Ground validation of presence-only modelling with rare species: a case study on barbastellesBarbastella barbastellus(Chiroptera: Vespertilionidae)

Bovine anaplasmosis in Zimbabwe: spatio-temporal distribution and environmental drivers

Understanding the spatial and temporal distribution of Bovine anaplasmosis is crucial for identifying areas of high prevalence for targeted disease control. This research was aimed at modelling and mapping the B. anaplasmosis potential distribution, and identify hotspots as well as significant variables explaining the occurrence of the disease. The Getis Ord Gi* statistic for Hotspot analysis was used as well as MaxEnt ecological niche modelling. The effects of time, land-use, and agro-ecological regions on B. anaplasmosis occurrence were tested using Analysis of Variance (ANOVA). Results showed that several districts in Zimbabwe are suitable for the occurence of the disease for example Binga, Seke, Buhera, Kwekwe, Gweru, Mhondoro, Chegutu, Sanyati, and in the North: Mbire, Muzarabani, Mt Darwin, Shamva, Bindura, Zvimba and Makonde. Morbidity and mortality hotspots were detected in Gokwe-south, Kwekwe, and Chirumhanzu districts. Binga, Gokwe-south, Gutu, Hurungwe, Mazoe, Nkayi, Shamva, and Kwekwe districts also experienced high disease incidences. Temperature seasonality, precipitation seasonality, mean diurnal range, and isothermality were the most important variables in explaining 93% of B. anaplasmosis distribution. Unlike land-use and agro-ecological regions, time (months) had a significant effect on B. anaplasmosis occurrence with July and September having significantly (p < 0.05) higher cases and deaths than the rest of the months. The results of this study provide insights into the management strategies and control of B. anaplasmosis in Zimbabwe. It is thus concluded that geo-spatial techniques, combined with ecological niche modelling can provide useful insights into disease prevalence and distribution and hence can contribute to effective management and control of B. anaplasmosis in Zimbabwe.

Potential for spatial coexistence of a transboundary migratory species and wind energy development

Global expansion in wind energy development is a notable achievement of the international community's effort to reduce carbon emissions during energy production. However, the increasing number of wind turbines have unintended consequences for migratory birds and bats. Wind turbine curtailment and other mitigation strategies can reduce fatalities, but improved spatial and temporal data are needed to identify the most effective way for wind energy development and volant migratory species to coexist. Mexican free-tailed bats (Tadarida brasiliensis mexicana) account for a large proportion of known bat fatalities at wind facilities in the southwestern US. We examined the geographic concordance between existing wind energy generation facilities, areas of high wind potential amenable for future deployment of wind facilities, and seasonally suitable habitat for these bats. We used ecological niche modeling to determine species distribution during each of 4 seasons. We used a multi-criteria GIS-based approach to produce a wind turbine siting suitability map. We identified seasonal locations with highest and lowest potential for the species' probability of occurrence, providing a potential explanation for the higher observed fatalities during fall migration. Thirty percent of 33,606 wind turbines within the southwestern US occurred in highly suitable areas for Mexican free-tailed bats, primarily in west Texas. There is also broad spatial overlap between areas of high wind potential and areas of suitable habitat for Mexican free-tailed bats. Because of this high degree of overlap, our results indicate that post-construction strategies, such as curtailing the timing of operations and deterrents, would be more effective for bat conservation than strategic siting of new wind energy installations.

Combining the Optimized Maximum Entropy Model to Detect Key Factors in the Occurrence of Oedaleus decorus asiaticus in the Typical Grasslands of Central and Eastern Inner Mongolia

Grasshoppers pose a significant threat to both natural grassland vegetation and crops. Therefore, comprehending the relationship between environmental factors and grasshopper occurrence is of paramount importance. This study integrated machine learning models (Maxent) using the kuenm package to screen MaxEnt models for grasshopper species selection, while simultaneously fitting remote sensing data of major grasshopper breeding areas in Inner Mongolia, China. It investigated the spatial distribution and key factors influencing the occurrence of typical grasshopper species in grassland ecosystems. The modelling results indicate that a typical steppe has a larger suitable area. The soil type, above biomass, altitude, and temperature, predominantly determine the grasshopper occurrence in typical steppes. This study explicitly delineates the disparate impacts of key environmental factors (meteorology, vegetation, soil, and topography) on grasshopper occurrence in typical steppes. Furthermore, it provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be instrumental in formulating future management measures to guarantee grass ecological environment security and the sustainable development of grassland.

A new threshold selection method for species distribution models with presence-only data: Extracting the mutation point of the P/E curve by threshold regression

Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real-world applications and model assessments. Prevalent threshold selection methods for presence-only data require unproven pseudo-absence data or subjective researchers' decisions. This study proposes a new method, Boyce-Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo-absence data. We summarize that the mutation point is a typical shape feature of the predicted-to-expected (P/E) curve after reviewing relevant articles. Analysis based on source-sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point. To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods.

Prediction of suitable habitat shifts and assessment of ecological niche overlaps for three Tridentiger species with intertidal and subtidal characteristics under future climate changes

To show how dramatic global climate change affects marine ecosystem species in different habitats. We used a joint species distribution model (SDM) and an ecological niche model (ENM) to investigate the suitable habitat shifts and ecological niche overlaps of the Tridentiger fishes. In the present study, the SDM results showed that 5 hotspots were identified for T. trigonocephalus and T. barbatus, and 4 hotspots for T. bifasciatus. The study on center-of-mass transfer revealed notable reductions in the habitual range of the three Tridentiger species with future climate change and no significant bipolar shifts in the center of mass. The ENM results indicated that T. trigonocephalus and T. barbatus exhibited the greatest ecological niche overlap with Schoener's D (D) and Hellinger-based I (I) values of 0.4719 and 0.7690, respectively. Both SDM and ENM results have suggested that T. trigonocephalus occupied a wider distribution and greater adaptability to future climate change. This study sought to measure the variations in the effects of global climate change on marine species in different habitats. Our study first found that intertidal species with specific life histories may be more resilient to environmental change.

