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CLIMATE CHANGE Improving the forecast for biodiversity ...
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REVIEW SUMMARY◥
CLIMATE CHANGE
Improving the forecast forbiodiversity under climate changeM. C. Urban,* G. Bocedi, A. P. Hendry, J.-B. Mihoub, G. Pe’er, A. Singer, J. R. Bridle,L. G. Crozier, L. De Meester, W. Godsoe, A. Gonzalez, J. J. Hellmann, R. D. Holt,A. Huth, K. Johst, C. B. Krug, P. W. Leadley, S. C. F. Palmer, J. H. Pantel, A. Schmitz,P. A. Zollner, J. M. J. Travis
BACKGROUND: As global climate change ac-celerates, one of the most urgent tasks for thecoming decades is to develop accurate pre-dictions about biological responses to guide theeffective protection of biodiversity. Predictivemodels in biology provide ameans for scientiststo project changes to species and ecosystemsin response to disturbances such as climatechange. Most current predictive models, how-ever, exclude important biological mechanismssuch as demography, dispersal, evolution, andspecies interactions. These biological mech-anisms have been shown to be important inmediating past and present responses to cli-mate change. Thus, current modeling effortsdo not provide sufficiently accurate predic-
tions. Despite the many complexities involved,biologists are rapidly developing tools thatinclude the key biological processes neededto improve predictive accuracy. The biggestobstacle to applying these more realistic mod-els is that the data needed to inform themare almost always missing. We suggest waysto fill this growing gap betweenmodel sophis-tication and information to predict and preventthe most damaging aspects of climate changefor life on Earth.
ADVANCES: On the basis of empirical andtheoretical evidence, we identify six biologicalmechanisms that commonly shape responsesto climate change yet are too often missing
from current predictive models: physiology;demography, life history, andphenology; speciesinteractions; evolutionary potential and popula-tion differentiation; dispersal, colonization, andrangedynamics; and responses to environmentalvariation. We prioritize the types of informationneeded to inform each of these mechanisms andsuggest proxies for data that are missing ordifficult to collect. We show that even for well-studied species,weoften lack critical informationthatwould be necessary to applymore realistic,mechanistic models. Consequently, data limi-tations likely override the potential gains inaccuracy of more realistic models. Given the
enormous challenge ofcollecting this detailedinformation on millionsof species around theworld, we highlight prac-tical methods that pro-mote the greatest gains
in predictive accuracy. Trait-based approachesleverage sparse data to make more generalinferences about unstudied species. Target-ing species with high climate sensitivity anddisproportionate ecological impact can yieldimportant insights about future ecosystemchange. Adaptive modeling schemes provideameans to target themost important datawhilesimultaneously improving predictive accuracy.
OUTLOOK: Strategic collections of essentialbiological information will allow us to buildgeneralizable insights that inform our broaderability to anticipate species’ responses to climatechange and other human-caused disturbances.By increasing accuracy andmakinguncertaintiesexplicit, scientists can deliver improved pro-jections for biodiversity under climate changetogether with characterizations of uncertaintyto support more informed decisions by policy-makers and land managers. Toward this end,a globally coordinated effort to fill data gapsin advance of the growing climate-fueled bio-diversity crisis offers substantial advantagesin efficiency, coverage, and accuracy. Biologistscan take advantage of the lessons learnedfrom the Intergovernmental Panel onClimateChange’s development, coordination, and inte-gration of climate change projections. Climateandweather projectionswere greatly improvedby incorporating important mechanisms andtesting predictions against global weather sta-tion data. Biology can do the same.Weneed toadopt this meteorological approach to predict-ing biological responses to climate change toenhance our ability to mitigate future changesto global biodiversity and the services it pro-vides to humans.▪
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Environment
Evolution
PhysiologyDemography
Dispersal
Species interactions
Energy and mass balance to predict physiological responses
Predicting land-use changes at relevant scales
Quantitative genetic or genetically explicit models to predict adaptive responses
Interaction matrices to predict novel communities
Climate-dependent dispersal behavior to predict spatial responses
Climate-dependent demography to predict population dynamics
Cane toad
Simulated land use
Dengue mosquito
Duskywingskipper & oaks
Meadow brown
Emperor penguin
Emerging models are beginning to incorporate six key biological mechanisms that canimprove predictions of biological responses to climate change. Models that include biologicalmechanisms have been used to project (clockwise from top) the evolution of disease-harboringmosquitoes, future environments and land use, physiological responses of invasive species such ascane toads, demographic responses of penguins to future climates, climate-dependent dispersalbehavior in butterflies, and mismatched interactions between butterflies and their host plants. Despitethesemodelingadvances,weseldomhave thedetaileddataneeded tobuild thesemodels, necessitatingnew efforts to collect the relevant data to parameterize more biologically realistic predictive models.
The list of author affiliations is available in the full article online.*Corresponding author. Email: [email protected] this article as M. C. Urban et al., Science 353, aad8466(2016). DOI: 10.1126/science.aad8466
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REVIEW◥
CLIMATE CHANGE
Improving the forecast forbiodiversity under climate changeM. C. Urban,1* G. Bocedi,2 A. P. Hendry,3 J.-B. Mihoub,4,5 G. Pe’er,5,6 A. Singer,6,7,8
J. R. Bridle,9 L. G. Crozier,10 L. De Meester,11 W. Godsoe,12 A. Gonzalez,13
J. J. Hellmann,14 R. D. Holt,15 A. Huth,7,6,16 K. Johst,7 C. B. Krug,17,18 P. W. Leadley,17,18
S. C. F. Palmer,2 J. H. Pantel,19 A. Schmitz,5 P. A. Zollner,20 J. M. J. Travis2
Newbiologicalmodels are incorporating the realistic processes underlying biological responsesto climate change and other human-caused disturbances. However, these more realisticmodels require detailed information,which is lacking formost species onEarth.Currentmonitoringefforts mainly document changes in biodiversity, rather than collecting the mechanistic dataneeded to predict future changes.We describe and prioritize the biological information needed toinformmore realistic projections of species’ responses to climate change.We also highlight howtrait-based approaches and adaptive modeling can leverage sparse data to make broaderpredictions.Weoutlineaglobal effort to collect thedatanecessary tobetter understand, anticipate,and reduce the damaging effects of climate change on biodiversity.
