Species Occurrence Data Research Articles

Taking advantage of opportunistically collected historical occurrence data to detect responses to climate change: The case of temperature and Iberian dung beetles.

This study introduces a novel approach to leverage high-resolution historical climate data and opportunistically collected historical species occurrence data for detecting adaptive responses to global change. We applied this procedure to the temperature data and the most comprehensive Iberian dataset of dung beetle occurrences as an illustrative example. To understand how populations of different species are responding, we devised a procedure that compares the temporal trend of spatial and temperature variables at the locations and times of all the occurrence data collection (overall trend) with the specific temporal trends among the occurrences of each species. The prevalence of various species responses is linked to life history or taxonomic characteristics, enabling the identification of key factors influencing the propensity to experience different effects from climate change. Our findings suggest that nearly half of the Iberian dung beetle species may be adversely affected by temperature increases, with a geographic shift being the most common response. The results generated through the proposed methodology should be regarded as preliminary information, serving to formulate hypotheses about the diverse responses of species to climate change and aiding in the selection of candidate species capable of coping with challenges posed by changing temperatures.

Suitable habitat of the scad fish (Decanters spp.) in Northern South China Sea predicted by MaxEnt model

As a pivotal economic pelagic fish species inhabiting in upper-middle layers, the scad fish (Decapterus spp.) was assumed a vital role in relieving the fishing pressure imposed upon demersal fish in Northern South China Sea (nSCS). To enhance production and sustain the prominence of the scad fish, this study combined species occurrence data of spanning the spring (March to May) and the autumn (September to November) seasons from 2018 to 2020, and contemporaneous environmental data for prognosticating habitat distribution patterns by the maximum entropy model (MaxEnt). The environmental factors considered were sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and chlorophyll-a concentration (Chla). The findings highlighted that the application of MaxEnt for each month yielding exemplary results typified by AUC values exceeding 0.9. During the spring, the suitable habitats for the scad fish were observed from the east waters of Hainan to the eastern Guangdong waters, where the centers of gravity was within. Similarly, the suitable habitats in the autumn were delineated within the bounds of the eastern Hainan Island waters to and few waters in eastern Guangdong. The key factors influencing occurrence probabilities of the scad fish varied seasonally, SST and SSS were most important in the spring, SSH and Chla manifested the greatest contribution to in the autumn. By elucidating spatiotemporal habitat suitability, this study may furnish robust scientific foundations to optimize catches and identify primary fishing grounds for the ecologically and economically vital species, facilitating sustainable resource utilization.

Which bird traits most affect the goodness-of-fit of species distribution models?

Species distribution models (SDMs) are numerical tools that combine species occurrence (or abundance) data with environmental variables, to predict the species’ distribution spatially. SDMs are increasingly used for purposes of conservation planning and management of ecosystems. The model performance can be measured as the goodness-of-fit (GOF), which describes how well it fits (e.g., the discrepancy between the statistical model and the data observed). However, there is still a need for a deeper understanding of the ecological characteristics of the modelled species which can affect the accuracy of those models. Here, we compared the goodness-of-fit of SDMs, considering several ecological characteristics of 56 bird species: Most frequently used environment, body mass, home-range, species specialization index (SSI), diet specialization and detectability. All SDMs were performed on the same dataset, and the relative frequency of each species was also incorporated to account for occurrence heterogeneity. GOF of SDMs was not significantly correlated with species’ frequency, home-range, body mass, degree of detectability or level of diet specialization. Overall, the birds with more accurate SDMs (GOF) were species of grasslands and the GOF was positively associated with SSI, indicating that more habitat-specialized species are better predictable. Our findings suggest that is important to focus not only on statistical issues potentially related to model performance but also on ecological characteristics of single species because can improve the performance of modellistic procedures, increasing their predictive power.

Evaluating effects of climate change on the spatial distribution of an atypical cavefish Onychostoma macrolepis

Comprehending endangered species' spatial distribution in response to global climate change (GCC) is of great importance for formulating adaptive management, conservation, and restoration plans. However, it is regrettable that previous studies mainly focused on geoclimatic species, while neglected climate-sensitive subterranean taxa to a large extent, which clearly hampered the discovery of universal principles. In view of this, taking the endemic troglophile riverine fish Onychostoma macrolepis (Bleeker, 1871) as an example, we constructed a MaxEnt (maximum-entropy) model to predict how the spatial distribution of this endangered fish would respond to future climate changes (three Global Climate Models × two Shared Socio-economic Pathways × three future time nodes) based on painstakingly collected species occurrence data and a set of bioclimatic variables, including WorldClim and ENVIREM. Model results showed that variables related to temperature rather than precipitation were more important in determining the geographic distribution of this rare and endemic fish. In addition, the suitable areas and their distribution centroids of O. macrolepis would shrink (average: 20,901.75 km2) and move toward the northeast or northwest within the study area (i.e. China). Linking our results with this species’ limited dispersion potential and unique habitat requirements (i.e. karst landform is essential), we thus recommended in situ conservation to protect this relict.

