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Estimating the Exposure of Carnivorous Plants to Rapid Climatic Change

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Estimating the Exposure of Carnivorous Plants to Rapid Climatic Change CHAPTER 28 Estimating the exposure of carnivorous plants to rapid climatic change Matthew C. Fitzpatrick and Aaron M. Ellison 28.1 Introduction carnivorous plants distributions constrained by cli- mate; and second, how readily, if at all, might car- Forecasting how carnivorous plant species will nivorous plants disperse to colonize new habitat as respond to climatic change is a key issue in their it becomes climatically suitable? conservation and management (Chapter 27) but In this chapter, we estimate the vulnerability of presents a number of challenges. These challenges carnivorous plants to climatic change in light of derive from interactions between the relatively challenges identified with SDMs in general and their simplistic statistical methods typically used to fore- particular application to these unique species. The cast species responses to climatic change, which to modeling approaches we use partially overcome date have been limited mainly to species distribu- some of these challenges, and may be applicable to tion models (“SDMs;” Elith and Leathwick 2009, other sparse or rare species. We begin by reviewing Franklin 2009), and particular aspects of the ecology the basics of SDMs. We then highlight specific eco- of carnivorous plants, including their rarity, habi- logical characteristics of carnivorous plants and their tat specialization (Chapter 2), and limited dispersal geographic distributions that limit the utility of clas- ability (Chapter 22). sical SDMs for forecasting their future distributions. The small ranges and oftentimes low local We combine two approaches: “ensembles of small abundance of carnivorous plants provide few oc- models” (Breiner et al. 2015), which attempt to deal currence records, which increase the potential for with the challenges of fitting SDMs for data-limited poorly or over-fitted SDMs and misspecification species; and “bioclimatic velocity” (Serra‐Diaz of relationships with their “optimal” environ- et al. 2014), which is an estimate of how fast a species ments. The unique habitats in which carnivorous would have to migrate to track its climatic niche (as plants often grow (Chapter 2) also are difficult to opposed to a prediction of the potential shift in dis- characterize using the basic temperature and pre- tribution, the typical output from SDM projections), cipitation data that often undergird SDMs. Rather, to provide initial assessments of the vulnerability of habitats in which carnivorous plants are common carnivorous plants to climatic change. often are decoupled from broader climatic patterns (e.g., many retain high soil moisture even during seasonal drought) and may be associated with fre- 28.2 The basics of species distribution quent disturbance (e.g., fire; Chapter 2). Last, dis- models persal limitation also may constrain range shifts of carnivorous plants as the climate changes. These Efforts to quantify the vulnerability of species to three issues raise two related questions that are crit- climatic change typically rely on SDMs. These mod- ical for understanding and forecasting the future of els (also called bioclimatic envelope models, habi- carnivorous plants. First, to what extent are current tat suitability models, or ecological niche models; Fitzpatrick, M. C., and Ellison, A. M., Estimating the exposure of carnivorous plants to rapid climatic change. In: Carnivorous Plants: Physiology, ecology, and evolution. Edited by Aaron M. Ellison and Lubomír Adamec: Oxford University Press (2018). © Oxford University Press. DOI: 10.1093/oso/9780198779841.003.0028 390 CARNIVOROUS PLANTS Guisan and Zimmermann 2000, Elith and Leath- footing, especially when used to forecast species wick 2009, Franklin 2009) are relatively simple sta- distributions under scenarios of climatic change tistical models that predict habitat suitability across (Hampe 2004, Heikkinen et al. 2006, Dormann 2007). an area of interest using empirical relationships be- The primary critique centers on whether empirical tween the distribution of a species (expressed as a species– climate relationships derived solely from set of point locations at which the species is known observations of point-occurrence records and asso- to occur) and coincident environmental variables ciated environmental conditions can reliably predict (typically derived from digital maps of interpolated responses of species to climatic change. Detractors climatic data). When applied to current climates or argue that multiple interacting abiotic and biotic simulations of future climate, SDMs respectively in- factors determine range limits (Gaston 2003), yet fer current species distributions or forecast potential SDMs typically