An Integrated Ecological Niche Modelling Framework for Risk Mapping of Peste des Petits Ruminants Virus Exposure in African Buffalo (Syncerus caffer) in the Greater Serengeti-Mara Ecosystem

Peste des petits ruminants (PPR) is a highly contagious viral disease of small ruminants that threatens livelihoods and food security in developing countries and, in some cases, wild ungulate species conservation. The Greater Serengeti-Mara Ecosystem (GSME) encompasses one of the major wildlife populations of PPR virus (PPRV)-susceptible species left on earth, although no clinical disease has been reported so far. This study aimed to gain further knowledge about PPRV circulation in the GSME by identifying which factors predict PPRV seropositivity in African buffalo (Syncerus caffer). Following an ecological niche modeling framework to map host-pathogen distribution, two models of PPRV exposure and buffalo habitat suitability were performed using serological data and buffalo censuses. Western Maasai Mara National Reserve and Western Serengeti National Park were identified as high-risk areas for PPRV exposure in buffalo. Variables related to wildlife-livestock interaction contributed to the higher risk of PPRV seropositivity in buffalo, providing supportive evidence that buffalo acquire the virus through contact with infected livestock. These findings can guide the design of cost-effective PPRV surveillance using buffalo as a sentinel species at the identified high-risk locations. As more intensive studies have been carried out in Eastern GSME, this study highlights the need for investigating PPRV dynamics in Western GSME.

Impacts of climate change on climatically suitable regions of two invasive Erigeron weeds in China

Introduction

Erigeron philadelphicus and Erigeron annuus are two ecologically destructive invasive plants from the Asteraceae family. Predicting the potential distribution pattern of two invasive alien Erigeron weeds can provide a scientific basis for prevent the further spread of these two weeds in China under climate change.

Methods

Based on historical occurrence datasets and environmental variables, we optimized a MaxEnt model to predict the potential suitable habitats of E. philadelphicus and E. annuus. We also analyzed the shifts of distribution centroids and patterns under climate change scenarios.

Results

The key variables that affect the potential geographical distribution of E. annuus and E. philadelphicus, respectively, are temperature seasonality and precipitation of the driest month. Moreover, topsoil sodicity and topsoil salinity also influence the distribution of E. philadelphicus. Under climate change, the overall suitable habitats for both invasive alien Erigeron weeds are expected to expand. The potential geographical distribution of E. annuus exhibited the highest expansion under the SSP245 climate scenario (medium forcing scenarios), whereas E. philadelphicus had the highest expansion under the SSP126 climate scenario (lower forcing scenarios) globally. The future centroid of E. annuus is projected to shift to higher latitudes specifically from Hubei to Hebei, whereas E. philadelphicus remains concentrated primarily in Hubei Province. The overlapping suitable areas of the two invasive alien Erigeron plants mainly occur in Jiangsu, Zhejiang, Fujian, Jiangxi, Hunan, Guizhou, and Chongqing, within China.

Discussion

Climate change will enable E. annuus to expand into northeastern region and invade Yunnan Province whereas E. philadelphicus was historically the only suitable species. E. annuus demonstrates a greater potential for invasion and expansion under climate change, as it exhibits higher environmental tolerance. The predictive results obtained in this study can serve as a valuable reference for early warning systems and management strategies aimed at controlling the spread of these two invasive plants.

Fungus under a Changing Climate: Modeling the Current and Future Global Distribution of Fusarium oxysporum Using Geographical Information System Data

The impact of climate change on biodiversity has been the subject of numerous research in recent years. The multiple elements of climate change are expected to affect all levels of biodiversity, including microorganisms. The common worldwide fungus Fusarium oxysporum colonizes plant roots as well as soil and several other substrates. It causes predominant vascular wilt disease in different strategic crops such as banana, tomato, palm, and even cotton, thereby leading to severe losses. So, a robust maximum entropy algorithm was implemented in the well-known modeling program Maxent to forecast the current and future global distribution of F. oxysporum under two representative concentration pathways (RCPs 2.6 and 8.5) for 2050 and 2070. The Maxent model was calibrated using 1885 occurrence points. The resulting models were fit with AUC and TSS values equal to 0.9 (±0.001) and 0.7, respectively. Increasing temperatures due to global warming caused differences in habitat suitability between the current and future distributions of F. oxysporum, especially in Europe. The most effective parameter of this fungus distribution was the annual mean temperature (Bio 1); the two-dimensional niche analysis indicated that the fungus has a wide precipitation range because it can live in both dry and rainy habitats as well as a range of temperatures in which it can live to certain limits. The predicted shifts should act as an alarm sign for decision makers, particularly in countries that depend on such staple crops harmed by the fungus.

