Combining Population-Dynamic and Ecophysiological Models to Predict Climate-Induced Insect Range Shifts

vol. 167, no. 6 the american naturalist june 2006 ൴ Combining Population-Dynamic and Ecophysiological Models to Predict Climate-Induced Insect Range Shifts Lisa Crozier* and Greg Dwyer† Department of Ecology and Evolution, University of Chicago, Webb and Bartlein 1992) and recent past (Walther et al. Chicago, Illinois 60637 2002; Parmesan and Yohe 2003). Projections of continued rapid warming over the next century (IPCC 2001) suggest Submitted October 26, 2005; Accepted March 3, 2006; Electronically published May 8, 2006 that many species will have to move quickly in order to stay within their physiological tolerances, or “bioclimatic Online enhancement: appendix. ranges.” Predicting how these climatically suitable zones will shift geographically is a crucial but difficult task. It is crucial because climate change is a major threat to bio- diversity in many ecosystems (Sala et al. 2000; Julliard et abstract: Hundreds of species are shifting their ranges in response al. 2004; Pounds and Puschendorf 2004). Thousands of to recent climate warming. To predict how continued climate warm- species are expected to go extinct because of a net loss of ing will affect the potential, or “bioclimatic range,” of a skipper area within their climate zones (McDonald and Brown butterfly, we present a population-dynamic model of range shift in 1992; Beaumont and Hughes 2002; Thomas et al. 2004). which population growth is a function of temperature. We estimate Identifying bioclimatic ranges is difficult, however, because the parameters of this model using previously published data for climate has complex effects on population dynamics (Law- Atalopedes campestris. Summer and winter temperatures affect pop- ton 1995; Logan and Powell 2001; Newman et al. 2003; ulation growth rate independently in this species and therefore in- teract as potential range-limiting factors. Our model predicts a two- Holt and Keitt 2005; Holt et al. 2005). These complexities phase response to climate change; one range-limiting factor gradually have made understanding the dynamics of species borders becomes dominant, even if warming occurs steadily along a thermally a central issue in ecology and evolution in both basic and linear landscape. Whether the range shift accelerates or decelerates applied research (Holt and Keitt 2005). and whether the number of generations per year at the range edge Various modeling strategies provide important insights increases or decreases depend on whether summer or winter warms into the ecological and evolutionary consequences of cli- faster. To estimate the uncertainty in our predictions of range shift, mate change, but there is a gap between existing ap- we use a parametric bootstrap of biological parameter values. Our results show that even modest amounts of data yield predictions with proaches. Existing models are typically either very general reasonably small confidence intervals, indicating that ecophysiolog- theoretical models (Pease et al. 1989; Lynch and Lande ical models can be useful in predicting range changes. Nevertheless, 1993; Burger and Lynch 1995; Holt 1996; Kirkpatrick and the confidence intervals are sensitive to regional differences in the Barton 1997; Case and Taper 2000) or statistical models underlying thermal landscape and the warming scenario. based on observations of particular species (see reviews in Guisan and Zimmermann 2000; Austin 2002; Pearson and Keywords: climate change, range shifts, insects, temperature, Dawson 2003). Results from general models can be dif- population-dynamic model. ficult to use in actual predictions either because the model parameters are hard to estimate from available data or because the models cannot be applied to complex land- Climate change has driven geographic range shifts in both scapes. Statistical models, on the other hand, are usually the distant (Davis 1976; Coope 1977; Atkinson et al. 1987; based on correlations between a species’ existing range and environmental factors. They therefore cannot reveal cli- * Present address: Northwest Fisheries Science Center, National Oceanic and Atmospheric Administration, 2725 Montlake Boulevard East, Seattle, Wash- matic niches that may be suitable but unoccupied. Fur- ington 98112; e-mail: [email protected]. thermore, correlations may have limited predictive value † E-mail: [email protected]. because factors other than weather may determine a spe- Am. Nat. 2006. Vol. 167, pp. 853–866. ᭧ 2006 by The University of Chicago. cies’ current range and because at large geographic scales, 0003-0147/2006/16706-41389$15.00. All rights reserved. multiple factors may covary (Lawton 1995; Guisan and 854 The American Naturalist Zimmermann 2000). Ecophysiological models usefully well (Crozier 2003, 2004b), we