Occurrence Points Research Articles

Risk zoning of Gynaephora alpherakii (Lepidoptera: Lymantriidae) on the Qinghai-Tibetan Plateau.

Gynaephora alpherakii (Grum-Grschimailo) (Lepidoptera: Lymantriidae) is a major pest in alpine meadow areas in the Qinghai-Tibetan Plateau (QTP) and causes severe losses in the local livestock production industry. Assessing areas at high risk for G. alpherakii infestation is critical for the effective management of this pest. In this study, an ensemble distribution model was used to analyze areas suitable for G. alpherakii on the QTP. Risk zoning was performed based on the vegetation and environmental conditions in areas with high-occurrence points, and differences between high-occurrence points and other occurrence points were compared. The results revealed that the suitable areas for G. alpherakii on the QTP amounted to 28.27 × 104 hm2, accounting for 10.94% of the total area of the QTP; the area of high-risk was 19.07 × 104 hm2, and these areas were located mainly in the eastern part of the QTP. Qinghai Province had the highest risk, accounting for 77% of the total area identified as high-risk. In terms of habitat, G. alpherakii preferred alpine Kobresia meadows, which have abundant sunshine, loose soil, and scarce precipitation. This study supports efforts to manage G. alpherakii outbreaks and contributes to the ecological protection of the QTP.

MaxEnt modeling of Klebsiella pneumoniae: predicting future distribution and evaluating the risk for public health

Klebsiella pneumoniae is a Gram-negative bacterium known for causing infections such as pneumonia, urinary tract infections, and bloodstream infections in humans. Its increasing drug resistance poses a significant threat. Understanding the effects of climate change on this bacterium is crucial to managing its impacts effectively. In this study, we employed MaxEnt modeling to explore the current and future distribution of K. pneumoniae under different climate change scenarios for 2050 and 2070. Our models utilized 280 valid occurrence points and 19 bioclimatic variables, with Mean Annual Temperature and Mean Annual Precipitation being the most influential variables. The models demonstrated high accuracy, with an AUC of 0.889. Our findings revealed that K. pneumoniae has a cosmopolitan distribution, with the highest suitability observed in Southeastern Asia, Central Africa, and Southern Europe. Future predictions indicated an apparent expansion of its range across almost all continents, particularly in the RCP 8.5 2070 scenario. Results have crucial implications for epidemiology, as they enable the identification of current and potential future disease hotspots and geographical patterns. This knowledge can inform the development of effective infection control strategies. Furthermore, it provides valuable insights for decision-makers and policymakers in allocating resources efficiently for medical control and prevention strategies.

Predicting the potential habitat of bears under a changing climate in Nepal.

In Nepal, the distributions of three bear species vary: sloth bears (Melursus ursinus) in the lowlands, Asiatic black bears (Ursus thibetanus) in the mid-hills, and brown bears (Ursus arctos) in the high Himalayas. We utilized 179 occurrence points for sloth bears, 199 for Asiatic black bears, and 41 points for brown bears to construct a habitat model incorporating climate and topographic variables. Employing various species distribution modeling algorithms in BIOMOD2, the model predicts suitable habitats spanning 10,971.75 km2 for sloth bears; 29,470.75 km2 for Asiatic black bears; and 6152.97 km2 for brown bears. Within protected areas, the habitat for sloth bears is 4120.56 km2, that for Asiatic black bears is 9688.67 km2, and that for brown bears is 4538.67 km2. Chitwan National Park emerged as the prime sloth bear habitat with a core area of 918.55 km2 and a buffer zone of 726.485 km2. The Annapurna Conservation Area was deemed suitable for Asiatic black bears and brown bears, covering 2802.23 km2 and 2795.91 km2, respectively. The models projected a significant reduction in the habitat of these bear species both inside and outside protected areas. As predicted under the Shared Socioeconomic Pathways (SSP)2-4.5 scenario, sloth bears may experience 54.9% (2050) and 44.7% (2070) losses, respectively, of habitat; Asiatic black bears, 11.2% (2050) and 16.8% (2070); and brown bears, 68.41% (2050) and 82.20% (2070) losses. The overlap between sloth bears and black bears spans 38.7 km2, and that between brown bears and black bears is 26.6 km2. Notably, all three bear species exhibited suitability correlations with the intermediate temperature of the driest quarter. Examining current and projected habitats provides essential information for guiding conservation strategies and ensuring the conservation of these bear species in the face of climate change.

