Olaf Bánki

Key message: The GUYADIV dataset compiles inventory data from over 100 hectares of lowland tropical forest plots across French Guiana, encompassing 90,000 trees from 1,393 species. It provides a solid foundation for investigating patterns of diversity and function in tropical tree communities at multiple spatial scales. It is available at https://doi.org/10.23708/RLYCVQ ; and the associated metadata at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/da2fb3a7-8669-499d-b8c1-258da68b8f85

Biodiversity data linkage is a prerequisite for addressing pressing societal and research questions. Digitisation of resources has progressed rapidly, but this has led to a number of parallel approaches for datasets and services, making it difficult for users to identify the appropriate tool for their requirements. While scientific names are a key element for data linkage, they are not fully suited to this role. Many of them are not unique identifiers for taxa (such as the species that form the basic building block of a large part of biodiversity information) because taxonomic research has resulted in name changes. Likewise, usage is prone to errors like misspellings or misapplication of nomenclatural rules. This complexity and unreliability may demotivate data providers from readying their valuable datasets for integration into an interlinked landscape of biodiversity information. Correct linking of scientific names is an important element in this process of linking data from various sources. Aggregated taxonomic datasets and name-matching services are an essential infrastructure to build tools for this purpose and are increasingly using unique, resolvable and persistent name identifiers. Accessing the aggregated dataset via these identifiers can also display connections between the names within the dataset, linking, orthographic variants to correct spelling, synonyms to accepted taxon name etc. External datasets that hold valuable research and usage information as taxon-associated data can also be linked directly via these identifiers. This helps them to meet the FAIR data criteria (Findable, Accessible, Interoperable, Reusable). Name matching finds the aggregator's name identifier, adding this identifier to the local data record may help to further interconnect biodiversity information, if data providers are motivated to partake in this process of enabling data linkage. We posit that this motivation can be increased by simplifying the process of choosing the right method for their specific needs, which may vary widely. We thus propose a framework that helps users to select the optimal workflow to address these needs. This involves: (i) identifying and categorising the specific criteria that characterise different user needs; (ii) based on these criteria, defining the necessary metadata both for taxonomic datasets accessed by name-matching services and the name-matching services themselves; (iii) providing tools based on these metadata that will allow users to interactively and confidently choose the best-fit target dataset and name matching service for their specific purpose.

Species occurrence data from in situ measurements constitutes the fundamental ingredient for analyses of status of biodiversity, including mapping of species distributions and estimating extinction risks, and thereby supplies core scientific evidence that informs biodiversity policy. These data are a critical component in monitoring progress towards targets to protect and conserve biodiversity, such as those included in the Kunming-Montreal Global Biodiversity Framework (GBF), the EU Nature Directives, the EU Biodiversity Strategy and the EU Nature Restoration Regulation. However, the availability of these data is still lacking for many parts of the world, and even for Europe, it is highly biased towards the North and West with poor coverage of Europe’s most diverse regions in the South and East.The Horizon Europe project ‘Biodiversity Meets Data’ - BMD - is developing a single access point providing users with: a) platforms for high-throughput biodiversity monitoring using camera traps, acoustic devices, and eDNA sampling combined with AI-based species identification, and b) biodiversity analysis tools (BATs) to analyse the captured data in the terrestrial, freshwater, and marine realms. Combining species occurrence information with spatial and temporal explicit Earth Observation (EO) and abiotic data in the BATs allows for powerful quantification of the effects of habitat and climate change on the status of biodiversity and the trends in species distributions and population sizes. BMD is focussed on the effectiveness of the EU Nature Directives and their implementation in the EU Natura 2000 network, and provides Natura 200 site managers and policymakers with monitoring and analyses tools.In this presentation we identify the gaps and biases in European biodiversity data and indicate which of the high-throughput biodiversity monitoring devices can be deployed most effectively to monitor the species for which the Natura 2000 sites were designated. EO data, and their derived habitat metrics, guide Natura 2000 site managers to localities for optimal deployment of high-throughput biodiversity monitoring devices.

Taxonomic lists, usually of species, have many functions. However, there is currently no reliable and convenient way to determine whether a list contains the information that a user requires other than by reading the list in detail. We therefore developed 24 indicators to characterise list contents. The indicators aim to describe the extent to which the scored list covers their intended class of organisms, the quality of their taxonomic scholarship, and additional information they provide for each taxon. We tested the indicators on 16 lists drawn from a wide range of vertebrates, invertebrates, and plants. A list content score was derived from individual indicator scores after they had been weighted to reflect the preferences that taxonomists had expressed in a global survey. The indicators aim to help list creators provide the details taxonomists and list users consider important. We expect indicators to be refined after public debate..

