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Climate Change and Its Effect on Species Range Boundaries: A Case Study of the Sachem Skipper Butterfly, Atalopedes campestris
Atalopedes campestris is a butterfly on the move. While the temperature has been warming in the Pacific Northwest, the typically southern A. campestris has advanced its northern range edge ~700 kilometers in 35 years (Dornfeld 1980, Hinchliff 1994, 1996; pers. obs.). In the warmest decade on record, the 1990s (IPCC 2001), the butterflies established their first known populations in Washington State. In the hottest year of the century, 1998 (NCDC 1999a), A. campestris dispersed another 125 kilometers into new territory (pers. obs.). Is the warm weather responsible for the range expansion? If so, A. campestris may provide a good example of the biological consequences of global warming. Many insects, including pests and disease vectors, are prime candidates to expand their ranges in response to warming, given their sensitivity to temperature, rapid generation times, and high mobility (Walker 1991, Lawton 1995, Sutherst et al. 1995). But constraints on distribution are neither simple, nor fully understood. This species presents an unusual opportunity to study the process of range shift during climate change.
Many recent studies have documented changes in animal distribution or phenology associated with climate change (Grabherr et al. 1994, Barry et al. 1995, Parmesan 1996, Crick et al. 1997, Holbrook et al. 1997, Mantua et al. 1997, Myneni et al. 1997, McCleery and Perrins 1998, Alward et al. 1999, Brown et al. 1999, Parmesan et al. 1999, Post and Stenseth 1999, Sagarin et al. 1999, Thomas and Lennon 1999). However, a gap exists between observations of an association between distributions and various environmental factors, and a mechanistic understanding of what physiological, ecological, and evolutionary processes maintain range edges, especially in animals (Bartholomew 1958, Hoffmann and Parsons 1991). While correlation studies have great value in suggesting multiple hypotheses, experimental tests are necessary to pin down actual range-limiting factors. Understanding the true underlying mechanisms is crucial for predicting the biological outcomes of a changing climate. Unfortunately, in very few cases have hypothesized range-limiting mechanisms actually been tested.
This chapter presents my investigation into what constrains the distribution of the native, northwardly moving Atalopedes campestris. The range expansion occurred during relevant changes in both habitat and temperature, which led to multiple hypotheses regarding the causal mechanism of the range shift. My approach involves three steps: (1) Develop hypotheses of range-limiting factors based on correlation with the range change and the biology of the species. (2) Identify the physiological constraints that could constitute a mechanism by which climate could directly limit the range of Atalopedes campestris. (3) Test hypotheses experimentally with field transplantation to determine whether the proposed mechanism is acting on the current range boundary. This type of case study into the actual mechanisms of range limits in a geographically dynamic species should provide a model from which to make testable predictions about the biological consequences of climate change for a variety of species.
Analyzing Range Shifts: From Global Change to Local Experiments
Anticipating specific biogeographical consequences of climate change requires understanding how species borders depend on climatic conditions. Our current understanding of this relationship is limited by the "scale gap" (Root and Schneider 1995). An average increase in global mean temperature is a landscape-scale prediction based on climate models using 500 × 500 km grids (Schneider 1993). These climate model predictions apply to "the big picture" of a species' range: large-scale patterns of abundance and distribution. Analyses of distributions at this scale show that combinations of temperature and precipitation criteria are frequently good predictors of species' ranges (e.g., Pigott 1975, Hengeveld 1985, Caughley et al. 1987, Root 1988, Cammell and Knight 1992). This association suggests that a change in environmental conditions may lead to a corresponding change in species' ranges.
Unfortunately, large-scale correspondence cannot differentiate essential and coincidental environmental associations. There are a large number of potential environmental variables that may yield spurious associations with species ranges. Many environmental factors are also correlated with each other in today's climate, obfuscating their relative importance for organisms (Dennis 1993). However, paleontological evidence shows that temperature, precipitation, and seasonality all vary independently during climate change (Brubaker 1988, Overpeck et al. 1992). Climates have existed in the past that do not occur today; thus it is likely that future regimes will differ from today's. If "no-analog" conditions result, present ranges do not necessarily predict a species' future distribution. Furthermore, species' response times to climate change can vary by orders of magnitude, leading to strong transient effects of rapid climate change (Davis 1976, Huntley 1991, Lawton 1995, Huntley et al. 1997). Response times are difficult to predict from static distributions due to the important but stochastic role of long-distance dispersal (Pitelka and Plant Migration Workshop 1997, Clark et al. 1998). Therefore, current large-scale associations alone are insufficient for predicting biogeographical consequences of climate change.
