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Released 4 years ago, the Wallace EcoMod application (R package wallace) provided an open‐source and interactive platform for modeling species niches and distributions that served as a reproducible toolbox and educational resource. wallace harnesses R package tools documented in the literature and makes them available via a graphical user interface that runs analyses and returns code to document and reproduce them. Since its release, feedback from users and partners helped identify key areas for advancement, leading to the development of wallace 2. Following the vision of growth by community expansion, the core development team engaged with collaborators and undertook a major restructuring of the application to enable: simplified addition of custom modules to expand methodological options, analyses for multiple species in the same session, improved metadata features, new database connections, and saving/loading sessions. wallace 2 features nine new modules and added functionalities that facilitate data acquisition from climate‐simulation, botanical and paleontological databases; custom data inputs; model metadata tracking; and citations for R packages used (to promote documentation and give credit to developers). Three of these modules compose a new component for environmental space analyses (e.g., niche overlap). This expansion was paired with outreach to the biogeography and biodiversity communities, including international presentations and workshops that take advantage of the software's extensive guidance text. Additionally, the advances extend accessibility with a cloud‐computing implementation and include a suite of comprehensive unit tests. The features in wallace 2 greatly improve its expandability, breadth of analyses, and reproducibility options, including the use of emerging metadata standards. The new architecture serves as an example for other modular software, especially those developed using the rapidly proliferating R package shiny, by showcasing straightforward module ingestion and unit testing. Importantly, wallace 2 sets the stage for future expansions, including those enabling biodiversity estimation and threat assessments for conservation.

The understanding of spatial distribution patterns of native riparian tree species in Europe lacks accurate species distribution models (SDMs), since riparian forest habitats have a limited spatial extent and are strongly related to the associated watercourses, which needs to be represented in the environmental predictors. However, SDMs are urgently needed for adapting forest management to climate change, as well as for conservation and restoration of riparian forest ecosystems. For such an operative use, standard large-scale bioclimatic models alone are too coarse and frequently exclude relevant predictors. In this study, we compare a bioclimatic continent-wide model and a regional model based on climate, soil, and river data for central to south-eastern Europe, targeting seven riparian foundation species—Alnus glutinosa, Fraxinus angustifolia, F. excelsior, Populus nigra, Quercus robur, Ulmus laevis, and U. minor. The results emphasize the high importance of precise occurrence data and environmental predictors. Soil predictors were more important than bioclimatic variables, and river variables were partly of the same importance. In both models, five of the seven species were found to decrease in terms of future occurrence probability within the study area, whereas the results for two species were ambiguous. Nevertheless, both models predicted a dangerous loss of occurrence probability for economically and ecologically important tree species, likely leading to significant effects on forest composition and structure, as well as on provided ecosystem services.

Movement behavior is an important contributor to habitat selection and its incorporation in disease risk models has been somewhat neglected. The habitat preferences of host individuals affect their probability of exposure to pathogens. If preference behavior can be incorporated in ecological niche models (ENMs) when data on pathogen distributions are available, then variation in such behavior may dramatically impact exposure risk. Here we use data from the anthrax endemic system of Etosha National Park, Namibia, to demonstrate how integrating inferred movement behavior alters the construction of disease risk maps. We used a Maximum Entropy (MaxEnt) model that associated soil, bioclimatic, and vegetation variables with the best available pathogen presence data collected at anthrax carcass sites to map areas of most likely Bacillus anthracis (the causative bacterium of anthrax) persistence. We then used a hidden Markov model (HMM) to distinguish foraging and non-foraging behavioral states along the movement tracks of nine zebra ( Equus quagga ) during the 2009 and 2010 anthrax seasons. The resulting tracks, decomposed on the basis of the inferred behavioral state, formed the basis of step-selection functions (SSFs) that used the MaxEnt output as a potential predictor variable. Our analyses revealed different risks of exposure during different zebra behavioral states, which were obscured when the full movement tracks were analyzed without consideration of the underlying behavioral states of individuals. Pathogen (or vector) distribution models may be misleading with regard to the actual risk faced by host animal populations when specific behavioral states are not explicitly accounted for in selection analyses. To more accurately evaluate exposure risk, especially in the case of environmentally transmitted pathogens, selection functions could be built for each identified behavioral state and then used to assess the comparative exposure risk across relevant states. The scale of data collection and analysis, however, introduces complexities and limitations for consideration when interpreting results.