Expanded distribution and predicted suitable habitat for the critically endangered yellow-tailed woolly monkey (Lagothrix flavicauda) in Perú

The Tropical Andes Biodiversity Hotspot holds a remarkable number of species at risk of extinction due to anthropogenic habitat loss, hunting, and climate change. One of these species, the critically endangered yellow-tailed woolly monkey (Lagothrix flavicauda), was recently observed in the region Junín, 206 km south of its previously known distribution. This range extension, combined with continued habitat loss, calls for a reevaluation of the species distribution, and available suitable habitat. Here, we present novel data from surveys at 53 sites in the regions of Junín, Cerro de Pasco, Ayacucho, and Cusco. We encountered L. flavicauda at 9 sites, all in Junín, and the congeneric Lagothrix lagotricha tschudii at 20 sites, but never in sympatry. Using these new localities along with all previous geographic localities for the species, we made predictive species distribution models based on ecological niche modeling using a generalized linear model and maximum entropy. Each model incorporated bioclimatic variables, forest cover, vegetation measurements, and elevation as predictor variables. The model evaluation showed >80% accuracy for all measures. Precipitation was the strongest predictor of species presence. Habitat suitability maps illustrate potential corridors for gene flow between the southern and northern populations, although much of this area is inhabited by L. l. tschudii whereas L. flavicauda has yet to be officially confirmed in these areas, by these or any other scientific surveys. An analysis of the current protected area (PA) network showed that ~75% of remaining suitable habitat is unprotected. With this, we suggest priority areas for new PAs or expansions to existing reserves that would conserve potential corridors between L. flavicauda populations. Further surveys and characterization of the distribution in intermediate areas, combined with studies on gene flow through these areas, are still needed to protect this species.

Effects of Climatic Change on Potential Distribution of Spogostylum ocyale (Diptera: Bombyliidae) in the Middle East Using Maxent Modelling

Spogostylum ocyale (Wiedemann 1828) is a large robust species of bee fly (family Bombyliidae), known to be a larval ectoparasitoid as well as an important flower pollinator as an adult. This species has become extremely rare or has disappeared from many of its historic habitats due to substantial changes in floral and faunal compositions in recent years. Climate change and urbanisation, together with other anthropogenic activities, may be to blame for these changes. Distribution modelling based on environmental variables together with known occurrences is a powerful tool in analytical biology, with applications in ecology, evolution, conservation management, epidemiology and other fields. Based on climatological and topographic data, the current and future distributions of the parasitoid in the Middle East region was predicted using the maximum entropy model (Maxent). The model performance was satisfactory (AUC mean = 0.834; TSS mean = 0.606) and revealed a good potential distribution for S. ocyale featured by the selected factors. A set of seven predictors was chosen from 19 bioclimatic variables and one topographic variable. The results show that the distribution of S. ocyale is mainly affected by the maximum temperature of the warmest period (Bio5) and temperature annual range (Bio7). According to the habitat suitability map, coastal regions with warm summers and cold winters had high to medium suitability. However, future scenarios predict a progressive decline in the extent of suitable habitats with global climate warming. These findings lead to robust conservation management measures in current or future conservation planning.

Analysis of desertification combating needs based on potential vegetation NDVI-A case in the Hotan Oasis

Combating desertification is vital for arresting land degradation and ensuring sustainable development of the global ecological environment. This study has analyzed the current desertification status and determined its control needs based on the difference between potential normalized difference vegetation index (PNDVI) and actual normalized difference vegetation index (ANDVI) in the Hotan desertoasis. The MaxEnt model, combined with the distribution point data of natural vegetation with long-term stable normalized difference vegetation index (NDVI) and 24 environmental factors was used to predict the PNDVI spatial distribution of different vegetation coverage grades and compared it with ANDVI. Excluding the areas of intense human activity such as arable land, the simulation results show that PNDVI with high, medium, and low vegetation cover was mainly distributed in the southwest and southeast of Hotan Oasis, in the midstream and downstream of Kalakash River and Yulong Kashi River, and the desert or Gobi area outside the oasis, respectively. The distribution of PNDVI with high, medium, and low vegetation cover accounted for 6.80%, 7.26%, and 9.17% of Hotan oasis, respectively. The comparison between ANDVI and PNDVI shows that 18.04% (ANDVI < PNDVI, about 3900 km²) of the study area is still suffering from desertification, which is mainly distributed in the desert-oasis ecotone in Hotan. The findings of this study implied that PNDVI could be used to assess the desertification status and endorsement of desertification control measures in vulnerable ecosystems. Hence, PNDVI can strengthen the desertification combating efforts at regional and global scales and may serve as a reference point for the policymakers and scientific community towards sustainable land development.

Demographic and ecogeographic factors limit wild grapevine spread at the southern edge of its distribution range

The spatial distribution of plants is constrained by demographic and ecogeographic factors that determine the range and abundance of the species. Wild grapevine (Vitis vinifera ssp. sylvestris) is distributed from Switzerland in the north to Israel in the south. However, little is known about the ecogeographic constraints of this species and its genetic and phenotypic characteristics, especially at the southern edge of its distribution range in the Levant region. In this study, we explore the population structure of southern Levantine wild grapevines and the correlation between demographic and ecogeographic characteristics. Based on our genetic analysis, the wild grapevine populations in this region can be divided into two major subgroups in accordance with a multivariate spatial and ecogeographical clustering model. The identified subpopulations also differ in morphological traits, mainly leaf hairiness which may imply adaptation to environmental stress. The findings suggest that the Upper Jordan River population was spread to the Sea of Galilee area and that a third smaller subpopulation at the south of the Golan Heights may represent a distinguished gene pool or a recent establishment of a new population. A spatial distribution model indicated that distance to water sources, Normalized difference vegetation index, and precipitation are the main environmental factors constraining V. v. sylvestris distribution at its southern distribution range. These factors in addition to limited gene flow between populations prevent further spread of wild grapevines southwards to semi-arid regions.

