Aim: Separating the biotic and abiotic factors controlling species distributions has been a long-standing challenge in ecology and biogeography. Joint species distribution models (JSDMs) have emerged as a promising statistical framework towards this objective by simultaneously modeling the environmental responses of multiple species and approximating species associations based on patterns in their (co-)occurrences. However, the signature of biotic interactions should be most evident at fine spatial resolutions. Here, we test how the resolution of input data affects the inferences from JSDMs.

Location: Switzerland

Taxon: Birds

Methods: Using standardized survey data of 43 woodland bird species and eight climatic, topographic and vegetation structural predictors, we fit JSDMs at different spatial resolutions (125m to 1000m) and sampling periods (1 and 5years). In addition, we calculate functional similarity among all species as an independent proxy of biotic interactions, specifically competition. We then assess how JSDM performance and estimates vary with the spatial resolution of the input data and test whether species associations are consistent across grain sizes and with the alternative approach based on functional similarity.

Results: Our results show better model performance at coarser spatial resolutions and for longer sampling periods. Although pairwise species associations estimated in JSDMs were generally shifted towards positive values, we found a higher proportion of negative associations at fine spatial resolutions. Strikingly, estimates were not consistent across spatial scales and frequently switched between positive and negative values. Moreover, estimated species associations tended to be more positive for functionally similar species.

Main conclusions: Our results show that species associations are more differentiated, i.e. cover a broader range of values, at finer spatial resolutions. Yet, their positive correlation with functional similarity and the general over-representation of positive associations suggest that shared responses to unobserved environmental predictors rather than biotic interactions underlie these scaling effects, cautioning against a naïve interpretation of species associations estimated by JSDMs at any spatial scale.

Data collection and processing is described in detail in the article.