Species distribution models in conservation biogeography: developments and challenges

How to make use of unlabeled observations in species distribution modeling using point process models

Species distribution modeling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit such models. However, the species observations used can have varying levels of quality and can have incomplete information, such as uncertain or unknown species identity.In this paper, we develop two algorithms to classify observations with unknown species identities which simultaneously predict several species distributions using spatial point processes. Through simulations, we compare the performance of these algorithms using 7 different initializations to the performance of models fitted using only the observations with known species identity.We show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that did not use the misspecified data. We applied the best-performing methods to a dataset of three frog species (Mixophyes).These models represent a helpful and promising tool for opportunistic surveys where misidentification is possible or for the distribution of species newly separated in their taxonomy.

Determinants of habitat suitability models transferability across geographically disjunct populations: Insights from Vipera ursinii urs inii

Transferability of habitat suitability models (HSMs), essential to accurately predict outside calibration conditions, has been seldom investigated at intraspecific level. We targeted Vipera ursinii ursinii, a meadow viper from southeastern France and central Italy, to assess determinants of transferability among geographically disjunct populations. We fitted HSMs upon occurrences of the Italian and French populations separately, as well as on the combined occurrence dataset. Internal transferability of HSMs, on spatially independent test data drawn from the calibration region, and their external transferability on the geographically disjunct populations were evaluated according to (a) use of full or spatially rarefied presence datasets; (b) ecology-driven or statistics-driven filtering of predictors; (c) modeling algorithm, testing generalized additive models and gradient boosting models; and (d) multivariate environmental novelty within test data. Niche overlap between French and Italian populations was also tested. Niche overlap was low, but niche divergence between the two populations' clusters was not corroborated. Nonetheless, wider niche breadth and heterogeneity of background environmental conditions characterizing the French populations led to low intercluster transferability. Although models fitted on the combined datasets did not attain consistently higher internal transferability than those separately fitted for the French and Italian populations, ensemble projection from the HSMs fitted on the joint occurrences produced more consistent suitability predictions across the full range of V. u. ursinii. Spatial thinning of occurrences ameliorated internal transferability but did not affect external transferability. The two approaches to predictors filtering did not differ in transferability of the respective HSMs but led to discrepant estimated environment-occurrence relationships and spatial predictions, while the two algorithms attained different relative rankings depending on the considered prediction task. Multivariate novelty of projection sites was negatively correlated to both internal transferability and external transferability. Our findings clarify issues researchers should keep in mind when using HSMs to get predictions across geographically disjunct populations.

Defining Conservation Requirements for the Suweon Treefrog (Dryophytes suweonensis) Using Species Distribution Models

Numerous amphibian species are declining because of habitat loss and fragmentation due to urbanization of landscapes and the construction of roads. This is a mounting threat to species restricted to habitats close to urban areas, such as agricultural wetlands in North East Asia. The Suweon treefrog (Dryophytes suweonensis) falls into the list of species threatened with habitat loss and most populations are under threat of extirpation. Over the last decades, sub-populations have become increasingly disconnected and specifically the density of paved roads has increased around the only site connecting northern and southern Seoul populations. We surveyed this locality in Hojobeol, Siheung, Republic of Korea in 2012, 2015 and 2019 to first confirm the decline in the number of sites where D. suweonensis was present. The second objective was to analyze the habitat characteristics and determine the remaining suitable habitat for D. suweonensis through a species distribution model following the maximum entropy method. Our results show that rice paddy cover and distance from the paved road are the most important factor defining suitable habitat for D. suweonensis. At this locality, uninterrupted rice paddies are a suitable habitat for the species when reaching at least 0.19 km2, with an average distance of 138 ± 93 m2 from the roads. We link the decrease in the number of sites where D. suweonensis is present with the decrease in rice paddy cover, generally replaced by localized infrastructures, greenhouses and habitat fragmentation. Rice paddies should remain connected over a large area for the protection of the remaining populations. In addition, habitat requirements should be integrated in the requisites to designate protected areas.

Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates

While the international pet trade and habitat destruction have been extensively discussed as major threats to the survival of the pancake tortoise (Malacochersus tornieri), the impact of climate change on the species remains unknown. In this study, we used species distribution modelling to predict the current and future distribution of pancake tortoises in Zambezian and Somalian biogeographical regions. We used 224 pancake tortoise occurrences obtained from Tanzania, Kenya and Zambia to estimate suitable and stable areas for the pancake tortoise in all countries present in these regions. We also used a protected area network to assess how many of the suitable and stable areas are protected for the conservation of this critically endangered species. Our model predicted the expansion of climatically suitable habitats for pancake tortoises from four countries and a total area of 90,668.75 km2 to ten countries in the future and an area of 343,459.60-401,179.70 km2. The model also showed that a more significant area of climatically suitable habitat for the species lies outside of the wildlife protected areas. Based on our results, we can predict that pancake tortoises may not suffer from habitat constriction. However, the species will continue to be at risk from the international pet trade, as most of the identified suitable habitats remain outside of protected areas. We suggest that efforts to conserve the pancake tortoise should not only focus on protected areas but also areas that are unprotected, as these comprise a large proportion of the suitable and stable habitats available following predicted future climate change.

Predicting multi-species foraging hotspots for marine turtles in the Gulf of Mexico

Quantifying the distribution of animals and identifying underlying characteristics that define suitable habitat are essential for effective conservation of free-ranging species. Prioritizing areas for conservation is important in managing a geographic extent that has a high level of disturbance and limited conservation resources. We examined the potential use of a species distribution model ensemble for multi-species conservation in marine habitats. Using satellite telemetry locations during foraging as input data, and ensemble ecological niche models, we predicted foraging areas for 2 nesting marine turtle species within the Gulf of Mexico (GoM): Kemp’s ridley Lepidochelys kempii (n = 63) and loggerhead Caretta caretta (n = 63). We considered 7 geophysical, biological, and climatic variables and compared contributing factors for each species’ foraging habitat selection. For both species, predicted suitable foraging habitats encompassed large areas along the GoM coast, but only intersected with each other in relatively small areas. Highly parameterized models resulted in overall greater fits, suggesting that multiple factors influence habitat selection by these species. Model validation results were mixed: cross-validation resulted in high prediction accuracy for both species, but an evaluation against independent data resulted in a low omission rate (5%) for Kemp’s ridleys and a high omission rate (72%) for loggerheads. The relatively small intersection of model-predicted foraging areas for these 2 species within the study area may indicate possible niche differentiations. The high omission rate for loggerheads indicates our samples likely underrepresent the population and illustrates the challenges in predicting suitable foraging extents for species that make dynamic movements and have greater individual variability.

Conservation status of American pikas (Ochotona princeps)

The American pika (Ochotona princeps) is commonly perceived as a species that is at high risk of extinction due to climate change. The purpose of this review is two-fold: to evaluate the claim that climate change is threatening pikas with extinction, and to summarize the conservation status of the American pika. Most American pikas inhabit major cordilleras, such as the Rocky Mountain, Sierra Nevada, and Cascade ranges. Occupancy of potential pika habitat in these ranges is uniformly high and no discernible climate signal has been found that discriminates between the many occupied and relatively few unoccupied sites that have been recently surveyed. Pikas therefore are thriving across most of their range. The story differs in more marginal parts of the species range, primarily across the Great Basin, where a higher percentage of available habitat is unoccupied. A comprehensive review of Great Basin pikas revealed that occupied sites, sites of recent extirpation, and old sites, were regularly found within the same geographic and climatic space as extant sites, and suggested that pikas in the Great Basin tolerated a broader set of habitat and climatic conditions than previously understood. Studies of a small subset of extirpated sites in the Great Basin and in California found that climate variables (most notably measures of hot temperature) were associated more often with extirpated sites than occupied sites. Importantly, upward contraction of the lower elevation boundary also was found at some sites. However, models that incorporated variables other than climate (such as availability of upslope talus habitat) often were better predictors of site persistence. Many extirpations occurred on small habitat patches, which were subject to stochastic extinction, as informed by a long-term pika metapopulation study in Bodie, California. In addition, several sites may have been compromised by cattle grazing or other anthropogenic factors. In contrast, several low, hot sites (Bodie, Mono Craters, Craters of the Moon National Monument and Preserve, Lava Beds National Monument, Columbia River Gorge) retain active pika populations, demonstrating the adaptive capacity and resilience of pikas in response to adverse environmental conditions. Pikas cope with warm temperatures by retreating into cool interstices of their talus habitat and augment their restricted daytime foraging with nocturnal activity. Pikas exhibit significant flexibility in their foraging tactics and are highly selective in their choice of available vegetation. The trait that places pikas at greatest risk from climate change is their poor dispersal capability. Dispersal is more restricted in hotter environments, and isolated low-elevation sites that become extirpated are unlikely to be recolonized in a warming climate. The narrative that American pikas are going extinct appears to be an overreach. Pikas are doing well across most of their range, but there are limited, low-elevation losses that are likely to be permanent in what is currently marginal pika habitat. The resilience of pikas in the face of climate change, and their ability or inability to persist in marginal, hot environments, will continue to contribute to our understanding of the impact of climate change on individual species.