Modelling the current and future distribution potential areas of Peperomia abyssinica Miq., and Helichrysum citrispinum Steud. ex A. Rich. in Ethiopia

BACKGROUND

The aim of this study is to investigate how climate change influences the distribution of economically and environmentally important species of P. abyssinica and H. citrispinum in Ethiopia. The species distribution modeling intends to forecast species' ecological niche ranges and habitat suitability by employing a variety of environmental parameters as predictors, which is vital for conservation planning and restoration success. Six representative concentration pathways (RCP 2.6, 4.5, and 8.5 for the years 2050 and 2070) with the same resolution of 2.5 min that shows the emission scenarios were used for the prediction. To predict the current and future distributions of H. citrispinum and P. abyssinica 56 and 45 occurrence records from National Herbarium, Addis Ababa University, GBIF, and available literatures were used respectively.

RESULTS

The MaxEnt model predicted habitat suitability for H. citrispinum species with an Area Under Curve (AUC) value of 0.961 ± 0.027, and 0.809 ± 0.045 for P. abyssinica, indicating excellent discriminatory ability or accuracy under the current climate scenario. The Future distribution of suitable habitat for both H. citrispinum and P. abyssinica plant species was accurately predicted with AUC values of 0.960 ± 0.017 and 0.780 ± 0.35, respectively under future climatic scenarios. The jackknife test result indicates that environmental variables such as topographic position index (92.5%), precipitation of the driest quarter (3%) and precipitation in the coldest quarter (1.8%) are associated with the distributions of H. citrispinum, while topographic position index (36.6%), precipitation of driest quarter (21.4%), precipitation of warmest quarter (16.2%) and precipitation seasonality (13.9%) were found to be limiting environmental variables for P. abyssinica under current and future climatic conditions in Ethiopia. The prediction map and interception calculation for both present and projected (in the 2050s and again in the 2070s) climate change scenarios indicate significant habitat loss, decreased, and fragmentation under all RCPs (2.6, 4.5, and 8.5) scenarios for P. abyssinica while habitat gain, and increasing for H. citrispinum in Ethiopia.

CONCLUSIONS

Topographic position index (TPI) is the most impactful predictor variable on the distribution of the two species. Consequently, potentially habitable areas (with diverse aspects and slopes) are increasing for H. citrispinum while decreasing for P. abyssinica.

MaxEnt brings comparable results when the input data are being completed; Model parameterization of four species distribution models

Species distribution models (SDMs) are practical tools to assess the habitat suitability of species with numerous applications in environmental management and conservation planning. The manipulation of the input data to deal with their spatial bias is one of the advantageous methods to enhance the performance of SDMs. However, the development of a model parameterization approach covering different SDMs to achieve well-performing models has rarely been implemented. We integrated input data manipulation and model tuning for four commonly-used SDMs: generalized linear model (GLM), gradient boosted model (GBM), random forest (RF), and maximum entropy (MaxEnt), and compared their predictive performance to model geographically imbalanced-biased data of a rare species complex of mountain vipers. Models were tuned up based on a range of model-specific parameters considering two background selection methods: random and background weighting schemes. The performance of the fine-tuned models was assessed based on recently identified localities of the species. The results indicated that although the fine-tuned version of all models shows great performance in predicting training data (AUC > 0.9 and TSS > 0.5), they produce different results in classifying out-of-bag data. The GBM and RF with higher sensitivity of training data showed more different performances. The GLM, despite having high predictive performance for test data, showed lower specificity. It was only the MaxEnt model that showed high predictive performance and comparable results for identifying test data in both random and background weighting procedures. Our results highlight that while GBM and RF are prone to overfitting training data and GLM over-predict nonsampled areas MaxEnt is capable of producing results that are both predictable (extrapolative) and complex (interpolative). We discuss the assumptions of each model and conclude that MaxEnt could be considered as a practical method to cope with imbalanced-biased data in species distribution modeling approaches.

Modeling of historical and current distributions of lone star tick, Amblyomma americanum (Acari: Ixodidae), is consistent with ancestral range recovery

The lone star tick, Amblyomma americanum L., is a three-host hard tick notorious for aggressive feeding behavior. In the early to mid-20th century, this species' range was mostly limited to the southern USA. Since the 1950s, A. americanum has been detected in many new localities in the western, northcentral, and northeastern regions of the country. To examine the influence of climate on this apparent expansion, we used historical (1748-1950) lone star locations from the literature and museum records to model areas suitable for this species based on past environmental conditions in the late 1800s - early 1900s. We then projected this model forward using present (2011-2020) climatic conditions and compared the two for evidence of climate-associated distributional shifts. A maximum entropy distribution or Maxent model was generated by using a priori selected climatic variables including temperature, precipitation, and vapor pressure deficit. Temperature and vapor pressure deficit were selected as the most important factors in creating a sensitive and specific model (success rate = 82.6 ± 6.1%) that had a good fit to the existing data and was significantly better than a random model [partial ROC (receiver operating characteristic) to AUC (area under the ROC curve) ratio = 1.97 ± 0.07, P < 0.001]. The present projected model was tested with an independent dataset of curated museum records (1952-2020) and found to be 95.6% accurate. Comparison of past and present models revealed > 98% A. americanum niche overlap. The model suggests that some areas along the western fringe are becoming less suitable for A. americanum, whereas areas in some Great Lakes and coastal northeastern regions are becoming more suitable, results that are compatible with possible effects of climate change. However, these changes are minor, and overall climate in North America does not appear to have changed in ways significant to A. americanum's distribution. These findings are consistent with an alternative hypothesis that recent changes in A. americanum's distribution are a result of this species re-occupying its historical range, driven predominantly by factors other than climate, such as shifts in land use and population densities of major hosts.

