Essential Biodiversity Variable Research Articles

Important first steps towards designing the freshwater, marine and terrestrial Essential Biodiversity Variable (EBV) workflows for the European Biodiversity Observation Network

The EuropaBON project aims to co-design a European Biodiversity Observation Network by utilising Essential Biodiversity Variables (EBVs) as the foundation for its monitoring system. To co-design the workflow steps for each EBV, the project organised a virtual workshop to engage a diverse group of experts and stakeholders. The workshop focused on describing various workflow components, identifying future needs for EBV implementation and specifying the relative importance of different monitoring techniques for each EBV. With 520 participants from 49 countries, the workshop benefited from a large stakeholder engagement and a wide range of expertise across realms, EBV classes, monitoring techniques and workflow components. During the 3-day workshop (2 hours per day), participants captured different workflows components (i.e. data collection and sampling, data integration and modelling), specified current EU or national initiatives and identified emerging tools and future needs for all 70 currently proposed EBVs. By the end of the workshop, all 70 templates of EBV workflows contained details about workflow components and future needs. Specific future needs for data collection and sampling highlighted by participants were to increase sampling efforts (e.g. number of sites, geographic coverage, sampling frequency and taxonomic scope), to develop and better incorporate novel monitoring techniques (e.g. eDNA, remote sensing and digital sensors) and to create new or improved sampling designs at a European scale. For data integration, combining and harmonising data from diverse sources and data collectors and developing standards and protocols were mentioned as key needs. For modelling, participants especially highlighted the need to develop spatially-explicit models or improve other types of existing models, ideally with open-source software and code. Next steps for designing EBV workflows are to analyse the gathered workshop information, to provide detailed descriptions of EBV workflows and to formulate specific recommendations for the development of a European Biodiversity Observation Network. Recommendations for each monitoring technique (structured in-situ monitoring, citizen science, digital sensors, genetics, satellite remote sensing and aerial remote setting) will also be identified. The gathered information will contribute to the co-design of the European Biodiversity Observation Network and to supporting the establishment of a Biodiversity Monitoring Coordination Centre in Europe.

An operational workflow for producing periodic estimates of species occupancy at national scales.

Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined 'regions' (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the 'ideal' and 'minimal' criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products.

Building Essential Biodiversity Variable netCDFs with the ebvcube R Package

The concept of Essential Biodiversity Variables (EBVs) was conceived to study, report, and manage biodiversity change. The EBV netCDF structure was developed in order to support publication and interoperability of biodiversity data. This standard is based on the Network Common Data Format (netCDF). Additionally, it follows the Climate and Forecast Conventions (CF, version 1.8) and the Attribute Convention for Data Discovery (ACDD, version 1.3). The standard allows several datacubes per netCDF file (see Fig. 1). These cubes have four dimensions: longitude, latitude, time and entity, whereby the last dimension can, for example, encompass different species or groups of species, ecosystem types or other aspects. The usage of hierarchical groups enables the coexistence of multiple EBV cubes (see Fig. 2). The first level (netCDF group) are scenarios, e.g., the modelling for different Shared Socioeconomic Pathways (SSP) scenarios. The second level (netCDF group) are metrics, e.g., the percentage of protected area per pixel and its proportional loss over a certain time span per pixel. All metrics are repeated per scenario, if any are present. The result is a rather complex raster dataset (see example dataset in Fig. 3). This is where the ebvcube R package comes into play. This R package enables scientists to create their own netCDFs in the EBV cube standard. Its functionality covers the creation, opening/reading and visualizing the EBV netCDFs. The ebvcube package is part of the overall EBV infrastructure and works together with the EBV Data Portal. Users can work with the downloaded EBV netCDFs or upload their own EBV netCDFs to the portal. Generally, the package aims to condense the output for the users and assist in the understanding of the file structure to overcome the complexity. The output is reduced to the necessary information, e.g., not displaying coordinate variables or any technical attributes. Moreover, functionality for a quick data exploration is implemented.