A globally synthesised and flagged bee occurrence dataset and cleaning workflow

Species occurrence data are foundational for research, conservation, and science communication, but the limited availability and accessibility of reliable data represents a major obstacle, particularly for insects, which face mounting pressures. We present BeeBDC, a new R package, and a global bee occurrence dataset to address this issue. We combined >18.3 million bee occurrence records from multiple public repositories (GBIF, SCAN, iDigBio, USGS, ALA) and smaller datasets, then standardised, flagged, deduplicated, and cleaned the data using the reproducible BeeBDC R-workflow. Specifically, we harmonised species names (following established global taxonomy), country names, and collection dates and, we added record-level flags for a series of potential quality issues. These data are provided in two formats, “cleaned” and “flagged-but-uncleaned”. The BeeBDC package with online documentation provides end users the ability to modify filtering parameters to address their research questions. By publishing reproducible R workflows and globally cleaned datasets, we can increase the accessibility and reliability of downstream analyses. This workflow can be implemented for other taxa to support research and conservation.

A new resource for monitoring reef ecosystems: The background of recreational diver photographs contains valuable habitat data

Abstract Vast numbers of nature photographs are shared online on various social media and citizen science platforms, providing an important and growing source of ecological data. In addition to the focal subject, which provides species occurrence data, photographs often contain secondary information such as habitat captured incidentally in the background. Here, we test whether the background of underwater photographs taken by recreational divers can be used to monitor reef habitat composition by comparing them to photoquadrats from standardised scientific surveys at three locations in Sydney, Australia. Furthermore, we assessed the effect of different photographic styles (wide‐angle vs. close‐up) and photographic subjects (fish vs. gastropods) on the habitat captured. Over 90% of underwater photographs displayed habitat in the background demonstrating the considerable amount of data available within this unutilised resource. Overall, the habitat composition captured in the background of photographs was broadly similar to that recorded by standardised survey quadrats. Although significant differences between photographs and photoquadrats were detected for most habitats, the differences were small. Furthermore, we found the difference between the two methods could be reduced by using subsets of photographs; for example, fish photographs detected kelp habitat at a similar rate to photoquadrats. Synthesis and applications: The background of underwater photographs from recreational divers contains valuable data that can be used to monitor habitat taxa that are typically under‐represented. To reduce the potential for bias we recommend restricting photographs to a single photographic subject (e.g. fish), using wide‐angle photographs only and obtaining at least 50 photographs per site from different divers when using recreational photographs to assess changes in habitat‐forming species.

Safeguarding Access to 500 Years of Biodiversity Data: Sustainability planning for the Biodiversity Heritage Library

