Can Ecological Interactions be Inferred from Spatial Data?

Abstract

The characterisation and quantication of ecological interactions, and the construction

of species distributions and their associated ecological niches, is of fundamental

theoretical and practical importance. In this paper we give an overview of a Bayesian

inference framework, developed over the last 10 years, which, using spatial data, offers

a general formalism within which ecological interactions may be characterised and

quantied. Interactions are identied through deviations of the spatial distribution

of co-occurrences of spatial variables relative to a benchmark for the non-interacting

system, and based on a statistical ensemble of spatial cells. The formalism allows for

the integration of both biotic and abiotic factors of arbitrary resolution. We concentrate

on the conceptual and mathematical underpinnings of the formalism, showing

how, using the Naive Bayes approximation, it can be used to not only compare and

contrast the relative contribution from each variable, but also to construct species

distributions and niches based on arbitrary variable type. We show how the formalism

can be used to quantify confounding and therefore help disentangle the complex

causal chains that are present in ecosystems. We also show species distributions and

their associated niches can be used to infer standard "micro" ecological interactions,

such as predation and parasitism. We present several representative use cases that

validate our framework, both in terms of being consistent with present knowledge of

a set of known interactions, as well as making and validating predictions about new,

previously unknown interactions in the case of zoonoses.