si

+fi:io A; :i q5;

=E

x=xYa;:l=. =rD=x(v):fro 3?ii6=)6'=^

rDQ-O-O_-i^a:

E- &

aD!,i.aXA:-!

NJ

UJ

x € 3P+E L-H

-IJ()^;^L

{z9ItsE = gE-=) = xx=ocb_]J

3 # ,v 3

:-+(,

qiFi* 1;= t.a i? EsZlirsErir ii i;it8-ii'ei=q iii7 5a [=E ;iig ei 11 iliEi Ell ig i6EEg ;tg sg *iei:+Es ia;x }f ;iii: ie {;Ef tsi=35:; is ril;i Egf Ei

Y

E

o(D

H

="

=i

of field

Moderate

E; [=sg liEgE lisF i =t s 7 tiz,Z|:i : i 1 ! aii 9 i I r =: r i. I i Ei l I 11 i e?iii5g ii*iig sEcr: x is #: + iia sE r J ''. i.;;I ). a a = j o = '==-z jE a=''' 7 i t':; oe, i V {= af -#iE fi: i:fII = .iE i-!r6I;a;i=1 riE+ii!3;i i: i 13 2i5 i 6t rE ZlfrE 1(??ti+: :;? I 7i:= EE = i = r sElEi+ryE i1fli; e1 iaE =:? +;FE;F,aqsEB? =fi?iE5ie= E* gEg5 ,..?iE:Ii=B ;i'gET;i itr i_._E[;,*g: iEZ i-1ZLE??=€ +;

?=i

quality of Kodachrome 25,

used only when extra speed is necessary

lr

V-L-J

cv--;\:f:.O-O aD--=

il

a

\3

o F,t (D

@

x a F-t CD

NJ il

? a

vAAA

6-

6 x_M

) 5 0 a 3 5)-{ xFSd; .LLlXcT 58 r.-B:H x==? Z +

i-P56S.

3i.g

d, x f B'

=-rdJ='.D oj 3aI H o :E +2. *' ? = ?

=3 -

B-$

il

9.5-

,, + :7

i

PHOTOGRAPHY

DATA-COLLECTION EQUIPMENT

Static electricity caused by rewinding film too rapidly in cold weather will cause Table 9.lO Reciprocity ancl rec'ontmenclecl f stop correc'tirttts.for 35 rum color Jilms

streaks or dots on the film. Also, X-ray inspection units in airports, despite their claims, may damage film whether it is new or exposed. X-ray damage is cumulative

Film type

1s

l0s

Kodachrome 25

*% stop

*2

Kodachrome 64

* % stop * 1 stop * % stop

N/A

Ektachrome 400

*% stop

* lrl

Fujichrome 50

No change No change No chage

*% stop *% stop

viewing. A cataloging system will organize your photos and may be based on: l. separate research projects; 2. behavior types; 3. species; or 4. field seasons.

* 1 stop

Computer programs are available that will catalog by numbers and captions, search fbr specific slides, and print out labels lor slides. Ethologists must develop a system

Ektachrome 100 Ektachrome 200

Fujichrome

100

Fujichrome 400

*

and may not show up until after additional exposure. Check film through by hand

or protect it in special, lead-lined bags available at camera stores.

stops

Prints, negatives and slides should be kept in a cool, dry area where they will be sale from damage, but where they can be easily retrieved. Store negatives and slides

1rl stops

in archival. plastic pages which can be put into three-ring binders or stored in a file cabinet. This will protect your original photos and provide for easy access and

N/A stops

Note: One stop:doubling the exposure

Reciprocityfailure is the loss of a film's light sensitivity during long exposures, normally longer than one second. This can be corrected by doubling the exposure (differing time for shots of one second. or more. Color films may show a color-shift sensitivity to different wavelengths of light) during exposures over two seconds. Table 9.10 lists the reciprocity and recommended J' stop corrections for several common films: Most negative films require an increase of one stop with exposures longer than one second. In additio n, inJiarefl films are available for special uses. Kodak High Speed Infrared film is available in 36-exposure rolls for 35-mm cameras. It is fine-grained with moderately high contrast, and medium resolving power and sharpness' It can be used to photograph through haze or to record behavior of nocturnal animals lighted by infrared bulbs. The speed of the film is highly variable, depending on the ratio of visible to infrared light available. Storageis an important consideration lor all types of film. All films are damagcd packby high temperature and high humidity. Films can be obtained in vapor-tight extctttls filnr Refrigerating aging if you anticipate working in areas of high humidity. its useful life well beyond the expiration date printed on the box. Betol'e using lilrn that has been refrigerated, allow 2-3 hours for the film to reach anrbicnt tcrllpcrrtKotlir k ture in it's plastic container (condensation may form on the film il' retllovccl). lilnr: and whitc makes the following storage recommendations for black

For storage periods of uP Keep lilm

below:

ttl:

2

7'5

l;

6

I?

60 Ir

50 l"

tttottllts

Kccp lilrrr lrwlry l'nlnt irrtlrrstri;rl llrtst's. nt()l()t t'rlt;tttsl.;tttrl tltl,rlr ol lllotlrlr;rllr lirr nt;rlrlt.lrl,rlt', solrt'ttlr, t lt'ltll('l\. ilttrl ttttltlt'tt ()l llllll'll" l)l('\('lll'tlt\t'''

which they find most useful. In addition, attempt to reduce possible losses or damage in the mail by sending photos properly packaged in separate packages: if possible. send duplicates instead of original slides and prints instead of negatives. Another storage medium is provided by digital photograph'!-. As examples, Kodak's models DCS 420 and DCS 460 (high resolution) combine digital imaging with a Nikon N90 SLR camera body. They are available in monochrome, color and infrared models which store the images on removable 170 MB RAM cards. One card will store from 30 high-resolution images (6 million pixels; DCS 460) to 100 images (DCS 420): by changing storage cards, 300 images can be captured on a single I hour battery charge. Using appropriate interfaces, the images can be downloaded to Apple Macintosh il, Powerbook. Quadra and IBM-PC (and compatibles) computers. They can then be used in computer displays, made into prints or slides, or stored in portfolio CDs. Additional information can be obtained frorn: Digital & Applied Imaging, L&MS. MC 00532, Eastman Kodak Co., PO Box 92894, Rochester,

NY

14692-9939.

e.tr.2 Motion-picture photography The obvious advantage of both motion pictures and videotape is that they allow you to record a two-dimensional visual representation of entire behavior patterns. The two-dimensional restriction can be overcome, in part. by the use of two or more

stratcgically located cameras. In addition, it provides the capacity synchronously to rccortl souncl (produced by the animals or the environs, or dictated by the observer).

llult

rrntl

llult

(1974) list fivc situatit'rr.rs in which motion pictures and videotape

;rrc especilrll-y rrsel'rrl: l. swil.l :tcliorr: 2. conrplcx actiorr; 3. strbtle bchavioral t'll;ttt1'1'r''.1. t'otultlt'r lrr'lrrrviot;tl s('(lu('n('('\. rtnrl 5. lltc rtcctl lilt'grt'ccisc nrcitsurcntt'trls rll P:rr ittil('l('t\ I lft'lfr',1 r ltort ('\(ril lt,t\t'lrr nr,rLr'r', llr, lrltrr ',r.', l,rr trr,tl \rllt \\,lltl trl tt\(' l ltr'trr.r

PHOTOGRAPHY

DATA-COLLECTION EQUI PMENT

Table

9.ll

Relative advantage of Super 8 mm and I6 mmfilming

Super 8 mm

l6 mm

1. Cheaper cameras, film and processing

l.

Pictures with greater sharpness,

2. Lighter equiPment

2.

resolution, and definition Pictures brighter when projected

to same

3.

29s

Convenience of cartridge film

size

3. Cameras often more durable 4. Film often easier to handle for editing and analYsis

5. Larger film caPacitY 6. Better for sync. sound

film has essentially disapbasic choices are 16 mm and Super 8 mm; standard 8-mm 9' 11; however, it is basiTable in listed are each of peared. The relative advantages

(16 mm)' If you cally a choice between lower cost (Super 8 mm) and higher quality If you intend don't intend to do much filming, borrow or rent a Super 8 mm camera' 16 mm (Figure to make filming an integral part of your studies and can afford it, use

e.22). and In selecting a camera you will be confronted with a trade-off between cost etc')' durability' certain features (e.g. lenses, built-in exposure meter, filming speeds,

what you need, these features are discussed below Remember to purchase but not more than you need. Also, if possible, try before you buy. for your equipLensesshould be selected with an eye toward the uses you intend such as 10 lenses, the camera has a lens turret, then you might select three Some

of

ment. If necessary to mm (wide-angle), 26mm(standard) ,andJ5 mm (telephoto). It may be Kloot 1964) or 1000 mm' use a telephoto lens as large as 600 mm (Dan and van der (26 mm to 75 or 100 mm) is zoom a If the camera will handle only one lens, then tubes are very useful. If you are working with insects, a close-up lens and extension g.fl'l ' often desirable. Select high-quality lenses with large apertures approachin thc confuse as to great so is movie-making for available of

The diversity

neophyte. Selection

filnts

of the proper film

is generally a trade-otT between film speetl

(amount of light necessary for proper exposure) and picture cluality' Illirck-antlwhile color lilltt white film is cheaper to purchase but more expensive ttl proccss. bttl ol'tctt provides an additional dimension which is not only csthctictlly Plcirsittg lilrrrs tlrrrl Kotl;tk ol' rr lisl provitlcs 9.12 necessary in some ethological studics. Tahlc t'rrtt lrt' lilttts slrr't'irtlizctl nr()t't. are uselirl lirr lilnting llinrll bclrirvior'. Atltlrtiorrrrl. (ittttlr" trtl: lllttlt't lirtrrrrl irr Ii.irstrrr,rr K,tlitk's lttr[lit'lrtigtt /( i I , hrtrhtl, 1t1,,'1,11',l.rtltlttt r''ttl lrt'tl"t'tl ltl "'lt Its u't'llits llolll olltt't ttt;tttttl;tr'ltll('l\ l(rl ('\:ltttl,11" tttlt;ttt'rlltltrr

Fig.9.22 Bolex H-16 l6 rnm movie camera with 75 mm telephoto lens.

junction with infrared lighting (e.g photofloods and infrared filters) to obtain motion pictures under nocturnal conditions. (Delgado and Delgado 1964). The.filnring speetlyou choose will depend on the purpose of the filming. Normal projection speeds for Super 8 mm and l6 mm are l8 and 24 frames/second, respectively. The ellect of accelerated motion is produced by filming a slower speeds (e.g. 2 l0 frames/second), and slow motion is produced with greater filming speeds (e.g. 32-64 frames/second). If you are interested in frame-by-frame analysis (see below) then the faster you film. the smaller the change in the animal's position from frame to frame. Faster filming speeds also allow for unsteadiness by the cameraman; but it means changing film more olten and increased costs in purchasing and processing the larger amount of film.

Various filming speeds and the authors'rationale for their use can be found in

of frames per second. 2-7 or 48 (EiblEibesfeldt, 1972), l6 (Clayton, 1976. Havkin and Fentress, 1985), l8 (Fleishman, 1988), 22 (Diakow, 1975), 24 (Kruijt, 1964; Dane and Van der Kloot" 196{), 32 (Duncan ancl Wood-Gush, 1gl2),64 (Bekoff, 1917a),128 (Hildebrand, 1965), and

the literature. For example, in terms

800 ancl 1000 (Grobecker and Pietsch . 1979) have all been used. Time-lapse photog-

nrphy can be used to clbtain instantaneous samples of behavior over extended pcriorls ol' linrc (ulso sce scction 9.11.3. on lilr-r-r analysis). For example, Capen (l()7ti) ttserl rrtt i"i ttrlrr rrrovic cluncl'irs scl lo llrkc lr ll'lrnc lrl citlrcr rlrtc-. l.-5 or twtltttittrrtt'nrl('rvill', trr ltrs slrrrlv ol rrt'stllrl' l',r''1r,tr',.rr irr rrltilt'rllisr.'s. Wlrerr llrr't';rntcr';rs \\('1r".('l,tllt\l tttttlttlt'tttlt'tt.rl', lrr,',1,t\'.,t1 l,lt,rlr,'.tr,rtlrllrr'olrl;11111'llllotttottr'ltlttt

PHOTOGRAPHY


Table 9.12. Selected Kodak rever,vul motion-picture.films Daylight

Speed (lSO)

Film

16 mm/

Super

8

Characteristics

Suggested uses

High degree of sharp-

General outdoor

Black-and-rt:hite

Plus-X

I

6/8

ness. good contrast.

2.5.

Fig.

of the bower to a reflector. When the beam is interrupted, a super-8 motion-picture camera exposes one lranre every two seconds. Birds were also observed from blinds. The system enabled the researcher to monitor the behavior and identity of bower owners and visitors at 33 bowers for the -50 day mating season. The researchers are now using a more sophisticated system based on videocameras. From Borgia ( 1986). Copyright O 1986 by Scientific American, Inc. All rights reserved.

gradation 200

l618

Excellent tonal gradation

9.23 The monitoring system used in a study of the satin bowerbird. An inlrared beam, invisible to the bowerbird, is projected through the avenue

photography

and excellent tonal

Tri-X

3 METERS

Under adverse lighting conditions

Color Kodachrome

Good color rendition

40

Ceneral outdoor photography

40

[]ktachrome

Higher speed

Adverse lighting

Good color rendition

General outdoor

160

l6

E,ktachrome

and sharp images

1239

Ektachrome

400

High speed

photography Adverse lighting

high speed

and high-speed

daylight

photography

cartridge. Borgia ( 1986) used an infrared system to trigger a super 8 motion picturc camera (Figure 9.23) in his study of bowerbird behavior' Both Super 8 mm and l6 mm films and cameras are available tbr simultitneotts recording on a sound truc'k. The sound reproduction is generally not of high quality.

but can be useful for recording the observer's commentary durillg lilnling. (iootlquality sound recordings are best made with l6 mm cameras (e.9.. Bolcx Il-16' Figure 9.22) that will synchronize with a high-quality tape recordcr. sttclt rts tltc Nagra IV-L.

(Milinski,

individual movements (Hailnnn,l96J', Havkin and Fentress, (Hildebrand, 1965; see also Chapter l0) and social displays (Barlow, 1977: Bekoff, l9l7 a,b); c) intra-individual sequences (Tinbergen, 1960a); Balgooyen,l9l6); (d) inter-individual sequences (Diakow 1975): and (e) 1984); (b)

1985), including locomotion

spatial relationships (Dane and Van der Kloot, 1964). Analysis of film is conducted either frame-by-frame or by sampling frames at regular intervals, e.g. every 24th frame (Golani, 1973).If frames are to be selected at intervals for analysis, an intervalometer can be coupled with the camera to expose frames at set intervals (Figure 9.24). This provides a more efficient use of film. Steele and Partridge (1988) projected Super-8

film of courting male Drosophila

onto the underside of a glass table and copied the males'movements onto tracing paper;from these tracings they measured each r.nale's maximum angular lag and top speed during their courtship dance. Analyses are generally conducted with either film editors that have a built-in projection screen (Hutt and Hutt, 1914). an optical data analyzer (e.g. LW International; Milinski, 1984;Havkin and Fentress, 1985), or an analyzer-projector (e.g. Lafayette Analyzer; Lafayette Instrument Co., Lafayette, Indiana). The latter projects the film onto a large screen (Dane and Van cler

Kloot.

1964). Whichever system is used,

cor,rrtter. Thc fiame counter, coupled with the

it should

have a reverse and a frame

filming speed, provides

a time base

lor

nreasuring the Iatencies, durations and inter-act periods of behaviors.

A rligitizing tablet can be used to record directly into a computer, data on the e.t

sprttirtl positiort ol'un irninral

1.3 Film analysis

Ethologists take motittn pictLrrcs lor bitsicrrllV two l)ttl'l)()ses: l. lo ltltvt'rt vtstt;tl recrtrcl o1'thc hclrirvior lirr illrrslt'trlivc l)lll'l)()ses (ptt'sr'ttltlti()lls;tttrl pttlrlit';tllolls, t'1' .1.M. l)lryis lr)75): lrrrtl/pr

.) lirr lulrlv:ts ol (lrl spr't tltr' ttt,ltvtrltt;tl

lrt'ltrtt'tot'.

r>r part of its body. For example, Fleishman ( 1988) digitizerl llre ltc;rtl rrrrtl tlcwlrrl'r 1'rositiort ol-tlispllying /troli,t' lizards lrom Super-8 movie llttttt's. ( l ltt'sr'siun('l('('llrritlrrt's rrrr'rlt'st'tilretl lirr vitlcotll-lcs in ir lirtcr section.)

It;ttttt'l11'lt;ttttt'lrtt;tlYstslt;tslrr','rtil\('(ll()nt('itsUt('lltt'tttovetttcrtlsrll'lltcl0ngttc

VIDEOTAPE RECORDING AND ANALYSIS

DATA.COLLECTION EQUIPM ENT

299

actively interacting. If this were the case, correlations which actually exist might be overlooked. (4) Finally, though unlikely, a movement which was too subtle to be detected on the film, might be a stimulus for another individual. IDune and Van der Kloot, 1964.285J

A computer system lor frame-by-frame analysis of film, FIDAC (Ledley, 1965), has been described by Watt (1966). The system consists

of

a cathode-ray-tube gen-

erator which projects an ordered array of rows and columns of spots of light through the film frame, where the intensity of the light transmitted is measured by a photocell as one of seven different levels of gray. This information is then transmitted to a digital computer. The computer can be programmed to control the location of the array of spots of light, their density in the array, and the area covered. The system has both high speed and high resolution. This system, or a similar one, may

find useful application in ethological studies of movement where the animal

is

filmed against a light background.

Fig.9.Z4 An 8 mm sequence camera and intervalonteter inside a weatherproof housing.

