The Geographic Scaling of Biotic Interactions
Araújo, Miguel B., and Alejandro Rozenfeld. 2014. “The Geographic Scaling of Biotic Interactions.” Ecography 37 (5): 406–15. doi:10.1111/j.1600-0587.2013.00643.x.
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Araujo and Rozenfeld (2014) address an important theoretical question in biogeography: at what scales do abiotic and biotic factors matter in determining species ranges? In general, it is thought that abiotic factors, such as climate, are more important at large scales, while biotic interactions influence species presence at local scales. This theory is based mostly on competitive interactions, whose effects are diluted at the scale of the biome, however, species interactions that are mutualistic or commensual may have lasting spatial effects are broader scales. This study aimed to identify the relative importance of different types of species interactions in determining species co-occurrence across a gradient of spatial scales.
The authors develop a point-process model that simulates species co-occurence resulting from all types of pairwise species interactions(e.g. +/+,+/-,+/0, -/-,-/0), and a range of interaction strengths. The point-process is a probability model that is a function of both species’ prevalences and their interaction strengths, calculating the probability that the two species co-occur in a cell. The model explicitly includes space by modeling occurence over a 100 x 100 lattice, using both a homogenous and spatially autocorrelated landscape scenario. These models were used to simulate species presences across landscapes, which were then evaluated at different spatial scales for the effect of species interactions.
They used a hierarchical framework for scaling to estimate how the effect of species interactions depends on spatial scale. This was implemented by ‘measuring’ co-occurence at increasing large spatial scales, from the individual lattice cell to the the size of the overall landscape. They also replicate field sampling methods by comparing ‘true’ co-occurence, when species co-occur in the same cell, to ‘sampled’ co-occurence, when species are present in the larger block, but not necessarily co-occuring the same cell. By comparing the difference between true and sampled co-occurence probabilities, the authors quantify the scale-dependence of each type of biotic interaction.
When comparing scale-dependence across different types of interactions, they find that negative interactions (e.g. competition) are more scale-dependent than mutalistic or neutral interactions. This also depends on the interaction strength, with stonger positive interactions being more scale independent, manifesting at both local and landscape scales, and stronger negative interactions becoming less apparent at larger spatial scales. This also applies for non-reciprocal interactions, for example consumer-resource interactions when one species has a strong positive dependency.
The simulation method in this approach is relatively simple, and based on how probabilities of species co-occurences may be impacted by different interaction types, without modeling any of the fine-scale mechanisms or processes happening within a cell. Given the nature of this approach, it does not provide much insight into the mechanisms or reasons why these patterns may appear, however it does confirm what other studies have found using observational data at biogeographical scales. Multi-scale modeling is important, as we know that fine-scale mechanisms may have disproportionately large impacts at broader spatial scales, however, this study finds that this is not necessarily true for all types of species interactions. This implies that any error resulting from the exclusion of negative species interactions would not scale up to a broader scale, and these species interactions need not be included in large-scale models. It also, however, highlights the importance of positive species interactions, with the implication that these types of interactions should be better characterized and included in models.