搜索结果：Biodiversity data journal

Global efforts to mitigate anthropogenic pressures on biodiversity and ecosystems will often be realised through management at landscape-scales (i.e., in the range of 100s-1000 s km2). In consequence, we need to measure biodiversity responses at landscape-scales to ensure mitigations are effectively protecting and restoring ecosystems. Yet many countries currently lack monitoring programmes that can generate indicators of biodiversity at these scales. Localised monitoring (e.g., 1 km2) is often amalgamated into national-scale indicators, however, this leaves a substantial gap in the middle of this spatial gradient, limiting availability of information at decision-relevant scales. Here, using the United Kingdom as a case study, we explored the suitability of seven sources of biodiversity data which could be used to construct landscape-scale indicators. We surveyed 70, mostly UK-based, monitoring experts for their opinions on structured and unstructured in-person surveys, camera traps, eDNA, drones, passive acoustic recorders, and satellite remote sensing. We assessed data source utility to construct indicators reflecting Essential Biodiversity Variables, i.e., as holistic measures of taxa or ecosystems rather than assessments of individual management interventions. All seven data sources were deemed suitable, and experts expected developments in technology and infrastructure to greatly increase this potential over the next decade. However, there are technical, analytical, logistical and financial barriers to establishing monitoring networks that could yield the requisite data for landscape-scale indicators. Resolving these issues requires substantial research, policy commitment and investment, but landscape-scale indicators will be essential for the UK to undertake adaptive management and monitor nature recovery.

Digitizing metadata on natural history specimen labels remains a critical bottleneck for biodiversity research. We present a transformative workflow integrating robotic imaging with artificial intelligence (AI)-driven transcription for rapid, comprehensive data extraction from specimen labels. Single high-resolution images of specimens and associated labels were submitted to Gemini 2.5 Flash and GPT-4 Turbo to extract verbatim textual information. This approach yielded ~600 verbatim transcriptions per hour, a 30-fold increase in efficiency compared to traditional manual methods, which yielded ~20 transcriptions per hour. Releasing historical specimen metadata facilitates information accessibility and provides temporal and spatial context for a variety of analyses. Our method fosters the reconnection of disparate biological datasets previously segregated among departments or institutions to unite ecologically interdependent components (e.g., host/parasite and pollinator/plant) for a more complete understanding of biodiversity dynamics.

Urbanization is one of the most extensive forms of land-use change globally and a major driver of biodiversity loss. Ground-dwelling arthropods are sensitive indicators of environmental change and play crucial roles in ecosystem functioning, yet trait-based data on urban arthropods remain limited. We present here an expert-curated checklist and dataset of ground-dwelling arthropods sampled across four major Italian cities-Turin, Milan, Florence and Rome-following a standardized sampling protocol. The dataset includes 297 species from six major taxonomic groups (Coleoptera Carabidae, Coleoptera Tenebrionidae, Isopoda Oniscidea, Chilopoda, Araneae, and Pseudoscorpiones), for which we report presence and activity density across cities and provide taxonomic, biogeographical, ecological, functional, morphological and genetic information. Ecological and functional traits datasets were compiled from literature, expert knowledge, and direct measurements, covering feeding habits, autoecology, habitat preference, dispersal ability, circadian activity, and body size. To our knowledge, this is the first multi-taxon, trait-based dataset of urban ground-dwelling arthropods for the Italian peninsula, providing a valuable baseline for exploring taxonomic and functional diversity across urban environments and for advancing research on how urbanization shapes ecological and evolutionary processes in ground-dwelling arthropod communities.

In recent years, biodiversity data management has emerged as a critical pillar in global conservation efforts. Today, the ability to efficiently collect, structure, and analyze biodiversity data is central to breakthroughs in conservation, drug development, disease monitoring, ecological forecasting, and agri-tech innovation. However, due to the vastness and heterogeneity of biodiversity data, it is often confined to databases for specific research areas in isolated formats and disconnected from other relevant resources. Crucial components of such data in kingdom Plantae comprise of metabolomes-the vast array of compounds produced by plants; traits-measurable characteristics of plants that influence their growth, survival, and reproduction, and that affect ecosystem processes; and biotic interactions-relationships of plants with other living organisms, affecting the ecosystem functions. In this work, we present METRIN-KG (MEtabolomes, TRaits, and INteractions-Knowledge Graph) a powerful data resource simplifying the integration of diverse and heterogeneous data resources such as plant metabolomes, traits, and biotic interactions. The proposed knowledge graph provides an interface to interactively search for data relating plant metabolomes, traits, and interactions. This, in turn, will facilitate development of research questions in life-sciences. In this context, we provide representative case studies on how to frame queries that can be used to search for relevant data in the knowledge graph.