In summary, I have not mentioned the vast array of additional equipment (e.g. light meters, fllters, tripods) that may be necessary for proper filming. These items should be discussed with your local camera dealer. Likewise, the various techniques which will improve your motion pictures and their analysis can best be gathered through discussions, experience and literature (Dewsbury, 1975; Matzkin, 1975: Wildi,

of boas (Csnstrictt)r constrictor) (Ulinski,1972) and the loot of a mollusc (Cardium echinatum) (Ansell, 1967).Illustrations of the results of their analyses are shown in Figure 9.25. Head movements relative to particular behaviors have been analyzed frame by frame lor the Burmese red jungle fowl (Galltt,; gallus spadic'eous) (Krut1t, 1964),laughing full chick (Laru,s utricillu) (Hailman, 1967). and domestic duck

(Clayton. 1 97 6 ; Figu r e 9 .26). Spatial relationships between courting goldeneyes were Ineasured by Dane ancl Van der Kloot (1964)by projecting film frame by frame onto a screen that they hacl divided with 16 equally spaced vertical lines. Distances perpendicular to the

(

A nas p la r y r hy nc' h o s)

camera's line of sight are relatively easy to lneasllre; but the perspective tlf depth is lost in measurements parallel to the line of sight. Dane and Van cler Kloot list tlthcr' complications and restrictions which are common to sin'rilar types ttl' {rlni atlalvsis:

(l) Birds are olten passing in and out of the field of'vicw ol'thc cttrltcril. When the final analysis is undertaken, there is always thc cltittlcc lltrtl lttt action given by a bir* ,-J

Ia a

i

/ r-'TP

R

FLICK CLUSTER

3

a

t

I

I

7

/''+Pl

/P\ / ---->/ / ,''l_--

rpa

/'-:a>J l' '

/

J:-

/'',-'"\

aV "l ,

'--,,1

7.5 cm.

END FLlCK CLUSTER

7

Fig.9.254 Pattern of boa tongue movements in lateral view. Tracings of each frame in motion pictures of two complete flick clusters are illustrated. Successive pictures are about 42 ms apart in time. The ends of the protrusion phase (P), the oscillation phase (O), and the retraction phase (R) are indicated by vertical lines. The figures should be studied from left to right in each line. (from Ulinski, 1972).

The lightweight, compact, battery-powered VHS-C and 8 mm cassette palnrcorders (Figure 9.27) make videotaping in the field relatively easy. Motor drivcrr

I:ig 9.25B Thc ntovements ol the foot of the bivalve mollusc, Cartliunt e(hinutum,during a singlc lcap. shown with relerence to the shell as a fixed object. The positions were titkctt ll'om motion picture of the movcment, the numbers indicating the number ol' t hc ll'itme corresponding to each position ( I 6 frames/second). Active (frames l2 lo 2.j)rttttl rccovery (lrames 23 to 50) are shown separately (from Ansell,196l\.

zoom lenses allow the researcher to obtain a broad or locused vicw ol' bclurvior'. Built-in microphones record environmental sounds (those from thc uninrirls lrc

svslcttt spccilically clcsigned lbr field use. It is an integrated, all weather, compact, tertl-litttc v'itlco tttortitot', r'cnrotc camera (infrared and visible light sensitive) and

generally not of sufficient quality for analysis) and also allow thc rcsclrrcltct' lo rtt:rke

rr't'ortlirr;:systerrr. llirttcricswillpowertheexternalcamerasystemlorupto20hours

verbal notations while recording. Although these populur canrcortlcrs

;ttltl lltt'r'lttt)t'riirlcl'ltttrl Ittoniltlr lilr up to l2 h; the camcorder will recorcl up to 120 lirlx' S('\'('rrrl lf iclrlclrrtt syslcrrrs irrc lrv;rilablc ll-or-r-r Fuhrman Diversifiecl.

ir

rc rclrrt ive lv

resistant to moisture and light impact. tlicy urc rtot rlcsignctl lirr llrc lt:rtslt t'orttli tionstowhich manyfielcl cthologists nriglrI cxposc lltcrn. As witlt still('iun('r'irs. v()u shoulcl chcck thc clr;l:rhililics lirr l)11)l)e r rrst' ltttl tr'sisllur('(' lo ;rlrrrst' lirr ;rttY t:rtrt cot'tlct' V()u ittt' t'ottsitlt'titt1' ttslt1, l ltt' liit'ltlt lun rs irrr ti rrrrrr t'l,rr..'rl t rrt rrrl vrtlt'rr

llrler tllrl:r rccrlrtling

virlt'ot:tpt'rl ll ll-i lr

VIDEOTAPE RECORDING AND ANALYSIS


27

I 18

Table 9.13. Relative advantages and disadvantages of videotape and ntovie .film ethological studies

for

Movie Film

Videotape

17 16

I

Advantages

l.

Immediate playback

1. Better quality

2.

Reusable

2. Easily analyzable frame by frame providing an accurate time base for studies of movements 3. With wind-up cameras, time in the field limited only by the amount of film 4. Equipment generally light

6

5

3. Tape relatively inexpensive

a

3

4. Easily duplicated Disadvantages

l.

I 1

This composite Fis.9.26 The duckling's drinking response illustrating the bill-lift element. line drawing is based on frames liom a motion picture lilm (16 frames/second). The sequence ol numbers corresponds to the frame numbers beginning as the bill leaves the water (from Clayton, 1976)'

videotrials in their study of mating behavior in water striders; they point out that taping 'allows the detection and accurate quantification of short-lived behaviour patterns and continuous monitoring of behaviour of long durations'(p.895)' Data from videotapes can be recorded on check sheets or input directly into a computer

using standard data-collection programs (see section 9'10'1d)' For example'

comRoberts (lgg4), in his research on vigilance sequences in sanderlings, used a puter-based event recording System to record the times of behavior events from videotape. Also. several specialized systems and programs have been designed the specifically for recording data from videotapes. Krauss et ul. (1988) describe

hardware and software

of a

computerized multichannel event rectlrder'

lor analyzingvideotapes. It records a starting and stoppitrg sigtlal on audio track of the videotape to mark the beginrritlg atrtl clltl ol' tltc

Videologger,

the second

its itrtcrrrlrl segment being analyzed. The microcomputer uses thc sigtritls to t'csct store in rnem6ry tlrc tlnscl tirnc ittttl tlttl'ltliott ol'kcyllt'csscs lot ltttv clock ancl

I( wirs tlcsigncrl to ltrrr ()n lttl Apgrte llt'otttllttlr.'t lrttl rVlttt lt c0nvctlerl lql lttl ltlM lirt tlr;tt Ilrt'svslt'tll t'ollslsls ol liVt'solitt;tl('l)l(),'l;tttts ruunrbcr o('hcltlrvi0r.s.

t'ltrt lrt'

1. Time delay lbr developing

Poorer-quality picture with less expensive video recorders

l.

2. Film

Most now analyzable frame by

usable only once

and stop action

l.

3. Film relatively

Equipment run off batteries with

expensive

limited chargeable life

4. Duplicating more

-1. Equipment sometimes heavy

expensive

;rrc available gratis from the authors. The Behavior Chronicles software (see section (). 10.

lcl) includes a videotape analysis mode in which the computer screen clock is

svrrchronized with the VCR and an icon on the computer screen allows the r('scarcher to control the VCR with the computer's mouse. Scvcral programs designed for recording data from videotapes are available comi:rlly. CAME,RA is a system which includes software and a keyboard which the r('scrrchcr intcrlaces with an IBM-PC; each button on the keyboard generates a ',r,untl with rr unicluc pitch providing the researcher with immediate auditory feedrrrr're

lr;rt'k.

('n MlrltA wts

reviewed by van der Vlugt et al. (1992) and is available from

l'ro(iAMMn. l'.(). Ilox 841, 9700 AV Groningen, The Netherlands. PRO( ()l{l)l:lt is:rnollrcrptl)gratrlirrrecorclingbehavioraldatafromvideotape;itwas r,'r'rr'\r't'tl lrv lrrpp rrrrtl Wrlrlcn ( lt)t).1)irrtcl is:rvailable lrom Jon Tapp and Associates ,

/,r hrrr

I;r1rp. l0(r l.ibclty l.trne.

l.rrve

t'grtc.

'l'N

370tJ6. Nolclus Inlormation

l,'. lrtr,r1111'1' lrllcr s lr Vitlt',r'l;ryrt' Atutlvsis Svsl('nt lirt' ttsc lvitlr'l'lrc Ohscrvt:r 3.0 stlli\\,r!(' rl t',;tr;ttl:tltlt'ttt Ilttr'r'rltllt'tt'ttl rrl)ll()ll l,lt, kltl't's

VIDEOTAPE RECORDING AND ANAI-YSIS

DATA-COLLECTION EQU I PM ENT

Angle of attack

palmcorder, model PV-S62' to Fig.9.21 Stephanie Bestelmeyer using a Panasonic VHS-C behavior. record waterfowl researcher's Videotape can be reviewed at slower or faster Speeds to enhance the her study ( in ability to observe and measure behavior. For example, Grandin 1989)' of pigs, found that high-speed reviewing of videotape recorded at 0'9 frames/s

I

rr ().lll Schematic representation ol a spoonbill's sweeping, showing the various geometric parameters. Also shown are the simulated prey items placed on the bottom to test displacement (undisturbed pattern on the left) and the bill tip vortcx streamlines, indicating shell motion on the right. U: Sweeping velocity; L: lili; A A: cross section of the bill; D: distance of the tip of the bill to bottom; VTX: induced vortex; SH: empty snail shells ('prey') (from Weihs and Katzir, l99zl). Copyrighted by Academic Press.

of the revealed subtle nosing and rooting movements as easily seen vibrations (1994) to Katzir and Weihs snout. Frame-by-frame analysis of videotape allowed (Figure demonstrate the hydrodynamic function of bill sweeping in the spoonbill 9.28).

samples of Time-lapse vicleotaping is often useful to obtain instantaneous/scan time-lapse (1987) used Grant over long periods of time. For example,

behavior

a beagle bitch video-recording to provide a 'continuous' record of the behavior of has also been used and her pups over a three-week period. Time-lapse vicleotaping red jr-rnglein studies of the behavior of calves (Dellmeier et crl..l985), and Burmese

fbwl (Hogan and Boxel, 1993). trsitrg Movements ancl spatial relationships of animals are frequetrtly tlleasttrccl lttl 9tt itttitltitl videotapes. Earlier, researchers often trace

q N

co

o o .f

c{

o q (o

!,7

il l, ti

o -Q

jI,il

So '6 I

ilt/

C)

o.

fl

i(r

t

(\,

)'"

o lrl/

ol ol

lul

T1

lt

EI (ol

,,J

!\*

eJ

sl Fig.

10.8

I

The Eskol Wachmann coordiuate system superimposed on a owl's head in order to denote the movement. In this example the head has moved 90" to the lelt from point; to point i. (S"e text for explanation.)

t0

24.00

l()

-16.00

-8.00 0.00

8.00

16.00

24.00 32.00

width

Front-vicw ol' Cal-comp graph of body position for two human subjects (fiom

Trochim. 1976).

o

2

0

--t

t0.9 Eshkol-Wachmann notation lor

o

6 the owl's head movement in Figure 10.8.

Measurements of dominant subordinant relationships are gerierally conducted an using one, or both, of the following methods (contexts): l. the resettrchcr stages is. clrch equal number of dyadic interactions between all individuals in a grotrp: that

individual is matched with every other individual in the group att ctluitl trttlttbct'ol' times (e.g. Smith and Hale, 1969); or 2. the researcher rectlrds trittttrltlly occttrittg interactions in wild or captive groLlps (c.g. ('hcncy irnrl Scyllrrlh. l()()0). soltrt'litttcs manipulating thc cpvironnrcn( lo cncoul'lp,c crtttllict (e.g. irllt'otlttt'ilt1', rr lirrrilt'tl 'llris scr',rrtl rrrt'llrorl l'('n('rilllv tt'srrlts itt tltllt'tt'tll rrr.rtlrr,t rll' prcle'r.rctl lilotl). Itrt,tltr,ts ()l i.l(,1.(.li.rrs llt.lryt,t'rr rltllr.tt.ll ,lt;r,lrt r ontlrnlttltottr ol ttttltVttltt;tl', {t'1'

llr,rnrcks ancl Hunte. 1983). The two methods can provide conflicting data. For in two of six llocks of chickens studied by Guhl ( 1953), he found correlaI rr,rrs bctwccn numbcr of contests won (method I ) and number of individuals domrrr,rlcrl rn tlrc l)ock (n-rethod 2) were less than 0.50. Data collection in the second rrirturirl. gnrrrp) contcrt is nrore tinre consuming. but it will generally allow you to

, r.rrrrplc.

,

(,n.,lrucl lr nr()l'c virlrtl hicrarchy. llrc helurvior':rl trnits sclected fi>r measurement (e.g. Hausfater, 1975), the

r .1111'ql(s) rrr

u'lriclr irrlcrircliorrs urc obsel'ved (described above), and the criteria of

,l,,lllllf :rrrt'r' lrrvr' vrrrrctl rvitlcly (llckoll. lt)llb, Kaufhrann, 1983). Ivan Chase (pers. r r,nunun ) lr;rs srrl'1'1'slerl tlurl lltcrc ltt'c lrt lclst tltt'cc tnethttcls of deciding when one

.rrur,rl

lr.r'. tlorrrrrr;tlt'rl iur()lll('r'rlrrtittp,;ul irpprcssivc inlct':tctirltt. Irirst. yoLl cein use

l)rlr,u \ \('l urlrrrlrrr'. t rrlt'r lotr lr;rsr'rl rr;lorr ollsr'tt;tliotts ol'tltc itttitttitls'behitvIot ('\,unIl1'. ('lr;rst'(l')S ))rrst'rl llrt'l,rll,r\\nll'( lrl('ulr in ltts sttttly ol'lricnuclty

,rrr .rr

I',t l,

r1 111,11

lr

)ll ltl ltt'tt r lttt l.t'lt',

SOCIAL BEHAVIOR

EXAMPLESoFDATACoLLECTIONANDDESCRIPTION

One animal was considered to dominate another if she: (l) delivered any jump ons, combination of three strong aggressive contact actions (pecks, and claws) to the other and (2) there was a 30 minute period following

Table 10.1. An example of a dyadic interac'tion ntutrix (see text.for explanation) Loser

the third action during which the receiver of the actions did not attack IChase, l9B2:220] the initiator. an Secondly, you can use a binomial approach; that is, in each aggressive interaction e.g' fleeing; or submission (based on individual is scored as either a winner or loser it wins 1963). An animal is considered dominant over another individual if

Brown,

significantly more (e.g. binomial test; see chapter l4) than it loses in encounters with the other individual. Thirdly. Chase has suggested that you could use a combination of the first two methods. priAnother common criterion for the expression of a dominance relationship is ority of access to a limited resource (e.g. lbod, shelter, space, estrus female, etc')' demonstrated through the supplanting of one individual by another without overt aggression being displayed (see Richards' l0 measures and below). Dominance does not always provide priority of access to all valued resources; hence, there may be dilferent dominant-subordinant relation-

Priority of

access can be

D D Winner

10. I ).

C 24

E

0

A

2t

11

C

t2

B

5t

t6 3l

a

J

0

0

13

0

0

0

t4

The dyadic interaction matrix that results provides the basis for generating a

tIominance hierarchy.

Brown (197 5) provided the following list of steps to lollow in the construction of rr clyadic interaction matrix (dominance matrix):

limited

Observation.r:

BlD. C>A, B>A, C>B, B>D, etc.: BID

(Huntinglord and Turneq 1987). When individuals are ranked by different criteria, the rankings are often not comparable (Bekoff, 1977b; Bernstein, 1970; Syme, 1974). However, Richards of (1974) used the ten factors listed below to assess dominance rank in six groups

an encounter

with D. In most

captive rhesus monkeys and lound that they produced comparable results'

matrix. as illustrated in Table 10.1.

ships for different resources

t

Starting matrix'. Enter the number of wins and loses observed in the Treutment of'reversals: A win by one individual over another that has won the majority of encounters with the first is termed a reversal. Rearrange the order so that only reversals fall below the diagonal, so far as possible;

that is, change the above order to CBDAE or CBEDA or CBAED. Trt'utntent of intransivity: An order in which an individual dominates another (wins the majority of encounters) that dominates the first is tcnncd intransitive or circular. Rearrange to minimize the inevitable

encounters

rrnrbiguity. F'rom the circular relationship shown in Figure

+ Gestures for fear-submission

l0.l I there are

llrrcc nurin irlte rnatives, as shown. In the three alternatives not shown. the

a Yielding ground/avoidances b Cautious aPProaches c Nonsexual presentations/mountings d Fear-grins lo.2.2b Dominance hierarchies tlctcttrtirttrtp, The data collectecl on aggrcssive intcrirclions. blsctl ort lltc ct'itcrilr lirr (lrr'[',1()tll) is listt'tl ilr itrtlivitlrr;rl l'lrt'lt nr:rlrix. lr irrlo crrlcrctl winncr-s rrntl loscr.s. lrc

Irl.tt',t.:rt'ltltxisol'tlrt.nt:trti\.()n(.;t\lslsllrllr'lr'rl

forms of

Starting order: Choose an arbitrary order, e.g. DEACB.

Order to approach experimenter during food offers

z Agonistic : DisPlaYs

means B won

cases, these encounters take the

supplanting rather than fighting.

PrioritY to food incentives a Order to dailY food ration b Order to milk bottle c Interactions at milk bottle

d

0

t7 4t

\\nllr('r'.. lltt'otltt't lost'ts(st't'l;tlrlt'

I

) ---=---=. [i 24

lr,'lttll

I )t.tl,t,tttt,,1 llr,' rttlt,tn',tlttr' lroll llrr' trr,rlrrr ttr l,rlrlr' lll

rrrtlivirlrrlrls

A. I) lrrrtl l:

SOCIAL BEHAVIOR

ITXAMPLE,S OF DATA COLLECTION AND DESCRIPTION

332

o

t33

one' Place departure from linearity involves two individuals rather than pro(lowest relationships ambiguous the individuals that are in tlie least the minimize to tends procedure portion of reversals) in linear orcler. This

inclividuals except the alpha, the third-ranking individual (gamma) dominates all inclividuals except alpha and beta. and so on down the hierarchy. In nonlinear lricrarchies. there are one or more intransitive (circular) relationships, such as indi-

total number of encounters entered below the diagonal. Final mati.r'.The one order that best reflects the order of dorninance within the gror.rp is then CBADE. A matrix may then be constructed'

vidr-ral

A dominating B, and B dominating C, but C dominating A. Rankings in a lricrarchy and type of hierarchy can change. For example, Murchison (1935) found tlrat the ranking in a group of six domestic fowl roosters changed from a nonlinear Io a linear hierarchy as they matured (Figure 10. l2). Perfectly linear hierarchies are relatively rare, making most hierarchies techni-

Best

r'llly nonlinear. Perfectly linear hierarchies are unidirectional. They can contain

DE

rcvcrsals (i.e., a subordinate wins an occasional encounter with a dominant individ-

D

A D

A

E

E

D

A

rutl), but they cannot contain any individuals of equal status or have any circularity ''trclt as: A---B---,C. The nonlinearity is, however, of varying degrees, and some so r

losel! approximate perfectly linear hierarchies that they should be considered

Irrrcitt'.

r

BADEWinsLosses

I-utrclau's index oJ' linearity has been discussed by Bekoff (1977b) and Chase l')71\. The index (ft) is calculated according to the following formula:

,r:(fr;)

59

C

109

t4

32

14

D

2l

70

l',

E

l3

t25

f

B

)[,

,-@-2lttlL

rr lrcl'C:

A

an ordinal scale The dominance hierarchies described above rank individuals on Boyd and Silk ( 1983) (see chapter 8);that is. C ranks above B, and B ranks above A' cardinal index of domdescribe a more complex, statisticalmethod lbr generating a

paired comparisons' It inance rank (versus the ordinal heirarchy above) based upon information on interactions that result in wins. losses ar-rd ties' They

incorporates

.then -r1.96, the kurtosis is not significantly rliflerent from zero at the 0.05 level; that is, this distribution of song durations is neither significantly leptokurtic nor platykurtic. Since the calculated

Z of -0.891

is

4.7000

0.6400

0.s 120

0.4096

5.1000

1.4400

1.7280

2.0736

3.2000

0.4900

-0.3430

0.2401

4.2000

0.0900

0.0270

0.0081

3.7000

0.0400

-0.0080

0.0016

0.0400

0.0080

0.0016

I-his test determines whether the variances

4.1000

0.3600

0.2160

rrre

4.5000

0.t296

3.6000

0.0900

-0.0270

0.0081

2.9000

1.0000

- 1.0000

1.0000

3.0000

0.8100

-0.1290

0.6561

39.0000

5.0000

0.3840

4.5284

If Z>t

t2.t.tb F-max

variance of Pop.

z

0'0384 :o.lo86 M1 on"o: o.50(o.7o7l) MrlM, __ 3)

:

:

.7

421:9.55

largest variance smallest

_0.68_ l '23 variance 0.55 ,

Obtain the tabular value of Flrom Table A4. Two diflerent degrees of

the smaller variance. In our case both degrees of freedom are 9

(cll'-

,_ SK - 0.1086 -0.1086-0.158 --Vvst 10 use: '',,':V-*,, If an1'measurement (10 use: *,,':f {t,,+0'5) normal distribution (e'g' Mendl' This transformation has also been used to create a 1988 ).