Toward reliable habitat suitability and accessibility models in an era of multiple environmental stressors

Global biodiversity declines, largely driven by climate and land-use changes, urge the development of transparent guidelines for effective conservation strategies. Species distribution modeling (SDM) is a widely used approach for predicting potential shifts in species distributions, which can in turn support ecological conservation where environmental change is expected to impact population and community dynamics. Improvements in SDM accuracy through incorporating intra- and interspecific processes have boosted the SDM field forward, but simultaneously urge harmonizing the vast array of SDM approaches into an overarching, widely adoptable, and scientifically justified SDM framework. In this review, we first discuss how climate warming and land-use change interact to govern population dynamics and species' distributions, depending on species' dispersal and evolutionary abilities. We particularly emphasize that both land-use and climate change can reduce the accessibility to suitable habitat for many species, rendering the ability of species to colonize new habitat and to exchange genetic variation a crucial yet poorly implemented component of SDM. We then unite existing methodological SDM practices that aim to increase model accuracy through accounting for multiple global change stressors, dispersal, or evolution, while shifting our focus to model feasibility. We finally propose a roadmap harmonizing model accuracy and feasibility, applicable to both common and rare species, particularly those with poor dispersal abilities. This roadmap (a) paves the way for an overarching SDM framework allowing comparison and synthesis of different SDM studies and (b) could advance SDM to a level that allows systematic integration of SDM outcomes into effective conservation plans.

SDMtune: An R package to tune and evaluate species distribution models

Balancing model complexity is a key challenge of modern computational ecology, particularly so since the spread of machine learning algorithms. Species distribution models are often implemented using a wide variety of machine learning algorithms that can be fine-tuned to achieve the best model prediction while avoiding overfitting. We have released SDMtune, a new R package that aims to facilitate training, tuning, and evaluation of species distribution models in a unified framework. The main innovations of this package are its functions to perform data-driven variable selection, and a novel genetic algorithm to tune model hyperparameters. Real-time and interactive charts are displayed during the execution of several functions to help users understand the effect of removing a variable or varying model hyperparameters on model performance. SDMtune supports three different metrics to evaluate model performance: the area under the receiver operating characteristic curve, the true skill statistic, and Akaike's information criterion corrected for small sample sizes. It implements four statistical methods: artificial neural networks, boosted regression trees, maximum entropy modeling, and random forest. Moreover, it includes functions to display the outputs and create a final report. SDMtune therefore represents a new, unified and user-friendly framework for the still-growing field of species distribution modeling.

Climate change models predict decreases in the range of a microendemic freshwater fish in Honduras

Despite their incredible diversity, relatively little work has been done to assess impacts of climate change on tropical freshwater organisms. Chortiheros wesseli is a species of Neotropical cichlid (Cichlidae: Cichlinae) restricted to only a few river drainages in the Caribbean-slope of Honduras. Little is known about this species and few specimens had been collected until recently; however, our work with this species in the wild has led to a better understanding of its ecology and habitat preferences making it an excellent model for how freshwater fishes can be affected by climate change. This study assesses the distribution and habitats of Chortiheros wesseli using a combination of field data and species distribution modeling. Results indicate this species is largely limited to its current range, with no realistic suitable habitat nearby. Empirical habitat data show that this species is limited to narrow and shallow flowing waters with rapids and boulders. This habitat type is highly influenced by precipitation, which contributed the greatest influence on the models of present and future habitat suitability. Although several localities are within boundaries of national protected areas, species distribution models all predict a reduction in the range of this freshwater fish based on climate change scenarios. The likelihood of a reduced range for this species will be intensified by adverse changes to its preferred habitats.