We need to predict how climate changewillalter biodiversity if we are to preventserious damage to the biosphere (1). Biol-ogists develop predictivemodels to antic-ipate how environmental changes might
affect the future properties of species and eco-systems (2, 3). Manymodels have been developedto understand climate change impacts (fig. S1) (4),but biological responses remaindifficult to predict(5, 6). One reason is that most models forecastingbiodiversity change ignoreunderlyingmechanisms,such as demographic shifts, species interactions,
and evolution, and instead extrapolate correlationsbetween current species’ ranges and climate (Fig. 1)(4). These omissions are troubling because weknow that these missing biological mechanismsplayed key roles in mediating past and presentbiotic responses to climate change (7–9).Moreover,models ignoring biological mechanisms oftenbecome unreliable when extrapolated to novelconditions (10–13). As climates and ecologicalcommunities without historical precedent becomemore common and correlations between currentspecies distributions and climate become uncoupled(10, 14, 15), we cannot rely on tools based onstatistical descriptions of the past. Given the essen-tial role of biological processes inmediating species’responses to climate change, accurate forecastsof future biodiversity likely will require morerealistic models.Emerging models incorporate fundamental
biologicalmechanisms rather than relying solely onstatistical correlations (16–19). Unlike correlativeapproaches, mechanistic models do not assumethat a species’ range reflects its niche perfectly,has reached equilibrium with the environment,or is independent of species interactions—allcommonly violated assumptions (7, 13, 20, 21).Mechanistic models can also integrate multiple,interacting biological processes, as well as non-linear and stochastic dynamics (Fig. 2) (17, 18, 22),and canbetter characterize uncertainty by directlymodeling error sources (2, 21, 23).By incorporating realistic features such as
demography and dispersal, mechanistic modelscommonly outperform correlative approaches inprojecting climate change responses (13, 20). Forexample,mechanisticmodels consistently predictedsimulated species’ range dynamics over a periodof 75 years, whereas correlative models becameincreasingly inaccurate over this same time frame(20). Mechanistic models improve predictive accu-
racy, especially when species face strong bioticinteractions, experience novel climates, or cannotdisperse far (13, 20, 24). Moreover, mechanisticmodels can informpredictive efforts by indicatingprocesses (e.g., biotic limits on ranges) hidden bycurrent associations between environments andspecies distributions (24). Althoughmorework isneeded to craft more sophisticated and accuratemechanisticmodels that are customizable for indi-vidual species andecosystems, the tools are alreadymature enough to improve projections (2, 16, 19).Mechanistic models, however, require high-
quality data about how a species’ unique biologygoverns its responses to climate. Parametersprovidethis information. For example, a parameter such aspopulation growth rate determines how popula-tion abundances change through time. In contrast,a model variable such as population abundancedescribes emergent properties. Differentiating be-tween parameters and variables is importantgiven the recent focus on harmonizing effortsto collect variables that monitor the state of globalbiodiversity (25). We believe that such endeavorsshould focus not solely on collecting variablesthat indicate the state of biodiversity, but alsoon measuring mechanistic parameters criticalfor predicting future responses.Here, we identify the mechanistic data needed
tomake substantial gains in predictivemodeling.Rather than focusing on one particular mecha-nism (15, 18, 22, 26, 27), we take a comprehensiveapproach, assess data availability for each mech-anism, prioritize data needs, demonstrate how toleverage sparse data tomake general predictions,and suggest how global coordination could facili-tate these efforts. By synthesizing this informationin one framework, we aim to inspire the futureresearch agenda needed to develop the full pre-dictive potential ofmechanisticmodels. Consistentwith the Intergovernmental Panel on Climate
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1Institute of Biological Risk, Ecology and EvolutionaryBiology, University of Connecticut, Storrs, CT, USA. 2Instituteof Biological and Environmental Sciences, University ofAberdeen, Aberdeen, UK. 3Redpath Museum, Department ofBiology, McGill University, Montreal, Canada. 4SorbonneUniversités, UPMC Université Paris 06, Muséum Nationald’Histoire Naturelle, CNRS, CESCO, UMR 7204, Paris,France. 5Conservation Biology, UFZ-Helmholtz Centre forEnvironmental Research, Leipzig, Germany. 6German Centrefor Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany. 7Ecological Modelling, UFZ-Helmholtz Centre for Environmental Research, Leipzig,Germany. 8Swedish University of Agricultural Sciences,Swedish Species Information Centre, Uppsala, Sweden.9School of Biological Sciences, University of Bristol, Bristol,UK. 10NOAA Fisheries Northwest Fisheries Science Center,Seattle, WA, USA. 11Laboratory of Aquatic Ecology, Evolutionand Conservation, KU Leuven, Leuven, Belgium. 12Bio-Protection Research Centre, Lincoln University, Lincoln, NewZealand. 13Biology, McGill University, Montreal, Canada.14Institute on the Environment; Ecology, Evolution, andBehavior, University of Minnesota, St. Paul, MN, USA.15Biology, University of Florida, Gainesville, FL, USA.16Institute for Environmental Systems Research, Departmentof Mathematics/Computer Science, University of Osnabrück,Osnabrück, Germany. 17Ecologie Systématique Evolution,University Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Orsay, France. 18DIVERSITAS, Paris, France. 19Centred’Ecologie fonctionnelle et Evolutive, UMR 5175 CNRS-Université de Montpellier-EPHE, Montpellier Cedex, France.20Forestry and Natural Resources, Purdue University, WestLafayette, IN, USA.*Corresponding author. Email: [email protected]
No mechanism
1 or more mechanisms
Dispersal
Demography
Physiology
Species interactions
Population interactions
Adaptive potential
0 10 20 30 40 50 60 70 80
Percent of total studies
Fig. 1. Most models of biological responses to cli-mate change omit important biological mecha-nisms.Only 23% of reviewed studies (4) includeda biological mechanism. Models that included onemechanism usually incorporated others, but nomodel included all six mechanisms. All models in-cluded environmental variation, generally via cor-relations, but usually did not explicitly incorporatespecies’ sensitivities to environmental variation atrelevant spatiotemporal scales.