High-resolution climate data are essential for understanding local climate impacts, assessing vulnerability, managing resources, and developing adaptation strategies in regions sensitive to climate change. This is the case for the Balearic Islands, located in the Western Mediterranean, which are characterized by rich biodiversity, pronounced exposure to global warming, and strong socio-economic dependence on climate-sensitive sectors such as tourism, agriculture, and water resources. We present Balear1km, a new climate dataset of dynamically downscaled climate simulations over the Balearic Islands at 1 km spatial resolution and hourly time steps for the period 2009-2023. It includes two simulations produced with the Weather Research and Forecasting (WRF) model: a historical simulation driven by ERA5 reanalysis data, and a future simulation using the Pseudo-Global Warming approach, which applies a climate change signal from 30 global climate models (CMIP6, high-emission scenario SSP5-8.5) to current conditions. This dataset provides physically consistent climate information across land and sea, enabling exploration of how recent weather events may respond under future warming conditions. It can support research and applications in hydrology, ecology, agriculture, public health, and resource management.

Entanglement in fishing gear and marine debris is a global threat to pinnipeds. Successful mitigation requires standardized methods and cooperation. The international Pinniped Entanglement Group (PEG), formed in 2009, is dedicated to this effort, through entanglement prevention, response, and education. Here, we report that at least 76% of pinniped species are affected by entanglement (25 of 33 extant species) with fur seals and sea lions more affected than true seals. Commercial and recreational fishing gear caused more harm than other marine debris. Global maps of entangled pinnipeds indicate that unreported species likely represent data deficiency rather than lack of impact. Entanglement data collection methods affect results, and while standardization is difficult to achieve, transparent and detailed methods will aid robust comparisons to target mitigation. We demonstrate the scale of entanglement threat and provide a contemporary review of the literature, PEG member data and mitigation including outreach and working with industry.

Biogeographic breaks, that is, shifts in overall species composition, are expected to be associated with phylogeographic breaks because of shared ecological or evolutionary factors operating at both the interspecific and intraspecific level. Here, we test the hypothesis that biogeographic and phylogeographic boundaries are congruent using mountain species of the Iranian Plateau as our model system. To this end, we analysed the genetic structure from RAD-sequencing data of four montane (i.e., mid-elevation) and five alpine plant species endemic to yet widely distributed in the Iranian mountains. Phylogeographic boundaries (breaks) were inferred via the Monmonier maximum difference algorithm and compared to biogeographic breaks identified previously based on floristic data. Major phylogeographic break zones, supported by several montane and alpine species, were identified between Alborz and Zagros as well as between the Azerbaijan Plateau and Zagros (each of those areas corresponding to an area of endemism), thus supporting the biogeography and phylogeography concordance hypothesis. Deviations from this pattern of congruence between biogeographic and phylogeographic breaks mostly concern the presence of additional phylogeographic breaks within areas of endemism. Moreover, the genetic structure is stronger in alpine than in montane species, which can at least partly be attributed to the stronger isolation of high-elevation habitats acting as sky islands.

Glaciers are retreating rapidly worldwide, particularly at high elevations, changing the environments and habitats of microorganisms, plants, and animals drastically and leaving behind nutrient-poor sediment. We sought to explore seasonal, elevational, and soil age differences in microbial community diversity found in moraine deposits exposed by recent deglaciation and previously exposed during the Little Ice Age in the Cordillera Vilcanota of southeastern Peru. In the wet and dry seasons of 2023, JMU students and other researchers collected soil samples from 35 sites across a 2.5 square kilometer range in the Andes mountains. Each sample was assigned to the season collected, elevation of collection, and age of exposure. Total DNA was extracted from samples and the 16S rRNA gene was amplified and sequenced on an Illumina MiSeq platform. The data were then processed and analyzed using the QIIME2 bioinformatics pipeline. This dataset will be useful to the field for studying ecological community and ecosystem formation in glacier forefields emerging from climate change.

