Interspecific interactions and range limits: contrasts among interaction types

Abstract

30There is a great deal of interest in the effects of biotic interactions on geographic distributions. Nature contains many different types of biotic interactions (notably mutualism, commensalism, predation, amensalism and competition), and it is difficult to compare the effects of multiple interaction types on species' distributions. To resolve this problem, we analyze a general, flexible model of 35 pairwise biotic interactions that can describe all interaction types. In the absence of strong positive feedback, a species' ability to be present depends on its ability to increase in numbers when it is rare and the species it is interacting with is at equilibrium. This insight leads to counterintuitive conclusions. Notably we often predict the same range limit when the focal species experiences competition, 40 predation or amensalism. Similarly, we often predict the same range margin or when the species experiences mutualism, commensalism or benefits from prey. In the presence of strong positive density dependent feedback different species interactions produce different range limits in our model. In all cases, the abiotic environment can indirectly influence the impact of biotic interactions on range 45 limits. We illustrate the implications of this observation by analyzing a stress gradient where biotic interactions are harmful in benign environments but beneficial in stressful environments. Our results emphasize the need to consider the effects of all biotic interactions on species' range limits, and provide a systematic comparison of when biotic interactions affect distributions. 50

Context‐dependent biotic interactions control plant abundance across altitudinal environmental gradients

“…Indeed, simulation work questions whether the consequences of biotic interactions can be inferred from distribution models at all (Godsoe et al 2017a). Our results may reflect unmeasured variables that co-vary with our set of predictors, rather than indicating an effect of biotic interactions (Godsoe et al 2017b), which may explain why distribution models using co-occurrence as a metric of competition do a poor job at predicting experimental outcomes (Barner et al 2018). However, by predicting abundance rather than presence/absence, our models may more accurately identify when biotic interactions are important for regulating populations (but see Godsoe et al 2017a).…”

Interactive range‐limit theory (iRLT): An extension for predicting range shifts

“…Our findings highlight the different types of spatial patterns that biotic interactions can impart along range limits (Bull, ; Holt & Barfield, ) and provide insight into the underlying processes. Competition can create a variety of range‐limit patterns (abrupt–diffuse) depending on phylogenetic and ecological similarity (Bull, ; Godsoe, Holland, et al, ; Wisz et al, ). For example, competition between highly similar carnivore species pairs (e.g.…”

“…Comparatively, the patterns that predation and parasitism create along range limits are less well understood (Godsoe, Holland, et al, 2017). These biotic interactions can confer patterns similar to competition (e.g.…”

When can competition and dispersal lead to checkerboard distributions?

“…These explanations, as well as the role of founder effects, environmental filtering and interactions with other community members, are all possible mechanisms underlying the observed checkerboard distributions in natural systems. Further, while interspecific competition is unlikely to be strong enough to result in checkerboard distributions, competition may have the ability to influence geographic range limits of interacting populations (a closely related problem; Godsoe et al., ; Price & Kirkpatrick, ). This suggests that a suite of influences may lead to checkerboard distributions in natural systems, though it appears that interspecific competition is unlikely to be the root cause.…”