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Change (IPCC), we use “projection” to define alldescriptions of the future and reserve “forecast”for the most likely projections.
Crucial biological information
In Table 1, we identify six mechanisms thatdetermine biological responses to climate change.On the basis of these mechanisms, we assess dataavailability for four well-studied species (Fig. 3).We find that although information on the six keymechanisms partly exists for species with higheconomic value, it is incomplete for even the best-studied species and absent for the vast majorityof Earth’s species. Consequently, themost realisticmodels usually rely on sparse data or data ex-trapolated from nonrepresentative populations,environments, or species.We next describe each mechanism in further
detail, highlighting key parameters and discussingchallenges of measurement, uncertainty, and sen-
sitivity. Here, uncertainty encompasses bothlimited knowledge and random outcomes; sen-sitivity denotes how changes in a parameter valueinfluencemodel outcomes. After describing thesemechanisms, we recommend how to collect dataefficiently and leverage imperfect data.
Physiology
Physiologymediates how climate conditions suchas temperature, growing degree-days, water avail-ability, and potential evapotranspiration influencesurvival, growth, development, movement, andreproduction (18, 28–30). Physiological parametersinclude critical thermal minima or maxima (thelow and high temperatures at which organismscease organized movement), evaporative waterloss, photosynthetic rate, andmetabolic rate. Theseindividual physiological responses often are usedto informhigher-level processes such as populationpersistence and range shifts (29, 31). For example,
knowledge of the proportion of time a lizardremains active outside its burrow, where it isthermally neutral, can help in predicting its ex-tinction risk under future climates (32).Physiologistsmeasure parameters fromnatural
observations or experiments in climate-controlledchambers (28). However, using natural observa-tions risks confounding responses to climatewithother environmental factors (28). High-prioritytraits include responses to extremeheat or dryness,where survival often declines steeply. Uncertaintyabout physiological responses increases when welack information on habitat heterogeneity, localadaptation, and physiological impacts on overallfitness.
Demography, life history, and phenology
Demographic (birth, death, migration), life his-tory (schedule of life cycle events), and phenological(timing of life history events) traits play critical
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Cold ColdHot
Evolution
Dispersal Dispersal
Cold
Hot
Cold ColdHot
Species interactionsEvolution
EnvironmentD
ispe
rsal
Demography Physiology
Additive genetic variation + gene flow
Spatial climate gradient
+ climate change
Temperature-dependentpopulation growth
Logistic populationgrowth
Gaussian dispersalfunction
Competition
A B
C D
Model components Model equations
Change in population (N)
Populationdynamics
Dispersal
Fitness
max exp
Temperature-dependent growth Competition
Change in trait (z)
Directional selection
Gene flow
Model spatiotemporal dynamics Model results
Tim
e
Space
Spec
ies
abun
danc
es
Space
Fig. 2. A generic model integrates six biological mechanisms to predictclimate change responses. (A to C) The six mechanisms (A) are matchedby color to their representation in equations (B) simplified from (11) (see tableS1 for symbol descriptions). Results suggest how dispersal (blue-purple),adaptive evolution (yellow), and their combination (red-orange) determine thematch between community-wide thermal traits and changing local temper-
atures (C). Temperatures increase before stabilizing at the white dashed line.Black indicates no trait change. In cold regions,warm-adapted species disperseinto newly suitable, warmer habitats. In warm regions, evolution dominatesbecause no species with higher thermal tolerances exist. (D) Equilibriumabundances of five hypothetical species (each indicated by differently coloredlines) after climate change.
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roles in climate change responses (29, 33, 34).Important parameters include birth and deathrates, age at maturity, development rate, and re-productive investment. Parameters are best collectedonmarked individuals across representative popu-lations spanning different densities and climates.
However, these efforts require long-term, costlycommitments. Changes in population abundancesfrom short-term weather variation can provideproxies, but these become unreliable over time.Long-term vegetation plots can provide detaileddemographic information for plants. Citizen scien-
tists can collect data over large regions, on traitssuch as flowering time or breeding date, butconcerns about data quality likely limit their use-fulness for less easily measured traits such asgenetic variation.Certain demographic parameters are especially
important. For example, adult survival often affectspopulation growth rate more than fecunditydoes in long-lived species (35). Density depen-dence and generation length also strongly affectextinction risk from climate change (22). Addi-tional uncertainty stems from local adaptation,responses to novel environments, mismatchedphenology, community shifts, and interactionswith nonclimate stressors (15, 36, 37).
Evolutionary potential andlocal adaptation
Assaying genetic variation is crucial for predictingfuture responses (27, 38) because it could allowpopulations to adapt to climate change in situ.Unfortunately, scientists seldom know if, or howquickly, populations can evolve climate-sensitivetraits (36). Moreover, species usually comprisemany locally adapted populations that each re-spond differently to climate change (39). Speciesmight not shift their ranges with climate changeif locally adapted populations become isolatedand cannot colonize new habitats (39). Alterna-tively, individuals dispersing from locally adaptedpopulations might track optimal climates acrosslandscapes, and thus might not need to adaptlocally (Fig. 2) (11).The breeder’s and Price equations can be used
to predict responses to natural selection basedon selection strength and genetic (co)variances(40). Genetic (co)variances are commonly mea-sured through controlled breeding experimentsor pedigrees. However, these estimates can be-come unreliable over long time scales or in novelenvironments if selection regimes or adaptivepotential change (41). Also, genetic (co)variancesoften vary among populations and environments,thus requiring broad sampling and careful sen-sitivity analyses.Other approaches involve trackingevolution using long-term observations, recon-structing evolution from layered propagule banks,or applying experimental evolution (42, 43). Forinstance, comparingBrassica rapa plants grownfrom seeds collected before and after a droughtrevealed rapid evolution of flowering time (43).Past local adaptations to spatial climatic gra-dients are easier to assess. However, these pat-terns suggest past adaptive potential, not futureevolutionary rates (36). By scanning entire ge-nomes, next-generation sequencing offers a pro-mising tool to uncover fine-scale evolutionarydiversification (44), and declining cost for genomicscould rapidly expand our limited knowledge ofadaptive potential. Other frequently applied ap-proaches include common garden experiments,natural transplants, and observations of pheno-typic variation (Table 1).Adaptive potential and population differen-
tiation represent high-priority parameters becauseignoring them contributes high levels of uncer-tainty (12, 27, 36, 43). For example, the Quino
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Fence lizard (Sceloporus undulatus) Sockeye salmon (Oncorhynchus nerka)
Speckled wood butterfly (Pararge aegeria)
High
Medium
Low
PhysiologyDemography& life history
Disp
ersa
l
EvolutionSp. interactions
Envi
ronm
ent
European beech (Fagus sylvatica)
A B
C D
Data quality
Fig. 3. Data gaps exist even for well-studied species. We rated data quality for some of the best-studied species in climate change research: (A) fence lizard, (B) sockeye salmon, (C) speckled woodbutterfly, and (D) European beech. Data quality: high = near-complete information; medium = informationavailable but missing critical components; low = information mostly absent.We evaluated data availabilityby examining models of climate responses, reviewing species-specific literature, and contacting experts.
