3c: Using spatial data for understanding biodiversity and ecosystems

3c:     Using spatial data for understanding biodiversity and ecosystems

Conveners:     Jenny Lazebnik (Wageningen Environmental Research)

                       Daniel Kissling (University of Amsterdam)

                       Stanley Nmor (Royal Netherlands Institute for Sea Research)

1. Integrating spatial information on vegetation structure derived from national LiDAR surveys with ecological and biodiversity monitoring data

Daniel Kissling, Yifang Shi, Jinhu Wang & Arie Seijmonsbergen, University of Amsterdam

Quantifying ecosystem structure is essential for ecology and biodiversity science. LiDAR can provide detailed and high-resolution information on vegetation structure but processing the massive raw data is often challenging for ecologists. We show how statistical properties derived from LiDAR point clouds can be integrated with biodiversity data, e.g. presence-only records and presence-absence and abundance data from transects and point counts. This can provide deeper insides into ecology and conservation, e.g. by analysing habitat preferences, niche overlap and species distributions. We further present recent developments to make spatial information on vegetation structure derived from national LiDAR surveys better accessible to ecologists. This includes the development of high-throughput workflow (‘Laserfarm’) that enables the efficient, scalable, and distributed processing of country-wide LiDAR point clouds, and making high-resolution LiDAR vegetation metrics available over the whole country. Moreover, new workflows for mapping individual trees and impacts of large herbivores on wetland vegetation are currently developed.

2. Rushing to reach 30% by 2030: but are we improving seabed habitat quality for the wider region?

Karin van der Reijden, Daniel van Denderen, Sebastian Valanko,  Technical University of Denmark, National Institute of Aquatic Resources

Global concerns about biodiversity have resulted in the ambitious plan to protect 30% of all seas with Marine Protected Areas (MPAs). By eliminating bottom fishing that physically disturbs the seabed, habitat quality within MPAs is expected to improve. However, it is unclear what effects these MPAs have on the wider seabed quality, due to the displacement of fishing activities into neighboring areas. In this study, we evaluate MPA impact on regional seafloor quality. We combine spatial information of seafloor habitats, fishing activities, benthic sensitivity to bottom fishing, and MPAs, and determine the regional habitat quality under various management scenarios. Results show that the implementation of MPAs can result in a deterioration of seabed quality, as prohibited fishing can be relocated from MPAs to more sensitive habitats in the region. This assessment evaluates the wider impacts of MPAs and can help develop MPAs that promote seabed quality for the entire region.

3. Climate and land cover change transform functional identity and reduce functional diversity in the European grassland ecosystem

Chang Liu, Koenraad Van Meerbeek, Katholieke Universiteit Leuven

European grasslands are highly species-rich at small scales but face threats to ecosystem functions (EFs) from land use and climate change. Functional traits, particularly community-level functional identity (FI) and functional diversity (FD), better explain EFs than taxonomic diversity. This study investigates the impacts of climate and land cover changes on grassland FI and FD by modeling trait-environment relationships using vegetation-plot databases (sPlotOpen, GrassPlot, NBGVD), historical gridded climate data, and remote-sensing products. We mapped the 18 community-weighted mean (CWM) traits and functional richness (FRic) using future spatial global climate and land cover change datasets under two scenarios. Preliminary findings include: (i) trait correlations do not improve predictive accuracy; (ii) plant height is expected to increase across Europe with rising temperatures, while seed numbers are predicted to increase at higher latitudes and decline at lower latitudes; and (iii) the mountain ranges are liekly to experience increases in FRic.

4. Microclimate data reveal microrefugia potential in European forests

Xiaqu Zhou, Ward Fonteyn, Stef Haesen, Alexander Sentinella, Koenraad Van Meerbeek, Katholieke Universiteit Leuven

Microrefugia are critical for species survival under climate change. This study uses fine-scale (25 * 25 meters) temperature data from the ForestClim database, which combines in situ near-surface (15 cm above ground) forest temperature with a machine learning model, to identify potential microrefugia in European forests. A multifunctionality (MF) index was developed, combing microclimate temperature offset, warming magnitude, and climate change velocity. Results reveal higher MF indices in mountainous and coastal areas due to their unique topography and climate. Tree cover density, slope, and relative elevation significantly enhance microrefugia potential, with dense canopies providing thermal insulation. Seasonal dynamics affect microclimate offsets, while differences in forward and backward climate change velocities identify regions vulnerable to climate shifts. By combining projected microclimate data with species distribution modeling, this study supports forest conservation prioritization and emphasizes a multidimensional approach to understanding and managing microrefugia under climate change.

5. Living Dunes, a novel software package for simulating dune development across scales: coupling vegetation, environmental drivers, and aeolian transport

Frederik van Daele, Dries Bonte, Ghent University

To inform coastal management decisions, we developed Living Dunes - a modelling framework that aims to integrate diverse spatial datasets to simulate dune dynamics. The model integrates three key data streams: (1) plant trait data collected across European coastal dunes following a standardized protocol, capturing both morphological traits (e.g. root architecture, shoot structure) and demographic responses (e.g. germination, growth, dispersal); (2) high-resolution LiDAR-derived digital terrain and surface models providing detailed dune topography and vegetation structure; and (3) global environmental datasets accessed through APIs (e.g. Copernicus Coastal Zones, WorldClim, EFCAS coastal flooding) that are dynamically downscaled to drive fine-grained vegetation processes. This multi-source data integration enables the simulation of biogeomorphological feedbacks between vegetation and dune development. By combining continental-scale trait variation with local environmental conditions, Living Dunes provides data-driven predictions for dune development under different climate scenarios, supporting evidence-based coastal management and restoration planning. For an initial beta preview, see https://users.ugent.be/~frevdael/.