Modelling the distribution and abundance of species at risk is extremely important for their conservation and management. We used ecological niche models (ENMs) to predict the occurrence of western rattlesnakes (Crotalus oreganus) in British Columbia (BC), Canada. We applied this to existing population estimates to support a threshold of occurrence for management and conservation. We also identified predictors influencing rattlesnake distribution and abundance in this region. Using a Geographic Information Systems platform, we incorporated ENMs, capture–mark–recapture (CMR) and radio-telemetry results, province-wide observations, Landsat imagery and provincial databases for agricultural land use to produce quantitative, spatially explicit, population estimates across BC. Using available western rattlesnake habitat estimated at 183.9 km2 and averaging estimates calculated from densities in three study populations, we generated a mean adult population size of 9722 (±SD 3009; 0.8 relative index of occurrence [RIO] threshold). Only a small area (21.6 km2) of suitable land cover was located within protected areas, potentially protecting an estimated 1144 (±354) adults. Most suitable land cover was within 500 m of roads (170.6 km2), representing potential habitat being used by an estimated 9017 (±2791) adults. At the threshold RIO value chosen (0.8), only a very small area of farmland provided suitable land cover. Our results highlight the possibility of high mortality rates for western rattlesnakes near roads and the fact that protected areas do not provide sufficient coverage to conserve the population. Given that this species has relatively low mobility and high site fidelity to home ranges, our population estimate for BC provides a useful reference for the northern part of the species’ range. It also fulfills a need to estimate population size within political jurisdictions where conservation management decisions are made, as well as presenting a method that can be applied to other parts of the range, including the southern United States. Our study provides an important benchmark for future monitoring of western rattlesnakes in BC using a repeatable and transparent approach. Similar applications can be extrapolated and applied for other threatened species to identify and quantify population distributions and threats, further supporting conservation prioritization tools to be used to maximize the effectiveness of conservation strategies under financial constraints.

With respect to the epidemiology of leishmaniasis, it is crucial to take into account the ecoclimatic and environmental characteristics that influence the distribution patterns of the vector sand fly species. It is also important to consider the possible impact of on-going climate changes on the emergence of this disease. In order to map the potential distribution of Phlebotomus perniciosus, the main vector species of canine leishmaniasis in north-eastern Italy, geographical information systems tools, ecological niche models (ENM) and remotely sensed environmental data were applied for a retrospective analysis of an entomological survey conducted in north-eastern Italy over 12 years. Sand fly trapping was conducted from 2001 to 2012 in 175 sites in the provinces of Veneto, Friuli-Venezia Giulia and Trentino-Alto Adige. We developed a predictive model of potential distribution of P. perniciosus using the maximum entropy algorithm software, based on seasonal normalized difference vegetation index, day and night land surface temperature, the Corine land cover 2006, a digital elevation model (GTOPO30) and climate layers obtained from the WorldClim database. The MaxEnt prediction found the more suitable habitat for P. perniciosus to be hilly areas (100-300 m above the mean sea level) characterised by temperate climate during the winter and summer seasons, high winter vegetation cover and moderate rainfall during the activity season of vector sand fly. ENM provided a greater understanding of the geographical distribution and ecological requirements of P. perniciosus in the study area, which can be applied for the development of future surveillance strategies.

Aim Climate variability is a major force affecting diversification processes and restricting species to specific areas, and thus, it has important impacts on species biogeographic patterns. This study aims to infer the role of climate in the evolutionary history of the endemic Iberian adder Vipera seoanei. Location Northern Iberian Peninsula and south-western France. Methods We combined genetic analyses with ecological niche-based modelling. Genetic analyses, based on sequencing of mitochondrial markers (cyt b, ND4), include phylogenetic and phylogeographic analyses, spatial interpolations of genetic variability and diversity, and identification of putative geographical origin of the most recent common ancestor of the species. Ecological modelling involved the combination of six modelling algorithms and projections to past conditions (Last Interglacial – LIG, Last Glacial Maximum – LGM) and the identification of climatic stable areas. Results The species shows a shallow phylogeographic structure, dated at middle-upper Pleistocene, and low haplotype diversity, with the highest genetic diversity located in north-western Iberia. This region is identified as the putative origin of the ancestral populations. Projections to past periods spatially fit genetic results, indicating range contractions to north-western Iberia during the LIG and expansions during the LGM. Main conclusions This study exemplifies how the combination of phylogeographic and ecological niche-based models is a powerful tool for inferring evolutionary scenarios and responses of species to Pleistocene climatic oscillations. Vipera seoanei responded accordingly to a cold temperate model and fits a simplified example of 'R' type species where interglacial warming periods during the Pleistocene probably caused major range reductions with persistence in a single refuge in north-western Iberia. The single mtDNA lineage observed in this study does not support the differentiation at subspecific level in V. seoanei. Our work highlights the importance of climate in explaining evolutionary processes and current biogeographical patterns of species with restrictive ranges.

