Biodiversity Conservation Management Research Articles

World of Crayfish™: a web platform towards real-time global mapping of freshwater crayfish and their pathogens

Freshwater crayfish are amongst the largest macroinvertebrates and play a keystone role in the ecosystems they occupy. Understanding the global distribution of these animals is often hindered due to a paucity of distributional data. Additionally, non-native crayfish introductions are becoming more frequent, which can cause severe environmental and economic impacts. Management decisions related to crayfish and their habitats require accurate, up-to-date distribution data and mapping tools. Such data are currently patchily distributed with limited accessibility and are rarely up-to-date. To address these challenges, we developed a versatile e-portal to host distributional data of freshwater crayfish and their pathogens (using Aphanomyces astaci, the causative agent of the crayfish plague, as the most prominent example). Populated with expert data and operating in near real-time, World of Crayfish™ is a living, publicly available database providing worldwide distributional data sourced by experts in the field. The database offers open access to the data through specialized standard geospatial services (Web Map Service, Web Feature Service) enabling users to view, embed, and download customizable outputs for various applications. The platform is designed to support technical enhancements in the future, with the potential to eventually incorporate various additional features. This tool serves as a step forward towards a modern era of conservation planning and management of freshwater biodiversity.

Community identification and carbon storage monitoring of Heritiera littoralis with UAV hyperspectral imaging

The Heritiera littoralis, an important rare semi-mangrove species found in coastal protective forests, plays a crucial role in carbon storage and cycling within mangrove wetland ecosystems. Despite its importance, previous studies have overlooked remote monitoring of its carbon storage. Taking the world’s oldest and largest natural community of H. littoralis in Shenzhen, China, as the study area, this research pioneers the use of UAV hyperspectral imaging to identify the H. littoralis community and estimate its above-ground, below-ground, and total carbon storage. The impacts of five geo-environmental factors (elevation, slope, aspect, vegetation communities, and inland distance from the coastline) on the spatial variability of carbon storage using SHapley Additive exPlanations (SHAP) and multiscale geographically weighted regression (MGWR) methods were also investigated. The results demonstrate that the first derivative bands within the red edge and near-infrared regions, the anthocyanin reflectance index 2 (ARI2) and newly-developed three-band VIs, were sensitive features for community identification and carbon storage estimation within the H. littoralis community. Among the four machine learning models (eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR)), the best identification accuracy for the H. littoralis community was achieved by XGBoost. Among the four machine learning models (XGBoost, RF, SVM and kernel ridge regression (KRR)), RF model achieved the best performance in estimating above-ground (R2 = 0.749, RMSE=1.723 kg/m2, EV (explained variance) = 0.704), below-ground (R2 = 0.636, RMSE=0.6 kg/m2, EV=0.606) and total carbon storage (R2 = 0.613, RMSE=2.592 kg/m2, EV=0.597). SHAP and MGWR analysis showed that elevation and inland distance from the coastline were key factors influencing the spatial variability of carbon storage. In conclusion, this study showcases significant advantages in community identification and carbon storage monitoring of rare mangrove communities, providing valuable insights for biodiversity conservation efforts and management within the H. littoralis ecosystem.

The impacts of fire vary among vertical strata: Responses of ant communities to long-term experimental burning.

Fire is a powerful tool for conservation management at a landscape scale, but a rigorous evidence base is often lacking for understanding its impacts on biodiversity in different biomes. Fire-induced changes to habitat openness have been identified as an underlying driver of responses of faunal communities, including for ants. However, most studies of the impacts of fire on ant communities consider only epigeic (foraging on the soil surface) species, which may not reflect the responses of species inhabiting other vertical strata. Here, we examine how the responses of ant communities vary among vertical strata in a highly fire-prone biome. We use a long-term field experiment to quantify the effects of fire on the abundance, richness, and composition of ant assemblages of four vertical strata (subterranean, leaf litter, epigeic, and arboreal) in an Australian tropical savanna. We first document the extent to which each stratum harbors distinct assemblages. We then assess how the assemblage of each stratum responds to three fire-related predictors: fire frequency, fire activity, and vegetation cover. Each stratum harbored a distinct ant assemblage and showed different responses to fire. Leaf litter and epigeic ants were most sensitive to fire because it directly affects their microhabitats, but they showed contrasting negative and positive responses, respectively. Subterranean ants were the least sensitive because of the insulating effects of soil. Our results show that co-occurring species of the same taxonomic group differ in the strength and direction of their response to fire depending on the stratum they inhabit. As such, effective fire management for biodiversity conservation should consider species in all vertical strata.