use only a small number of climatic changes in species distributions from predictions of variables (most frequently temperature and precipi- habitat suitability (Guisan and Thuiller 2005). tation) in model fitting and often ignore other causal The field of species distribution modeling has abiotic drivers, biotic interactions (Wisz et al. 2012), grown rapidly over the last few decades (Guisan dispersal processes (Fitzpatrick et al. 2008), or adap- et al. 2013), fueled by three primary factors: in- tation (Fitzpatrick and Keller 2015). creased availability of species occurrence and Although carnivorous plants are found world- environmental datasets (Graham et al. 2004); the de- wide and several species have large geographic velopment of more powerful statistical techniques ranges, all of them occupy patchy, restricted mi- and user-friendly software packages (Phillips crohabitats within their geographic range (Juniper et al. 2006, Thuiller et al. 2009); and an overall need et al. 1989; Chapter 2). Local environmental con- for comprehensive information on species distribu- ditions including soils and hydrology likely are tions, including quantitative assessments of the vul- at least as important, if not more so, than climate nerability of species to climatic change (Chapter 27). per se in determining habitat suitability and occur- rence patterns. In terms of dispersal constraints, few species have any obvious mechanisms for long- 28.2.1 Challenging species distribution models distance seed dispersal. Moreover, the successful es- with sparse or rare species tablishment of some carnivorous plants far outside Despite advancements in data and algorithms, their native ranges suggests that distributions of modeling the impacts of climatic change on sparse some species are limited by their ability to disperse species (sensu Rabinowitz 1981), rare species, or and colonize suitable, but distant, habitats (e.g., El- habitat specialists like carnivorous plants using lison and Parker 2002; Chapter 22). These dispersal SDMs remains a major challenge. These challenges constraints suggest carnivorous plants may not be arise primarily from fitting models with insufficient able to track rapid climatic shifts by shifting their or unreliable point-occurrence records and envi- geographic ranges. ronmental predictors that inadequately character- Even if all habitat factors could be included in ize habitats. Whereas rare species may be under perfectly calibrated SDMs, fitted species–climate greatest threat from climatic change and thus might relationships still may not reflect true distribu- benefit most from SDM-based climate-impact as- tional constraints. There also is no reason to expect sessments, they are often are most difficult to model that current species–climate relationships will re- using SDMs, a conflict Lomba et al. (2010) summa- main constant in altered ecological contexts (e.g., rized as the “rare species modeling paradox.” Fitzpatrick et al. 2007, Veloz et al. 2012) or novel climates of the future (Williams and Jackson 2007, Fitzpatrick and Hargrove 2009). 28.2.2 Critiques of species distribution models Last, when applied to future scenarios of climatic Even when data are adequate and statistical is- change, SDMs forecast potential changes in species sues can be minimized, numerous critiques have distributions, not actual changes in where popula- argued that SDMs stand on weak theoretical tions occur on the landscape. The extent to which EstimatiNG THE EXPOSURE OF CARNIVOROUS PLANTS TO RAPID climatic CHANGE 391 species will be able to follow these forecasted range Besides limiting information content, low numbers shifts over the next several decades will depend on of point-occurrence records can bias fitted statisti- dispersal and population dynamics, both of which cal relationships that are extrapolated across the are uncertain and stochastic. Moreover, the increas- study area to map habitat current and future suit- ing isolation and fragmentation of natural habits ability (Barry and Elith 2006). Loss of populations and the rapid rates of projected climatic change because of habitat conversion or over-collecting likely will make rapid dispersal unfeasible for all (Chapter 27) only adds to the challenges of reliably but the most vagile and widespread species (Hill applying SDMs to carnivorous plants: not only are et al. 1999, Malcolm et al. 2002, Loarie et al. 2009). the number of occurrences reduced, but also rela- Pragmatic users of SDMs are quick to acknowl- tionships between their distributions and climate edge these, and other, shortcomings of the models, may be altered or obscured. but counter that SDMs are one of the only tools A small geographic distribution is not necessar- available that can be applied across multiple taxa, ily problematic; SDMs often perform best for nar- regions, times, and spatial scales
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