Over the last century, intensification and conversion of land use has caused habitat destruction and species extinctions throughout Europe. However, these extinctions can be delayed by time lag effects, i.e., extinction debts, which are typically measured by species richness. The aim of this study was to investigate whether such extinction debts can be detected in dung beetles of the Pannonian region of Austria and the Czech Republic, where extensive pasture systems were converted into intensive agricultural areas. We modeled the impact of this land use change and the use of anthelmintics as an additional stressor on both dung beetle species richness and individual abundance. We found that the use of veterinary anthelmintics significantly reduces dung beetle species richness and that historic land cover data from the 19th century best explained not only current dung beetle species richness, but showed even stronger effects on individual abundance. This suggest that population declines may precede species extinctions, potentially masking the true extent of biodiversity loss in short-term studies. Biodiversity assessments using only species richness may therefore underestimate ongoing species losses, as they don't account for temporal trends in population size. Our findings further underscore the necessity of incorporating historical land-use data when assessing biodiversity trends and conservation strategies, as well as the need for long-term ecological monitoring to capture delayed responses to environmental change. Moreover, the observed impact of anthelmintics on dung beetle communities calls for reconsideration of veterinary practices to mitigate their unintended consequences on biodiversity. Overall, the results add a crucial temporal dimension to understanding insect declines, reinforcing that past land-use decisions shape present and future biodiversity in complex and often underestimated ways.

Global agricultural production is currently limited by ongoing climate change. Approximately 90% of crop species and numerous wild plants are dependent on pollinators for reproduction. The global threat to pollinators posed by climate change has grown considerably, as higher temperatures, shifting rainfall patterns, and more frequent extreme weather events disrupt the fragile relationships between plants and their pollinators. The decline in pollinators is also linked to shifts in land use, the widespread adoption of monocropping, and heavy reliance on agrochemicals. Therefore, the protection of pollinators and the preservation of agrobiodiversity are essential to uphold global food systems. Here, we synthesize the adverse impact of climate change on plant-pollinator interactions; throughput assay for phenotyping floral traits; assessing variability and molecular basis of floral display (flower size, shape, color, attractants etc.) and reward (nectar volume and composition, pollen, and fragrance in case of ornamental plants) traits; crop domestication and inbreeding, ploidy and mating systems differences impacting plant-pollinator interactions; volatiles and metabolites mediating plant-pollinator relationships; trade-offs involving reproductive and pollinator traits; and finally, progress in developing pollinator-friendly crop cultivars through conventional plant breeding and biotechnological interventions. Pollinator-assisted phenotyping and selection platform (DARkWIN) combined with other high-throughput phenotyping assays, has the potential to simultaneously quantify multiple interactions impacting pollinators' visitation and foraging behaviors, and generate data on other parameters like stress tolerance, yield, and nutrition in the target populations. Assessing and exploiting functional diversity for plant-pollinator interactions, combined with the use of functionally characterized genes and associated markers for floral display (AT2G31010, AT4G17080, CmGEG, CmCYC2c, CmJAZ1-like-CmBPE2, Cyc2CL-1, Cyc2CL-2) and reward (SWEET9, BrCWINV4A, EOBI, EOBII) traits, can be deployed in breeding programs to develop pollinator-friendly crop cultivars. Numerous candidate genes, reported herein, must be functionally validated before being deployed in crop breeding programs.