Analyzing the distribution patterns and dynamic niche of Magnolia grandiflora L. in the United States and China in response to climate change

IntroductionMagnolia grandiflora L. (southern magnolia) is native to the southeastern coastal areas of the United States, from North Carolina to eastern Texas (USDA Cold Hardiness Zone 8). It is currently widely cultivated in Zones 5-10 in the U.S. and in southern Yangtze River regions in China. Limited studies have examined the effects of climate change and human activities on the geographical distribution and adaptability of M. grandiflora during its introduction to China.MethodsWe selected 127 occurrence points in the U.S. and 87 occurrence points in China, along with 43 environmental variables, to predict suitable habitat areas for M. grandiflora using present climate data (1970-2000) and projected future climate data (2050-2070) based on a complete niche ensemble model (EM) using the Biomod2 package. We also predicted the niche change of M. grandiflora in both countries using the 'ecospat' package in R.ResultsThe ensemble models demonstrated high reliability, with an AUC of 0.993 and TSS of 0.932. Solar radiation in July, human impact index, and precipitation of the wettest month were identified as the most critical variables influencing M. grandiflora distribution. The species shows a similar trend of distribution expansion under climate change scenarios in both countries, with predicted expansions towards the northwest and northeast, and contractions in southern regions.DiscussionOur study emphasizes a practical framework for predicting suitable habitats and migration of Magnoliaceae species under climate change scenarios. These findings provide valuable insights. for species conservation, introduction, management strategies, and sustainable utilization of M. grandiflora.

Maxent modelling reveals suitable habitat remains unchanged for Carcinus maenas in Australia

ABSTRACT This study assesses the habitat suitability of the invasive green crab, Carcinus maenas, in Australia using MaxEnt modelling under current and future climate scenarios. The species has significant ecological impacts, displacing native species and altering habitats. Using 90 occurrence points from the Global Biodiversity Information Facility, the MaxEnt model shows high performance (Area Under the Curve value of 0.99), indicating reliable predictions. Currently, the green crab's habitat suitability along the Australian coast ranges from high to medium in Tasmania, Victoria, New South Wales and South Australia and low in Western Australia and South Queensland. Future projections suggest a decrease in suitable habitats, confined to Tasmania, Victoria, Adelaide and New South Wales. These findings can guide the development of long-term management and conservation plans for C. maenas, with an emphasis on areas with consistent habitat suitability. Additionally, further field and laboratory experiments are recommended to understand the species’ tolerance to environmental changes. Recognising the ecological impact of C. maenas is crucial for informed decision-making to preserve Australia's ecosystems and ensure the sustainable coexistence of native species amidst invasive threats.

Leveraging social media and community science data for environmental niche models: A case study with native Australian bees