The Biodiversity Heritage Library (BHL) is the world’s largest open access digital library for biodiversity literature and archives. Headquartered at Smithsonian Libraries and Archives (SLA), BHL is a global consortium of research institutions working together to build and maintain a critical piece of biodiversity data infrastructure. BHL provides free access to over 60 million pages of biodiversity content from the 15th–21st centuries. BHL works with the biodiversity community to develop tools and services to facilitate greater access, interoperability, and reuse of content and data. Through taxonomic intelligence tools developed by Global Names Architecture, BHL has indexed more than 230 million instances of taxonomic names throughout its collection, allowing researchers to locate publications about specific taxa. BHL also works to bring historical literature into the modern network of scholarly research by retroactively assigning DOIs (digital object identifiers) and making this historical content more discoverable and trackable. Biodiversity databases such as the Catalogue of Life, International Plant Names Index, Tropicos, World Register of Marine Species, and iNaturalist, rely on literature housed in BHL. Locked within its 60 million pages are valuable species occurrence data and observations from expeditions. To make this data FAIR (findable, accessible, interoperable, and reusable), BHL and its partners are working on a data pipeline to transform textual content into actionable data that can be deposited into data aggregators such as the Global Biodiversity Information Facility (GBIF). BHL’s shared vision began in 2006 among a small community of passionate librarians, technologists, and biodiversity researchers. Uniting as a consortium, BHL received grant funding to build and launch its digital library. BHL partners received additional grant funding for further technical development and targeted digitization projects. When initial grant funding ended in 2012, BHL established an annual dues model for its Members and Affiliates to help support central BHL operating expenses and technical development. This dues model continues today, along with in-kind contributions of staff time from Members and Affiliates. Significant funding is also provided by the Smithsonian in the form of an annual U.S. federal allocation, endowment funds, and SLA cost subvention, to host the technical infrastructure and Secretariat staff. BHL also relies on user donations to support its program. Though BHL has diversified funding streams over the years, it relies heavily on a few key institutions to cover operating costs. Though these institutions have overarching open access, research, and sustainability goals, priorities and resources to achieve these goals shift over time. Without long-term commitments, institutions may choose to prioritize new projects over established programs. Many BHL contributors have experienced funding loss for digitization projects, reducing the rate at which new content is added to BHL. Further loss of funding for central staff and technical infrastructure would reduce BHL from a data-rich technology project to an unsupported and deprecated platform. Without a long-term commitment to maintain and improve the technical infrastructure, BHL’s termination would result in countless broken links from biodiversity databases, library catalogs, Wikidata, and other aggregators across the web; detrimental impact on existing third-party projects relying on BHL citation and species data; and the elimination of more equitable and free access to biodiversity knowledge. To continue its mission, BHL must increase and improve its data integration with the wider biodiversity infrastructure and secure a sustainable future. Securing that future will require external expertise to diversify funding sources, re-engage support from existing partners, and identify new stakeholders for support. During the founding discussions of BHL, stakeholders agreed that the only way to do biodiversity science globally is through collaboration. One institution could not lead alone. Going forward, this imperative must also include collaborative funding models. Partnering with initiatives such as the Global Biodata Coalition (GBC) can lead to a stronger and more resilient biodiversity infrastructure. With ongoing collaboration, innovation, and an unwavering commitment to open access, BHL will continue to transform research on a global scale and provide researchers with the tools they need to study, explore, and conserve life on Earth.

Mobilising Long-Term Natural Environment and Biodiversity Data and Exposing it for Federated, Semantic Queries

Biodiversity and ecosystems cannot be studied without assessing the impacts of changing environmental conditions. Since the 1980s, the U.S. National Science Foundation's Long Term Ecological Research (LTER) Network has been a major force in the field of ecology to better understand ecosystems. In Europe, the LTER developments are led by the the Integrated European Long-Term Ecosystem, critical zone and socio-ecological system Research Infrastructure (eLTER RI), a currently project-based infrastructure initiative with the aim to facilitate high impact research and catalyse new insights about the compounded impacts of climate change, biodiversity loss, soil degradation, pollution, and unsustainable resource use on a range of European ecosystems and socio-ecological systems. The European LTER network, which forms the basis for the up-coming eLTER RI, is active in 26 countries and has 500 registered sites that provide legacy data e.g., historical time-series data about the environment (not only biodiversity). Its site information and dataset metadata with the measured variables are available to be searched at the Dynamic Ecological Information Management System - Site and dataset registry (DEIMS-SDR, Wohner et al. 2019). While DEIMS-SDR data models utilize parts of the Ecological Metadata Language (EML) schema 2.0.0, location information follows the European INSPIRE specification. The future eLTER data is planned to consist of site-based, long-term time-series of ecological data. The eLTER projects have defined eLTER Standard Observations (SO), which will include the minimum set of variables as well as the associated method protocols that can characterise adequately the state and future trends of the Earth's systems. (Masó et al. 2020, Reyers et al. 2017). The current eLTER network consists of sites that differ in terms of infrastructure maturity or environment type and may focus on one or several of the future SOs or they are not yet executing any holistic monitoring scheme. The main objective is to convert the eLTER site network into a distributed research infrastructure that incorporates a clearly outlined mandatory monitoring program. Essential to this effort are the suggested variables for eLTER SOs and the corresponding methods and protocols for relevant habitat types according to the European Nature Information System (EUNIS) in each domain. eLTER variables are described by using the eLTER thesaurus "EnvThes". These descriptions are currently enhanced by the use of the InteroperAble Descriptions of Observable Property Terminology (I-ADOPT, Magagna et al. 2022) framework to provide the necessary level of detail required for seamless data discovery and integration. Variables and their associated methods and protocols will be formalised to enable automatic site classifications, by building on existing observation representations such as the Extensible Observation Ontology (OBOE), Open Geospatial Consortium's Observation and Measurement, and the future eLTER Standard Observation ontology. DEIMS-SDR will continue to be used as a core service with an RDF representation of its assets (sites, sensors, activities, people) currently being implemented. This action is synced with the Biodiversity Digital Twin (BioDT) project to ensure maximum findability, accessibility, interoperability and re-usability (FAIRness; Wilkinson et al. 2016) of data through FAIR Digital Objects (FDO). Other (digital) assets such as datasets, models and analytical workflows will be documented in the Digital Asset Register (DAR) alongside semantic mapping and crosswalk techniques, to provide machine-actionable metadata (Schultes and Wittenburg 2019, Schwardmann 2020). The Biodiversity Digital Twin (BioDT) project is bringing together biodiversity and natural environment data from seven thematic use cases for modeling. BioDT prototypes rely on openly available data that comes from multiple heterogeneous sources using a multitude of standards and formats. In the pilot phase, merging data requires "hand picking" from selected sources, and automation of workflows would still require many additional steps. There are ongoing efforts in both the BioDT and eLTER projects to find best ways and practices to bring the raw data together by using suitable standards but also to harmonise the other environment variables by referring to vocabularies and possibly express the data as FDOs. Currently both the EML schema and Darwin Core standard (Darwin Core Task Group 2009; with registered extensions) allow referring to external schemas and vocabularies, which give flexibility but may still prove to be too narrow for the multitude of data types and formats the natural environment data requires. We welcome discussion about how to create good practices for enriching and harmonising natural environment data and species occurrence data in a meaningful way. GBIF's new data model and enriching the raw data with semantic artefacts may prove to be the way to provide thematic data products that combine data from multiple sources.

