Ensemble habitat mapping of invasive plant species

Climate as a Predictive Factor for Invasion: Unravelling the Range Dynamics of Carpomya vesuviana Costa

Invasive alien species (IAS) significantly affect global native biodiversity, agriculture, industry, and human health. Carpomya vesuviana Costa, 1854 (Diptera: Tephritidae), a significant global IAS, affects various date species, leading to substantial economic losses and adverse effects on human health and the environment. This study employed biomod2 ensemble models, multivariate environmental similarity surface and most dissimilar variable analyses, and ecological niche dynamics based on environmental and species data to predict the potential distribution of C. vesuviana and explore the environmental variables affecting observed patterns and impacts. Compared to native ranges, ecological niche shifts at invaded sites increased the invasion risk of C. vesuviana globally. The potential geographical distribution was primarily in Asia, Africa, and Australia, with a gradual increase in suitability with time and radiation levels. The potential geographic distribution centre of C. vesuviana is likely to shift poleward between the present and the 2090s. We also show that precipitation is a key factor influencing the likely future distribution of this species. In conclusion, climate change has facilitated the expansion of the geographic range and ecological niche of C. vesuviana, requiring effective transnational management strategies to mitigate its impacts on the natural environment and public health during the Anthropocene. This study aims to assess the potential threat of C. vesuviana to date palms globally through quantitative analytical methods. By modelling and analysing its potential geographic distribution, ecological niche, and environmental similarities, this paper predicts the pest's dispersal potential and possible transfer trends in geographic centres of mass in order to provide prevention and control strategies for the global date palm industry.

Modeling habitat suitability for the lesser-known populations of endangered mountain nyala (Tragelaphus buxtoni) in the Arsi and Ahmar Mountains, Ethiopia

Habitat suitability models have become a valuable tool for wildlife conservation and management, and are frequently used to better understand the range and habitat requirements of rare and endangered species. In this study, we employed two habitat suitability modeling techniques, namely Boosted Regression Tree (BRT) and Maximum Entropy (Maxent) models, to identify potential suitable habitats for the endangered mountain nyala (Tragelaphus buxtoni) and environmental factors affecting its distribution in the Arsi and Ahmar Mountains of Ethiopia. Presence points, used to develop our habitat suitability models, were recorded from fecal pellet counts (n = 130) encountered along 196 randomly established transects in 2015 and 2016. Predictor variables used in our models included major landcover types, Normalized Difference Vegetation Index (NDVI), greenness and wetness tasseled cap vegetation indices, elevation, and slope. Area Under the Curve model evaluations for BRT and Maxent were 0.96 and 0.95, respectively, demonstrating high performance. Both models were then ensembled into a single binary output highlighting an area of agreement. Our results suggest that 1864 km2 (9.1%) of the 20,567 km2 study area is suitable habitat for the mountain nyala with land cover types, elevation, NDVI, and slope of the terrain being the most important variables for both models. Our results highlight the extent to which habitat loss and fragmentation have disconnected mountain nyala subpopulations. Our models demonstrate the importance of further protecting suitable habitats for mountain nyala to ensure the species' conservation.

Early warning and management of invasive crop pests under global warming: estimating the global geographical distribution patterns and ecological niche overlap of three Diabrotica beetles

Invasive alien pests (IAPs) pose a major threat to global agriculture and food production. When multiple IAPs coexist in the same habitat and use the same resources, the economic loss to local agricultural production increases. Many species of the Diabrotica genus, such as Diabrotica barberi, Diabrotica undecimpunctata, and Diabrotica virgifera, originating from the USA and Mexico, seriously damaged maize production in North America and Europe. However, the potential geographic distributions (PGDs) and degree of ecological niche overlap among the three Diabrotica beetles remain unclear; thus, the potential coexistence zone is unknown. Based on environmental and species occurrence data, we used an ensemble model (EM) to predict the PGDs and overlapping PGD of the three Diabrotica beetles. The n-dimensional hypervolumes concept was used to explore the degree of niche overlap among the three species. The EM showed better reliability than the individual models. According to the EM results, the PGDs and overlapping PGD of the three Diabrotica beetles were mainly distributed in North America, Europe, and Asia. Under the current scenario, D. virgifera has the largest PGD ranges (1615 × 104 km2). In the future, the PGD of this species will expand further and reach a maximum under the SSP5-8.5 scenario in the 2050s (2499 × 104 km2). Diabrotica virgifera showed the highest potential for invasion under the current and future global warming scenarios. Among the three studied species, the degree of ecological niche overlap was the highest for D. undecimpunctata and D. virgifera, with the highest similarity in the PGD patterns and maximum coexistence range. Under global warming, the PGDs of the three Diabrotica beetles are expected to expand to high latitudes. Identifying the PGDs of the three Diabrotica beetles provides an important reference for quarantine authorities in countries at risk of invasion worldwide to develop specific preventive measures against pests.

Seasonal distribution patterns of Scomberomorus commerson in the Taiwan Strait in relation to oceanographic conditions: An ensemble modeling approach

The decline in the stock of the narrow-barred Spanish mackerel in the Taiwan Strait has sparked interest in conservation efforts. To optimize conservation and restoration efforts, it is crucial to understand their habitat preference in response to changing environments. In this study, ensemble modeling was used to investigate the seasonal distribution patterns of Spanish mackerel. Winter was identified as the most productive season, followed by fall; productivity was the lowest in summer. Five single-algorithm models were developed, and on the basis of their performance, four were selected for inclusion in an ensemble species distribution model. The spatial distribution of Spanish mackerel was primarily along the latitudinal range 23°-25°N in spring and summer. However, in fall and winter, the geographical range increased toward the southern region. The findings of this study will contribute to the understanding of this specific species and the approach used in this study may be applicable to other fisheries stocks also.

Reconstructed Global Invasion and Spatio-Temporal Distribution Pattern Dynamics of Sorghum halepense under Climate and Land-Use Change

Sorghum halepense competes with crops and grass species in cropland, grassland, and urban environments, increasing invasion risk. However, the invasive historical dynamics and distribution patterns of S. halepense associated with current and future climate change and land-use change (LUC) remain unknown. We first analyzed the invasive historical dynamics of S. halepense to explore its invasion status and expansion trends. We then used a species distribution model to examine how future climate change and LUC will facilitate the invasion of S. halepense. We reconstructed the countries that have historically been invaded by S. halepense based on databases with detailed records of countries and occurrences. We ran biomod2 based on climate data and land-use data at 5' resolution, assessing the significance of environmental variables and LUC. Sorghum halepense was widely distributed worldwide through grain trade and forage introduction, except in Africa. Europe and North America provided most potential global suitable habitats (PGSHs) for S. halepense in cropland, grassland, and urban environments, representing 48.69%, 20.79%, and 84.82%, respectively. The future PGSHs of S. halepense increased continuously in the Northern Hemisphere, transferring to higher latitudes. Environmental variables were more significant than LUC in predicting the PGSHs of S. halepense. Future PGSHs of S. halepense are expected to increase, exacerbating the invasion risk through agricultural LUC. These results provide a basis for the early warning and prevention of S. halepense worldwide.