A nonparametric statistical test is a test whose modeldoes not specily conditions about the parameters of the population from which the sample was

drawn.

tsiegal, t9-t6..1l

I

Since there are lewer constraints on nonparametric tests, they are usually lcss powerful when used to analyze data where parametric tests are applicable (howcvcr

Blair and Higgins, 1985). Therefore, researchers often proceed with paranrctrie without having necessarily satisfied the four criteria listed above (p. 373). This is supportable, in part. by the fact that some parametric tests are robust; that is, they can be used with reasonable validity even when some ol the parametric model assumptions are violated. For example, Student's /-test can be used even when there is considerable deviation from normality and/or homogeneity of varianoe, except in an independent-samples design with unequal numbers of scores; however, analysis of variance is highly sensitive to the kurtosis of a population ( Govindarajulu, 197 6). Overall, there are several factors which should be considered when selecting tretween parametric and nonparametric tests. Gibbons (1993) has compiled a list which serves as the basis for a safe (yet sometimes conservative) guideline. According to Gibbons (1993) (Jse a nonparafiTetric statistic'al test then uny o./'tha see

tests

Iollow'ing zre true:

t2.l.2h Logarithmic ffanqformation

t

create a norntal distribution in The logarithmic transformation is generally used to used when the measurethe measurements (e.g. Lawrence 1985): it is commonly

categories: nominal scale of measurement).

z r

is given by: ments are skewed to the right. The transformed measurement

some

of

the measurements are zero. or very small" use:

rtnk rttlwrt.

n)()l'c rcprcscntutive than tlre mean.

irrrtl pl'()P()l'lirrtts (o ctclttc lt The arcsine transformatior-r is used with percentagcs

tlistrr' norrrrnldislrihuti,, (e.g.Shcrryt'ltrl..l98l:Mc.tll. lgltl{)'cs1'rcciirllVtvltctrtlre

\,r

.r;tlt'slttt'\

shapes of'the distributions from which the samples are drawn are

: TItc sarnple size is small. r' 'f ltc tncasurentents are imprecise. u l'hcrc ltrc outliers and/or extreme values in the data, making the median

2.1.2c At'csine transformation

blrti.n al-,-rcirsrrr.cnrurts

The assumptions required for the validity of the corresponding paramet-

+ The

,r,,':log,,,(x,rf l)

t

The data are measured on an ordinal scale.

ric procedure are not met or cannot be verified.

r -':log,u('t,r) If

The data are counts or frequencies of dilferent types of outcomes (i.e.

ts is birrorrriirl.'l'lrc lnrnslirt'tttctl tttt'ltsttte tttt'ttt

r,,

I'irt'tt lt\

ll

tlrt'tllrlrt rnccl llrc rrsstrnrpliorrs lirr prrrrrurctric tcsts then parametric

tests

will

lrr'tttr)r('l)()\\'('rlrtl. lr,ru't'r,r't. llrt'nrotr.'lltc rlltl:t viol:rlr'(ltc lrssrrrttl'rtions litr paramet-

ttt'lr'sls.lltt'tttt)t('l)()\\'('tlttllr,,tr;)iu;rttl('lttt (/;rr. l')S.l)

lt'rl',ltt'r'otttt'tt'l;tlirt'lrlllltt':ttttctt'ictcsts

SELEC.TION OF A STATISTICAL TT]ST

Powar-e/ficiency is a measure of the amount of increase in sample size necessary

to make test B as powerful as test tests which can be

I

t3 Parametric statistical tests

(Siegel, 1956). It can be used to compare any two

validly applied to the data, such as comparable parametric and

nonparametric tests (Welkowitz et al., 1916). Given that the data meet the criteria fbr use of parametric test, then fbr a given difference between population rleans, a given alpha level and a specified power, power-eflficiency is a ratio expressed as a percent as lollows: Power-efficiency of nonparametric Where:

{:sample P

t.rt:9x

size for the parametric test.

t/ilp :sample size necessary for the nonparametric test to make it as the

Since much of the data gathered in ethology do not meet the assumptions necessary to use parametric statistical tests, only a few of the more commonly applied para-

100"1,

N,,t,

as

metric tests are describecl below. Also, nonparametric statisticaltests can be applied to data which meet the assumptions for parametric tests; however, in those cases the parametric tests will be more powerful.

powerful

parametric test.

For example. if the parametric test requires a sample size of 80 and the nonparametric test requires a sample size of 100 to make it as powerful as the parametric test, then the power-efficiency is: Power-efficiency of nonparametric

In this

test:

case, the nonparametric test is 80'2, as

Jqx 100

I3.I CoMPLETELY RANDOM TZED DESIGN 100'Zr:80'Zr

13.r.l Two independent samples

powerlul as the parametric test.

The power-efficiency is provided for several of the nonparametric tests discussed in

tJ.l.te Standard ewor of

the

the foll owing ch apters.

dffirence betneen means

we can compare means from two samples ancl determine if they are significantly tlifrerent, that is. whether they came frorn significantly different populations or

rvhether there was a significant treatment eflect. The standard error of the difference means is computed according to the foilowing rormura:

rl'the

sEo-rri:-:

l, 1 //'t'-*t:-\ -

{ \1,r,

1\

l,r,

I

/

The symbols.r,z, s,2 and Np N, represent the variances and sample sizes of s:tttrplcs I and 2" respectively. If the difl-erence between the two means is larger than tNo titres the standard error of the difference. they are significantly different. Iirr exitmple. assume we want to test the research hypothesis

that the mean dura-

lr.tl ol'sottg bottts in Population A of a bird species is significantly different than it

rs

itt l)opttlittiort

IJ.

we randomly sample l0 males lrom each population and recorcl

llrt'tlttt':ttiorl ol'ottc ranclomly selected song bout from each male rr.trkl rr.r'rr:rlly rirkc, rnuch larger sample than this).

t

(see below; we

( 'rtlt'trlrrtc lllc lotitl rtttrl tttclrr song bout duration (in seconds) lor each poprrl;rtiorr

382

COM PLETELY RAN DOM

PARA M ETRIC STATISTICAL TESTS

Sample Ponulation

b

SamPle

A

PoPulatiort B

0.8

4.8

5.1

1.2

1.44

6.4

-1./

-0.1

0.49

5.3

4.2

0.3

0.09

3'l

3.1

-0.2

0.04

5.0

4.1

0.2

0.04

4.4

4.5

0.6

0.36

5.2

3.6

-0.3

0.09

4.9

2.9

-

4.7

3.0

-0.9

Total:49.0

39.0 3.9

sr:

(-t,-X)' 0.09

0.81

Total:ffi: f(.r,- X )r 9

J

We can then calculate the standard

0.01 2.25

sE--: /tn'*tu': 'A'B NB

0.16 3.24

error of the diflerence of the means:

floos.oi'ru): Vo. 124:0. r24:0.35

Vt

V/Vo

tO

The diflerence between the means (4.9-3.9:

0.01 0.25

sr-\A rR - :0.35x2:0.10;

0.09

significant.

1.0)

is larger than twice the

therefbre, the difference between the means is statistically

0.00 0.04

Total= 6.14-X

\,-.\'r)

!1"I {)':94:o.u, 9 N-l

,^:,/[

1.00

1.0

N-t

Sample: PoPulation A

(,.,-X) 0.3 -0.1 1.5 0.4 - 1.8 0.1 -0.5 0.3 0.0 -0.2

0.64

Ir.r,-xrr ' !_ : 5.00 :0.55

Calculate the stanclctrtl tleviution for both samples'

a

$,-*)

(x,- x )'

4.1

4.9

3ul

Sample: Population B:

5.2

Mean:X:

lZEl) I)l:S l( ; N

!''i

l'l:vo

t.1.t.lh Student's t-test Student's /-test is also used to test for significant difl-erences between two sets of data

comparison of means. We will use the same data on song duration ll'orn the two populations that we used in the previous examples. In those examples wc testecl lirr the assumptions of most parametric tests and found: 1. there is homo-

rund is based on a

oa:0 8]

gcrrcity ol' variirnr.:c bctween Populations A and B, and 2. the data from Population

Ii irrc rrcithcr siunilicantly skewed nor leptokurtic or platykurtic.

We should check

tlrc norrrr:rlitv ol'llrc riatit in Population A belore we proceed, but we also krtow that tlre l-test is srrllicicrrlly nrbrrst so llrat lhcsc r.rssunrptions can be violated to a reason;rlrlr.'t'xlr'rrl u'illrorrt ;rllt'c'tirrp'llrc vrrlitlity ol'lhc tcst. Also. all the lirctors in the l( )r

lulrl;r lt:rr t' lr l t t';trlV l)('('n

(';r

lt ul;rlt'rl ( rtlro1

1'

1.

(x^-x,)l( /t

COMPLETELY RAN DOM IZED I)trs t( ; N

ETRIC STATISTICAL TESTS

PA RA M

,ffi)

luo- I xsA:)+( Nu-

Table 13.1. Song bout tluration

| x,sB2)

(s)

Population samples

D

// ro, rot 4.9-3.9 | ! \ l0+ l0/

Row totals

(r)

I

/r

lo-

I

V

xo.7l )+( ro+

lo-

ro, -

4.7

3.9

5.1

18.9

5.1

4.2

5.9

20.0

6.4

3.2

3.9

4.8

18.3

5.3

4.2

3.1

'oV(^ l

5.2

17.8

3.1

_t.

I

3.6

4.9

15.3

5.0

4.1

4.1

5.3

18.5

4.4

4.5

3.2

5.4

t7.5 16.6

VL r8

|

l

(1.0\(2.24t 2.24i-:2-84 2.24 i-- ; v0.62 0.]e l(6.12+s.4 \

V\ r8 )

difference between means (above).

13.1.2 Three or more independent samples

3.6

3.0

4.8

2.9

2.7

5.2

15.7

4.7

3.0

2.9

5.5

l6.l

39.0

34.6

52.1

Table 13.2 Sum Source of variation

df

Mean square

lletween samples (columns)

BSSS

BSMS

Within samples

WSSS

WSMS

lirtal

TSS

tests fbr significant diflerences between

applied to a wide variety of experimental designs; see Meddis (1973) for a clear and

calculate the correction Term:

concise overview. The one-way ANOVA described in the example below is fbr thc

completely randomized design. We will once again use the hypothetical clata orr song duration that we used in the examples above; however, wc will expantl our hypothesis and samples from Populations A and B to include two nlorc popr.rlirtions C and D (Table 13.1).

z

of

squares

three or more independent samples of measurements. Variations of this test can be

t

114.7:GT

(rows)

l3.I.2a One-way analysis of variance

of variance (ANOVA)

5.2 4.9

Column totals(r):{9.9

We then obtain the tabular value for I from Table A,5 for l8 degrees of freedom (dl) and a significance level of P:0.05. Since our calculated I of 2.84 is larger than the tabular r value of 2.101, we conclude that the data are from two distinctly different populations. That is, song duration in Population A is statistically greater than it is in Population B; this agrees with our comparison using the standard error of the

One-way analysis

5.2 4.8

/r roo i /f rqrto.os r +r2y1o.s6)

l:

xo.s6)

I

Cirlctrlitlc lhc lrlt:rls lirr c:rclr slrrrrlllt'(t'olrrnrll.

tr

I

t

//:total

5.lr t4.lJr1(r.4r...

-5.5r

ll.0-l

.

17 0-l I

'/()li

.)

1

.10.()(r

.

10.25

I

( 'ttlt ttl;tlt'lltr'lol.rl,,urrt ol ,.rltr.rrt.., ( ISS)

,,,,

I iI I

number of measurements:40

('alculutc thc sum of squares of the measurements (),r,r2); that is, square circh ol'thc inclividual measurements and sum them.

)'\,,

Complete the analysis ol variance (Tahlc 13.2) hclow by nrlkirrg llrc r':rlculations in Steps 2 13. I{csults irrc lorrtttl irr 'ljrble I l.l

t ('ltlt'ttlrttt'lltt'tol;tls lirt t'rtt'lt r()\\' , | I ('rrlr rrlrrlt'llrt'1,1.ur(llol,rl(( i1 I t iI

whcre:

174'12 :763 CT:GT': N 40

ISS )r

( I

1,)li'l

'(,

I

r'',1

PARA M ETRIC STATISTICAL TESTS

RANDOMIZED BLOCK. MAT('ttt:t) t,n tRs

Calculate the between sample sum of squares (BSSS): L2+ t.2+ r,2+ r *^2

BSSS:''

'

n(,

both conditions (treatments) or they could be meersurements paired by some characteristic (e.g. litter, time, location).

-CT

t

where: n,,:number of scores in each column (sample) 240 t

+ t s2r + t te7l9

a)!4

_ t 63

Calculate:

: 20.36

It:

- l-o* 7115'', ! V N-I

Calculate the within samples sum of squares (WSSS):

N

WSSS:TSS-BSSS

:35.21-20.36:14.85

Where:

Caiculate the degrees of freedom (d0: Between-samples

df:Number of

samples (columns)-

1:3

l

Within-samples df:(Number of rows - 1)(Number of columns) : ( 1 0- I )(4): 36 Total df:Number of measurements (4,1- l:39

ll Calculate each mean square (MS) by dividing the sum of squares by the

il

D:diflerence between each pair of measurements D:mean of the diflerence between each pair ol measurements ly':number of pairs of measurements

z

Compare the calculated t to the tabular varue (Table A5), where df:N - 1. If the calculated value of r>tabular value. then the null hypothesis of no signiflcant difference between the samples is rejected.

Table 13.3

corresponding df Between samples mean square

Within

20.36

Sum

(BSMS):

-:6.78 y!:O.orrt samples mean square (WSMS): '36

Source of variation

l3

Fof

16.445 is larger than the tabular value

(2.81), we reject the null hypothesis of no significant clifl-crer.rce between the samples.

13.2

RANDOMIZED BLOCK. MATCHED PAIRS OIt I{l:l'>lrA'l'lrl) MEASURES DESIGN

l3.z.t Two rclated or matched samplcs 1.1.2.1t Paircil t-tast

l'lrt.1l:rirt.,l I tr'sl rs rrst'tl lo tt'sl lirr sil,tttltt:rnl rltllt'r('lt(("' lrr'lttt't'tt ltto tr'l;tlt'tl ot ttt;tlt llt'tl ";tttr1,l,"' llr."'t'

r

ilttltl lrt' ttlr',t',tll('lll('lll ' Ill lll'",tlll('

Within-samples

20.36

6.78

36

14.85

0.4t

39

35.21

16.44

(rows)

Total

16.445

Compare the calculated Fvalue to the tabular value (Table A6) using the between-samples df (3), the within-samples df (36)and the appropriate alpha level. The tabular value for P:0.05 is 2.87 (extrapolated lrom 2.92 and 2.84). Since our calculated

Mean square

(columns)

duration between the populations calculate the between-samples Fvalue:

t?TPlgnl4! : r: q:l:ttn Within-samples MS

of

squares

Between-samples

To test the hypothesis that there is a significant dillerence in song bout

Between-samples

dr

lll(ltt t'ltt'tl" rttttlt't

As an example, we will provide hypothetical data on songbird species

r

(Table

l3'4)' similar to that recorded by Reid (1987) lor Ipswich sparrows (pas.serculus princcp.r). our research question is whether time spent singing is greater than time spent fbraging in a habitat with an abundant food supply. We t'itttcloltlly selccted l0 individual males from a population of l8 and took focal,rundwiL'hcn,si,s

lrtlitllitl/itll-occurrences samples, measuring the time spent singing and foraging tlttriitg lltc ltours 06(X) to 0900. Total observation time for each male was l0 hours.

PA RA M

388

Table

RANDOMIZED BLOCK. MAT( llI:l) I)AlRS

ETRIC STATISTICAL TESTS

Table 13.5. Copulutions hy males w,ith eli//ert'nt mutirtg histories to virginJbmulc

hy ten male 13.4. H1-pothetical dota on time ,spent singing ancl.fctroging

monarch butterflies

songbirds

Singing

Individual

Foraging

pz

D

A

105

152

47

2209

B

97

202

r05

11025

C

ll5

117

2

D

95

233

E

120

105

l5

F

87

215

188

G H

103

176

89

260

I

t12

131

l9

J

109

139

30

| 032

| 190

Totals:

I

Time since

Total time 1min.)