Museum occurrence records are popular sources of information for creating Environmental Niche Models (ENMs), which allow the mapping of the potential niche ranges of species. Occurrence data is often downloaded en masse from established databases. However, the use of non-traditional data sources, such as occurrence records from community/citizen science outreach and social media, is increasing in use and abundance. Data from non-traditional data sources are potentially valuable records of information, particularly for species where museum occurrence records may be comparatively scarce. In the current study, we aimed to determine the impact of adding occurrence data from non-traditional databases to ENMs that were originally created using traditional databases with a group of comparatively understudied species, native Australian bees. We used the Maxent algorithm to model the potential environmental niches of eight species. We created three models for each species: 1) one consisting of only location data from museum specimen collection records from the Atlas of Living Australia (ALA) (a traditional database), 2) one combining ALA and geo-tagged social media (Flickr) data, and 3) a model combining ALA and geo-tagged community science data from iNaturalist. This resulted in 24 different models. By comparing the models produced from each of the augmented data sets with the traditional species data set (ALA vs. ALA & Flickr; ALA vs. ALA & iNaturalist) we showed that there were significant differences, not only in predicted ranges, but also in the weighting of environmental variables used by the models to predict the environmental niche. Differences were more greatly influenced by the geographic location of the extra occurrences rather than the number of additional occurrence points. We demonstrate the potential value and risks of including social media and community science geo-tagged image data in supplementing knowledge of species distributions, particularly for relatively under-sampled species such as native bees.

Current and future habitat suitability modelling of Bambusa teres outside forest areas in Nepal under climate change scenarios

Bambusa teres Buch.-Ham. ex Munro (syn. Bambusa nutans subsp. capulata Stapleton) is a fast-growing perennial bamboo that has ecological, economic, cultural and climate change mitigation benefits. However, information on its current and future potential distribution outside forest areas across Nepal and the key factors affecting its growth and distribution are little known. We used a total of 298 occurrence points obtained from the National Bamboo Resource Assessment and 23 environmental variables to project the distribution of B. teres throughout its potential range in Nepal. Maximum entropy model (MaxEnt) was utilized for this study. We assessed the performance of the model using a receiver operating characteristic curve and evaluated the relative importance of predictor variables through a Jackknife procedure. The model achieved a high level of performance with an area under the curve value of 0.928. Precipitation of the coldest quarter (bio_19), temperature seasonality (bio_4) and precipitation seasonality (bio_15) were the significant contributing variables for the distribution of B. teres. The most suitable habitat for B. teres, with a suitability index >0.6, covered 9264.6 km2, with large sections in Eastern and Central Nepal. However, under future climate change scenarios, the area of suitable habitat for the species is projected to increase across Nepal. This study serves as a baseline for assessing potential climate change impacts on B. teres and will enable the development of adaptive measures to protect and establish various bamboo populations outside forest areas in Nepal and globally.

Amphibian hotspots in Iran: Effectiveness of current protected area and priorities for conservation planning

The identification of conservation priority areas for amphibians in Iran remains unexplored. Although protected areas play a vital role in biodiversity conservation, 40 percent of amphibians are at risk of extinction. The present study aimed to identify the priority areas for amphibian conservation and assess the effectiveness of the current protected conservation areas. 2192 species occurrence points for 23 amphibians, including 16 species of Anura and 7 species of Caudata, were curated. These occurrence points in addition to bioclimatic, anthropogenic, and geographical variables were used to model the species distribution. Then, the richness and rarity maps of amphibians were determined and hotspots identified based on the top 10 % area of the country with the highest richness and rarity values. Finally, Iran's protected areas were overlaid to evaluate the current coverage of hotspots and identify future conservation priorities. Although approximately 12 % of Iran is currently protected, the present study findings indicate that the current network of protected areas is ineffective for amphibian conservation; over 90 % of hotspots for amphibians lie outside the current national protected area network. The most important hotspots are located in the Caspian Hyrcanian Mixed Forest, the western margin of the North Zagros, and the Central Zagros Mountains Forest-Steppe ecoregions of the country. Among different types of protected areas that overlap with amphibian hotspots, protected areas and wildlife refuges respectively ranked the highest in quantity and size, while national parks ranked the lowest. In the current study, a baseline of top candidate areas for expanding protected areas where habitats can be managed to protect amphibians in Iran was provided. To further improve protected areas' coverage, priority conservation areas in the Zagros Mountains Forest-Steppe hotspots were recommended.

