landscape fragmentation affects responses of avian … · to test this hypothesis, we quantiﬁed...

Landscape fragmentation affects responses of aviancommunities to climate changeMARTA A JARZYNA1 W ILL IAM F PORTER 1 BR IAN A MAURER 1 2 B EN JAMIN

ZUCKERBERG3 and ANDREW O FINLEY4

1Department of Fisheries and Wildlife Michigan State University East Lansing MI 48824 USA 2Center for Statistical Training

and Consulting Michigan State University East Lansing MI 48824 USA 3Department of Forest and Wildlife Ecology

University of Wisconsin-Madison Madison WI 53706 USA 4Departments of Forestry and Geography Michigan State

University East Lansing MI 48824 USA

Abstract

Forecasting the consequences of climate change is contingent upon our understanding of the relationship between

biodiversity patterns and climatic variability While the impacts of climate change on individual species have been

well-documented there is a paucity of studies on climate-mediated changes in community dynamics Our objectives

were to investigate the relationship between temporal turnover in avian biodiversity and changes in climatic condi-

tions and to assess the role of landscape fragmentation in affecting this relationship We hypothesized that commu-

nity turnover would be highest in regions experiencing the most pronounced changes in climate and that these

patterns would be reduced in human-dominated landscapes To test this hypothesis we quantified temporal turn-

over in avian communities over a 20-year period using data from the New York State Breeding Atlases collected dur-

ing 1980ndash1985 and 2000ndash2005 We applied Bayesian spatially varying intercept models to evaluate the relationship

between temporal turnover and temporal trends in climatic conditions and landscape fragmentation We found that

models including interaction terms between climate change and landscape fragmentation were superior to models

without the interaction terms suggesting that the relationship between avian community turnover and changes in cli-

matic conditions was affected by the level of landscape fragmentation Specifically we found weaker associations

between temporal turnover and climatic change in regions with prevalent habitat fragmentation We suggest that

avian communities in fragmented landscapes are more robust to climate change than communities found in contigu-

ous habitats because they are comprised of species with wider thermal niches and thus are less susceptible to shifts in

climatic variability We conclude that highly fragmented regions are likely to undergo less pronounced changes in

composition and structure of faunal communities as a result of climate change whereas those changes are likely to be

greater in contiguous and unfragmented habitats

Keywords biodiversity bird communities climate change global change habitat fragmentation temporal turnover

Received 8 September 2014 and accepted 11 January 2015

Introduction

The ability to predict the ecological consequences of cli-

mate change and develop sound conservation strategies

depends on our understanding of the relationship

between changes in biodiversity patterns and climatic

variability To date the majority of research regarding

the implications of climate change to biodiversity

has evaluated responses of individual species (eg

Parmesan amp Yohe 2003 La Sorte amp Thompson 2007

Marini et al 2009 Zuckerberg et al 2009) The variabil-

ity of individual species responses is predicted to lead

to disruptions of communities and ecosystems (Brotons

amp Jiguet 2010) but the complex nature of ecological

interactions makes it difficult to extrapolate from the

scales of individuals to the community or ecosystem

level (Walther et al 2002) To fully understand conse-

quences of climate change however it is imperative to

develop a more comprehensive understanding on

whether climate change can lead to broadscale changes

in community structure and composition

Measuring community turnover has potential to offer

a useful and simple way of evaluating the implications

of climate change to ecological communities (eg

Tuomisto 2010ab Barton et al 2013) Community

turnover can be quantified in multiple ways but gener-

ally is an aggregate of gains and losses of species

comprising a community (eg Tuomisto 2010b) Tradi-

tionally turnover has been used to describe biodiver-

sity patterns across space rather than time (spatial

turnover) but it can also be viewed in terms of a

Correspondence Marta A Jarzyna Department of Ecology and

Evolutionary Biology Yale University 165 Prospect Street

New Haven CT 06520-8106 USA tel +1 203 432 1597

fax +1 203 432 5176

e-mail martajarzynayaleedu

2942 copy 2015 John Wiley amp Sons Ltd

Global Change Biology (2015) 21 2942ndash2953 doi 101111gcb12885

temporal change in community composition that has

occurred at the same site across a particular time period

(temporal turnover) Given that spatial turnover in

species composition is thought to be at least partially

related to spatial variation in environmental variables

(Gaston et al 2007 Buckley amp Jetz 2008 Gutierrez-

Canovas et al 2013 Siefert et al 2013) it is likely that

temporal turnover reflects temporal changes in climate

and could be a useful indicator of climate change

impacts on biodiversity

Even though the impacts of climate change on biodi-

versity are likely to be felt across a wide range of ecosys-

tems different communities are likely to respond to

climate change in disparate ways because a wide range

of environmental factors might influence speciesrsquo vulner-

abilities to climate change For example simulation stud-

ies suggest that the interaction between climate and land

cover will influence the way in which biodiversity

responds to climate change (Travis 2003 Jeltsch et al

2011) Specifically habitat fragmentation is thought to

affect the rate of distributional shifts associated with cli-

mate change allowing species in less fragmented habi-

tats to disperse faster and farther (Warren et al 2001

Opdam amp Wascher 2004) Lack of suitable habitat

between speciesrsquo current distribution and the future suit-

able climatic conditions might prevent a species with

some limiting intrinsic characteristics such as sedentary

life style or short dispersal distances from migrating to

these new climatically suitable locations If species in

contiguous (ie unfragmented) habitats shift their distri-

butions faster as a result of climate change than species

in fragmented habitats then changes in communities

should also follow the same pattern Thus stronger asso-

ciations of temporal turnover with changing climatic

conditions in contiguous habitats might be expected

In addition to more pronounced distributional shifts

other mechanisms might also lead or contribute to

stronger associations of temporal turnover with chang-

ing climate in unfragmented landscapes First commu-

nities in contiguous habitats tend to be comprised

largely of habitat specialists (Estavillo et al 2013)

which generally have narrower habitat and climatic

niches (Barnagaud et al 2012) and thus might be more

vulnerable to changing climatic conditions Conse-

quently the same amount of change in climatic condi-

tions would be expected to exert a stronger effect on

communities in contiguous landscapes Secondly frag-

mented habitats tend to support smaller populations

than contiguous unfragmented regions Small local

population sizes generally increase the effect of demo-

graphic or environmental stochasticity (Gaublomme

et al 2014) resulting in higher turnover in community

composition compared with that of regions supporting

large populations (Banks-Leite et al 2012)

While recent research suggests that climate change is

one of the factors causing temporal shifts in community

composition (Devictor et al 2008 Kampichler et al

2012 Prince amp Zuckerberg 2015) the influence of land-

cover and habitat fragmentation is still unclear For

example Devictor et al (2008) found that composition

of avian communities in France changed to include

higher proportion of high-temperature dwelling species

and suggested (although not explicitly tested) that this

response of communities to climate warming might dif-

fer between different types of land-cover and levels of

landscape fragmentation Kampichler et al (2012) sug-

gested a connection between land-cover and climate

change and bird community change by showing a

directional change in the proportion of high-tempera-

ture dwelling species as well as in the level of habitat

specialization of species comprising the community In

eastern North America Prince amp Zuckerberg (2015)

found that the composition of winter birds has become

increasingly dominated by warm-adapted species and

this community reshuffling was greatest in regions

experiencing long-term warming patterns However

despite these initial investigations the relationship

between temporal turnover in biodiversity and the

interaction of climatic changes and landscape fragmen-

tation to the best of our knowledge has not yet been

explicitly evaluated We intend to fill this research gap

by investigating the drivers of temporal turnover in

avian biodiversity across New York using Breeding

Bird Atlas data (Andrle amp Carroll 1988 McGowan amp

Corwin 2008) Our primary hypothesis was that tem-

poral turnover in avian assemblages is related to cli-

mate change but the strength of this relationship varies

with the degree of habitat fragmentation Specifically

we predict high rates of temporal turnover in regions

experiencing greater climate change and that these cli-

mate-mediated turnover rates would be higher in land-

scapes characterized by more contiguous habitat

Contiguous habitat is likely to facilitate higher rates of

turnover via three ecological mechanisms (i) allowing

species to disperse faster in response to climate change

(ii) supporting communities comprised of species more

vulnerable to changing climatic conditions and (iii)

increasing the ratio of deterministic to stochastic

processes

Materials and methods

Site description

The study area is the State of New York USA New York cov-

ers 128 401 km2 including 4240 km2 of inland water The cli-

mate of New York is affected by the statersquos broad elevation

gradient as well as by the proximity to lakes Erie and Ontario

copy 2015 John Wiley amp Sons Ltd Global Change Biology 21 2942ndash2953

RESPONSES OF COMMUNITIES TO CLIMATE CHANGE 2943

and the Atlantic Ocean The Adirondack and Catskill Moun-

tains in the east are among the highest regions of the state

with elevation varying between 600 and 1500 m The elevation

of the southwestern New York ranges from approximately

300ndash900 m while northwestern New York as well as Hudson

River valley and New York City are low-lying with elevation

ranging from approximately 0ndash200 m

In general New York State is covered by approximately

512 forest (deciduous evergreen and mixed forests) 134

pasture and hay 82 cultivated crops 29 scrub and shrub

10 grassland and herbaceous 80 wetland cover 87

developed land and 02 barren land (Homer et al 2004)