Modeling the Potential Distribution of the Malaria Vector Anopheles (Ano.) pseudopunctipennis Theobald (Diptera: Culicidae) in Arid Regions of Northern Chile

The extreme north of Chile presents a subtropical climate permissive of the establishment of potential disease vectors. Anopheles (Ano.) pseudopunctipennis is distributed from the south of the United States to the north of Argentina and Chile, and is one of the main vectors of malaria in Latin America. Malaria was eradicated from Chile in 1945. Nevertheless, the vector persists in river ravines of the Arica and Tarapacá regions. The principal effect of climate change in the north of Chile is temperature increase. Precipitation prediction is not accurate for this region because records were erratic during the last century. The objective of this study was to estimate the current and the projected distribution pattern of this species in Chile, given the potential impact due to climate change. We compiled distributional data for An. (Ano.) pseudopunctipennis and constructed species distribution models to predict the spatial distribution of this species using the MaxEnt algorithm with current and RCP 4.5 and 8.5 scenarios, using environmental and topographic layers. Our models estimated that the current expected range of An. (Ano.) pseudopunctipennis extends continuously from Arica to the north of Antofagasta region. Furthermore, the RCP 4.5 and 8.5 projected scenarios suggested that the range of distribution of An. (Ano.) pseudopunctipennis may increase in longitude, latitude, and altitude limits, enhancing the local extension area by 38 and 101%, respectively, and local presence probability (>0.7), from the northern limit in Arica y Parinacota region (18°S) to the northern Antofagasta region (23°S). This study contributes to geographic and ecologic knowledge about this species in Chile, as it represents the first local study of An. (Ano.) pseudopunctipennis. The information generated in this study can be used to inform decision making regarding vector control and surveillance programs of Latin America. These kinds of studies are very relevant to generate human, animal, and environmental health knowledge contributing to the "One Health" concept.

Modeling the Invasion of the Large Hive Beetle, Oplostomusfuligineus, into North Africa and South Europe under a Changing Climate

Simple Summary

Large Hive Beetles (LHBs) are common pests of honeybee colonies, especially in the African continent. The ability of this pest to invade new regions in North Africa and Europe is highlighted in the present study using a species distribution modeling technique in current and future climate change scenarios in 2050 and 2070. In brief, this pest will be a new burden on the beekeeping sector outside Africa, and therefore the development of early monitoring strategies is recommended.

Abstract

Some beetle species can attack honeybee colonies, causing severe damage to beekeeping. These pests include Oplostomus fuligineus, which is also known as the Large Hive Beetle (LHB). This beetle is native to Sub-Saharan Africa and has recently also been recorded in some parts of North Africa. It feeds mainly on young bee larvae and stored food within the colonies, causing severe damage to weak colonies. The present work sheds light on the current and future distribution (from 2050 to 2070) of this beetle in Africa and South Europe using species distribution modeling. Maxent was used to model the invasion of LHB. The Shared Socioeconomic Pathways (SSPs) 126 and 585 were used to model the future distribution of LHB. The Maxent models showed satisfactory results with a high Area Under Curve (AUC) value (0.85 ± 0.02). Furthermore, the True Skill Statistics (TSS) value was equal to 0.87. The current and future maps showed a high risk of invasion because of temperature variation in most of the parts of North Africa and South Europe. The maps also predicted the future invasion of LHB into other countries, mainly through southern Europe. These predictive risk maps will help quarantine authorities in highly relevant countries to prevent the expansion of this pest outside of its natural range.

The Impact of Human Pressure and Climate Change on the Habitat Availability and Protection of Cypripedium (Orchidaceae) in Northeast China

Human pressure on the environment and climate change are two important factors contributing to species decline and overall loss of biodiversity. Orchids may be particularly vulnerable to human-induced losses of habitat and the pervasive impact of global climate change. In this study, we simulated the extent of the suitable habitat of three species of the terrestrial orchid genus Cypripedium in northeast China and assessed the impact of human pressure and climate change on the future distribution of these species. Cypripedium represents a genus of long-lived terrestrial orchids that contains several species with great ornamental value. Severe habitat destruction and overcollection have led to major population declines in recent decades. Our results showed that at present the most suitable habitats of the three species can be found in Da Xing’an Ling, Xiao Xing’an Ling and in the Changbai Mountains. Human activity was predicted to have the largest impact on species distributions in the Changbai Mountains. In addition, climate change was predicted to lead to a shift in distribution towards higher elevations and to an increased fragmentation of suitable habitats of the three investigated Cypripedium species in the study area. These results will be valuable for decision makers to identify areas that are likely to maintain viable Cypripedium populations in the future and to develop conservation strategies to protect the remaining populations of these enigmatic orchid species.

