A new analysis of the nearly century‐old Lotka–Volterra theory allows us to link species interactions to biodiversity patterns, including: species abundance distributions, estimates of total community size, patterns of community invasibility, and predicted responses to disturbance. Based on a few restrictive assumptions about species interactions, our calculations require only that the community is sufficiently large to allow a mean‐field approximation. We develop this analysis to show how an initial assemblage of species with varying interaction strengths is predicted to sort out into the final community based on the species’ predicted target densities. The sorting process yields predictions of covarying patterns of species abundance, community size, and species interaction strengths. These predictions can be tested using enrichment experiments, examination of latitudinal and productivity gradients, and features of community assembly.
In this quantitative review, we investigate the degree to which landscape ecology studies that use spatial data address spatial uncertainty when conducting analyses. We identify three broad categories of spatial uncertainty that are important in determining the characterisation of landscape pattern and affect the outcome of analysis in landscape ecology: i) classification scheme uncertainty, ii) spatial scale and iii) classification error. The second category, spatial scale, was further subdivided into five scale dependent factors i) pixel size ii) minimum mappable unit, iii) smoothing, iv) thematic resolution and v) extent. We reviewed all articles published in the journal Landscape ecology in 2007 and recorded how spatial data was used and whether spatial uncertainty was addressed or reported in ecological analyses. This review found that spatial uncertainty was rarely addressed and/or reported. Most articles used the default pixel size of the sensor. Only 23% of articles addressed one or more scale dependent factors and 47% reported one or more as issues. A single study of the 59 investigated the effect of classification accuracy on ecological analyses. We demonstrate that spatial uncertainty is not being addressed as standard practice in analyses in landscape ecology, and then describe methods to test for spatial uncertainty and potential solutions that can be developed in the future.
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