Predicting the distributional range shifts of Rhizocarpon geographicum (L.) DC. in Indian Himalayan Region under future climate scenarios

Himalaya, the highest mountain system in the world and house of important biodiversity hotspot, is sensitive to projected warming by climate change. Rhizocarpon geographicum (map lichen), a crustose lichen, grows in high mountain ranges, is a potential indicator species of climate change. In the present study, MaxEnt species distribution modeling algorithm was used to predict the suitable habitat for R. geographicum in current and future climate scenarios. Nineteen bioclimatic variables from WorldClim database, along with elevation, were used to predict the current distribution and three representative concentration pathway (RCP) scenarios by integrating three general circulation models (GCMs) for future distribution of species covering years 2050 and 2070. Furthermore, we performed change analysis to identify the precise difference between the current and future distribution of suitable areas of the species for delineating habitat range expansion (gain), habitat contraction (loss), and stable habitats. The final ensemble model obtained had average test value 0.968, and its predicted ~ 27.5% of the geographical area in the Indian Himalayan Region is presently climatically suitable for the species. The predicted highly suitable area for R. geographicum is observed to be declining in Northwestern Himalaya, and it is shifting towards the higher elevation areas of the Eastern Himalaya. The projected distribution in future under the RCP scenarios (RCP 4.5, 6.0, and 8.5) showed the range expansion towards higher elevations, and it is more pronounced for the extreme future scenarios (RCP 8.5) than for the moderate and intermediate climate scenarios (RCP 4.5 and RCP 6.0). However, assuming that species can migrate to previously unoccupied areas, the model forecasts a habitat loss of 10.86-16.51% for R. geographicum, which is expected due to increase in mean annual temperature by 1.5-3.7 °C. The predictive MaxEnt modeling approach for mapping lichen will contribute significantly to the understanding of the impact of climate change in Himalayan ecosystems with wide implications for drawing suitable conservation plans and to take adaptation and mitigation measures.

Identifying priority areas for bat conservation in the Western Ghats mountain range, peninsular India

Understanding patterns of species distribution and diversity plays a vital role in biodiversity conservation. Such documentation is frequently lacking for bats, which are relatively little studied and often threatened. The Western Ghats biodiversity hotspot in peninsular India is a bat hotspot with 63 species. We conducted a comprehensive bat survey across the southern Western Ghats and used maximum entropy modeling (MaxEnt) to model the potential distribution of 37 bat species for which sufficient data were available. We generated binary maps of each species using species-specific thresholds to estimate suitable habitat areas and overlaid binary maps of species to produce bat hotspots (we use the term “bat hotspot” for regions that were suitable for more than 25 bat species). We also estimated species richness across protected area networks in the southern Western Ghats to assess the level of protection. The highest levels of species richness were found mainly along the southmost Periyar–Agastyamalai landscape. The study also identified a 1,683 km2 area of potential bat hotspot and 726 km2 (43%) of the total bat hotspots are currently within the protected area network. However, more than 50% of suitable habitats for each of the 37 species remain unprotected. Therefore, conservation decisions are needed to take into account both bat hotspots and species with restricted distributions.

Introduction of high-value Crocus sativus (saffron) cultivation in non-traditional regions of India through ecological modelling

Crocus sativus L. (saffron) is a globally used expensive spice. There are a few countries like Iran, Greece, Morocco, Spain, Italy, Turkey, France, Switzerland, Pakistan, China, Japan and Australia where this spice is cultivated and exported to other countries. India contributes 5% of the world's total production of which 90% is supplied only from its Jammu and Kashmir (J&K) regions. In India, the production of saffron from J&K is 3.83 tonnes whereas its annual demand is approximately 100 tonnes. In this country, there are geographical regions that have similar environmental and ecological conditions to J&K and possess the possibility of introducing this crop. Identification of such regions can be made using Ecological Niche Modelling (ENM). Therefore, 'MaxEnt' ENM was carried out using 103 environmental variables, 20 presence data and topographic parameters (elevation, slope and aspect) to find suitable regions for saffron production in unconventional areas of India. The achieved area under the curve for the model was 0.99. The precipitation and temperature were the main environmental variable influencing its cultivation. The saffron was sowed in these new modelled locations in India representing its various states such as Himachal Pradesh, Uttarakhand, Arunachal Pradesh, Sikkim, Manipur and Tamil Nadu. The quality, as well as yield of saffron produced in some of these regions, were evaluated and found at par with the saffron grown traditionally in India. Based on the promising results obtained in this work, we are expanding saffron cultivation to more modelled areas in India to meet our national demand.

MaxEnt Modelling and Impact of Climate Change on Habitat Suitability Variations of Economically Important Chilgoza Pine (Pinus gerardiana Wall.) in South Asia

Chilgoza pine is an economically and ecologically important evergreen coniferous tree species of the dry and rocky temperate zone, and a native of south Asia. This species is rated as near threatened (NT) by the International Union for Conservation of Nature (IUCN). This study hypothesized that climatic, soil and topographic variations strongly influence the distribution pattern and potential habitat suitability prediction of Chilgoza pine. Accordingly, this study was aimed to document the potential habitat suitability variations of Chilgoza pine under varying environmental scenarios by using 37 different environmental variables. The maximum entropy (MaxEnt) algorithm in MaxEnt software was used to forecast the potential habitat suitability under current and future (i.e., 2050s and 2070s) climate change scenarios (i.e., Shared Socio-economic Pathways (SSPs): 245 and 585). A total of 238 species occurrence records were collected from Afghanistan, Pakistan and India, and employed to build the predictive distribution model. The results showed that normalized difference vegetation index, mean temperature of coldest quarter, isothermality, precipitation of driest month and volumetric fraction of the coarse soil fragments (>2 mm) were the leading predictors of species presence prediction. High accuracy values (>0.9) of predicted distribution models were recorded, and remarkable shrinkage of potentially suitable habitat of Chilgoza pine was followed by Afghanistan, India and China. The estimated extent of occurrence (EOO) of the species was about 84,938 km2, and the area of occupancy (AOO) was about 888 km2, with 54 major sub-populations. This study concluded that, as the total predicted suitable habitat under current climate scenario (138,782 km2) is reasonably higher than the existing EOO, this might represent a case of continuous range contraction. Hence, the outcomes of this research can be used to build the future conservation and management plans accordingly for this economically valuable species in the region.