New York offers a broad gradient of landscape fragmentation

The regions of the Adirondack and Catskill Mountains are the

least fragmented with expansive regions of forest land The

land cover of the Hudson River valley is fragmented by

residential and commercial development whereas the

landscapes of western New York are characterized mostly by

agriculturendashforest mosaic

Breeding Bird Atlas

We used the New York State Breeding Bird Atlas (BBA) to

characterize changes in avian communities through time BBA

is a statewide survey that documented the distribution of

breeding birds in New York To date BBA has been conducted

in two time periods 1980ndash1985 (hereafter BBA1980 Andrle amp

Carroll 1988) and 2000ndash2005 (hereafter BBA2000 McGowan

amp Corwin 2008) For both BBAs a grid system was used to

define the basic unit for reporting data The BBA reporting

unit (a block) was a square measuring 5 9 5 km a total of

5335 blocks covered the entirety of New York State This data-

set represents one of the largest and finest resolution atlases in

the world (Gibbons et al 2007)

A total of 242 species were recorded for BBA1980 and 248

species were recorded for BBA2000 (Andrle amp Carroll 1988

McGowan amp Corwin 2008 see Table S1 in Appendix S1 for

the list of all species used in this analysis) Observations were

made by skilled birders who spent at least 8 h in each block

visited all cover types in each block and included at least one

nighttime visit to document nocturnal species Observer effort

was recorded for each BBA and reported as a number of per-

son hours (measured as the sum of the number of hours spent

in each block x the number of people surveying each block

McGowan amp Zuckerberg 2008) The BBA represents a detec-

tionnondetection dataset nondetection indicates that species

could not be found given search criteria (McGowan amp Corwin

2008)

Habitat fragmentation

Habitat fragmentation was identified using the National Land

Cover Data (NLCD) derived from the Landsat Thematic Map-

per satellite data (Homer et al 2004) Even though the NLCD

are available for four time periods (ie 1992 2001 2006 and

2011) there is no land-cover data readily available for the time

period of BBA1980 Thus to assess whether temporal turnover

is related to landscape fragmentation and whether landscape

fragmentation affects responses of communities to climate

change we used NLCD 2001 To ensure that land-cover or

land-use change could not have significantly affected changes

in avian community composition over the time period of

1980ndash2005 we used the NLCD 19922001 Retrofit Land Cover

Change Product (Fry et al 2009) We quantified the amount of

land-cover change that occurred between 1992 and 2001 and

found that on average only 17 of the BBA blockrsquos area chan-

ged over this time period (median = 13 min = 0

max = 216) and approximately 97 of all blocks underwent

land-cover change of less than 5 (see Fig S1 in Appendix

S1) We thus felt confident that land-cover change had minor

if any effect on changes in avian community composition

Prior to landscape analysis we consolidated the following

land-cover classes open water and perennial ice and snow

classes into one class of waterice open space developed low

intensity developed medium intensity developed high inten-

sity developed into one class of developed land cultivated

crops and pasturehay into one class of agriculture and

deciduous forest evergreen forest mixed forest and forested

wetland into one class of forest Consolidation of these land-

cover types improved accuracy of classification and simplified

the environment for purposes of our evaluation Upon consoli-

dation we examined the following land-cover classes water

ice developed barren land forest scrubshrub grassland

herbaceous agriculture and wetlands

We used FRAGSTATS 41 (McGarigal et al 2012) and the

Geospatial Modelling Environment (GME httpwwwspa-

tialecologycomgme) to quantify landscape fragmentation

Because we focused our analysis on a diverse suite of species

with varying habitat requirements we chose a landscape-scale

variable to capture broadscale variation in habitat fragmenta-

tion We chose edge density (ED) as a measure of landscape

fragmentation because an increase in habitat edge is a primary

outcome of habitat fragmentation (Hargis et al 1998) ED was

also reported as an effective tool for evaluating landscape

fragmentation and performed better than other landscape

fragmentation indices (Hargis et al 1998) However in situa-

tions when landscape consists entirely of one cover type the

ED would be 0 regardless of the type of land cover present

Therefore to differentiate between landscapes that consisted

entirely of natural land cover (eg forest) and those mostly

developed (eg cities) we also calculated the proportion of

developed land (DEVEL) metric and included it as a variable

in our models (see Fig S2 in Appendix S1 for maps of all

explanatory variables)

Climatic trends

The climate data were derived from the PRISM (Parameter-ele-

vation Regressions on Independent Slopes Model) climate

mapping system (Daly amp Gibson 2002) PRISM consists of inter-

polated monthly maximum and minimum temperatures and

precipitation at a 25-arcmin resolution from 1891 to 2010 for

the entire contiguous United States We calculated the magni-

tude of the 25-year (1980ndash2005) trend in average monthly max-

imum and minimum temperatures and in total monthly

precipitation using ordinary least squares regression The


2944 M A JARZYNA et al

slope of the OLS regression indicated the magnitude of the

trend and reflected the amount of change in climatic variables

that occurred between 1980 and 2005 We then interpolated

the trend magnitudes for each BBA block and averaged the

monthly values to reflect average breeding season (May

through September) trend magnitude in maximum and mini-

mum temperatures (TMAXTrend and TMINTrend respec-

tively) and in total precipitation (PRECIPTrend) TMAXTrend

and TMINTrend were expressed in degC25 years PRECIP-

Trend was expressed in mm25 years An average increase in

maximum temperatures of the breeding season was 063degC(median = 065 degC) over the 1980ndash2005 time period while an

average increase in minimum temperatures was 141 degC(median = 141 degC) over the same time period On average

precipitation increased by 10 mm (median = 10 mm) over the

1980ndash2005 time period (see Figs S2 and S3 in Appendix S1 for

maps and histograms of all explanatory variables)

Other model covariates

We included elevation in all the models to account for topo-

graphical variation of New York State We used digital eleva-

tion models for New York State to quantify mean elevation in

each block (ELEV) Additionally increasing survey effort often

results in a higher number of recorded species (Tobler et al

2008) Drastically different survey effort between BBA1980

and BBA2000 could increase the values of temporal turnover

which could be a result of different number of recorded spe-

cies rather than actual changes in species identities through

time To account for potential survey effort bias for each BBA

block we calculated the difference in the number of person

hours between BBA2000 and BBA1980 standardized by the

number of person hours in BBA1980 (EFF = (EFF2000 ndashEFF1980)EFF1980) and included it as a covariate in our

models We did not detect significant correlation among any

of our explanatory variables (Fig S3 in Appendix S1)

Statistical analysis

We quantified temporal turnover (TURN) as an aggregate of

species losses (ie species recorded at a particular site during

BBA1980 but no longer detected during BBA2000) and species

gains (ie species not detected at a particular site during

BBA1980 but recorded during BBA2000) within a particular

site Specifically temporal turnover was calculated as follows

TURN =Ethorn C

Ethorn Cthorn Peth1THORN

where E is the number of species lost in a particular BBA block

between BBA1980 and BBA2000 C is the number of species

gained in a particular BBA block between BBA1980 and

BBA2000 and P is number of species present in a particular

BBA block during both BBAs (ie persistence) TURN metric

is the complement of Jaccardrsquos similarity index (Jaccard 1912)

In addition to temporal turnover we quantified its compo-

nents proportion of species lost (ie species recorded at a par-

ticular site during BBA1980 but no longer detected during

BBA2000 hereafter called extinction EXT) and proportion of

species gained (ie species not detected at a particular site

during BBA1980 but recorded during BBA2000 hereafter

called colonization COL) in each block between BBA1980 and

BBA2000 EXT and COL were calculated as follows

EXT =E

Ethorn Peth2THORN

COL =C

Cthorn Peth3THORN

For TURN we used the sum of E C and P as the denomi-

nator of Eqn (1) For EXT we used the sum of E and P as the

denominator because only species present in a block in

BBA1980 could have gone extinct For colonization we used

the sum of C and P as the denominator We recognize that

species already present in a block in BBA1980 (ie those

included in P metric) could not have colonized that block and

that species other than those included in C and P metrics

could have potentially colonized a site However we were

simply interested in the proportion of species that colonized

the site relative to all the species found within the block in the

second time period Furthermore using the sum of C and P as

the denominator of Eqn (3) allows COL metric to be equiva-

lent to EXT The values of TURN EXT and COL are bounded

by 0 and 1 values approaching 0 indicate low temporal turn-

over extinction or colonization in a BBA block between

BBA1980 and BBA2000 while values approaching 1 indicate

high temporal turnover extinction or colonization in a BBA

block between BBA1980 and BBA2000

To ensure that observed temporal turnover extinction and

colonization are at least partially a result of deterministic fac-

tors rather than stochastic processes we used North American

Breeding Bird Survey (httpswwwpwrcusgsgovbbs) as

independent dataset to verify whether change in community

composition increases with temporal lag We calculated tem-

poral turnover extinction and colonization that occurred

between 1980 and any subsequent year until 2005 and found

that each of the three community change metrics increased

with increasing temporal lag (see Fig S4 in Appendix S1)