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checkerspot butterfly was expected to becomeextinct from climate change, but it persists afteradapting to live on a new host plant (45). Givenlimited genetic and evolutionary information,we will often need to generalize adaptive ratesacross species based on characteristics such asgeneration time, genetic isolation, phenotypicvariation, and phylogenetic position. Fortunately,even coarse estimates of maximum adaptiverate compared to climate change suggest tippingpoints, where minor changes in climate initiatemajor biological disruptions and thus representtargets for facilitating adaptation in threatenedpopulations (46).
Species interactions
Species interactions often underlie unexpectedresponses to climate change (10, 15), and mostextinctions attributed to climate change to datehave involved altered species interactions (47).Surprises occur when specialist interactions suchasmutualism constrain species’ responses (48),phenologicalmismatches alter species interactions(37), or top consumers propagate climate changeeffects throughout food webs (8). For instance,high temperatures along the Pacific Coast exa-cerbated predation by sea stars onmussels, whichcaused local extirpations (49). Yet few modelsaccount for species interactions explicitly, insteadassuming that each species responds independentlyto climate change (6, 15) (Fig. 1).High-quality information on species interactions
requires well-resolved information about inter-acting species, interaction types and strengths,spatiotemporal variation, and phenology. Un-fortunately, such detailed information is usuallymissing. One approach to overcome this deficitis to analyze important subsets of strongly inter-acting species (15). Less robust alternatives in-clude estimating trophic position using isotopes,understanding competition via diet breadth orspecies co-occurrence patterns, extrapolating fromcorrelations between body size and trophic level,or discerning species co-occurrence patterns frommetagenomics. High-priority parameters includethose characterizing specialist interactions, top-down foodweb interactions, and timingmismatchesamong interacting species.High uncertainty arisesfrom changes in species interactions themselves(e.g., shifts from competition to facilitation) andcomplex indirect effects that propagate throughfoodwebs (9). Additional uncertainties arise fromspecies’differential abilities to track climate changein space, creating previously unseen communitiesas coevolved interactions disappear and novelinteractions form (10).
Dispersal, colonization,and range dynamics
To persist, species often must track suitable cli-mates into new regions through dispersal, colo-nization, and subsequent range shifts (50, 51).Mostmodels unrealistically assume that all orga-nisms disperse comparably and across any land-scape (Fig. 1) (26). In reality, dispersal dependson the interplay among individual behavior, fit-ness, habitat quality, and landscape configuration
(52). Range shifts are particularly sensitive todynamics at range boundaries where low abun-dances challenge accurate estimation (53).Global positioning system units can record
fine-scaled individual movement but are costlyand unsuitable formany small organisms. Passiveintegrated transponders, acoustic tags, and telem-etry devices track smaller individuals at lowercost, but these require strategically placed recorders.
Neutral genetic variation across landscapes canindicate movement patterns, but demographichistory can confound these estimates. Citizen sciencesometimes enables cost-effective, coordinated, andlarge-scale data collection, assuming adequatequality control. Dispersal distances also can beinferred from proxies [e.g., body size–dispersalrelationships in animals (51) and growth form,seedmass, and vegetation type in plants (54)] until
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Revise model to includenew parameters or different relationships
Improve estimatesfor sensitive parameters
Target sensitive parameters
Define model
Popu
latio
n
Estimate parameter sensitivities
Use available parameters
Obtainprojections frompreliminarymodel
Projections improve across iterations
0 100
0 20
05
10
Temperature
Gro
wth
rate
1
2
dN
dt= rN N 2
Validate with monitoring data
Define model
0 100
010
YearYear
20
010
20
Fig. 4. Biological models improve iteratively through time by applying an adaptive modelingscheme. Steps include parameterizing models using available data, estimating parameter sensitivities,targeting better measurements for sensitive parameters, validating projections with observations, anditeratively refining and updating the model to improve predictive accuracy and precision through time.
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SCIENCE sciencemag.org 9 SEPTEMBER 2016 • VOL 353 ISSUE 6304 aad8466-5
Table
1.Biological
param
eters,
colle
ctionmethods,
proxies
,priorities
,andke
yunce
rtainties
.We
listsixclas
sesof
biologica
lmec
hanism
s,ex
ample
param
eters,
metho
dsto
colle
ctthem
,pos
sible
proxy
relation
shipsthat
could
fillin
gapsforpoo
rlystud
ied
taxa
,priorityparam
eters,
and
key
remaining
unce
rtainties.
