Broennimann, Olivier, et al. “Measuring ecological niche overlap from occurrence and spatial environmental data.” Global Ecology and Biogeography 21.4 (2012): 481-497.
Authors put forth a new method that measures niche overlap between two similar species or the same species but in different geographic regions (endemic and invasive). The framework follows three steps: first calculate the density of species occurrence and of environmental factors along the environmental axes of a multivariate analysis, second measure the niche overlap along the gradients in the multivariate analysis, and third compute niche equivalency and similarity. To account for differences in sampling strategy, researchers use a kernel density function in the environmental space for species occurrence. The same function is also applied to the occurence of environmental cells.
Comparison of niche overlap is then determined by using the D metric.
Where Z1ij is species 1 occupancy and Z2ij is species 2 Occupancy, output varies between 0 (no overlap) and 1 (complete overlap). Comparing the two niches statistically entail investigating niche similarity in two geographic ranges (equivalency) and the same location (similarity).
In order to evaluate the proposed method, researchers conducted a simulation study of two virtual entities with varying degrees of niche overlap. However, the environmental parameters that drive species distribution were based off of climate conditions found in North America and Europe. Researchers also tested the provided method against two cases of species invasion. Finally, researchers compared their framework between species distribution models (EG: MaxEnt) and ordination techniques.
Results in niche detection were variable with traditional SDM methods (figures 3 – 5). Among ordination methods that did not depend on prior grouping, PCA-env performed best on both EU and NA sets of data. No method was considered bester amongst those that depended on prior grouping. For the SDM methods, MaxEnt achieved the best result in measuring niche overlap.
Figure 4. Sensivity analysis of simulated versus detected niche overlap for different SDM algorithsm. (a) generalized linear models, (b) MaxEnt, (c) gradiemt boosting machine, and (d) random forests.
Results demonstrate their ability to determine range overlap between and within species. Methods presented here improve on previous first in two ways. First, it removes the dependency of species occurrence from the frequency of different climatic conditions that can occur across a region. Secondly, smoothing species densities allows for species occurrence to be independent of both sampling effort and of the resolution of environmental. Both of these improvements help minimize the influence of of data resolution on the measurement of niche overlap.