Finding rare species and estimating the probability that all occupied sites have been found

Detecting occupied sites of rare species, and estimating the probability that all occupied sites are known within a given area, are desired outcomes for many ecological or conservation projects. Examples include managing all occupied sites of a threatened species or eradicating an emerging invader. Occupied sites may remain undetected because (1) sites where the species potentially occurs had not been searched, and (2) the species could have been overlooked in the searched sites. For rare species, available data are typically scant, making it difficult to predict sites where the species probably occurs or to estimate detection probability in the searched sites. Using the critically endangered Rose's mountain toadlet (Capensibufo rosei), known from only two localities, we outline an iterative process aimed at estimating the probability that any unknown occupied sites remain and maximizing the chance of finding them. This includes fitting a species distribution model to guide sampling effort, testing model accuracy and sampling efficacy using the occurrence of more common proxy species, and estimating detection probability using sites of known presence. The final estimate of the probability that all occupied sites were found incorporates the uncertainties of uneven distribution, relative area searched, and detection probability. Our results show that very few occupied sites of C. rosei are likely to remain undetected. We also show that the probability of an undetected occupied site remaining will always be high for large unsearched areas of potential occurrence, but can be low for smaller areas intended for targeted management interventions. Our approach is especially useful for assessing uncertainty in species occurrences, planning the required search effort needed to reduce probability of unknown occurrence to desired levels, and identifying priority areas for further searches or management interventions.

European Ground Squirrels at the Edge: Current Distribution Status and Anticipated Impact of Climate on Europe’s Southernmost Population

The European ground squirrel (Spermophilus citellus) is an endangered semifossorial small mammal of grassland/agricultural ecosystems. In the last few decades, the species’ population has declined throughout its range in Europe. The Greek populations represent the southernmost limit of the species’ range and are notably small, scattered, and located mainly in human-modified areas. The goal of the present research is to understand the environmental and anthropogenic variables associated with its distribution in the Mediterranean habitats, assess possible drivers of observed local extinctions, and propose conservation and land-use management actions in light of near-future climate change scenarios. We used presence records since 2000 across all known populations (107 colonies) and maximum entropy conditional probability models (MaxEnt) to calculate both the habitat suitability (bioclimatic variables) and habitat availability (anthropogenic/land-use variables) within the European ground squirrel’s historical range in northern Greece. We report a projected 39% to 94.3% decrease in habitat suitability by 2040–2060 due to climate change. Based on our findings, we provide guidance by proposing nascent conservation actions to protect the few existing colonies in Greece via improved land management practices and identify in situ climate refugia that could be prioritized as sites for future reintroductions.

Fine-Tuned Ecological Niche Models Unveil Climatic Suitability and Association with Vegetation Groups for Selected Chaetocnema Species in South Africa (Coleoptera: Chrysomelidae)

Despite beetles (Coleoptera) representing most existing animal species, the ecological and biogeographical factors shaping their distribution are still unclear in many regions. We implemented state-of-the-art ecological niche models (ENMs) and niche overlap analysis to investigate climate–occurrence patterns for five flea beetle species of the genus Chaetocnema in South Africa (C. brincki, C. danielssoni, C. darwini, C. gahani, and C. natalensis). ENMs were fitted through Maxent and Random Forests, testing various parameterizations. For each species, tuned ENMs attaining good discrimination on spatially independent test data were selected to predict suitability across the study region and individuate its main climatic drivers. Percentage coverage of climatically suitable areas by seventeen Afrotropical vegetation formations was also computed. Predicted suitable areas do not extend far away from known presence localities, except for C. brincki and C. gahani in north-eastern South Africa. Temperate grasslands and shrublands cover most of suitable areas for C. brincki and C. gahani, along with warm temperate forests, as well as for C. danielssoni, in this case being followed by tropical flooded and swamp forests. Climatic suitability for C. darwini mainly relates to the Mediterranean grasslands and scrublands of the southern coastal region, while suitable areas for C. natalensis encompass various vegetation formations, coherently with its wide distribution. The environmental niche of C. danielssoni significantly overlaps with those of the wide-ranging C. darwini and C. natalensis, suggesting that historical factors, rather than low climatic tolerance, has determined its restricted distribution in the Western Cape Province. Maxent and Random Forests were confirmed to be of great help in disentangling the environment–occurrence relationships and in predicting suitability for the target species outside their known range, but they need to be properly tuned to perform at their best.

Potential distribution of the extremely endangered species Ostrya rehderiana (Betulaceae) in China under future climate change

Global climate change is a major threat to biodiversity, which may increase the extinction risk of rare species, particularly those like Ostrya rehderiana Chun (Betulaceae) with very few remaining extant wild individuals. We aimed to estimate the potential distribution of O. rehderiana under climate change and to analyze possible relevant climatic factors. Maximum entropy (Maxent) was employed to model the potential distribution of O. rehderiana under present and future climate scenarios. Suitable habitat areas in different periods and the main contributing climate factors were identified using species distribution models. The minimum temperature in winter and precipitation seasonality were the principal climatic factors influencing the establishment of O. rehderiana. The proportion of high potential distribution area in China was 3.91% and would further shrink significantly under changing climate, especially reduce by 97% under high radiative forcing. The extinction risk of O. rehderiana would still be extraordinarily high under future climate scenarios. The Tianmu and Luoxiao Mountains would be the only potential refugia for O. rehderiana in the future. Special conservation efforts are urgently required to rescue extremely endangered species as O. rehderiana. We propose priorities for the conservation region and suggestions for conservation management strategies.

Bioacoustics for in situ validation of species distribution modelling: An example with bats in Brazil

Species distribution modelling (SDM) gained importance on biodiversity distribution and conservation studies worldwide, including prioritizing areas for public policies and international treaties. Useful for large-scale approaches and species distribution estimates, it is a plus considering that a minor fraction of the planet is adequately sampled. However, minimizing errors is challenging, but essential, considering the uses and consequences of such models. In situ validation of the SDM outputs should be a key-step—in some cases, urgent. Bioacoustics can be used to validate and refine those outputs, especially if the focal species’ vocalizations are conspicuous and species-specific. This is the case of echolocating bats. Here, we used extensive acoustic monitoring (>120 validation points over an area of >758,000 km², and producing >300,000 sound files) to validate MaxEnt outputs for six neotropical bat species in a poorly-sampled region of Brazil. Based on in situ validation, we evaluated four threshold-dependent theoretical evaluation metrics’ ability in predicting models’ performance. We also assessed the performance of three widely used thresholds to convert continuous SDMs into presence/absence maps. We demonstrated that MaxEnt produces very different outputs, requiring a careful choice on thresholds and modeling parameters. Although all theoretical evaluation metrics studied were positively correlated with accuracy, we empirically demonstrated that metrics based on specificity-sensitivity and sensitivity-precision are better for testing models, considering that most SDMs are based on unbalanced data. Without independent field validation, we found that using an arbitrary threshold for modelling can be a precarious approach with many possible outcomes, even after getting good evaluation scores. Bioacoustics proved to be important for validating SDMs for the six bat species analyzed, allowing a better refinement of SDMs in large and under-sampled regions, with relatively low sampling effort. Regardless of the species assessing method used, our research highlighted the vital necessity of in situ validation for SDMs.

