Biology subjects

Klinger, Y. P.

Publications and source records attributed to Klinger, Y. P..

2 recordsLinked to original sources

bioRxiv2026

Data sources shape species niches: integrating citizen science and state agency data expands habitat suitability models and improves biological invasion predictions

Species distribution models (SDMs) are widely used to support risk assessment for invasive non-native plant species (INNPS), but their performance is constrained by the coverage of occurrence data. Combining occurrences from citizen science (CS) platforms with data from structured state agency (StAg) monitoring provides unique advantages, yet they are rarely integrated. Here, we systematically compare how CS, StAg, and combined (COM) occurrence data influence the inferred environmental niches, predictive performance, and spatial applicability of SDMs for three widespread INNPS (A. altissima, H. mantegazzianum, I. glandulifera) in central Germany. We quantified niche overlap between datasets using PCA and Schoeners D and applied a hierarchical SDM utilizing boosted regression trees, while the Area of Applicability (AOA) was assessed to identify monitoring gaps. CS data were strongly biased toward lower-elevation, urbanized environments, whereas StAg data captured higher-elevation, remote habitats, particularly along watercourses. Niche overlap reflected both invasion stage and habitat preferences: A. altissima, a species that is spreading, showed the lowest overlap. H. mantegazzianum, associated with linear habitats like watercourses and infrastructure, exhibited intermediate overlap, while I. glandulifera, a widespread species, displayed the highest overlap. Overall, combined models achieved the highest predictive performance (AUC: 0.85, TSS: 0.58), reduced uncertainty along environmental gradients and produced more ecologically plausible suitability patterns. AOA analysis revealed high applicability ([≥]59%) across data sources and species, with COM models consistently reducing extrapolation uncertainty. Our findings highlight that integrating CS and StAg data reduces spatial biases and enhances SDM robustness, which is vital to improve INNPS risk assessments and management. HighlightsO_LICitizen science and state agency data capture distinct environmental spaces. C_LIO_LIOverlap between data sources is related to invasion stage and habitat preference. C_LIO_LICombined data improves invasive species niche representation and model accuracy. C_LIO_LIAOA analysis reveals monitoring gaps, especially in remote and high-elevation areas. C_LI

ecology↗

bioRxiv2024

Impact of land-use intensity, productivity, and aboveground richness on seed rain in temperate grasslands

O_LISeed rain, the amount of seeds reaching an area via primary or secondary dispersal, affects the regeneration of plant communities and shapes the trajectory of future community composition. In agricultural grasslands, the composition and density in seed rain are mainly driven by land use, but drivers of seed rain quality and quantity along land-use gradients are poorly understood. We studied the effects of land-use intensity (LUI), its components (i.e. fertilization, mowing, and grazing), productivity, and aboveground vegetation composition and richness on seed rain. C_LIO_LIWe collected the seed rain over a five-month period in 142 grasslands and identified emerging seedlings. Grass seedlings dominated seed rain most likely due to their high abundance in vegetation and intense and early seed set. Only ten species accounted for approximately 80 % of seedlings, with grasses such as Lolium perenne and Alopecurus pratensis being most abundant. Forbs such as Cerastium holosteoides and Veronica arvensis were abundant in seed rain despite lower cover, probably due to early and prolonged flowering and high seed production. C_LIO_LISeed rain of grasses and forbs reacted differently to LUI and vegetation richness, LUI effects on grass seed rain mainly determined total seed density. Seed rain richness first increased with LUI, but decreased at higher LUI levels. Consequently, seed rain richness consistently increased with vegetation richness. This is reflected by a decrease in the abundance of stress strategists and an increase in ruderals in seed rain with increasing LUI and decreasing vegetation richness. C_LIO_LIAmong LUI components, fertilization intensity most strongly affected seed rain density and composition, with negative effects at intermediate fertilization intensities. Mowing once a year increased seed rain density, whereas it decreased at higher mowing frequencies. Grazing intensity reduced overall seed density and richness by reducing grass seed density, while forb seed density remained unaffected. C_LI Synthesis: Land-use intensity and aboveground productivity significantly influence the species composition and seed densities in the seed rain of temperate agricultural grasslands. Higher land-use intensity and productivity increased seed production but reduced taxonomic and functional diversity in seed rain and may negatively impact ecosystem stability and resilience.

ecology↗