Welistmetho
dsin
anillus
trativedes
cend
ingorder
ofdataac
curacy
.The
orderingof
colle
ctionmetho
dsisillus
trativeon
lyan
dwillclea
rlych
ange
dep
endingon
theparticu
lar
attributes
ofsp
eciesan
dsystem
s.The
bes
tmetho
ds,
howev
er,m
ight
notbeea
sily
implemen
tedfor
sometaxa
,ne
cessitating
morepractical
metho
dsfollo
wed
byse
nsitivityan
alys
is.The
ywill
also
chan
gethroug
htime—
forex
ample,as
emerging
metho
dsbec
omeless
costly.In
reality,
theidea
l
approac
hforco
llectingdataon
ake
yproce
sswill
invo
lvejointus
eof
more
than
onemethod
.For
exam
ple,for
dispersa
lwemight
curren
tlywan
tto
colle
cthigh
-qua
litytelemetry
dataforthemov
e-men
tof
arelative
lysm
allnu
mber
ofdisperse
rsbec
ause
ofco
stco
nstraintswhile
also
obtaining
population
-lev
eles
timates
ofdispersa
lthrough
either
land
scap
ege
netics
ormark-releas
e-
reca
pture
method
s(orboth).Ween
courag
eread
ersto
tailo
rco
stsan
dben
efitsof
thealternative
and
complemen
tary
approac
hes
totheirow
nsy
stem
and
adjust
dec
isions
forinve
stmen
tof
reso
urces
appropriately.
Note
that
each
ofthes
emec
hanism
slik
ely
interacts
with
othe
r
mec
han
isms.
Biologica
l
mec
hanism
s
Exam
ple
parameters
Alterna
tive
and
complem
entary
metho
dsProxy
relation
ships
Priority
parameters
Key
unce
rtainties
Phy
siolog
yThe
rmal,d
esicca
tion
,
andch
emical
toleranc
es;
environm
ent-dep
enden
t
perform
ance
and
metab
olic
rate;
pho
tosy
nthe
sis
•Exp
erim
entalu
nderstan
ding
ofph
ysiologica
l
resp
onse
sto
environm
entalc
onditio
ns
inna
ture
orthelabo
ratory
•Obs
ervedco
rrelations
betw
eenph
ysiologica
l
resp
onse
san
den
vironm
entalc
onditio
nsin
timeor
spac
e
•Trait-ba
sedprox
ies(e.g.,bo
dymass
formetab
olism)
•Bod
ymas
sco
rrelates
strong
lywithen
ergy
requiremen
ts
•Water
andlig
ht
requiremen
tsin
vege
tation
mod
els
Phy
siolog
ical
resp
onse
s
inex
trem
een
vironmen
ts
(e.g.,perform
ance
under
hotor
dry
conditions)
•How
does
beh
avior
modify
phys
iology
?
•To
what
deg
reedoorgan
isms
evolvedifferen
tphys
iologica
l
resp
onse
sac
ross
arange
?
•How
dophys
iologica
l
sensitivities
ofdifferen
t
perform
ance
traits
scale
towhole-organ
ism
fitnes
s?............................................................................................................................................................................................................................................................................................................................................................................................................................................
Dem
ography
,
lifehistory,
andphe
nology
Birth
anddea
thrates,
includ
ingag
eor
stag
e
structure,
ageof
maturity,
dev
elop
men
t
andgrow
thrates,
environm
ental
dep
enden
ce,tim
ing,
andindividua
lvariability
•Lo
ng-term
markreca
pture
paren
tage
stud
iesor
long
-term
dem
ographicdata
from
vege
tation
plots
•Exp
erim
entals
tudiesof
environmen
t-dep
enden
tbirth
anddea
th
ratesin
nature
(bes
t)or
thelaboratory
•Pop
ulationgrow
thratesfrom
obse
rved
abun
dan
cedata
Dem
ographicparam
eters
correlatewithlife
historytraits
(e.g.,slow
-fas
tco
ntinuu
m)
andnich
esp
ecialization
Vital
ratesthat
mos
tinflu
ence
pop
ulationgrow
thrates
(e.g.,ad
ultsu
rvival
for
long
-lived
organ
isms,
gene
rationlength,
mismatch
esin
timing
oflifehistory
even
ts)
•To
wha
tde
gree
doorganism
sevolve
diffe
rent
lifehistoriesacross
arang
e?
•Doe
srapidad
aptatio
nto
clim
atech
ange
play
arole?
•Whe
ndo
esph
enolog
yde
pend
onclim
ateversus
nonc
limate
trigge
rs(e.g.,da
yleng
th)?
•How
doothe
ren
vironm
ental
chan
ges(e.g.,ha
bitatde
grad
ation)
interact
with
clim
ateresp
onses?
............................................................................................................................................................................................................................................................................................................................................................................................................................................
Evolutiona
ry
poten
tial
andloca
l
adap
tation
Additivege
netictrait
(co)va
rian
ce/h
eritab
ility
andad
ditivege
netic
cova
rian
cebetwee
n
traits
andfitne
ss
•Qua
ntitativege
neticva
riationin
keytraits
estimated
from
controlle
dbreed
ingdes
igns
,
from
pop
ulations
withped
igrees
,orfrom
individualsraised
under
common
cond
itions
•Exp
erim
entalo
rco
rrelationa
lestim
ationof
selectiongrad
ients
•Gen
eex
press
ionpatternsforun
derstan
ding
func
tiona
ltraitva
riationun
der
differen
t
environmen
talc
onditions
•Phe
notypic
variationwithinpop
ulations
•Ev
olutiona
ryrates
correlatene
gative
ly
withge
neration
leng
th
•Gen
etic
variation
withinpop
ulations
pos
itivelyco
rrelates
withpop
ulationsize
•Spac
e-for-timesu
bstitutions
Adap
tive
potential,
loca
ladap
tationof
clim
ate-se
nsitive
param
etersac
ross
spec
iesrange
•To
what
deg
reeis
traitch
ange
determined
byge
netics
versusen
vironmen
t?
•How
welld
osh
ort-term
mea
suremen
tsof
adap
tive
mec
han
isms
perform
inthelongrun?
•How
does
loca
ladap
tation
within
arange
altersp
ecies-leve
l
resp
onse
sto
clim
atech
ange
?............................................................................................................................................................................................................................................................................................................................................................................................................................................
Fitnes
sdifferen
ces
amon
gpop
ulations
anden
vironm
ents;
gene
ticva
riationam
ong
pop
ulations
;phe
notypic
variation,
includ
ing
plastic
ity,a
mon
gpo
pulatio
ns
•Rec
iproca
ltrans
plant
andco
mmon
garden
experim
ents
that
reve
alfitne
ss
andtraitdifferen
cesam
ong
pop
ulations
inresp
onse
toreleva
nt
environm
entalg
radients
•Statistical
search
forva
riationin
loci
under
selection
•Gen
etic
variationam
ong
pop
ulations
pos
itively
correlates
withrang
esize
............................................................................................................................................................................................................................................................................................................................................................................................................................................