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.

A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

Impact of Past and Future Climate Change on the Potential Distribution of an Endangered Montane Shrub Lonicera oblata and Its Conservation Implications

Climate change is an important driver of biodiversity patterns and species distributions, understanding how organisms respond to climate change will shed light on the conservation of endangered species. In this study, we modeled the distributional dynamics of a critically endangered montane shrub Lonicera oblata in response to climate change under different periods by building a comprehensive habitat suitability model considering the effects of soil and vegetation conditions. Our results indicated that the current suitable habitats for L. oblata are located scarcely in North China. Historical modeling indicated that L. oblata achieved its maximum potential distribution in the last interglacial period which covered southwest China, while its distribution area decreased for almost 50% during the last glacial maximum. It further contracted during the middle Holocene to a distribution resembling the current pattern. Future modeling showed that the suitable habitats of L. oblata contracted dramatically, and populations were fragmentedly distributed in these areas. As a whole, the distribution of L. oblata showed significant migration northward in latitude but no altitudinal shift. Several mountains in North China may provide future stable climatic areas for L. oblata, particularly, the intersections between the Taihang and Yan mountains. Our study strongly suggested that the endangered montane shrub L. oblata are sensitive to climate change, and the results provide new insights into the conservation of it and other endangered species.

Validation of presence-only models for conservation planning and the application to whales in a multiple-use marine park

Identification of species' Biologically Important Areas (BIAs) is fundamental to conservation planning and species distribution models (SDMs) are a powerful tool commonly used to do this. Presence-only data are increasingly being used to develop SDMs to aid the conservation decision-making process. The application of presence-only SDMs for marine species' is particularly attractive due to often logistical and economic costs of obtaining systematic species' distribution data. However, robust model validation is important for conservation management applications that require accurate and reliable species' occurrence data (e.g., spatially explicit risk assessments). This is commonly done using a random subset of the data and less commonly with fully independent test data. Here, we apply a spatial block cross-validation (CV) approach to validate a MaxEnt presence-only model using independent presence/absence survey data for a highly mobile, marine species (humpback whale, Megaptera novaengliae) in the Great Barrier Reef (GBR). A MaxEnt model was developed using opportunistic whale sightings (2003-2007) and then used to identify areas differing in habitat suitability (low, medium, high) to conduct a systematic, line-transect, aerial survey (2012) and derive a density surface model. A spatial block CV buffering strategy was used to validate the MaxEnt model, using the opportunistic sightings as training data and independent aerial survey sightings data as test data. Moderate performance measures indicate MaxEnt was reliable in identifying the distribution patterns of a mobile whale species on their breeding ground, indicated by areas of high density aligned to areas of high habitat suitability. Furthermore, we demonstrate that MaxEnt models can be useful and cost-effective for designing a sampling scheme to undertake systematic surveys that significantly reduces sampling effort. In this study, higher quality information on whale reproductive class (calf vs. non-calf groups) was obtained that the presence-only data lacked, while sampling only 18% of the GBR World Heritage Area. The validation approach using fully independent data provides greater confidence in the MaxEnt model, which indicates significant overlap with the main breeding ground of humpback whales and the inner shipping route. This is important when evaluating presence-only models within certain conservation management applications, such as spatial risk assessments.

Climate change has different predicted effects on the range shifts of two hybridizing ambush bug (Phymata, Family Reduviidae, Order Hemiptera) species

AIM

A universal attribute of species is that their distributions are limited by numerous factors that may be difficult to quantify. Furthermore, climate change-induced range shifts have been reported in many taxa, and understanding the implications of these shifts remains a priority and a challenge. Here, we use Maxent to predict current suitable habitat and to project future distributions of two closely related, parapatrically distributed Phymata species in light of anthropogenic climate change.

LOCATION

North America.

TAXON

Phymata americana Melin 1930 and Phymata pennsylvanica Handlirsch 1897, Family: Reduviidae, Order: Hemiptera.

METHODS

We used the maximum entropy modeling software Maxent to identify environmental variables maintaining the distribution of two Phymata species, Phymata americana and Phymata pennsylvanica. Species occurrence data were collected from museum databases, and environmental data were collected from WorldClim. Once we gathered distribution maps for both species, we created binary suitability maps of current distributions. To predict future distributions in 2050 and 2070, the same environmental variables were used, this time under four different representative concentration pathways: RCP2.6, RCP4.5, RCP6.0, and RCP8.5; as well, binary suitability maps of future distributions were also created. To visualize potential future hybridization, the degree of overlap between the two Phymata species was calculated.

RESULTS

The strongest predictor to P. americana ranges was the mean temperature of the warmest quarter, while precipitation of the driest month and mean temperature of the warmest quarter were strong predictors of P. pennsylvanica ranges. Future ranges for P. americana are predicted to increase northwestward at higher CO2 concentrations. Suitable ranges for P. pennsylvanica are predicted to decrease with slight fluctuations around range edges. There is an increase in overlapping ranges of the two species in all future predictions.

MAIN CONCLUSIONS

These evidences for different environmental requirements for P. americana and P. pennsylvanica account for their distinct ranges. Because these species are ecologically similar and can hybridize, climate change has potentially important eco-evolutionary ramifications. Overall, our results are consistent with effects of climate change that are highly variable across species, geographic regions, and over time.