Climate covariate selection influences MaxEnt model predictions and predictive accuracy under current and future climates

The performance and transferability of species distribution models (SDMs) depends on a number of ecological, biological, and methodological factors. There is a growing body of literature that explores how the choice of climate covariate combinations and model parameters can affect predictive performance, but relatively few that delve into covariate reduction methods and the optimisation of model parameters, and the resulting spatial and temporal transferability of those models. The present work used the citrus pest, Diaphorina citri Kuwayama (Hemiptera: Psyllidae), to illustrate how MaxEnt models trained on the insect’s native range in Asia varied in their predictions of climatic suitability across the introduced range when eight different covariate reduction methods were applied during model building. Additionally, it showed how model sensitivity varied across these different covariate combinations using three sets of independently validated occurrence points in the invaded range of the psyllid. Climatically suitable areas for D. citri differed by as much as two-fold between the best and worst-performing models in selected areas. Great care should be taken in the selection of the highest-performing predictor combinations and model parameter settings for SDMs, particularly in the case of invasive species where the assumption of environmental equilibrium is likely violated in the introduced range. Understanding how the predictive ability of SDMs can be influenced by the methodological choices made during the model building phase is vital to ensuring that ecological and invasion management programmes do not over- or underestimate climatic suitability and subsequent invasion risk.

Effect of climate change on Clinopodium polycephalum (Vaniot) C. Y. Wu & S. J. Hsuan distribution adopting temporal data, ArcGIS, and the MaxEnt model.

Clinopodium polycephalum (Vaniot) C. Y. Wu & S. J. Hsuan, a vital plant in traditional Chinese medicine, has been used for its hemostatic properties since 1220 AD. Despite its recognized medicinal benefits including anti-inflammatory and cardiovascular applications and increasing market demands, research on this plant remains limited, particularly from the perspective of plant ecology. Due to global warming and the resultant climate change, studies on the distribution and conservation of C. polycephalum are of great importance, especially when a clear trend that its habitat shifts to the north was observed. To predict the potential distribution of C. polycephalum under distinct climate situations, the MaxEnt model was used along with the ArcGIS software. As a result, an AUC value of 0.931 was achieved, indicating high predictive accuracy of the model. By analyzing 135 occurrence points and their corresponding bioclimatic factors (including precipitation), soil data, and other environmental variables (49 in total), 16 key factors including pH value and basic saturation were selected for downstream analysis. It was found that solar radiation in May, precipitation in May and April, and the lowest temperature in the coldest month are important factors influencing the growth and distribution of C. polycephalum. Compared to the current climate scenario, the future suitable habitat for C. polycephalum is expected to shift northwest, and under the SSP245-2061-2080 climate scenario, its highly suitable habitat area is projected to increase by 886,000 km2. These findings provide crucial insights into the environmental drivers of C. polycephalum distribution and aid in its preservation and sustainable use in traditional medicine. Based on the findings of this study, future research should focus on factors such as solar radiation in May and the lowest temperature in the coldest month within the suitable habitat to ensure its effective conservation.

A Multi‐Modelling Approach for Informing the Conservation of a Cold‐Adapted Terrestrial Amphibian in the Face of Climate Change

ABSTRACTAimWe aimed to assess potential climate change impacts on the distribution of a cold‐adapted terrestrial amphibian that shows strong intraspecific differentiation.LocationAlps and Dinarides.TaxonAlpine salamander (Salamandra atra).MethodsWe built SDMs for each of two major intraspecific lineages (S. a. atra, which is found throughout the Northern Alps, and S. a. prenjensis, which appears sparsely along the Dinarides) and for the entire species, using occurrence points from a carefully curated database, climate data with 1 km2 spatial resolution and eight modelling techniques. We projected climatically suitable areas to 2070 under two climate scenarios (RCP2.6 and RCP8.5), using 24 general circulation models.ResultsFor S. a. atra, under RCP2.6 scenario, we detected a 3% increase in the extent of climatically suitable areas in the Alps (although with low model agreement), yet a 6% decrease under the RCP8.5 scenario. For S. a. prenjensis, we detected a decrease in the extent of climatically suitable areas in the Dinarides between 7% and 45% (RCP2.6 and RCP8.5, respectively). The SDM predictions for the entire species were most in line with those of the S. a. atra model. Although climate variables were not equally strong in predicting the climatic suitability for both lineages, the minimum temperature of the coldest month revealed an important predictor for the entire species, with clear decreases in suitability towards higher temperature.Main ConclusionsAll models reveal a considerable decrease in climate suitability throughout most of the range of S. atra, which is particularly visible in the Dinarides. We highlight the importance of considering intraspecific variation when modelling climate change impacts on geographically differentiated species whose populations vary in their climatic niche. We provide our study results (occurrence data and maps) via a web application that can be useful for guiding conservation efforts.