Ecological experiments are designed to identify causal mechanisms underlying broader patterns. For logistical reasons most ecological experiments occur on a relatively small scale, studying a single population or locality, usually for a short period of time (Kareiva and Anderson 1988). Unfortunately, experimental results cannot necessarily be extrapolated to larger scales (Levin 1992, Root and Schneider 1995). There are three potential problems with scaling up. First, larger-scale constraints may not be apparent at a smaller spatial or temporal scale or level of organization. For example, a short-term experiment would be unlikely to detect a range limit set by occasional extreme events, or subtle differences in extinction probability. Second, constraints appearing at a small scale may not be very important at a larger scale. We know, for example, that the key factor limiting population growth in one part of a species' range may differ from that in another location (e.g., Pollard 1979, Shaw 1981, Dempster 1983, Thrush et al. 2000). Although there is increasing interest in this problem (e.g., Connolly and Roughgarden 1999, Thrush et al. 2000), it is not yet clear how to predict when certain classes of factors will dominate either within a species or across species and thus how small-scale processes may generate large-scale patterns.
Third, all levels of organization from individual, to population, to species, to multiple species—and a corresponding range of spatial and temporal scales—interact to shape a species' distribution. For example, physiological limits to tolerance of environmental stress certainly exist and correlate broadly with geographic distribution. However, it is not necessarily clear whether this relationship demonstrates a fundamental physiological constraint in the species, or is contingent on population dynamics that inhibit local adaptation. Genetic variation in stress tolerance exists within natural populations (Hoffmann and Parsons 1991), suggesting that species could adapt to different climatic regimes. Intraspecific comparisons of populations from climatically different regions (e.g., Lamb 1977) complemented by many artificial selection experiments demonstrate that physiological tolerance can evolve rapidly (e.g., White et al. 1970, Tucic 1979, Huey and Kingsolver 1989). However, a comparison of the environmental correlates of the ranges of closely related species suggests that these niches tend to be conserved in evolutionary time (Peterson et al. 1999).
We currently know relatively little about range edge populations and how they may differ from central populations (Hoffmann and Parsons 1991, Hoffmann and Blows 1993). Little is known, for example, about the extent to which marginal populations are locally adapted, whether they are more likely to go extinct than equal-sized central populations, or what percentage of edge populations are demographic sinks, dependent on migration from central populations for persistence. There may be trade-offs between stress tolerance and fitness (Mongold et al. 1996, Bradshaw et al. 1998) such that migration from central-range populations opposes local adaptation (Stearns and Sage 1980, Kirkpatrick and Barton 1997). Thus population-level and species-level processes could interact with a physiological process to define a range limit.
One solution to the scaling problem is to alternate between scales interactively (Root 1991, Root and Schneider 1995). For example, an initial comparative analysis may reveal a pattern consistent with a particular hypothesized mechanistic relationship between factors. This hypothesis should then be explicitly tested at a local level to explore its mechanistic basis (e.g., Gilbert 1980, Muth 1980). Once the mechanism is understood, additional experiments in different localities or with different species can be targeted to more efficiently detect that mechanism at a larger scale and to predict under what circumstances it should be most important (e.g., Root 1991).
The approach I present in this chapter fits into this paradigm at the mechanistic level. After presenting some background information on what we already know about the relationship between butterflies and climate, I will focus on the mechanism controlling the leading edge of an advancing range in the sachem skipper. Understanding the direct impact of an environmental gradient in a natural population along the range edge is a first step toward integrating the individual- and population-level dynamics. Studying a population that may already be responding to rapid environmental change will further provide insight into the transient effects of rapid climate change, and hence the population-and species-level phenomenon of range change. Further experiments involving other locations and species will add to the results of this study and build a better bridge across the scale gap.
Butterfly Ranges and Climate
Research at many scales clearly shows that climate is important for butterfly abundance and distribution. The basic association between climate and insect distribution and abundance has been studied for at least 70 years (e.g., Uvarov 1931, Andrewartha and Birch 1954, Birch 1957, Dennis 1993). The challenge now is to focus on experiments that will integrate our mechanistic understanding of individual and population processes with distribution limits and change.
Large-scale patterns in butterfly distribution are generally consistent with the hypothesis that environmental conditions may constrain the northern boundaries of many species' ranges, although alternative hypotheses have been proposed (see discussion in Dennis 1993). As with many taxa, butterfly species richness characteristically declines along latitudinal and elevational gradients (Scriber 1973, Gieger 1987, Dennis et al. 1991, Sanchez-Rodriguez 1995). Dennis (1993) conducted a multifactorial analysis of species richness within Great Britain based on a systematic 10-km grid database on butterfly abundance (from the Butterfly Monitoring Program, Pollard 1977). Dennis (1993) recorded that butterfly species richness correlates with July temperature (correlation coefficient, r = 0.85), but also with January temperature (r = 0.59), the number of frost-free days (r = 0.58), and precipitation (r = -0.49). This analysis demonstrates a strong correspondence with environmental factors, particularly summer temperature.
