Monitoring and Estimating Monitoring and Estimating Species RichnessSpecies Richness

Paul F. Doherty, Jr.Paul F. Doherty, Jr.Fishery and Wildlife Biology DepartmentFishery and Wildlife Biology Department

Colorado State UniversityColorado State UniversityFort Collins, COFort Collins, CO

BackgroundBackground Species diversity or richness may be an important Species diversity or richness may be an important

objective of monitoring/management programsobjective of monitoring/management programs Especially with careful definitionsEspecially with careful definitions

e.g. maximizing the number neotropical migrants, bats, frogse.g. maximizing the number neotropical migrants, bats, frogs

BackgroundBackground In estimating species diversity the most common In estimating species diversity the most common

measures are:measures are: Measure of evenness (relative abundances)Measure of evenness (relative abundances)

Simpson’s Index Simpson’s Index

Shannon Index (a.k.a. Shannon-Weiner, Shannon-Weaver)Shannon Index (a.k.a. Shannon-Weiner, Shannon-Weaver)

Species richness (presence/absence)Species richness (presence/absence) No need to tell individuals apartNo need to tell individuals apart

Often-made assumptions Often-made assumptions detection rates = 1 detection rates = 1 all species sampled with the same probability.all species sampled with the same probability.

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In estimating species diversity the most common In estimating species diversity the most common measures are:measures are:

Measure of evenness (relative abundances)Measure of evenness (relative abundances) Simpson’s Index Simpson’s Index

Shannon Index (a.k.a. Shannon-Weiner, Shannon-Weaver)Shannon Index (a.k.a. Shannon-Weiner, Shannon-Weaver)

Species richness (presence/absence)Species richness (presence/absence) No need to tell individuals apartNo need to tell individuals apart

Often-made assumptions Often-made assumptions detection rates = 1 detection rates = 1 all species sampled with the same probability.all species sampled with the same probability.

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ChallengesChallenges Different species will have different detectabilitiesDifferent species will have different detectabilities

Certainly some species will be missedCertainly some species will be missed e.g. Compare a thrush with a gnatcatcher songe.g. Compare a thrush with a gnatcatcher song Abundance will vary for each speciesAbundance will vary for each species

To use Shannon or Simpson’s Indices To use Shannon or Simpson’s Indices must estimate abundance of each speciesmust estimate abundance of each species Not clear how to interpret these indices (notions of evenness and Not clear how to interpret these indices (notions of evenness and

dominance)dominance)

Focus on specie richnessFocus on specie richness Easier to tell species apart than individualsEasier to tell species apart than individuals In past studies species richness and these indices are often In past studies species richness and these indices are often

highly correlated.highly correlated.

Direct connection in estimating Direct connection in estimating richness to estimating abundancerichness to estimating abundance

Instead of estimating abundance (# of Instead of estimating abundance (# of individuals), estimate species richness (# individuals), estimate species richness (# of species)of species)

Instead of estimating survival, estimate Instead of estimating survival, estimate extinction, etc.extinction, etc.

Time scaleTime scale

Local or globalLocal or global Local probably most relevant in our caseLocal probably most relevant in our case

EstimationEstimation

Importance of detection probability (C = Importance of detection probability (C = count, p = probabilty of detection, S = count, p = probabilty of detection, S = richness)richness)

Relative change – issue does not go awayRelative change – issue does not go away

SpC pCS

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2

1

1

2

2

1

1

2

1

Cpx

pC

pC

pC

SS

EstimationEstimation Can use ‘heterogeneity’ estimators such as Burnham Can use ‘heterogeneity’ estimators such as Burnham

and Overton’s (1978,1979) Jackknife.and Overton’s (1978,1979) Jackknife. Makes sense to use estimator such as this.Makes sense to use estimator such as this. Desirable to have equal sampling effort (e.g. quadrats, time Desirable to have equal sampling effort (e.g. quadrats, time

spent listening).spent listening). Can be used with ‘capture history’ or X matrixCan be used with ‘capture history’ or X matrix

N = number of species detected, t = number of quadrats N = number of species detected, t = number of quadrats sampled, f = number of species found on only one quadratsampled, f = number of species found on only one quadrat

For stats-minded folks – this is probably one area where For stats-minded folks – this is probably one area where growth will occurgrowth will occur

tf)1t(NS 1

SamplingSampling Sampling over time or spaceSampling over time or space Capture history for each speciesCapture history for each species

010110010110 Can generate frequencies (# of species seen exactly once, twice, etc)Can generate frequencies (# of species seen exactly once, twice, etc) Can estimate richness using widely available software (Capture, Can estimate richness using widely available software (Capture,

SpecRich, Comdyn).SpecRich, Comdyn).