French Biodiversity Data Hub: Linking local to global biodiversity through international initiatives and open science clouds

The French national biodiversity data hub (“Pôle National de Données de Biodiversité” - PNDB) is a national e-infrastructure created in 2018 and led by the National Museum of Natural History, contributing to the Open Science policy of the Ministry of Higher Education, Research and Innovation (MESRI). PNDB contributes to building an integrative framework taking into account biodiversity over the long term (from the origins of life to future models), at all biological scales (from the molecule to the socio-ecosystem), and in all its interactions, by providing tools and services for the description, access, validation, analysis and reuse of biodiversity data. With the diversity and complementary type of research biodiversity data (information systems, institutional data repositories, research infrastructures as observatories, experimental devices, natural history collections, etc.), but also from public policy data, the missions of the PNDB are deeply based on the FAIR approach (making data Findable, Accessible, Interoperable, Reusable). Thanks to its nomination in 2022 as a thematic reference center of the MESRI, PNDB will contribute to promoting the FAIR approach, will increase the skills (e.g., by training, good practices) of the scientific communities around open science, and stimulate interactions between producers and users of biodiversity data. PNDB has led the French participation to GEO BON (Group on Earth Observations Biodiversity Observation Network) since 2018 and recently shared the lead with public policies information system coordination. Thanks to this co-lead, this national BON proposes an innovative coordination of all biodiversity monitoring programs, from expertise to research around an innovative Essential Biodiversity Variable (EBV) operationalization pilot. This pilot is made of open practical solutions providing a particular high degree of FAIRNess of biodiversity research objects, from data to source codes. PNDB is also a major European point of contact for the DataOne network, who, in combination with the strong link between PNDB and French Global Biodiversity Information Facility (GBIF) node colleagues, allows the dissemination of all types of data through the world in the best manner!

Essential Biodiversity for Understanding Change

The biodiversity crisis we are experiencing requires more than ever the establishment of an observation and monitoring system to help us understand where we have the greatest problems, to inform actions to help halt and reverse biodiversity loss in those places, and to anticipate the impact of our future actions. The Essential Biodiversity Variable framework concept (EBV; Pereira et al. 2013), was conceptually conceived to provide an analytical framework for biodiversity monitoring, which includes functional, structural, and compositional aspects at different levels of organization, from genes to ecosystems, which, as opposed to reductionist approaches, tries to think of biodiversity from a holistic and systemic viewpoint. After some years of working on refining this concept (e.g., Proença et al. 2017, Kissling et al. 2017, Jetz et al. 2019), in collaboration with the Group on Earth Observations Biodiversity Observation Networks (GEO BON) user community, we have taken the next step, which is precisely to work on grounding this concept through a proposed structure, standard, and tools, which facilitates interoperability and the process of sharing and accessing this information. Our vision for boosting the adoption and use of the EBV framework, named for the different dimensions of the planetary life system that it is intended to capture, is to facilitate access to multiple EBV data products organized under a consistent data structure and with standardized annotation across EBV classes. We propose to leverage the Network Common Data Form (NetCDF) data structure in combination with the Attribute Convention for Data Discovery (ACDD) terms and the Ecological Metadata Language (EML) syntax and adapt them to describe hierarchically organized, spatially explicit, gridded data on biodiversity observations, model predictions, and scenarios using a combination of a web portal and an R package for publication and exploration. We believe that this data structure metadata and supporting tools, initially tested using diverse biodiversity datasets that cover the full scope of the Essential Biodiversity Variables framework, will allow us to better serve the community of users interested in different aspects of global change, to be able to ask questions framed in specific times and spaces in order to obtain, analyze, and visualize the time series registered somewhere on the planet and gain knowledge and insights that will inform decision-making and help us drive and direct development trajectories towards a more sustainable future.

