Individual-level biotic interactions and species distribution models

Abstract

Aim

Accounting for biotic interactions in species distribution models is complicated by the fact that interactions occur at the individual-level at unknown spatial scales. Standard approaches that ignore individual-level interactions and focus on aggregate scales are subject to the modifiable aerial unit problem (MAUP) in which incorrect inferences may arise about the sign and magnitude of interspecific effects.

Location

Global (simulation) and North Carolina, United States (case study).

Taxon

None (simulation) and Aves (case study).

Methods

We present a hierarchical species distribution model that includes a Markov point process in which the locations of individuals of one species are modelled as a function of both abiotic variables and the locations of individuals of another species. We applied the model to spatial capture-recapture (SCR) data on two ecologically similar songbird species—hooded warbler (Setophaga citrina) and black-throated blue warbler (Setophaga caerulescens)—that segregate over a climate gradient in the southern Appalachian Mountains, USA.

Results

A simulation study indicated that the model can identify the effects of environmental variation and biotic interactions on co-occurring species distributions. In the case study, there were strong and opposing effects of climate on spatial variation in population densities, but spatial competition did not influence the two species' distributions.

Main Conclusions

Unlike existing species distribution models, the framework proposed here overcomes the MAUP and can be used to investigate how population-level patterns emerge from individual-level processes, while also allowing for inference on the spatial scale of biotic interactions. Our finding of minimal spatial competition between black-throated blue warbler and hooded warbler adds to the growing body of literature suggesting that abiotic factors may be more important than competition at low-latitude range margins. The model can be extended to accommodate count data and binary data in addition to SCR data.