Effectiveness of South Africa's network of protected areas: Unassessed vascular plants predicted to be threatened using deep neural networks are all located in protected areas.

Globally, we are in the midst of a biodiversity crisis and megadiverse countries become key targets for conservation. South Africa, the only country in the world hosting three biodiversity hotspots within its borders, harbours a tremendous diversity of at-risk species deserving to be protected. However, the lengthy risk assessment process and the lack of required data to complete assessments is a serious limitation to conservation since several species may slide into extinction while awaiting risk assessment. Here, we employed a deep neural network model integrating species climatic and geographic features to predict the conservation status of 116 unassessed plant species. Our analysis involved in total of 1072 plant species and 96,938 occurrence points. The best-performing model exhibits high accuracy, reaching up to 83.6% at the binary classification and 56.8% at the detailed classification. Our best-performing model at the binary classification predicts that 32% (25 species) and 8% (3 species) of Data Deficient and Not-Evaluated species respectively, are likely threatened, amounting to a proportion of 24.1% of unassessed species facing a risk of extinction. Interestingly, all unassessed species predicted to be threatened are in protected areas, revealing the effectiveness of South Africa's network of protected areas in conservation, although these likely threatened species are more abundant outside protected areas. Considering the limitation in assessing only species with available data, there remains a possibility of a higher proportion of unassessed species being imperilled.

Predicting climate change impact on the habitat of Ethiopia’s spot-breasted lapwing using ensemble model

Endemic species are usually confined in a restricted geographical range with specialized ecological requirements. Majority of these species, therefore, are under the risk of extinction due to climate change coupled with other anthropogenic pressures. The impact is increasingly recognized as severe in tropical highland endemic bird species by either shifting or contracting their geographical range. In this study, we tried to show the impact of climate change on one of Ethiopian highland endemic bird species, the spot-breasted lapwing (Vanellus melanocephalus). Although this species is considered Least Concern, it is restricted in the fragmented Afro-alpine open grassland a long streams or seasonal pond ecosystem of Ethiopia. We predicted the current suitable habitat of this species and projected its future suitable habitat range under two shared socio-economic pathways (SSP4.5 and SSP8.5) of the year 2050 and 2070. We used 117 georeferenced occurrence points and 11 ecologically important variables to develop an ensemble suitable habitat modeling by averaging nine-species distribution algorithms with threshold of TSS > 0.7. The result indicated 104,117.62 km² area as a predicted suitable habitat range in various northwestern and southeastern Ethiopian highlands. The largest range of this suitable habitat (78.02 %) is outside the protected areas of the country. Mean temperature of the driest quarter was the leading bioclimatic variable for the prediction of suitable habitat of this species followed by iso-thermality and vegetation. Habitat suitability declined as mean temperature of the driest quarter is beyond 15ºC. Under future climatic scenarios, substantial suitable habitat loss will occur with more than 50 % of net range size change. This loss of suitable habitat will be 86.60 % under SSP8.5 by the year 2070. Such range contractions suggest the potential risk of extinction of this species in the future unless conservation action is taken. The presence of a large range of current suitable habitat outside of protected areas reveal the need for species-specific conservation plan in both protected and unprotected areas. Moreover, adequate ecological, genetic and geographical studies are also mandatory to have full understanding regarding the impact of climate change and anthropogenic pressure on this endemic bird species in the future.

