Biology subjects

Kleinebecker, T.

Publications and source records attributed to Kleinebecker, T..

9 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↗

bioRxiv2025

Accounting for spatial interactions in the upscaling of ecosystem services

O_LIMaps of ecosystem service (ES) supply are frequently used to guide spatial planning, policy making, and ecosystem management. However, these are typically based upon coarse land-cover proxies. This approach lacks a strong mechanistic basis, and neglects spatial biodiversity dynamics and interactions among landscape properties that can modify ES provision. C_LIO_LIWe present an analytical framework for ES upscaling that incorporates spatial interactions between landscape properties to determine ES supply. The resulting models can be viewed as a spatially informed ES production function. The approach comprises seven steps that include several elements absent from most existing approaches, notably a procedure for identifying geodata variables that represent the true mechanistic drivers, the inclusion of spatial interactions in the upscaling model, and modification following expert feedback on the selected model. C_LIO_LIWe demonstrate the approach using two example ES from German grasslands: biodiversity conservation and water supply. We show that the inclusion of spatial interactions in the upscaling model improved model predictions from 15% to 33% depending on the ES evaluated. In addition, inclusion of spatial interactions led to reduced error associated with the upscaled estimates. C_LIO_LIBy overcoming several shortcomings of existing, upscaling approaches we generate resulting maps of ES supply that can more reliably inform spatial planning Further, the underlying models allow for simulation of changes in the drivers of ES supply and estimation of respective outcomes. These advantages have the potential to better link detailed local-scale ecological understanding and land management with large-scale ES supply mapping, and thus better inform decision making and spatial planning. 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↗

bioRxiv2023

A fast-slow trait continuum at the level of entire communities

Across the tree of life, organismal functional strategies form a continuum from slow-to fast-growing organisms, in response to common drivers such as resource availability and disturbance. However, the synchronization of these strategies at the entire community level is untested. We combine trait data for >2800 above-and belowground taxa from 14 trophic guilds spanning a disturbance and resource availability gradient in German grasslands. Most guilds consistently respond to these drivers through both direct and trophically-mediated effects, resulting in a slow-fast axis at the level of the entire community. Fast trait communities were also associated with faster rates of whole ecosystem functioning. These findings demonstrate that slow and fast strategies can be manifested at the level of whole ecosystems, opening new avenues of ecosystem-level functional classification.

ecology↗

bioRxiv2021

Soil conditions drive belowground trait space in temperate agricultural grasslands

O_LIPlant belowground organs perform essential functions, including water and nutrient uptake, anchorage, vegetative reproduction and recruitment of mutualistic soil microbiota. Determining how belowground traits jointly determine dimensions of the trait space and how these dimensions are linked to environmental conditions would further advance our understanding of plant functioning and community assembly. C_LIO_LIHere, we investigated belowground plant-trait dimensionality and its variation along 10 soil and land-use parameters in 150 temperate grasslands plots. We used eight belowground traits collected in greenhouse and common garden experiments, as well as bud-bank size and specific leaf area from databases, for a total of 313 species, to calculate community weighted means (CWMs). C_LIO_LIUsing PCA, we found that about 55% of variance in CWMs was explained by two main dimensions, corresponding to a mycorrhizal collaboration and a resource conservation gradient. Frequently overlooked traits such as rooting depth, bud-bank size and root branching intensity were largely integrated in this bidimensional trait space. The two plant-strategy gradients were partially dependent on each other, with outsourcing communities along the collaboration gradient being more often slow. These outsourcing communities were also more often deep-rooting, and associated with soil parameters, such as low moisture and sand content, high topsoil pH, high C:N and low {delta}15N. Slow communities had large bud-banks and were associated with low land-use intensity, high topsoil pH, and low nitrate but high ammonium concentrations in the soil. We did not find a substantial role of phosphorus-availability as an indicator along the collaboration gradient. C_LIO_LIIn conclusion, the collaboration and conservation gradients previously identified at the species level scale up to community level in grasslands, encompass more traits than previously described, and vary with the environment. C_LI

ecology↗

bioRxiv2020

Context-dependency in biodiversity-multifunctionality relationships is driven by nitrogen availability and plant functional composition

The ability of an ecosystem to deliver multiple functions at high levels (multifunctionality) typically increases with biodiversity but there is substantial variation in the strength and direction of biodiversity effects, suggesting context-dependency. A better understanding of the drivers of this context dependency is essential to predict effects of global change on ecosystems. To determine how different factors modulate the effect of diversity on multifunctionality, we established a large grassland experiment with 216 communities, crossing a manipulation of plant species richness (1, 4, 8, 20 species) with manipulations of resources (nitrogen enrichment), plant functional composition (gradient in mean specific leaf area [SLA] to manipulate abundances of exploitative, fast-growing vs. conservative, slow-growing species), plant functional diversity (variance in SLA) and enemy abundance (foliar fungal pathogen removal). We measured ten above- and belowground functions, related to productivity, nutrient cycling and energy transfer between trophic levels, and calculated ecosystem multifunctionality. Plant species richness and functional diversity both increased multifunctionality, but their effects were context dependent. Species richness increased multifunctionality only when communities were assembled with fast growing (high SLA) species. This was because slow species were more redundant in their functional effects, whereas different fast species tended to promote different functions. Functional diversity also increased multifunctionality but this effect was dampened by nitrogen enrichment. However, unfertilised, functionally diverse communities still delivered more functions than low diversity, fertilised communities. Our study suggests that a shift towards fast-growing exploitative communities will not only alter ecosystem functioning but also the strength of biodiversity-functioning relationships, which highlights the potentially complex effects of global change on multifunctionality.