Potential habitat distribution of Himalayan red panda and their connectivity in Sakteng Wildlife Sanctuary, Bhutan

Survival of endangered Himalayan red panda is threatened by ever-growing anthropogenic activities leading to an unprecedented rate of habitat degradation and loss. However, limited studies have been conducted in the context of the spatial distribution of habitats and habitat connectivity for the species in the landscape of Sakteng Wildlife Sanctuary (SWS). Lack of such information remains a challenge while implementing effective and holistic conservation initiatives. Therefore, this study identifies the distribution of potential habitats and their connectivity using maxent and linkage mapper, respectively. Precipitation-related predictor variables exhibited a significant influence on the prediction of habitat distribution. The model predicted 27.7% of the SWS as a potential habitat (fundamental niche). More than 75% of the predicted habitats fall outside the existing core zones where anthropogenic disturbance is relatively high, indicating the need to reassess existing management options. In SWS, 15 core habitats (CH) are predicted which are connected by a least-cost corridor (length µ = 2.91 km) with several pinch points in it. Centrally located CH5 and CH11 are identified as the most important habitat in maintaining overall connectivity within SWS. However, CH located in the peripheries could be equally important in facilitating the transboundary movement of the species. Overall, SWS can play a critical role as a connecting link between the larger landscape of Bhutan and the adjacent Indian state of Arunachal Pradesh in the conservation of Himalayan red panda that exhibits narrow dispersal with special habitat needs. Based on our findings, we recommend initiating GPS/satellite telemetry of the species to enable SWS to understand the precise interaction of Himalayan red panda to widespread herder communities, livestock, and free-roaming dogs dwelling in the same landscape. It will also help to evaluate the functionality of the predicted habitats, linkages, and feasibility of transboundary conservation initiatives.

Predicting the Invasion Potential of the Lily Leaf Beetle, Lilioceris lilii Scopoli (Coleoptera: Chrysomelidae), in North America

Invasive species are among the leading threats to global ecosystems due to impacts on native flora and fauna through competition and predation. The lily leaf beetle, Lilioceris lilii Scopoli (Coleoptera: Chrysomelidae), is an invasive pest of lilies (Lilium spp.) and other genera of Liliaceae (Liliales). A habitat suitability model was created using Maxent, to help predict if L. lilii will be able to establish in locations were native North American Liliaceae species grow. The model was created using georeferenced occurrence records from the beetle's native, naturalized, and invasive range. Model results indicate that precipitation in the driest quarter and annual average temperatures are most strongly correlated with L. lilii distribution, and suggest that the species will perform poorly in very dry, hot, or cold environments. The model also indicates that the beetle should be able to establish throughout the range of most North American Liliaceae genera, including species of special conservation concern. This model can be used by natural area managers to identify areas of high habitat suitability that overlap with vulnerable North American Liliaceae species, and prioritize L. lilii monitoring and control activities as the beetle continues to expand its range.

Combining species distribution modeling and distance sampling to assess wildlife population size: A case study with the northern yellow-cheeked gibbon (Nomascus annamensis)

Population size and distribution data for wildlife species play an important role in conservation and management, especially for endangered species. However, scientists seriously lack data on the population status of many species. The northern yellow-cheeked gibbon (Nomascus annamensis) is found in southern Lao PDR, central Vietnam, and northeastern Cambodia. The population of the species has significantly declined due to hunting, habitat loss, and the wildlife trade. To examine the population size and distribution of N. annamensis, we conducted a field survey in Song Thanh Nature Reserve, Quang Nam Province, central Vietnam from February to April 2019 using the audio point count method. We combined Distance Sampling and Ecological Niche Modeling to estimate the population of the gibbons. Results showed that the total suitable area for the gibbons was about 302.32 km² , with the two most important variables of the habitat model being the distance-to-villages and forest type. We detected 36 gibbon groups through field surveys and estimated 443 (95% CI, 278-707) gibbon groups in Song Thanh Nature Reserve. Our results indicate that the gibbon population in Song Thanh Nature Reserve is the largest known population of N. annamensis in Vietnam. In addition, our study was the first to combine species distribution modeling with distance sampling to estimate gibbon density and population size. This approach might be useful in surveying and monitoring gibbon populations because it takes imperfect detection probability into account in estimating gibbon population density while estimating the area of potential habitat using environmental variables.

