Malinowska, K. | bioRxiv Science

bioRxiv2026

Landscape heterogeneity as a main driver of avian population dynamics

The ongoing decline in biodiversity highlights the need for understanding the causes of population changes. This study uses 25-year, large-scale monitoring dataset to investigate the influence of climate and landscape structure on the annual population growth rates of 84 bird species across Poland. Our methodological framework involves the spatiotemporal decomposition of these environmental drivers to decouple demographic effects of long-term carrying capacities from the short-term effects of environmental perturbations. Using species-specific demographic models followed by a community-wide meta-analysis, we evaluated how individual species responses scale up to shape community-level dynamics. The results reveal significant variation in species-specific responses to individual drivers. At the community level, our findings suggest that bird populations are mainly regulated by the long-term spatial constraints rather than short-term disturbances. Persistent environmental heterogeneity had the strongest positive demographic effect on birds, followed by temperature, forest dominance over croplands, and precipitation. In contrast, rapid temporal shifts in environmental heterogeneity and precipitation anomalies negatively affected population growth, whereas urbanisation consistently exerted a negative effect across both spatiotemporal dimensions. Our results highlight the significance of protecting existing heterogeneous and ecotonal habitats, as well as the need to incorporate features that enhance habitat heterogeneity into urban development. Article impact statementPreserving heterogeneous habitats is essential for the conservation of bird populations.

ecology↗

bioRxiv2025

Comparing analytical protocols for identifying causes of population changes

Conservation decision-making requires accurate identification of causes of population changes. Ecologists often rely on analytical protocols that aggregate high-dimensional monitoring data. We hypothesise that compressing data - either spatially, as in conventional time series (TS) analysis, or temporally, as in static species distribution models (SDMs) - destroys covariance structures and obscures the identification of causal drivers. To quantify this aggregation cost, we conducted a rigorous simulation experiment using virtual species to establish a known ground truth of population drivers. We then employed a virtual ecologist approach to mimic a 20-year large-scale bird monitoring scheme, and generate realistic spatiotemporal datasets to evaluate the analytical pipelines. We benchmarked the causal attribution accuracy of aggregated TS and SDM protocols against a full-resolution spatiotemporal (FRST) framework, which retains native data dimensions and integrates mechanistic spatiotemporal covariance structures. Our simulations revealed that spatial compression severely compromises causal inference: unpenalised TS models failed to detect any true underlying drivers (accuracy = 0.50, sensitivity = 0.00). Temporal compression (SDMs) performed moderately better (accuracy = 0.68), while the FRST model achieved superior accuracy (0.88), sensitivity (0.84), and specificity (0.93). Furthermore, we identified a variable selection paradox: double penalty shrinkage marginally improved underpowered TS models, although it degraded the specificity of SDM and FRST frameworks by forcing spurious, correlated variables to absorb residual variance. Our findings demonstrate that protocols that involve data aggregation reduce the informational value of large-scale monitoring datasets. Full-resolution, mechanistically informed frameworks are essential for reliable causal attribution and robust biodiversity monitoring. HighlightsO_LISpatiotemporal data aggregation obscures causal drivers in biodiversity monitoring. C_LIO_LISpatial compression in time series models fails to detect true population drivers. C_LIO_LIFull-resolution spatiotemporal models accurately identify true drivers. C_LIO_LIAutomated variable selection introduces false positives in high-resolution models. C_LI

ecology↗