SAFARIS: a spatial analytic framework for pest forecast systems

Non-native pests and diseases pose a risk of economic and environmental damage to managed and natural U.S. forests and agriculture. The U.S. Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) Plant Protection and Quarantine (PPQ) protects the health of U.S. agriculture and natural resources against invasive pests and diseases through efforts to prevent the entry, establishment, and spread of non-native pests and diseases. Because each pest or disease has its own idiosyncratic characteristics, analyzing risk is highly complex. To help PPQ better respond to pest and disease threats, we developed the Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS), an integrated system designed to provide a seamless environment for producing predictive models. SAFARIS integrates pest biology information, climate and non-climate data drivers, and predictive models to provide users with readily accessible and easily customizable tools to analyze pest and disease risks. The phenology prediction models, spread forecasting models, and other climate-based analytical tools in SAFARIS help users understand which areas are suitable for establishment, when surveys would be most fruitful, and aid in other analyses that inform decision-making, operational efforts, and rapid response. Here we introduce the components of SAFARIS and provide two use cases demonstrating how pest-specific models developed with SAFARIS tools support PPQ in its mission. Although SAFARIS is designed to address the needs of PPQ, the flexible, web-based framework is publicly available, allowing any user to leverage the available data and tools to model pest and disease risks.

Assessing potential European areas of Pierce's disease mediated by insect vectors by using spatial ensemble model

Pierce's disease (PD) is a serious threat to grape production in Europe. This disease is caused by Xylella fastidiosa and is mediated by insect vectors, suggesting its high potential for spread and necessity for early monitoring. In this study, hence, potential distribution of Pierce's disease varied with climate change and was spatially evaluated in Europe using ensemble species distribution modeling. Two models of X. fastidiosa and three major insect vectors (Philaenus spumarius, Neophilaenus campestris, and Cicadella viridis) were developed using CLIMEX and MaxEnt. The consensus areas of the disease and insect vectors, along with host distribution, were evaluated using ensemble mapping to identify high-risk areas for the disease. Our predictions showed that the Mediterranean region would be the most vulnerable to Pierce's disease, and the high-risk area would increase three-fold due to climate change under the influence of N. campestris distribution. This study demonstrated a methodology for species distribution modeling specific to diseases and vectors while providing results that could be used for monitoring Pierce's disease by simultaneously considering the disease agent, vectors, and host distribution.

Climate change has increased the global threats posed by three ragweeds (Ambrosia L.) in the Anthropocene

Invasive alien plants (IAPs) substantially affect the native biodiversity, agriculture, industry, and human health worldwide. Ambrosia (ragweed) species, which are major IAPs globally, produce a significant impact on human health and the natural environment. In particular, invasion of A. artemisiifolia, A. psilostachya, and A. trifida in non-native continents is more extensive and severe than that of other species. Here, we used biomod2 ensemble model based on environmental and species occurrence data to predict the potential geographical distribution, overlapping geographical distribution areas, and the ecological niche dynamics of these three ragweeds and further explored the environmental variables shaping the observed patterns to assess the impact of these IAPs on the natural environment and public health. The ecological niche has shifted in the invasive area compared with that in the native area, which increased the invasion risk of three Ambrosia species during the invasion process in the world. The potential geographical distribution and overlapping geographical distribution areas of the three Ambrosia species are primarily distributed in Asia, North America, and Europe, and are expected to increase under four representative concentration pathways in the 2050s. The centers of potential geographical distributions of the three Ambrosia species showed a tendency to shift poleward from the current time to the 2050s. Bioclimatic variables and the human influence index were more significant in shaping these patterns than other factors. In brief, climate change has facilitated the expansion of the geographical distribution and overlapping geographical distribution areas of the three Ambrosia species. Ecomanagement and cross-country management strategies are warranted to mitigate the future effects of the expansion of these ragweed species worldwide in the Anthropocene on the natural environment and public health.

Feast or famine: How is global change affecting forage supply for Yellowstone's ungulate herds?

The ecological integrity of US national parks and other protected areas are under threat in the Anthropocene. For Yellowstone National Park (YNP), the impacts that global change has already had on the park's capacity to sustain its large migratory herds of wild ungulates is incompletely understood. Here we examine how two understudied components of global change, the historical increase in atmospheric CO₂ and the spread of nonnative, invasive plant species, may have altered the capacity of YNP to provide forage for ungulates over the last 200-plus years. We performed two experiments: (1) a growth chamber study that determined the growth rates of important invasive and native YNP grasses that are forages for ungulates under preindustrial (280 ppm) versus modern (410 ppm) CO₂ levels and (2) a field study that compared the effect of defoliation (clipping) on the shoot growth of invasive and native mesic grassland plants under ambient CO₂ conditions in 2019. The growth chamber experiment revealed that modern CO₂ increased the growth rates of both invasive and native grasses, and invasive grasses grew faster regardless of CO₂ conditions. The field results showed a continuum of positive to negative responses of shoot growth to defoliation, with a subgroup of invasive species responding most positively. Altogether the results indicated that the historical increase in CO₂ and the spread of invasive species, some of which were planted to provide forage for ungulates in the early and mid-1900s, have likely increased the capacity of forage production in YNP. However, rising CO₂ has also resulted in regional warming and increased aridity in YNP, which will likely reduce grassland productivity. The challenge for global change biologists and park managers is to determine how competing components of global change have already affected and will increasingly affect forage dynamics and the sustainability of Yellowstone's iconic ungulate herds in the Anthropocene.