Assessing congruence of opportunistic records and systematic surveys for predicting Hispaniolan mammal species distributions

Comparative assessment of the relative information content of different independent spatial data types is necessary to evaluate whether they provide congruent biogeographic signals for predicting species ranges. Opportunistic occurrence records and systematically collected survey data are available from the Dominican Republic for Hispaniola's surviving endemic non-volant mammals, the Hispaniolan solenodon (Solenodon paradoxus) and Hispaniolan hutia (Plagiodontia aedium); opportunistic records (archaeological, historical and recent) exist from across the entire country, and systematic survey data have been collected from seven protected areas. Species distribution models were developed in maxent for solenodons and hutias using both data types, with species habitat suitability and potential country-level distribution predicted using seven biotic and abiotic environmental variables. Three different models were produced and compared for each species: (a) opportunistic model, with starting model incorporating abiotic-only predictors; (b) total survey model, with starting model incorporating biotic and abiotic predictors; and (c) reduced survey model, with starting model incorporating abiotic-only predictors to allow further comparison with the opportunistic model. All models predict suitable environmental conditions for both solenodons and hutias across a broadly congruent, relatively large area of the Dominican Republic, providing a spatial baseline of conservation-priority landscapes that might support native mammals. Correlation between total and reduced survey models is high for both species, indicating the substantial explanatory power of abiotic variables for predicting Hispaniolan mammal distributions. However, correlation between survey models and opportunistic models is only moderately positive. Species distribution models derived from different data types can provide different predictions about habitat suitability and conservation-priority landscapes for threatened species, likely reflecting incompleteness and bias in spatial sampling associated with both data types. Models derived using both opportunistic and systematic data must therefore be applied critically and cautiously.

Inclusion of trophic interactions increases the vulnerability of an alpine butterfly species to climate change

Climate change is expected to have significant and complex impacts on ecological communities. In addition to direct effects of climate on species, there can also be indirect effects through an intermediary species, such as in host-plant interactions. Indirect effects are expected to be more pronounced in alpine environments because these ecosystems are sensitive to temperature changes and there are limited areas for migration of both species (i.e. closed systems), and because of simpler trophic interactions. We tested the hypothesis that climate change will reduce the range of an alpine butterfly (Parnassius smintheus) because of indirect effects through its host plant (Sedum sp.). To test for direct and indirect effects, we used the simulations of climate change to assess the distribution of P. smintheus with and without Sedum sp. We also compared the projected ranges of P. smintheus to four other butterfly species that are found in the alpine, but that are generalists feeding on many plant genera. We found that P. smintheus gained distributional area in climate-only models, but these gains were significantly reduced with the inclusion of Sedum sp. and in dry-climate scenarios which resulted in a reduction in net area. When compared to the more generalist butterfly species, P. smintheus exhibited the largest loss in suitable habitat. Our findings support the importance of including indirect effects in modelling species distributions in response to climate change. We highlight the potentially large and still neglected impacts climate change can have on the trophic structure of communities, which can lead to significant losses of biodiversity. In the future, communities will continue to favour species that are generalists as climate change induces asynchronies in the migration of species.

Modeling the Spatial Distribution of Plateau Pika (Ochotona curzoniae) in the Qinghai Lake Basin, China

The plateau pika (Ochotona curzoniae) is a keystone species in the alpine rangeland ecosystem of the Qinghai-Tibetan Plateau. Most previous studies of habitat selection by plateau pika have been conducted at a local microhabitat scale; however, little is known about the relationship between the distribution of plateau pika and macrohabitat factors at broad spatial scales. Using a presence-only ecological niche model (maximum entropy, Maxent), we predicted the distribution of plateau pika in the Qinghai Lake basin based on a set of environmental and anthropogenic variables at 1-km spatial resolution, and identified key macrohabitat factors that contribute to the predictive performance. Our results showed suitable area for plateau pika in the Qinghai Lake basin being approximately 3982 km2, which is 15.8% of the land area in the whole watershed. The distance to road emerged as the most important predictor of the distribution patterns of plateau pika, while the soil type was of ancillary importance. Mean air temperature of wettest quarter, distance to resident site and altitude also produced high gains in defining plateau pika's distribution. A higher predictive accuracy was achieved by the model that combined environmental and anthropogenic variables. With the constraint of human factors, the presence probability of plateau pika in about 1661 km2 will increase. These findings demonstrate the impact of human activities on the distribution of plateau pika, and the importance of vegetation reservation for plateau pika control.

Past distribution of epiphyllous liverworts in China: The usability of historical data

Epiphyllous liverworts form a special group of bryophytes that primarily grow on the leaves of understory vascular plants in tropical and subtropical evergreen broadleaf forests. Being sensitive to moisture and temperature changes, epiphyllous liverworts are often considered to be good indicators of climate change and forest degradation. However, they are a poorly collected and taxonomically complicated group, with an only partly identified distribution pattern. In this study, we built four models based on 24 environmental variables at four different spatial resolutions (i.e., 1 km, 5 km, 10 km, and 15 km) to predict the past distribution of epiphyllous liverworts in China, using Maxent model and 63 historical location records (i.e., presence-only data). Both area under the curve of the receiver operating characteristic (AUC) and true skill statistic (TSS) methods are used to assess the model performance. Results showed that the model with the predictors at a 15-km resolution achieved the highest predictive accuracy (AUC=0.946; TSS=0.880), although there was no statistically significant difference between the four models (p > 0.05). The most significant environmental variables included aridity, annual precipitation, precipitation of wettest month, precipitation of wettest quarter, and precipitation of warmest quarter, annual mean NDVI, and minimum NDVI. The predicted suitable areas for epiphyllous liverworts were mainly located in the south of Yangtze River and seldom exceed 35°N, which were consistent with the museum and herbarium records, as well as the historical records in scientific literatures. Our study further demonstrated the value of historical data to ecological and evolutionary studies.

