Overview
- commonmethodsinanimalbehavior
- myinterests
- rationale
- goals/aims
- preliminarydata
- preliminaryanalysis
TraditionalSamplingObservationalmethods(Altmann,1974;Martin&Bateson,2007)
Ethogram– adetailedcatalogueofbehaviors,usuallymutuallyexclusiveandasobjectiveaspossible.Ideally,itshouldbeabletoinstructtwodifferentpeopletorecordthesamebehaviorsthesameway
Typesofsampling:
- Alloccurrence
- 1/0sampling
- Instantaneousscansampling
Mostlyuseintuitiontodecideuponsampling,timescales,durations.
MyInterests– patternandstructure- Howtomeasurebehavior
- Howpatternsrelatestofunction
- Howanimalsperceiveandrespondtopatterns
Dissertation:
1.Patternperception
2.Individualbehaviorpatterns
3.Grouplevelnetworkstructure
Systemisananimal– itsbehavior isaproductofmanydifferent thingsonmanydifferenttimescales:evolutionaryhistoryofthespecies,geneticcomposition, rearinginfluences,interactionswithotheranimals,currentenvironment,pastenvironment, hormones, healthstate,physiology, etc.etc.etc.(andallof thesethingsalsointeractwitheachother).
Ananimalisbasicallyablackbox,withbehavioraloutputswecanmeasure.
Complexityinbiologicalsystems
‘Theoryofcomplexity lossinaginganddisease’ (Goldbergeretal,2002)- healthysystemsoutputcomplexmulti-scaled variabilitythatallowsforadaptivecapabilities andbreaksdownwithageorillness.- the long-rangescalingpatternsofhumanheartbeatandgaitdynamicshavebeenshowntodeteriorateinmarkedlypathologicalconditions(Goldbergeretal,2002b).
“Acompletelyordereduniverse,however,wouldbedead.Chaosisnecessaryforlife.Behaviouraldiversity,totakeanexample,isfundamentaltoanorganism’ssurvival.Noorganismcanmodeltheenvironmentinitsentirety.Approximationbecomesessentialtoanysystemwithfiniteresources.Chaos,aswenowunderstandit,isthedynamicalmechanismbywhichnaturedevelopsconstrainedandusefulrandomness.Fromitfollowdiversityandtheabilitytoanticipatetheuncertainfuture.Thereisatendency,whoselawswearebeginningtocomprehend,fornaturalsystemstobalanceorderandchaos,tomovetotheinterfacebetweenpredictabilityanduncertainty.Theresultisincreasedstructuralcomplexity.”– Crutchfield(2011)
Complexityinbehaviorpatterns
- animal-environmentinteraction(i.e.resourceacquisition)
- self-regulationofbehaviorinresponsetoachangingenvironment
- pathologicalbehavioralstereotypy
Themeasurementofbehavioralcomplexityorstructuremaybeanuntappedyetfundamentalsourceofinformationaboutananimal’sbehavioralecologyandhealth.
BriefreviewofliteraturePreviousstudieshavereportedthatcertainanalysesofbehaviorpatterncandetectsubtlechangesin‘hidden’animalbehavioralstructurethataremissedwhenusingtraditionalmeasuressuchasaveragedurations(Escós etal.,1995;Asheretal.,2009;Seuront &Cribb,2011).
Variabilityinbehavioralsequencecomplexityhasbeenlinkedtomanycharacteristics:
- differencesbysex:socialbehaviorofchimpanzees(Alados &Huffman,2000);foragingandlocomotionbehaviorinJapanesemacaques(MacIntosh etal.,2011)
- stress level:boattrafficdisturbance indolphinsurfacebehavior(Seuront &Cribb,2011);resourcedeprivationinchickens(María etal.,2009)
- age:youngchickens(María etal.,2009);adultmacaques(MacIntosh etal.,2011)
- statesknowntobeenergeticallytaxingsuchasreproduction,clinically impairedhealth,andparasiteinfection:Japanesemacaques(MacIntosh etal.,2011),SpanishIbex(Alados etal,1996),chimpanzees(Alados &Huffman,2000).
AnimalMovement
- fairlyobjectivebehaviortoscore
- fundamentaltosurvivalofmobilespecies,multi-purpose
- alreadyassociatedwithhealth(self-tracking)
- tonsofthistypeofdataisbeingcollected
“Movement data provide a window - often our only window -into the cognitive, social, and biological processes that underlie the behavioral ecology of animals in the wild.” - Gurarie et al 2016
Goals:
Describestructureinmonkeymovement
- Relatepatternattributestofunction◦ behavioralstate,terrain,individual characteristics(age,health,rank,etc.)
- Informfuturemeasurementandsampling
Koshima,Summer2015- EAPSIprogram(NSF/JSPS)
- Pilotdata
- collaborationwithDr.AndrewMacIntosh
- behavioralstructure~parasiteload
Data
- 56sequences(4sequencesfromeachof14monkeys)
- 1-0activitydowntothesecond*
- 1,000-4,000datapoints/sequence
- Activityrangedfrom.5%to52%(mean17.51%)
0.00
0.02
0.04
0.06
0 200 400d1$length
density
typeact
inact
"density plots of lengths of activity bouts"
Sequences
ReturnMaps
0 20 40 60 80 100 120 140
0100
200
300
400
500
Activity followed by inactivity bout lengths
ilength alength
ilength
0 20 40 60 80 100 120 140
020
4060
80100
120
140
Activity bout lengths n+1~n
n
n.1
0 100 200 300 400 500
0100
200
300
400
500
Inactivity bout lengths n+1~n
n
n.1
H(L)fordifferentL,usingwordfrequencies
o
o
o
o
o
o
o
o
o
2 4 6 8
0.5
1.0
1.5
2.0
2.5
HxL for different monkey sequences
d$lcb
ind(
d$so
, d$s
k, d
$sku
, d$s
s, d
$sso
, d$s
u)
oo
oo
oo
oo
o
oo
oo
oo
oo
o
oo
oo
oo
oo
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
(Goldenmean)
random random bout real
2
4
6
8
0.0 2.5 5.0 7.5 0.0 2.5 5.0 7.5 0.0 2.5 5.0 7.5
word length
entropy
monkeykibana
omoto
sone
usu
Entropy by word length for randomized and real sequences
Nextsteps- E-machines◦ BayesianInferencetogetinfocalculations◦ Alternating renewalprocessmodeltype
Marzen etal,2015