If most species' ranges are constrained by temperature, then during a century of warming the prevailing direction of range shifts should be toward higher latitude and elevation. The 0.7°C warming trend in Europe since the 1890s should favor butterfly range expansion (Dennis 1993), based on physiological and ecological principles of butterfly biology. Parmesan et al. (1999) analyzed butterfly range shifts throughout Europe, and found that out of 35 species with data on their whole range, 63% have shifted northward, whereas only 3% have shifted southward. However, this analysis excluded species for which nonclimatic factors seemed to affect the range. In a separate analysis of the ranges of all British butterflies at a finer resolution, Pollard and Eversham (1995) discovered that many more species have contracted their ranges overall than have shifted. Of 59 resident British butterflies, 30 have experienced major range contractions and only 3 have expanded overall. Pollard and Eversham (1995) and Warren (1992) attribute this discrepancy to widespread habitat loss and degradation in Britain. It is not clear to what extent habitat degradation is sufficiently widespread to affect these species' entire ranges.
Importantly, all expanding British species but one (Ladoga camilla) are common and widespread within their ranges (Pollard and Eversham 1995), thus there may be a connection between local abundance and distribution change. Pollard and Eversham (1995) define as common and widespread those species found at > 80% of survey sites within their range. Fifty-five percent of all common species have expanded their range over the past three decades, while only 3% (a single species) of all localized species have expanded their range in Britain. Autecological studies have revealed a large number of associations between a species' population abundance over time in a given location, and climatic variation (Pollard 1979, Ehrlich 1980, Pollard and Lakhani 1985, Pollard 1988, Pollard 1991, Pollard and Yates 1993, Thomas et al. 1998). Multiple regression analyses show a significant correlation between population numbers and summer temperature for 6 out of 11 species that have expanded range since the 1940s (Pollard and Eversham 1995). Thus common butterfly species seem to track climatic fluctuations in both population size and range size better than other butterflies.
One possible mechanism of range shift resulting from climate change is that populations in an increasingly less favorable location will be more likely to go extinct and less likely to colonize new sites than populations in an increasingly favorable part of the range. This will lead to a shift in abundance, and over time, may lead to a range shift. Parmesan (1996) documented evidence of this type of shift in a North American species, Edith's checker-spot (Euphydryas editha). Population surveys since the 1930s documented the locations of E. editha populations from Canada to Mexico. Parmesan resurveyed populations, recording the number of populations that no longer survived. Significantly more population extinctions had occurred in the southern part of the range and at lower elevations than in the north or at higher elevations, resulting in a 92 km northward and 105 m upward shift in mean population location. This magnitude and direction of shift closely matched mean annual temperature isotherm shift (105 km northward, 105 m upward).
While these results support the hypothesis that climate trends influence the abundance and distribution of many species, there are very few cases where the mechanistic nature of the relationship between climate and distribution change is known. The white admiral butterfly, Ladoga camilla, is one of the best examples in which a detailed historical record is complemented by study of an edge population to attribute a causal mechanism to the association between distribution and climate in Britain. Using key factor analysis, Pollard (1979) determined that the limiting factor for a Ladoga population near the range edge is late larval and pupal development time in June. A cool June yields slow development, prolonged vulnerability to predation, and lower adult population size. Furthermore, a cool July reduces the available flight time for oviposition. The net result is that in cool years mortality is higher and fecundity is lower than in warm years. Pollard compared population sizes and appearance at new sites with May, June, and July temperature since 1900. He found a strong correlation between anomalously warm years and an increase in both new site records and population abundance in existing sites. It is not currently known whether this is a common mechanism linking distributions to climate.
Excerpted from Wildlife Responses to Climate Change by Stephen H. Schneider, Terry L. Root. Copyright © 2002 Island Press. Excerpted by permission of ISLAND PRESS.
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|Introduction: The Rationale for the National Wildlife Federation Cohort of Young Scientists Studying Wildlife Responses to Climate Change|
|Climate Change: Overview and Implications for Wildlife||1|
|Ch. 1||Climate Change and Its Effect on Species Range Boundaries: A Case Study of the Sachem Skipper Butterfly, Atalopedes campestris||57|
|Ch. 2||Butterflies as Model Systems for Understanding and Predicting Climate Change||93|
|Ch. 3||Historical Studies of Species' Responses to Climate Change: Promises and Pitfalls||127|
|Ch. 4||Community Responses to Climate Change: Links Between Temperature and Keystone Predation in a Rocky Intertidal System||165|
|Ch. 5||Testing Climate Change Predictions with the Subalpine Species Delphinium nuttallianum||201|
|Ch. 6||Modeling Potential Impacts of Climate Change on the Spatial Distribution of Vegetation in the United States with a Probabilistic Biogeography Approach||251|
|Ch. 7||Climate Change and the Susceptibility of U.S. Ecosystems in Biological Invasions: Two Cases of Expected Range Expansion||277|
|Ch. 8||Climate Change, Whitebark Pine, and Grizzly Bears in the Greater Yellowstone Ecosystem||343|
|Conclusion: Climate Change and Wildlife - A Look Ahead||415|