Climate change could fuel urinary schistosomiasis transmission in Africa and Europe.

The freshwater snail Bulinus truncatus is an important intermediate host for trematode parasites causing urogenital schistosomiasis, a tropical disease affecting over 150 million people. Despite its medical importance, uncertainty remains about its global distribution and the potential impacts of climate change on its future spread. Here, we investigate the distribution of B. truncatus, combining the outputs of correlative and mechanistic modelling methods to fully capitalize on both experimental and occurrence data of the species and to create a more reliable distribution forecast than ever constructed. We constructed ensemble correlative species distribution models using 273 occurrence points collected from different sources and a combination of climatic and (bio)physical environmental variables. Additionally, a mechanistic thermal suitability model was constructed, parameterized by recent life-history data obtained through extensive lab-based snail-temperature experiments and supplemented with an extensive literature review. Our findings reveal that the current suitable habitat for B. truncatus encompasses the Sahel region, the Middle East, and the Mediterranean segment of Africa, stretching from Southern Europe to Mozambique. Regions identified as suitable by both methods generally coincide with areas exhibiting high urogenital schistosomiasis prevalence. Model projections into the future suggest an overall net increase in suitable area of up to 17%. New suitable habitat is in Southern Europe, the Middle East, and large parts of Central Africa, while suitable habitat will be lost in the Sahel region. The change in snail habitat suitability may substantially increase the risk of urogenital schistosomiasis transmission in parts of Africa and Southern Europe while reducing it in the Sahel region.

Potential distribution prediction of Pomacea canaliculata in China based on the Biomod2.

Pomacea canaliculata is a globally significant invasive species that poses substantial threats to agricultural production, ecosystems, and human health in China. To evaluate its habitat suitability in China, we collected and collated 741 county-level occurrence points of P. canaliculata in China through database query, literature search and news reports, and obtained the five most important climatic factors by variance inflation factor and Pearson correlation coefficient from 19 bioclimatic factors. We investigated the potential suitable distribution areas of P. canaliculata under current and future climate scenarios using the Biomod2 ensemble model, and identified the primary environmental variables influencing their distributions. The results showed that under the current climate, the suitable habitat areas were concentrated in southern provinces or municipalities, such as Zhejiang, Shanghai, Fujian, Jiangxi, Guangdong, Guangxi, Hainan, and Yunnan. Under different climate scenarios in the future, the suitable habitat area would show large-scale expansion in the southern provinces or municipalities, and spread to the northern region. The most two important environmental factors affecting the distribution of suitable habitat areas were precipitation of wettest month and mean temperature of wettest quarter. A combination model to predict the potential distribution of P. canaliculata under current and future climate conditions would help manage the risk of its invasion and spread, and provide a reference for relevant regions and departments to take active measures in advance to prevent the spread, monitor and mitigate its invasion.

Different species or altitudinal morphotypes? Testing the taxonomic value of Dianthus brachycalyx (Caryophyllaceae)

AbstractDefining species boundaries within plant groups distributed along wide elevational and geographic gradients may lead to unstable taxonomic delimitations using morphological data only. Within the taxonomically challenging genus Dianthus, through an integrative approach we tested the taxonomic value of D. brachycalyx, a putative species endemic to mountain peaks of the central‐southern Apennines, with respect to the widespread central‐Mediterranean D. virgineus. We measured 30 morphological characters in 452 individuals from 25 populations and obtained 3202 single nucleotide polymorphisms using ddRAD‐seq techniques in 394 individuals from 36 populations. For climatic niche comparison, we built a dataset of 348 occurrence points. By comparing morphometric, genetic, and climatic niche data, we showed that D. brachycalyx cannot be considered a distinct species. Morphometric separation between the two species is detectable, but high‐elevation populations of D. virgineus are similar to D. brachycalyx. Genetic analyses revealed that the genetic structure of populations of D. brachycalyx and D. virgineus is mainly shaped by isolation‐by‐distance, irrespective of their taxonomic attribution. The climatic niches of the two species are overlapping, and the niche differences are just due to different availability of climatic conditions in their ranges. Accordingly, multiple lines of evidence do not support a separation of D. brachycalyx from D. virgineus, and the former should be considered a heterotypic synonym of the latter.

