Background: Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way.Results: This package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric.Discussion: Use of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes.

Acarological surveys in areas outside the currently believed leading edge of the distribution of lone star ticks (Amblyomma americanum), coupled with recent reports of their identification in previously uninvaded areas in the public health literature, suggest that this species is more broadly distributed in North America than currently understood. Therefore, we evaluated the potential geographic extent under present and future conditions using ecological niche modeling approach based on museum records available for this species at the Walter Reed Biosystematics Unit (WRBU). The median prediction of a best fitting model indicated that lone star ticks are currently likely to be present in broader regions across the Eastern Seaboard as well as in the Upper Midwest, where this species could be expanding its range. Further northward and westward expansion of these ticks can be expected as a result of ongoing climate change, under both low- and high-emissions scenarios.

The North American distributional potential of the recently invaded tick, Haemaphysalis longicornis, was estimated using occurrence data from its geographic range in other parts of the world and relevant climatic data sets. Several hundred candidate models were built using a correlative maximum entropy approach, and best-fitting models were selected based on statistical significance, predictive ability, and complexity. The median of the best-fitting models indicates a broad potential distribution for this species, but restricted to three sectors—the southeastern United States, the Pacific Northwest, and central and southern Mexico.

Ecological niche models and species distribution models (ENM and SDM, respectively) are tools that have seen massive use and considerable improvement during the last twenty years. The choice of calibration areas for such models has strong effects on model outcomes and model interpretation, as well as on model transfer to distinct environmental settings. However, approaches to selecting these areas remain simple and/or unlinked to biological concepts. Such models should be calibrated within areas that the species of interest has explored throughout its recent history, the accessible area (M). In this paper, we provide a simulation approach for estimating a species’ M considering processes of dispersal, colonization, and extinction in constant current climate or glacial-interglacial climate change frameworks, implemented within a new R package we developed called grinnell. Using the avian genus Aphelocoma, we explored different parameterizations of our simulation, and compared them to current approaches for M selection, in terms of model performance and risk of extrapolation using the algorithm Maxent and mobility-oriented parity analyses. Model calibration exercises from all approaches resulted in at least one model meeting optimal performance criteria for each species; however, we noted high variability among taxa and M selection methods. More importantly, M hypotheses derived directly from simulations of key biological processes, rather than being based on simple proxies of those processes, and as such are better suited to erecting biologically appropriate contrasts in model calibration, and to characterizing the potential for model extrapolation more rigorously. Major factors in our simulations were environmental layer resolution, dispersal kernel characteristics, and the inclusion of a changing framework of climatic conditions. This contribution represents the first simulation-based method for selecting calibration areas for ENM and SDM, offering a quantitative approach to estimate the accessible area of a species while considering its dispersal ability, along with patterns of change in environmental suitability across space and time.

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.