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Svidzinska, D.

Publications and source records attributed to Svidzinska, D..

2 recordsLinked to original sources

Global Convergence of Plant Functional Trait Composition in the Anthropocene

Since the onset of European colonial expansion, humans have accelerated species migration across continents, reshaping plant functional composition and associated ecosystem processes. Plant functional traits-such as leaf area, plant height, or rooting depth-are structured along major axes of variation, including size and leaf economics, that reflect ecological strategies. While human-mediated changes in this trait space have been documented regionally or for specific taxa, there exists no global, grid cell-level quantification of past shifts across major axes of trait variation. Here, we link global citizen science plant occurrence data with data on 37 above- and below-ground traits, and information on native and introduced status for each occurrence. Using dimension-reduction on grid cell-level trait means and introduced species status as a proxy for anthropogenic change, we identify three major axes of functional variation: the size, leaf economics, and life-span axes. By comparing past (native-only) and present-day trait distributions in 3D trait space and geographically, we find prominent region-specific shifts along all three axes. Overall, functional composition converges toward (mostly) smaller, more acquisitive, and shorter-lived assemblages, with region-specific differences in which axis shifts are most pronounced. These results provide the first global estimate of how human-mediated plant introductions have altered ecosystem functional composition in the past centuries, highlighting the spatial patterns and trait dimensions most affected by anthropogenic pressures.

ecology↗

From smartphones to satellites: Uniting crowdsourced biodiversity monitoring and Earth observation to fill the gaps in global plant trait mapping

Plant functional traits are fundamental to ecosystem dynamics and Earth system processes, but their global characterization is limited by the availability of field surveys and trait measurements. Recent expansions in biodiversity data aggregation, including large collections of vegetation surveys, citizen science observations, and trait measurements, offer new opportunities to overcome these constraints. Here we demonstrate that combining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km resolution. Our approach achieves high predictive power, reaching correlations up to 0.63 (15 of 31 traits exceeding 0.50) and improved spatial transferability, effectively bridging gaps in under-sampled regions. By capturing a broad range of traits with high spatial coverage, these maps can enhance our understanding of plant community properties and ecosystem functioning globally, and can serve as useful tools in modeling global biogeochemical processes and informing worldwide conservation efforts. Ultimately, our framework highlights the power and necessity of crowdsourced biodiversity data in high-resolution plant trait modeling. We anticipate that advancements in biodiversity data collection and remote sensing capabilities will further refine global trait mapping, fostering a dynamic trait-based understanding of the biosphere.

ecology↗