Habitat Suitability Assessment for Two Burrowing Rodents on the Island of Lesvos: A Niche-Based Approach.

We conducted a habitat suitability assessment for two burrowing rodents, Anatolian or Nehring's blind mole rat (Nannospalax xanthodon) and Harting's vole (Microtus hartingi), on the island of Lesvos using a niche-based approach. We collected data on the presence of the two species across the island and selected several environmental variables, including land cover, geology, and habitat topography, to assess their influence on habitat suitability. We used the Maxent species distribution modelling algorithm to predict suitable habitats. The results showed that both species preferred habitats with low slopes and specific geological substrates, i.e., alluvial deposits and volcanic rocks. M. hartingi showed a preference for open habitats such as saltmarshes and non-irrigated arable land, while N. xanthodon preferred non-irrigated arable land, pastures, and discontinuous urban fabric. The model predicted a wider area of suitable habitats for Microtus hartingi compared to N. xanthodon. Interestingly, the two species are absent from the southeastern part of the island despite our model predicting high suitability; this indicates that a natural barrier of hilly terrain, extensive pine forests, and limestone rock formations may exist that impedes dispersal. Our study provides valuable insights into the habitat preferences of these two burrowing rodents on the island of Lesvos, which can inform biodiversity conservation and ecosystem management strategies.

Precipitation and Soil Nitrogen as Key Drivers of Species and Phylogenetic Diversity Across Growth Forms in Picea purpurea Forests of the Eastern Qinghai–Tibet Plateau

ABSTRACTPicea purpurea forests are a fundamental part of the coniferous forests of the Qinghai–Tibet Plateau. These forests support an extremely rich diversity of organisms while providing crucial ecological functions. Despite their importance, the species and phylogenetic diversity (PD) of these forests remain understudied. The extent to which different plant growth forms in the forest are influenced by the environment varies according to differences in the evolutionary history and characteristics of these taxa. Here, we investigate the variations in all, woody, and herbaceous plant species diversity (SD) and PD in the eastern Qinghai–Tibet Plateau's Picea purpurea forests, along with the associated environmental factors influencing these patterns. Our results show that both SD and PD exhibit similar patterns across all, woody, and herbaceous plants. Notably, mean annual precipitation, total nitrogen, and altitude were identified as the primary factors accounting for the majority of variance in SD and PD within plant communities. Specifically, we found positive correlations between SD and PD with soil total nitrogen content, while negative correlations were observed with mean annual precipitation and altitude. Furthermore, we find that the influence of environmental variables on all plants was found to be more pronounced compared to woody and especially herbaceous plants in terms of both SD and PD. Environmental factors had a direct effect on SD and PD in all woody and herbaceous plants. However, the strength of these effects varied. Importantly, our results indicate that environmental factors influence SD independently of PD. Our findings suggest that precipitation and total soil nitrogen were the most influential environmental factors affecting plant diversity across different growth forms in Picea purpurea communities. However, inferring SD from PD remains a challenging task. These findings of our study will provide guidance for forest biodiversity conservation and sustainable forest management in the Qinghai–Tibet Plateau.

Diversity of Birch and Alder Forests in the Kostanay Region of Kazakhstan

This study delves into the phytocenotic structure and biodiversity of forest ecosystems dominated by species of the family Betulaceae (Betula pendula, Betula pubescens, and Alnus glutinosa) in the Kostanay region of Kazakhstan. The research is a significant step toward understanding the influence of environmental factors, particularly hydrological regimes and soil conditions, on the formation and functioning of these forest communities. Field studies were conducted across multiple plots, where detailed geobotanical descriptions and analyses of species diversity were performed. The results underscore the significant species diversity, with 146 vascular plant species identified across various forest types, and are of great importance. Birch forests exhibit higher species diversity than alder forests, with the highest diversity observed in plots with favourable moisture conditions. The study concludes that the hydrological regime is critical in shaping these forest ecosystems’ phytocenotic structure and productivity. These conclusions underline the importance of biodiversity conservation and sustainable forest management in the region, making the implications of this research significant and far reaching.