Increase in community turnover with temporal lag implies

that shifts in community composition were unlikely to be a

result of stochastic processes alone but rather were partly dri-

ven by deterministic factors

We evaluated five competing statistical models (Table 1)

Model 1 included all climatic variables (TMAXTrend TMIN-

Trend and PRECIPTrend) as well as elevation (ELEV) and

survey effort (EFF) Model 2 included all climatic variables

ED ELEV and EFF Model 3 included all climatic variables

ED percent developed land (DEVEL) ELEV and EFF Model

4 included all climatic variables ED ELEV EFF and the inter-

action terms between the climatic variables and ED Model 5

included all climatic variables ED DEVEL ELEV ED and

the interaction terms between all climatic variables and ED

(Table 1) To ease the comparison and interpretation of the

coefficient estimates we standardized all the explanatory vari-

ables to z-scores with a mean = 0 and standard deviation = 1

We used a Bayesian model estimation procedure Each

response variable (temporal turnover TURN extinction EXT



or colonization COL) at each location (s) was modeled as a

binomial random variable Let y(s) be a response variable

then y(s) ~ B(p(s)N(s)) where N(s) is the total number of spe-

cies found at least once in a block from BBA1980 to BBA2000

(for models of TURN) the total number of species found in

BBA1980 (for models of EXT) or the total number of species

found in BBA2000 (for models of COL) For each BBA block

let p(s) = y(s)N(s) Also let x(s) be a vector containing the

explanatory variables for each model In all models we

included spatially structured random effects by adding all the

variance associated with the spatial process to the model inter-

cept thus accounting for the potential spatial autocorrelation

A generalized linear model approach was used to relate

responses to explanatory variables Using the logit link g(s) = log[p(s)1-p(s)] the spatially varying intercept model

was

gethsTHORN frac14 wethsTHORN thorn bxethsTHORN eth4THORNwhere b is a vector of regression coefficients and w(s) is a ran-

dom effect representing spatially varying adjustments to the

intercept We assume each element in w(s) arises from a spa-

tial Gaussian process (GP) (see eg Banerjee et al 2004 or

Cressie amp Wikle 2011 for more details) Specifically w(s) ~ GP

(0 C(s s0)) where s and s0 are any two locations within the

study area the spatial covariance C(s s0) = rq(s s0 ) with

variance parameter r2 correlation function q( ) and spatial

decay parameter The exponential spatial correlation was

assumed for q()Prior distributions on the parameters complete the hierar-

chical model specification (eg Gelman et al 2004) The glo-

bal regression coefficients brsquos followed a normal distribution

priors with mean and variance parameters equal to 0 and 100

respectively (ie N(0 100)) while the variance component r2

was assigned inverse gamma distribution prior with shape

and scale parameters of 2 and 1 respectively (ie IG(2 1))

The spatial decay parameter followed an informative uni-

form prior with support that ranged from 1 km to the maxi-

mum distance between any two grid cells (ie approximately

350 km) We ran three MCMC chains for 20 000 iterations

each Convergence was diagnosed using the GelmanndashRubin

diagnostic (Gelman amp Rubin 1992) As appropriate for a

Bayesian framework candidate model fit to the observed data

was assessed using the deviance information criterion (DIC

Spiegelhalter et al 2002) Lower values of DIC indicate

improved fit Model explanatory power was evaluated using

pseudo-R2 Pseudo-R2 was calculated as 1 ndash SSresSStot where

SSres is the sum of squared differences between the observed

and the predicted values of the dependent variable and SStot is

the sum of squared differences between the observed and the

mean value of the dependent variable Larger values of R2

suggest better fit Model accuracy was compared using root

mean square error (RMSE) that was calculated as the square

root of the mean squared deviations between fitted and

observed outcomes Lower values of RMSE indicate improved

accuracy

Prior to statistical analysis we removed all blocks that did

not have continuous land-cover coverage blocks with more

than 50 open water coverage and those that did not have

survey effort data We withheld 10 of the data for theTable

1Fivecompetingmodelsoftemporalturnoverextinctionan

dcolonizationincluded

thefollowingexplanatory

variablesmag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

emax

imum

temperature

ofthebreed

ingseason(TMAXTrend)mag

nitudeofthe25

-year(198

0ndash200

5)tren

din

averag

eminim

um

temperature

ofthebreed

ingseason

(TMIN

Trend)

mag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

etotalprecipitation

ofthebreed

ingseason(PRECIPTrend)

edgeden

sity

(ED)

percentdev

eloped

land

(DEVEL)interactionbetweenthemag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

emax

imum

temperature

ofthebreed

ingseasonan

ded

geden

sity

(TMAXTrendED)inter-

actionbetweenthemag

nitudeofthe25

-year(198

0ndash200

5)tren

din

averag

eminim

um

temperature

ofthebreed

ingseasonan

ded

geden

sity

(TMIN

TrendED)interactionbetween

mag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

etotalprecipitationofthebreed

ingseasonan

ded

geden

sity

(PRECIPTrendED)elev

ation(ELEV)an

dsu

rvey

effort(EFF)

Model

Explanatory

variables

TMAXTrend

TMIN

Trend

PRECIPTrend

ED

DEVEL

TMAXTrendED

TMIN

TrendED

PRECIPTrendED

ELEV

EFF

Model

1x

xx

xx

Model

2x

xx

xx

x

Model

3x

xx

xx

xx

Model

4x

xx

xx

xx

xx

Model

5x

xx

xx

xx

xx

x



validation of modelrsquos predictive performance Ultimately we

used a total of N = 4271 blocks to fit the models and 473

blocks to validate the models Holdout set predictions were

compared to the observed values using root mean square

error (RMSEpred) where lower values indicate improved per-

formance

The models were run in R 2151 statistical package (R

Development Core Team 2013 httpwwwr-projectorg)

using package spBayes (Finley et al 2007) In a Bayesian

framework parameter estimates have valid posterior distribu-

tions and inferences are made using mean or median as well

as the credible intervals of these posterior distributions Credi-

ble intervals overlapping zero imply that the parameter in

question is not different from zero Summaries of parameter

estimates were generated using the R coda package (Plummer

et al 2006)

Results

Temporal turnover

The mean value of TURN was 0384 (variance 00097)

This indicates that on average approximately 62 of

the species were common to both BBAs and 38 of spe-

cies had either colonized or become extinct within the

average block (Fig 1) The northeastern part of the

state which is the location of the Adirondack Moun-

tains experienced the highest rates of temporal turn-

over across BBA1980 and BBA2000 (Fig 1) Central and

northwestern New York underwent lowest temporal

turnover (Fig 1)

Model selection indicated that the variation in tempo-

ral turnover in avian communities was best accounted

for by the most complex model (Model 5 Table 2) Val-

ues of pseudo-R2 implied that Model 5 also explained a

slightly larger proportion of variation than other

models (Table 2) Similarly values of RMSE suggested

that the most complex model had slightly higher accu-

racy than the remaining models (Table 2) Selection of

the most complex model points to the importance of

the interactions between landscape fragmentation and

change in climatic conditions in explaining temporal

turnover in species composition between BBA1980 and

BBA2000 The values of RMSEpred indicated that pre-

dictive abilities of the most complicated model were

better than predictive abilities of the remaining models

(Table 2) However despite Model 5 having the best

predicting abilities it tended to overpredict low values

of TURN while high values tended to be underpredict-

ed (see Fig S5 in Appendix S1 for model validation plot

for the best model)

Parameter estimates from the top model indicated

that temporal turnover was positively related to

TMAXTrend suggesting that higher temporal turnover

was observed in regions where maximum temperatures

increased the most (Fig 2 see Table S2 in Appendix S1

for parameter estimates resulting from all temporal

turnover models) Temporal turnover was also posi-

tively associated with TMINTrend but the effect of

trends in minimum temperatures on TURN interacted

with ED (Fig 3a) In regions of low landscape fragmen-

tation large increases in minimum temperatures were

associated with high temporal turnover but the effect

of TMINTrend reversed and significantly diminished

in locations with high landscape fragmentation There

was a negative association of temporal turnover and

ED but the presence of multiple ED interaction terms

in the model makes it difficult to interpret the main

effects of ED on temporal turnover We found a posi-

tive relationship between temporal turnover and

DEVEL (Fig 2) indicating that highly urbanized

(a) (b) (c)

Fig 1 Temporal turnover (a) proportion of extinction events (b) and proportion of colonization events (c) observed between 1980ndash

1985 and 2000ndash2005 in avian communities across New York Temporal turnover was calculated using New York State Breeding Bird