Habitat suitability and connectivity modeling predict genetic population structure and priority control areas for invasive nutria (Myocastor coypus) in a temperate river basin

The nutria (Myocastor coypus), also known as the coypu, is a semi-aquatic, invasive rodent native to South America that causes damage to natural riverine and wetland habitats in many parts of the world, including South Korea. Understanding habitat use, connectivity, and gene flow of nutria populations is critical for the sound management of local and regional ecosystems. Here, we assessed habitat suitability and connectivity in relation to the genetic structure of nutria populations in the Nakdong River Basin of South Korea. A total of 321 nutria occurrence sites and seven environmental variables were used to perform ensemble habitat suitability modeling using five species distribution models (SDMs), including boosted regression trees, maximum entropy model, random forest, generalized linear model, and multivariate adaptive regression splines. Using graph and circuit theory approaches, we assessed the population gene flow and current flow betweenness centrality (CFBC) of suitable habitats derived from the ensemble SDM. All SDMs performed well with a range of test AUC values from 0.962 to 0.970 (mean = 0.966) with true skill statistic values over 0.8. The minimum temperature of the coldest month, mean temperature of the warmest quarter, precipitation of the driest quarter, and distance from water bodies were important predictors in nutria habitat modeling. Nutria population gene flow was significantly correlated with the least-cost path distance on a cost resistance surface based on ensemble habitat suitability modeling and roads (Mantel's r = 0.60, p < 0.05). Finally, the CFBC positively correlated with the genetic diversity of nutria populations was used to identify priority control areas. Habitat suitability and connectivity modeling not only revealed environmental conditions and areas that support the survival and spread of nutrias, but also improved our understanding of the animals' genetic population structure, thereby indicating priority areas to target for eradication.

Clay and climatic variability explain the global potential distribution of Juniperus phoenicea toward restoration planning

Juniperus phoenicea is a medicinal conifer tree species distributed mainly in the Mediterranean region, and it is IUCN Red Listed species, locally threatened due to arid conditions and seed over-collection for medicinal purposes, particularly in the East-Mediterranean region. Several studies have addressed the potential distribution of J. phoenicea using bioclimatic and topographic variables at a local or global scale, but little is known about the role of soil and human influences as potential drivers. Therefore, our objectives were to determine the most influential predictor factors and their relative importance that might be limiting the regeneration of J. phoenicea, in addition, identifying the most suitable areas which could be assumed as priority conservation areas. We used ensemble models for species distribution modelling. Our findings revealed that aridity, temperature seasonality, and clay content are the most important factors limiting the potential distribution of J. phoenicea. Potentially suitable areas of the output maps, in which J. phoenicea populations degraded, could be assumed as decision-support tool reforestation planning. Other suitable areas, where there was no previous tree cover are a promising tool for afforestation and conservation planning. Finally, conservation actions are needed for natural habitats, particularly in the arid and semi-arid regions, which are highly threatened by global warming.

Modelling the distribution of Oxytenanthera abyssinica (A. Richard) under changing climate: implications for future dryland ecosystem restoration

Bamboo is the world's most widely exploited plant resource, with significant socio-economic and cultural values. In most parts of Africa, the population is in jeopardy due to the high pressure from human and natural forces. Of these, Oxytenanthera abyssinica (A. Richard) is among the threatened bamboo species. Furthermore, the effect of climate change on the distribution of bamboo has not yet been adequately studied. Therefore, this study aims to model and map the current and future distribution of O. abyssinica in Africa under four representative concentration pathways (RCPs), such as RCP2.6, RCP4.5, RCP6, and RCP8.5. The future projections were done for the years 2050 and 2070 using SDM ensemble approaches. To model the current and future distribution of O. abyssinica in Africa, 737 presence data were collected from various sources. For this study, a total of eight (8) temperature and precipitation-related variables were used as inputs to the Species Distribution Model (SDM). Finally, the model performance was assessed based on the area under the curve (AUC) and true skills statistics (TSS) measures of statistics. Our results showed an upsurge in the distribution of O. abyssinica across the study area for the low and moderate suitability classes for the climatic conditions considered in this study. However, a steady shrinkage in the habitat was found for the higher suitability classes. The model indicated climatic-related factors such as precipitation during the cold and warm quarters (57.8%), followed by mean temperature during the coldest quarter, isothermality (41.9%) and topographic factors such as elevation and slope (31.6%) were identified as the main limiting factors for the growth of O. abyssinica. Precipitation and temperature during the dry period, on the other hand, had the least impact on the growth of O. abyssinica. Except for RCP2.6, the majority of south-western African countries and the Sahel region remain the most climatically stable ecosystems for O. abyssinica growth under the three climatic scenarios RCP45, RCP6 and RCP8.5. Our results revealed a steady increase in the future suitable habitat for O. abyssinica all over the continent under the considered climatic scenarios. Therefore, to support the future restoration of dryland ecosystems, countries should scheme a restoration policy that allows the sustainable utilization of O. abyssinica tree species. The future policy direction for biodiversity conservation and management should encourage the use of O. abyssinica as a major plant species for improving the livelihoods of people living in dryland areas.

Phylogeography reveals the origin of the two phenological forms of large blue, Phengaris arion (Lepidoptera: Lycaenidae)

Our main goal was to investigate the phylogeography of the butterfly Phengaris arion to reveal the evolutionary origin of its ‘spring’ and ‘summer’ forms. Molecular analyses based on highly variable microsatellites, together with Wolbachia screening, were carried out on 34 populations in Europe. We found three well-defined genetic lineages of different origins: the Apennine, the central and the eastern. The highly distinct Apennine lineage is limited by the Alps and evaluated as an Evolutionary Significant Unit (ESU). Therefore, the taxon name ligurica, described from the Ligurian coast (Italy), should not be applied to denote the ‘summer form’ of the Pannonian region. The central lineage is limited by the Carpathians and the most eastern ranges of the Alps, and lacks major range fluctuations related to glaciations, although there is evidence for extra-Mediterranean refugia in the Carpathian Basin. The eastern clade could have had refugia in central Asia. Our results exclude the potential allopatric origin of the ‘spring’ and ‘summer’ arion, and support the hypothesis that the existence of the two forms could be a result of local adaptation to the distinctive phenology of host plant flowering which is manifested in the genetic differences between them. Wolbachia infection has been ruled out as a driver of sympatric speciation in P. arion.

A Scoping Review of Species Distribution Modeling Methods for Tick Vectors

Background

Globally, tick-borne disease is a pervasive and worsening problem that impacts human and domestic animal health, livelihoods, and numerous economies. Species distribution models are useful tools to help address these issues, but many different modeling approaches and environmental data sources exist.

Objective

We conducted a scoping review that examined all available research employing species distribution models to predict occurrence and map tick species to understand the diversity of model strategies, environmental predictors, tick data sources, frequency of climate projects of tick ranges, and types of model validation methods.