Occurrence of apomictic conspecifics and ecological preferences rather than colonization history govern the geographic distribution of sexual Potentilla puberula

The geographic distribution of sexual-apomictic taxa (i.e., comprising individuals usually reproducing either sexually or asexually via seeds) is traditionally thought to be driven by their ecological preferences and colonization histories. Where sexuals and apomicts get into contact with each other, competitive and reproductive interactions can interfere with these factors, an aspect which hitherto received little attention in biogeographic studies. We disentangled and quantified the relative effects of the three factors on the distribution of tetraploid sexuals in Potentilla puberula in a latitudinal transect through the Eastern European Alps, in which they are codistributed with penta-, hepta-, and octoploid apomictic conspecifics. Effects were explored by means of binomial generalized linear regression models combining a single with a multiple predictor approach. Postglacial colonization history was inferred from population genetic variation (AFLPs and cpDNA) and quantified using a cost distance metric. The study was based on 235 populations, which were purely sexual, purely apomictic, or of mixed reproductive mode. The occurrence of apomicts explained most of the variation in the distribution of sexuals (31%). Specifically, the presence of sexual tetraploids was negatively related to the presence of each of the three apomictic cytotypes. Effects of ecological preferences were substantial too (7% and 12% of the total variation explained by ecological preferences alone, or jointly with apomicts' occurrence, respectively). In contrast, colonization history had negligible effects on the occurrence of sexuals. Taken together, our results highlight the potentially high impact of reproductive interactions on the geographic distribution of sexual and apomictic conspecifics and that resultant mutual exclusion interrelates to ecological differentiation, a situation potentially promoting their local coexistence.

Histoecology: Applying Ecological Principles and Approaches to Describe and Predict Tumor Ecosystem Dynamics Across Space and Time

Cancer cells exist within a complex spatially structured ecosystem composed of resources and different cell types. As the selective pressures imposed by this environment determine the fate of cancer cells, an improved understanding of how this ecosystem evolves will better elucidate how tumors grow and respond to therapy. State of the art imaging methods can now provide highly resolved descriptions of the microenvironment, yielding the data required for a thorough study of its role in tumor growth and treatment resistance. The field of landscape ecology has been studying such species-environment relationship for decades, and offers many tools and perspectives that cancer researchers could greatly benefit from. Here, we discuss one such tool, species distribution modeling (SDM), that has the potential to, among other things, identify critical environmental factors that drive tumor evolution and predict response to therapy. SDMs only scratch the surface of how ecological theory and methods can be applied to cancer, and we believe further integration will take cancer research in exciting new and productive directions. Significance: Here we describe how species distribution modeling can be used to quantitatively describe the complex relationship between tumor cells and their microenvironment. Such a description facilitates a deeper understanding of cancers eco-evolutionary dynamics, which in turn sheds light on the factors that drive tumor growth and response to treatment.

Do traits of plant species predict the efficacy of species distribution models for finding new occurrences?

Species distribution models (SDMs) are used to test ecological theory and to direct targeted surveys for species of conservation concern. Several studies have tested for an influence of species traits on the predictive accuracy of SDMs. However, most used the same set of environmental predictors for all species and/or did not use truly independent data to test SDM accuracy. We built eight SDMs for each of 24 plant species of conservation concern, varying the environmental predictors included in each SDM version. We then measured the accuracy of each SDM using independent presence and absence data to calculate area under the receiver operating characteristic curve (AUC) and true positive rate (TPR). We used generalized linear mixed models to test for a relationship between species traits and SDM accuracy, while accounting for variation in SDM performance that might be introduced by different predictor sets. All traits affected one or both SDM accuracy measures. Species with lighter seeds, animal-dispersed seeds, and a higher density of occurrences had higher AUC and TPR than other species, all else being equal. Long-lived woody species had higher AUC than herbaceous species, but lower TPR. These results support the hypothesis that the strength of species-environment correlations is affected by characteristics of species or their geographic distributions. However, because each species has multiple traits, and because AUC and TPR can be affected differently, there is no straightforward way to determine a priori which species will yield useful SDMs based on their traits. Most species yielded at least one useful SDM. Therefore, it is worthwhile to build and test SDMs for the purpose of finding new populations of plant species of conservation concern, regardless of these species' traits.