Aquaculture plays a crucial role in global food security; however, disease outbreaks remain a major constraint to sustainable production. Rapid and reliable detection of fish diseases is essential to reduce mortality, economic losses, and the misuse of antimicrobials in aquaculture systems. Conventional diagnostic approaches, such as clinical observation and bacterial culturing, are time-consuming, costly, and require specialized expertise. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown promise in automating image-based disease detection. This study aimed to compare a lightweight three-layer CNN model with pre-trained deep learning architectures (VGG16, InceptionV3, and ResNet50) for multi-class classification of infected freshwater fish species using a balanced image dataset collected from aquaculture farms in Thailand. Images from clinically infected freshwater fish were collected during routine farm inspections across six provinces in Thailand. The dataset included 424 images of four species: Asian seabass (Lates calcarifer), red tilapia (Oreochromis sp.), snakeskin gourami (Trichopodus pectoralis), and snakeheads (Channa striata). After preprocessing, a balanced dataset of 56 images per class (totaling 224) was created. The dataset was divided into training (80%) and testing (20%) subsets. On-the-fly data augmentation techniques, such as rotation, brightness adjustment, flipping, shifting, shearing, and zooming, were applied to the training data to reduce overfitting. A lightweight, three-layer CNN model with stochastic gradient descent was used and compared with pre-trained architectures (VGG16, InceptionV3, and ResNet50). Model performance was assessed through accuracy, precision, recall, F1-score, confusion matrix analysis, and five-fold cross-validation. Among the evaluated models, InceptionV3 achieved the highest classification accuracy (56.82%), followed by VGG16 (43.18%) and the proposed CNN (38.64%), while ResNet50 performed poorly (25%). The InceptionV3 model also demonstrated higher average precision (63%), recall (57%), and F1-score (56.75%), indicating superior classification capabilities. Confusion matrix analysis revealed that InceptionV3 correctly classified 25 out of 44 test images, outperforming the proposed CNN (17 correct predictions) and VGG16 (19 correct predictions). Five-fold cross-validation further confirmed the stability and relatively better performance of the InceptionV3 model. The comparative evaluation shows that pre-trained CNN architectures, especially InceptionV3, outperform a lightweight three-layer CNN when trained on small, balanced datasets of infected fish images. Although the proposed lightweight CNN has limited accuracy, its low computational needs suggest it could be useful in resource-limited aquaculture settings. Incorporating deep learning-based image analysis into aquaculture health monitoring systems could enable quick disease triage, support timely management decisions, and promote better biosecurity and sustainable fish production. Future research should increase the dataset size, include more fish species and disease types, and test model performance across different farming environments to improve its generalizability and practical use.

In recent years, budburst, the timing of leaf emergence, has advanced less than expected despite continued spring warming, suggesting counteracting ecological forces. One of these forces might be increased and earlier herbivory on young leaves under climate warming. Here using 5 years of satellite radar data from 27,500 pixels (10 ×10 m2) across 60 temperate oak forest sites under experimental manipulation of insect herbivore loads in Central Europe, we show that prior-year leaf herbivory delayed budburst by 3 days, cancelling the phenological advance observed during a decade of warming. This delay reduced subsequent herbivory by 55%, exceeding the effects of parasitoids or pathogens, and persisted even during pest outbreaks. Across landscapes, the delay was strongest where it probably provided the highest benefit, that is, where a given amount of delay most effectively reduced following herbivory, which suggests an adaptive tree defence. Ultimately, trees may be trapped between responding to two opposing consequences of global change: warming selects for earlier budburst, whereas herbivory selects for delay. Our results underscore the need to consider not only climate, but also plant-herbivore interactions and adaptive evolution to predict tree responses to a changing world.

Biobanks represent organized repositories of biological samples linked to associated data, designed for long-term scientific, clinical, and forensic utilization. In veterinary medicine, animal biobanks facilitate biomedical research, genetic resource preservation, species conservation, and forensic investigations. The present systematic review aimed to synthesize advances, persistent challenges, and practical applications of biobanks in veterinary forensic medicine, with particular emphasis on their contribution to detection, investigation, and suppression of wildlife trafficking. A systematic literature search was conducted across Periódicos Capes, PubMed, SciELO, and ScienceDirect databases, covering publications from 2013 to 2023. Search strings combined terms such as "animal biobank", "animal biorepository", "wildlife forensic", "wildlife trafficking", and "forensic veterinary" (English), together with Portuguese equivalents. Only peer-reviewed articles published in English or Portuguese that explicitly addressed biobanks in veterinary forensic contexts or wildlife crime were included. The review adhered to PRISMA 2020 guidelines. Screening involved title/abstract evaluation followed by full-text assessment. Data were narratively synthesized. Of 1,495 records identified, 15 studies fulfilled all inclusion criteria after exclusion of 1,460 irrelevant or non-qualifying publications. No eligible articles appeared between 2013 and 2014. From 2015 onward, publications demonstrated progressive refinement, transitioning from molecular barcoding for species identification toward integrated applications in geographic origin assignment, chain-of-custody documentation, and evidentiary support in judicial proceedings. Key materials included DNA from muscle, scales, claws, and feathers; cryopreserved gonadal tissues; and somatic cells derived from minimally invasive sources (e.g., feather follicles) or roadkill specimens. Studies highlighted particular utility in identifying fraudulently labeled fishery products, counterfeit mammalian derivatives (e.g., fake tiger claws), and confiscated pangolin scales, as well as in tracing trafficking routes in high biodiversity regions. Veterinary forensic biobanks offer substantial potential for accurate species and geographic provenance determination, thereby strengthening enforcement against illegal wildlife trade. Nevertheless, implementation remains constrained by absent standardized operating procedures, limited practitioner awareness, fragmented reference databases, inadequate inter-institutional connectivity, and elevated logistic/financial demands. Regionalized biobanks integrated with wildlife screening centers (CETAS), harmonized chain-of-custody protocols, and artificial intelligence-supported data curation are proposed as priority strategies to translate existing scientific advances into routine forensic and conservation practice.