Design

Following the PRISMA-ScR checklist, we searched scientific databases for eligible articles, their references, and explored related publications through a graphical tool (www.connectedpapers.com). Two independent reviewers performed article selection and characterization using a priori criteria.

Results

We describe data collected from 107 peer-reviewed articles that met our inclusion criteria. The literature reflects that tick species distributions have been modeled predominantly in North America and Europe and have mostly modeled the habitat suitability for Ixodes ricinus (n = 23; 21.5%). A wide range of bioclimatic databases and other environmental correlates were utilized among models, but the WorldClim database and its bioclimatic variables 1–19 appeared in 60 (56%) papers. The most frequently chosen modeling approach was MaxEnt, which also appeared in 60 (56%) of papers. Despite the importance of ensemble modeling to reduce bias, only 23 papers (21.5%) employed more than one algorithm, and just six (5.6%) used an ensemble approach that incorporated at least five different modeling methods for comparison. Area under the curve/receiver operating characteristic was the most frequently reported model validation method, utilized in nearly all (98.9%) included studies. Only 21% of papers used future climate scenarios to predict tick range expansion or contraction. Regardless of the representative concentration pathway, six of seven genera were expected to both expand and retract depending on location, while Ornithodoros was predicted to only expand beyond its current range.

Conclusion

Species distribution modeling techniques are useful and widely employed tools for predicting tick habitat suitability and range movement. However, the vast array of methods, data sources, and validation strategies within the SDM literature support the need for standardized protocols for species distribution and ecological niche modeling for tick vectors.

Pet distribution modelling: Untangling the invasive potential of Trachemys dorbigni (Emydidae) in the Americas

Human activities have been changing the global biogeographic patterns by the introductions of invasive species. For reptiles, the invasion rate increase of non-native species is remarkably related to the pet trade, especially for freshwater turtles. Here we estimated the invasive potential of the South American turtle Trachemys dorbigni in the Americas using a combination of climatic and human activity variables. We built species distribution models based on data from the native and invasive ranges, using the ensemble model from five different algorithms (GAM, MAXENT, BRT, RF and GBM). We compared the two models' performance and predictions, one calibrated with only climatic variables (climate-driven), and the second also included a descriptive variable of human activity (climate plus human-driven). Suitable areas for T. dorbigni covered occurrence areas of its congeners and highly diversified ecoregions, such as the eastern USA, the islands of Central America, and the south eastern and eastern Brazilian coast. Our results indicate that human activities allow T. dorbigni to establish populations outside of its original climatic niche. Including human activity variables proved fundamental to refining the results to identify more susceptible areas to invasion and to allow the efficient targeting of prevention measures. Finally, we suggested a set of actions to prevent T. dorbigni becoming a highly impacting species in the areas identified as more prone to its invasion.

Ensemble evaluation of the potential risk areas of yellow-legged hornet distribution

Invasion of alien species facilitated by climate change and human assistant is one of global threats that cause irreversible damages on the local flora and fauna. One of these issued species, Vespa velutina nigrithorax du Buysson, 1905 (Hymenoptera:Vespidae), is a significant threat to entomofauna, including honeybees, in the introduced regions. This wasp is still expanding its habitats, prioritizing the development of a reliable species distribution model based on recently updated occurrence data. Therefore, the aim of this study was to evaluate the potential areas that are climatically exposed to V. v. nigrithorax invasion globally and in South Korea, where the wasp has caused severe damage to local ecosystems and apiculture after its recent introduction. We developed a new global scale ensemble model based on CLIMEX and Maxent models and applied it to South Korea using field survey data. As a result, risky areas were predicted to be temperate and subtropical climate regions, including the eastern USA, western Europe, Far East Asia, and small areas in South America and Australia. In particular, South Korea has a high potential risk throughout the country. We expect that this study would provide fundamental data for monitoring the environmental risks caused by V. v. nigrithorax using advanced species distribution modeling.

Mapping habitat suitability for Asiatic black bear and red panda in Makalu Barun National Park of Nepal from Maxent and GARP models

Habitat evaluation is essential for managing wildlife populations and formulating conservation policies. With the rise of innovative powerful statistical techniques in partnership with Remote Sensing, GIS and GPS techniques, spatially explicit species distribution modeling (SDM) has rapidly grown in conservation biology. These models can help us to study habitat suitability at the scale of the species range, and are particularly useful for examining the overlapping habitat between sympatric species. Species presence points collected through field GPS observations, in conjunction with 13 different topographic, vegetation related, anthropogenic, and bioclimatic variables, as well as a land cover map with seven classification categories created by support vector machine (SVM) were used to implement Maxent and GARP ecological niche models. With the resulting ecological niche models, the suitable habitat for asiatic black bear (Ursus thibetanus) and red panda (Ailurus fulgens) in Nepal Makalu Barun National Park (MBNP) was predicted. All of the predictor variables were extracted from freely available remote sensing and publicly shared government data resources. The modeled results were validated by using an independent dataset. Analysis of the regularized training gain showed that the three most important environmental variables for habitat suitability were distance to settlement, elevation, and mean annual temperature. The habitat suitability modeling accuracy, characterized by the mean area under curve, was moderate for both species when GARP was used (0.791 for black bear and 0.786 for red panda), but was moderate for black bear (0.857), and high for red panda (0.920) when Maxent was used. The suitable habitat estimated by Maxent for black bear and red panda was 716 km2 and 343 km2 respectively, while the suitable area determined by GARP was 1074 km2 and 714 km2 respectively. Maxent predicted that the overlapping area was 83% of the red panda habitat and 40% of the black bear habitat, while GARP estimated 88% of the red panda habitat and 58% of the black bear habitat overlapped. The results of land cover exhibited that barren land covered the highest percentage of area in MBNP (36.0%) followed by forest (32.6%). Of the suitable habitat, both models indicated forest as the most preferred land cover for both species (63.7% for black bear and 61.6% for red panda from Maxent; 59.9% black bear and 58.8% for red panda from GARP). Maxent outperformed GARP in terms of habitat suitability modeling. The black bear showed higher habitat selectivity than red panda. We suggest that proper management should be given to the overlapping habitats in the buffer zone. For remote and inaccessible regions, the proposed methods are promising tools for wildlife management and conservation, deserving further popularization.