Taxonomic lists are important tools for efficient communication about biodiversity. The processes by which they are created and maintained need to be robust, scientifically sound, and transparent. Articulating and scoring a set of governance quality indicators provides a way to assess the relative strengths of list management, gives list users a means to assess the quality of this process, augments the information available to list aggregators, and allows patterns to be measured over time and among forms of life. Based on published principles, we created 12 governance quality indicators which we tested on 16 lists spanning a range of taxonomic groups. Independence of taxonomy from nomenclature scored most strongly, but scores for local and regional involvement were lower. The governance quality indicators may eventually provide a rubric for assessing best practice species list governance but now need a period of further testing, review, and refinement before they are institutionalized.

Climate change and increasing availability of resources such as carbon dioxide are modifying forest functioning worldwide, but the effects of these changes on forest structure are unclear. As additional resources become available, for example, through CO ₂ fertilization or nitrogen deposition, large trees, with greater access to light, may be expected to gain further advantages. Conversely, smaller light-suppressed trees might benefit more if their light compensation point changes, while bigger trees may be the most negatively impacted by increasing heat and drought. We assessed recent changes in the structure of Earth’s largest tropical forest by analysing 30 years of Amazonian tree records across 188 mature forest plots. We find that, at a stand level, trees have become larger over time, with mean tree basal area increasing by 3.3% per decade (95% CI 2.4; 4.1). Larger trees have increased in both number and size, yet we observed similar rates of relative size gain in large and small trees. This evidence is consistent with a resource-driven boost for larger trees but also a reduction in suppression among smaller trees. These results, especially the persistence and consistency of tree size increases across Amazonian forest plots, communities and regions, indicate that any negative impacts of climate change on forests and large trees here have so far been mitigated by the positive effects of increased resources.

Wood density is a critical control on tree biomass, so poor understanding of its spatial variation can lead to large and systematic errors in forest biomass estimates and carbon maps. The need to understand how and why wood density varies is especially critical in tropical America where forests have exceptional species diversity and spatial turnover in composition. As tree identity and forest composition are challenging to estimate remotely, ground surveys are essential to know the wood density of trees, whether measured directly or inferred from their identity. Here, we assemble an extensive dataset of variation in wood density across the most forested and tree-diverse continent, examine how it relates to spatial and environmental variables, and use these relationships to predict spatial variation in wood density over tropical and sub-tropical South America. Our analysis refines previously identified east-west Amazon gradients in wood density, improves them by revealing fine-scale variation, and extends predictions into Andean, dry, and Atlantic forests. The results halve biomass prediction errors compared to a naïve scenario with no knowledge of spatial variation in wood density. Our findings will help improve remote sensing-based estimates of aboveground biomass carbon stocks across tropical South America.

Understanding the capacity of forests to adapt to climate change is of pivotal importance for conservation science, yet this is still widely unknown. This knowledge gap is particularly acute in high-biodiversity tropical forests. Here, we examined how tropical forests of the Americas have shifted community trait composition in recent decades as a response to changes in climate. Based on historical trait-climate relationships, we found that, overall, the studied functional traits show shifts of less than 8% of what would be expected given the observed changes in climate. However, the recruit assemblage shows shifts of 21% relative to climate change expectation. The most diverse forests on Earth are changing in functional trait composition but at a rate that is fundamentally insufficient to track climate change.

Leaf and wood functional traits of trees are related to growth, reproduction, and survival, but the degree of phylogenetic conservatism in these relationships is largely unknown. In this study, we describe the variability of strategies involving leaf, wood and demographic characteristics for tree genera distributed across the Amazon Region, and quantify phylogenetic signal for the characteristics and their relationships. Leaf and wood traits are aligned with demographic variables along two main axes of variation. The first axis represents the coordination of leaf traits describing resource uptake and use, wood density, seed mass, and survival. The second axis represents the coordination between size and growth. Both axes show strong phylogenetic signal, suggesting a constrained evolution influenced by ancestral values, yet the second axis also has an additional, substantial portion of its variation that is driven by functional correlations unrelated to phylogeny, suggesting simultaneously higher evolutionary lability and coordination. Synthesis. Our results suggest that life history strategies of tropical trees are generally phylogenetically conserved, but that tree lineages may have some capability of responding to environmental changes by modulating their growth and size. Overall, we provide the largest‐scale synopsis of functional characteristics of Amazonian trees, showing substantial nuance in the evolutionary patterns of individual characteristics and their relationships. Read the free Plain Language Summary for this article on the Journal blog.

Measuring the use of natural history collections is essential to understand their past and present impact on science, to underpin decisions about their management and to assist with deploying them optimally to address societal challenges. Using the vast natural history collections of Naturalis Biodiversity Center as an example, this paper assesses the significance and relevance of quantifying collection use. Four aspects are discussed: 1. standardisation, 2. relevance of having standardised metrics on collection use, 3. the level of detail and completeness of the information and 4. the interactions between digitisation of collections and physical collection use. Based on a set of transparent and objective parameters to describe collection use, it is proposed to further develop these into international standards.