The increasing frequency of freshwater cyanobacterial blooms has emerged as a critical ecological and environmental concern, yet long-term time series data documenting such blooms remain scarce. This study presents a 13-year dataset (2010-2022) from two adjacent subtropical reservoirs (Shidou and Bantou) in Xiamen, Fujian Province, Southeast China. It provides a monthly and quarterly overview of 20 physicochemical parameters (348 samples), microscope-based phytoplankton (348 samples), and DNA sequence-based data for bacteria (342 samples) and microeukaryotes (348 samples). The dataset highlights recurrent cyanobacterial blooms dominated by Raphidiopsis raciborskii (basionym Cylindrospermopsis raciborskii). This long-term dataset serves as a valuable resource for investigating, predicting, and controlling cyanobacterial blooms, and will support efforts in biodiversity forecasting, ecological restoration, and targeted management of freshwater ecosystems.

Knowledge of the feeding habits of cetaceans is critical to the development of system-wide conservation strategies in marine ecosystems, yet dietary data are often lacking. To investigate the foraging ecology of long-finned pilot whales (Globicephala melas), we analysed stable carbon and nitrogen isotopes in skin tissue from 50 adult and juvenile animals that mass stranded in July 2023 on the Isle of Lewis, Scotland, in the Northeast Atlantic. We interpreted our isotope data with reference to published data from six delphinid species and baseline prey data from the region. We compared isotopic niche breadth among delphinid species and estimated dietary composition for long-finned pilot whales. The average isotopic values were -17.4 ± 0.9 ‰ for δ13C and +11.0 ± 0.7 ‰ for δ15N. The core isotopic niche of long-finned pilot whales overlapped with striped dolphin only, with a core niche overlap of 8.6%, suggesting some shared habitat and low trophic level prey, or foraging in habitats with lower baseline δ15N values. Adult male and female long-finned pilot whales showed complete isotopic niche overlap, although females displayed a wider niche. Estimated dietary contributions suggest a primarily benthopelagic foraging strategy linked to continental shelf edge and slope food webs. Our findings demonstrate the importance of deep-water prey resources to long-finned pilot whales, providing valuable insights into the early spring-summer feeding habits of the species and build baseline ecological data for the Northeast Atlantic. These results highlight the value of stable isotope analysis to advance our understanding of cetacean trophic ecology and better inform marine mammal conservation management.

Across the tropics, hunting for wildmeat is essential in supporting diets, livelihoods, and food security for millions, and is also central to many cultures. Wildmeat consumption has however increased rapidly over recent decades as human population growth has increased and threatens biodiversity. Measuring hunting impacts at global scales is challenging as hunting can vary even at local scales between localities, social groups, habitat types, motivations, hunting techniques, and governance. A national scale focus would capture local literature and give context specific evidence. This evidence would inform national and subnational policies and decision-making that would be locally acceptable and sustainable. Here, we focus on hunting in Peru, where wildlife and forest research policy is motivated in developing local economies through sustainable wildlife use management and seeks scientific knowledge to meet its needs in this area. A systematic map of the literature would provide an overview of the state of knowledge on hunting and identify research gaps, which is currently lacking and would help inform policy. This protocol describes the process for conducting a systematic map to address the following question: what evidence exists on the drivers, ecological and socio-economic outcomes, and distribution of hunting in Peru? A study is included if it presents information on hunting of at least one species and a location description. Using Spanish and English languages, relevant bibliographic databases will be searched, including Peruvian-specific databases, as well as grey literature on organisational websites. Articles will be title and abstract screened, and those meeting the criteria will have meta-data extracted. Extracted data will include location and habitat, hunting details (e.g., techniques, motivations, and governance), species hunted, and reported ecological and socio-economic outcomes and how these were measured. To identify knowledge gaps, we will map the distribution of hunting studies by region, province, and habitat type. We will also describe how hunting has been studied by summarising the frequency of species hunted, hunting characteristics, and reported ecological and socio-economic outcomes, including the associated metrics and methods used.