The optimized Maxent model reveals the pattern of distribution and changes in the suitable cultivation areas for Reaumuria songarica being driven by climate change.

Reaumuria songarica, a drought-resistant shrub, is widely distributed and plays a crucial role in the northern deserts of China. It is a key species for desert rehabilitation and afforestation efforts. Using the Maxent model to predict suitable planting areas for R. songarica is an important strategy for combating desertification. With 184 occurrence points of R. songarica and 13 environmental variables, the optimized Maxent model has identified the main limiting factors for its distribution. Distribution patterns and variation trends of R. songarica were projected for current and future climates (2030s, 2050s, 2070s, and 2090s) and different scenarios (ssp_126, ssp_370, and ssp_585). Results show that setting parameters to RM (regulation multiplier) = 4 and FC (feature combination) = LQHPT yields a model with good accuracy and high reliability. Currently, R. songarica is primarily suitable for desert control in eight provinces and autonomous regions, including Inner Mongolia, Xinjiang, Qinghai, and Ningxia. The total suitable planting area is 148.80 × 104 km2, representing 15.45% of China's land area. Precipitation (Precipitation of the wettest month, Precipitation of the warmest quarter, and Annual precipitation) and Ultraviolet-B seasonality are the primary environmental factors limiting the growth and distribution of R. songarica. Mean temperature of the warmest quarter is the primary factor driving changes in the distribution of suitable areas for R. songarica under future climate scenarios. In future climate scenarios, the suitable planting area of R. songarica will shrink, and the distribution center will shift towards higher latitude, potentially indicate further desertification. The area of highly suitable habitat has increased, while moderately and less suitable habitat areas have decreased. Increased precipitation within R. songarica's water tolerance range is favorable for its growth and reproduction. With changes in the suitable cultivation area for R. songarica, priority should be given to exploring and utilizing its germplasm resources. Introduction and cultivation can be conducted in expanding regions, while scientifically effective measures should be implemented to protect germplasm resources in contracting regions. The findings of this study provide a theoretical basis for addressing desertification resulting from climate change and offer practical insights for the development, utilization, introduction, and cultivation of R. songarica germplasm resources.

Fine-scale habitat suitability modelling of Northern red muntjac (Muntiacus vaginalis) in the Chitwan Annapurna Landscape, Nepal

Abstract Factors associated with the habitat suitability of northern red muntjac (Muntiacus vaginalis) especially outside protected areas in the human-dominated landscape are still lacking. Fine-scale environmental variables can influence the habitat suitability of the animals. This study aimed to explore the different eco-geographic fine-scale variables for the distribution of the northern red muntjac; and predict suitable habitats using the maximum entropy (Maxent) model in the Chitwan Annapurna landscape (CHAL). The presence points of the northern red muntjac (n = 265) were collected between 2018 to 2021 using 150 transects of various lengths in four blocks. Density-based occurrence points rarify and performance-based variable selection were applied to improve the output of the model. The model was evaluated based on the area under the curve (AUC) value of operator characteristic (ROC) and analyzed on the basis of the response curve, the relative importance of variables, Jackknife test and suitability map. Results indicated the model was statistically satisfactory (mean AUC > 0.75). The distance to the nearest cropland was the most contributed variable followed by Normalized Difference Built-up Index (NDBI), distance to developed/settlement area and distance to grassland that explained suitability of Northern red muntjac. The species distribution model predicted 6.52% highly suitable and 23.77% suitable area for northern red muntjac. Therefore, this area is important for the muntjac and provides a possible alternative habitat for other wild animals outside the protected areas. Our research suggests that human dominated landscape should be prioritized in management plans for the conservation of muntjac.