Snub-nosed monkeys (Rhinopithecus): potential distribution and its implication for conservation

Many threatened species have undergone range retraction, and are confined to small fragmented populations. To increase their survival prospects, it is necessary to find suitable habitat outside their current range, to increase and interconnect populations. Species distribution models may be used to this purpose and can be an important part of the conservation strategies. One pitfall is that such mapping will typically assume that the current distribution represents the optimal habitat, which may not be the case for threatened species. Here, we use maximum entropy modelling (Maxent) and rectilinear bioclimatic envelope modelling with current and historical distribution data, together with the location of protected areas, and environmental and anthropogenic variables, to answer three key questions for the conservation of Rhinopithecus, a highly endangered genus of primates consisting of five species of which three are endemic to China, one is endemic to China and Myanmar and one is endemic to Vietnam; Which environmental variables best predict the distribution? To what extent is Rhinopithecus living in an anthropogenically truncated niche space? What is the genus’ potential distribution in the region? Mean temperature of coldest and warmest quarter together with annual precipitation and precipitation during the driest quarter were the variables that best explained Rhinopithecus’ distribution. The historical records were generally in warmer and wetter areas and in lower elevation than the current distribution, strongly suggesting that Rhinopithecus today survives in an anthropogenic truncated niche space. There is 305,800–319,325 km² of climatic suitable area within protected areas in China, of which 96,525–100,275 km² and 17,175–17,550 km² have tree cover above 50 and 75%, respectively. The models also show that the area predicted as climatic suitable using Maxent was 72–89% larger when historical records were included. Our results emphasise the importance of considering historical records when assessing restoration potential and show that there is high potential for restoring Rhinopithecus to parts of its former range.

Quantifying the degree of bias from using county-scale data in species distribution modeling: Can increasing sample size or using county-averaged environmental data reduce distributional overprediction?

Citizen-science databases have been used to develop species distribution models (SDMs), although many taxa may be only georeferenced to county. It is tacitly assumed that SDMs built from county-scale data should be less precise than those built with more accurate localities, but the extent of the bias is currently unknown. Our aims in this study were to illustrate the effects of using county-scale data on the spatial extent and accuracy of SDMs relative to true locality data and to compare potential compensatory methods (including increased sample size and using overall county environmental averages rather than point locality environmental data). To do so, we developed SDMs in maxent with PRISM-derived BIOCLIM parameters for 283 and 230 species of odonates (dragonflies and damselflies) and butterflies, respectively, for five subsets from the OdonataCentral and Butterflies and Moths of North America citizen-science databases: (1) a true locality dataset, (2) a corresponding sister dataset of county-centroid coordinates, (3) a dataset where the average environmental conditions within each county were assigned to each record, (4) a 50/50% mix of true localities and county-centroid coordinates, and (5) a 50/50% mix of true localities and records assigned the average environmental conditions within each county. These mixtures allowed us to quantify the degree of bias from county-scale data. Models developed with county centroids overpredicted the extent of suitable habitat by 15% on average compared to true locality models, although larger sample sizes (>100 locality records) reduced this disparity. Assigning county-averaged environmental conditions did not offer consistent improvement, however. Because county-level data are of limited value for developing SDMs except for species that are widespread and well collected or that inhabit regions where small, climatically uniform counties predominate, three means of encouraging more accurate georeferencing in citizen-science databases are provided.

Modeling of spatial distribution for scorpions of medical importance in the São Paulo State, Brazil

Aim:

In this work, we aimed to develop maps of modeling geographic distribution correlating to environmental suitability for the two species of scorpions of medical importance at São Paulo State and to develop spatial configuration parameters for epidemiological surveillance of these species of venomous animals.

Materials and Methods:

In this study, 54 georeferenced points for Tityus serrulatus and 86 points for Tityus bahiensis and eight environmental indicators, were used to generate species distribution models in Maxent (maximum entropy modeling of species geographic distributions) version 3.3.3k using 70% of data for training (n=38 to T. serrulatus and n=60 to T. bahiensis) and 30% to test the models (n=16 for T. serrulatus and n=26 for T. bahiensis). The logistic threshold used to cut models in converting the continuous probability model into a binary model was the “maximum test sensitivity plus specificity,” provided by Maxent, with results of 0.4143 to T. serrulatus and of 0.3401 to T. bahiensis. The models were evaluated by the area under the curve (AUC), using the omission error and the binomial probability. With the data generated by Maxent, distribution maps were produced using the “ESRI^® ArcGIS 10.2.2 for Desktop” software.

Results:

The models had high predictive success (AUC=0.7698±0.0533, omission error=0.2467 and p<0.001 for T. serrulatus and AUC=0.8205±0.0390, omission error=0.1917 and p<0.001 for T. bahiensis) and the resultant maps showed a high environmental suitability in the north, central, and southeast of the state, confirming the increasing spread of these species. The environmental variables that mostly contributed to the scorpions species distribution model were rain precipitation (28.9%) and tree cover (28.2%) for the T. serrulatus and temperature (45.8%) and thermal amplitude (12.6%) for the T. bahiensis.

