Biology subjects

Bauknecht, R.

Publications and source records attributed to Bauknecht, R..

3 recordsLinked to original sources

bioRxiv2026

Landscape restoration strategies differ in their predicted benefits for species persistence

Global biodiversity recovery demands restoration at landscape scales, where ecological outcomes are shaped not only by habitat amount but also spatial arrangement. Alternative landscape restoration strategies can increase habitat amount, improve quality, and alter configuration in fundamentally different ways and may therefore vary in their efficiency. Here, we conceptualise five landscape restoration strategies and use a spatially explicit metapopulation framework to compare their ability to increase predicted species persistence across heterogeneous landscapes and trait space defined by dispersal ability and specialisation. We find that restoration outcomes are strongly trait-dependent, with restoration of existing degraded habitats yielding the highest benefits - particularly for specialists. Improvement of the landscape matrix to create low-quality habitat primarily benefits generalists, while strategies creating new habitat patches or enlarging patches provide more consistent but modest gains. These results reveal fundamental trade-offs among restoration strategies and highlight the utility of trait-explicit approaches to inform their implementation and evaluation.

ecology↗

bioRxiv2026

Movement mechanisms explain current animal density patterns and predict their reorganization under global change

Aim: To test whether individual movement mechanisms inferred from animal tracking data can be scaled up to explain landscape-scale patterns of population density and improve predictions of population redistribution under future environmental change. Location: Texas Gulf Coast, USA Time Period: 2009-2018 Major Taxa Studied: Whooping Crane (Grus americana) Methods: We parameterized individual-level step-selection functions using 14,898 GPS locations from 22 Whooping Cranes and propagated the resulting movement and habitat-selection mechanisms across the landscape to derive steady-state distributions of space use. We compared these movement-informed predictions with correlative species distribution models fitted to the same data. Predictions were evaluated using withheld tracking observations and fully independent population-level aerial-survey data. We then projected both approaches under sea-level-rise scenarios to assess how future habitat loss and fragmentation alter crane space use. Results: Movement mechanisms derived from individual-level tracking data captured the mechanistic dependence of population density on habitat quality and spatial configuration. When projected across the landscape, these mechanisms reproduced fully independently observed density patterns and outperformed conventional species distribution models. Because predictions arose from movement mechanisms rather than occurrence-environment correlations, movement-informed models also captured the effects of habitat reconfiguration under sea-level rise. They projected pronounced population redistribution and an expansion of core space-use areas of up to 80% which conventional models missed. Main Conclusions: Individual movement mechanisms derived from biologging data can scale up to explain current population structure and predict future redistribution under environmental change. By mechanistically accounting for how habitat configuration constrains accessibility, movement-informed models reveal severe impacts of fragmentation that are missed by conventional distribution models. Rapidly expanding biologging datasets therefore offer a scalable basis for connecting individual behavior to macroecological patterns and improving biodiversity forecasts in changing landscapes.

ecology↗

bioRxiv2026

Large river eDNA sampling designs with remote-sensing-based clustering stratification

Environmental DNA (eDNA) provides a powerful tool for biodiversity monitoring in large river ecosystems. However, current studies often rely on subjective site selection and lack systematic sampling designs. This can limit the ability to capture the full spectrum of environmental conditions that species depend on, thereby compromising sampling efficiency. To address this challenge, we propose utilizing remote sensing-based clustering for environmental stratification of sampling designs, thereby enhancing detection capabilities and increasing the objectivity of eDNA sampling. Using GBIF-based fish species distribution models and simulated distributions along the Danube, we demonstrate that this approach enhances detection efficiency compared to conventional random and regular sampling methods. To facilitate practical implementation, we developed a tool to help fieldwork planners of river sampling campaigns automatically apply this method and select stratified sampling sites without the need for extensive data processing. Finally, we demonstrate that eDNA detection occurred most frequently within the range of 0-20km downstream of the expected modeled distribution of species, suggesting that the diffusion of the signal should be further considered in the sampling design process. Our findings highlight the potential of remote sensing-based stratification to create more efficient and objective sampling designs but suggest that sampling design should be further combined with hydrological information to optimize cost-efficient sampling. The development of standard and robust sampling protocols will help advance more cost-effective eDNA-based biodiversity monitoring in riverine ecosystems.

ecology↗