focus here specifically on complement correlative models by instead attempting to the bioclimatic range or fundamental niche (Holt et al. predict species distributions from data relating survival or 2005). This is the region in which populations are self- phenology to climate (Hodkinson 1999). The ecophysio- sustaining (l ≥ 1 ), so we define the range edge as the point logical approach thus allows us to make mechanistic con- at whichl p 1 , and we explore how this edge moves in nections between climate and range and so may afford response to climate change. The general questions that we greater confidence in the importance of particular climatic ask are then “What conditions will cause ranges to track variables. Many ecophysiological models, however, are too particular isotherms?” and “What is the effect of unequal specific for general interpretation and application; more- warming rates in different seasons on skipper population over, in most cases in which ecophysiological models have dynamics at the range edge?” Because the basic phenom- been used to predict insect range shifts, a single physio- enon of interacting range-limiting factors is common to logical trait is assumed to be the only limiting factor. For many species, we suspect that a population-dynamic ap- the temperate-zone insects that are the focus of most such proach may have general application. models, this has meant modeling the effects of temperature on either overwinter survival (e.g., Virtanen et al. 1998) or phenology (e.g., Porter 1995; Logan and Powell 2001) Methods: Population Dynamics, Range Edges, but not both. Most species, however, respond to multiple and Climate Change limiting factors. To see the importance of multiple range-limiting factors, In this article, we use experimental data to develop an we first consider the model in an abstract form. We focus ecophysiological model that is simple enough to elucidate on summer and winter temperatures as potential range- a pattern of response to climate change at large spatial limiting factors because first, they are often good predic- and temporal scales. We use the model to predict range tors of plant and insect distributions (Sutherst et al. 1995; shifts in a particular species, the sachem skipper butterfly Ungerer et al. 1999; Bryant et al. 2000); second, they have Atalopedes campestris. This species has extended the north- well-known and profound physiological impacts (Uvarov ern edge of its range dramatically over the past few decades 1931; Messenger 1959); third, they are predicted to change in response to increasing temperatures (Crozier 2003, at different rates over the next century (IPCC 2001); and 2004b). Our model is realistic enough that its parameters finally, they change systematically along latitudinal gra- can be easily estimated from existing data, but it is simple dients. Other factors, such as precipitation, could readily enough that it could also be adapted to other insect or be substituted, although the geographic characteristics plant species that have different limiting factors. A useful of precipitation are quite different. Because we focus feature of this approach is that it considers how multiple on temperate-zone insects, our underlying population- p range-limiting factors may complicate responses to climate dynamic model isNtϩ1 lNt , where Nt is the population change. We use the model to show how differences in density in the year t and l is the annual per capita pop- warming rates across seasons can affect temporal patterns ulation growth rate. Most insects in the temperate zone of range shift. This is important for predictions of range have an active period in the summer, in which they com- shifts because warming rates are expected to be different plete one to three generations, and a dormant stage in the in different seasons and locations (Hassol 2004). winter (Bale 1991). We therefore split the life cycle into The application of our model to the sachem skipper two parts: winter, in which survival is J, and summer, in allows us to address an important criticism of ecophysi- which net recruitment is R, the number of survivors at ological models, which is that they require too many ex- the end of the summer produced by an individual at the perimental data to be practical. This is a serious concern beginning of the summer. Note that R includes both fe- because the relationship between population dynamics and cundity and survival and allows for the possibility of mul- climatic factors can be especially uncertain. Because our tiple generations. Because temperatures are a function of model is based directly on experimental data, we can use latitude and time, as temperatures increase, the location the uncertainty in parameter estimates to place confidence of the range edge moves, and we can track

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