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aad8466-6 9 SEPTEMBER 2016 • VOL 353 ISSUE 6304 sciencemag.org SCIENCE
Biologica
l
mec
hanism
s
Exam
ple
parameters
Alterna
tive
and
complem
entary
metho
dsProxy
relation
ships
Priority
parameters
Key
unce
rtainties
............................................................................................................................................................................................................................................................................................................................................................................................................................................
•Gen
eex
press
ionpatterns
forun
derstan
dingfunc
tion
altrait
variationun
der
differen
ten
vironm
ental
cond
itions
•Pop
ulationge
netics
withne
utralloc
ito
understan
dpop
ulationdifferen
tiation
throug
hbarriersto
gene
flow
•Obse
rvationof
phe
notypic
variationwithin
andam
ongpop
ulations
............................................................................................................................................................................................................................................................................................................................................................................................................................................
Spe
cies
interactions
Interactionweb
s
withsp
atiotemporal
variationan
dphe
nology
,
interactiontypes
and
streng
ths,
commun
ity
mod
ule,
dietor
reso
urce
overlap,
trop
hic
pos
ition
•Exp
erim
entale
valuationof
spec
ies
interactionstreng
than
ddirec
tion
inna
ture
(bes
t)or
thelaboratory
•Natural
historyob
servations
ofinteractions
•Isotop
ean
alys
isto
reve
altrop
hic
leve
lsan
dfood
web
links
•Statistical
co-occ
urrenc
e
patterns(e.g.,ch
ecke
rboa
rd
patternsforco
mpetition)
•Trop
hicleve
linc
reas
es
withbod
ysize
•Sim
ilartrop
hicleve
ls
aresh
ared
by
phy
loge
netica
lly
simila
rsp
ecies
Spec
ialistinteractions,
sens
itivityoftop
cons
umers,
phe
nologica
l
mismatch
esbetwee
n
interactingsp
ecies
•What
hap
pen
sas
coev
olved
interactionsdisap
pea
ran
dnew
spec
iesinteractionsform
?
•How
sensitive
arefoodweb
sto
top-dow
nve
rsusbottom-up
clim
atedisturban
ces?
•To
what
deg
reeca
nsp
ecies
adap
tto
nov
elsp
ecies
interactions?
............................................................................................................................................................................................................................................................................................................................................................................................................................................
Dispe
rsal,
colonizatio
n,
andrang
e
dyna
mics
Dispersa
lbeh
aviors;
mov
emen
tan
d
settlemen
trules;
interind
ividua
l
variab
ility;e
nviron
men
t-,
den
sity-,an
dco
ndition-
dep
enden
tdispersa
l;
land
scap
epermea
bility
(e.g.,leas
t-co
stpath
analys
is)
•Satellitetelemetry
ofmov
ingorga
nism
s
toreve
alland
scap
emov
emen
ttrac
ks
•Mark-reca
pture
and
reloca
tion
sto
evalua
teab
solute
mov
emen
t
•Exp
erim
ents
(e.g.,lin
ked
mes
ocos
ms)
toun
derstan
dmov
emen
t
•La
ndscap
ege
netic
sto
reveal
land
scap
eco
nnec
tivity
amon
gpo
pulatio
ns
•Historica
lrec
onstructionof
movem
ent
patterns
during
expa
nsion
•Incide
ncefunc
tions
inmetap
opulations
tode
term
inepo
pulatio
nco
nnec
tivity
•Citizenscienc
eto
trac
k
orga
nism
s(e.g.,tagg
edbirds)
•La
rger-bod
iedan
imals
disperse
farthe
r
•Smallerse
edstrav
elfarthe
r
•Animal-disperse
dse
eds
trav
elfarthe
r
•La
rger-w
inge
dorga
nism
s
disperse
farthe
r
•Pelag
ican
imalsdisperse
farthe
rthan
ben
thic
ones
Long
-distance
dispersa
l,fitnes
sat
rang
eboundaries
•How
importan
tis
long-range
dispersa
lforrange
dyn
amics?
•How
does
fitnes
sva
ry
across
arange
?
............................................................................................................................................................................................................................................................................................................................................................................................................................................
Respo
nses
to
environm
ental
varia
tion
Func
tion
alrelation
ships
betwee
ntraits
and
environm
ents;
iden
tific
ationan
d
qua
ntifica
tion
ofke
y
environm
entalg
radients
across
spec
ies-releva
nt
scales
ofsp
ace
andtime
•Exp
erim
entalm
anipulationof
keyen
vironm
ents
toun
derstan
dfunc
tion
alresp
onse
s
•Statistical
analys
isof
environm
entalg
radients
andresp
onse
s
•Cha
racterizationof
environm
entalg
radientsat
biologica
llyreleva
ntsc
ales
♦Surve
ysof
environm
entalp
aram
eters
cond
uctedat
releva
ntsp
atial
andtemporal
scales
♦Groun
d-truthed
map
sto
beus
edin
environm
entalg
radient
analys
es
♦Statistical
interpolationof
coarse
map
data
•Determiningne
tworks
ofco
-acting
environm
ental
variab
les
•Correlating
easily
colle
cted
remotely
sens
eddatato
othe
rfactors
such
asreso
urce
s
Iden
tifyingke
ygrad
ients,
spatials
cale
dep
enden
ce
ofen
vironmen
tal
resp
onse
s,dyn
amic
chan
gein
grad
ients
•Are
therege
neral
way
sto
predict
thereleva
ntsc
ales
atwhich
differen
tsp
ecieswill
resp
ond
toen
vironmen
talv
ariation?
•What
biologica
lparam
etersare
linke
dwiththeen
vironmen
tal
factors
andhow?
•How
areim
portan
ten
vironmen
tal
grad
ients
chan
gingthrough
time?
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better estimates become available. Long-distancedispersal and fitness at range edges are high-priority parameters because they introduce highuncertainty in model outcomes (26), yet are dif-ficult to measure.