Want to Describe and Share Biodiversity Inventory and Monitoring Data? The Humboldt Extension for Ecological Inventories Can Help!

Access to high-quality ecological data is critical to assessing and modeling biodiversity and its changes through space and time. The Darwin Core standard has proven to be immensely helpful in sharing species occurrence data (see Wieczorek et al. 2012, Global Biodiversity Information Facility, GBIF) and promoting biodiversity research following the FAIR principles of findability, accessibility, interoperability and reusability (Wilkinson et al. 2016). However, it is limited in its ability to fully accommodate inventory data (i.e., linked records of multiple taxa at a specific place and time). Information about the inventory processes is often either unreported or described in an unstructured manner, limiting its potential re-use for larger-scale analyses. Two key aspects that are not captured in a structured manner yet are: i) information about the species that were not detected during an inventory, and ii) ancillary information about sampling effort and completeness. Non-detections (i.e., reported counts of zero) potentially enable more accurate and precise estimates of distribution, abundance, and changes in abundance. This becomes possible when variation in effort is used to estimate the likelihood that a non-detection represents a true absence of that taxon during the inventory. Currently, ecological inventory data, when shared at all, are typically discoverable through dataset catalogs (e.g., governmental data repositories) and supplementary materials to publications. With few exceptions, indexing of such data with the detail and structure needed has not been attempted at broad temporal and spatial scales, despite the potentially high value resulting from making inventory data more readily accessible. To address these limitations in documenting inventory data using the Darwin Core, Guralnick et al. (2018) proposed the Humboldt Core. Subsequent discussions within the biodiversity standards community made it clear that greater integration could be achieved by creating an extension of the Darwin Core, rather than developing a new standard in isolation. Extension design work began in 2021 and progress has been reported by Brenton (2021) and Sica et al. (2022). Over the last year the Humboldt Extension Task Group has sought advice from data providers and aggregators and updated its vocabulary terms. A challenging aspect has been creating terminology for the parent-child relationships (see Properties of Hierarchical Events) needed to describe surveys that may be as simple as a collection of checklists (one level of hierarchy) or as complex as species records from traps within plots along transects across habitats over multiple years (at least four levels of hierarchy). The Task Group has committed to completing a User Guide for the Humboldt Extension. Group members who contributed to the Darwin Core (Darwin Core Task Group 2009) and the Vocabulary Maintenance Specification (Vocabulary Maintenance Specification Task Group 2017) have provided valuable expertise on term refinement and process. Through ratification of the Humboldt Extension as a Darwin Core Event extension, we expect to provide the community with a usable solution, tied to well-established data publication mechanisms, for sharing and using inventory data. This effort promises to overcome a key bottleneck in the sharing of critically important ecological data, enhancing data discoverability, interoperability and re-use while lowering reporting burden and data and metadata heterogeneity. Global data aggregation initiatives, such as GBIF, will benefit from this development as they develop their data models and the range of standards and extensions they support. We anticipate that the Humboldt Extension will be attractive both to data publishers and data users, by facilitating the representation and indexing of data in richer, more meaningful ways. Despite the data-intensive nature of fundamental ecological research and applied monitoring for management and policy, ecological data have remained as one of the FAIR data frontiers. We anticipate that the Humboldt Extension will address most data exchange needs of all professional communities involved.