Atlas (BBA) as a proportion of species gained or lost between 1980ndash1985 (ie BBA1980) and 2000ndash2005 (ie BBA2000) in a particular

site (ie a Breeding Bird Atlas block) relative to all species recorded during at least one time period extinction was calculated as a pro-

portion of species lost between BBA1980 and BBA2000 in a particular site relative to all species present in a block during BBA1980 colo-

nization was calculated as a proportion of species gained between BBA1980 and BBA2000 in a particular site relative to all species

present in a block during BBA2000 High values of all three measures indicate high temporal turnover extinction and colonization



regions generally experienced increased temporal turn-

over The relationship between TURN and ELEV was

positive (Fig 2) suggesting that high-elevation regions

experienced higher temporal turnover

Extinction

The mean value of EXT for all species was 0210 (vari-

ance 00139) which indicates that on average approxi-

mately 21 of species of the original assemblage had

become extinct within the average block (Fig 1) The

northeastern part of the state (ie the Adirondack

Mountains) and regions surrounding the Finger Lakes

displayed higher than average rates extinction (Fig 1)

Model selection indicated that the most complex

model (Model 5) provided the best fit to the variation

in the proportion of extinction events (Table 2) Values

of pseudo-R2 implied that explanatory abilities of

Model 5 were slightly better than those of other models

(Table 2) Similarly values of RMSE suggested that

Model 5 had slightly higher accuracy than the remain-

ing models (Table 2) Selection of the most complex

model indicates that interactions between land-cover

fragmentation and change in climatic conditions con-

tribute to explaining extinction rates observed in avian

communities The values of RMSEpred indicated that

predictive abilities of Model 5 were slightly lower than

those of Model 3 but better than predictive abilities of

the remaining models (Table 2) Model 5 in general un-

derpredicted low values of extinction and overpredict-

ed high values of extinction (see Fig S5 in Appendix

S1)

Parameter estimates of the best model indicated that

extinction in avian communities was positively related

to TMINTrend although the effect of changes in mini-

mum temperatures on extinction rates interacted with

ED (Fig 2 see Table S3 in Appendix S1 for parameter

estimates resulting from all extinction models) In

regions of low landscape fragmentation large increases

in minimum temperatures were associated with high

proportion of extinction events but the effect of TMIN-

Trend diminished or reversed in locations with high

landscape fragmentation (Fig 3b) Extinction was posi-

tively associated with PRECIPTrend (Fig 2) but this

positive relationship between EXT and PRECIPTrend

was only detected in regions with high landscape frag-

mentation (Fig 3c) There was a negative association of

extinction and ED but the presence of multiple ED

interaction terms in the model makes it difficult to

interpret the main effects of ED on extinction in avian

communities There was a positive relationship

between extinction and DEVEL (Fig 2) indicating

increased proportion of extinction events in highly

urbanized regions We found a positive relationship

Table 2 Comparison of five competing models of temporal turnover proportion of extinction events and proportion of coloniza-

tion events using deviance information criterion (DIC) pseudo-R2 and root mean square error (RMSE) pD indicates the effective

number of parameters Predictive performance of the models was evaluated using RMSEpred Pseudo-R2 was calculated as 1 ndash SSres

SStot where SSres is the sum of squared differences between the observed and the predicted values of the dependent variable and

SStot is the sum of squared differences between the observed and the mean value of the dependent variable Larger values of

pseudo-R2 suggest better fit RMSE was calculated as the square root of the mean squared deviations between fitted and observed

outcomes RMSEpred was calculated as the square root of the mean squared deviations between the observed values from the hold-

out set and the predicted values resulting from all the models Lower values of RMSE and RMSEpred indicate improved accuracy

and better predictive power respectively For description of the models see Table 1

Measure of community change Model DDIC pD Pseudo-R2 RMSE RMSEpred

Temporal turnover Model 5 00 1093 0299 00825 00803

Model 3 195 1061 0296 00826 00805

Model 4 353 1075 0297 00826 00806

Model 2 591 1050 0294 00828 00808

Model 1 946 1023 0291 00830 00813

Extinction Model 5 00 1136 0316 00976 00971

Model 4 380 1135 0314 00978 00975

Model 3 398 1114 0313 00977 00970

Model 2 768 1099 0312 00978 00973

Model 1 1514 1107 0308 00981 00978

Colonization Model 5 00 1133 0205 01008 01006

Model 3 05 1098 0204 01008 01007

Model 4 91 1109 0204 01008 01007

Model 2 127 1089 0204 01009 01007

Model 1 231 1086 0203 01009 01008



between EXT and ELEV indicating that high-elevation

regions experienced higher levels of temporal turnover

in avian communities

Colonization

The mean value of COL was 025 (variance 00128)

indicating that on average 25 of all species found in a

block during BBA2000 were the ones that had colonized

the site since BBA1980 Colonization was in general

higher in eastern and western parts of the state while

central and southern New York underwent lower colo-

nization rates (Fig 1) However the spatial pattern of

proportion of colonization events was less clumped

than the one displayed by temporal turnover or

extinction

Model selection procedure selected more than one

model as the top model accounting for the variation

in the proportion of colonization events The top two

models included the most complex model (Model 5)

and the model with main effects of all explanatory

variables but without the interaction terms (Model 3

Table 2) Values of pseudo-R2 implied that Model 5

also explained a slightly larger proportion of variation

than other models (Table 2) The values of RMSE sug-

gested that the most complex model also had slightly

higher accuracy than other models (Table 2) How-

ever despite the fact that the most complex model

was selected among the top models none of the inter-

action terms was associated with COL The most com-

plex model also showed the poorest predictive

abilities as indicated by the values of RMSEpred

(Table 2) Model validation also indicated that it un-

derpredicted the low values of colonization and over-

predicted high values of colonization (see Fig S5 in

Appendix S1)

Colonization was positively related to TMAXTrend

and negatively related to PRECIPTrend (Fig 2 see

Table S4 in Appendix S1 for parameter estimates result-

ing from all colonization models) Although ED was

negatively related to COL presence of multiple ED


interpret the main effects of ED We found a positive

relationship between colonization and DEVEL (Fig 2)

which indicates that highly urbanized regions

experienced highest colonization rates

Discussion

Our findings supported our hypothesis that changes in

avian community composition are related to changes in

climate but the strength of the relationship between

temporal turnover and climate change is affected by

the magnitude of landscape fragmentation Associa-

tions were generally weaker between community

change and climatic change in regions with widespread

habitat fragmentation but these relationships varied

based on temporal turnover extinction and coloniza-

tion dynamics We found that temporal turnover and

extinction were generally associated with increasing

rates of minimum temperature but these climate-medi-

ated patterns interacted strongly with fragmentation

and were diminished in fragmented areas Colonization

events were more likely in regions experiencing

increases in maximum temperature and decreases in

precipitation but unlike extinction patterns these rela-

tionships were not affected by fragmentation Although

we found that overall rates of community turnover

were higher in regions experiencing warming the

climatic drivers of temporal turnover extinction and

Fig 2 Coefficient estimates (ie median values of the posterior

distribution and associated 95 credible intervals) of the best

model for temporal turnover proportion of extinction events

and proportion of colonization events Abbreviations of the

explanatory variables are as follows magnitude of the 25-year

(1980ndash2005) trend in average maximum temperature of the

breeding season (TMAXTrend) magnitude of the 25-year (1980ndash

2005) trend in average minimum temperature of the breeding

season (TMINTrend) magnitude of the 25-year (1980ndash2005)

trend in average total precipitation of the breeding season (PRE-

CIPTrend) edge density (ED) percent developed land

(DEVEL) interaction between the magnitude of the 25-year


breeding season and edge density (TMAXTrendED) interac-

tion between the magnitude of the 25-year (1980ndash2005) trend in

average minimum temperature of the breeding season and edge

density (TMINTrendED) interaction between magnitude of the

25-year (1980ndash2005) trend in average total precipitation of the

breeding season and edge density (PRECIPTrendED) elevation

(ELEV) and survey effort (EFF)