138

t-)

t7l

788(rD)

985

1986

last mating

Number

0/

Number

/0

(days)

tested

Mated

tested

Matctl

0{
x)'] [Atrr

j]Y)21

Where: 3.162

_ 78.8 _78.8:3.66 67 .97 21.5 3.162

2'262' Silcc ttttr citlcttThe tabular / value (Table A5) for df:9 and P:0'05 is //,,' irtrtl cottcltrtlc tltit( the reject we lated f (3.66) is larger than the tabular value, singirrg. trrttl there is a significant difference between time spcnt lirraging itt ttl:tlc As another example, oberhauser (1988) sttrtlictl tttittittg slt'ltlcp,it's rtl'tttltlcs tttltlctl itl l')s(t tlt:rtt monarch butterflies ancl lilLrnrl thirt ir lowcr'1'rcrccrrl:rgc () ()ll)' ;l tt.sttll sltr' ;tlttilltttt.tl lrr lttl in 1985 (Trrhlc 13..5; |llrir.ctl l l.l' (ll. l.i. /, lrr, lltt'ttttltllrt't .l ttttttstrtlly t.rl.l srrnunt.r. Nolt. llutl llrr'nr('ir\lnr'rttt'ttl\ itl('l);lttt'rl

tllrVs stltt t' t lrt' llr'.t ttt:tl tttl'

l/: Xf: X: l: .\'r )"'

Number of pairs of scores

of the products of the paired scores of the scores of one varil,, -'(X) sr.rrtr of the scores of the other v. , iable ( Y) surn ol'the squared scores of the Xvariable sr.rnr ()l'thc squared scores of the I'variable sum

sunr

l'lrc nrn1,,t'ol'

r'is

l.(X) to + l.(X). antl thc sign of r denotes whether the correla-

Ironisposilivt'orrrcp,lrlive. l'lrcllrrgcr llrcr vrrlrrc.tltcnrorchighlycorrelatedarethe trro st'ls ol rl;rllr Ilrt'srllnilir'rurt'r'ol r (Irottt rl to llrt'r.rlrrr','l r. ur l;rl,lt','\

/

I lrr., r',.r

l\\,'

r

O)r'rrtt l'rr'tlclct'lrtittctl hy crtrtrpltt'ing

l,nlr'rl tt'sl li,t ;t stl,ttilit';tttl t'ott'cllt(iort.

rt';';rr,llt".', rtl lltr",tf,n ol r ll r t', l,rt1',', llr.rtr r llr,'rr \r)u r('l('( I llrr.'//, ;rtttl tottt'ltttlt' llr,rl llrr'l\\l t,tt t,tlrl,'', ( \ ) l,rt, ,t1'tttlt, ,rrrll\ ,,'t t''l,tl, rl

RAM ETRIC STATISTICAL TESI'S

PA

390

on a songbird species that As an example, we will use the same hypotheticaldata since this is only a hypothetical was used for the paired /-test (above). However, by l0 in order to keep the calculaexample, we will divide each of the measurements to calculate Pearson's coelficient tions more manageable. All the variables necessary assign silging as the X variable and are provicled in Table 13.6. We will arbitrarily

t4 Non par ametric statistical

tests

foraging as the Yvariable' t

:

tI

:Vtt :

-!t!1? [L?2!I - I 0rz'l t t ot :s 3s'94) -

I 0( I 7 87. 60)

V{

0(

r

oio-os)

l7 876.00- l8 472.80 ro zoo'so- ro oioi+L(-rs 3se'40-

va, ,r.*!"9' *o,,:

1

79'021

}

Nonparametric statistical tests are the most commonly used statistical tests in ethological research. They are simple tests that can easily be calculated by hand or with a

-32 041'00)l

hand-held calculator. This means they can be used reliably while you are in the field access to a computer.

without

;t'2#: -o'e8

Since nonparametric tests are so easily conducted, it is tenrpting to apply them to alldata. However, parametric statistical tests should be r-rsed when the data meet the

criteria (see section

Table 13.6 Total time (min.;

Male

Singing

Foragtng

(n

(v)

Y

XY

,n 231.04

r0.5

15.2

159.6t)

9.7

195.94

94.09

408.04

B

20.2

C

I 1.5

tr.1

134.55

t32.2s

136.89

90.25

542.89

l)

9.5

23.3

221.35

E

12.0

10.5

126.00

144.00

21.5

239.2s

G

10.3

11.6

181.28

106.09

309.16

8.9

26.0

231.40

19.21

676.00

H

I

11.2

13. I

146.12

125.44

171 .61

13.9

I 51.51

193.21

J

10.9

I 18.81

03.2

179.0

I 076.08

I535.94

Sums:

()x')

(;n

1

787.60

()xr)

GXN

One variable

l1.l.lq

One sample

7

(f l'l

S,

S,.,

,rl

:

-r,:

Sample of variable rl (or Treatment) Measurement on Inclividual No. I

_Y1

s6.25

8.7

r

l.t.l.l

I 10.25

15.69

F

l. I ), since they are more powerful.

I4,I COMPLETELY RANDOMIZED DESIGN

110.25

A

12.

:

.Y,,

()ttc .sutttpIL' run,t I(,tI 'l'his tcst tlctcrntines whether a sequence of two dillerent items (in time or space) is n()n-ril D(l()ln.

Slrrrplc sc(lr.rctrcc: A A B B B A B B B B A A B A The calculated coefficient

of -0.98

Since the calculated r.or- --0.gg is rargcr

inclicates a vcry strotlg ttcg:ttivc cttt't'clrttiott' tlic tubrrl.r'r', .r'0 (r-rr. u'e rr'rr'('t tlrr' //,,

trrir.

slrcrrr ar- na c.rrcl.titlrr irrtr c.rtclrrrrc thrrt lirrrc clt tlt lv. ttel'ltt ively t'ot.t t'l;ttt'tl'

lir*r,irr,

rrrrtl sirrl'rrrr';rr. sr,'rrili

As cxlrrrrplr.'s. !ltc sc(lucncc irbove cotrkl lrc the:

I '

St'r

1

t

t('tl('(' t tl' or'r'u

Sr't1ttt'nlt;tl,rs1lr,'I It'lt'Pltr)n('\\

t't

r'n('('

(

)l l tlo lrr'lrlrvirlrs

(

A. l|

).

ol ttr;rlt't,\)iut(ll('nlitlt'(ll)st;rrlingsg'lcrclrctl irlrtng:r

il('

1 St'rIl('nlr.rlurrlt'r

,'l ',1r,', 1,1 \ l.urrlr rrrr , tllt lrr,'tl rrl,rl rr lt'r'tl lrtttth

NON PARAM

COMPLETELY RAN DOM lZF.l) I) llS I ( ; N

ETRIC STATISTICA L T.I:STS

or separated in want to determine whether the two items are clustered chance' The two non-random the sequence more than would be expected by extremes,for the example above, would be:

ln any

case we

Table 14.1. Numbers of ./bmale grasshopper,\ thut hucl rutt given response chirps to males that subsequently mated or did not mute Trials which ended with:

BBBB Non-randomly clusten:r/: A A A A A A B B B B

q Non-randomly separared:B A A EA Example: Ho: The sheep

1

q4E4E4B

B

Mating

No mating

ll

23

in2h

Female did not give

bunk' (A) and cows (B) are randomly distributed along the feed

response stridulation Sourc'e: From

Determine the number of runs (r)'

Butlin et al. (1985).

AABBBABBBBAABA,,-,

T 2 3 4 Ti'7'-'

Table 14.2. Calc'ulation of chi-square .from data in Table

14.

I

Determine:

Q-D2

No:number of A items:6 Nu:number of B items:8

Response

o

A82) for No and N'' The Compare r with the tabular values (Tables A8t' (r) is significant at the 0.05 level, if: r is less than or equal

number of runs

Mating No mating

23

t7 t7

totheisvalueinTableA8,orrisgreaterthanorequaltothevaluein

'fotals

34

34

Table A8,.

than

Q-q2

-6

36

2.1

+6

36

2.1 18

l8

12

In our example, 7 is larger than 3 (Table 18,) and smaller (Table,48,);therefore,wecannotrejectthef/othatthesheepandcowsare

Expected

randomlY distributed' One samPle c'hi-stluare te'\t

can make one of only two This test is used to analyze clata in which individuals or right, fly or don,t fly). It should be used responses (e'g. accept or reject, turn left less than 5, use the binontial test when the expected values are >5. If they are only

the chi-squared statistic in (described below). Additional precautions when using (1992)' ethology are given by Kramer and Schmidhammer

(E):total

number of responses equally distributed between

each category

of response:

ll +23:34

3412:17

We would expect 17 to mate and 17 not to mate

if they had

an equal

(50:50) chance of reponding either way.

f :2:4.236 .t ('orrpare the calculated I to the the value in Table ,{9 with a degree of ll'ccclonr ol' I (df:no. categories- I ). If the calculated f is greater than or

:

. 2 \x.,-: -[(observed-exPetttq]-0'5]1 exPected

Calculate chi-square,

ctltral to thc tabular value, then the

F10

that there is no significant differ-

crrcc in thc lcnralcs' responses is rejected.

'l'ltc ('llculatcd

f'enlalc gritsshoppcrs For example, Butlin et al. (1935) measured whether ttl tl]alcs strhsctltrctrtly (Chorthippus brunnneus) that did not give respc-rnse chirps mated (Table 14.1).

lo

H,,: Females that clid not givc rcspottsc cllit'Ps nratc. as ttot tl-tittc.

I ('otttplt'tt'tltt'( l:rlrlt' I I )) ( )lr.'r'l tr',1 ( t )) t'' tltt'tl,tl'l ll(llll l 'rlrlt' I I

ll

(o-D

f

ot-

4.236 is larger than the tabular value of 3.841

10.05 lcvcl ol'sisrrilicirncc); thcrcfirre, we reject the I1,,and conclude that

tlrt' li'turrlcs rttlrlctl lcss ol'lcn lltirrt woultl bc cxpccted by chance.

tttrtlcs wct'c etlttitlly likcly ltr lJtttrtttttrtl lt'.tl I rl.t'lltt't

ltt ',(ltlitt('lt'rl;tlrort'

rtr rr ltt. lr llrr't('t',,r I

lltt'l)lll()lttt,rl

lrrr'rltr lr'rl tlrlr rlrnlr,lr

1,".1 t'.;t ()tt('slttttPlt','rttltltless-rll-lit

l,r'lr\r'r'r

l\\o rlt',t rt'lt'r ,tlr'1,ot tt's ll is ll

lcst l'1r1y1l

394

N

ON PA RAM ETR

IC STATISTICA L I. I,STS

COMPLETELY RANDOM \ZF.D I)t:S l(

replacement fbr the chi-square test when any o1'the expected tiequencies are (5. The binomial test determines the probability of obtaining ,r events (the smallest observed value), or fewer, in one category and l/-"r events in the other category, out

of

a

Table 11.3. Alurnt reac,liotr of lttutl luryut, to 600 s) towards

total of Iy'events.

Experimental half

Calculate the probability,p(x), using the lollowing fbrmula: N!

/(v): ,t t,ny- rttPt 2A

395

(ue,\

total of l/ events. The researcher must specify the expected probabilities.

l.

;N

Control half

N

l1

20

t

Sourt,e: From Hews (I9gg).

where: N! means the factorial of

l/

2' A more appropriate pr-obability to calculate for most ethological experiments probability of obtaining .y, or fewer, events in one category and N-r events in the other category out of a total of Nevents.

P:expected proportion of r

Q:l-

is the

P

__ N!_ : : o,'omial .r! (I/-r)! (T)

coerficient

This part of the formula can be calculated using lactorials (Table

Al) or by

determining the binomial coefficient using Table A3.

As an example, Hews (1988) tested the alarm reaction of toad (Bu.fo boreas) larvae to chemical cues released from predation by a waterbug on conspecific and heterospecific larvae. The data in Table 14.3 below are only fbr the tests with preda-

tion on a conspecific larva in the experimental half of the test tank. The 20 larvae could choose the half of the tank where the predation was occurring (experimental half) or the other half (control hal0. We will use an expected probability of 0.50 in eacl-t half of the tank. That is,

P:0.5

and

To calculate this probability, sum the probability of the observed events r and all the more extreme distribr-rtions. This is accomplishecl by successively reducing,r by I and calculating the probability lor each value including zero. This total probability is the probability of obtaining the observed distribution of events, or more extreme values. For our example, the probability of having 3. 2, I or 0 larvae

choose the experimental lialf of the tank is the sum of each of the individual prob-

abilities.

/ A/\

1(-t):\,

p(3):0.00 I (calculated above)

N:20 -r:smallest number of larvae choosing half of tank:3

,('):(T)Pxe'r-' :(

/,(' I

i()ll

).

+

):(/20\,ir0.s,Xo 5,',):20(0.5X l.9l,,): 1.90-s /

Thereft>re.0.001 is tltc problrbility ol'lrirvirrg exrtcllv lltt'cc lrtt'r'rtc t'lloosc tlte t'rpt'tintcntirl lurll'ol'llrc llrrrk. il'tlte c\[rr'('t!'11 rtrrrnlrt't is l0 (lr;tst'tl ()ll iln t't1tt;tltltsltilrtt I

r,il

l0\

(

1t(0)

140x0.5r)(0.5r7)

I 140X0. 125\(l .63 n):0.(X)

'

t0:(20)(0.5r)(o 5IB): I90(0.2511.1.8t n):l.8l

Q: I -P:0.5

:(l

)r'Q'

.l)

t ,,i"''s",10'5r{);: l(l)(9.53'):9.53

0.(x)l+l.gl

11 1.90 5+9.53 7:0.0012

I lrt'rt'lirrt'. llrt- pr6[lr[ilily 6l' lrrrvirrg lh.cc. .r' rcw'cr, Iarvae choose the experimental lr,rll ol tlrt.t;rrrk rs0 ()()l-)

ir i

I

NONPARAM ETRIC STATISTICA L TI:STS

396

t4.l.tb

COMPLETELY RANDOM IZED I)trs t(

Table l4'4' Hypothetic'al dattt on song tlurution,t fiLtnt ttto populations oJ'bircls ancl rankings used in the Mann-Whitney L) te.sr (,rce te.ut )

Two independent ssmples

Ar

Az

xt

r -trz

xzt

,\,I -1

-Y^. t7

xzz

l,:

Sample or Treatment No. I

Song duration (s)

1,, :Measurement on Individual No. I in Sample No.

I

i: .r^

_Y.

lil

Mann-Whitne1, U test The Mann-Whitney [/ test is the nonparametric counterpart of Student's l-test lor independent samples. Whereas tl-re r-test determines significant differences between means, the Mann-Whitney U test uses the medians to test for a significant difference in the location

;N

of the sample data. It

rs 95"1, as powerf

ul as the Student's /-test

(Mood, 1954). This test can be used when the data are, at least, ordinal. For large samples, the Mann-Whitney U Test is more powerful than the Kolmogorov-Smirnov Test; for

Ranks

Population A

Population B

sample

Population A

Population B

sample

sample

sample

4.7

8.t

l0

5.3

t5

4.2

t2

3.6

7

6.7

J

5.1

9.5

4.0

ll

t3

l6

2.7

5

4.1

2

r.8

6

3.8

I

7.8

4

t4

1.3

8

1.4

9

Sum

of the ranks

: ?": 68

very small samples, the Kolmogorov-Smirnov Test is more powertul (Siegel and

Castellan, 1988). If the samples are correlated (paired or matched), use the Wilcoxon matched-pairs signed-rank test. The use of both the Mann-Whitney U test and the Wilcoxon matched-pairs signed-rank test on independent and paired

where:

N":number of measures in the smaller sample ly'.:number of measures in the larger sample

data, respectively, is illustrated by Breitwisch's (1988) study of parental defense in

(J

mockingbirds. As an example, we will use samples of song durations from two populations in which the variances are obviously ditferent; that is, an F-max test would be expected

to show a significant difference in the variation of bird-song duration from

these

two populations. Therefore. we will use a nonparametric statisticaltest.

r:

(7

x9 ) +1(7

%:trrN. 4

-_

P

Ur:

-,68

:

63 + 28

1l1p1- 23

:

-

68

:

23

40

Obtain the tabular value lor N,.:7, tabular value: l2

N.:9

in Table

Al0.

Llr:23 Procedure:

t

Ur:40

Rank the data (Table 14.4) using both samples. The smallest measurement gets rank no.1.

Determine the surn of the ranks in the smaller sample

it is Population B).If both samples are the same lirst sample. ?":68 Calculatc tlre {/, irnrl t/r s(:rtistics:

{',

,NrN,

' ,' I ,ry.(,ry.

ll)

I

(f;

in our cxanrplc

size. sLrnr thc ranks

ol'thc

'[.lrcrc is a sigrlilicant difference if either of the observed values (u, or ur) is r'tlttttl ltt ttr lr't't' lltutr lhc tabular vetlue. Hence, in our example the song durations are rr,t 5;ig11ilit'lrrrtly tlillcrent between the two populations. This you would suspect, 'tttt't' lllct'c is cl).t1gl1 v'ariubility in Sample B to overlap the values in Sample A; we ( iur scc tlrrs bcltcr.itr gr.irphic litrm (Figure l4.l). It, t f 1 1

1

1

1.1,,

1

1.,,

t,,\'

t t t i t. t t t t

t. I lt.r t

.t, t t t

t

t

I t I t,

I t,,s,

I

llrts lt'sl ts;t sttlrsltltllt'lirt lltt'M;rrrrr wlrrlrrt'v li lt.sl. csPcci:rlly whcn the sample ''ll('l\ rttt;tll ll rlt'lt.unnt(',, rr lr,.llr..r l\\o,,,trrrplr.r tlrllt.r siprrilit.trrrtly irr cif ltcr. litrrtr rtr Iot;lltoil I lr,rl r', rl 1t..,1,. llr,' //, llr,rl lltr.l\\,r,,.trrr;r11.,,,p(.p1t si,,rrilit.lrrrtlf,tlilli,r.t.rr(.

NON PA RAM ETRIC STATISTI('A t.'l' I,STS

398

COMPLETELY RANDOM IZED I)I:S I(;

10

N

Table 14.5. Hvpotheticuldata on tht, lurtrtiort

399

o.f

/eecling bouts in tottt herds o.f'tlecr

Feeding bout duration (rnin.)

Herd A

Herd B

43

24

62

3l

Song Duration

t

47

IJ

35

ll

Population 14.

l9

69 1-

AB Fig.

8l

29

64 Graph of hypothetical data on song duration from two populations of songbirds.

The scale of measurement must be at least ordinal. Even

if

l8

l8

the data are interval or

21

89

43

67

tl

59

ratio, they are analyzed as an ordinal scale, which means that resolution is lost. Therefore, a parametric statistical test (Student's l-test) wor-rld normally be used if

65

t2

28

the criteria (section l2.l.l) are met; however, when the sample size is small the Kolmogorov-Smirnov test is96"l' as powerful as Student's l-test (Dixon, 1954).

