Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide‐spread plant species

Abstract

The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic‐alpine cushion plant Silene acaulis, we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific‐level SDMs with a species‐level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic‐ and habitat‐informed SDMs are considerably more accurate than a species‐level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific‐level SDMs. We emphasize the need to carefully examine how to best define intraspecific‐level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring population performance or biotic interactions from SDM predictions, as these often‐assumed relationships are not supported in our study.

Species versus within-species niches: a multi-modelling approach to assess range size of a spring-dwelling amphibian

“…high sensitivity). A comparable pattern was recently reported by Lecocq et al 22 , and Chardon et al 30 . Still, our inferences are based on small sample size (n < 30), and although we used a robust algorithm capable to deal with small sample size 35 , our findings must be considered with caution 36 .…”

“…Canopy cover appears to supply the required shade for the northern populations, while topography provides shade for the southern populations. Several studies indicated a dependency of intraspecific lineages on distinct environmental drivers [30][31][32] . In our case, the S lineage could compensate for the harsh climate of the southern region by being limited to local topographic refugia 33 .…”

Ecological niche models display nonlinear relationships with abundance and demographic performance across the latitudinal distribution of Astragalus utahensis (Fabaceae)

“…Bean et al, 2014;Bayly & Angert, 2019). In addition, suitability based upon a model built for the entire distribution may not reflect the climatic optimum for all populations and may poorly predict demographic responses to climate variation across the distribution (Chardon et al, 2020;Peterson et al, 2019).…”

“…On the other hand, a recent analysis of the relationship between ENM-predicted suitability and abundance of 396 mammal and tree species found no such relationship (Dallas & Hastings, 2018). The smaller number of studies that have evaluated the relationship between demographic performance and projected suitability yield similarly equivocal results: positive suitability-demography relationships have been observed in some cases (Brambilla & Ficetola, 2012;McLane & Aitken, 2012;Monnet, Hardouin, Robert, Hingrat, & Jiguet, 2015;Searcy & Shaffer, 2016;Sheppard, Burns, & Stanley, 2014), but appear to be the exception rather than the rule (Bacon et al, 2017;Bayly & Angert, 2019;Chardon, Pironon, Peterson, & Doak, 2020;Csergő et al, 2017;Oliver et al, 2012). The lack of consensus regarding the ubiquity of suitability-abundance and suitability-demography relationships may be due to local-scale constraints not captured in the large-scale predictors often used in ENMs (Davis, Jenkinson, Lawton, Shorrocks, & Wood, 1998;Guisan & Thuiller, 2005;Lembrechts, Nijs, & Lenoir, 2019;Pearson & Dawson, 2003;Varner & Dearing, 2014;Wisz et al, 2013), such that suitability predicts a maximum abundance or performance at a site that is then altered by attributes of the local environment (VanDerWal, Shoo, Johnson, & Williams, 2009).…”

Challenges and opportunities of species distribution modelling of terrestrial arthropod predators

“…There are proportionally more such studies for plants and marine invertebrates (see e.g. Chardon et al, 2020;Webb et al, 2020) than animals, because large spatial data sets needed for integrating physiological trait variation are available (Chown and Gaston 2016). While all these applications are still rare when it comes to terrestrial arthropods (see Maino et al 2016), recently there have been studies that have successfully addressed biotic interaction (Mammola and Isaia 2017), dispersal limitations (Monsimet et al 2020), and metapopulations (Giezendanner et al 2020), thereby showing promising directions for future research.…”