T1 T2 T3 T4 T5 T6

L1

L2

L3

L4

L5

L6

Community dynamicsCommunity dynamics

Change in species richnessChange in species richness ExtinctionExtinction TurnoverTurnover ColonizationColonization

Use Pollock’s Robust DesignUse Pollock’s Robust Design

X1X2X3 Y1Y2Y3 Z1Z2Z3

closed closed closed

open open

Community dynamicsCommunity dynamics

Change in species richness over timeChange in species richness over time

Not changes in composition, just numbersNot changes in composition, just numbers ‘‘Stability’ (temporal variance)Stability’ (temporal variance)

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SS

Local ExtinctionLocal Extinction Probability that a species present in Probability that a species present in

sampling period sampling period ii is not present in a later is not present in a later period period jj..

iijij R/)RM(11

Local Species TurnoverLocal Species Turnover Probability that a species selected at Probability that a species selected at

random from the community at time random from the community at time jj is a is a “new” species as compared to the “new” species as compared to the community at time community at time i.i. 1 if every species is “new”1 if every species is “new” 0 if every species is “old”0 if every species is “old”

jjiji R/)RM(11

Number of colonizing speciesNumber of colonizing species The number of species not present at time The number of species not present at time ii

that colonize and are present at time that colonize and are present at time jj..

iijjij SˆSB

Spatial AnalogsSpatial Analogs Comparing two sitesComparing two sites Relative species richnessRelative species richness

Species co-occurance and omissionSpecies co-occurance and omission Comparing reference and altered sitesComparing reference and altered sites (Forest Plan)(Forest Plan)

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SS

ExampleExample

Comdyn, SpecRichComdyn, SpecRich http://www.mbr-pwrc.usgs.gov/software.html#comdynhttp://www.mbr-pwrc.usgs.gov/software.html#comdyn

ExampleExample Compare a BBS route (WI in 1970 and 1990) Compare a BBS route (WI in 1970 and 1990)

SummarySummary

Community richness and dynamics can be Community richness and dynamics can be estimated nicely and easilyestimated nicely and easily

May be useful in monitoring programsMay be useful in monitoring programs Especially where monitoring concerns are Especially where monitoring concerns are

‘broad’.‘broad’. Data are often easier/cheaper to collect than Data are often easier/cheaper to collect than

those associated with abundancethose associated with abundance

Example:Example:

Sexual Selection affects local extinction and Sexual Selection affects local extinction and turnover in bird communitiesturnover in bird communities

Doherty P.F., Sorci, G., Royle, A., Nichols, Doherty P.F., Sorci, G., Royle, A., Nichols, J.D., and T. BoulinierJ.D., and T. Boulinier

BackgroundBackground

Sexual selection is thought to drive the Sexual selection is thought to drive the evolution of secondary sexual traits such evolution of secondary sexual traits such as bright colors and plumage dimorphismas bright colors and plumage dimorphism

Benefit of these traitsBenefit of these traits Increased reproductive success (Andersson Increased reproductive success (Andersson

1994)1994)

Costs of such traitsCosts of such traits

Increased predation and sensitivity to Increased predation and sensitivity to environmental and demographic environmental and demographic stochasticity (Haskell 1996, Legendre et stochasticity (Haskell 1996, Legendre et al. 1999)al. 1999) Prediction: Extinction rates should be higher Prediction: Extinction rates should be higher

for species with intense natural selection for species with intense natural selection (Tanaka 1996)(Tanaka 1996)

Support for predictionSupport for prediction

Dichromatic bird species have been Dichromatic bird species have been shown to have higher mortality rates than shown to have higher mortality rates than monochromatic (Promislow et al. 1994)monochromatic (Promislow et al. 1994)

Dichromatic bird species introduced onto Dichromatic bird species introduced onto islands went extinct at a higher rate than islands went extinct at a higher rate than monochromatic species (McLain et al. monochromatic species (McLain et al. 1995, Sorci et al. 1998)1995, Sorci et al. 1998)

Methodological IssuesMethodological Issues Detection errorDetection error

Dimorphic and monomorphic species may not be Dimorphic and monomorphic species may not be detected with equal probabilitydetected with equal probability

Differ with habitatDiffer with habitat Temporal differencesTemporal differences

Variation in spaceVariation in space Local populations close together may be synchronousLocal populations close together may be synchronous Spatial autocorrelation shown to be important is island Spatial autocorrelation shown to be important is island

study of species richness (Selmi and Boulinier 2001)study of species richness (Selmi and Boulinier 2001)

Objective of our studyObjective of our study To test the prediction that dichromatic To test the prediction that dichromatic

species have higher local extinction ratesspecies have higher local extinction rates ImprovementsImprovements

Also examined turnover ratesAlso examined turnover rates Tested on continental scale.Tested on continental scale. Incorporated detection probabilitiesIncorporated detection probabilities Incorporated spatial autocorrelationIncorporated spatial autocorrelation

Data setData set

US Breeding Bird Survey data (1975-96)US Breeding Bird Survey data (1975-96) Restricted dataset to daytime species from Restricted dataset to daytime species from

6 orders and grouped them as either 6 orders and grouped them as either having dichromatic (n = 153) or having dichromatic (n = 153) or monochromatic (n=185) plumage.monochromatic (n=185) plumage.