ecology↗

bioRxiv2020

Landscape management for grassland multifunctionality

Land-use intensification has contrasting effects on different ecosystem services, often leading to land-use conflicts. While multiple studies have demonstrated how landscape-scale strategies can minimise the trade-off between agricultural production and biodiversity conservation, little is known about which land-use strategies maximise the landscape-level supply of multiple ecosystem services (landscape multifunctionality), a common goal of stakeholder communities. We combine comprehensive data collected from 150 German grassland sites with a simulation approach to identify landscape compositions, with differing proportions of low-, medium-, and high-intensity grasslands, that minimise trade-offs between the six main grassland ecosystem services prioritised by local stakeholders: biodiversity conservation, aesthetic value, productivity, carbon storage, foraging, and regional identity. Results are made accessible through an online tool that provides information on which compositions best meet any combination of user-defined priorities (https://neyret.shinyapps.io/landscape_composition_for_multifunctionality/). Results show that an optimal landscape composition can be identified for any pattern of ecosystem service priorities. However, multifunctionality was similar and low for all landscape compositions in cases where there are strong trade-offs between services (e.g. aesthetic value and fodder production), where many services were prioritised, and where drivers other than land use played an important role. We also found that if moderate service levels are deemed acceptable, then strategies in which both high and low intensity grasslands are present can deliver landscape multifunctionality. The tool presented can aid informed decision-making by predicting the impact of future changes in landscape composition, and by allowing for the relative roles of stakeholder priorities and biophysical trade-offs to be understood by scientists and practitioners alike. HighlightsO_LIAn online tool identifies optimal landscape compositions for desired ecosystem services C_LIO_LIWhen the desired services are synergic, the optimum is their common best landscape composition C_LIO_LIWhen the desired services trade-off, a mix of grassland intensity is most multifunctional C_LIO_LISuch tools could support decision-making processes and aid conflict resolution C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/208199v5_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@3273f6org.highwire.dtl.DTLVardef@5b3a5aorg.highwire.dtl.DTLVardef@153f526org.highwire.dtl.DTLVardef@103cbea_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2019

The results of biodiversity-ecosystem functioning experiments are realistic

A large body of research shows that biodiversity loss can reduce ecosystem functioning, thus providing support for the conservation of biological diversity1-4. Much of the evidence for this relationship is drawn from biodiversity-ecosystem functioning experiments (hereafter: biodiversity experiments), in which biodiversity loss is simulated by randomly assembling communities of varying species diversity, and ecosystem functions are measured5-9. This random assembly has led some ecologists to question the relevance of biodiversity experiments to real-world ecosystems, where community assembly may often be non-random and influenced by external drivers, such as climate or land-use intensification10-18. Despite these repeated criticisms, there has been no comprehensive, quantitative assessment of how experimental and real-world plant communities really differ, and whether these differences invalidate the experimental results. Here, we compare data from two of the largest and longest-running grassland biodiversity experiments globally (Jena Experiment, Germany; BioDIV, USA) to related real-world grassland plant communities in terms of their taxonomic, functional, and phylogenetic diversity and functional-trait composition. We found that plant communities of biodiversity experiments have greater variance in these compositional features than their real-world counterparts, covering almost all of the variation of the real-world communities (82-96%) while also containing community types that are not currently observed in the real world. We then re-analysed a subset of experimental data that included only ecologically-realistic communities, i.e. those comparable to real-world communities. For ten out of twelve biodiversity-ecosystem functioning relationships, biodiversity effects did not differ significantly between the full dataset of biodiversity experiments and the ecologically-realistic subset of experimental communities. This demonstrates that the results of biodiversity experiments are largely insensitive to the inclusion/exclusion of unrealistic communities. By bridging the gap between experimental and real-world studies, these results demonstrate the validity of inferences from biodiversity experiments, a key step in translating their results into specific recommendations for real-world biodiversity management.

ecology↗

bioRxiv2019

Decomposition disentangled: a test of the multiple mechanisms by which nitrogen enrichment alters litter decomposition

O_LINitrogen (N) enrichment has direct effects on ecosystem functioning by altering soil abiotic conditions and indirect effects by reducing plant diversity and shifting plant functional composition from dominance by slow to fast growing species. Litter decomposition is a key ecosystem function and is affected by N enrichment either by a change in litter quality (the recalcitrance of the plant material) or through a change in soil quality (the abiotic and biotic components of the soil that affect decomposition). The relative importance of soil and litter quality and how the direct and effects of N alter them remains poorly known.\nC_LIO_LIWe designed a large grassland field experiment manipulating N enrichment, plant species richness and functional composition in a full factorial design. We used three complementary litter bag experiments and a novel structural equation modelling approach to quantify the relative effects of the treatments on litter and soil quality and their importance for total decomposition.\nC_LIO_LIOur results indicate that total decomposition was mostly driven by changes in litter quality rather than soil quality. Litter quality was affected by the nutrient contents (N and calcium) and structural components of the litter (leaf dry matter content, fibres). N enrichment increased litter decomposition mostly indirectly through a shift in functional composition toward faster growing plant species producing higher quality litter. N enrichment also had effects on soil, by directly and indirectly affected vegetation cover, but this had relatively few consequences for the total decomposition rate.\nC_LIO_LISynthesis. Our approach provides a mechanistic tool to test the drivers of litter decomposition across different ecosystems. Our results show that functional composition is more important than richness or soil quality in determining litter decomposition and that N enrichment effects mainly occur via above- rather than belowground processes. This highlights the importance of considering shifts in plant species composition when assessing the effects of N enrichment on decomposition.\nC_LI

ecology↗