A Rapid Survey of the Critically Endangered Zapata Toad, Peltophryne florentinoi (Anura: Bufonidae), in Cuba: New Locality and Conservation Approaches

We provide new distributional data for the most geographically restricted and threatened Cuban toad, the Zapata Toad (Peltophryne florentinoi), and evaluate this information for its potential conservation implications. Prior to this survey, the species was known from only one locality. Although our records extend the geographic distribution about 20 km to the east from the previous locality, unsuitable habitats limited the species’ true extent of occurrence. Intermittent salt-water lagoons, local lithographic features, and anthropogenic disturbances impede the continuity of the toad’s distribution along the coastal forests. New surveys and long-term monitoring protocols could offer better clues about the distribution, biology, and conservation status of this species

Detecting Signals of Species’ Ecological Niches in Results of Studies with Defined Sampling Protocols: Example Application to Pathogen Niches

Ecological niches are increasingly appreciated as a long-term stable constraint on the geographic and temporal distributions of species, including species involved in disease transmission cycles (pathogens, vectors, hosts). Although considerable research effort has used correlative methodologies for characterizing niches, sampling effort (and the biases that this effort may or may not carry with it) considerations have generally not been incorporated explicitly into ecological niche modeling. In some cases, however, the sampling effort can be characterized explicitly, such as when hosts are tested for pathogens, as well as comparable situations such as when traps are deployed to capture particular species, etc. Here, we present simple methods for testing the hypothesis that non-randomness in occurrence or detection exists with respect to environmental dimensions (= a detectable signal of ecological niche); i.e., whether a pathogen occurs nonrandomly with respect to environment, given the occurrence and sampling of its host. We have implemented a set of R functions that presents an overall test for nonrandom occurrence with respect to a set of environmental dimensions, and, a posteriori, a set of exploratory tests that identify in which dimension(s) and in which direction or form the nonrandom occurrence is manifested. Our tools correctly detected signals of niche in most of our example cases. Although such signal may not be detectable in cases in which the niche of interest is broader than the universe sampled, such a possibility was correctly discarded in our analyses, preventing further interpretations. This kind of testing can constitute an initial step in a process that would conclude with development of a more typical ecological niche model. The particular advantage of the analyses proposed is that they consider the biases involved in sampling, testing, and reporting, in the context of nonrandom occurrence with respect to environment before proceeding to inferential and predictive steps

Kuenm: An R package for detailed development of ecological niche models using Maxent

This work is licensed under a Creative Commons Attribution 4.0 International License.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.PAPIIT UNAM IN116018CONACyT-FORDECyT 27364

Current and Future Distribution of the Lone Star Tick, Amblyomma americanum (L.) (Acari: Ixodidae) in North America

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

Climatic suitability of the eastern paralysis tick, Ixodes holocyclus, and its likely geographic distribution in the year 2050

The eastern paralysis tick, Ixodes holocyclus is one of two ticks that cause potentially fatal tick paralysis in Australia, and yet information on the full extent of its present or potential future spatial distribution is not known. Occurrence data for this tick species collected over the past two decades, and gridded environmental variables at 1 km2 resolution representing climate conditions, were used to derive correlative ecological niche models to predict the current and future potential distribution. Several hundreds of candidate models were constructed with varying combinations of model parameters, and the best-fitting model was chosen based on statistical significance, omission rate, and Akaike Information Criterion (AICc). The best-fitting model matches the currently known distribution but also extends through most of the coastal areas in the south, and up to the Kimbolton peninsula in Western Australia in the north. Highly suitable areas are present around south of Perth, extending towards Albany, Western Australia. Most areas in Tasmania, where the species is not currently present, are also highly suitable. Future spatial distribution of this tick in the year 2050 indicates moderate increase in climatic suitability from the present-day prediction but noticeably also moderate to low loss of climatically suitable areas elsewhere

General Theory and Good Practices in Ecological Niche Modeling: A Basic Guide

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

rangemap: An R Package to Explore Species' Geographic Ranges

Data exploration is a critical step in understanding patterns and biases in information about species’ geographic distributions. We present rangemap, an R package that implements tools to explore species’ ranges based on simple analyses and visualizations. The rangemap package uses species occurrence coordinates, spatial polygons, and raster layers as input data. Its analysis tools help to generate simple spatial polygons summarizing ranges based on distinct approaches, including spatial buffers, convex and concave (alpha) hulls, trend-surface analysis, and raster reclassification. Visualization tools included in the package help to produce simple, high-quality representations of occurrence data and figures summarizing resulting ranges in geographic and environmental spaces. Functions that create ranges also allow generating extents of occurrence (using convex hulls) and areas of occupancy according to IUCN criteria. A broad community of researchers and students could find in rangemap an interesting means by which to explore species’ geographic distributions

rangemap: An R Package to Explore Species' Geographic Ranges

Data exploration is a critical step in understanding patterns and biases in information about species’ geographic distributions. We present rangemap, an R package that implements tools to explore species’ ranges based on simple analyses and visualizations. The rangemap package uses species occurrence coordinates, spatial polygons, and raster layers as input data. Its analysis tools help to generate simple spatial polygons summarizing ranges based on distinct approaches, including spatial buffers, convex and concave (alpha) hulls, trend-surface analysis, and raster reclassification. Visualization tools included in the package help to produce simple, high-quality representations of occurrence data and figures summarizing resulting ranges in geographic and environmental spaces. Functions that create ranges also allow generating extents of occurrence (using convex hulls) and areas of occupancy according to IUCN criteria. A broad community of researchers and students could find in rangemap an interesting means by which to explore species’ geographic distributions

Acknowledging uncertainty in evolutionary reconstructions of ecological niches

Reconstructing ecological niche evolution can provide insight into the biogeography and diversification of evolving lineages. However, comparative phylogenetic methods may infer the history of ecological niche evolution inaccurately because (a) species' niches are often poorly characterized; and (b) phylogenetic comparative methods rely on niche summary statistics rather than full estimates of species' environmental tolerances. Here, we propose a new framework for coding ecological niches and reconstructing their evolution that explicitly acknowledges and incorporates the uncertainty introduced by incomplete niche characterization. Then, we modify existing ancestral state inference methods to leverage full estimates of environmental tolerances. We provide a worked empirical example of our method, investigating ecological niche evolution in the New World orioles (Aves: Passeriformes: Icterus spp.). Temperature and precipitation tolerances were generally broad and conserved among orioles, with niche reduction and specialization limited to a few terminal branches. Tools for performing these reconstructions are available in a new R package called nichevol

Acknowledging uncertainty in evolutionary reconstructions of ecological niches

Reconstructing ecological niche evolution can provide insight into the biogeography and diversification of evolving lineages. However, comparative phylogenetic methods may infer the history of ecological niche evolution inaccurately because (a) species' niches are often poorly characterized; and (b) phylogenetic comparative methods rely on niche summary statistics rather than full estimates of species' environmental tolerances. Here, we propose a new framework for coding ecological niches and reconstructing their evolution that explicitly acknowledges and incorporates the uncertainty introduced by incomplete niche characterization. Then, we modify existing ancestral state inference methods to leverage full estimates of environmental tolerances. We provide a worked empirical example of our method, investigating ecological niche evolution in the New World orioles (Aves: Passeriformes: Icterus spp.). Temperature and precipitation tolerances were generally broad and conserved among orioles, with niche reduction and specialization limited to a few terminal branches. Tools for performing these reconstructions are available in a new R package called nichevol