I Small suntples The procedure below is used when the number of measurements is

':25 in both

samples. As a hypothetical erample, we

will determine whether

the

duration of leeding bouts are significantly dilferent between two sn-rall herds of deer lrorn dilferent habitats but with the same sex and age composition (Table 14.5).

lormula would be:

Procedure:

I

1J:rnaximum [q,-^t,,]

Convert ratio or interval data to an ordinal scale. Since we are working

with ratio data. the scale of measurements must be divided into intervals. An interval should be selected such that no single interval contains more than two to three ffreasurements. Each sarriple is then arrangcd in orcler ol'

Tcst Statistic: DXmXn

llrc litrgest

dill'erence in the pretlit'ted direction is at the intervals of 45*-49 and ,{) 5-l rnirrulcs whcre .1,:0.916 and S,,ltt:0.333.

rnagnitude, and the cumulative frequencics of observutions lirr each

l)

sample up to each intervalare determined (Trrble 14.6).

:

Calculate the ratios of the cumulative freqr-rencies til thc lolrrl rrurnbcr ol' measurernents in each sample.

where:/??:no. measLlremcnts in Slnrplc I

/,:

n(). nrcirstn'cnrurts irr Srrnrplc

I

(llcxl A) (I

lctrl lf t

,S,,, t'ltlio tll' r'tttttttllrtivt' lrt'r1rtr'ttt'\, lo lr

,\

Find D, the largest clilference between {,, and S,. For a one-tailed test, D is calculated as the maximurn difference in the pretlic'ted direction. For example, if our research hypothesis was that the feeding durations in Herd A were significantly longer than in Her6 B, the

t;llto ol ( untrrl;rltrt'ltr'tIrt'rrt \ lo

//

I

0 9l

(r

0.333:0.583

cst st:rlisric-0.-5t{3X I 2X

l

2:

g3.95

( onrl)itrc tlrc t':rlt'rrllrlctl lcsl strrlislic lo thc lirbular.vulue (Table Al l) for the appro_ l)rr,rlt'r;tlttt's ttl tn.n lrntl k.r,r.l ol sil,ltilit.;rrrt.c.'l'lrc lirbtrlis.vltltrc fbrlll: 12,n:12at l' O O\ tr /.) Srrrr't.()ut (.;tlcrrl;rlt.rl r,;rlrrc rrl 51 t11 rs llrrllr,r. lllrrt lltc tlrbrtlltr

''

rrr

vitlue of.

\\r'rr';t't l llrr'//,,rlrrr rlrt'lt't'rlrrr1,,lrrr,rrr.r,, r, Ilr.rtl,A lrrt.r.l l1r;lggt rl,ttt llrcvrtt.c

ll,'r,l ll

COMPLETELY RANDOMIZED I)lrsl(i

L TI:STS NON PARAMETRIC STATISTICA

Smirttov fir'o samPle test./br small Table 14.6. Calcttlations .fbr the Kolmogorlt' 14.5 samples using the data.from Table

Interval (no. minutes)

Rearranged

Cumulative

measurements

frequencies

Herd A

Herd B

Herd A

Herd B

N

Table 14.7. The number o.f duys it look rccd v,arhlers to re.ject model cuckoo eggs by either ejection or desertion

Ratios

(s,,,) Herd

A

(s,,)

Number of nests with rejection

Herd B

Rejected during

By ejection

By desertion

0

0

0.000

0.000

0-4

Day I

l0

0

0.000

0.000

5-9

0

Day 2

4

7

2

0

0.166

0.000

Day

3

2t

t4

J

J

0.250

0.250

Day 4

2t

18

0.411

Day

l0-14

I 1,12

15-19

18

20-24 25-29 30-34

17, I 8,

l9

21,24

3

5

0.250

5

25

20

28,29

J

1

0.250

0.583

Day 6

29

23

3l

J

8

0.250

0.667

Day

3l

24

0.250

0.750

_1

9

31

24

t0

0.333

0.833

0.333

0.916

4

il ll

0.333

0.916

5

1l

0.411

0.916

7

ll

0.583

0.916

9

t2

0.750

1.000

l0 l0

l2 l2

0.833

1.000

('ompare the calculated test statistic to the tabular value (Table Al2) for the appro-

0.833

1.000 1.000

lrriate values of m, n and level of significance. The tabular value for nt:12, n:12 at /':0.05 is 72. Since our calculated value of 83.95 is larger than the tabular value of

1.000

72we reject our

35-39

35

40_/.4

43

4

47

4

43

4549 50-54 s5-59

59

60-64

62,64 67,69

65-69 7

65

t-\

70-74

4

5-19

80-84

8l

ll

t2

0.916

85-89

89

l2

l2

1.000

m:12

Totals:

n--12

7

Total rejected:

Source: From Davies and Brooke ( 1988).

D:0.916-0.333:0.583 Test statistic

F1o

llcrd A and Herd l.(tr,q(

:

0.583

x l2x l2:83.95

of no significant difference in duration of feeding bouts between

B.

wntltlas If the number of measurements in either sample is >25, a different

trrblc and procedures are necessary depending on whether you are conducting a one-

the nlaximlm ub,;olutt,differencc For a two-tailed test, D is calculated as between

S,,,

and

trrilcd or two-tailed test.

As an cxample, Davies and Brooke (1988) studied nest parasitism by cuckoos

S,,.

D:maximum 1S,,,-S,, Test statistic:

I

Dxmxn

rr';ts

lirt ottt ottt' l;ttlt'rl lr'sl

( 'trt'tt ltt.s t'rt trur

tt,;)

I

lrcv nrclrstrrcrl

t hc nunrber

on reed warblers (Acroc'ephalus scirpaceus). As part of the research,

of days it took reed warblers to reject model cuckoo eggs by

I \\'( ) r rrclhorls, c' jcct ion of'the egg

from the nest and desertion of the nest (Table 14.1).

'l lrr rrscrrlch tlucstitln wirs whcther the distribution of times to reject the model

AswestatedatthebeginningofthisexampleoLlr(]uCSti()llwllswlrctltcr.tllct.cislr .r'the rlcc'i. thcsc tw. rrc.trs' rlre'clir'e' significant duration in the t-eeding b.uts tlrr' r-rcrwccrr rrrc ri'ctrirrg trrrrrri.rrs irr .ur H,, is that thcrc is .. sig.iricurt triflL.r.c,cc \'l \O lttttl 'lr) 't'.c llr'gcs I ttltsttlttlt' tlil'li'rcrlt'e is ;rt llrt' ittlt'trltls .l 'l\ twtr lrerrls. tttittttlt's (lltt's;ttltt';ts il

I

)

,'l'l's lry

rlr'se

rli()n wlts sigrtilicirrtlly rlill'crcnt than by eiection.

l'tot't'rltltt':

I

I

)t'lt'r trrrrrr'llrr't rrrnrrl,rlrrr' lrr'r1ttt'rrr'r('\ iul(l t;rlios lilt'clrcIt 1'rct'itltl, tts irt

l;rlrlt' l,l

li

NON PARAM ETRIC STATISTICA L

402

.I.I]S

COM PLETELY RANDOM IZED I)

I.S

1,S

I(

403

;N

than the tabular value (5.99), we lail to rcject our hypothesis that the nests Sntirrutv tt:o santple test.fbr large Table 14.8. Calcttlations .fbr the Kolmogttrov 14.7 ,samples using thc data.fiont Table

are rejected by desertion sooner than by ejection. F-or a

two-tailed test, D is calculated as the maximum absolute differ-

ence betweefl S,,,and S,,. Since we are testing whether there is a significant

Ratios of cumulative frequencies to total

Cumulative no. nests with rejection bY: Rejected Desertion

Ejection

within

Ejection

(S,,,)

ence.

Desertion (S,,)

4

0.322

2 days

l0 l4

0.166

ll

0.451

0.458

3 days

2l

l4

0.677

0.583

4 days

ZI

18

0.611

0.750

5 days

25

20

0.806

0.833

6 days

29

23

0.935

0.958

7 days

3l

24

1.000

r.000

I day

difference in either direction. we will use the maximum absolute differ-

D:maximum 1S,,,-S,,

I

The largest absolute dillerence is at Day I where S,,,:0.322 and S,,:0.166.

D:0.322-

0.

166:0.

166

This calculated D is compared to the D value obtained by entering the observed values of lzl and

ru

in the expression given in Table A l3 at the

appropriate P value. For our example, we I

Total

z

critical value of

24:n

3l:tn

rejected:

will

use the expression in Table

A I 3 lor P:0.05 as follows:

D:

I

.-16 /f '"

*t

)

V \mxn/

:trrlt:-,;)

For a one-tailed test' D Find D, the largest difference between S,,, and 'S,,' rnthe pretlit'ted dtrection, just as is calculatecl as the maximum difference if our hypothesis is that the in the small sample case above. For example, by ejection, the formula for D nests are rejected by desertion Sooner than

:(1.36)v0.0739 :0.370

would be:

:

D:maxirnum (S,,-S,,,) is at 4 days where The largest dill'erence in the predicted direction : 0'677' S,, : 0.750 and S,,,

to reject the mocleleggs by clesertion and by ejection.

Wull Wollowit:

D:0.150-0.671:0'073 rForalargesample,one-tailecltest.achi-squarevalueisnowcalculatedas follows: - tttXn

/\i:4D: tltl

tt

:4(0.07_t r.

:,1(o.oo5)

3l x24

,, (I

ttt'tt sttmple rltns test

l.ike the Marnn Whitney U test and the Kolmogorov-Smirnov test this test is used to tcsl thc 11,, that that there is no significant difference between two indepenclent srrrnplcs. It wrll re'ject F1,, if the two samples difler significantly in either lorm or locatirrrt. lt is appttxiniately J5'Y' as powerful as Student's l-test for sample sizes of rr1'rpnrxinrirtcly 20 (Srnith. 1953). Since it is less powerful than the Mann Whitney L/ It'st lrrrtl llrc Kolrnog()rov Smirnov test, the primary advantage of this test is its simlrlrcity.

n2O

3.53) -0.21

of 0.166 is smaller than 0.370 we fail to reject the H,,that there is no significant difference between the distribution of times Since our calculated D

As rrn ('\iu))l)lc. r.rc I

tll ') lltr' .l ('rlnr1'llr|cllte t'ltlt'rtllrtctl 1'totlle lltlrttllrt r';tlttt'(llrlrlt'A())lirr l) t)() Sttlt t'trlll t 'llt rrl'rlt'rl 1'rrl '/ I r'' rttt;tllt't l:rlrrrllrt r;rlrrt.;rl l' O gr ts:

will tlclcrrnirtc rvlrcthcr the lrypothetical frequency of agonis-

ol sorrllrirtls tlilli'rs si1'nilit';rnllv lrelrvccrr birrls trt fcec'lerType A and at It'r'rlt't ltpt' ll Wt'rrtll rrsrttttt,'llr,tl ttt'r oll1'1 | lltt'lryPollrclictrl tlitllr rl rr ring rrinc Irc lrt'lr:rrior

11'1

rrnr'lltt111 ',;1111lrltttl, l)r'ttrrrl'.,rt

t,trltlr'r'rl,t II1rr'II,rlrlt'

1,1

tr,

COMPLETELY RAN DOM IZED I) l:S

NON PARAMETRIC STATISTICA L T IISTS

Feeder Type B

sample

sample

(;N

dent measurements when using chi-square has bcen emphasized by many authors, including Kramer and Schmidhammer (1992). Whether the chi-square is a good-

Table 14.9. Hypothetical data on the.lrequertcy oJ' agonistic hehavior oJ' songbirds at tv'tt types o'f'feeders Feeder TyPe A

I

ness-of-fit test or a test of independence, the measurements that are summed to provide the observed cell frequencies must be independent in order to have a valid test. The assumption of independence may be violated if an individual contributes more than once to a data set.

16

ll

The application of chi-square with two samples is described below; its use with three, or more, samples will be discussed later in this chapter. It is used with nominal

9

6

data and compares observed frequencies with frequencies that would be expected in

l8

J

6

15

t2

t1

8

13

7

10 15

5

t4

5

Procedure:

and cast them into Rank all the measurements in order of increasing size from which each score comes a single order. Then identify the population and rJetermine the number of runs accordingly' populations: Measurements listed in order and their corresponding

355 667 8 910 tll2 131415161718 BBBBBBB AA B B AAAAAA 1234

Runs:

Number of runs

less

rnore often:

' ' ' '

Parental choice:

Maternal, 35 Paternal,12

Total47 Procedure:

Determine the expected by either assuming: l. a random expected distrib-

('):4

2 obtain the tabular value from Table A8 r. If the observed value is equal or

uniform or random distribution. As an example, Vives (1988) studied parent choice by larval cichlids (Cichlasoma nigro.fasciatum) that were reared under two treatments: l. in the presence of predators of fry; and 2. not in the the presence of predators of fry. Later, the free-swimming lry were placed in an aquarium where they could choose to stay in close proximity to their mother, lather or neither (see the analysis of Vives'2x2 tnatrix of data in the discussion of Fischer's exact test later in this chapter). Vives combined the data from the two treatments and tested whether the larval cichlids chose to stay in proximity to either their maternal or paternal parent significantly a

to

0'05 level' thanthetabular value, then the //o is rejected at the

tl,:number of measurements in the first sample:9 nr:number of measurements in the second sample:9 r(4) is stnttlle r In our example the tabular value:5. Since our calculated sigrtilictrrrt tlilno is there that than the tabular value of 5, we reject the 11,, the trequency of agonistic behaviors betwce tt sottghirtls

ution and randomly assigning each of the 47 measurements to one of the two categories (maternal or paternal); or 2. a uniform expected distributiorr and assigning 50'X, of lhe 47 measurements to each category, as has been clone in Table 14. 10. Calcr"rlate the

I

lor each category (as shown in the Table l4.l l):

(o-Ef ti

ference between

ll

at the two feeder tYPes'

t'lri-stluru'c lcsls whct'c lltc rlcgrcc ol'll'ecrlonr is I (e.9. Parker, 1979). This

is ol'tcrr rccor)mlcnclcd

that Yutas' torrct'tion.frtr t'ontinuitybe used in

t'onsislsol'tr'tlrtt'in1, lltt'nulnt't;tlor lry0 5bclirrcitisscluared,asfollows: C

lt

i'.s

t1

tut

rt'

gt tt t tl t t t"s't - r t l - I i

t

t

t"t

t'

I tt' t )'\(

tt)

t

I

)I

t'

\

rlt'tt't rtttrtt'tVltt'lltt'r lrr"o. ()l lll()l(" Thc chi-st1'llt-c g()(xlrtcss-trl-[il tcst clrn bc rrsctl to I lr.'trrrlt(,l l,tllt t'r'l ltltr llll' lll(l('l)('ll irrtlelrt.ntlr.rrt slrrrrPk.s;rrt.si1,rrilit.:rrrllv tlillt'rr'rrt

l{( )lrst'rvr'tl l'rlrt't

tt'tl)

l r pr't lr't l

{l

',1'

406

COMPLETELY RANDOMIZE D l)lrsI(i

NON PARAM ETR lC STATISTICA L'l'l:S'l-S

Table

gootlnessTable 14.10. Caluilation o/'the Ohi-squurc t'ic'hlids lurval clrcice purental b' ol-fit test on clatu.fitr

(o-

(O)

Maternal

35

23.5

5.63

Paternal

t2

23.5

5.63

Total

47

47

Srturce ;

n.26

'n:

i,

N,- Ntlttlbcl-rll'

re

\rl

\'

N,P2 - -: N.

pl1

'ry'

r I

I

il

vt 0.4-s

0.

l-i.s

-0.63s)+0.635(r -0.6.rs)l

I

22

r

l

3' 103

Sinct' \ l0l litllsorrlsitlctlrclirrrilsol. I l.()(rlo -l.96.weconcludethatthetwo sp()tlsc itr Stttuplc No'

i1

22(.41)+22(.86\':0.635 22+22

0.41-0.86

N,

Il'tcitsrtrclllcllls irt Slrrrtlllc

,ryr/'r Lry,/',

l'

+

/l o.o.rs1r

Pr P: nt*rr t -rrrl

Percetltage of

/\y'r

the percent-

rtt -

where:

:

N tPt+

2l- ,, Pt-l): Lll t tl pl p(l ll Nr -t

Calculate:

P,

44

Nr:22 Pr:0.86

Vt

of l\t'| Percentlges

{L

22

(fed intact. live honeybees) and control toads (t-ed dead honeybees, with stinging apparatus removed) ate droneflies (honeybee mimic) in different proportions (Table

I 1.26 is larger

1/, '

22

l6

N,:22 P-0.41

significa.t dirlerence This test is used to determine whether-there is a samples (or treatments)' ages (proportions) of a response in two

fl

(14'Y,,)

For example, Brower and Brower (1962) determined whether experimental toads

parent, and we conclude by chance' significantly more often than would be expected

-

3

(59"1,)

0.05 level.

of than the tabular value (3.84) we reject the H,, paternal and the maternal a unifbrm distribution of choices between parent that the larval cichlids chose the maternal

7,

28

or 20:

assigned X and Y Rank the measurements for each variable, arbitrarily

Convert rho to the't'statistic:

(Table 14.24).

I

ll N-2 \ I:rho ll /v - I

either measurements within a variable are equal, then been have would that ranks assign to each of them the average of the tau (below)' especially assigned had they not been equal, or use Kendall's

If two or more

V\l-rhc,f

Compare the calsulated 'r'with the tabular value (Table .{5), where: df: N -2.|f the calculated r is larger than the tabular /, at the appropriate

if

there are several ties. pair of measureDetermine the difference between the ranks for each (Table 14'25)' (r/r) clifferenoe ments (r/) and calculate the square of that Determine the total for the d2s (as in Table 14'25)'

P value. then the Huof no correlation is rejected. Although some statisticians have recommended always converting rlio to 'r', Siegel and

Castellan

Calculate SPearman's rho:

(

1988) recommend using Table A

l9 whenever

ly' is less

than 50.

('onvcrt rho to the ':'statistic:

6(>d2l

-1r_1U

where:

,d2

significant correlation between activity and song frequency.

Procedure:

t

: 6:

we reject the 1/,,of no correlation and conclude that there is a statistically

determinewhetheritisastatisticallysignificantcorrelation.

,.:

I

Total

z-r'ho V'N-

lirr

N:no. of paired measurements:7

I

ir two-t:rilecl test. rho is significant at the 0.05 level

rrrrc-lrrilctl lcst, rho is signilicant at the 0.05 level if

6(6) :,_19:l_o.l07:0.89 _ _,_ t- l+t-l-'P,: 336

,\' llrr rrl l)lllt('(l lll('il\lllt'tttt'ttls

il :

is > I .96. For a

is >1.64.

lrrrr I rkc SPr'ru lnlul s rlto. Kcrttltrll's tlrrr rlctcrrnines the tendenc:y of two r.utI r,trlt'ts ol rlrrl;t lo llt':rtntl.rt hr'trtllrll's llru rs l)r('lr'rrctl wltctt thcrc ttrc several hr'tttltrll':

When N[x( x-ll1 nl_

Ta-

433

M:a*d:4*3:7 U: b* t: I -11:2

of two, three, etc')

and divide by 2 to obtain )l lor each set of ties together

R,:

1A Calculate

lltS

Frog B

vocalizes'l

vocalizes?