Rapid assessment of plant diversity using MODIS biophysical proxies

The spectral information derived from satellite data provides important inputs for assessing plant diversity. If a suitable satellite-derived biophysical proxy is applicable to assess and monitor plant diversity of different biogeographic regions will be of interest to policy makers and conservationists. We selected four biogeographic regions of India, i.e., semi-arid, Eastern Ghats, Western Ghats, and Northeast as the test sites on the basis of variations in moisture availability. The flora data collected for the study sites are the extract of the national biodiversity project ‘Biodiversity Characterization at Landscape Level’. The available Moderate Resolution Imaging Spectroradiometer (MODIS)-derived biophysical proxies at high temporal frequencies was considered to compare the biophysical proxies: surface reflectance-red and near-infrared, normalized difference vegetation index-NDVI, enhanced vegetation index-EVI, leaf area index-LAI, and fraction of absorbed photosynthetically active radiation-FAPAR at different temporal scales (monthly, post-monsoon, seasonal, annual) in each selected biogeographic regions of India. Generalized linear model (GLM) and multivariate adaptive regression spline (MARS) were utilized to evaluate the relationship between plant diversity and MODIS-derived biophysical proxies. MARS summarized the suitable biophysical proxies at monthly scale in descending order for the total forest area in semi-arid was red, NDVI, and FAPAR; for Eastern Ghats was EVI, FAPAR, and LAI; for Western Ghats was EVI, LAI, and FAPAR; and for Northeast was NDVI, near-infrared, and red. Furthermore, monthly FAPAR commonly found to be the suitable proxy to large scale monitoring of plant diversity in the moisture-varied biogeographic regions of India, except Northeast. Using artificial neural network, the relationship of plant diversity and monthly FAPAR/NDVI were modeled. The correlation between the predicted and reference plant diversity was found to be r = 0.56 for semi-arid, r = 0.52 for Eastern Ghats, r = 0.52 for Western Ghats and r = 0.61 for Northeast at p-value < 0.001. The study affirms that FAPAR is potentially an essential biodiversity variable (EBV) for carrying out rapid/indicative assessment of plant diversity in different biogeographic regions, and thereby, meeting various international commitments dealing with conservation and management measures for biodiversity.

A USER-FRIENDLY REMOTE-SENSING WEB-PLATFORM FOR BIODIVERSITY CONSERVATION AND MANAGEMENT IN PROTECTED AREAS

Abstract. The Chilean SNASPE is a complex network of 104 protected areas covering 18.5 million hectares of continental and insular Chile in South America. The geographical complexity and high biodiversity of the SNASPE make difficult to develop a unified monitoring system for conservation and management. In this contribution, we introduce a novel and remote-sensing web-platform for monitoring SNASPE units based completely in open acces data and software. The platform was designed in close cooperation with the Chilean forest service CONAF in order to make it applicable to the whole SNASPE. Following the framework of the Group on Earth Observation - Biodiversity Observation Network (GEO-BON), we used the Essential Biodiversity Variable (EBV) Phenology and MODIS Enhanced Vegetation Index (EVI) data to detect in near-real-time anomalies from the normal annual phenological cycle of vegetation. The platform is based on a flexible non-parametric probabilistic algorithm (the “npphen” R package) capable to reconstruct any type of leaf phenology and to quantify its inter-annual variation by means of confidence intervals around the most probable annual curve. Phenological anomalies are then calculated as a deviation from the expected annual cycle and judged based on their location within the confidence intervals. Anomalies located above 95% confidence interval trigger a “red alert” which is displayed on the web application as soon as the MODIS data become available. This user-friendly platform was implemented in the La Campana National Park giving early alerts of a severe drought in 2019, warning Conaf to implement actions to protect the park from potential wild fires.

Remotely Sensed Variables of Ecosystem Functioning Support Robust Predictions of Abundance Patterns for Rare Species