Potential distribution of selected invasive alien plants under current and future climate change scenarios in South Africa

Invasive alien plants are one of the main causes for the decline of native biodiversity worldwide. Hence, it is crucial to understand the dynamics of invasive plants in the context of a changing climate. The main aim of this study was to evaluate the potential distribution of two major invasive alien plants, Prosopis spp and Acacia mearnsii, under current and future climate change scenarios across South Africa. The maximum entropy (MaxEnt) model was used with species occurrence data and bioclimatic variables. The Species occurrence data was obtained from the Global Biodiversity Information Facility (GBIF), while the bioclimatic variables were downloaded from the WorldClim database. The model evaluation metrics for training and test samples were the area under curve (AUC) of 0.76 and 0.77 for Prosopis spp, and 0.91 and 0.89 for A. mearnsii, respectively. It showed that MaxEnt performed well in mapping the distribution of both species. Model results indicated that the near-current potential distribution of Prosopis spp and A. mearnsii in South Africa is significant (93.8% and 9.7% of the total land area, respectively). With the projected climate, Prosopis spp showed an inconsistent result across the General Circulation Models (GCMs), projection times and climate change scenarios. However, with respect to the current potential distribution, the geographical ranges of A. mearnsii will significantly contract (by about 75%) due to climate change. Therefore, it is imperative that policy makers, environmental managers and other stakeholders implement integrated management and control strategies to restrict the distribution of Prosopis spp.

Mapping suitable habitats for globally endangered raptors in Kenya: Integrating climate factors and conservation planning.

Raptors face global threats like electrocution, collisions, and habitat fragmentation. Many species remain understudied, and their distribution patterns are unknown. Understanding their current and future distribution is crucial for conservation. Protecting these top predators requires knowledge of their spatial distribution and environmental influences. This study addresses knowledge gaps in raptor habitats and distributions in Kenya, considering current and future climate changes. Using species distribution models and occurrence data from the Global Biodiversity Information Facility, we evaluated suitable habitats for four endangered Kenyan raptor species: Martial eagle, Secretarybird, Bateleur, and Steppe Eagle. We assessed the impact of climatic predictors on their distribution, considering two climate change scenarios for 2020-2040. Our findings reveal that raptor distribution in Kenya is predominantly concentrated in the southwestern region, extending into the central region of the country. The most significant predictors of raptor species distribution varied for each species, with Steppe eagle and Secretarybird being highly influenced by precipitation during the warmest quarter, Martial eagle being influenced by mean temperature during the driest quarter, and Bateleur being primarily influenced by precipitation during the coldest quarter. When projecting our model into the climate change scenarios for 2020-2040, all species except the Bateleur exhibited a negative range shift. The results of our study suggest that climate change may have adverse impacts on the raptor species examined. In light of these findings, we recommend implementing targeted monitoring and conducting surveys in accordance with our current model predictions. Specifically, our focus should be on monitoring areas that exhibit the highest climate suitability, as these areas are likely to undergo significant shifts in the near future. By conducting regular monitoring and engaging in further research, we can enhance our understanding of these raptor species and gather valuable data to improve the accuracy and reliability of our model predictions.

Applying the concept of niche breadth to understand urban tree mortality in the UK

Accelerated climate change has raised concerns about heightened vulnerability of urban trees, spurring the need to reevaluate their suitability. The urgency has also driven the widespread application of climatic niche-based models. In particular, the concept of niche breadth (NB), the range of environmental conditions that species can tolerate, is commonly estimated based on species occurrence data over the selected geographic range to predict species response to changing conditions. However, in urban environments where many species are cultivated out of the NB of their natural distributions, additional empirical evidence beyond presence and absence is needed not only to test the true tolerance limits but also to evaluate species' adaptive capacity to future climate. In this research, mortality trends of Acer and Quercus species spanning a 21-year period (2000–2021) from tree inventories of three major UK botanic gardens - the Royal Botanic Gardens, Kew (KEW), Westonbirt, the National Arboretum (WESB), and the Royal Botanic Garden Edinburgh (RBGE) - were analyzed in relation to their NB under long-term drought stress. As a result, Acer species were more responsive to drought and heat stress. For Acer, positioning below the lower limits of the precipitation of warmest quarter led to an increase in the probability of annual mortality by 1.2 and 1.3 % at KEW and RBGE respectively. In addition, the mean cumulative mortality rate increased corresponding to an increase in the number of niche positions below the lower limits of the selected bioclimatic variables. On the other hand, Quercus species in general exhibited comparable resilience regardless of their niche positions. Moreover, Mediterranean oaks were most tolerant, with cumulative mortality rates that were lower than those of native oaks in the UK. These findings further highlight the importance of incorporating ecological performance and recognizing species-specific adaptive strategies in climatic niche modeling.