Distribution and extent of suitable habitats of Ruspoli’s Turaco (Tauraco ruspolii) and White-cheeked Turaco (Tauraco leucotis) under a changing climate in Ethiopia

BackgroundUnderstanding the distribution pattern of species and their suitable habitat is key to focus conservation efforts. Climate change has had notable impact on the distribution and extent of suitable habitats, and the long-term survival of various species. We aim to determine the distribution and extent of suitable habitats for Tauraco ruspolii and T. leucotis in Ethiopia and predict their range in the 2050s and 2070s using MaxEnt algorithm. We used 25 and 29 rarified occurrence points for T. ruspolii and T. leucotis, respectively, and 13 environmental variables. Three regularization multipliers and two cut-off thresholds were used to map the potential suitable habitats for each species under current and future climates. Maps were assembled from these techniques to produce final composite tertiary maps and investigated the habitat suitability overlap between the two species using the UNION tool in the geographical information system.ResultAll model run performances were highly accurate for both species. Precipitation of the driest month and vegetation cover are the most influential variables for the habitat suitability of T. ruspolii. The habitat suitability of T. leucotis is also mainly influenced by mean temperature of the driest quarter and vegetation cover. Under the current climate, the suitable habitat predicted for T. ruspolii covered about 24,639.19 km2, but its range size change shows a gain and increase by 156.00% and 142.68% in 2050 and 2070, respectively. The T. leucotis‘s current suitable habitat ranges about 204,397.62 km², but this is reduced by 40.84% and 68.67% in 2050 and 2070, respectively. Our modeling also showed that there was suitable habitat overlap between them at the margin of their respective habitat types in time series.ConclusionWe concluded that there is a direct or indirect impact of climate change on the suitable habitat range expansion for T. ruspolii and contraction for T. leucotis as well as overlapping of these turaco species in different regions of Ethiopia. Therefore, understanding the distribution of current and future suitable habitats of the two turaco species can provide valuable information to implement conservation practices for the species and the regions as well.

Prognostic value of a disintegrin and metalloproteinase Domain-8 in heart failure

BackgroundHeart failure (HF) is a severe disease threatening people's health. The aim of this study is to find a significant biomarker inducive to predicting the prognosis of HF. MethodsGSE135055 and GSE161472 datasets were reanalyzed for exploring key genes related to HF. This single-center, prospective, observational cohort study enrolled 298 patients with or without HF from the Cardiology Department of Zhongda Hospital. Levels of ADAM8 were measured using ELISA kits. Major adverse cardiovascular events (MACEs) were defined as the composite end points of the first occurrence of rehospitalization because of HF or cardiac-related death during one-year follow-up. Results(1) Bioinformatics analysis showed that ADAM8 was a key gene in HF via mainly regulating the mechanisms of extracellular matrix (ECM) organization. (2) Levels of ADAM8 were significantly increased in the HF group, compared to the non-failing (NF) group (p < 0.001), especially in patients with HFrEF (p < 0.05), and HFmEF (p < 0.05). The prevalence of HF in the high ADAM8 group (≧472.916 pg/mL) was significantly higher than in the low ADAM8 group (<472.916 pg/mL) (41.95 % vs 30.54 %, p < 0.01). (3) Correlation analysis revealed that ADAM8 was negatively correlated to the left ventricular ejection fraction (LVEF) (r = −0.272, p < 0.001). ROC analysis showed that the AUC of ADAM8 in predicting HF and predicting the MACE were 0.701 (p < 0.0001) and 0.683 (p < 0.0001), respectively. (4) Logistic and Cox regression both indicated that high ADAM8 expression can predict adverse prognosis of HF. ConclusionsADAM8 may be a risk factor for HF, especially in cases of HFrEF and HFmEF. High ADAM8 expression in plasma was related to the decreased heart function, and can predict the adverse prognosis of HF.