Confronting species distribution model predictions with species functional traits

Abstract

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day−1. Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = −0.26, 95% CI (−0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.

Species’ traits as predictors of range shifts under contemporary climate change: A review and meta‐analysis

“…However, heterogeneous change in climatic variables related to temperature and precipitation may cause some species to move in counterintuitive directions to track favorable climate (Crimmins, Dobrowski, Greenberg, Abatzoglou, & Mynsberge, ; Tingley et al., ; Wolf, Zimmerman, Anderegg, Busby, & Christensen, ). Species’ traits may be stronger predictors of range shifts when investigated in the context of niche tracking and environmental matching (Sol et al., ; Wittmann, Barnes, Jerde, Jones, & Lodge, ; Wogan, ). For example, temperature and water flow preference of invertebrates in New South Wales explained whether range shifts occurred at warm vs. wet range edges (Chessman, ).…”

Phenotypic plasticity in life‐history traits of Daphnia galeata in response to temperature – a comparison across clonal lineages separated in time

“…; Wittmann et. al ), or assuming that dispersal is restricted, to either local extinction or local persistence through phenotypic plasticity or micro‐evolutionary adaptation (Bellard et al. ).…”

Phenotypic distribution models corroborate species distribution models: A shift in the role and prevalence of a dominant prairie grass in response to climate change

“…Whether SDM output is interpreted as a probability of occurrence or an index thereof, it describes only one dimension of potential response to environmental change. However, recent work demonstrates predictions from SDMs correlate with population‐level or physiological traits that directly relate to species’ persistence and roles within their communities (Michel et al., ; Wittmann et al., ). Hence, expanding the remit of ‘species’ distribution modeling to include functional and physiological traits opens promising avenues for forecasting responses to global change (Michel et al., ).…”

“…In this situation, the persistence of locally adapted populations could be threatened if they are unable to respond in situ to changing conditions through rapid selection (Shaw & Etterson, ), phenotypic plasticity (Jump & Peñuelas ), or dispersal. Thus, incorporating intraspecific phenotypic variation in climate vulnerability analyses is an important challenge in predicting population responses to global change (Wittmann, Barnes, Jerde, Jones & Lodge, ).…”

“…Thus, incorporating intraspecific phenotypic variation in climate vulnerability analyses is an important challenge in predicting population responses to global change (Wittmann, Barnes, Jerde, Jones & Lodge, 2016).…”