Applying species distribution models in public health research by predicting snakebite risk using venomous snakes' habitat suitability as an indicating factor

Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified. This study aims to use venomous snakes' habitat suitability as an indicator of snakebite risk, to identify high-priority areas for snakebite management across the country. Thus, an ensemble approach using five distribution modelling methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy Modelling, Generalized Linear Models, and Random Forest was applied to produce a spatial snakebite risk model for Iran. To achieve this, four venomous snakes' habitat suitability (Macrovipera lebetinus, Echis carinatus, Pseudocerastes persicus and Naja oxiana) were modelled and then multiplied. These medically important snakes are responsible for the most snakebite incidents in Iran. Multiplying habitat suitability models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi province) has the highest snakebite risk in the country. In addition, villages that were at risk of envenoming from the four snakes were identified. Results revealed that 51,112 villages are at risk of envenoming from M. lebetinus, 30,339 from E. carinatus, 51,657 from P. persicus and 12,124 from N. oxiana. Precipitation seasonality was identified as the most important variable influencing distribution of the P. persicus, E. carinatus and M. lebetinus in Iran. Precipitation of the driest quarter was the most important predictor of suitable habitats of the N. oxiana. Since climatic variables play an important role in shaping the distribution of the four venomous snakes in Iran, thus their distribution may alter with changing climate. This paper demonstrates application of species distribution modelling in public health research and identified potential snakebite risk areas in Iran by using venomous snakes' habitat suitability models as an indicating factor. Results of this study can be used in snakebite and human-snake conflict management in Iran. We recommend increasing public awareness of snakebite envenoming and education of local people in areas which identified with the highest snakebite risk.

Hidden in our pockets: building of a DNA barcode library unveils the first record of Myotis alcathoe for Portugal

Background

The advent and boom of DNA barcoding technologies have provided a powerful tool for the fields of ecology and systematics. Here, we present the InBIO Barcoding Initiative Database: Portuguese Bats (Chiroptera) dataset containing DNA sequences of 63 specimens representing the 25 bat species currently known for continental Portugal. For that, we sequenced tissues samples obtained in a vast array of projects spanning the last two decades.

New information

We added four new Barcoding Index Numbers (BINs) to existing Chiroptera barcodes on BOLD, two belonging to Myotisescalerai, one to Plecotusauritus and the other to Rhinolophushipposideros. Surprisingly, one of the samples initially identified in the field as Myotismystacinus turned out to be Myotisalcathoe, which represents the first record of this species for Portugal. The presence of Nyctalusnoctula in Portugal was also genetically confirmed for the first time. This case study shows the power and value of DNA barcoding initiatives to unravel new data that may be hidden on biological collections.

Identification of skipjack tuna (Katsuwonus pelamis) pelagic hotspots applying a satellite remote sensing-driven analysis of ecological niche factors: A short-term run

Skipjack tuna (SJT) pelagic hotspots in the western North Pacific (WNP) were modelled using fishery and satellite remotely sensed data with Ecological Niche Factor Analysis (ENFA) models. Our objectives were to model and predict habitat hotspots for SJT and assess the monthly changes in sub-surface temperatures and mixed layer depths at fishing locations. SJT presence-only monthly resolved data, sea surface temperature, chlorophyll-a, diffuse attenuation coefficient, sea surface heights and surface wind speed were used to construct ENFA models and generate habitat suitability indices using a short-term dataset from March-November 2004. The suitability indices were then predicted for July-October (2007 and 2008). Monthly aggregated polygons of areas fished by skipjack tuna pole and line vessels were also overlaid on the predicted habitat suitability maps. Distributions of sub-surface temperatures and mixed layer depths (MLD) at fishing locations were also examined. Our results showed good fit for ENFA models, as indicated by the absolute validation index, the contrast validation index and the continuous Boyce index. The predicted hotspots showed varying concurrences when compared with 25-degree polygons derived from fished areas. Northward shifts in SJT hotspots corresponded with declining MLDs from March to September. The MLDs were shallower in summer and deeper in autumn and winter months. The habitat hotspots modeled using ENFA were consistent with the known ecology and seasonal migration pattern of SJT. The findings of this work, derived from a short-term dataset, enable identification of SJT hotspots in the WNP, thus contributing valuable information for future research on SJT habitat prediction models.

An African bat in Europe, Plecotus gaisleri: Biogeographic and ecological insights from molecular taxonomy and Species Distribution Models

Because of the high risk of going unnoticed, cryptic species represent a major challenge to biodiversity assessments, and this is particularly true for taxa that include many such species, for example, bats. Long-eared bats from the genus Plecotus comprise numerous cryptic species occurring in the Mediterranean Region and present complex phylogenetic relationships and often unclear distributions, particularly at the edge of their known ranges and on islands. Here, we combine Species Distribution Models (SDMs), field surveys and molecular analyses to shed light on the presence of a cryptic long-eared bat species from North Africa, Plecotus gaisleri, on the islands of the Sicily Channel, providing strong evidence that this species also occurs in Europe, at least on the islands of the Western Mediterranean Sea that act as a crossroad between the Old Continent and Africa. Species Distribution Models built using African records of P. gaisleri and projected to the Sicily Channel Islands showed that all these islands are potentially suitable for the species. Molecular identification of Plecotus captured on Pantelleria, and recent data from Malta and Gozo, confirmed the species' presence on two of the islands in question. Besides confirming that P. gaisleri occurs on Pantelleria, haplotype network reconstructions highlighted moderate structuring between insular and continental populations of this species. Our results remark the role of Italy as a bat diversity hotspot in the Mediterranean and also highlight the need to include P. gaisleri in European faunal checklists and conservation directives, confirming the usefulness of combining different approaches to explore the presence of cryptic species outside their known ranges-a fundamental step to informing conservation.

Effects of Temperature Rise on Multi-Taxa Distributions in Mountain Ecosystems

Mountain biodiversity is associated with rare and fragile biota that are highly sensitive to climate change. To estimate the vulnerability of biodiversity to temperature rise, long-term field data are crucial. Species distribution models are an essential tool, in particular for invertebrates, for which detailed information on spatial and temporal distributions is largely missing. We applied presence-only distribution models to field data obtained from a systematic survey of 5 taxa (birds, butterflies, carabids, spiders, staphylinids), monitored in the northwestern Italian Alps. We estimated the effects of a moderate temperature increase on the multi-taxa distributions. Only small changes in the overall biodiversity patterns emerged, but we observed significant differences between groups of species and along the altitudinal gradient. The effects of temperature increase could be more pronounced for spiders and butterflies, and particularly detrimental for high-altitude species. We observed significant changes in community composition and species richness, especially in the alpine belt, but a clear separation between vegetation levels was retained also in the warming scenarios. Our conservative approach suggests that even a moderate temperature increase (about 1 °C) could influence animal biodiversity in mountain ecosystems: only long-term field data can provide the information to improve quantitative predictions, allowing us to readily identify the most informative signals of forthcoming changes.