Global environmental changes are affecting both the distribution and abundance of species at an unprecedented rate. To assess these effects, species distribution models (SDMs) have been greatly developed over the last decades, while species abundance models (SAMs) have generally received less attention even though these models provide essential information for conservation management. With population abundance defined as an essential biodiversity variable (EBV), SAMs could offer spatially explicit predictions of species abundance across space and time. Satellite-derived ecosystem functioning attributes (EFAs) are known to inform on processes controlling species distribution, but they have not been tested as predictors of species abundance. In this study, we assessed the usefulness of SAMs calibrated with EFAs (as process-related variables) to predict local abundance patterns for a rare and threatened species (the narrow Iberian endemic ‘Gerês lily’ Iris boissieri; protected under the European Union Habitats Directive), and to project inter-annual fluctuations of predicted abundance. We compared the predictive accuracy of SAMs calibrated with climate (CLI), topography (DEM), land cover (LCC), EFAs, and combinations of these. Models fitted only with EFAs explained the greatest variance in species abundance, compared to models based only on CLI, DEM, or LCC variables. The combination of EFAs and topography slightly increased model performance. Predictions of the inter-annual dynamics of species abundance were related to inter-annual fluctuations in climate, which holds important implications for tracking global change effects on species abundance. This study underlines the potential of EFAs as robust predictors of biodiversity change through population size trends. The combination of EFA-based SAMs and SDMs would provide an essential toolkit for species monitoring programs.

From Raw Biodiversity Data to Indicators, Boosting Products Creation, Integration and Dissemination: French BON FAIR initiatives and related informatics solutions

Most biodiversity research aims at understanding the states and dynamics of biodiversity and ecosystems. To do so, biodiversity research increasingly relies on the use of digital products and services such as raw data archiving systems (e.g. structured databases or data repositories), ready-to-use datasets (e.g. cleaned and harmonized files with normalized measurements or computed trends) as well as associated analytical tools (e.g. model scripts in Github). Several world-wide initiatives facilitate the open access to biodiversity data, such as the Global Biodiversity Information Facility (GBIF) or GenBank, Predicts etc. Although these pave the way towards major advances in biodiversity research, they also typically deliver data products that are sometimes poorly informative as they fail to capture the genuine ecological information they intend to grasp. In other words, access to ready-to-use aggregated data products may sacrifice ecological relevance for data harmonization, resulting in over-simplified, ill-advised standard formats. This is singularly true when the main challenge is to match complementary data (large diversity of measured variables, integration of different levels of life organizations etc.) collected with different requirements and scattered in multiple databases. Improving access to raw data, and meaningful detailed metadata and analytical tools associated with standardized workflows is critical to maintain and maximize the generic relevance of ecological data. Consequently, advancing the design of digital products and services is essential for interoperability while also enhancing reproducibility and transparency in biodiversity research. To go further, a minimal common framework organizing biodiversity observation and data organization is needed. In this regard, the Essential Biodiversity Variable (EBV) concept might be a powerful way to boost progress toward this goal as well as to connect research communities worldwide. As a national Biodiversity Observation Network (BON) node, the French BON is currently embodied by a national research e-infrastructure called "Pôle national de données de biodiversité" (PNDB, formerly ECOSCOPE), aimed at simultaneously empowering the quality of scientific activities and promoting networking within the scientific community at a national level. Through the PNDB, the French BON is working on developing biodiversity data workflows oriented toward end services and products, both from and for a research perspective. More precisely, the two pillars of the PNDB are a metadata portal and a workflow-oriented web platform dedicated to the access of biodiversity data and associated analytical tools (Galaxy-E). After four years of experience, we are now going deeper into metadata specification, dataset descriptions and data structuring through the extensive use of Ecological Metadata Language (EML) as a pivot format. Moreover, we evaluate the relevance of existing tools such as Metacat/Morpho and DEIMS-SDR (Dynamic Ecological Information Management System - Site and dataset registry) in order to ensure a link with other initiatives like Environmental Data Initiative, DataOne and Long-Term Ecological Research related observation networks. Regarding data analysis, an open-source Galaxy-E platform was launched in 2017 as part of a project targeting the design of a citizen science observation system in France (“65 Millions d'observateurs”). Here, we propose to showcase ongoing French activities towards global challenges related to biodiversity information and knowledge dissemination. We particularly emphasize our focus on embracing the FAIR (findable, accessible, interoperable and reusable) data principles Wilkinson et al. 2016 across the development of the French BON e-infrastructure and the promising links we anticipate for operationalizing EBVs. Using accessible and transparent analytical tools, we present the first online platform allowing the performance of advanced yet user-friendly analyses of biodiversity data in a reproducible and shareable way using data from various data sources, such as GBIF, Atlas of Living Australia (ALA), eBIRD, iNaturalist and environmental data such as climate data.

LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA

Abstract. Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 &lt; R2 &lt; 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.

LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA

Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 &amp;lt; R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; &amp;lt; 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.

Contrasting changes in the abundance and diversity of North American bird assemblages from 1971 to 2010.

Although it is generally recognized that global biodiversity is declining, few studies have examined long-term changes in multiple biodiversity dimensions simultaneously. In this study, we quantified and compared temporal changes in the abundance, taxonomic diversity, functional diversity, and phylogenetic diversity of bird assemblages, using roadside monitoring data of the North American Breeding Bird Survey from 1971 to 2010. We calculated 12 abundance and diversity metrics based on 5-year average abundances of 519 species for each of 768 monitoring routes. We did this for all bird species together as well as for four subgroups based on breeding habitat affinity (grassland, woodland, wetland, and shrubland breeders). The majority of the biodiversity metrics increased or remained constant over the study period, whereas the overall abundance of birds showed a pronounced decrease, primarily driven by declines of the most abundant species. These results highlight how stable or even increasing metrics of taxonomic, functional, or phylogenetic diversity may occur in parallel with substantial losses of individuals. We further found that patterns of change differed among the species subgroups, with both abundance and diversity increasing for woodland birds and decreasing for grassland breeders. The contrasting changes between abundance and diversity and among the breeding habitat groups underscore the relevance of a multifaceted approach to measuring biodiversity change. Our findings further stress the importance of monitoring the overall abundance of individuals in addition to metrics of taxonomic, functional, or phylogenetic diversity, thus confirming the importance of population abundance as an essential biodiversity variable.

Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions

AbstractAlthough satellite‐based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The growth of interest in biodiversity variables observable from space has been partly underpinned by the development of the essential biodiversity variable (EBV) framework by the Group on Earth Observations – Biodiversity Observation Network, which itself was guided by the process of identifying essential climate variables. This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing (SRS) EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space. Progress toward the identification of SRS‐EBVs will require a clear understanding of what makes a biodiversity variable essential, as well as agreement on who the users of the SRS‐EBVs are. Technological and algorithmic developments are rapidly expanding the set of opportunities for SRS in monitoring biodiversity, and so the list of SRS‐EBVs is likely to evolve over time. This means that a clear and common platform for data providers, ecologists, environmental managers, policy makers and remote sensing experts to interact and share ideas needs to be identified to support long‐term coordinated actions.

Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets

AbstractBiodiversity is continuing to decline. This crisis has been recognised by the Convention on Biological Diversity (CBD), whose members have set ambitious targets to avert ongoing declines in the state of biodiversity by 2020. These so called “Aichi Biodiversity Targets” (ABTs) are organized around five strategic goals, with indicators showing the level of progress made towards each target. Currently, measurements of many ABT indicators are not available. The Essential Biodiversity Variable (EBV) framework, developed by the Group on Earth Observations Biodiversity Observation Network (GEO BON), attempts to form a coherent and harmonised set of observations of biodiversity. In this paper, we explore the potential role of Earth Observation (EO) as a tool to support biodiversity monitoring against the ABT and EBV frameworks. We show that EO‐based measurements are adequate for assessing progress towards 11 out of 20 ABTs. In addition, 14 of the 22 candidate EBVs have a fully or partly remotely‐sensed component and can be considered as Remote Sensing Essential Biodiversity Variables (RS‐EBVs). Those with a partial EO component require further in‐situ data and/or modelling effort to complete the EBV. While the status of biodiversity can be assessed with both fully and partly measured RS‐EBVs, assessing trends is more challenging, particularly for partly measured RS‐EBVs, as coincident time series of EO and supporting data are lacking. A synthetic pathway for developing generic biodiversity indicators using RS‐EBVs is proposed.