Local adaptations can determine the potential of populations to respond to environmental changes, yet adaptive genetic variation is commonly ignored in models forecasting species vulnerability and biogeographical shifts under future climate change. Here we integrate genomic and ecological modeling approaches to identify genetic adaptations associated with climate in two cryptic forest bats. We then incorporate this information directly into forecasts of range changes under future climate change and assessment of population persistence through the spread of climate-adaptive genetic variation (evolutionary rescue potential). Considering climate-adaptive potential reduced range loss projections, suggesting that failure to account for intraspecific variability can result in overestimation of future losses. On the other hand, range overlap between species was projected to increase, indicating that interspecific competition is likely to play an important role in limiting species’ future ranges. We show that although evolutionary rescue is possible, it depends on a population’s adaptive capacity and connectivity. Hence, we stress the importance of incorporating genomic data and landscape connectivity in climate change vulnerability assessments and conservation management.

Aim The idea of combining predictions from different models into an ensemble has gained considerable popularity in species distribution modelling, partly due to free and comprehensive software such as the R package BIOMOD. However, despite proliferation of ensemble models, we lack oversight of how and where they are used for modelling distributions, and how well they perform. Here, we present such an overview. Location Global. Methods Since BIOMOD is freely available and widely used by ensemble species distribution modellers, we focused on articles that apply BIOMOD, filtering the initial 852 papers identified in our structured literature search to a relevant final subset of 224 eligible peer‐reviewed journal articles. Results BIOMOD‐based ensembles are used across many taxa and locations, with terrestrial plants being the most represented group of species (n = 72) and Europe being the most represented continent (n = 106). These studies often focus on forecasting distributions in the future (n = 109), and commonly use presence‐only species data (n = 139) and climatic environmental predictors (n = 219). An average of six models are used in ensembles, and approximately half of ensembles weight contributions of models by their cross‐validation performance. However, discussion about choices made in the modelling process and unambiguous information on the performance of ensemble models versus individual models are limited. The use of independent data to validate model performance is particularly uncommon. Main conclusions We document the breadth of ensemble applications, but could not draw strong quantitative conclusions about the predictive performance of ensemble models, due to lack of unambiguous information reported. Understanding how and where ensembles are best used when modelling species distributions is important for enabling best choices for different applications. To enable this objective to be achieved, we provide recommendations for thorough reporting practices in a BIOMOD‐based ensemble workflow.

Schistosomiasis mansoni remains an important parasitic disease of man, endemic in large parts of sub-Saharan Africa, the Middle East, South America and the Caribbean. The aetiological agent is the trematode Schistosoma mansoni, whereas aquatic snails of the genus Biomphalaria act as intermediate hosts in the parasite life cycle. In Brazil, the distribution of Biomphalaria spp. is closely associated with the occurrence of schistosomiasis. The purpose of this study was to map and predict the spatial distribution of the intermediate host snails of S. mansoni across Brazil. We assembled snail \"presenceonly\" data and used a maximum entropy approach, along with climatic and environmental variables to produce predictive risk maps. We identified a series of risk factors that govern the distribution of Biomphalaria snails. We find that high-risk areas for B. glabrata are concentrated in the regions of Northeast and Southeast and the northern part of the South region. B. straminea are found in the Northeast and Southeast regions, and B. tenagophila are concentrated in the Southeast and South regions. Our findings confirm that the presence of the intermediate host snails is correlated with the occurrence of schistosomiasis mansoni. The generated risk maps of intermediate host snails might assist the national control programme for spatial targeting of control interventions and to ultimately move towards schistosomiasis elimination in Brazil.

The discriminating capacity (i.e. ability to correctly classify presences and absences) of species distribution models (SDMs) is commonly evaluated with metrics such as the area under the receiving operating characteristic curve (AUC), the Kappa statistic and the true skill statistic (TSS). AUC and Kappa have been repeatedly criticized, but TSS has fared relatively well since its introduction, mainly because it has been considered as independent of prevalence. In addition, discrimination metrics have been contested because they should be calculated on presence–absence data, but are often used on presence-only or presence-background data. Here, we investigate TSS and an alternative set of metrics—similarity indices, also known as F-measures. We first show that even in ideal conditions (i.e. perfectly random presence–absence sampling), TSS can be misleading because of its dependence on prevalence, whereas similarity/F-measures provide adequate estimations of model discrimination capacity. Second, we show that in real-world situations where sample prevalence is different from true species prevalence (i.e. biased sampling or presence-pseudoabsence), no discrimination capacity metric provides adequate estimation of model discrimination capacity, including metrics specifically designed for modelling with presence-pseudoabsence data. Our conclusions are twofold. First, they unequivocally impel SDM users to understand the potential shortcomings of discrimination metrics when quality presence–absence data are lacking, and we recommend obtaining such data. Second, in the specific case of virtual species, which are increasingly used to develop and test SDM methodologies, we strongly recommend the use of similarity/F-measures, which were not biased by prevalence, contrary to TSS.