Effectiveness of rapid sampling assessments for wetland-breeding amphibians

A primary response to the Clean Water Act has been the development of numerous indices of biotic integrity (IBI) using various taxonomic groups to assess the condition of water bodies in the United States. The IBI is a frequently used approach for assessing the ecological integrity of streams; however, it is less commonly applied to wetland systems, despite the scientific and policy needs to assess wetland condition and develop ecological performance goals for wetland monitoring, creation, and restoration. Although a great deal of attention has been given to wetland acreage due to the “no net loss” policy over the past two decades, much less attention has been given to the ecological integrity of these same wetlands. To assess wetland health and function, regulatory agencies and land managers need clear performance standards that are based on the ecology of wetland organisms. To address this need, we sampled 242 wetlands distributed across 45 managed properties in Illinois, U.S.A. over three years (2012–2014) for adult and larval amphibians (12 samples per wetland). Amphibian assemblages make up critical ecological components of many wetland ecosystems and are considered useful bioindicators of environmental change because they are sensitive to various forms of environmental and habitat alteration. We developed an amphibian-based IBI (hereafter aIBI) for the assessment of seasonal wetlands and optimal sampling strategies to best quantify these wetlands (i.e., assessment of time and effort). The aIBI applied a conservation coefficient to all species captured; conservation coefficients were developed through a survey of amphibian experts (n = 16). We captured 25 amphibian species with an average of 7.11 amphibian species (range 1–13) per site. Using the species occurrence data collected from both dipnet and minnow trap surveys over 12 survey periods, we assessed the average percent error in the estimation of aIBIs and species richness. Our results show that a wetland can be accurately evaluated with amphibians in one field season, but that multiple samples during that field season will be necessary. We show that 4–5 surveys using multiple methods distributed throughout the spring and summer are required. This level of effort may be incongruent with existing definitions of ‘rapid assessment’, but it is the necessary level of effort needed to confidently and accurately evaluate wetlands using amphibians as an indicator taxon. Additionally, aIBI provides a more robust framework to assess wetland integrity compared to simple species richness or other similar methodologies.

The ASV Portal Allows for Efficient Handling of DNA Derived Data in the Living Atlas

The Living Atlas platform functions as the national hub for integration of biodiversity data with environmental and contextual information in many countries, but it has had limited functionality for species occurrence data derived from DNA sequences. Meanwhile, molecular methods for species observation, such as metabarcoding of environmental DNA, are becoming increasingly important for documenting the diversity of life. To incorporate this rapidly growing data source into the platform, we developed the Amplicon Sequence Variant (ASV) portal: a web interface for DNA sequence-derived observations in the Living Atlas. We identified three key features that would enhance the utility of the atlas platform for species occurrence data derived from metabarcoding: 1) storage of the processed barcode sequences (ASVs) underlying such occurrences, along with the ability to locate them using sequence alignment search; 2) the option to search for data based on sequencing details, such as target genes and primers; and 3) the possibility to update the taxonomic annotation of observed ASVs as reference databases evolve. In this presentation, we demonstrate how these features have been implemented in the ASV portal, which now operates as a module within the Swedish Biodiversity Data Infrastructure (SBDI). We provide a brief overview of the underlying architecture and its integration with the Living Atlas and Global Biodiversity Information Facility (GBIF) platforms, showcase the online functionality, and conclude by discussing challenges and future development plans.