Climate change and micro-topography are facilitating the mountain invasion by a non-native perennial plant species

Mountainous areas and their endemic plant diversity are threatened by global climate change and invasive species. Mountain plant invasions have historically been minimal, however, climate change and increased anthropogenic activity (e.g. roads and vehicles) are amplifying invasion pressure. We assessed plant performance (stem density and fruit production) of the invasive non-native forb Linaria dalmatica along three mountain roads, over an eight-year period (2008–2015) in the Greater Yellowstone Ecosystem (GYE), USA. We evaluated how L. dalmatica performed in response to elevation, changed over time, responded to climate and how the climate of our sites has changed, and compared elevation, climate, micro-topography (slope aspect and angle), and fruit production among sites with differing temporal trends. Linaria dalmatica stem density and fruit production increased with elevation and demonstrated two temporal groups, those populations where stem densities shrank and those that remained stable or grew over time. Stem density demonstrated a hump-shaped response to summer mean temperature, while fruit production decreased with summer mean maximum temperature and showed a hump-shaped response to winter precipitation. Analysis of both short and long-term climate data from our sites, demonstrated that summer temperatures have been increasing and winters getting wetter. The shrinking population group had a lower mean elevation, hotter summer temperatures, drier winters, had plots that differed in slope aspect and angle from the stable/growing group, and produced less fruit. Regional climate projections predict that the observed climate trends will continue, which will likely benefit L. dalmatica populations at higher elevations. We conclude that L. dalmatica may persist at lower elevations where it poses little invasive threat, and its invasion into the mountains will continue along roadways, expanding into higher elevations of the GYE.

Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

The Relict Ecosystem of Maytenus senegalensis subsp. europaea in an Agricultural Landscape: Past, Present and Future Scenarios

Maytenus senegalensis subsp. europaea is a shrub belonging to the Celastraceae family, whose only European populations are distributed discontinuously along the south-eastern coast of the Iberian Peninsula, forming plant communities with great ecological value, unique in Europe. As it is an endangered species that makes up plant communities with great palaeoecological significance, the development of species distribution models is of major interest under different climatic scenarios, past, present and future, based on the fact that the climate could play a relevant role in the distribution of this species, as well as in the conformation of the communities in which it is integrated. Palaeoecological models were generated for the Maximum Interglacial, Last Maximum Glacial and Middle Holocene periods. The results obtained showed that the widest distribution of this species, and the maximum suitability of its habitat, occurred during the Last Glacial Maximum, when the temperatures of the peninsular southeast were not as contrasting as those of the rest of the European continent and were favored by higher rainfall. Under these conditions, large territories could act as shelters during the glacial period, a hypothesis reflected in the model’s results for this period, which exhibit a further expansion of M. europaea’s ecological niche. The future projection of models in around 2070, for four Representative Concentration Pathways according to the fifth report of the Intergovernmental Panel on Climate Change, showed that the most favorable areas for this species would be Campo de Dalías (southern portion of Almería province) as it presents the bioclimatic characteristics of greater adjustment to M. europaea’s ecological niche model. Currently, some of the largest specimens of the species survive in the agricultural landscapes in the southern Spain. These areas are almost totally destroyed and heavily altered by intensive agriculture greenhouses, also causing a severe fragmentation of the habitat, which implies a prospective extinction scenario in the near future.

Assessing the Potential Replacement of Laurel Forest by a Novel Ecosystem in the Steep Terrain of an Oceanic Island

Biological invasions are a major global threat to biodiversity and often affect ecosystem services negatively. They are particularly problematic on oceanic islands where there are many narrow-ranged endemic species, and the biota may be very susceptible to invasion. Quantifying and mapping invasion processes are important steps for management and control but are challenging with the limited resources typically available and particularly difficult to implement on oceanic islands with very steep terrain. Remote sensing may provide an excellent solution in circumstances where the invading species can be reliably detected from imagery. We here develop a method to map the distribution of the alien chestnut (Castanea sativa Mill.) on the island of La Palma (Canary Islands, Spain), using freely available satellite images. On La Palma, the chestnut invasion threatens the iconic laurel forest, which has survived since the Tertiary period in the favourable climatic conditions of mountainous islands in the trade wind zone. We detect chestnut presence by taking advantage of the distinctive phenology of this alien tree, which retains its deciduousness while the native vegetation is evergreen. Using both Landsat 8 and Sentinel-2 (parallel analyses), we obtained images in two seasons (chestnuts leafless and in-leaf, respectively) and performed image regression to detect pixels changing from leafless to in-leaf chestnuts. We then applied supervised classification using Random Forest to map the present-day occurrence of the chestnut. Finally, we performed species distribution modelling to map the habitat suitability for chestnut on La Palma, to estimate which areas are prone to further invasion. Our results indicate that chestnuts occupy 1.2% of the total area of natural ecosystems on La Palma, with a further 12–17% representing suitable habitat that is not yet occupied. This enables targeted control measures with potential to successfully manage the invasion, given the relatively long generation time of the chestnut. Our method also enables research on the spread of the species since the earliest Landsat images.

Accessing habitat suitability and connectivity for the westernmost population of Asian black bear (Ursus thibetanus gedrosianus, Blanford, 1877) based on climate changes scenarios in Iran

Climate change, as an emerging phenomenon, has led to changes in the distribution, movement, and even risk of extinction of various wildlife species and this has raised concerns among conservation biologists. Different species have two options in the face of climate change, either to adopt or follow their climatic niche to new places through the connectivity of habitats. The modeling of interpatch landscape communications can serve as an effective decision support tool for wildlife managers. This study was conducted to assess the effects of climate change on the distribution and habitat connectivity of the endangered subspecies of Asian black bear (Ursus thibetanus gedrosianus) in the southern and southeastern Iran. The presence points of the species were collected in Provinces of Kerman, Hormozgan, and Sistan-Baluchestan. Habitat modeling was done by the Generalized Linear Model, and 3 machine learning models including Maximum Entropy, Back Propagation based artificial Neural Network, and Support Vector Machine. In order to achieve the ensemble model, the results of the mentioned models were merged based on the method of "accuracy rate as weight" derived from their validation. To construct pseudo-absence points for the use in the mentioned models, the Ensemble model of presence-only models was used. The modeling was performed using 15 habitat variables related to climatic, vegetation, topographic, and anthropogenic parameters. The three general circulation models of BCC-CSM1, CCSM4, and MRI-CGCM3 were selected under the two scenarios of RCP2.6 and RCP8.5 by 2070. To investigate the effect of climate change on the habitat connections, the protected areas of 3 provinces were considered as focal nodes and the connections between them were established based on electrical circuit theory and Pairwise method. The true skill statistic was employed to convert the continuous suitability layers to binary suitable/unsuitable range maps to assess the effectiveness of the protected areas in the coverage of suitable habitats for the species. Due to the high power of the stochastic forest model in determining the importance of variables, this method was used. The results showed that presence/absence models were successful in the implementation and well distinguished the points of presence and pseudo-absence from each other. Based on the random forests model, the variables of Precipitation of Driest Quarter, Precipitation of Coldest Quarter, and Temperature Annual Range have the greatest impact on the habitat suitability. Comparing the modeling findings to the realities of the species distribution range indicated that the suitable habitats are located in areas with high humidity and rainfall, which are mostly in the northern areas of Bandar Abbas, south of Kerman, and west and south of Sistan-Baluchestan. The area of suitable habitats, in the MRI-CGCM3 (189731 Km2) and CCSM4 (179007 Km2) models under the RCP2.6 scenario, is larger than the current distribution (174001 Km2). However, in terms of the performance of protected areas, the optimal coverage of the species by the boundary of the protected areas, under each of the RCP2.6 and RCP8.5 scenarios, is less than the present time. According to the electric circuit theory, connecting the populations in the protected areas of Sistan-Baluchestan province to those in the northern Hormozgan and the southern Kerman would be based on the crossing through the heights of Sistan-Baluchestan and Hormozgan provinces and the plains between these heights would be the movement pinch points under the current and future scenarios. Populations in the protected areas of Kerman have higher quality patch connections than that of the other two provinces. The areas such as Sang-e_Mes, Kouh_Shir, Zaryab, and Bahr_Aseman in Kerman Province and Kouhbaz and Geno in Hormozgan Province can provide suitable habitats for the species in the distribution models. The findings revealed that the conservation of the heights along with the caves inside them could be a protective priority to counteract the effects of climate change on the species.