Predicting the potential distribution of the parasitic Cuscuta chinensis under global warming

Background

The climate is the dominant factor that affects the distribution of plants. Cuscuta chinensis is a stem holoparasitic plant without leaves or roots, which develops a haustorium and sucks nutrients from host plants. The potential distribution of the parasitic plant C. chinensis has not been predicted to date. This study used Maxent modeling to predict the potential global distribution of C. chinensis, based on the following six main bioclimatic variables: annual mean temperature, isothermality, temperature seasonality, precipitation seasonality, precipitation of the warmest quarter, and precipitation of the coldest quarter.

Results

The optimal annual average temperature and isothermality of C. chinensis ranged from 4 to 37 °C and less than 45, respectively, while the optimal temperature seasonality and precipitation seasonality ranged from 4000 to 25,000 and from 50 to 130, respectively. The optimal precipitation of the warmest season ranged from 300 to 1000 mm and from 2500 to 3500 mm, while that of the coldest season was less than 2000 mm. In Asia, C. chinensis is mainly distributed at latitudes ranging from 20° N to 50° N. During three specific historical periods (last glacial maximum, mid-Holocene, and 1960–1990) the habitats suitable for C. chinensis were concentrated in the central, northern, southern, and eastern parts of China. From the last glacial maximum to the mid-Holocene, the total area with suitability of 0.5–1 increased by 0.0875 million km²; however, from the mid-Holocene to 1960–1990, the total area with suitability of 0.5–1 decreased by 0.0759 million km². The simulation results of habitat suitability in the two representative concentration pathways (RCP) 2.6 (i.e., the low greenhouse gas emissions pathway) and 8.5 (i.e., the high greenhouse gas emissions pathway) indicate that the habitat suitability of C. chinensis decreased in response to the warming climate. Compared with RCP2.6, areas with averaged suitability and high suitability for survival (RCP8.5) decreased by 0.18 million km².

Conclusion

Suitable habitats of C. chinensis are situated in central, northern, southern, and eastern China. The habitat suitability of C. chinensis decreased in response to the warming climate. These results provide a reference for the management and control of C. chinensis.

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

Accurate mapping is a main challenge for endangered small-sized terrestrial species. Freely available spatio-temporal data at high resolution from multispectral satellite offer excellent opportunities for improving predictive distribution models of such species based on fine-scale habitat features, thus making it easier to achieve comprehensive biodiversity conservation goals. However, there are still few examples showing the utility of remote-sensing-based products in mapping microhabitat suitability for small species of conservation concern. Here, we address this issue using Sentinel-2 sensor-derived habitat variables, used in combination with more commonly used explanatory variables (e.g., topography), to predict the distribution of the endangered Cabrera vole (Microtus cabrerae) in agrosilvopastorial systems. Based on vole surveys conducted in two different seasons over a ~176,000 ha landscape in Southern Portugal, we assessed the significance of each predictor in explaining Cabrera vole occurrence using the Boruta algorithm, a novel Random forest variant for dealing with high dimensionality of explanatory variables. Overall, results showed a strong contribution of Sentinel-2-derived variables for predicting microhabitat suitability of Cabrera voles. In particular, we found that photosynthetic activity (NDI45), specific spectral signal (SWIR1), and landscape heterogeneity (Rao’s Q) were good proxies of Cabrera voles’ microhabitat, mostly during temporally greener and wetter conditions. In addition to remote-sensing-based variables, the presence of road verges was also an important driver of voles’ distribution, highlighting their potential role as refuges and/or corridors. Overall, our study supports the use of remote-sensing data to predict microhabitat suitability for endangered small-sized species in marginal areas that potentially hold most of the biodiversity found in human-dominated landscapes. We believe our approach can be widely applied to other species, for which detailed habitat mapping over large spatial extents is difficult to obtain using traditional descriptors. This would certainly contribute to improving conservation planning, thereby contributing to global conservation efforts in landscapes that are managed for multiple purposes.

Molossid unlimited: extraordinary extension of range and unusual vocalization patterns of the bat, Promops centralis

The big crested mastiff bat, Promops centralis, occurs in Central and South America, but knowledge of its ecology is limited due to its open space hunting strategy, making captures extremely challenging. Notwithstanding, members of the species produce echolocation calls that are easy to identify. After recording calls of P. centralis 1,500 km away from its known range in Brazil, we hypothesized that the distribution range of this species was probably greatly underestimated. To improve the accuracy of P. centralis’ real distribution, we employed acoustic surveys throughout parts of Brazil, conducted after a bibliographic review to gather additional records, and used MaxEnt to model the species’ potential distribution. We have found that P. centralis has a much wider distribution in South America than previously thought, adding more than 3.8 million km2 to its former known area. We also describe an unusual vocalization pattern of P. centralis, with individuals emitting at least three very distinct but highly variable calls. This study shows that bioacoustic surveys and species distribution models can complement traditional methodologies in studying species that are difficult to capture, such as P. centralis, potentially contributing to more effective conservation and management plans.

Using Sentinel-2 Multispectral Images to Map the Occurrence of the Cossid Moth (Coryphodema tristis) in Eucalyptus Nitens Plantations of Mpumalanga, South Africa

Coryphodema tristis is a wood-boring insect, indigenous to South Africa, that has recently been identified as an emerging pest feeding on Eucalyptus nitens, resulting in extensive damage and economic loss. Eucalyptus plantations contributes over 9% to the total exported manufactured goods of South Africa which contributes significantly to the gross domestic product. Currently, the distribution extent of the Coryphodema tristis is unknown and estimated to infest Eucalyptus nitens compartments from less than 1% to nearly 80%, which is certainly a concern for the forestry sector related to the quantity and quality of yield produced. Therefore, the study sought to model the probability of occurrence of Coryphodema tristis on Eucalyptus nitens plantations in Mpumalanga, South Africa, using data from the Sentinel-2 multispectral instrument (MSI). Traditional field surveys were carried out through mass trapping in all compartments (n = 878) of Eucalyptus nitens plantations. Only 371 Eucalyptus nitens compartments were positively identified as infested and were used to generate the Coryphodema tristis presence data. Presence data and spectral features from the area were analysed using the Maxent algorithm. Model performance was evaluated using the receiver operating characteristics (ROC) curve showing the area under the curve (AUC) and True Skill Statistic (TSS) while the performance of predictors was analysed with the jack-knife. Validation of results were conducted using the test data. Using only the occurrence data and Sentinel-2 bands and derived vegetation indices, the Maxent model provided successful results, exhibiting an area under the curve (AUC) of 0.890. The Photosynthetic vigour ratio, Band 5 (Red edge 1), Band 4 (Red), Green NDVI hyper, Band 3 (Green) and Band 12 (SWIR 2) were identified as the most influential predictor variables. Results of this study suggest that remotely sensed derived vegetation indices from cost-effective platforms could play a crucial role in supporting forest pest management strategies and infestation control.