Biodiversity conservation and ecological restoration dominated vegetation dynamics during the 1980s-2010s in Yunnan, China

Changes in both vegetation type and productivity can affect the structure, functioning, and services of terrestrial ecosystems. Understanding vegetation dynamics and their drivers is critical for biodiversity conservation and ecosystem management. Although land cover datasets, climate-vegetation models, and remote sensing vegetation indices have been frequently used to reflect vegetation dynamics, they generally lack biological information about vegetation, such as species compositions, community structures, and succession status. Yunnan, recognized as the most biodiverse province in China, has undergone considerable vegetation changes over recent decades. However, the roles of climate change and human activities remain unclear. This study integrated detailed vegetation maps, climate factors, and vegetation indices obtained in the 1980s (1986–1995) and 2010s (2006–2015) to comprehensively evaluate the coverage transformations and productivity changes of vegetation types in Yunnan and unravel the drivers of decadal vegetation changes. The results indicated that: 1) A greening trend was observed across all vegetation types, particularly in coniferous and temperate forests. 2) The decadal vegetation changes were dominated by: the restoration of savanna and shrubland, cropland expansion, and artificial afforestation, accounting for 23.7 %, 22.9 %, and 19.1 % of all observed changes, respectively. 3) Conservation and restoration efforts dominated vegetation greening in Yunnan (55 %), followed by artificial afforestation (23 %), and agricultural expansion (16 %). By comparing vegetation maps of separated stages, the inclusion of vegetation class information provided critical knowledge for a better understanding of the intrinsic mechanisms behind vegetation greening and range shifting. Our study highlighted the significant role of ecological conservation and restoration policies and practices in influencing the spatiotemporal dynamics of biodiversity and ecosystem functioning at a regional scale, within merely a few decades.

Integrating UAV imagery and machine learning via Geographic Object Based Image Analysis (GEOBIA) for enhanced monitoring of Yucca gloriosa in Mediterranean coastal dunes

Effective monitoring and early detection of invasive alien plant species (IAPs) are crucial for mitigating their spread and safeguarding native habitats. Unmanned Aerial Vehicles (UAVs) offer a cost-efficient solution, providing high resolution images. In this study, we aimed to develop a semi-automated methodology using a machine learning algorithm, spatial metrics, and clustering techniques on UAV images to monitor, map, and suggest management measures to counteract Yucca gloriosa, an invasive plant colonizing coastal fixed dunes in central Italy.UAV flights were conducted using two drones: one for the visible spectrum and the other for multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared) along with a Digital Surface Model (DSM). Derived vegetation indices were also utilized. For mapping Y. gloriosa distribution, a Geographic Object Based Image Analysis (GEOBIA) approach was applied to the orthophoto segmentation, followed by a Random Forest algorithm in a training phase, considering three variable combinations (DSM + vegetation indices, DSM + spectral bands, DSM + mixed variables). The most accurate Y. gloriosa map was used to suggest management measures combining the spatial pattern of invaded patches (size, height, isolation level, and aggregation degree) and a mixed clustering approach (hierarchical and partitioning).The results highlighted that the most accurate prediction map was based on the DSM + mixed variables dataset, showing the important role of using a combination of spectral bands and vegetation indices. In all three cases, the DSM emerged as the pivotal variable for discriminating Y. gloriosa from the surrounding environment. Additionally, our results demonstrate the advantages of incorporating vegetation indices in discerning the target invasive alien plant (IAP) from the broader environment, particularly considering its distinctive photosynthesis process and biomass production. From a managerial standpoint, our pilot study indicates that the UAV-based mapping methodology represents an optimal balance between field efforts and costs. This approach allows for the precise identification of containment and removal areas of Y. gloriosa, without compromising the accuracy of the method. The generated prediction maps also hold potential significance for the conservation of coastal dune ecosystems, providing a promising tool for the effective management of invasive species and biodiversity conservation by suggesting management measures for Y. gloriosa.