Modeling the potential distribution of Epiphyllum phyllanthus (L.) Haw. under future climate scenarios in the Caatinga biome

The climate change projections for the Caatinga biome this century are for an increase in temperature and reduction in rainfall, leading to aridization and plant cover dominated by Cactaceae. The objective of this study was to model the potential distribution of Epiphyllum phyllanthus (L.) Haw., a cactus that is native to the Caatinga biome, considering two possible future climate scenarios, to assess this species' spatio-temporal response to these climate change, and thus to evaluate the need or not for conservation measures. For this purpose, we obtained biogeographic information on the target species from biodiversity databases, choosing nine environmental variables and applying the MaxEnt algorithm. We considered the time intervals 2041-2060 and 2061-2080, centered on 2050 and 2070, respectively, and the greenhouse gas scenarios RCP4.5 and 8.5. For all the scenarios considered, the models generated for 2050 and 2070 projected drastic contraction (greater than 80%) for the areas of potential occurrence of the species in relation to the present potential. The remaining areas were found to be concentrated in the northern portion of the biome, specifically in the northern part of the state of Ceará, which has particular characteristics.

Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling

Inland valleys (IVs) in Africa are important landscapes for rice cultivation and are targeted by national governments to attain self-sufficiency. Yet, there is limited information on the spatial distribution of IVs suitability at the national scale. In the present study, we developed an ensemble model approach to characterize the IVs suitability for rainfed lowland rice using 4 machine learning algorithms based on environmental niche modeling (ENM) with presence-only data and background sample, namely Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Maximum Entropy (MAXNT) and Random Forest (RF). We used a set of predictors that were grouped under climatic variables, agricultural water productivity and soil water content, soil chemical properties, soil physical properties, vegetation cover, and socio-economic variables. The Area Under the Curves (AUC) evaluation metrics for both training and testing were respectively 0.999 and 0.873 for BRT, 0.866 and 0.816 for GLM, 0.948 and 0.861 for MAXENT and 0.911 and 0.878 for RF. Results showed that proximity of inland valleys to roads and urban centers, elevation, soil water holding capacity, bulk density, vegetation index, gross biomass water productivity, precipitation of the wettest quarter, isothermality, annual precipitation, and total phosphorus among others were major predictors of IVs suitability for rainfed lowland rice. Suitable IVs areas were estimated at 155,000-225,000 Ha in Togo and 351,000-406,000 Ha in Benin. We estimated that 53.8% of the suitable IVs area is needed in Togo to attain self-sufficiency in rice while 60.1% of the suitable IVs area is needed in Benin to attain self-sufficiency in rice. These results demonstrated the effectiveness of an ensemble environmental niche modeling approach that combines the strengths of several models.

A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales

Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. To ensure efficiency, we used largely automated modeling approaches and human input only at key junctures. We explore and present uncertainty by using two alternative sources of background samples, including five statistical algorithms, and constructing model ensembles. We demonstrate the use and efficiency of the Software for Assisted Habitat Modeling [SAHM 2.1.2], a package in VisTrails, which performs the majority of the modeling analyses. Our workflow includes solicitation of expert feedback on model outputs such as spatial prediction results and variable response curves, and iterative improvement based on new data availability and directed field validation of initial model results. We highlight the utility of the models for decision-making at regional and local scales with case studies of two plant species that invade natural areas: fountain grass (Pennisetum setaceum) and goutweed (Aegopodium podagraria). By balancing model automation with human intervention, we can efficiently provide land managers with mapped predicted distributions for multiple invasive species to inform decisions across spatial scales.

Using species distribution models to predict potential hot-spots for Rift Valley Fever establishment in the United Kingdom

Vector borne diseases are a continuing global threat to both human and animal health. The ability of vectors such as mosquitos to cover large distances and cross country borders undetected provide an ever-present threat of pathogen spread. Many diseases can infect multiple vector species, such that even if the climate is not hospitable for an invasive species, indigenous species may be susceptible and capable of transmission such that one incursion event could lead to disease establishment in these species. Here we present a consensus modelling methodology to estimate the habitat suitability for presence of mosquito species in the UK deemed competent for Rift Valley fever virus (RVF) and demonstrate its application in an assessment of the relative risk of establishment of RVF virus in the UK livestock population. The consensus model utilises observed UK mosquito surveillance data, along with climatic and geographic prediction variables, to inform six independent species distribution models; the results of which are combined to produce a single prediction map. As a livestock host is needed to transmit RVF, we then combine the consensus model output with existing maps of sheep and cattle density to predict the areas of the UK where disease is most likely to establish in local mosquito populations. The model results suggest areas of high suitability for RVF competent mosquito species across the length and breadth of the UK. Notable areas of high suitability were the South West of England and coastal areas of Wales, the latter of which was subsequently predicted to be at higher risk for establishment of RVF due to higher livestock densities. This study demonstrates the applicability of outputs of species distribution models to help predict hot-spots for risk of disease establishment. While there is still uncertainty associated with the outputs we believe that the predictions are an improvement on just using the raw presence points from a database alone. The outputs can also be used as part of a multidisciplinary approach to inform risk based disease surveillance activities.