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

No No

No

Yes

Ntr

Ntr

Ntr

Yt's

Yt's

Ncr

researcher's concept

each

of how an association should be measured.

Irrtcrpretution o./'an ttssot'iution A significantly large tau or rho indicates a high rlcgree of association whether positive or negative. There are basically three interprctations lor a high degree of association (i.e. high correlation):

t : r

Frog B

Change in variable

I

causes a change in variable B.

Change in variable B causes a change in variable

Neitlier interpretations I or

2 are

l.

correct, but rather a change in some

other variable (C)causes a change in both A and B.

'l'lrc corrclation coefficients will not tell us which of these interpretations is r orrccl. Wc can only judge (with varying degrees of validityl which is correct ,rt

Frog A

t'orrlirrg to our prrior knowledge of the variables and their relationships.

434

NON PARAM ETRIC STATTSTI('A L'l'l:S'l

RANDOMIZED BLOCK" MA-I('ll I:l)

S

"'

435

blocked

intct three time periods

Sumple,s

A1

ltS

tt britl,v./rutm.fbur habitat,y

Table 14.28. Hvpothetic'al duta on song durutiort

t4.2.lb Three or more matched samples

I'>A I

Mean song duration (s)

Au

Habitat A

r:r -Y::

\.

Habitat B

Habitat C

Habitat D

10.2

6.5

Sunrise

6.7

8.6

Noon

3.4

4.3

6.5

2.5

Sunset

6.3

8.3

9.9

7.1

_1t

Frieclnutis two-w'ul' trnulysis of' turiunce is a nonparametric test used to deterTlie Friedman two-way analysis of variance blockecl measurements or repeated mine if several (three or more) samples with significantly diflerent. It requires ordinal data measures on the same inclivi6gals are compared to the paratnetric ANOVA' (at least) and tests fbr differences in location. (van as powerful for ten samples iI is 12,,/uas powerful fbr three samples and 87"/u Elteren and Noether, 1959)' durations. tiom four habitats; but this As an examPle, we will again sample sc'rng time periocJs: sunrise' noon' and sunset time we will block our samPles into three

Table 14.29. Ruks of'eot'h rov'o.f nl(osltcments in Tuble 14.28 /br t'alculution Friedman's lw'o-\ruy unulltsis of variunc'e (,see tert )

o.f'

Habitat A

Habitat B

Habitat C

Habitat D

Sunrise

2

3

4

I

Noon

2

J

4

I

Sunset

I

-)

4

2

-5

R*:9

t2

Rr:4

R^

R.:

(Table 14.28).

r

\ y::( / . ^ll-- x266 I-gtst:53.20-45:8.10 "t \12(5) I

Procedure: (blocks) separately (Table 14'29)' Rank the measurefilents for each row and provides a more powerThis minimizes the between-row differences

6

fultestofthebetween-column(sample;habitat)differences. Calculateeachoftlreranksums(i.e.columntotals;R.,,Table14.29). Calculate each of the R.r}s'

Rn':25 Rot:81

Rr't:144

df:N- l:2 tabular 1, ,,.1:5.99

R,rr: l6

Sirrce our calculate:i ;,

: 26s1263 te t s) :

Itl

1

s

Therefore, the expected frequency of threats in adult males in this changing pop-

ulation is 75 compared with the observed number of 210. Once again we could make

the same calculations for the adult females and immatures, and then use a chisquare test to determine

when the the conditions are the b. The expected frequency of behavior per class composition changes' is calculated same as in (a) (above), except that the population

if

the differences are significant.

Uniform, class-specific rates of behavior are expressed as a mean number of occurrences per individual per unit time, according to the lormula below:

according to the following formula:

s.

ur:*t*'L: u L;21t1t11;1

Eu

:2,2f fl

E

:the hypothetical

ri

expected frequency of behavior a for

members of class x

where:

E,,:expectedfrequencyofbehaviorrllormembersofclassr

N 2/

fr,,

total number of occurrences of behavior

:

all classes in the samPle total sample time for all individuals of class -t in the entire

a

Lambda,:hypothetical mean participation rate per member of class x

for all individuals of

:

study

2Ff ,, :total

(lambda) for adult males by referring to Table 15.1.

Lambda,:

x)

2,2,tyt,,:totalsampletimeforallindividualsofallclassesfortheentire study (time in sample period oneXnumber of individuals in all

classes)*(timeinsampleperiocltwoXnumberofindividualsin + . . (time in final sample period x number of indiall classes)

' viduals in all classes)

Asanillustration,wewillusethehypotheticaldatairtTablel5.2(seeAltmann determine the expected freand Altmann. lgJJ, for another example).we can of changing cort]position as quency of threats in adult males in this population lollows:

N 2l

:260 r,,, :[(5x l0)+( ''- -50 +

in the entire

study

individuals in .y)+ . . . (time in final sample periodXnumber of

:

r

As an illustration we can obtain the hypothetical mean participation rate

=ltimeinsampleperiodoneXnumberofindividualsinclass 't)+(timeinsampleperiodtwoXnumberofindividualsinclass class

sample time for all individuals of class

l(r5 I

IIx

l5)+14x l2)l

4l{ 261

)',)',/,/il,, .)(rl ll(5' l:'i)l(ll

.)o)

l('l " lt'11

2/ lrr,

Eu

:0.80x263:210

Therelore, the hypothetical expected frequency of threats for adult males during the entire sample period (20 hours, Table 15.2) is 210. The total number of males observed was 37, so that the mean rate per individual was 21 0137

:5.7

threats for the

l0-hour sample. This hypothetical mean rate can then be compared to the observed nrcirn rate in this sample or from observations gathered later from the same or a diflcrcnt population.

Altnrirnn and Altmann (1977) proceed to the consideration of interactions bctwccn intlividuals. They provide procedures lor calculating expected rates of lrclurvior lirr- syrnruetric and asymmetric interactions at constant rates and interaclions wrth lrypotlrcticrrl clrrss-spccific ratcs ol- bchavior. Michener (1980) noted that

llrt'

;rssrrrrrptiorr

pr'rrotl

'

80

100:0.80 :(5x l0)+(1 1 x l5)+(4x12):263

ol

in Allnlrrur lrnrl Allrrr:rnn's (l()77) lirrnrulae that lor any given

lrnrt'rrrrv rrrtlrvrrlrurl lrrs llrr'polt'rrlr;rl lo irrlt'nrct wilh trny olhcr intlividual.

rvlttlt'ltttt'lrlt l't;tl,ittlott',',r('(t(", t',ttol lttlr'lot ln,rn\'\Pt't'tr'su,ltett'ccl'lltittifttlivirl

fr-

l

444

RATES OF BEHAVIOR & ANALYSIS

()l

SI:QLIENCES

ANALYSIS OF SEQUENCES

Tlie following discr-rssion of behavior sequence anarysis is cursory and meant only as an overview' More detailed discussions of the general topic of sequence analysis' including in-clepth descriptions of specific methocls not discussed i, this book' can be lound in: Bakernan and Gottnran

Table 15.2. HypothetiL'al cluta on the nutnber of'tltrcut,s itt u lterd of mule tleer

Adult

Adult

males

females

Immatures l3

4l

60

l8 l3

2

t5

Number of individuals

l5

20

l5

50

Observed number of threats

80

l5

5

100

Sumplapariod2

Suntple periocl

Totals

( l 9g6), castellan (lg7g).Fagen and Young (1978), Gottman and Roy (1990). Haccou anS Triad A------'B---'C Tetrad A--'-+B-----)g---+P

What constitutes a sufficient sumple size lor analyzing Markov chains? Fagen ancl Yor"rng ( I978)provided the following'rule-of-thumb'(based on simulations run by I'agcn) Ibr analyses of first-order Markov chains (Table 15.4): I'or': /l:number of individual behaviors for which sequences will be ntclsurecl

Ilrrrr: l/(r:

i. which it c.. bc sh.w, t'irt Markov chains are sequences or behavi.rs (). ().('lrrr.lltct'ltl s,l'tlt' rlI trrc hchirvit)rs:r'c trcPe rrtrr.'rrr sitions betwccn twa ar 'r.rc

lcvcltll.prtrlllrlrilitvptcltlct.tltlttlelt:tttec.ltlsrr.tlrt'lr'rt.|()l|)l(,lr:tlrtlttVisltsstttttt.tlt.'

insLrllicicnt srrnrplc sizc

5/t': lrortlcr lirrc

tlrc t.rrrrI

0/i'

slrrrrPlc sizr'

strllit'tr'nl \iuttPlr'

s171'

ol

RATES OF BEHAVIOR & ANALYSIS

450

Table 15.4. Orders of Markov

Table 15.5. Transition.frequent'ies urnong bchuviot' puttern categories in c'onlests involving .supplemented and unsupplementetl ol'ner.\ o.f poor-quality \grrit,,r'.t

c'hains

(N:25

Definition

Order of Markov chain 'zeroth-ord er'

ANALYSIS OF SEQUENCES

SIQtJENCE'S

The behavioral events are independent

(A.8,()

The probabilitY of occureuce of

first-order (B'--C)

a

particular

behavior is dePendent on onlY the immediatelY preceding behavior

second-ord er (A-'-

B-

The probabilitY ol occurence of a particular behavior is dePendent on the two immediatelY

C)

preceding behaviors

5.2.1

h

Locate

130

80

0

0

0

73

435

l5

l0

30

0

8

30

-1

0

0

-:r

0

l0 l0

3

R

0

0

5

Signal Threat Contact Retreat

25

J

1

0

5

Signal

13

85

20

0

28

Threat

0

8

l8

0

20

Contact

0

0

0

0

0

Retreat

-l

8

0

0

5

included are not indepenclent

ow' ne r s

Source: From Riechert ( 1984). Copyrighted by Bailliere Tindall.

9

l5

that appear to difll-er greatly between the two samples. For example. Reichert (1984) rrieasured transition frequencies of agonistic behaviors in spiders tliat owned poor-

4

t7

30

5

8

32

Observed occrlrrences

Following behavior B

C

I

6

20

B

9 19

behavior

29

Have we collectccl en.ugh tlata'l Accorcling

d

Row totals

Transition matrix

34

te

should be sufficient. In our example, R:3 and 10 Rr:90; therefbre, since our sample size of 97 is larger than I0R2, it should be sufficient. The matrix of observed frequencies can initially be inspected lor cells which show large frequencies. For example, Lemon and Chatfield (1971) contructed a transition matrix of preceding and lollowing song types for cardinals (Richntondena t'urdinuli,s); their initial inspection of the matrix showed that switches between certain song types occurred very frequently. Two matrices, each from a dillerent value of the independent variable, or treatment, can be initially examined for cells

of each other. Below is a hypothetical transition

A

LI n s Lrp p I e me n

of

matrix lor three behaviors'

Column t otals

Retreat

25

based on observeg.49, we infer that the (iom

ln fact, you can see difl.erent trom random chance. value at P:0'001' lated I is larger than the tabular

Table A9. that our calcu-

of occurrence of dilferent

song types between the hrst and second halves of their sample periods. To overcome the stationarity problem, Oden (1917) developed a rnethod for analyzing behavior seqLrences based on fitting ascending-order nonstationary Markov pl'ocesses to tlre data.

ties

ThetabularValueforffor4dfatP=0.05isg.4g.Sinceourcalculatedvalueof observed are significantly

For a more complete discussion of the analysis of sequences using Markov chains and transition matrices see Gottman and Notarius

Cruner"sPhi .:-^r..-. a matrix of any slze ls association between cells in of measure convenient Another 0 to I and is calcuto a coefficient that varies from cramer,s phi. lt converts the I latecl as follows (2ar,1984)"

/. '(: lY

J

E

o

Eo

t,) 3 .9

!o

o

:

Principal component analysis (also called principal-axis lactor analysis) utilizes an orthogonal rotation of the data (e.g. Huntingford, 1982). Factors analyzed using

rotation account for maximum possible variance among the observed behaviors. Cooley and Lohnes ( 1971 ) provide a FORTRAN program listing lor Varimax rotation. Giles and Huntinglord (1984) use principal components analysis to describe the anti-predator responses of sticklebacks to a model heron or a live pike.

'l

o E 2

p^

) io

n

:

o)

:

o- n

I6.3 GROUPING INDIVIDUALS

n

If it is suspected that groups of individuals

are behaving in a similar way, then Qf actor analysis can be applied to the data in Matrix A. The analysis extracts individ-

Fig.l6.lThreetypicalmatricesinwhichethologicaldatacirnbeorganized:A.Different B' Irrteractic'rns between individuals:

ual-related lactors based on their observed behaviors.

inclividutls: behaviors performecl by rJiflerent

sequences' C. Inter- or intra-individual behavior

In contrast to principal component analysis, powered-vector factor analysis It places maximum emphasis on biological rele-

cxtracts factors without rotation.

the objectives of analysis shoul<J be matched to the characteristics of the particular

vancy, whereas the principal-axis method emphasizes parsimony (i.e. maximum variance accounted for by a f-ew f actors) (Aspey and Blankenship, l91l).

the stuclY (see Gottrnan' 1978)' Since one of the advantages

rrrollusc Aplysia bru.silianu. They recorded the occurrence

they allow of these multivariate methorjs is that

selected graphical displays (Rohlf' 1968)' visualization of the variables through examPles will be Provided'

I(.1.2

GROU PIN G

B

E'HAVIOR

S

'factors'basecl on their into a smaller number of Figure factors A. this analysis extracts the behavior-related ties. when applied to Matrix varialnce' fbr a large percentage of the total 16.1)

which account

Forexarnple,Aspey(|971c)recorcledtheoccurrenceof20ditferentbehaviors

in40individualspiders.HeuseclR-factoranalysistoextracttburbehaviorlor 20 different behaviors) which accounted related factors (groups from the after then descriptively labeled the tttctors 14.3,,/uof the total variance. Aspey labelecl

them. consequently, Factor I was exarnining the behaviors comprising .rur.r/retrcat.' liitcttlr lV .approach/signal', Factor II .vigorous pursuit,, Factor III labelecl 'tl.tr-li.ki,g" uninterpretable' but was then since none

of the

linkcd witlt composite behavio.s was significantly

tcrs in 32 individuals (Matrix Type A). Q-factor analysis extracted three factors (groups of individuals) which accounted lor 80.2"/u of the burrowers. Burrowing characteristics, interpreted relative to ef-ficiency, were examined and the three r11)ups were labeled 'inefhcient burrowers'. 'efficient burrowers'and 'intermediate

(e'g' behaviors in Matrix A' large number of variables R-fnctor analysis organizes a underlying similari-

at first seemed biologically

Aspey and Blankenship (1976) studied burrowing behavior in the marine ol l0 burrowing parame-

..y

.tltct'

sl:tlctl

interactitlns. Altlrotrgh it is ctlttlttltlttlY behavior cluring inter.indivicltral t'e cxislcrr.c ,l' . c()rll'r(),',1()livltti.ttltl that trse .r- r.ctar irn.lysis irssrr.rctl [spe v ltlts lttt' l':tt'tot (Sllrtt'r' l() / \) ttolt' 11t"1 strttc' tttttlcrlvirlu cltclt cxtt'ltc(ctl ritlt.tlIlritlr,tlt'st'rrlrtir(..lillltt'tllt:tttltttlt'lttrtt.tll.rl,.'1,'ltltrrtt.'lt;tsl.Wtt'1rk('lllil

irrrrrowers'. The three-dimensionalrepresentation of these 32 individuals relative to

tlrc three extracted factors illustrates their location into three distinct groups t

lrigure 16.?).

Ilicrarchial cluster analysis groups variables (e.g. individuals or behaviors) on tlre birsis ol' similarities (or differences) among oommon characteristics. Simple ,lrstrrncc-l'unctiorl cluster analysis is sensitive to variability in the data and tends to .,1rlit'thc vuriablcs intc> more groups (Aspey, pers. commun.). Cluster analysis olten rrrrkcs ll'wcr assunrptions than other methods and therefore is easier to understand

gln tt ttl.. 1976; Sparling and Williams, 1978). llrcr':rrehrll cltrstcr anulyses begin with a matrix of similarities (or dissimilaritrr'r). rurtl r;rrirrblcs rlc sc(lucntially.itlincd on the basis of their relative similarities rrrlo tlr't)tlol'llll)ls (liigttrc 16.11. lvlticlt ltt'c silttplc. vistrllly iptcrpretable representaI rrlll\ (rl tlrt' rr'srtlls ol' lltt' ;'to1111i;11,1. l\ttrt1'lttr t't rtl (l()/(r) rrst'tl lrtrrrr,ltr,rlt ltt:lr't ;rtt;tlVsis ott rl;tllt l'l'rrttt lt ttutlt'ix tll' llrr'tttt'r'lt;rttir.'sol'r'otttlttr'titttl lrlr'll;ln(l l)torrtlt'rl;r',lr,rr1'lrllor\\,rrrlrlr",tttllrr)nr)l ,r'.rrr1'11' lrrrl' , lrr..lr'r ,rrr,rlr',r', {\l t \ I ,r', rrr'll ,r'.,1r',r rr',',rtr1'llrr'Porllnt':rltrI ttt'1';11i11'

t N'l or

M U LTI VAR IATE, ANALYSES

470

GROUPING INDIVIDUALS

4tt

+

o o

.$ |{

o

87

}\ 9 6

100

e

o

t.z Indlvidual Doe Deer

o

o

/C

u*, on, v"7

nuo'tt

16.2 Factor loadings for Aplt'siu burrowing behavior projected onto coordinate axes corresponding to the three factors extracted by Q-factor analysis. The origin falls in the center of the tliree-dimensional space. Factor I (large circles) represents 'inelficient burrowers'. Factor II (small stippled circles), 'eflicient burrowers'; and Factor III (triangles), 'intermediate burrowers'(lrom Aspey and Blankenship. 1976\. i

l

attributes

of the method. Their paper lormed the basis lor the discussion which

follows.