Predicting the range of a regionally threatened, benthic fish using species distribution models and field surveys

Understanding a species' historical and current distribution is critical when making conservation and management decisions. Recent observations in headwater streams of northern Illinois, USA, where no previous records of Iowa Darters Etheostoma exile occurred, revealed the need to re-evaluate its state-wide distribution. We conducted a series of species distribution modelling procedures coupled with targeted field surveys to generate historical and contemporary distribution models. The historical distribution model indicated E. exile distributions were concentrated across extreme northern Illinois. Both contemporary models included results of the historical model as a model variable. Based on the initial contemporary model, 30 potential Iowa Darter sites, 10 each of three groups representing low, medium and high model scores, were sampled during the summer of 2016. Field surveys provided nine new E. exile localities and presence-absence data from field surveys were then applied to revise the contemporary model. The revised contemporary model suggests a decrease in certain areas of its historic Illinois range, as well as areas where it has potentially expanded its range.

Determining threatened species distributions in the face of limited data: Spatial conservation prioritization for the Chinese giant salamander (Andrias davidianus)

The purpose of this study was to determine whether limited occurrence data for highly threatened species can provide useful spatial information to inform conservation. The study was conducted across central and southern China. We developed a habitat suitability model for the Critically Endangered Chinese giant salamander (Andrias davidianus) based on one biotic and three abiotic parameters from single‐site locality records, which represent the only relevant environmental data available for this species. We then validated model quality by testing whether increased percentage of predicted suitable habitat at the county level correlated with independent data on giant salamander presence. We randomly selected 48 counties containing historical records which were distinct from, and independent of, the single‐site records used to develop the model, and 47 additional counties containing >50% predicted suitable habitat. We interviewed 2,812 respondents near potential giant salamander habitat across these counties and tested for differences in respondent giant salamander reports between counties selected using each method. Our model predicts that suitable giant salamander habitat is found widely across central and southern China, with counties containing ≥50% predicted suitable habitat distributed in 13 provinces. Counties with historical records contain significantly more predicted suitable habitat than counties without historical records. There are no statistical differences in any patterns of respondent giant salamander reports in surveyed counties selected from our model compared with the areas of known historical giant salamander distribution. A Chinese giant salamander habitat suitability model with strong predictive power can be derived from the restricted range of environmental variables associated with limited available presence‐only occurrence records, constituting a cost‐effective strategy to guide spatial allocation of conservation planning. Few reported sightings were recent, however, with most being over 20 years old, so that identification of areas of suitable habitat does not necessarily indicate continued survival of the species at these locations.

How to deal with ground truthing affected by human-induced habitat change?: Identifying high-quality habitats for the Critically Endangered Red Siskin

Species distribution models (SDM) can be valuable for identifying key habitats for conservation management of threatened taxa, but anthropogenic habitat change can undermine SDM accuracy. We used data for the Red Siskin (Spinus cucullatus), a critically endangered bird and ground truthing to examine anthropogenic habitat change as a source of SDM inaccuracy. We aimed to estimate: (1) the Red Siskin's historic distribution in Venezuela; (2) the portion of this historic distribution lost to vegetation degradation; and (3) the location of key habitats or areas with both, a high probability of historic occurrence and a low probability of vegetation degradation. We ground‐truthed 191 locations and used expert opinion as well as landscape characteristics to classify species' habitat suitability as excellent, good, acceptable, or poor. We fit a Random Forest model (RF) and Enhanced Vegetation Index (EVI) time series to evaluate the accuracy and precision of the expert categorization of habitat suitability. We estimated the probability of historic occurrence by fitting a MaxLike model using 88 presence records (1960–2013) and data on forest cover and aridity index. Of the entire study area, 23% (20,696 km²) had a historic probability of Red Siskin occurrence over 0.743. Furthermore, 85% of ground‐truthed locations had substantial reductions in mean EVI, resulting in key habitats totaling just 976 km², in small blocks in the western and central regions. Decline in Area of Occupancy over 15 years was between 40% and 95%, corresponding to an extinction risk category between Vulnerable and Critically Endangered. Relating key habitats with other landscape features revealed significant risks and opportunities for proposed conservation interventions, including the fact that ongoing vegetation degradation could limit the establishment of reintroduced populations in eastern areas, while the conservation of remaining key habitats on private lands could be improved with biodiversity‐friendly agri‐ and silviculture programs.

Land Cover and Climate Change May Limit Invasiveness of Rhododendron ponticum in Wales

Invasive plant species represent a serious threat to biodiversity precipitating a sustained global effort to eradicate or at least control the spread of this phenomenon. Current distribution ranges of many invasive species are likely to be modified in the future by land cover and climate change. Thus, invasion management can be made more effective by forecasting the potential spread of invasive species. Rhododendron ponticum (L.) is an aggressive invasive species which appears well suited to western areas of the UK. We made use of MAXENT modeling environment to develop a current distribution model and to assess the likely effects of land cover and climatic conditions (LCCs) on the future distribution of this species in the Snowdonia National park in Wales. Six global circulation models (GCMs) and two representative concentration pathways (RCPs), together with a land cover simulation for 2050 were used to investigate species' response to future environmental conditions. Having considered a range of environmental variables as predictors and carried out the AICc-based model selection, we find that under all LCCs considered in this study, the range of R. ponticum in Wales is likely to contract in the future. Land cover and topographic variables were found to be the most important predictors of the distribution of R. ponticum. This information, together with maps indicating future distribution trends will aid the development of mitigation practices to control R. ponticum.