Species Distribution Modeling of Sassafras Tzumu and Implications for Forest Management

Sassafras tzumu (Chinese sassafras) is an economically and ecologically important deciduous tree species. Over the past few decades, increasing market demands and unprecedented human activity in its natural habitat have created new threats to this species. Nonetheless, the distribution of its habitat and the crucial environmental parameters that determine the habitat suitability remain largely unclear. The present study modeled the current and future geographical distribution of S. tzumu by maximum entropy (MAXENT) and genetic algorithm for rule set prediction (GARP). The value of area under the receiver operating characteristic curve (AUC), Kappa, and true skill statistic (TSS) of MAXENT was significantly higher than that of GARP, indicating that MAXENT performed better. Temperate and subtropical regions of eastern China where the species had been recorded was suitable for growth of S. tzumu. Relative humidity (26.2% of permutation importance), average temperature during the driest quarter (16.6%), annual precipitation (12.6%), and mean diurnal temperature range (10.3%) were identified as the primary factors that accounted for the present distribution of S. tzumu in China. Under the climate change scenario, both algorithms predicted that range of suitable habitat will expand geographically to northwest. Our results may be adopted for guiding the preservation of S. tzumu through identifying the habitats susceptible to climate change.

Habitat Suitability Estimation Using a Two-Stage Ensemble Approach

Biodiversity conservation is important for the protection of ecosystems. One key task for sustainable biodiversity conservation is to effectively preserve species’ habitats. However, for various reasons, many of these habitats have been reduced or destroyed in recent decades. To deal with this problem, it is necessary to effectively identify potential habitats based on habitat suitability analysis and preserve them. Various techniques for habitat suitability estimation have been proposed to date, but they have had limited success due to limitations in the data and models used. In this paper, we propose a novel scheme for assessing habitat suitability based on a two-stage ensemble approach. In the first stage, we construct a deep neural network (DNN) model to predict habitat suitability based on observations and environmental data. In the second stage, we develop an ensemble model using various habitat suitability estimation methods based on observations, environmental data, and the results of the DNN from the first stage. For reliable estimation of habitat suitability, we utilize various crowdsourced databases. Using observational and environmental data for four amphibian species and seven bird species in South Korea, we demonstrate that our scheme provides a more accurate estimation of habitat suitability compared to previous other approaches. For instance, our scheme achieves a true skill statistic (TSS) score of 0.886, which is higher than other approaches (TSS = 0.725 ± 0.010).

From topography to hydrology-The modifiable area unit problem impacts freshwater species distribution models

Species distribution models (SDMs) are statistical tools to identify potentially suitable habitats for species. For SDMs in river ecosystems, species occurrences and predictor data are often aggregated across subcatchments that serve as modeling units. The level of aggregation (i.e., model resolution) influences the statistical relationships between species occurrences and environmental predictors-a phenomenon known as the modifiable area unit problem (MAUP), making model outputs directly contingent on the model resolution. Here, we test how model performance, predictor importance, and the spatial congruence of species predictions depend on the model resolution (i.e., average subcatchment size) of SDMs. We modeled the potential habitat suitability of 50 native fish species in the upper Danube catchment at 10 different model resolutions. Model resolutions were derived using a 90-m digital-elevation model by using the GRASS-GIS module r.watershed. Here, we decreased the average subcatchment size gradually from 632 to 2 km2. We then ran ensemble SDMs based on five algorithms using topographical, climatic, hydrological, and land-use predictors for each species and resolution. Model evaluation scores were consistently high, as sensitivity and True Skill Statistic values ranged from 86.1-93.2 and 0.61-0.73, respectively. The most contributing predictor changed from topography at coarse, to hydrology at fine resolutions. Climate predictors played an intermediate role for all resolutions, while land use was of little importance. Regarding the predicted habitat suitability, we identified a spatial filtering from coarse to intermediate resolutions. The predicted habitat suitability within a coarse resolution was not ported to all smaller, nested subcatchments, but only to a fraction that held the suitable environmental conditions. Across finer resolutions, the mapped predictions were spatially congruent without such filter effect. We show that freshwater SDM predictions can have consistently high evaluation scores while mapped predictions differ significantly and are highly contingent on the underlying subcatchment size. We encourage building freshwater SDMs across multiple catchment sizes, to assess model variability and uncertainties in model outcomes emerging from the MAUP.

Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis

The rhizomes of Coptis chinensis Franch., Coptis deltoidea C. Y. Cheng et Hsiao and Coptis teeta Wall, are sources of renowned traditional Chinese medicines. Recently, human activities and climate change has caused degeneration of the natural habitats of these pharmacological plants. Analyzing the impact of climate change on the possible distribution of Coptis herbs is essential for their future conservation and domestication. The purpose of this study was to predict the potential distribution of these valuable plants and identify the potential effects of climate change on three Coptis species, using of species distribution modeling (SDM). In this study, we first predict the distribution size variations of the three plant species, under present and future conditions. Secondly, we carried out field sampling of these three species and analyzed the chemical composition by high performance liquid chromatography (HPLC). Results show that the predicted distributions of all three Coptis herbs were not limit to the reported regions, but also cover other potential areas. Among the environmental variables, annual precipitation range (Bio2) induced the largest impact on SDMs for C. chinensis (72.2%) and C. deltoidea (37.9%), while C. teeta was more significantly affected by isothermally (Bio3, 39.2%). When comparing the possible future distribution to the present distribution of these species, a decreasing tendency was observed in the highly suitable areas of C. chinensis and the generally suitable areas of C. teeta, indicating that the environmental changes would affect the distribution of these two species. In addition, the average alkaloid content was found to be the highest in highly suitable areas, while it was decreased in moderately and generally suitable areas, indicating that alkaloid content may be related to environmental factors. In summary, these findings improve our understanding of the ecological impact of climate on the distribution of three Coptis species.

Shifted distribution baselines: neglecting long-term biodiversity records risks overlooking potentially suitable habitat for conservation management

Setting appropriate conservation measures to halt the loss of biodiversity requires a good understanding of species' habitat requirements and potential distribution. Recent (past few decades) ecological data are typically used to estimate and understand species' ecological niches. However, historical local extinctions may have truncated species-environment relationships, resulting in a biased perception of species' habitat preferences. This may result in incorrect assessments of the area potentially available for their conservation. Incorporating long-term (centuries-old) occurrence records with recent records may provide better information on species-environment relationships and improve the modelling and understanding of habitat suitability. We test whether neglecting long-term occurrence records leads to an underestimation of species' historical niche and potential distribution and identify which species are more vulnerable to this effect. We compare outputs of species distribution models and niche hypervolumes built using recent records only with those built using both recent and long-term (post-1500) records, for a set of 34 large mammal species in South Africa. We find that, while using recent records only is adequate for some species, adding historical records in the analyses impacts estimates of the niche and habitat suitability for 12 species (34%) in our dataset, and that this effect is significantly higher for carnivores. These results show that neglecting long-term biodiversity records in spatial analyses risks misunderstanding, and generally underestimating, species' niches, which in turn may lead to ill-informed management decisions, with significant implications for the effectiveness of conservation efforts. This article is part of a discussion meeting issue 'The past is a foreign country: how much can the fossil record actually inform conservation?'

Phylogenetic signals in pest abundance and distribution range of spider mites

Background

Attributes of pest species like host range are frequently reported as being evolutionarily constrained and showing phylogenetic signal. Because these attributes in turn could influence the abundance and impact of species, phylogenetic information could be useful in predicting the likely status of pests. In this study, we used regional (China) and global datasets to investigate phylogenetic patterns in occurrence patterns and host ranges of spider mites, which constitute a pest group of many cropping systems worldwide.

Results

We found significant phylogenetic signal in relative abundance and distribution range both at the regional and global scales. Relative abundance and range size of spider mites were positively correlated with host range, although these correlations became weaker after controlling for phylogeny.

Conclusions

The results suggest that pest impacts are evolutionarily constrained. Information that is easily obtainable – including the number of known hosts and phylogenetic position of the mites – could therefore be useful in predicting future pest risk of species.