Responses to environmental variation
Responses to climate change depend on species-specific sensitivities and exposures to climate andhabitat variation at relevant spatiotemporal scales.For instance, birds respond idiosyncratically todifferent climate variables, depending on theirindividual sensitivities to temperature andprecipi-tation change (55). Researchers must carefullyidentify which specific climate components actu-ally affect species. Many organisms respond notto average annual temperature or precipitation,but rather to temperature thresholds, season length,humidity, potential evapotranspiration, or extremeevents such as droughts. Species also differ in therelevant spatiotemporal scales of environmentalvariation. Researchers should evaluate the envi-ronment through the eyes of the organism. Thescales relevant to focal organisms often aremetersand minutes rather than the measurements inkilometers andmonths typically available. Despitethe increasing availability of fine-scaled informa-tion, most predictions are still made at coarsescales, which can substantially reduce predictiveaccuracy (56). Hierarchical sampling canmaximizeinformation content by combining large-scalesampling with targeted fine-scale measurementsthat capture relevant gradients. Species charac-teristics such as body size or generation lengthalso can provide proxies for missing data on spe-cies’ environmental responses.In addition, we need to integrate predictions
of climate change with other human disturbances,including land use, pollution, invasive species, andharvesting, to gauge the full extent of future envi-ronmental change. Improving predictions of thesedisturbances [e.g., (57)] and downscaling data torelevant ecological resolutions are critical forreducing future uncertainty.
Interacting mechanisms
Eachmechanismpotentially interacts withmanyothers. Specifically, climate responses dependprox-imately on dispersal and demography; demog-raphy in turn depends on physiology, speciesinteractions, and environments; and each traitcan evolve. For example, great tit birds in theNeth-erlands do not lay eggs earlier in warmer springs(involving demography, phenology, and environ-mental responses), whereas their caterpillar prey(species interaction) emerge earlier. This pheno-logical mismatch between birds and their preydecreases nestling fitness (demography) (37). Yetgreat tits from the United Kingdom do breedearlier in warmer springs, suggesting populationgenetic differentiation (58). A challenge is to inte-gratemultiple interactingmechanismswithout un-necessarily increasing model complexity (Fig. 2).
A practical way forward
We recognize that the complexity of naturalsystems will add uncertainty to even the best-
parameterized and most realistic models (59).Collecting the relevant information and devel-oping realistic biological models will requiresubstantial investment in time and resources.Despite these challenges, we believe that collect-ing mechanistic data will both enhance ourfundamental understanding of the biological pro-cesses that underlie climate responses and con-tribute tomore accurate, longer-term projectionsthat facilitate more effective conservation. Mech-anistic models might not make accurate pre-dictions initially, but learning from those failuresprovides the insights that ultimately improveprojections. Predictive science advances mostquicklyvia iterativeprediction-failure-improvementcycles, and mechanistically grounded models oftenquicken the pace of these advances (2, 3, 19). Evensmall gains in understanding can improve futuremodels by indicating criticalmissing information,highlighting key uncertainties, suggesting generaltrait-based predictions for nonmodeled organisms,and delimiting the best options for retaining bio-diversity under a range of future policy scenarios.Given limited time and resources, however, we
need to develop strategies that leverage existingdata and target essential information. Towardthis end, we advocate for an adaptive modelingscheme that facilitates cost-effective model devel-opment anddata collection (Fig. 4). The process ofmodel testing and revision—steps rarely taken to-day, but facilitatedbyamore systematic approach—can reveal data of particular importance for im-provingpredictions.Researchers first parameterizemodelswith available data. In Table 1, we demon-strate how to tailor data collection efforts to system-specific constraints by listing ideal methods alongwith more easily collected proxies. Researchersthen use independently collected variables frommonitoring efforts to test outcomes and fit un-certain relationships. Sensitivity analyses identifythemost important parameters to collect, ensuringthat resources go toward producing the greatestgains in accuracy. On the basis of these analyses,researchers can collect improved or new param-eter estimates and revise the model throughsuccessive iterations of the approach. Crucially,results frommultiple independentmodels shouldbe combined because ensemble forecasts oftenprove more accurate (3, 60). Researchers alsoneed to articulate clearly how uncertainty in pa-rameter estimates and model choice propagatesat each modeling step. We recommend adoptingthe IPCC’s standards (1) for classifyingmodel con-fidence and probabilistic uncertainty.Several approaches are available to extend pro-
jections from a few carefully studied species tomany unstudied ones.We often possess extensiveinformation that is spread across many speciesbut is incomplete foranyparticularspecies.Emergingphylogenetic and trait-based approaches couldfill these data gaps. Trait-based approaches usetrait correlations (e.g., between adult survival andfecundity) to predict missing parameters forspecies (50). Researchers also can simulate theclimate responses of virtual species with realisticcombinations of traits. For example, this virtualapproach predicted that 30% of terrestrial mam-
mals might not keep pace with climate change(61). Minimally, these efforts provide qualitativeinsights about which types of species are mostvulnerable to climate change and therefore shouldbe targeted for future, in-depth study (22). Anothercost-effective strategy is to prioritize research onspecies with both high climate sensitivity and dis-proportionately large impacts on ecosystems. Theseso-called biotic multipliers—often, top predatorsandother keystone species—amplify small changesin climate to produce large ecological effects (8)such that their future dynamics drive overall eco-system changes (9).Conservation sometimes focuses on overall bio-
diversity rather than focal species. Estimates fromsubsets of species might be cautiously extrapo-lated to overall biodiversity, assuming suitablerepresentation across taxonomic and phylogeneticdiversity. However, trait-based approaches mightmore efficiently suggest species that have vulner-able trait combinations or amplify community-wide impacts of climate change. For example,focusing on top consumers and other keystonespecies can indicate how their responses rever-berate through entire food webs (8), thus furtherextending the value of single-species forecasts.Lastly, hybrid correlative-mechanistic approaches
offer a pragmatic initial approach to improvingpredictions by adding key mechanisms to simplemodels. For example, adjusting predicted rangesfrom correlativemodels with species-specific dis-persal abilities (62) or interacting species’ ranges(48) can add realism and improve predictions.Given the simplicity of most current approaches(Fig. 1), even minimally more realistic modelsmight improve projections untilmore complicatedmodels can be developed (13, 19).
