RESUMEN
BACKGROUND: Ecological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many ß-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs. METHODS: We simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure. RESULTS: Overall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA's error rates under almost all scenarios. CONCLUSION: We encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.
RESUMEN
Tubastraea tagusensis, a coral native to the Galapagos Archipelago, has successfully established and invaded the Brazilian coast where it modifies native tropical rocky shore and coral reef communities. In order to understand the processes underlying the establishment of T. tagusensis, we tested whether Maxent, a tool for species distribution modeling, based on the native range of T. tagusensis correctly forecasted the invasion range of this species in Brazil. The Maxent algorithm was unable to predict the Brazilian coast as a suitable environment for the establishment of T. tagusensis. A comparison between these models and a principal component analysis (PCA) allowed us to examine the environmental dissimilarity between the two occupied regions (native and invaded) and to assess the species' occupied niche breadth. According to the PCA results, lower levels of chlorophyll-a and nitrate on the Atlantic coast segregate the Brazilian and Galapagos environments, implying that T. tagusensis may have expanded its realized niche during the invasion process. We tested the possible realized niche expansion in T. tagusensis by assuming that Tubastraea spp. have similar fundamental niches, which was supported by exploring the environmental space of T. coccinea, a tropical-cosmopolitan congener of T. tagusensis. We believe that the usage of Maxent should be treated with caution, especially when applied to biological invasion (or climate change) scenarios where the target species has a highly localized native (original) distribution, which may represent only a small portion of its fundamental niche, and therefore a violation of a SDM assumption.