colonization events varied geographically across the

study region

There are at least three plausible not mutually exclu-

sive ecological mechanisms that might be driving the

pattern of associations between temporal community

change climatic change and landscape fragmentation

First communities in contiguous landscapes might be

able to respond more quickly to changes in tempera-

tures and precipitation perhaps due to fewer barriers

to dispersal Such an interpretation would align with

research suggesting that unfragmented habitats allow

higher rates of species distributional shifts associated

with climate change (Opdam amp Washer 2004) If that

were the case communities in fragmented habitats

would lag behind changing climatic conditions and

therefore be less capable of adapting to shifting climate

space This lag would initially be reflected in communi-

ties being compositionally similar across time and thus

showing no temporal turnover However through

time it is likely that species will eventually disappear

from locations in which they can no longer find suitable

climatic conditions Such localized species extinctions

will ultimately result in higher community temporal

turnover and higher extinction rates perhaps on a par

with those currently observed in unfragmented

landscapes

While birds are highly mobile organisms not all

birds display similar dispersal abilities (Gaston amp

Blackburn 2002) There are significant differences in

dispersal distances between migratory and resident

bird species with migratory birds capable of dispersing

farther (Sutherland et al 2000) The differences in dis-

persal abilities might suggest that distributional shifts

of migratory birds might not be restricted by landscape

fragmentation to the same extent as those of resident

birds although research suggests otherwise For exam-

ple Villard et al (1995) showed that the area of land-

scape patches affects the spring resettlement by

Neotropical migratory birds suggesting that landscape

matrix might be one of the factors influencing resettle-

ment of returning migrants However mechanisms

through which migratory birds reoccupy their breeding

ranges are still poorly known and further research is

needed to explore whether landscape fragmentation

might be impeding distributional shifts of migratory

birds to the same extent as shifts of resident birds

How plausible is the unimpeded movement explana-

tion in New York State Using the same dataset

Zuckerberg et al (2009) documented shifts in breeding

ranges of 129 species of birds They found that the birds

exhibiting shifts in their range boundaries all had

different breeding habitat associations suggesting that

land cover was not a factor in the observed range

expansion Specifically they found no evidence for

more pronounced range shifts of forest breeding birds

in unfragmented habitats such as those of the Adiron-

dack and Catskill Mountains Given their findings it

seems unlikely that contiguous habitats facilitating the

climate change-driven range shifts may be at the root of

the higher rates of temporal turnover extinction and

colonization we are observing

A second plausible mechanism is that avian commu-

nities in fragmented landscapes are more robust to

changes in temperatures and precipitation resulting

from climate change than communities found in contig-

uous habitats If that were the case we would expect

little or no temporal change in community composition

and weaker associations with changing climatic

(a) (b) (c)

Fig 3 Effects size plots resulting from the best models for temporal turnover (a) and proportion of extinction events (b c) for the inter-

action of the magnitude of the 25-year (1980ndash2005) trend in average minimum temperature of the breeding season (TMINTrend) and

edge density (ED) and the interaction of the magnitude of the 25-year (1980ndash2005) trend in total precipitation of the breeding season

(PRECIPTrend) and edge density (ED) The interaction terms shown here are the ones whose credible intervals did not overlap zero

Yellow (temporal turnover) and blue (extinction) points are the observed data points and are plotted to indicate the levels of confidence

for the predicted surface



conditions in fragmented landscapes Indeed heteroge-

neous and fragmented habitats are often composed of a

large proportion of habitat generalists (Tscharntke

et al 2012 Estavillo et al 2013) Habitat generalists

tend to have wider thermal breadths than habitat spe-

cialists (Barnagaud et al 2012) which potentially

allows them to tolerate greater changes in climatic con-

ditions On the other hand narrower thermal niches of

habitat specialists (Barnagaud et al 2012) might make

them vulnerable to even small increases in tempera-

tures Thus in this scenario we would expect commu-

nities in contiguous or less fragmented habitats (ie

comprised mainly of habitat specialists) to continue to

undergo relatively large compositional changes as a

result of climate change while those found in frag-

mented regions (ie comprised largely of generalists)

to show relatively small changes until perhaps some

thermal threshold is reached

Preliminary analysis of temporal turnover and pro-

portion of extinction and colonization events in com-

munities of birds belonging to different habitat

associations revealed that temporal community change

in generalist birds was lower than that of birds with

other more specialized habitat associations (eg forest

grassland wetland specialists Jarzyna MA data not

shown) This lower temporal change in communities of

generalist birds perhaps suggests that generalist birds

are indeed more robust to climatic changes lending

support to our second interpretation of the pattern of

stronger associations between community change and

climatic change in regions with unfragmented land-

scapes Furthermore Kampichler et al (2012) found

that highly contiguous regions such as forests and

heathlands in the Netherlands underwent large tempo-

ral change in community composition but the level of

community specialization remained constant and rela-

tively high Thus we suggest that relative contributions

of habitat generalists and specialists in ecological com-

munities might be at least partially contributing to the

divergent patterns of the associations between temporal

community change and climate change

Lastly the relative importance of stochastic and

deterministic processes in fragmented and contiguous

landscapes might be yet another factor contributing to

the observed pattern of relationships between temporal

turnover and changes in climatic conditions Turnover

in community composition in highly fragmented land-

scapes is generally pervasive (Krauss et al 2003

Borgella amp Gavin 2005 Banks-Leite et al 2012) This

high compositional turnover is expected to result from

small local population sizes which increase the effect

of demographic or environmental stochasticity (Gau-

blomme et al 2014) On the other hand stochastic

events tend to be less important in contiguous

landscapes because independent individual events tend

to average out in populations of larger sizes (Lande

1993 Goulart et al 2013) Thus in unfragmented habi-

tats deterministic processes such as changes in climatic

conditions are expected to be a major contributor to

turnover in community composition We were able to

attribute a larger portion of variation in temporal turn-

over to changing climatic conditions in regions of con-

tiguous land cover than in fragmented landscapes

While determining which of the last two alternative

explanations ndash relative contributions of habitat general-

ists and specialists or increased importance of deter-

ministic processes in contiguous habitats ndash is the main

mechanism driving the divergent community changendashclimate change associations is beyond the scope of this

manuscript we suggest that both processes are likely to

have played important roles

We found that high-elevation regions experienced

more pronounced changes in community composition

Elevation had the strongest influence on the values of

overall temporal turnover while proportion of extinc-

tion events was slightly less strongly affected by this

variable Indeed a positive effect of elevation on turn-

over in community composition is well supported by

scientific literature For example Stegen et al (2013)

found a positive relationship between habitat heteroge-

neity ndash measured as elevation range ndash and spatial and

temporal turnover in communities of breeding birds

across the continental United States while Coyle et al

(2013) found a positive effect of elevation on species

richness of North American birds In New York high-

elevation regions tend to also be more contiguous By

accounting for the influence of elevation on community

turnover we were able to disentangle the effects of

landscape fragmentation and elevation on the response

of avian communities to climate change

We recognize that rare species might be occasionally

missed during surveying potentially leading to percep-

tion that temporal turnover is low Additionally non-

random variation in the proportion of rare or poorly

detectable species across sites could potentially lead to

apparent patterns of high temporal turnover While

such bias resulting from presence of rare species within

communities has been well-documented in the litera-

ture (Vellend 2001 Bacaro amp Ricotta 2007) so far few

solutions have been offered

Our findings provide new insights into the drivers of

temporal change in avian communities and have signif-

icant implications for conservation theory and practice

Current conservation strategies commonly focus on

conserving large regions with undisturbed and contigu-

ous habitats that generally support high species diver-

sity often comprising of species with narrow habitat

niches (ie habitat specialists) Our research suggests



that while ecological communities in these protected

areas will be less subject to stochastic variation they

will be more sensitive to changing environmental con-

ditions and will likely undergo the most drastic compo-

sitional changes as a result of climate change It is

therefore necessary that current conservation strategies

are put in the context of climate change

Acknowledgements

We would like to thank thousands of volunteers who partici-pated in both New York State Breeding Bird Atlases We alsothank Kimberley Corwin and Kevin McGowan for supplyingatlas databases and Colin M Beier Daniel Bishop and JohnWiley for supplying climate data We would like to thankAllen Hurlbert and an anonymous reviewer for helpful com-ments on the earlier versions of the manuscript The manu-script benefited from discussions with members of the Booneand Crockett Quantitative Wildlife Center at Michigan StateUniversity This study received financial support from NASAGrant NNXO9AK16G and the Boone and Crockett ClubAndrew O Finley was supported by the National ScienceFoundation grants DMS-1106609 EF-1137309 EF-1241868 andEF-125322

References

Andrle RF Carroll JR (1988) The Atlas of Breeding Birds in New York State Cornell Uni-

versity Press Ithaka NY

Bacaro G Ricotta C (2007) A spatially explicit measure of beta diversity Community

Ecology 8 41ndash46

Banerjee S Carlin BP Gelfand AE (2004) Hierarchical Modeling and Analysis for Spatial

Data Chapman amp HallCRC Boca Raron FL

Banks-Leite C Ewers RM Metzger JP (2012) Unraveling the drivers of community

dissimilarity and species extinction in fragmented landscapes Ecology 93 2560ndash

2569

Barnagaud J-Y Devictor V Jiguet F Barbet-Massin M Le Viol I Archaux F (2012)

Relating habitat and climatic niches in birds PLoS ONE 7 e32819

Barton PS Cunningham SA Manning AD Gibb H Lindenmayer DB Didham RK

(2013) The spatial scaling of beta diversity Global Ecology and Biogeography 22

639ndash647

Borgella R Jr Gavin TA (2005) Avian community dynamics in a fragmented tropical

landscape Ecological Applications 15 1062ndash1073

Brotons L Jiguet F (2010) Bird communities and climate change In Effects of Climate

Change on Birds (eds Moller AP Fielder W Berthold P) pp 275ndash294 Oxford Uni-

versity Press Oxford

Buckley LB Jetz W (2008) Linking global turnover of species and environments Pro-

ceedings of the National Academy of Sciences of the United States of America 105

17836ndash17841

Coyle JR Hurlbert AH White EP (2013) Opposing mechanisms drive richness pat-

terns of core and transient bird species The American Naturalist 181 E83ndashE90

Cressie N Wikle CK (2011) Statistics for Spatio-Temporal Data John Wiley and Sons