Global coordination
Global coordination will be critical at all stages,including defining projection goals, developingbettermodels, collating and incorporating existingdata, determining which additional data mightimprove forecasts, collecting newdata,monitoringbiodiversity changes, and organizing and main-taining data. Researchers and policymakers firstmust agree on the nature of the projection itself,including the accuracy, coverage, and timehorizonof forecasts. A global clearinghouse would beuseful to organize trait data, standardize termi-nology (e.g., dispersal versus migration), andmonitor climate responses.It would also be useful to form regionalworking
groupswith local experts. Regionalworking groupswould define representative ecosystems and cli-matic and environmental gradients in their region,while taking advantage of existing data and long-termmonitoring sites. Groupswould select speciesrepresenting abroad rangeof regional trait diversityand build initial models with available data toestimate parameter sensitivity. To address imme-diate extinction threats, regional working groupsmight also characterize the climate change riskfor threatened species on the International UnionforConservationofNatureRedList. Groups shouldthendevelop plans to refine sensitive parametersthrough targeted fundingopportunities and citizen
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science. Collected biological informationmust beaccessible, quality-checked, standardized, andmain-tained in databases such as Encyclopedia of Life’sTraitBank (traits) and Global Biodiversity Infor-mation Facility (species occurrences).The IPCC’s development of climate change
predictions provides a template for how to achievecomparable progress in biodiversity projections.The IPCC’s biodiversity analog, the Intergovern-mental Platform on Biodiversity and EcosystemServices, can also help to coordinate this effort.Already, the Group on Earth Observations–Biodiversity Observation Network is developinga list of essential biodiversity variables (EBVs) formonitoring global biodiversity (25) and isworkingto address monitoring gaps (19). Despite someoverlap between the modeling parameters out-lined here and EBVs, the two collection schemeshave divergent objectives. The EBVs monitorchanges in biodiversity and provide variables forinitializing and testing mechanistic predictions.Mechanisticmodels, however, also require param-eters governing key processes, which often man-date more detailed observations or experimentsthan monitoring programs currently entail.
Combining predictive modeling withrobust scenario analysis
Collecting the data necessary to informmechanisticbiologicalmodels presents an enormous challengegiven the vast diversity of life, its complexity, andour inadequate knowledge about it. This inherentcomplexity and stochasticity limits the accuracy ofbiological predictions for policy and management(59, 63), especially over long forecast horizons (3).Wemust accept that even thebest-informedpredic-tions could fail for a variety ofunanticipated reasons.An alternative approach to planning for climate
change develops conservation strategies robust toa broad range of future scenarios (64), thus in-suring against inevitable surprises. For example,applying this “robust scenario” approach mightinclude maintaining dispersal corridors, pre-serving existing natural habitat and geneticdiversity, and facilitating monitoring and flex-ible, adaptive management (59, 65). This strat-egy broadly protects biodiversity and dependsless on accurate predictions. However, practicalconsiderations will often limit the number of op-tions that are feasible, especially whenmanage-ment options for one species trade off againstanother.The two approaches are notmutually exclusive,
and we believe that they work best in tandem.Mechanistic approaches likely will improve pre-dictions at intermediate time horizons (e.g., 25 to50 years), when current environmental correla-tions break down and correlative approachesbecome less accurate (3). Beyond this time frame,even the best mechanistic models become un-certain as keyparameters can shift anduncertaintypropagates. Yet predictive models are still neededto delimit plausible expectations, place boundson uncertainty, and direct limited resources towardstrategies that target themost threatened regionsand species (23, 59). Hence, a tandem approachbuilds general insights from key representative
species while preserving flexible options thatwork when models fail.
Conclusions
Climate scientists in 1975 acknowledged theirinability to predict climate accurately and high-lighted the many challenges to reaching thisobjective (66). Despite these challenges, they out-lined an ambitious long-term research programaimedatunderstandingkeymechanismsgoverningclimate change and collecting key pieces of missinginformation. This program ultimately produced theimprovements in forecasting weather and climatechange that society benefits from today. We believethat biology can and must do the same.We advocate for a renewed global focus on
targeting the natural history information neededto predict the future of biodiversity. Such effortswould more than compensate for their cost byimproving our ability to understand, anticipate,and thereby prevent biodiversity loss and dam-age to ecosystems from climate change as well asother disturbances. Ultimately, understanding hownature works will provide innumerable benefits forlong-term sustainability and human well-being.
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ACKNOWLEDGMENTS
This paper originates from the “Ecological Interactions and RangeEvolution Under Environmental Change” and “RangeShifter” workinggroups, supported by the Synthesis Centre of the German Centre forIntegrative Biodiversity Research (DFG-FZT-118), DIVERSITAS, and itscore projects bioDISCOVERY and bioGENESIS. Supported by theCanada Research Chair, Natural Sciences and Engineering ResearchCouncil of Canada, and Quebec Centre for Biodiversity Science (A.G.);the University of Florida Foundation (R.D.H.); KU Leuven ResearchFund grant PF/2010/07, ERA-Net BiodivERsA TIPPINGPOND, andBelspo IAP SPEEDY (L.D.M.); European Union Biodiversity ObservationNetwork grant EU-BON-FP7-308454 (J.-B.M. and G.P.); KU LeuvenResearch Fund (J.P.); and NSF grants DEB-1119877 and PLR-1417754and the McDonnell Foundation (M.C.U.).
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(6304), . [doi: 10.1126/science.aad8466]353Science Zollner and J. M. J. Travis (September 8, 2016) P. W. Leadley, S. C. F. Palmer, J. H. Pantel, A. Schmitz, P. A. Gonzalez, J. J. Hellmann, R. D. Holt, A. Huth, K. Johst, C. B. Krug,Singer, J. R. Bridle, L. G. Crozier, L. De Meester, W. Godsoe, A. M. C. Urban, G. Bocedi, A. P. Hendry, J.-B. Mihoub, G. Pe'er, A.Improving the forecast for biodiversity under climate change
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