The Future Possible Distribution of Kasnak Oak (Quercus vulcanica Boiss. & Heldr. ex Kotschy) in Anatolia under Climate Change Scenarios

The deterioration of natural habitats for oak species has steadily occurred as a consequence of both climate change and human pressure. Therefore, detailed and reliable information about the geographic distribution of oak species under changing climate conditions is needed for diverse ecological and conservation practices. This study examined the habitat suitability of endemic Kasnak oak, Quercus vulcanica Boiss. & Heldr. ex Kotschy, an endemic that ranges across the Central Anatolia Region and surrounding mountains in Türkiye. The occurrence data were gathered through fieldwork, and new records were identified. Next, we applied ecological niche modeling to assess the past, present-day, and future potential geographic range of the species in Anatolia. Projections for the Last Glacial Maximum indicate that most of the suitable areas for Kasnak oak were in southern Anatolia. However, present-day estimates suggest projections estimate suitable habitats in northern Anatolia and around the Anatolian Diagonal. According to future projections, the distribution of the species seems to decrease by 2100, with habitat suitability reduction ranging from 3.27% to 7.88%. Projections suggest a decrease in habitat suitability for the species, particularly in the western and southern Türkiye in the future. Moreover, the projections indicated that suitable habitats for the northern range of the species would likely persist until 2100, although they would diminish towards the northeast. The results can be effectively applied to enhance biodiversity conservation planning and management, leading to the development of innovative strategies.

"Two zones and three centers" distribution and suitable areas shift of an evergreen oak in subtropical China under climate scenarios.

Understanding the impact of climate change on the geographical distribution of species is a fundamental requirement for biodiversity conservation and resource management. Quercus oxyphylla, an evergreen oak endemic to China, plays a crucial role in maintaining the ecological stability in subtropical regions and high economic value attributed to its dark and high-density heartwood, but the existing resources are close to endangered. Currently, limited knowledge exists regarding its distribution and potential influences of climate change on suitable areas. This study utilized 63 occurrence records and Biomod2 platform, to predict changes in suitable areas for Q. oxyphylla under future climate change. The results revealed that (1) Q. oxyphylla showed a pattern of three disjunctive geographical centers in the eastern subregion of subtropical evergreen broad-leaved forest region (IVA): Qinling-Daba Mountains, Nanling Mountains and Wuyi Mountains center. Currently, the highly suitable areas concentrated in two zones divided by the Yangtze River, that is, the northern subtropical evergreen and deciduous broad-leaved forest zone (IVAii) and the mid-subtropical evergreen broad-leaved forest zone (IVAi). (2) The temperature-related variables, such as annual temperature range (Bio7), the mean diurnal range (Bio2), and annual mean temperature (Bio1), were identified as the key determinants of the distribution pattern. Because of its considerable climatic variations in temperature and water conditions, Q. oxyphylla's habitat displayed a wider climate niche and strong physiological tolerance to climate change. (3) Under future climate scenarios, the suitable area of the species was expected to overall expand with significant regional differences. The suitable area in IVAi was expected to expand significantly northward while that in IVAii was expected to gradually shrink. To address the impact of climate change, it is necessary to develop conservation plans focused around the three distribution centers, implement localized and regional conservation policies, and conduct educational outreach among local people.

The role of big data and AI in enhancing biodiversity conservation and resource management in the USA

The rapid advancement of big data and artificial intelligence (AI) technologies has opened new frontiers in biodiversity conservation and resource management, particularly in the USA. This review paper explores the significant roles these technologies play in enhancing conservation efforts and resource management strategies. The paper begins by contextualizing the importance of biodiversity in the USA and the existing challenges in conservation practices. It then delves into the realm of big data, discussing its sources, collection methods, storage, integration, and real-world applications. Furthermore, it examines AI technologies, highlighting their capabilities in data analysis, predictive modeling, and habitat monitoring. The synergy between big data and AI is also analyzed, showcasing how their integration can lead to more informed decision-making and efficient management practices. Case studies illustrate the practical applications and benefits of these technologies. The paper concludes with a summary of key findings, recommendations for policymakers and practitioners, and suggestions for future research directions. Through this comprehensive review, the paper underscores the transformative potential of big data and AI in safeguarding biodiversity and managing natural resources effectively.