Hoboken NJ

Daly C Gibson W (2002) Parameter-estimation on Independent Slopes Model (PRISM)

The PRISM Climate Group Corvallis OR

Devictor V Julliard R Couvet D Jiguet F (2008) Birds are tracking climate warming

but not fast enough Proceedings of the Royal Society B 275 2743ndash2748

Estavillo C Pardini R da Rocha PLB (2013) Forest loss and the biodiversity threshold

an evaluation considering species habitat requirements and the use of matrix habi-

tat PLoS ONE 8 e823369

Finley AO Banerjee S Carlin BP (2007) spBayes an R package for univariate and

multivariate hierarchical point-referenced spatial models Journal of Statistical Soft-

ware 19 1ndash24

Fry JA Coan MJ Homer CG Meyer DK Wickham JD (2009) Completion of the

National Land Cover Database (NLCD) 1992ndash2001 Land Cover Change Retrofit

product pp 18 US Geological Survey Open-File Report 2008ndash1379

Gaston KJ Blackburn TM (2002) Large-scale dynamics in colonization and extinction

for breeding birds in Britain Journal of Animal Ecology 71 390ndash399

Gaston KJ Davies RG Orme CDL et al (2007) Spatial turnover in the global avifauna

Proceedings of the Royal Society B-Biological Sciences 274 1567ndash1574

Gaublomme E Eggermont H Hendrickx F (2014) Local extinction processes rather

than edge effects affect ground beetle assemblages from fragmented and urban-

ised old beech forests Insect Conservation and Diversity 7 82ndash90

Gelman A Rubin DB (1992) Inference from iterative simulation using multiple

sequences Statistical Science 7 457ndash511

Gelman A Carlin JB Stern HS Rubin DB (2004) Bayesian Data Analysis 2nd edn

Chapman amp HallCRC Boca Raton FL

Gibbons DW Donald PF Bauer H-G Fornasari L Dawson IK (2007) Mapping avian

distributions the evolution of bird atlases Bird Study 54 324ndash334

Goulart FF Salles P Machado RB (2013) How may agricultural matrix intensification

affect understory birds in an Atlantic Forest landscape A qualitative model on

stochasticity and immigration Ecological Informatics 18 93ndash106

Gutierrez-Canovas C Millan A Velasco J Vaughan IP Ormerod SJ (2013) Contrast-

ing effects of natural and anthropogenic stressors on beta diversity in river organ-

isms Global Ecology and Biogeography 22 796ndash805

Hargis CD Bissonette JA David JL (1998) The behavior of landscape metrics com-

monly used in the study of habitat fragmentation Landscape Ecology 13 167ndash186

Homer C Huang C Yang L Wylie B Coan M (2004) Development of a 2001 national

landcover database for the United States Photogrammetric Engineering and Remote

Sensing 70 829ndash840

Jaccard P (1912) The distribution of the flora in the alpine zone New Phytologist 11

37ndash50

Jeltsch F Moloney KA Schwager M Keuroorner K Blaum N (2011) Consequences of cor-

relations between habitat modifications and negative impact of climate change for

regional species survival Agriculture Ecosystems and Environment 145 49ndash58

Kampichler C Turnhout CAM Devictor V van der Jeugd HP (2012) Large-scale

changes in community composition determining land use and climate change sig-

nals PLoS ONE 7 e35272

Krauss J Steffen-Dewenter I Tscharntke T (2003) Local species immigration extinc-

tion and turnover of butterflies in relation to habitat area and habitat isolation

Oecologia 442 591ndash602

La Sorte FA Thompson FR (2007) Poleward shifts in winter ranges of North Ameri-

can birds Ecology 88 1803ndash1812

Lande R (1993) Risks of population extinction from demographic and environmental

stochasticity and random catastrophes The American Naturalist 142 911ndash927

Marini MA Barbet-Massin M Lopes LE Jiguet F (2009) Predicted climate-driven bird

distribution changes and forecasted conservation conflicts in a neotropical

savanna Conservation Biology 23 1558ndash1567

McGarigal K Cushman SA Ene E (2012) FRAGSTATS v4 Spatial Pattern Analysis

Program for Categorical and Continuous Maps Computer software program pro-

duced by the authors at the University of Massachusetts Amherst MA

McGowan M Corwin K (2008) The Second Atlas of Breeding Birds in New York State

Cornell University Press Ithaka NY

McGowan K Zuckerberg B (2008) Summary of results In The Second Atlas of Breeding

Birds in New York State (eds McGowan K Corwin K) pp 15ndash42 Cornell University

Press Ithaka NY

Opdam P Wascher D (2004) Climate change meets habitat fragmentation linking

landscape and biogeographical scale levels in research and conservation Biological

Conservation 117 285ndash297

Parmesan C Yohe G (2003) A globally coherent fingerprint of climate change impacts

across natural systems Nature 421 37ndash42

Plummer M Best N Cowles K Vines K (2006) CODA convergence diagnosis and

output analysis for MCMC R News 6 7ndash11

Prince K Zuckerberg B (2015) Climate change in our backyards the reshuffling

of North Americarsquos winter bird communities Global Change Biology 21 572ndash

585

Siefert A Ravenscroft C Weiser MD Swenson NG (2013) Functional beta-diversity

patterns reveal deterministic community assembly processes in eastern North

American trees Global Ecology and Biogeography 22 682ndash691

Spiegelhalter DJ Best NG Carlin B van der Linde A (2002) Bayesian measures of

model complexity and fit (with discussion) Journal of the Royal Statistical Society

Series B 64 583ndash639

Stegen JC Freestone AL Crist TO et al (2013) Stochastic and deterministic drivers of

spatial and temporal turnover in breeding bird communities Global Ecology and

Biogeography 22 202ndash212

Sutherland GD Harestad AS Price K Lertzman KP (2000) Scaling of natal dispersal

distances in terrestrial birds and mammals Conservation Ecology 4 Art 16



Tobler MW Carrillo-Percastegui SE Leite Pitman R Mares R Powell G (2008) An

evaluation of camera traps for inventorying large- and medium-sized terrestrial

rainforest mammals Animal Conservation 11 169ndash178

Travis JMJ (2003) Climate change and habitat destruction a deadly anthropogenic

cocktail Proceedings of the Royal Society B-Biological Sciences 270 467ndash473

Tscharntke T Tylianakis JM Rand TA et al (2012) Landscape moderation of

biodiversity patterns and processes ndash eight hypotheses Biological Reviews 87 661ndash

685

Tuomisto H (2010a) A diversity of beta diversities straightening up a concept gone

awry Part 2 Quantifying beta diversity and related phenomena Ecography 33

23ndash45

Tuomisto H (2010b) A diversity of beta diversities straightening up a concept gone

awry Part 1 Defining beta diversity as a function of alpha and gamma diversity

Ecography 33 2ndash22

Vellend M (2001) Do commonly used indices of b-diversity measure species turn-

over Journal of Vegetation Science 12 545ndash552

Villard M-A Merriam G Maurer BA (1995) Dynamics and subdivided populations

of Neotropical migratory birds in a fragmented temperate forest Ecology 76 27ndash

40

Walther GR Post E Convey P et al (2002) Ecological responses to recent climate

change Nature 416 389ndash395

Warren MS Hill JK Thomas JA et al (2001) Rapid responses of British butterflies to

opposing forces of climate and habitat change Nature 414 65ndash69

Zuckerberg B Woods AM Porter WF (2009) Poleward shifts in breeding bird distri-

butions in New York State Global Change Biology 15 1866ndash1883

Supporting Information

Additional Supporting Information may be found in the online version of this article

Appendix S1

Table S1 List of all bird species (N = 256) from the New York State Breeding Bird Atlas (BBA) used in the analysisTable S2 Parameter estimates (brsquos) of each covariate resulting from all tested models of temporal turnover (starting with the bestmodel as indicated by DIC)Table S3 Parameter estimates (brsquos) of each covariate resulting from all tested models of extinction (starting with the best model asindicated by DIC)Table S4 Coefficient estimates (brsquos) of each covariate resulting from all tested models of colonization (starting with the best modelas indicated by DIC)Figure S1 Percent of the New York State Breeding Bird Atlas blocks that underwent land cover or land use change between 1992and 2001 The NLCD 19922001 Retrofit Land Cover Change Product was used to quantify the amount of land cover changeFigure S2 Spatial distribution of all explanatory variables magnitude of the 25-year (1980ndash2005) trend in average maximum tem-peratures (a) magnitude of the 25-year (1980ndash2005) trend in average minimum temperatures (b) magnitude of the 25-year (1980ndash2005) trend in total precipitation (c) edge density (d) percent developed land (e) and elevation (f)Figure S3 Scatterplot and histograms of all explanatory variables Abbreviations of the explanatory variables are as follows magni-tude of the 25-year (1980ndash2005) trend in average maximum temperature of the breeding season (TMAXTrend) magnitude of the 25-year (1980ndash2005) trend in average minimum temperature of the breeding season (TMINTrend) magnitude of the 25-year (1980ndash2005) trend in average total precipitation of the breeding season (PRECIPTrend) edge density (ED) percent developed land(DEVEL) elevation (ELEV)Figure S4 Temporal turnover (a) proportion of extinction events (b) and proportion of colonization events (c) as a function of tem-poral lag Data from 128 Breeding Bird Survey routes in New York State surveyed between 1980 and 2005Figure S5 Model validation for the best model for temporal turnover proportion of extinction events and proportion of coloniza-tion events observed between BBA1980 and BBA2000 using plots of predicted vs observed values