Integrating anthropogenic factors into regional-scale species distribution models-A novel application in the imperiled sagebrush biome

Species distribution models (SDMs) that rely on regional-scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local-scale anthropogenic variables, including wildfire history, land-use change, invasive species, and ecological restoration practices can override regional-scale variables to drive patterns of species distribution. Incorporating these human-induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human-induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field-sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: -6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: -47.9%, -41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land-use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.

Geography alone cannot explain Tetranychus truncatus (Acari: Tetranychidae) population abundance and genetic diversity in the context of the center-periphery hypothesis

The center-periphery hypothesis (CPH) states that the genetic diversity, genetic flow, and population abundance of a species are highest at the center of the species' geographic distribution. However, most CPH studies have focused on the geographic distance and have ignored ecological and historical effects. Studies using niche models to define the center and periphery of a distribution and the interactions among geographical, ecological, and historical gradients have rarely been done in the framework of the CPH, especially in biogeographical studies of animal species. Here, we examined the CPH for a widely distributed arthropod, Tetranychus truncatus (Acari: Tetranychidae), in eastern China using three measurements: geographic distance to the center of the distribution (geography), ecological suitability based on current climate data (ecology), and historical climate data from the last glacial maximum (history). We found that the relative abundances of different populations were more strongly related to ecology than to geography and history. Genetic diversity within populations and genetic differentiation among populations based on mitochondrial marker were only significantly related to history. However, the genetic diversity and population differentiation based on microsatellites were significantly related to all three CPH measurements. Overall, population abundance and genetic pattern cannot be explained very well by geography alone. Our results show that ecological gradients explain the variation in population abundance better than geographic gradients and historical factors, and that current and historical factors strongly influence the spatial patterns of genetic variation. This study highlights the importance of examining more than just geography when assessing the CPH.

Predicting the Potential Distribution of Paeonia veitchii (Paeoniaceae) in China by Incorporating Climate Change into a Maxent Model

A detailed understanding of species distribution is usually a prerequisite for the rehabilitation and utilization of species in an ecosystem. Paeonia veitchii (Paeoniaceae), which is an endemic species of China, is an ornamental and medicinal plant that features high economic and ecological values. With the decrease of its population in recent decades, it has become a locally endangered species. In present study, we modeled the potential distribution of P. veitchii under current and future conditions, and evaluated the importance of the factors that shape its distribution. The results revealed a highly and moderately suitable habitat for P. veitchii that encompassed ca. 605,114 km2. The central area lies in northwest Sichuan Province. Elevation, temperature seasonality, annual mean precipitation, and precipitation seasonality were identified as the most important factors shaping the distribution of P. veitchii. Under the scenario with a low concentration of greenhouse gas emissions (RCP 2.6), we predicted an overall expansion of the potential distribution by 2050, followed by a slight contraction in 2070. However, with the scenario featuring intense greenhouse gas emissions (RCP 8.5), the range of suitable habitat should increase with the increasing intensity of global warming. The information that was obtained in the present study can provide background information related to the long-term conservation of this species.

kuenm: an R package for detailed development of ecological niche models using Maxent

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.

Modelling potential habitat for snow leopards (Panthera uncia) in Ladakh, India

The snow leopard Panthera uncia is an elusive species inhabiting some of the most remote and inaccessible tracts of Central and South Asia. It is difficult to determine its distribution and density pattern, which are crucial for developing conservation strategies. Several techniques for species detection combining camera traps with remote sensing and geographic information systems have been developed to model the habitat of such cryptic and low-density species in challenging terrains. Utilising presence-only data from camera traps and direct observations, alongside six environmental variables (elevation, aspect, ruggedness, distance to water, land cover, and prey habitat suitability), we assessed snow leopard habitat suitability across Ladakh in northern India. This is the first study to model snow leopard distribution both in India and utilising direct observation data. Results suggested that elevation and ruggedness are the two most influential environmental variables for snow leopard habitat suitability, with highly suitable habitat having an elevation range of 2,800 m to 4,600 m and ruggedness of 450 m to 1,800 m. Our habitat suitability map estimated approximately 12% of Ladakh's geographical area (c. 90,000 km2) as highly suitable and 18% as medium suitability. We found that 62.5% of recorded livestock depredation along with over half of all livestock corrals (54%) and homestays (58%) occurred within highly suitable snow leopard habitat. Our habitat suitability model can be used to assist in allocation of conservation resources by targeting construction of livestock corrals to areas of high habitat suitability and promoting ecotourism programs in villages in highly suitable snow leopard habitat.