A pollinator crisis can decrease plant abundance despite pollinators being herbivores at the larval stage

Pollinating insects are decreasing worldwide due to various environmental stresses (so-called pollinator crisis), raising concerns that plant productivity could be undermined in natural and agricultural ecosystems. To date, however, few studies have reported a concurrent decline in both pollinators and plants, and little is known about when a “plant crisis” occurs. Here, we propose that anthropogenic environmental stresses on pollinating insects (e.g. climate change, habitat loss, and pesticide usage) can negatively affect herbivorous insects (e.g., pollinator larvae and crop pests) as well, and effects of pollinator declines may be masked by positive effects of herbivore declines. To test the idea, we theoretically investigated plant population dynamics mediated by two insect groups: one representing a pollinator that is mutualistic at the adult stage but antagonistic at the larval stage, and the other representing a non-structured pest herbivore. Our model revealed that environmental stresses (increasing insect mortality) can have counterintuitive effects on plants. Nonetheless, plant abundance generally decreases with decreasing pollinator abundance, especially when plant populations grow slowly without pollinators, when pollinators are effective mutualists, or when pollinators are susceptible to environmental stresses. These findings offer a theoretical basis for assessing the pollinator crisis for biodiversity conservation and agricultural management.

Classification of forest cover of Ta Dung National Park, Vietnam using optical satellite images

Abstract The objective of this study was to classify the forest status of Ta Dung National Park, Vietnam using integrated satellite imagery and a machine learning algorithm to support biodiversity conservation and forest management. The complexity of land use poses a challenge to producing accurate land cover/land use maps using satellite imagery, particularly in tropical countries where farming often occurs in small, fragmented regions. This challenge is compounded when attempting to assess the natural forest status in tropical forests, which are inherently complex and have often experienced varying degrees of disturbance. Consequently, there is a need for approaches that enhance image classification accuracy while still allowing for the categorization of forest characteristics into reasonably homogeneous groups. In this study, we combined optical satellite images to classify the area of Ta Dung National Park, Vietnam into nine categories representing different forest statuses. Our results showed that integrating Sentinel-2 and Landsat 9 imagery using the random forest algorithm achieved a high classification accuracy of 84.75% with an overall accuracy and a kappa coefficient of 0.83. This approach can be applied to other areas facing similar challenges in classifying forest status.

Internet of Things (IoT) for Environmental Monitoring

Purpose: The general objective of this study was to explore the Internet of Things for environmental monitoring. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the Internet of Things for environmental monitoring. Preliminary empirical review revealed that IoT technologies have significantly enhanced environmental management practices by revolutionizing data collection and analysis across various ecosystems. By integrating IoT sensors with existing monitoring frameworks, real-time data on air and water quality, agriculture, wildlife habitats, and urban green spaces was efficiently gathered. This data facilitated proactive decision-making, early detection of environmental risks, and evidence-based policy formulation to address climate change, biodiversity conservation, and sustainable resource management challenges. Despite challenges like data security and interoperability, collaborative efforts among stakeholders paved the way for more effective environmental monitoring and sustainable development initiatives globally. Unique Contribution to Theory, Practice and Policy: The Complex Adaptive Systems Theory, Diffusion of Innovations Theory and Resource Dependence Theory may be used to anchor future studies on the Internet of Things technology. The study provided several recommendations that contributed significantly to theory, practice, and policy in environmental management. The study emphasized interdisciplinary approaches to enhance theoretical frameworks, advocating for advanced models and algorithms integrating IoT with environmental science and data analytics. In practice, it recommended widespread adoption of IoT-enabled sensor networks with enhanced capabilities for precise and reliable data collection. Policy-wise, the study called for regulatory frameworks supporting IoT integration, data standards, and international cooperation to address global environmental challenges collaboratively. Capacity building and continuous research and development were also highlighted to optimize IoT technologies for sustainable environmental monitoring and management globally.