temporal change in community composition that has

occurred at the same site across a particular time period

(temporal turnover) Given that spatial turnover in

species composition is thought to be at least partially

related to spatial variation in environmental variables

(Gaston et al 2007 Buckley amp Jetz 2008 Gutierrez-

Canovas et al 2013 Siefert et al 2013) it is likely that

temporal turnover reflects temporal changes in climate

and could be a useful indicator of climate change

impacts on biodiversity

Even though the impacts of climate change on biodi-

versity are likely to be felt across a wide range of ecosys-

tems different communities are likely to respond to

climate change in disparate ways because a wide range

of environmental factors might influence speciesrsquo vulner-

abilities to climate change For example simulation stud-

ies suggest that the interaction between climate and land

cover will influence the way in which biodiversity

responds to climate change (Travis 2003 Jeltsch et al

2011) Specifically habitat fragmentation is thought to

affect the rate of distributional shifts associated with cli-

mate change allowing species in less fragmented habi-

tats to disperse faster and farther (Warren et al 2001

Opdam amp Wascher 2004) Lack of suitable habitat

between speciesrsquo current distribution and the future suit-

able climatic conditions might prevent a species with

some limiting intrinsic characteristics such as sedentary

life style or short dispersal distances from migrating to

these new climatically suitable locations If species in

contiguous (ie unfragmented) habitats shift their distri-

butions faster as a result of climate change than species

in fragmented habitats then changes in communities

should also follow the same pattern Thus stronger asso-

ciations of temporal turnover with changing climatic

conditions in contiguous habitats might be expected

In addition to more pronounced distributional shifts

other mechanisms might also lead or contribute to

stronger associations of temporal turnover with chang-

ing climate in unfragmented landscapes First commu-

nities in contiguous habitats tend to be comprised

largely of habitat specialists (Estavillo et al 2013)

which generally have narrower habitat and climatic

niches (Barnagaud et al 2012) and thus might be more

vulnerable to changing climatic conditions Conse-

quently the same amount of change in climatic condi-

tions would be expected to exert a stronger effect on

communities in contiguous landscapes Secondly frag-

mented habitats tend to support smaller populations

than contiguous unfragmented regions Small local

population sizes generally increase the effect of demo-

graphic or environmental stochasticity (Gaublomme

et al 2014) resulting in higher turnover in community

composition compared with that of regions supporting

large populations (Banks-Leite et al 2012)

While recent research suggests that climate change is

one of the factors causing temporal shifts in community

composition (Devictor et al 2008 Kampichler et al

2012 Prince amp Zuckerberg 2015) the influence of land-

cover and habitat fragmentation is still unclear For

example Devictor et al (2008) found that composition

of avian communities in France changed to include

higher proportion of high-temperature dwelling species

and suggested (although not explicitly tested) that this

response of communities to climate warming might dif-

fer between different types of land-cover and levels of

landscape fragmentation Kampichler et al (2012) sug-

gested a connection between land-cover and climate

change and bird community change by showing a

directional change in the proportion of high-tempera-

ture dwelling species as well as in the level of habitat

specialization of species comprising the community In

eastern North America Prince amp Zuckerberg (2015)

found that the composition of winter birds has become

increasingly dominated by warm-adapted species and

this community reshuffling was greatest in regions

experiencing long-term warming patterns However

despite these initial investigations the relationship

between temporal turnover in biodiversity and the

interaction of climatic changes and landscape fragmen-

tation to the best of our knowledge has not yet been

explicitly evaluated We intend to fill this research gap

by investigating the drivers of temporal turnover in

avian biodiversity across New York using Breeding

Bird Atlas data (Andrle amp Carroll 1988 McGowan amp

Corwin 2008) Our primary hypothesis was that tem-

poral turnover in avian assemblages is related to cli-

mate change but the strength of this relationship varies

with the degree of habitat fragmentation Specifically

we predict high rates of temporal turnover in regions

experiencing greater climate change and that these cli-

mate-mediated turnover rates would be higher in land-

scapes characterized by more contiguous habitat

Contiguous habitat is likely to facilitate higher rates of

turnover via three ecological mechanisms (i) allowing

species to disperse faster in response to climate change

(ii) supporting communities comprised of species more

vulnerable to changing climatic conditions and (iii)

increasing the ratio of deterministic to stochastic

processes

Materials and methods

Site description

The study area is the State of New York USA New York cov-

ers 128 401 km2 including 4240 km2 of inland water The cli-

mate of New York is affected by the statersquos broad elevation

gradient as well as by the proximity to lakes Erie and Ontario






















Breeding Bird Atlas


























2008)























































Climatic trends


















































TURN =Ethorn C
















EXT =E

Ethorn Peth2THORN

COL =C

Cthorn Peth3THORN

































































was




































accuracy








variablesmag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

emax

imum

temperature

ofthebreed


nitudeofthe25

-year(198

0ndash200

5)tren

din

averag

eminim

um

temperature

ofthebreed

ingseason

(TMIN

Trend)

mag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

etotalprecipitation

ofthebreed


edgeden

sity

(ED)

percentdev

eloped

land


nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

emax

imum

temperature

ofthebreed

ingseasonan

ded

geden

sity

(TMAXTrendED)inter-

actionbetweenthemag

nitudeofthe25

-year(198

0ndash200

5)tren

din

averag

eminim

um

temperature

ofthebreed

ingseasonan

ded

geden

sity

(TMIN


mag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag


ingseasonan

ded

geden

sity

(PRECIPTrendED)elev

ation(ELEV)an

dsu

rvey

effort(EFF)

Model

Explanatory

variables

TMAXTrend

TMIN

Trend

PRECIPTrend

ED

DEVEL

TMAXTrendED

TMIN

TrendED

PRECIPTrendED

ELEV

EFF

Model

1x

xx

xx

Model

2x

xx

xx

x

Model

3x

xx

xx

xx

Model

4x

xx

xx

xx

xx

Model

5x

xx

xx

xx

xx

x








formance










et al 2006)

Results

Temporal turnover










turnover (Fig 1)




















for the best model)





















(a) (b) (c)














Extinction

























S1)


































Model 3 195 1061 0296 00826 00805

Model 4 353 1075 0297 00826 00806

Model 2 591 1050 0294 00828 00808

Model 1 946 1023 0291 00830 00813


Model 4 380 1135 0314 00978 00975

Model 3 398 1114 0313 00977 00970

Model 2 768 1099 0312 00978 00973

Model 1 1514 1107 0308 00981 00978


Model 3 05 1098 0204 01008 01007

Model 4 91 1109 0204 01008 01007

Model 2 127 1089 0204 01009 01007

Model 1 231 1086 0203 01009 01008






Colonization










extinction




















Appendix S1)











Discussion













































study region
























landscapes











































(a) (b) (c)





























































































































Acknowledgements


References




Ecology 8 41ndash46





2569





639ndash647








17836ndash17841




Hoboken NJ










ware 19 1ndash24





























37ndash50
























Press Ithaka NY










585





















685



23ndash45








40









Appendix S1























Breeding Bird Atlas


























2008)























































Climatic trends


















































TURN =Ethorn C
















EXT =E

Ethorn Peth2THORN

COL =C

Cthorn Peth3THORN

































































was




































accuracy








variablesmag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

emax

imum

temperature

ofthebreed


nitudeofthe25

-year(198

0ndash200

5)tren

din

averag

eminim

um

temperature

ofthebreed

ingseason

(TMIN

Trend)

mag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

etotalprecipitation

ofthebreed


edgeden

sity

(ED)

percentdev

eloped

land


nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

emax

imum

temperature

ofthebreed

ingseasonan

ded

geden

sity

(TMAXTrendED)inter-

actionbetweenthemag

nitudeofthe25

-year(198

0ndash200

5)tren

din

averag

eminim

um

temperature

ofthebreed

ingseasonan

ded

geden

sity

(TMIN


mag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag


ingseasonan

ded

geden

sity

(PRECIPTrendED)elev

ation(ELEV)an

dsu

rvey

effort(EFF)

Model

Explanatory

variables

TMAXTrend

TMIN

Trend

PRECIPTrend

ED

DEVEL

TMAXTrendED

TMIN

TrendED

PRECIPTrendED

ELEV

EFF

Model

1x

xx

xx

Model

2x

xx

xx

x

Model

3x

xx

xx

xx

Model

4x

xx

xx

xx

xx

Model

5x

xx

xx

xx

xx

x








formance










et al 2006)