Modeling the Distribution of Medically Important Tick Species in Florida

The lone star (Amblyomma americanum), black-legged (Ixodes scapularis) and American dog ticks (Dermacentor variabilis) are species of great public health importance as they are competent vectors of several notable pathogens. While the regional distributions of these species are well characterized, more localized distribution estimates are sparse. We used records of field collected ticks and an ensemble modeling approach to predict habitat suitability for each of these species in Florida. Environmental variables capturing climatic extremes were common contributors to habitat suitability. Most frequently, annual precipitation (Bio12), mean temperature of the driest quarter (Bio9), minimum temperature of the coldest month (Bio6), and mean Normalized Difference Vegetation Index (NDVI) were included in the final models for each species. Agreement between the modeling algorithms used in this study was high and indicated the distribution of suitable habitat for all three species was reduced at lower latitudes. These findings are important for raising awareness of the potential for tick-borne pathogens in Florida.

Evaluating Potential Distribution of High-Risk Aquatic Invasive Species in the Water Garden and Aquarium Trade at a Global Scale Based on Current Established Populations

Aquatic non-native invasive species are commonly traded in the worldwide water garden and aquarium markets, and some of these species pose major threats to the economy, the environment, and human health. Understanding the potential suitable habitat for these species at a global scale and at regional scales can inform risk assessments and predict future potential establishment. Typically, global habitat suitability models are fit for freshwater species with only climate variables, which provides little information about suitable terrestrial conditions for aquatic species. Remotely sensed data including topography and land cover data have the potential to improve our understanding of suitable habitat for aquatic species. In this study, we fit species distribution models using five different model algorithms for three non-native aquatic invasive species with bioclimatic, topographic, and remotely sensed covariates to evaluate potential suitable habitat beyond simple climate matches. The species examined included a frog (Xenopus laevis), toad (Bombina orientalis), and snail (Pomacea spp.). Using a unique modeling approach for each species including background point selection based on known established populations resulted in robust ensemble habitat suitability models. All models for all species had test area under the receiver operating characteristic curve values greater than 0.70 and percent correctly classified values greater than 0.65. Importantly, we employed multivariate environmental similarity surface maps to evaluate potential extrapolation beyond observed conditions when applying models globally. These global models provide necessary forecasts of where these aquatic invasive species have the potential for establishment outside their native range, a key component in risk analyses.

Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem

In recent years, an increasing number of distribution maps of invasive alien plant species (IAPS) have been published using different machine learning algorithms (MLAs). However, for designing spatially explicit management strategies, distribution maps should include information on the local cover/abundance of the IAPS. This study compares the performances of five MLAs: gradient boosting machine in two different implementations, random forest, support vector machine and deep learning neural network, one ensemble model and a generalized linear model; thereby identifying the best-performing ones in mapping the fractional cover/abundance and distribution of IPAS, in this case called Prosopis juliflora (SW. DC.). Field level Prosopis cover and spatial datasets of seventeen biophysical and anthropogenic variables were collected, processed, and used to train and validate the algorithms so as to generate fractional cover maps of Prosopis in the dryland ecosystem of the Afar Region, Ethiopia. Out of the seven tested algorithms, random forest performed the best with an accuracy of 92% and sensitivity and specificity >0.89. The next best-performing algorithms were the ensemble model and gradient boosting machine with an accuracy of 89% and 88%, respectively. The other tested algorithms achieved comparably low performances. The strong explanatory variables for Prosopis distributions in all models were NDVI, elevation, distance to villages and distance to rivers; rainfall, temperature, near-infrared and red reflectance, whereas topographic variables, except for elevation, did not contribute much to the current distribution of Prosopis. According to the random forest model, a total of 1.173 million ha (12.33% of the study region) was found to be invaded by Prosopis to varying degrees of cover. Our findings demonstrate that MLAs can be successfully used to develop fractional cover maps of plant species, particularly IAPS so as to design targeted and spatially explicit management strategies.

Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana, Kenya

AIM

Prosopis spp. are an invasive alien plant species native to the Americas and well adapted to thrive in arid environments. In Kenya, several remote-sensing studies conclude that the genus is well established throughout the country and is rapidly invading new areas. This research aims to model the potential habitat of Prosopis spp. by using an ensemble model consisting of four species distribution models. Furthermore, environmental and expert knowledge-based variables are assessed.

LOCATION

Turkana County, Kenya.

METHODS

We collected and assessed a large number of environmental and expert knowledge-based variables through variable correlation, collinearity, and bias tests. The variables were used for an ensemble model consisting of four species distribution models: (a) logistic regression, (b) maximum entropy, (c) random forest, and (d) Bayesian networks. The models were evaluated through a block cross-validation providing statistical measures.

RESULTS

The best predictors for Prosopis spp. habitat are distance from water and built-up areas, soil type, elevation, lithology, and temperature seasonality. All species distribution models achieved high accuracies while the ensemble model achieved the highest scores. Highly and moderately suitable Prosopis spp. habitat covers 6% and 9% of the study area, respectively.

MAIN CONCLUSIONS

Both ensemble and individual models predict a high risk of continued invasion, confirming local observations and conceptions. Findings are valuable to stakeholders for managing invaded area, protecting areas at risk, and to raise awareness.

Variable importance for sustaining macrophyte presence via random forests: data imputation and model settings

Data sets plagued with missing data and performance-affecting model parameters represent recurrent issues within the field of data mining. Via random forests, the influence of data reduction, outlier and correlated variable removal and missing data imputation technique on the performance of habitat suitability models for three macrophytes (Lemna minor, Spirodela polyrhiza and Nuphar lutea) was assessed. Higher performances (Cohen’s kappa values around 0.2–0.3) were obtained for a high degree of data reduction, without outlier or correlated variable removal and with imputation of the median value. Moreover, the influence of model parameter settings on the performance of random forest trained on this data set was investigated along a range of individual trees (ntree), while the number of variables to be considered (mtry), was fixed at two. Altering the number of individual trees did not have a uniform effect on model performance, but clearly changed the required computation time. Combining both criteria provided an ntree value of 100, with the overall effect of ntree on performance being relatively limited. Temperature, pH and conductivity remained as variables and showed to affect the likelihood of L. minor, S. polyrhiza and N. lutea being present. Generally, high likelihood values were obtained when temperature is high (>20 °C), conductivity is intermediately low (50–200 mS m⁻¹) or pH is intermediate (6.9–8), thereby also highlighting that a multivariate management approach for supporting macrophyte presence remains recommended. Yet, as our conclusions are only based on a single freshwater data set, they should be further tested for other data sets.