Conclusion:

The distribution model of these species of medical importance scorpions in São Paulo State revealed a higher environmental suitability of these species in the regions north, central, and southeast of the state, warning to emergencies actions for prevention and surveillance from scorpion stings in several counties. There is also a need to best conservation strategies related to neighboring territories, with the implementation of new environmental protected areas and measures of spread control of these species in urban areas of several counties.

A continental risk assessment of West Nile virus under climate change

Since first introduced to North America in 1999, West Nile virus (WNV) has spread rapidly across the continent, threatening wildlife populations and posing serious health risks to humans. While WNV incidence has been linked to environmental factors, particularly temperature and rainfall, little is known about how future climate change may affect the spread of the disease. Using available data on WNV infections in vectors and hosts collected from 2003-2011 and using a suite of 10 species distribution models, weighted according to their predictive performance, we modeled the incidence of WNV under current climate conditions at a continental scale. Models were found to accurately predict spatial patterns of WNV that were then used to examine how future climate may affect the spread of the disease. Predictions were accurate for cases of human WNV infection in the following year (2012), with areas reporting infections having significantly higher probability of presence as predicted by our models. Projected geographic distributions of WNV in North America under future climate for 2050 and 2080 show an expansion of suitable climate for the disease, driven by warmer temperatures and lower annual precipitation that will result in the exposure of new and naïve host populations to the virus with potentially serious consequences. Our risk assessment identifies current and future hotspots of West Nile virus where mitigation efforts should be focused and presents an important new approach for monitoring vector-borne disease under climate change.

Modeling the distribution of the West Nile and Rift Valley Fever vector Culex pipiens in arid and semi-arid regions of the Middle East and North Africa

BACKGROUND

The Middle East North Africa (MENA) region is under continuous threat of the re-emergence of West Nile virus (WNV) and Rift Valley Fever virus (RVF), two pathogens transmitted by the vector species Culex pipiens. Predicting areas at high risk for disease transmission requires an accurate model of vector distribution, however, most Cx. pipiens distribution modeling has been confined to temperate, forested habitats. Modeling species distributions across a heterogeneous landscape structure requires a flexible modeling method to capture variation in mosquito response to predictors as well as occurrence data points taken from a sufficient range of habitat types.

METHODS

We used presence-only data from Egypt and Lebanon to model the population distribution of Cx. pipiens across a portion of the MENA that also encompasses Jordan, Syria, and Israel. Models were created with a set of environmental predictors including bioclimatic data, human population density, hydrological data, and vegetation indices, and built using maximum entropy (Maxent) and boosted regression tree (BRT) methods. Models were created with and without the inclusion of human population density.

RESULTS

Predictions of Maxent and BRT models were strongly correlated in habitats with high probability of occurrence (Pearson's r=0.774, r=0.734), and more moderately correlated when predicting into regions that exceeded the range of the training data (r=0.666,r=0.558). All models agreed in predicting high probability of occupancy around major urban areas, along the banks of the Nile, the valleys of Israel, Lebanon, and Jordan, and southwestern Saudi Arabia. The most powerful predictors of Cx. pipiens habitat were human population density (60.6% Maxent models, 34.9% BRT models) and the seasonality of the enhanced vegetation index (EVI) (44.7% Maxent, 16.3% BRT). Maxent models tended to be dominated by a single predictor. Areas of high probability corresponded with sites of independent surveys or previous disease outbreaks.

CONCLUSIONS

Cx. pipiens occurrence was positively associated with areas of high human population density and consistent vegetation cover, but was not significantly driven by temperature and rainfall, suggesting human-induced habitat change such as irrigation and urban infrastructure has a greater influence on vector distribution in this region than in temperate zones.

Assessing the exposure of lion tamarins (Leontopithecus spp.) to future climate change

Understanding how biodiversity will respond to climate change is a major challenge in conservation science. Climatic changes are likely to impose serious threats to many organisms, especially those with narrow distribution ranges, small populations and low dispersal capacity. Lion tamarins (Leontopithecus spp.) are endangered primates endemic to Brazilian Atlantic Forest (BAF), and all four living species are typical examples of these aggravating conditions. Here, we integrate ecological niche modeling and GIS-based information about BAF remnants and protected areas to estimate the exposure (i.e., the extent of climate change predicted to be experienced by a species) of current suitable habitats to climate change for 2050 and 2080, and to evaluate the efficacy of existing reserves to protect climatically suitable areas. Niche models were built using Maxent and then projected onto seven global circulation models derived from the A1B climatic scenario. According to our projections, the occurrence area of L. caissara will be little exposed to climate change. Western populations of L. chrysomelas could be potentially exposed, while climatically suitable habitats will be maintained only in part of the eastern region. Protected areas that presently harbor large populations of L. chrysopygus and L. rosalia will not retain climatic suitability by 2080. Monitoring trends of exposed populations and protecting areas predicted to hold suitable conditions should be prioritized. Given the potential exposure of key lion tamarin populations, we stress the importance of conducting additional studies to assess other aspects of their vulnerability (i.e., sensitivity to climate and adaptive capacity) and, therefore, to provide a more solid framework for future management decisions in the context of climate change.