Results

Temporal turnover










turnover (Fig 1)




















for the best model)





















(a) (b) (c)














Extinction

























S1)


































Model 3 195 1061 0296 00826 00805

Model 4 353 1075 0297 00826 00806

Model 2 591 1050 0294 00828 00808

Model 1 946 1023 0291 00830 00813


Model 4 380 1135 0314 00978 00975

Model 3 398 1114 0313 00977 00970

Model 2 768 1099 0312 00978 00973

Model 1 1514 1107 0308 00981 00978


Model 3 05 1098 0204 01008 01007

Model 4 91 1109 0204 01008 01007

Model 2 127 1089 0204 01009 01007

Model 1 231 1086 0203 01009 01008






Colonization










extinction




















Appendix S1)











Discussion













































study region
























landscapes











































(a) (b) (c)





























































































































Acknowledgements


References




Ecology 8 41ndash46





2569





639ndash647








17836ndash17841




Hoboken NJ










ware 19 1ndash24





























37ndash50
























Press Ithaka NY










585





















685



23ndash45








40









Appendix S1










































TURN =Ethorn C
















EXT =E

Ethorn Peth2THORN

COL =C

Cthorn Peth3THORN

































































was




































accuracy








variablesmag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

emax

imum

temperature

ofthebreed


nitudeofthe25

-year(198

0ndash200

5)tren

din

averag

eminim

um

temperature

ofthebreed

ingseason

(TMIN

Trend)

mag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

etotalprecipitation

ofthebreed


edgeden

sity

(ED)

percentdev

eloped

land


nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

emax

imum

temperature

ofthebreed

ingseasonan

ded

geden

sity

(TMAXTrendED)inter-

actionbetweenthemag

nitudeofthe25

-year(198

0ndash200

5)tren

din

averag

eminim

um

temperature

ofthebreed

ingseasonan

ded

geden

sity

(TMIN


mag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag


ingseasonan

ded

geden

sity

(PRECIPTrendED)elev

ation(ELEV)an

dsu

rvey

effort(EFF)

Model

Explanatory

variables

TMAXTrend

TMIN

Trend

PRECIPTrend

ED

DEVEL

TMAXTrendED

TMIN

TrendED

PRECIPTrendED

ELEV

EFF

Model

1x

xx

xx

Model

2x

xx

xx

x

Model

3x

xx

xx

xx

Model

4x

xx

xx

xx

xx

Model

5x

xx

xx

xx

xx

x








formance










et al 2006)

Results

Temporal turnover










turnover (Fig 1)




















for the best model)





















(a) (b) (c)














Extinction

























S1)


































Model 3 195 1061 0296 00826 00805

Model 4 353 1075 0297 00826 00806

Model 2 591 1050 0294 00828 00808

Model 1 946 1023 0291 00830 00813


Model 4 380 1135 0314 00978 00975

Model 3 398 1114 0313 00977 00970

Model 2 768 1099 0312 00978 00973

Model 1 1514 1107 0308 00981 00978


Model 3 05 1098 0204 01008 01007

Model 4 91 1109 0204 01008 01007

Model 2 127 1089 0204 01009 01007

Model 1 231 1086 0203 01009 01008






Colonization










extinction




















Appendix S1)











Discussion













































study region
























landscapes











































(a) (b) (c)





























































































































Acknowledgements


References




Ecology 8 41ndash46





2569





639ndash647








17836ndash17841




Hoboken NJ










ware 19 1ndash24





























37ndash50
























Press Ithaka NY










585





















685



23ndash45








40









Appendix S1


















was




































accuracy








variablesmag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

emax

imum

temperature

ofthebreed


nitudeofthe25

-year(198

0ndash200

5)tren

din

averag

eminim

um

temperature

ofthebreed

ingseason

(TMIN

Trend)

mag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

etotalprecipitation

ofthebreed


edgeden

sity

(ED)

percentdev

eloped

land


nitudeofthe25

-year(198

0ndash20

05)tren

din

averag

emax

imum

temperature

ofthebreed

ingseasonan

ded

geden

sity

(TMAXTrendED)inter-

actionbetweenthemag

nitudeofthe25

-year(198

0ndash200

5)tren

din

averag

eminim

um

temperature

ofthebreed

ingseasonan

ded

geden

sity

(TMIN


mag

nitudeofthe25

-year(198

0ndash20

05)tren

din

averag


ingseasonan

ded

geden

sity

(PRECIPTrendED)elev

ation(ELEV)an

dsu

rvey

effort(EFF)

Model

Explanatory

variables

TMAXTrend

TMIN

Trend

PRECIPTrend

ED

DEVEL

TMAXTrendED

TMIN

TrendED

PRECIPTrendED

ELEV

EFF

Model

1x

xx

xx

Model

2x

xx

xx

x

Model

3x

xx

xx

xx

Model

4x

xx

xx

xx

xx

Model

5x

xx

xx

xx

xx

x








formance










et al 2006)

Results

Temporal turnover










turnover (Fig 1)




















for the best model)





















(a) (b) (c)














Extinction

























S1)


































Model 3 195 1061 0296 00826 00805

Model 4 353 1075 0297 00826 00806

Model 2 591 1050 0294 00828 00808

Model 1 946 1023 0291 00830 00813


Model 4 380 1135 0314 00978 00975

Model 3 398 1114 0313 00977 00970

Model 2 768 1099 0312 00978 00973

Model 1 1514 1107 0308 00981 00978


Model 3 05 1098 0204 01008 01007

Model 4 91 1109 0204 01008 01007

Model 2 127 1089 0204 01009 01007

Model 1 231 1086 0203 01009 01008






Colonization










extinction




















Appendix S1)











Discussion













































study region
























landscapes











































(a) (b) (c)





























































































































Acknowledgements


References




Ecology 8 41ndash46





2569





639ndash647








17836ndash17841




Hoboken NJ










ware 19 1ndash24





























37ndash50
























Press Ithaka NY










585





















685



23ndash45








40









Appendix S1









formance










et al 2006)

Results

Temporal turnover










turnover (Fig 1)




















for the best model)





















(a) (b) (c)














Extinction

























S1)


































Model 3 195 1061 0296 00826 00805

Model 4 353 1075 0297 00826 00806

Model 2 591 1050 0294 00828 00808

Model 1 946 1023 0291 00830 00813


Model 4 380 1135 0314 00978 00975

Model 3 398 1114 0313 00977 00970

Model 2 768 1099 0312 00978 00973

Model 1 1514 1107 0308 00981 00978


Model 3 05 1098 0204 01008 01007

Model 4 91 1109 0204 01008 01007

Model 2 127 1089 0204 01009 01007

Model 1 231 1086 0203 01009 01008






Colonization










extinction




















Appendix S1)











Discussion













































study region
























landscapes











































(a) (b) (c)





























































































































Acknowledgements


References




Ecology 8 41ndash46





2569





639ndash647








17836ndash17841




Hoboken NJ










ware 19 1ndash24





























37ndash50
























Press Ithaka NY










585





















685



23ndash45








40









Appendix S1








Extinction

























S1)


































Model 3 195 1061 0296 00826 00805

Model 4 353 1075 0297 00826 00806

Model 2 591 1050 0294 00828 00808

Model 1 946 1023 0291 00830 00813


Model 4 380 1135 0314 00978 00975

Model 3 398 1114 0313 00977 00970

Model 2 768 1099 0312 00978 00973

Model 1 1514 1107 0308 00981 00978


Model 3 05 1098 0204 01008 01007

Model 4 91 1109 0204 01008 01007

Model 2 127 1089 0204 01009 01007

Model 1 231 1086 0203 01009 01008






Colonization










extinction




















Appendix S1)











Discussion













































study region
























landscapes











































(a) (b) (c)





























































































































Acknowledgements


References




Ecology 8 41ndash46





2569





639ndash647








17836ndash17841




Hoboken NJ










ware 19 1ndash24





























37ndash50
























Press Ithaka NY










585





















685



23ndash45








40









Appendix S1







Colonization










extinction




















Appendix S1)











Discussion













































study region
























landscapes











































(a) (b) (c)





























































































































Acknowledgements


References




Ecology 8 41ndash46





2569





639ndash647








17836ndash17841




Hoboken NJ










ware 19 1ndash24





























37ndash50
























Press Ithaka NY










585





















685



23ndash45








40









Appendix S1





study region
























landscapes











































(a) (b) (c)





























































































































Acknowledgements


References




Ecology 8 41ndash46





2569





639ndash647








17836ndash17841




Hoboken NJ










ware 19 1ndash24





























37ndash50
























Press Ithaka NY










585





















685



23ndash45








40









Appendix S1
























































































































Acknowledgements


References




Ecology 8 41ndash46





2569





639ndash647








17836ndash17841




Hoboken NJ










ware 19 1ndash24





























37ndash50
























Press Ithaka NY










585





















685



23ndash45








40









Appendix S1











685



23ndash45








40









Appendix S1




landscape fragmentation affects responses of avian … · to test this hypothesis, we quantiﬁed...

Documents