Inferring the absence of an incipient population during a rapid response for an invasive species

Successful eradication of invasives is facilitated by early detection and prompt onset of control. However, realizing or verifying that a colonization has occurred is difficult for cryptic species especially at low population densities. Responding to the capture or unconfirmed sighting of a cryptic invasive species, and the associated effort to determine if it indicates an incipient (small, localized) population or merely a lone colonizer, is costly and cannot continue indefinitely. However, insufficient detection effort risks erroneously concluding the species is not present, allowing the population to increase in size and expand its range. Evidence for an incipient population requires detection of ≥1 individual; its absence, on the other hand, must be inferred probabilistically. We use an actual rapid response incident and species-specific detection estimates tied to a known density to calculate the amount of effort (with non-sequential detections) necessary to assert, with a pre-defined confidence, that invasive brown treesnakes are absent from the search area under a wide range of hypothetical population densities. We illustrate that the amount of effort necessary to declare that a species is absent is substantial and increases with decreased individual detection probability, decreased density, and increased level of desired confidence about its absence. Such survey investment would be justified where the cost savings due to early detection are large. Our Poisson-based model application will allow managers to make informed decisions about how long to continue detection efforts, should no additional detections occur, and suggests that effort to do so is significantly higher than previously thought. While our model application informs how long to search to infer absence of an incipient population of brown treesnakes, the approach is sufficiently general to apply to other invasive species if density-dependent detection estimates are known or reliable surrogate estimates are available.

Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees

Data aggregation is a necessity when working with big data. Data reduction steps without loss of information are a scientific and computational challenge but are critical to enable effective data processing and information delineation in data-rich studies. We investigated the effect of four spatial aggregation schemes on Landsat imagery on prediction accuracy of green photosynthetic vegetation (PV) based on fractional cover (FCover). To reduce data volume we created an evenly spaced grid, overlaid that on the PV band and delineated the arithmetic mean of PV fractions contained within each grid cell. The aggregated fractions and the corresponding geographic grid cell coordinates were then used for boosted regression tree prediction models. Model goodness of fit was evaluated by the Root Mean Squared Error (RMSE). Two spatial resolutions (3000 m and 6000 m) offer good prediction accuracy whereas others show either too much unexplained variability model prediction results or the aggregation resolution smoothed out local PV in heterogeneous land. We further demonstrate the suitability of our aggregation scheme, offering an increased processing time without losing significant topographic information. These findings support the feasibility of using geographic coordinates in the prediction of PV and yield satisfying accuracy in our study area.

Reconstructed historical distribution and phylogeography unravels non-steppic origin of Caucasotachea vindobonensis (Gastropoda: Helicidae)

Existing data on the phylogeography of European taxa of steppic provenance suggests that species were widely distributed during glacial periods but underwent range contraction and fragmentation during interglacials into "warm-stage refugia." Among the steppe-related invertebrates that have been examined, the majority has been insects, but data on the phylogeography of snails is wholly missing. To begin to fill this gap, phylogeographic and niche modeling studies on the presumed steppic snail Caucasotachea vindobonensis were conducted. Surprisingly, reconstruction of ancestral areas suggests that extant C. vindobonensis probably originated in the Balkans and survived there during the Late Pleistocene glaciations, with a more recent colonization of the Carpatho-Pannonian and the Ponto-Caspian regions. In the Holocene, C. vindobonensis colonized between the Sudetes and the Carpathians to the north, where its recent and current distribution may have been facilitated by anthropogenic translocations. Together, these data suggest a possible non-steppic origin of C. vindobonensis. Further investigation may reveal the extent to which the steppic snail assemblages consist partly of Holocene newcomers.

Habitat assessment of Marco Polo sheep (Ovis ammon polii) in Eastern Tajikistan: Modeling the effects of climate change

Identifying the factors predicting the high-elevation suitable habitats of Central Asian argali wild sheep and how these suitable habitats are affected by the changing climate regimes could help address conservation and management efforts and identify future critical habitat for the species in eastern Tajikistan. This study used environmental niche models (ENMs) to map and compare potential present and future distributions of suitable environmental conditions for Marco Polo argali. Argali occurrence points were collected during field surveys conducted from 2009 to 2016. Our models showed that terrain ruggedness and annual mean temperature had strong correlations on argali distribution. We then used two greenhouse gas concentration trajectories (RCP 4.5 and RCP 8.5) for two future time periods (2050 and 2070) to model the impacts of climate change on Marco Polo argali habitat. Results indicated a decline of suitable habitat with majority of losses observed at lower elevations (3,300-4,300 m). Models that considered all variables (climatic and nonclimatic) predicted losses of present suitable areas of 60.6% (6,928 km2) and 63.2% (7,219 km2) by 2050 and 2070, respectively. Results also showed averaged habitat gains of 46.2% (6,106 km2) at much higher elevations (4,500-6,900 m) and that elevational shifts of habitat use could occur in the future. Our results could provide information for conservation planning for this near threatened species in the region.

Assessing three fish species ecological status in Colorado River, Grand Canyon based on physical habitat and population models

Colorado River is a unique ecosystem and provides important ecological services such as habitat for fish species as well as water power energy supplies. River management for this ecosystem requires assessment and decision support tools for fish which involves protecting, restoring as well as forecasting of future conditions. In this paper, a habitat and population model was developed and used to determine the levels of fish habitat suitability and population density in Colorado River between Lees Ferry and Lake Mead. The short term target fish populations are also predicted based on native fish recovery strategy. This model has been developed by combining hydrodynamics, heat transfer and sediment transport models with a habitat suitability index model and then coupling with habitat model into life stage population model. The fish were divided into four life stages according to the fish length. Three most abundant and typical native and non-native fish were selected as target species, which are rainbow trout (Oncorhynchus mykiss), brown trout (Salmo trutta) and flannelmouth sucker (Catostomus latipinnis). Flow velocity, water depth, water temperature and substrates were used as the suitability indicators in habitat model and overall suitability index (OSI) as well as weight usable area (WUA) was used as an indicator in population model. A comparison was made between simulated fish population alteration and surveyed fish number fluctuation during 2000 to 2009. The application of this habitat and population model indicates that this model can be accurate present habitat situation and targets fish population dynamics of in the study areas. The analysis also indicates the flannelmouth sucker population will steadily increase while the rainbow trout will decrease based on the native fish recovery scheme.