Microclimatic challenges in global change biology

Despite decades of work on climate change biology, the scientific community remains uncertain about where and when most species distributions will respond to altered climates. A major barrier is the spatial mismatch between the size of organisms and the scale at which climate data are collected and modeled. Using a meta-analysis of published literature, we show that grid lengths in species distribution models are, on average, ca. 10 000-fold larger than the animals they study, and ca. 1000-fold larger than the plants they study. And the gap is even worse than these ratios indicate, as most work has focused on organisms that are significantly biased toward large size. This mismatch is problematic because organisms do not experience climate on coarse scales. Rather, they live in microclimates, which can be highly heterogeneous and strongly divergent from surrounding macroclimates. Bridging the spatial gap should be a high priority for research and will require gathering climate data at finer scales, developing better methods for downscaling environmental data to microclimates, and improving our statistical understanding of variation at finer scales. Interdisciplinary collaborations (including ecologists, engineers, climatologists, meteorologists, statisticians, and geographers) will be key to bridging the gap, and ultimately to providing scientifically grounded data and recommendations to conservation biologists and policy makers.

Predicting habitat distribution of five heteropteran pest species in Iran

In agroecosystems, potential species distribution models are extensively applied in pest management strategies, revealing species ecological requirements and demonstrating relationships between species distribution and predictive variables. The Maximum Entropy model was used to predict the potential distribution of five heteropteran key pests in Iran, namely Adelphocoris lineolatus (Goeze) (Hemiptera: Miridae), Lygus pratensis (L.), Apodiphus amygdali (Germar) (Hemiptera: Pentatomidae), Nezara viridula (L.), and Nysius cymoides (Spinola) (Hemiptera: Lygaeidae). A total of 663 samples were collected from different parts of Iran. The altitude and climate variable data were included in the analysis. Based on test and training data, the area under the receiver operating characteristic curve values were above 0.80, the binomial omission test with the lowest presence threshold for all species was statistically significant (< 0.01), and the test omission rates were less than 3%. The suitability of areas in Iran for A. amygdale (Germar) (Hemiptera: Pentatomidae), N. cymoides (Spinola) (Hemiptera: Lygaeidae), A. lineolatus (Goeze) (Hemiptera: Miridae), L. pratensis (L.), and N. viridula (L.) (Hemiptera: Pentatomidae), ranked as 78.86%, 68.78%, 43.29%, 20%, and 15.16%, respectively. In general, central parts of Iran including salt lakes, deserts, and sand dune areas with very high temperatures and windy weather were predicted to be less suitable, while other regions, mainly northern parts, were most suitable. These new data could be applied practically for the design of integrated pest management and crop development programs.

Assessing the Application of a Geographic Presence-Only Model for Land Suitability Mapping

Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1,300 sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the Maxent model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results.

Geographical distribution of the space-weaving spider, Chibchea salta, from northwestern Argentina: New records and bioclimatic modeling

New records of the spider Chibchea salta Huber 2000 (Araneae, Pholcidae) from northwestern Argentina are provided, and the potential range of this species is modeled. Two presence-only methods, Maxent and Bioclim, were run using 19 bioclimatic parameters at a resolution of 30 arc seconds. The climatic profile of C. salta is described, and the relative importance of the bioclimatic variables is explored. Temperature variables proved to be more decisive to the final range shape. The range predicted with Maxent is slightly larger than with Bioclim, but the latter appears to be more sensitive to the record set bias. Both methods performed well, resulting in predictive ranges consistent with the yungas ecoregion. These results provide an initial insight into the bioclimatic tolerance of C. salta, and by identifying potential areas with no records, such as the sierras on the Salta-Jujuy border, they also help in identifying sites for future sampling efforts.

Ecological niche modeling of potential West Nile virus vector mosquito species in Iowa

Ecological niche modeling (ENM) algorithms, Maximum Entropy Species Distribution Modeling (Maxent) and Genetic Algorithm for Rule-set Prediction (GARP), were used to develop models in Iowa for three species of mosquito - two significant, extant West Nile virus (WNV) vectors (Culex pipiens L and Culex tarsalis Coquillett (Diptera: Culicidae)), and the nuisance mosquito, Aedes vexans Meigen (Diptera: Culicidae), a potential WNV bridge vector. Occurrence data for the three mosquito species from a state-wide arbovirus surveillance program were used in combination with climatic and landscape layers. Maxent successfully created more appropriate niche models with greater accuracy than GARP. The three Maxent species' models were combined and the average values were statistically compared to human WNV incidence at the census block group level. The results showed that the Maxent-modeled species' niches averaged together were a useful indicator of WNV human incidence in the state of Iowa. This simple method for creating probability distribution maps proved useful for understanding WNV dynamics and could be applied to the study of other vector-borne diseases.