Neighborhood Diversity Promotes Tree Growth in a Secondary Forest: The Interplay of Intraspecific Competition, Interspecific Competition, and Spatial Scale.

Understanding the biodiversity-productivity relationship (BPR) is crucial for biodiversity conservation and ecosystem management. While it is known that diversity enhances forest productivity, the underlying mechanisms at the local neighborhood level remain poorly understood. We established a 9.6 ha dynamic forest plot to study how neighborhood diversity, intraspecific competition, and interspecific competition influence tree growth across spatial scales using linear mixed-effects models. Our analysis reveals a significant positive correlation between neighborhood species richness (NSR) and relative growth rate (RGR). Notably, intraspecific competition, measured by conspecific neighborhood density and resource competition, negatively impacts RGR at finer scales, indicating intense competition among conspecifics for limited resources. In contrast, interspecific competition, measured by heterospecific density and resource competition, has a negligible impact on RGR. The relative importance of diversity and intra/interspecific competition in influencing tree growth varies with scale. At fine scales, intraspecific competition dominates negatively, while at larger scales, the positive effect of NSR on RGR increases, contributing to a positive BPR. These findings highlight the intricate interplay between local interactions and spatial scale in modulating tree growth, emphasizing the importance of considering biotic interactions and spatial variability in studying BPR.

Trade-offs and Synergies between Economic and Environmental Cocoa Farm Management Decisions.

Optimizing sustainability among smallholder farms poses challenges due to inherent trade-offs. In the study of organic and conventional cocoa smallholder farming in Ghana, 398 farms are assessed using the Food and Agriculture Organsation of the United Nations (FAO) Sustainability Assessment of Food and Agriculture systems(SAFA) Guidelines and Sustainability Monitoring and Assessment Routine (SMART)-Farm Tool. Organic farming exhibited synergies in environmental aspects (e.g., soil quality, energy efficiency) and between biodiversity conservation and risk management. Conventional farming showed potential vulnerabilities, including trade-offs with long-range investments (e.g., chemical inputs) and species diversity. Both systems demand tailored approaches for short-term economic and environmental sustainability, aligning with community-wide long-term goals. To mitigate trade-offs in conventional farming, smallholders should adopt practices like material reuse, recycling, and recovery within their operations.

Exploring the distribution and habitat preferences of Polytrichaceae (Bryophyta) in Tibet, China

The Qinghai-Tibet Plateau stands as one of the most ecologically fragile and biodiversity-rich regions globally. Understanding the distribution of different taxa and their relationship with environmental factors is crucial for effective conservation and sustainable management. Polytrichaceae, a significant bryophyte family widely distributed in Tibet, displays distinct structural, morphological, and phylogenetic traits compared to other mosses. Despite its importance, the distribution of Polytrichaceae in Tibet and its correlation with environmental factors have yet to be explored. In this study, we used an optimized Maximum Entropy (MaxEnt) model to explore the potential suitable habitats of Polytrichaceae in Tibet, aiming to clarify their geographic distribution pattern as well as the key environmental influence factors. The model had high accuracy with an average Area Under the Curve (AUC) of 0.933 and True Skill Statistics (TSS) value of 0.789. The results showed that the potential suitability habitats of Polytrichaceae were mainly located in southeastern Tibet, and the low suitable, moderately suitable, and highly suitable habitats accounted for 12.53 %, 6.84 %, and 3.31 % of the total area of Tibet respectively. Unsuitable habitats were mainly located in northwestern Tibet, accounting for about 77.32 %. In Tibet, temperature factors (Mean Temperature of Coldest Quarter (Bio11) and Annual Mean Temperature (Bio1)) played a pivotal role in determining the potential suitable habitats for Polytrichaceae, and elevation, precipitation, and vegetation coverage also had an important influence. The family preferred warm, moist and densely vegetated habitats in Tibet. This study enriched our ecological understanding of bryophyte ecology in this region and provided data-driven support for biodiversity conservation and ecosystem management in Tibet.