Performance of inhomogeneous Poisson point process models under different scenarios of uncertainty in species presence-only data

Haphazard and opportunistic species occurrence (PO) data are widely used in species distribution models (SDMs) instead of high-quality species data gathered using appropriate and structured sampling methods, which is expensive and often spatially limited. Despite their widespread use in ecology, PO data are prone to errors and uncertainties, such as imperfect detectability, positional imprecision, and spatial niche truncation, which make their use analytically challenging for effective and adaptive biodiversity management and conservation. Using simulated data, this study investigates the effects of these uncertainties on the performance of spatial point process based presence-only and integrated SDMs. We investigated three SDMs in this study, one that ignores imperfect detectability: the presence-only model (PO model), and two that account for it: the thinned presence-only model (THINPO model) and the integrated model (PBPC model). The ability of these SDMs to produce accurate maximum likelihood estimates of intensity model coefficients and reliable predictions of species distributions under different data quality scenarios was investigated. The results show that SDMs that account for imperfect detectability (THINPO or PBPC models) are not applicable in situations of high detectability. In this situation, the PO model produces the most accurate maximum likelihood estimates of the models’ coefficients ({hat{beta }}_k), and consequently the most accurate predictions of species distributions ({hat{lambda }}(s)). The effects of positional uncertainty and spatial niche truncation on this SDM output are minimal. However, in situations of low detectability, it is preferable to use the PBPC model. Positional uncertainty and spatial niche truncation have negligible effects on the output of this SDM, except when positionally uncertain PO data are analyzed along with truncated PC data. These minimal effects of spatial niche truncation on SDM outputs demonstrate the transferability of SDMs. However, the effects of all these uncertainties may depend on the characteristics of the species. Prior to modeling species distributions, a multivariate environmental similarity surface analysis should be performed to test the similarity between data from the restricted region to be used for model calibration and data from the entire range. If this analysis reveals dissimilarities, larger spatial and ecological scales should be considered to address the issue of spatial niche truncation. Further efforts could address the effects of species characteristics on SDMs performance and assess the effects of species-specific uncertainties.

Wildlife habitat association over a twelve-year period (2008–2020) in the Greater Mahale Ecosystem, western Tanzania

The Greater Mahale Ecosystem (GME) in western Tanzania hosts high faunal biodiversity, including charismatic species such as elephants and chimpanzees. Across the GME, habitat occurs in differently administered, unprotected and protected areas, including village land, forest reserves, and national parks. Areas of lower protective status are prone to deforestation and land cover changes. These land cover changes influence suitable habitat itself as well as the connectivity between habitat patches, but the impact on different mammal species in this region is unknown. In this study, we investigated changes in habitat availability by creating species distribution models for 11 medium-large mammal species based on species occurrence data collected from 2008 to 2020. We tested habitat associations for each species and evaluated the importance of the static predictor variables elevation, slope, aspect and terrain-ruggedness and the dynamic predictor variables distance to deforestation, percent forest, vegetation intensity, and vegetation variability, retrieved through remotely sensed data analysis. Our models suggest habitat declined for most, but not all species. Whilst elephants and buffalo lost more than 50 % of available habitat during this period, primates exhibited habitat stability. Habitat losses were pronounced in the southeast of the GME. Slope, elevation and median tasseled-cap wetness index (vegetation intensity) and distance to deforestation were the most important predictor variables. Our study presents a spatially and temporally explicit estimation of habitat changes in a critically important, biodiverse region undergoing drastic land cover changes. We call for consideration in land-use planning to foster connectivity and landscape integrity to protect and conserve wildlife across the ecosystem.

Influence of environmental variables on the distribution of Piciformes in the Ivaí river basin

Birds occupy diverse habitats, and therefore the environmental gradient of the Atlantic Forest contributes to the distribution of species. In this sense, we sought to determine the structure of the Piciforme birds community in the Ivaí river basin, investigating spatial substitutions in the distribution of birds within the same genus and testing the influence of environmental characteristics on the distribution, in different vegetation types of the Atlantic Forest. Species occurrence data were obtained through the Global Biodiversity Information Facility platform (GBIF). The genera that presented substitution were Picumnus, Pteroglossus, Ramphastos and Veniliornis. The environmental characteristics tested were: climate, domain, designation, geomorphology, degradation and land use, obtained through data and Thematic Geospatial Information, from Instituto Água e Terra. The results obtained by the Principal Component Analysis (PCA) indicated that the vegetal typology is the main characteristic that determines the distribution of these species, as the variables that presented greater importance within the component.