In conducting a single-level cluster analysis, let us assume that wc warrt lo tlclcr'mine the relative association anlong intlividr.rals in a hcrrl ol'cight utlLrlt rloc tlccr irr rr wildlif-e prcscrvc. Wc rlbscrvc lhcrrt lirt'ir lrlllrl ol'5(X) ltotrt's lrtrrl rccortl (ltc lrtttorut( o(

tirtrc tlrirt irrtlivitlrurls lrre closer tlurrr li)ur nr('l('ts llrrolrl,ll tr.",rr s;unplirty' ('\'r'rv tttittttlt' l';tt'lt,)((tltl('ll(('(rl :ttt;tssot'i;tltt)lt l\,r'.''tlllr('(l l(l lt'Itt'it'tll ()ll('lllllltll('{)l

B'vuEBGE A lrriti:tltlerrtllirl'r.rtttt lirt.;tss.t'i;rti.rs i, rr lrvp.llrcticirl hcrcl of eighr adult cloc tlt'r'r. ll. I irr;rl rlt.rrrlrol,r;rrrr l,rr llrt.lrssot.ilrlrolll i11 ,\.

I

MU

472

Table

16.2.

GROUPING INDIVIDTJALS

LTIVARIATE ANALYSES

Hypotltetic'nl ussocitttion

claru

deer' Dut? Jbr u hertl o/ aight adult doe

rtre the nLtmher of-hours observetl in association

doe der

in Tuble 16.2

A

H

G

F

t)

C

A

Table 16.3. Similarities.lbr the us.sot'iution,; irt tlta hyytthctit'ul herd of'eight udult

D

C

B

G

H

A

A B

2l

C

18

D

30

E

l6

F

38

22

l1

ll

ll

G H

3t

67

48

4l

47

31

21

17

151

348

Total hours

3l

2l l0

48 22

28

3l

30

25

23

34

191

202

203

208

168

B

53

C

45

D

15

5l

t17

E

44

27

59

74

F

109

6l

30

46

34

G

69

122

87

74

9l

61

H

1l

69

56

50

82

61

76

t29

247

observed

The similarity is then multiplied by 1000 for convenience, so that we can deal with whole numbers.

lor a discussion association (see section 8.3 on sampling assumption). The procedure is as follows:

r

z

of the hazards of this

shown in Table 16'2' This The data are flrst organized into the association vertically and horizontally in table. a matrix of Type B, can be read both

ordertodetermineassociations'However,itisstilldifficulttoseemuch Hierarchialcluster pattern of associations from the data in the table' of the associations' presentation visual analysis will provide a better (associations) similarities of table The next step is to generate a triangular of time' periods total each deer was seen for different

similarity

:

0.053 X I 000

:

53

We then complete the table

of similarities (Table 16.3).

Next. we search the table for the range of similarities. The highest is 129 for H*G, and the lowest is 27 for E+B. The vertical axis of our dendrogram should include this range, so for convenience we will set up a vertical scale of from 0 to 150; note that the scale increases from top to bottom

(Figure 16.3A).

of association observed, which they derived from Dice's coelficient coefficient of assoCole's same as ( 1945). Note that this is essentially the

linking individuals on the basis of similarities, starting with the largest similarity and working to the smallest. Individuals H+G provide our first association. The next similarity l22is between G and B. Tlris nreans we link B up with both G and H at the lZ2level. This is an cxanrple of 'chaining', which is an undesirable characteristic of the mcthod, since it links through intermediates (Jardine and Sibson, 1968)

ciation ( 1949) (section 17'4' lb)'

irncl is clifTicult to interpret visually. That is, the apparent association

among deer. Since

wemustfirstnormalizethedatatoadjustforthosedifferences.

Morganetul.(|g16)describeamethodfornormalizingdatafortime

SimilaritY:;:., where:

Xy:totaltime when individuals Xand X:total time Xwas observed )/:total time Ywas observecl

Ywere observctl togctlicr

/l irl'l'rrblc l(r'l Thcrclilrc. thc sirlril:rrity lirr intlivitltrirls "l rttltl Sirrrilrrritv

,l r ll r()l r .)0.) o 0s l

We then begin

hctwcur [] and H in Figure 16.3A is really due to B's association with G. M orurr n c t ul. ( I 976) d iscuss the problems of 'chaining' in more detail. 'l'lrc next irssociution is D*C at thc I l7 level of similarity. We then t'orrlirtrrc thc sirrrillritics ('lhblc 16..1) trntil all the inclividuals have been

l lris lr;rppt'rrs tvltr'rr rt'r'nurkc lltc lrss

the tabular value then the sample directions

are not randomly distributed but are significantly clustered.

s If

0.24

Expected

Observed (O- E)llE

A

(50,'1,) 21.5

t4

2.61

C

(50"1,)

29

2.6t

your data are angular degrees from magnetic north (or time) then also

21.5

43

^):5.22

conduct a Ztest (below) fbr confirmation of significant clustering.

As an example, Sordahl (1986) tested whether the distraction displays

of

14

avocets and 5 stilts were directional, especially whether they would lead an observer

away from the nest. He stood

l0

20 m

Plays in the other two quadrants would be considered misleading,

nrost researchers.

from the nests and recclrded the position of

colored-banded, displaying birds in a circle with him at the center. F-or analysis, he divided the circle into lour equalquadrants with one of them subtended by an angle of 45o on either side of a line from him to the nest (Figure 17.3). He observed 8l clis-

rr

distribution) display distributions

IicirnI Iy

Il you use r.lnly the numbcr ol clisplays towru'tl

(tlrrirtlrirrrl A)irtttl rtwiry llont lltt'

('). tltcchi-srltrirrc lcsl sltorvs llrrl lltost'tlisplrrvs u.'r'tr'rlisltilrtttt'tl si1' trilierrrrllv rlillr'rt'ttl llt;ttt t:tnrloltt (st't' l';rlrlt' I / \) llo\\r'\r't ,lt"t,'.1';ltrlttt;' lltr tltr

rlilll'l'cnt.

I lris rr

lcsl ts trsctl lo 1lc1gl'111iltc whetlrer the sample of directions (or tirne) diflers sigilit'rrrr l lv ll ottt t'rttttlolt.t. -f hc tlatu nrust be in angular degrees from north (0.).

plays were in the quadrant away from the nest. the direction of'all dislnrciiorr tlisplays was not significantly different from random.

by

t"t.t.2 Raylcigh test

is given in Table 17.2.

The calculated clii-square value of 5.94 is smaller than the tabulitr vulLtc (3 dl. alpha level:0.05) of 7.81. This means that although most of the clistrlctiott dis-

if not invalid,

Tlre calculated chi-square ol 5.22is larger than the tabular value of 3.g4 (l di lPIla level :0'05 ); therefbre, the number of clisplays in these two quaclrants was sig-

rr i

traction displays from the l9 birds. The observed and expectecl (based ort a uniflorn.t

ncst (cluaclrant

l

Ey:

+ Compare the calculated If

3.78

18

Table 11.3. The c,hi-square gooclness-o/-fit tubte using only the duta.fiont Sec,tor,; A ( tot;-arcl the nest ) ancl C

Calculate the Chi-square value (see Table 17.1):

,.r:r(oALE

29

-

The expected number/sector must >4.

:

489

Table 17.2. The c,hi-scluare gorrlnt,.s,.s-o/-lit table /or

.1. Layout./br c'hi-squure goodness-o/-fit tesl of' data.fiom a c'ircular

Sector

ot, t)llrrr(,.t.tONS

Ittol'1'111111''

t

( 'trlt'rrl;rl('llt(.\lltn ,'\

\

'I t:ur )',rrrr.

1r1. 11.,,.,1

ol

llrr, \///r,\ iuttl r.r,r///r,r ttl.tltc slrtttltlc tlil.cctiOrts. T0ble

l,,t ,.tllrr.t ,rtr1,rtl,1

rlt.1,1t.r.r

pl

li1lr.,.

I

TESTS FOR RANDOMNESS

CIRCU LAR STATISTICS

490

northeast o/' c'ampus while blindfolcled

Alpha level N

0.0s

0.01

0.001

30

2.91

4.50

6.62

50

2.98

4.54

6.14

100

2.99

4.57

6.82

200

2.99

4.59

6.81

500 (or larger)

2.99

4.60

6.89

r

c2)

Calculate the test statistic Z'

Z:RzlN +

Directions pointed while blindfolded (angular degrees from magnetic north)

R'

R:V(sr+

where: N:number of sample directions

Clompare the calculated

Zto

the tabular Z"inTable

17

'4'

+0.2156

-0.9613

88

+0.9994

+0.0349

t44

+ t).5875

-0.8090

328

+0.8480

290

-0.5299 -0.9391

-0.4067

tt4

+0.9135

180

0.0000

128

+0.7880

t52

+0.4695

108

+0.9511

178

+0.0349

-0.469s Totals:

R:V(s2+ c\:t Z

Asanexample'Icollecteddataonthedirectionalorientationabilitiesofl3stu-

miles northeast of the

+0.0698

+0.9976

When the sample directiops are nonrancantly clustered about the mean directiorr' mean direction and cletermine whether dom, you can go on to calculate the sample direction (e'g'home)' it is significantly rJifferent from the predicted

10

Cosine

86

208

cues when they were deprived of normal visual dents in my ethological methods class to a van in a postion). They were blindfolded ancl transported

Sine

164

the directions are nonIf the calculated Z is greater than the tabular Zu,then directions appear to be unimodal' then random. If the distribution of the sample proves that the sample directions are signifisignificance in the Rayleigh test also

(landmarks and sun secludecl location opf .o*i,rutely

491

Table 17.5. Direc'tions l3 blind/bldecl stutlcnl,s poitttctl to intlit'ate the direction to the Colorudo State Univer,rity camplts a.f'ter huvittg bacn driyen approximately l0 niles

Table 17.4. Critical values o.f Zu

z Calculate

Ol l)llLl:("1'IONS

:

R2 I

N

:

S: +4.078

(16.630+3 1.047):\/ 4i .677

47 .67 7 I 13

:

:

+0.3420

-

L0000

-0.6157 -0.8829 -0.3090 -0.9994 -0.8829

C: -

5.512

6.904

3.661

Since the calculated Z of 3.661 is larger than 2,,,,(2.97) we reject the I/n that the

directions pointed by the students are random.

Colora{o State University

and to provicle data lor the students to analyze campus. The exercise was conducted use to ability their the methods' It was not a valid test of

to allow them to critique

Able' field (see Baker, 1980' 1987; Gould ancl other cues, such as the earth's magnetic stttclctlts were not coverecl' and some tll'tlic 1981); for example, the van windows rt cttc ttr its srlll afterngotr the the heat from sitting next to the windows reported using still w'ile v.rr trrc inclivicrually red fr..r westerly direction. The stude'ts were the

'l hcv

the canrpus fi-..r thcir r,r'sc.l blindtblded anci askecl the direction t. ''rrsiti,' (). i'r(l tlle. rir'st wrrrr rrrc hrirrtrirltl still were then askecr t. p.i.t t.war.trs c.,rrr:i. rlrr.strrrrt'rrrs p.irrt'rr lvrrirc lrrirrtil'rrrlt'rl. with trrc bli.tilirrtr r.e.r.vctr..r'rrc trir.ceti.rs

rlt'1't('("':ll('l'l\t'll lll l'rlrlt'l / \ Ittttllltt.siltt.:l:tntlt'ositlt'srrl llto\('illll'tll;lt

t7.1.3 V test Whctr tlte rc is art expected direction, the Ztest is prelerable to the Rayleigh test since

it

is rttorc powcr'f'ul. For example, when homing pigeons are clock-shifted 6 hours

lrrtc. llteir crpcctcrl tlisappcirrance direction upon release is 90" clockwise lrom the

'l'lrc I 'tcst lvill lcrrrl to sigtrilicance only if there is sufficient clustering lrl,,tttttl lltr' ptr'rlir'lr'tl tlitt't'ltott Ilr)\\'('vcr'. tlrc l'1cst shoulcl t>nly be ttsed to test Ior r;rtttl.rnrnt'ss. ll rl,'t's rrol lt",l rrlrt'llrcr llrt's;rrrrplt'rrrclrrrtlircctirlrt rlevitttcssignifi(:tttll\ ltotttlltr'Ptr',1t,lt',1rlttr'rltrrtt lltt'rlttttrlt'ttr't'tttlt'trltlsltottltlltettsctllirrlltlrt 1' )l l)llll)(r"t'(ll,rl"t ltt'lt'l lt)lil,('{",(r ltr,tt | Itorrrc tlirect rort.

492

SAMPLE MEAN AND SPE('ll

CIRCI]LAR STATISTICS

We determine the sinc arrd cosinc

Procedure:

I

R

cos(O-

r:

0,,)

of the mean angle:ffi/r': *0.3l4l5.3l l: +0.5912 Cosine of the mean angle:c-os lr: -0.429rc.531 l: -0.8017

R was calculated previously (see Rayleigh Test)

the test statistic

Using Table A23 we find that the angle whose sine approximates +0.5912 and whose cosine approximates -0.8077 is 143.5". This concurs with our

rz.

previous calculation.

Calculate test statistic lr.

':J(i)(')

@:sample mean angle: 143.5"

where: N:number of sample directions

0,,: predicted direction (campus): I 96'

Compare the calculated a to the tabular value lr,, (Table A24). tf the calcuand laled uzu,, then the sample directions are not randomly distributed

Vt:

R cos(@-

are clustered significantly about the expected direction.

143.5"- I 96o:

Irtrr t ": J(;)(

(unt of tirle they spent in each part

of the moving troop (front,

side, rear, middle or clusters; see Collins, 1984, for a

diagram). When associations between individuals are based on distances, the researcher must decide on a criterion distance between individuals in which the probability of their interacting greatly increases and beyond which they commonly approach one

another in order to re-establish the association; this decision is based on the researcher's experience. For example, Grant (1913) used as his 'measure of association'between individual grey kangaroos (Mocropu,s gigunteus) the number of times each animal occurred within 120 cm of another at set l5-minute intervals (instantaneous samples). With this procedure, the accuracy of observers in determining the distances between individual animals can be a problem, especially in field studies involving distant observations. For example, Morton (1993) measured the accuracy of observers in determining the locations of individual elk in small herds. The distance discrepancy between observed animal locations (from observer diagrams) and actual animal locations (from aerial photographs taken simultaneously with ground observations) averaged 5.6 body lengths. Sullivan and Morton

(

1994) measured the

ability of ground observers to judge inter-animal distances in groups of life-size 11.4.1

Animal-animal spatial relationships

Animal-animalspatialrelationshipscaninvolveintra-orinterspecificassociationsthe the research question can require between individuals or groups. Additionally,

or between known individuals over time' measurement of distanoes or associations simultanesampled being of the group spatial relationships among all members

artifical deer. They took photographs from different observer viewing angles of dif'f-erent herd sizes with the animals oriented in different directions (facing away or perpendicular to the line of sight); then observers diagrammed the animals' locations from the photographs. Larger herds, lower viewing angles, and perpendicular orientation of the animals produced greater discrepancies between observer perccived and actual animal locations.

ously.

your ability to accurately determine their The observability of the animals aud to each other will influence the type positions, either in the environment or relative

ofsamplingmethodemployed.Themethodsbasicallyinvolvetwodifl-erentproce-two recording when (frequency and/or duratiou) >- Boboon trocK

It

lir| gRrttP S lrrltl Fig. 17.6 Baboon group home ranges and core areas. A. Tcn-clay ritngcs llrc itltlicittcs linc sltitrp Tltc Rcscrvc. group Cape C. 2l day 11lges for ('lrrrtl S llv occttllictl At'ctts I|. ntttgc. hotnc gr()up's 9lclch limit lpproximatc irr o'nt'tlltP ol grogps ttrtrl si.;ullrclp lirrrit ol'N grrrtrp's lirtll'('. rrrrlitlrltitl':ttttottttl llrlttlc

t'ltttg.cs

lttttl loclttiolt ol'tolt'lttt':ts

(ltotlt I)t'\'ott

of

observations neces-

to rcitch thc l'Z,level varied with the species and the stage of the nesting cycle. Srurtlcrson ( 19(r(r) suggested that live-trapping mammals would probably provide lnstrllicicnt tlitlit to apply the observation-area curve method, but that radiotelemesru'y

'rrlrl Il;rll

lt)('r)

t

Ptobirhlv worrltl.

'llrc'u'rrlitlitv ol'llrc cirlcrrllrlctl hornc riurgc. or tcrritory. will depend on the techttirlttr'r'lttltlovr'tl. Wltr'tt rlitr't'l olrsr'ttlrlions rrr-c rrrirrlc. lhc irnimal's location can be r'onsirlt'tr'rl ;tlo111';ur ('\:r('nlltllY conlnrrrorrs rlislrilrrrliolr,'l'ltlrt is. it rnity bc corttirtuotl:.lV oltst'tvr'tl llttottl'ltr)ttl. itn(l lounrl,rl ,rtt\ l)('lll \\'tllrrrr. ils lt'ttr. ltolttC r':trtgC.'l-ltis ',,tlttltltlt;, 1,,,',lt,rrlr,ur ltt't otr',trlt'tr'rl trr',l,rnl,ltrr)ll',',,nrrIlnt1'ol ;t lirt':tl;tttinlrl ttsittlt,

\('t\ ',ttt,tll',,rttt1rlr'tttlr'tt,tl', \\,',',1( r ( l')("

I tt'rrr,' I i /)',,rrrrPl1'11 rrstrrl,lot'lrl

Pltrr.

CI RCTILAR STATISTICS

514

Wood

SPATIAL PATTERNS

all-occurrences of visits to dilferent cluarlrirts. Son-rc indirect measures, ssclr ir\ tracking in snow, can also provide continlroLls clata, ancl biotelemetry can plrvirlr.

Pewee

nearly continuous data by rapidly scan sanrpling inc'lividual locations (sec ('lrrrptcr

- 1--o/ /o

/o

e).