Assessing Reptile Conservation Status under Global Climate Change

Global climate change drives variations in species distribution patterns and affects biodiversity, potentially increasing the risk of species extinction. Investigating the potential distribution range of species under future global climate change is crucial for biodiversity conservation and ecosystem management. In this study, we collected distributional data for 5282 reptile species to assess their conservation status based on distributional ranges using species distribution models. Our predictions indicate that the potential distribution ranges for over half of these species are projected to decrease under different scenarios. Under future scenarios with relatively low carbon emissions, the increase in the number of threatened reptiles is significantly lower, highlighting the importance of human efforts. Surprisingly, we identified some endangered species that are projected to expand their distribution ranges, underscoring the potential positive effects of climate change on some special species. Our findings emphasize the increased extinction risk faced by reptile species due to climate change and highlight the urgent need to mitigate the effects of habitat degradation and human activities on their potential distribution in the future.

Fish beta diversity associated with hydrologic and anthropogenic disturbance gradients in contrasting stream flow regimes

Understanding the role of hydrologic variation in structuring aquatic communities is crucial for successful conservation and sustainable management of native freshwater biodiversity. Partitioning beta diversity into the additive components of spatial turnover and nestedness can provide insight into the forces driving variability in fish assemblages across stream flow regimes. We examined stream fish beta diversity across hydrologic and anthropogenic disturbance gradients using long-term (1916–2016) site occurrence records (n = 17,375) encompassing 252 species. We assessed total beta diversity (Sørensen dissimilarity), spatial turnover, and nestedness of fish assemblages in contrasting stream flow regimes across a gradient of decreasing flow stability: groundwater stable (n = 77), groundwater (n = 67), groundwater flashy (n = 175), perennial runoff (n = 141), runoff flashy (n = 255), and intermittent (n = 63) streams. Differences in total beta diversity among the stream flow regimes were driven predominantly (>86 %) by spatial turnover (i.e. species replacement) as opposed to nestedness (i.e. species loss or gain). Total fish beta diversity and spatial turnover were highest in streams with intermediate flow stability (groundwater flashy), while more flow-stable streams (groundwater stable and groundwater) had lower turnover and higher nestedness. Species turnover was also strongly associated with seasonal variation in hydrology across all flow regimes, but these relationships were most evident for assemblages in intermittent streams. Distance-based statistical comparisons showed significant correlations between beta diversity and anthropogenic disturbance variables, including dam density, dam storage volume and water withdrawals in catchments of groundwater stable streams, while hydrologic variables were more strongly correlated with beta diversity in streams with runoff-dominated and flashy flow regimes. The high spatial turnover of species implies that fish conservation actions would benefit from watershed-focused approaches targeting multiple streams with wide spatial distribution, as opposed to simply focusing on preserving sites with the greatest number of species.

Assembly and Comparative Analyses of the Chloroplast Genomes of the Threatened Plant Rosa anemoniflora

Due to insufficient molecular biology and genetic research on Rosa anemoniflora, this endangered plant has not yet received effective protection. Therefore, the complete chloroplast genome sequence of R. anemoniflora, along with comparative analysis of the chloroplast genomes of related species, is necessary and crucial for reconstructing phylogenetic relationships and developing genetic markers to conserve these species. A series of analyses, including genome structure, GC content, gene number, selection pressure, and nucleotide diversity, were performed by comparing the chloroplast genomes of R. anemoniflora and its relatives. The results indicate that the chloroplast genomes of R. anemoniflora and its close relatives are highly conserved in all genome characteristics, and all protein-coding genes in R. anemoniflora have not experienced significant positive or negative selection pressures. Comparative analysis revealed several variation hotspots, such as the atpH-atpI region, which can serve as a DNA barcode for distinguishing R. anemoniflora from its close relatives. Finally, the results confirmed that R. anemoniflora belongs to Rosa section Synstylae and that R. anemoniflora and its close relatives likely originated from the Fujian and Taiwan regions of China and diverged approximately 3.24 million years ago. This study provides crucial information for future biodiversity conservation and genetic resource management.