Effects of climate change on the distribution of plant species and plant functional strategies on the Canary Islands

AbstractAimOceanic islands possess unique floras with high proportions of endemic species. Island floras are expected to be severely affected by changing climatic conditions as species on islands have limited distribution ranges and small population sizes and face the constraints of insularity to track their climatic niches. We aimed to assess how ongoing climate change affects the range sizes of oceanic island plants, identifying species of particular conservation concern.LocationCanary Islands, Spain.MethodsWe combined species occurrence data from single‐island endemic, archipelago endemic and nonendemic native plant species of the Canary Islands with data on current and future climatic conditions. Bayesian Additive Regression Trees were used to assess the effect of climate change on species distributions; 71% (n = 502 species) of the native Canary Island species had models deemed good enough. To further assess how climate change affects plant functional strategies, we collected data on woodiness and succulence.ResultsSingle‐island endemic species were projected to lose a greater proportion of their climatically suitable area (x ̃ = −0.36) than archipelago endemics (x ̃ = −0.28) or nonendemic native species (x ̃ = −0.26), especially on Lanzarote and Fuerteventura, which are expected to experience less annual precipitation in the future. Moreover, herbaceous single‐island endemics were projected to gain less and lose more climatically suitable area than insular woody single‐island endemics. By contrast, we found that succulent single‐island endemics and nonendemic natives gain more and lose less climatically suitable area.Main ConclusionsWhile all native species are of conservation importance, we emphasise single‐island endemic species not characterised by functional strategies associated with water use efficiency. Our results are particularly critical for other oceanic island floras that are not constituted by such a vast diversity of insular woody species as the Canary Islands.

Citizen science can complement professional invasive plant surveys and improve estimates of suitable habitat

AbstractAimCitizen science is a cost‐effective potential source of invasive species occurrence data. However, data quality issues due to unstructured sampling approaches may discourage the use of these observations by science and conservation professionals. This study explored the utility of low‐structure iNaturalist citizen science data in invasive plant monitoring. We first examined the prevalence of invasive taxa in iNaturalist plant observations and sampling biases associated with these data. Using four invasive species as examples, we then compared iNaturalist and professional agency observations and used the two datasets to model suitable habitat for each species.LocationHawai'i, USA.MethodsTo estimate the prevalence of invasive plant data, we compared the number of species and observations recorded in iNaturalist to botanical checklists for Hawai'i. Sampling bias was quantified along gradients of site accessibility, protective status and vegetation disturbance using a bias index. Habitat suitability for four invasive species was modelled in Maxent, using observations from iNaturalist, professional agencies and stratified subsets of iNaturalist data.ResultsiNaturalist plant observations were biased towards invasive species, which were frequently recorded in areas with higher road/trail density and vegetation disturbance. Professional observations of four example invasive species tended to occur in less accessible, native‐dominated sites. Habitat suitability models based on iNaturalist versus professional data showed moderate overlap and different distributions of suitable habitat across vegetation disturbance classes. Stratifying iNaturalist observations had little effect on how suitable habitat was distributed for the species modelled in this study.Main ConclusionsOpportunistic iNaturalist observations have the potential to complement and expand professional invasive plant monitoring, which we found was often affected by inverse sampling biases. Invasive species represented a high proportion of iNaturalist plant observations, and were recorded in environments that were not captured by professional surveys. Combining the datasets thus led to more comprehensive estimates of suitable habitat.

Projecting shifts in the distributions of Chinese endemic vertebrate species under climate and land-use change

Human-induced climate and land-use change impact species’ habitats and survival ability. A growing body of research uses species distribution models (SDMs) to predict potential changes in species ranges under global change. We constructed SDMs for 411 Chinese endemic vertebrates using Maximum Entropy (MaxEnt) modeling and four shared socioeconomic pathways (SSPs) spanning to 2100. We compared four different approaches: (1) using only climatic and geographic factors, (2) adding anthropogenic factors (land-use types and human population densities), but only using current data to project into the future, (3) incorporating future estimates of the anthropogenic variables, and (4) processing species occurrence data extracted from IUCN range maps to remove unsuitable areas and reflect each species’ area of habitat (AOH). The results showed that the performance of the models (as measured by the Boyce index) improved with the inclusion of anthropogenic data. Additionally, the predicted future suitable area was most restricted and diminished compared to the current area, when using the fourth approach. Overall, the results are consistent with other studies showing that species distributions will shift to higher elevations and latitudes under global change, especially under higher emission scenarios. Species threatened currently, as listed by the IUCN, will have their range decrease more than others. Additionally, higher emission scenarios forecast more threatened species in the future. Our findings show that approaches to optimizing SDM modeling can improve accuracy, predicting more direct global change consequences, which need to be anticipated. We also show that global change poses a significant threat to endemic species even in regions with extensive protected land at higher latitudes and elevations, such as China.