No abstract

The ENMTools software package was introduced in 2008 as a platform for making measurements on environmental niche models (ENMs, frequently referred to as species distribution models or SDMs), and for using those measurements in the context of newly developed Monte Carlo tests to evaluate hypotheses regarding niche evolution. Additional functionality was later added for model selection and simulation from ENMs, and the software package has been quite widely used. ENMTools was initially implemented as a Perl script, which was also compiled into an executable file for various platforms. However, the package had a number of significant limitations; it was only designed to fit models using Maxent, it relied on a specific Perl distribution to function, and its internal structure made it difficult to maintain and expand. Subsequently, the R programming language became the platform of choice for most ENM studies, making ENMTools less usable for many practitioners. Here we introduce a new R version of ENMTools that implements much of the functionality of its predecessor as well as numerous additions that simplify the construction, comparison and evaluation of niche models. These additions include new metrics for model fit, methods of measuring ENM overlap, and methods for testing evolutionary hypotheses. The new version of ENMTools is also designed to work within the expanding universe of R tools for ecological biogeography, and as such includes greatly simplified interfaces for analyses from several other R packages.

The Red Queen hypothesis (RQH) is both familiar and murky, with a scope and range that has broadened beyond its original focus. Although originally developed in the palaeontological arena, it now encompasses many evolutionary theories that champion biotic interactions as significant mechanisms for evolutionary change. As such it de-emphasizes the important role of abiotic drivers in evolution, even though such a role is frequently posited to be pivotal. Concomitant with this shift in focus, several studies challenged the validity of the RQH and downplayed its propriety. Herein, we examine in detail the assumptions that underpin the RQH in the hopes of furthering conceptual understanding and promoting appropriate application of the hypothesis. We identify issues and inconsistencies with the assumptions of the RQH, and propose a redefinition where the Red Queen's reign is restricted to certain types of biotic interactions and evolutionary patterns occurring at the population level.

Ecological niche modeling (ENM) and species distribution modeling (SDM) are sets of tools that allow the estimation of distributional areas on the basis of establishing relationships among known occurrences and environmental variables. These tools have a wide range of applications, particularly in biogeography, macroecology, and conservation biology, granting prediction of species potential distributional patterns in the present and dynamics of these areas in different periods or scenarios. Due to their relevance and practical applications, the usage of these methodologies has significantly increased throughout the years. Here, we provide a manual with the basic routines used in this field and a practical example of its implementation to promote good practices and guidance for new users.

Large-scale biodiversity databases have become crucial information sources in many analyses in biogeography, macroecology, and conservation biology, often involving development of empirical models of species’ ecological niches and predictions of their geographic distributions. These analyses, however, can be impaired by the presence of errors, particularly as regards taxonomic identifications and accurate geographic coordinates. Here, we present a detailed data-cleaning exercise based on two contrasting datasets; we link these example data with a step-by-step guide to overcoming these problems and improving data quality for analyses based on these data.

Species invasions represent a significant dimension of global change yet the dynamics of invasions remain poorly understood and are considered rather unpredictable. We explored interannual dynamics of the invasion process in the Eurasian collared dove () and tested whether the advance of the invasion front of the species in North America relates to centrality (versus peripherality) within its estimated fundamental ecological niche. We used ecological niche modelling approaches to estimate the dimensions of the fundamental ecological niche on the Old World distribution of the species, and then transferred that model to the New World as measures of centrality versus peripherality within the niche for the species. Although our hypothesis was that the invasion front would advance faster over more favourable (i.e. more central) conditions, the reverse was the case: the invasion expanded faster in areas presenting less favourable (i.e. more peripheral) conditions for the species as it advanced across North America. This result offers a first view of a predictive approach to the dynamics of species' invasions, and thereby has relevant implications for the management of invasive species, as such a predictive understanding would allow better anticipation of coming steps and advances in the progress of invasions, important to designing and guiding effective remediation and mitigation efforts.

1. The increase of digitally available primary biodiversity data has been a positive result of sharing initiatives in the natural history museum community and among citizen scientists. Owing to the heterogeneity of sources, however, limitations related to data quality control emerge, as incomplete and/ or erroneous information at different stages of input must be overcome. To facilitate detection of spatial errors, species distribution modelling (SDM) has been suggested, but its efficiency in detection of different types of spatial errors has not been assessed.2. We investigate the utility of SDM-based assessments in detection of two types of spatial errors found in large biodiversity databases, random errors versus errors of misidentification as congeneric taxa. We used available distributional data for five closely related species of the tortoise beetle genus Mesomphalia (Coleoptera, Chyrsomelidae, Cassidinae) to test the suitability values associated with simulated erroneous points mimicking the two error types.3. Overall, we observed that habitat suitability values associated with random outliers were lower than those for congeneric outliers, fitting expectations based on the idea of niche conservatism. Also, detecting outliers in small datasets is more challenging, whereas in larger datasets, the detection of random outliers should be more efficient.4. Our results indicate that SDM tools can be useful in detection of outliers more efficiently when erroneous points fall outside the ecological niche profile of the species, as in the case of random typographical errors, but not as effective with errors of misidentification. This paper explores a potential tool to promote better assessment of the quality of biodiversity data.

Climatic variables have been the main predictors employed in ecological niche modeling and species distribution modeling, although biotic interactions are known to affect species’ spatial distributions via mechanisms such as predation, competition, and mutualism. Biotic interactions can affect species’ responses to abiotic environmental changes differently along environmental gradients, and abiotic environmental changes can likewise influence the nature of biotic interactions. Understanding whether and how to integrate variables at different scales in ecological niche models is essential to better estimate spatial distributions of species on macroecological scales and their responses to change. We report the leaf beetle Eurypedus nigrosignatus as an alien species in the Dominican Republic and investigate whether biotic factors played a meaningful role in the distributional expansion of the species into the Caribbean. We evaluate ecological niche models built with an additive gradient of unlinked biotic predictors—host plants, using likelihood-based model evaluation criteria (Akaike information criterion and Bayesian information criterion) within a range of regularization multiplier parameter values. Our results support the argument that ecological niche models should be more inclusive, as selected biotic predictors can improve the performance of models, despite the increased model complexity, and show that biotic interactions matter at macroecological scales. Moreover, we provide an alternative approach to select optimal combination of relevant variables, to improve estimation of potential invasive areas using global minimum model likelihood scores.