Paleogeography, and particularly the paleolatitude, provides key context in the interpretation of paleoclimatic and paleobiological data but these fields are typically studied by scientists in different disciplines. To facilitate communication between these disciplines, a decade ago the online Paleolatitude.org calculator was developed. This provided for any coordinate on stable tectonic plates a paleolatitude estimate for any chosen Phanerozoic time interval, including an uncertainty that includes paleogeographic uncertainty and age uncertainty of a sample/fossil. Here, we provide a major update to this tool. First, we include in the calculator the first global paleogeographic model, including GPlates reconstruction files, back to 320 Ma that also restores paleogeographic units that are now thrusted over each other in orogenic (mountain) belts. Second, we include a recent, more precise paleomagnetic reference frame with updated statistical procedures, and provide the first update of its underlying database. Third, we introduce a new online interface with an easy-to-use tool with a batch option, and data and graph export functions. Finally, we illustrate differences with previous reconstructions and show an application by calculating a paleolatitudinal biodiversity gradient for the late Jurassic in which we use a bootstrap approach to propagate paleolatitude and age uncertainty into the result.

While human activities are driving widespread declines in wildlife populations1,2, in Central Africa, the meat of wild animals, or wild meat, represents a major component of the diets of millions of people3. To halt faunal degradation while ensuring sustainable use of wildlife, it is crucial to understand the scale and drivers of wild meat consumption. Here, using data from over 12,000 households from 252 locations in Central Africa, we show that wild meat is a fundamental component of the diets of rural populations, accounting for 20% of the recommended daily protein intake, compared with 13% and 6% for those living in towns and cities. We estimate that the total annual biomass of wild meat consumed in Central Africa increased from 0.73 million to 1.10 million tonnes between 2000 and 2022, with increasing demand from towns and cities. To ensure that wild meat is available to rural communities, in accordance with the Sustainable Development Goals4 and the Kunming-Montreal Global Biodiversity Framework5, reducing wild meat consumption in urban metropolises is key. While our results are based on the most comprehensive dataset available, the geographical coverage is incomplete and the dataset represents a minimal fraction of the entire population of Central Africa. Targeted studies are needed to validate our model and assess critical areas of intervention.

Marine metabolomics has emerged as a powerful approach for elucidating the chemical basis of marine biodiversity, ecological interactions, and organismal responses to environmental change. By profiling low-molecular-weight metabolites, it provides a functional link between genotype, phenotype, and environment. This review is based on a structured survey of literature published from 2015 to 2025, retrieved from major scientific databases including Web of Science, Scopus, and PubMed, using keywords related to marine metabolomics, mass spectrometry, and chemical ecology, and focusing on peer-reviewed studies. This review synthesizes recent advances and core challenges in marine metabolomics. Specifically, the authors emphasize the strong context dependence of metabolite expression spatial, temporal, and ecological scales, driven by environmental heterogeneity, biological interactions, and exposure history. Despite significant methodological progress, the field continues to face persistent bottlenecks across the research pipeline, including preservation of in situ metabolic states during sampling, severe matrix interference, limited structural annotation of marine-specific metabolites, challenges in ecologically relevant functional validation, and difficulties in translating laboratory findings into real-world applications. Emerging integrated strategies, including multidimensional mass spectrometry, advanced data analytics, stable isotope tracing, spatial metabolomics, and ecologically realistic experimental systems, are highlighted as key pathways to improve structural resolution, functional interpretation, causal inference, and translational robustness.