Other methods, such as the commonly

Llsed capture-recapture mctlrorl l.r discontinuous spatial distribution of' l()cirlr()n:. restricted to trap sites. Thereflore, the validity of this method is affectctl h.y trrlr spacing (Stickel, 1954), as well as trapping interval (time of trap scl rp tr;r;, check), sample size (number of trapping intervals) and the responses ol'irrrlrrrrl Llal animals to the traps (Balph, 1968; see below). This sampling nrctlrprl t.i1r lrr. considered a one zero sample (Chapter 8) for each trap site. That is. lirr t.;rt.lr trap site each individual is either captured, or not captured, rmtttiotr abottt it bcliitviol'lllsYsl('lrr (('I' rcprocluctivc bclurvior) in an attclrpt to undcrstettttl bcttct'tllcir ciltlscli lttttl lttttt liprrs lrrrtl illustratc thc irrtcrrclltionships bctwectt bcltitvitlrs. Motlcls ttt.e l't'ltt't;tll\ ltypollrcticll irnrl tcutporlry. bcitrg chitngetl as Ilcw t'csttlts c()lllc lirrtlr. l'itt t'rrttttPlt', rrrl('l li:rc;crrtls(l()76)1tnr1'rosctlllttotlcl(l;igtrrclti.l2)tocxplititltltcoccttt't't'll('t'()l tttotlt'l l'rtrrkt' lltt' rrrPti'u'r. bclurvior tlrrrirrg lltc irrcrrbirlion itt lrcrrirtg gtrlls. llitcrctttls rrrlp'svslr.rrrs'.'srrlrsyslclts'. ittttl 'ttcls'wlrrt'lr ltt'tt'l;ttt's to'l irlllctgctr's ( I()\0)t':ttltt't

r.prrt'r'l)lrr;rl tprrtlr.l rll' llrt. lrir.rlrrt'lrit'ltl ()t,'lltttzlrltott

ol ltclltvittl (st't' :tlso l)lttt'kttls.

l()l.i\) lrrrrtirrl'lr)t(l ( l()l.i,l) rllrr'.lt.tlt". :ttttl tlist ltss('s s('\('t:tl ;trltlt l()/(rtr i1t(l Iro11;11 torrtr'Plrlrl rrrorlr'l: r,l lttollt;tllr,tt l\1, l,rrl,rtr,l (l')/l). l\lr Ilrtllrrr.l illl(l llpttrl6rr(l()lil),rrr.l lr,,rlr'.,(l()li(l)tolll,lltt,trlrltll,rll,llr'\,lllr1tlr"',rl '.\'-lt'ttt''ltt,,,lt'l', ( ilrs:..

Sotttr'llltt,',,,,11t,

I

1rrrnr('lrl',.ltttl

ttt,', lt,ttrt.tll.

r,lll

lrt

tt',,'.1 'l',,lll,llo1'lt',

u1

VISUAL REPR ESENTATI()N

INTERPRETATION AND PRESENTNTION OF RESULTS

S

WAVE AND WHIP

STATI C

DISPLAY lrig. I ti.l

I

Kinematic graph of the spermatophore-transler phase of the smooth newt's of arrows is proportional to the lrequency of transition. Arrows pointing to lhe left are returning to retreat display; arrows pointing outwards erre leaving sexual behavior, fbr example, to breathe. Br:brake; C:creep, C.O. :,creep-on; P.B. : push-back, e :quiver; R. D. - retreat display; S - spermatophore depositi on ; T.T. : touch -tail ( from sexual behavior sequence (Figure 18.10). Width

Halliday, 1975).

RETREAT D IS

PLAY rttctaphors to help visualize, and often better understand, behavioral processes. For ('xample, Lorenz's original (1950), and revised (1981), psycho-hydraulic model ol' rrrotivation has appeared to some to be analogous to a flush toilet (e.g. Goodenough

t't u\.,1993); however, it served as the basis for much early theorizing about innate ;rrtitrral behavior. For example, discussing Lorenz's early models, Thorpe (1919)

te EE SPERMATOPHORE TRAN

SF

ER

+

+\rFF ie'6

CREEP & FOLLOW

s

Some of his models were obviously analogous only - but the very of 'analogy'is its imperfections which challenges rethinking. One did not suppose them to be'true'but they were valuable in being

OUIVER

essence TOUCH TAIL

highly suggestive. DEPOSITION

Fig.

18.10

Kinematic graph of the sexual behavior sccprcncc is in black (from Halliday. I97-5;.

It:t'u'c I

BRAKE &

TOUCH TAIL

r()nr iln cvoltrtionat'y perspcctive (e.g. Maynard Smith, 1982). I

PUSH BACK

ol'thc snrootlt trcwt. lhc

IThorpe, I979:I03J

Irkcwisc. giultc theory models, such as Prisoner's Dilernma (e.g. Axelrod, 1984), sct'vetl its a usclirl rnetaphors (Sigmund. 1993) for envisioning animal conflict

+\ETrTi3il,

fl q

tlrtecl:

trurlc

rt

lt't ltct' 1'rct's1-rccl

ivc is provirlccl by generalized conceptual models which help the

,'tltologisl vistutlizc lltc cottrplcx ol'vuriahlcs wlrich impinge on behavior (Chapter ') Sottlc tttotlcls rtitl rcsclrrclrcrs rrr rccogrrizing how thcir rcscarch fits within the'big Irtt ltttr"rttttlltssisls itr itlcrttil'yirrg, irtr1rorllrrrI vtrritrblcs Io invcsligtrtc in lirture str-rrlies. \ tt'tV l'.t'ttr't;tl tttotlt'l ol' tltis lyllt'rr';rs tlt'st'rrllt'tl ;urtl tlist'rrssctl rrr tlcllril by ('rook cl ,tl (lt)l(t) I lrc lrr,rlrrl irst. ;rllorvt.rl lr1, llrt.rr rrrorlt.l rs illrrslrlrlt'tl rrr I,igrrr.c Ili.lj.

( oll':trt(l()/li )l)l()\'t(l(':.;r 1'oorl()\'('rirllrlrrrrri:.rortol llrt'r,,lr'ol

rrrlrlt.llrr,,, ll(.tltrll69-

r{,tll('\(';il( lt ( rrg1t1'1rl tt,tlttt,,,l,'l',,,11,'nlr',t,11,,7,1 ,',lt, lrtt'nrttrlr'/r rrlttrlt,tt,.,.tlrt(.,.,1(.(ltttttt;tlltr.

VISUAL REPRESENTATIoN

INTERPRE,TATION AND PRESENTNTION OF RESULTS

S

I

External Environmental Variables

m

(EEV)

i I I

Fig. 18.13

i

A conceptual model showing how externalenvironmental variables (EEV) are expected to interact with species parameters (SP, for example, morphological and physiological characteristics) to determine social structure (measured as the

I I

principal social system variables (PSSV) and social dynamics (changes in PSSV over time)). The dotted arrow takes note of the lact that EEVs also affect SP. but

a

c t

on a slower (evolutionary) time scale than the effects on PSSVs, which may of an individual through learning (from Crook et al..

o

I

change within the lifespan 1916).

matical terms to enable tests of their validity; that is, they should result in lalsifiable lrypotheses (e.g. Drickamer and Vessey, 1982). Predictive models are built from data sets. Generally, the larger and more accurate the data set, the more accurate the

rnodel; however, Gauoh (1993) has argued that a model can be more accurate than the tlata used to build it since the model amplifles hiddern patterns and discards noise.

Predictive models can be rather general, such as Regelmann's (1984) model for Itow competing individuals should distribute themselves between food resource pittches, or they can be more specific such as Altmann's (1980) mathematical model cxpressing the relationship

of

a baboon mother's feeding time requirement to her inlitnt's age. Tliesc models are beyond the scope of this book, but good discussions crtrt be lirLrncl in Colgan (1978), Hazlett and Bach (1977) and Mangel and Clark

(letili). corollirry on lhc input lor incubation. This input is fed llrrottlllt rt tutil (/). rtcccssrrly to crplain tlrc inhibition o['settling and building rvlrt'rt li'erlllttk ttutlcltcs c\l)c('tiur('v. Ilrc cllt'ct ol'll'ctlhlck tliscrcpancy on Iy' (:rrrtl /). /:,:rrtrl /'. trtn lrt'tr';rrl llorrr llrc iur()\\'s.'l lrc rrlrin syslcnls rnutually \tllrPll'1''()ll(';lll()llrt't. /'rrlltrlttl'ltl lrr01q111:tstttlt'tIrrPliVr'llr'ltlt'"'i0ttt tlurlttglt rlt.'tttlrtlrtll()r(|l \';tttrl / /'trrtr lrr':rtlrr'rrlr'rlrlrrt'rllYlr\t'rlt'ltrlrlslirrrttll likcrltrsl l.llll ol lr,ll,t"llr"' / (.ttl.tl',,,1,1'',lttttttl,tlt'rl lrr rlr',ltttl',ur,t", ollrt'l tlr;rtt rlr.litit.ttl l,',',11,,r, l. lr,'ur llr,', lrrlr lr (lrntrr ll,t, r, n,l,, l,lit') lrrr clle'r'cncc copy or

Fig. 18.12 Model for the explanation

of the occurrence ol'intcrruptivc hclutviot'tltu'irtg tltc

incubation ol a herring gull. The {ixccl actiort pitttcrns ut'c itt tltc t'igltt coltrltttt and superimposed control systcnrs tll'first lrrrtl sccorttl otrlct'ltt'c t'elltesr'ttlerl icll ol thcnr (IV=inctrbtrtiort syslctu. /i cscrrllt'\y\l('nr. /' ptt't'ttttt1, s\sl('ltt I I ltt' l;rr1't' vcrticttl ilt't()ws trl)t'esct)l olit'nl;tlion t otnltottr'ttl', tr tllt tr'1':tttl lo lltr' ttr'sl Itrt'rrlrlrlirrl,is llrt't'orrsrlrrrrlrlorv rrr'l I t't'rll,,r, l. ',lrnrrtl,rltotl ltr,ttt llt,'t lttlt lt.,tllt't lrt'ttt1'ptor't'rsr'tl rrt //'. llorrs l() it uilll (( / I ult,'t,'tl t. r,rtlrl),ttt'rl trtllt ('\lx'( l,tlt( \.

tii

INTERPRETATION AND PRESENTATI0N OF RESULTS

VISUAL REPRESENTATION

S

RANK

18.2.s Other illustrations

1970 The type of visual representation employed and its value in interpreting results are limited only by the ingenuity of the researcher. Simplicity in illustrations is generally

1971

420

022

a virtue worth pursuing. For example, Bercovitch (1988) used a pie-chart to illus-

06o

trate the percentages of the different types of consort change-overs (e.g. feed, fight) in adult male baboons. Patterson (1917) used a simple diagram which clearly

o2o 530

demonstrates the rank-order changes of male shelducks (Tadornu tadorna) over a

two-year observation period (Figure 18.14). The positional and relative extent of the changes in rank order are obvious and conducive to further interpretation. Hutt and Hutt (1910) followed up on a suggestion by Altmann (1965) and

012

of a phase structure grammar model to the analysis of

013

described the application

behavioral sequences. The model was first developed by Chomsky (1957) for the study of psycholinguistics. The model consists of the sequential partitioning of a sentence into its constituent parts based on its explicit meaning. The result is a tree

-

diagram of sequentially smaller clusters of words that together carry the meaning of the sentence. This hierarchical model, discussed by R.Dawkins (1976a) and

of syntax in the repro-

023

ductive behavior of the pigeon (Figure 18.15). The 'Catch-22' ol this method lor the ethologist is that to apply the model to gain understanding of the message in communication, we must first understand the

432

message.

This difficulty is illustrated by applying the analysis to the sentence'We fed her dog bones', which can have two meanings; hence it can be diagrammed in two ways (Figure 18. 16).

530 383 013

/ /.. ,//

015

010

572 613 380 019

321 330 007

624 017

523 /

this level of resolution in analyzing sequences of animal behavior'/ Altmann ( 1965a)

_y

with sufficient experience it can be done.

If one's

'lit stttttttt:tt'ize, rrll ol' tltr'sr' lt't'ltttirlttr.'s ol \ lsurrl tt'1111",1'111,r1ro11 (lrrrrl ollrt'rs rrol tlist'ttsst'tl)t':ttt;ttrltttlltt'tttlt'tPtt'l;rll()n ()l r(",ull'. llr,'\ ',lr,,rrl,ll,, , \,unnl('(ltrol orrl\

-

017

As Dale (1976) states, the ambiguity does not arise from a difference in words or

goal is to draw up an exclusive and exhaustive classification of the animals' repertoire of socially significant behaviour patterns, tl-ren these units of behaviour are not arbitrarily chosen. On the contrary,thcy can be empirically determined. One divides up the continuum ol'actiorr wherever the animals do. If the resulting recombination units lu'c themselves communicative, that is, if they affect the behaviour ol'othcr' members of the social group, then they arc social messagcs. J'htrs. tlrc splitting and lumping that onc clocs is. itlcrrlly. ir rcllcctiorr ol'thc splitting und lumping tlrirt lhc rrnirrrrls tlo f lltttrrttttt. l()lt:,tt .lt).' f

440

015

in their ordet but rather from a dillerence in their constituent structure. Do we have suggests that

563

/

005 531

Westman (L917), was used by Marshall (1965) in his study

546

408

395 053

il1

518

023

425

399 018 057

I rr ll'i l.l

('lr;rrry'r'r rrr r;rrrk ()t(l('t

ol slrt.lrlrrr k., lrt.lrr(.(.n \(.:rrs I lrt.lil,rrlt.s:rr.e llrc scrill ttt,ttkcrl trr,rl,",,rrr,rrr1,r'tl rrr r.rrrl. ortlr.t llttrls rrltitlt wet.c t,tttk,rl ttt lrollr \(',u.,,1t(.lotn(.(l lr\,uto\\, Ilr,.lrrl,lrr,r r.rrrl.rrr1,lrrrrls ttr l()/0 lr'ttrlr'rl lo tr'1',,,,,, lrrl,lr rrr l,l/l (lr,,rrtr I .,rrltil.utr,\\ ,) 1,s11 llr,.trrrtlrllt.lrrtrl., trr l()/0 tttttttlrt'ts

ol

11111;1t,ltt,tl

(lltttttr, t',,'lt,l.ilrrr\\',1 lr'ttrlr,l l,r 1,,,, r,rtrl r, l.rlrr, 1,,11r,,,, ,lllil\\',)(lt"lll

l'.tllr

t ,iltt

l'r'r)

1,,11 1,,

lrr/{l(rl,r.,lrr.rl

REVISING AND RESTARTIN(; ll\ l'()llll:SlrS

INTERPRETATION AND PRESENTNTION OF RESULTS

model'l Does your interpretation help you to tlcvclopr nr:w models and generate new hypotheses? At this point it is again importunt to consider what other research has

SSSeq

,/l\ /t\ /\ t

\

Prep

lnt

w

Wa

ASS

/\

Dr

lnt

D

Bi

lnt

Bw

Aoo Bw

/

Pre

/

M

\

shown.

Co

\

Co

I8.3 COMPARTSONS WITH PREVIOUS RESULTS How do your results compare with those of other researchers? Discuss your results with the same researchers you consulted before beginning your study (Chapter 4). Even though you reviewed the literature belbre beginning your study, it is wise to again search for relevant material in the light of your results. You may want to know rnore about similar behavior in other species or different behavior in the same

Wa

,/\ gE_iw

I

A

species. You may discover that your results have a bearing on a general concept or current theoretical issues. The importance of results are often unforeseen when a

study begins, but become apparent as the study proceeds and finally come to light as

\

the results are carefully interpreted.

qi

P.

of generative grammar in its recursive form to reproductive behavior of the male pigeon. SBSeq:sexual behavior sequence; Prep : preparatory behavior, Con : consummatory behavior; Int : introduce; Wa:warm up; Agg:aggressive behavior; Bw: bowing; Dr:driving; A:attacking; D:displacement preening; Bi:billing; M:mounting: Co:copulation. The underlining represents the final behavior that results from the previous steps. The dots indicate where the pigeon can backtrack in the

Fig. 18.15 Tree diagram showing application

sequence

(from Hutt and Hutt, 1970).

We

fod hor dog

-.Aed

know where you began, and you think you know what your results mean. Even lhough your results were seemingly conclusive, your study could have been better. I{e-evaluate the economics, efficiency and validity of your methods. Did you

plctccl.

fed her dog bones fod

hor

dog boner

-4r

her dog

-,A't.

her

Yrru've now reached the point where you can re-evaluate the entire study. You

sclect the proper species, study area, behavioral units, data-collection method, rrnalytical tests, etc.'J Re-evaluate your study at each phase of the ethological lpproach (Chapter l). You should improve your methods with each study, but tltis can only come through a critical re-evaluation of each study as it is com-

We fed her dog bones

fed her dog bones

I8.4 RE-EVALUATION

dog

III

dog

Fig. 18.16 Two-phase-structure grammar models of a single sentence to illustratc thc different meanings (adapted from Dale, 1976).

to understand better the particular bchavior stutliccl. but itlso to pttt it irt pcrspcctive relative to the various lcvcls ot'bchuvior (('ht1'rte r' | ).

Art'lltt'

5 REVISING AND RESTATING HYPOTHESES

bones

t'csttlls sirrrillrl'lrt lltost'

secl l(lr tltltcr hchitviors. itrrrl spccics illr(l ttnrlt't olltt't t'ttt it()lllllr'lllill t'olltlilitrtts'.' Arc tlre l'csrtltst'otlsistt'lll witlr;r ('()ll('('l)lttltlttt"rlt'l .'t r'tlrt;ll'lt'l.t ttrt'lll il l)lt'rltt ltr''t"

\irrr rtt:ry wunt to rcvisc or restatc your hypotheses whether your results were posilivc or ncglrtivc. Tcsting rcvisccl hypotheses can help reinlorce positive results and rsolrrlc llrc sotrrcc ol'ncgutivc resulls. Ytru might choose to isolate additional vari,rlrles ol tcsl tlrc cxlcrrurl virlirlity ol'yotrr rcsults on othcr spccies. Wlrt'tlteryott terisr'.r'eslltlc.()t !('neI'lltcItcwltypotltc:.ics.y()r.rilrcIl()wbackatthe l,r'l'itutinl'ol lltt'r'lltolo1,i1';11:tllPto;rclt t'vt'lr'(('lr;tPlt't I). r't'rrtly lo bcgitt irgitin. This Ittnt'yt)lt ;tI('ilt()l('('\l)('tl('n(('(1.;rttrl. lr()Pt'lrrlly. trtsr'r Sltr';rklttr ,'' ,rn r'\olttttottltt\ lrtttl,rl,t:.1 . I ( ) Wtl'-,rtt ollr'rr'tl tlrt' lirlltrtvittB ttr',t1'lrl

INTERPRETATION AND PRESENI'ATION OF RE,SULTS

Love the animals for themselves first, thell strain for general explanations, and, with good fortune, discoveries will follow.

APPENDIX A

lf

don't, the love and the pleasure will have been enou gh. Jwilson,

they 1994.

191

Statistical figures and

J

For ethologists, having had the pleasure of observing animals and learnedwhat they do is generally exceeclingly rewarding without having yet fully understood vthv.

tables

Table Al. Factorials. Values ctf n! nl

t7

0l 1l 22 36 424 5 6 7 8 9 l0 Ir 2 rI 14 t.5 r6 ll I ti () l )0 I

120 720

5040

40320 362 880 3 628 800

39916800 479001 600

r

6?.27 020800

87 t78 29t200

I

307 614 368 0(X)

20922 789 tt88 (XX) l-5-5 (,tt7 6

l -1

42fl 096 (XX)

402 17.3 705 72tt 0(x) (xx)

l l 6.t5 t(x)"101{ til2 t

I tlttr

(x

)l{ I 76

6-10 (x x )

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Handbook of Proteomic Methods

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Handbook of Proteomic Methods

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