DefinitionDefinition

Extinction - proportion of species Extinction - proportion of species becoming locally extinct (on a BBS survey becoming locally extinct (on a BBS survey route) between two successive years route) between two successive years among species present the first year.among species present the first year.

Turnover – proportion of new species Turnover – proportion of new species among species present the second year among species present the second year as compared to the firstas compared to the first

Detection ErrorDetection Error We used COMDYN (1999) to estimate species We used COMDYN (1999) to estimate species

richness, extinction rate, and turnover rate for richness, extinction rate, and turnover rate for dimorphic and monomorphic speciesdimorphic and monomorphic species

This program and associated metrics was This program and associated metrics was developed to better estimate these quantities as developed to better estimate these quantities as well as detection probabilitieswell as detection probabilities

Based on ‘closed’ capture models with Based on ‘closed’ capture models with heterogeneity (Mh) and robust design (Boulinier heterogeneity (Mh) and robust design (Boulinier et al. 1998, Nichols et al. 1998)et al. 1998, Nichols et al. 1998)

Spatial modelingSpatial modeling

We used a Bayesian approach We used a Bayesian approach Computed the difference between Computed the difference between

dichromatic and monchromatic extinction dichromatic and monchromatic extinction ratesrates

ticmonochromai

cdichromatiiiy

Spatial modeling Spatial modeling T-test would assume T-test would assume yyii independent normally independent normally

distributeddistributed However, However, yyii‘s probably not independent due to ‘s probably not independent due to

spatial proximityspatial proximity We could resort to a subjective stratification We could resort to a subjective stratification

scheme with associated restrictive forms of scheme with associated restrictive forms of spatial dependencespatial dependence

Or we could introduce spatial correlation into the Or we could introduce spatial correlation into the problem explicitly by adding an error term which problem explicitly by adding an error term which we assume is spatially correlatedwe assume is spatially correlated

Spatial modelingSpatial modeling

Many ways to parameterize the spatial correlation Many ways to parameterize the spatial correlation among the among the ααii’s’s

Could insert parameters from a variogram Could insert parameters from a variogram (parametric approach)(parametric approach)

We used a convolution approach suggested by We used a convolution approach suggested by Higdon (1998)Higdon (1998)

iii uy

Normally distributed random variable

Correlated spatial random variables

ResultsResults

Dichromatic species had a 23% higher local Dichromatic species had a 23% higher local extinction rate per survey than monochromatic extinction rate per survey than monochromatic species (0.079 species (0.079 ±± 0.001 vs. 0.064 0.001 vs. 0.064 ±± 0.001) 0.001)

Widespread result across continent indicating that Widespread result across continent indicating that sexual selection influences the communities of birds sexual selection influences the communities of birds in many differing habitats and places.in many differing habitats and places.

We also found that a model incorporating a spatial We also found that a model incorporating a spatial component fit better than one without (component fit better than one without (ΔΔAIC = 117), AIC = 117), and that we should qualify above statementand that we should qualify above statement

Mean difference in extinction rate

Lower 95% CI

ResultsResults If extinction rates are higher in dichromatic If extinction rates are higher in dichromatic

species, then some might expect the species, then some might expect the number of dichromatic species to number of dichromatic species to decrease over time.decrease over time.

We found the number of species in both We found the number of species in both groups increased over the time period groups increased over the time period (4.8% and 3.4% respectively).(4.8% and 3.4% respectively).

We examined turnover rates tooWe examined turnover rates too

Mean difference in turnover rate

Lower 95% CI

ConclusionsConclusions Higher local extinction and turnover rates Higher local extinction and turnover rates

for dichromatic vs. monochromatic speciesfor dichromatic vs. monochromatic species Spatial structure in these rates suggest Spatial structure in these rates suggest

metacommunities (Wilson 1992)metacommunities (Wilson 1992) Regionally connected communitiesRegionally connected communities

Regional or local factors may have dramatic Regional or local factors may have dramatic effects over a large area.effects over a large area.