Evidences for a shared history for spectacled salamanders, haplotypes and climate

The so-called glacial refugia, formed during the Pleistocene climatic oscillations, played a major role in shaping the distribution of European species, triggering migrations or isolating populations. Many of these events were recently investigated by genetic data, mainly for the European Last Glacial stage, in the Iberic, Italian and Greek-Balkan peninsulas. The amphibian genus Salamandrina, the most ancient living salamandrid lineage, was widespread in Europe until the climatic oscillations of Miocene probably forced it to shelter in the only suitable territory at that time, the Apennines. Nowadays this genus is endemic of peninsular Italy with two parapatric species, S. perspicillata and S. terdigitata, sharing an area of secondary contact formed after the Last Glacial Maximum. Climate is generally identified as the key factor for the interpretation of genetic data. In this research, we directly measure climate influences on the two Salamandrina known species through Ensemble Modelling techniques and post-modelling GIS analyses, integrating updated genetic data in this process. Our results confirm the hypotheses of southwards (and subsequent northwards) shifts, identify glacial refugia and corridors used for the post-glacial re-colonization. Finally, we map a contact zone deserving more sampling effort to disentangle the introgression and hybridization observed.

Spatial quantification of the world population potentially exposed to Zika virus

Background

Zika virus is an emerging Flaviviridae virus, which has spread rapidly in the last few years. It has raised concern because it has been associated with fetus microcephaly when pregnant women are infected. The main vector is the mosquito Aedes aegypti , distributed in tropical areas.

Methods

Niche modelling techniques were used to estimate the potential distribution area of A. aegypti. This was overlapped with human population density, determining areas of potential transmission risk worldwide. Afterwards, we quantified the population at risk according to risk level.

Results

The vector transmission risk is distributed mainly in Asia and Oceania on the shores of the Indian Ocean. In America, the risk concentrates in the Atlantic coast of South America and in the Caribbean Sea shores in Central and North America. In Africa, the major risk is concentrated in the Pacific and Atlantic coasts of Central and South Africa. The world population under high and very high risk levels includes 2.261 billion people.

Conclusions

These results illustrate Zika virus risk at the global level and provide maps to target the prevention and control measures especially in areas with higher risk, in countries with less sanitation and poorer resources. Many countries without previous vector reports could become active transmission zones in the future, so vector surveillance should be implemented or reinforced in these areas.

Coupling GIS spatial analysis and Ensemble Niche Modelling to investigate climate change-related threats to the Sicilian pond turtle Emys trinacris, an endangered species from the Mediterranean

The pond turtle Emys trinacris is an endangered endemic species of Sicily showing a fragmented distribution throughout the main island. In this study, we applied "Ensemble Niche Modelling", combining more classical statistical techniques as Generalized Linear Models and Multivariate Adaptive Regression Splines with machine-learning approaches as Boosted Regression Trees and Maxent, to model the potential distribution of the species under current and future climatic conditions. Moreover, a "gap analysis" performed on both the species' presence sites and the predictions from the Ensemble Models is proposed to integrate outputs from these models, in order to assess the conservation status of this threatened species in the context of biodiversity management. For this aim, four "Representative Concentration Pathways", corresponding to different greenhouse gases emissions trajectories were considered to project the obtained models to both 2050 and 2070. Areas lost, gained or remaining stable for the target species in the projected models were calculated. E. trinacris' potential distribution resulted to be significantly dependent upon precipitation-linked variables, mainly precipitation of wettest and coldest quarter. Future negative effects for the conservation of this species, because of more unstable precipitation patterns and extreme meteorological events, emerged from our analyses. Further, the sites currently inhabited by E. trinacris are, for more than a half, out of the Protected Areas network, highlighting an inadequate management of the species by the authorities responsible for its protection. Our results, therefore, suggest that in the next future the Sicilian pond turtle will need the utmost attention by the scientific community to avoid the imminent risk of extinction. Finally, the gap analysis performed in GIS environment resulted to be a very informative post-modeling technique, potentially applicable to the management of species at risk and to Protected Areas' planning in many contexts.