Wawrzynowicz, M. | bioRxiv Science

bioRxiv2026

Behavioural plasticity at the spatial-social interface: Predation risk modulates density-dependent breeding dispersion

The spatial organisation of breeding populations can influence fitness and population dynamics, yet the spatial expression of density dependence may vary with ecological context. We investigated whether predator abundance alters this expression in the northern lapwing (Vanellus vanellus), a loosely social ground-nesting wader. Using long-term breeding-bird monitoring data from agricultural landscapes, we quantified spatial organisation using a Normalised Spatial Dispersion Index (NSDI) and modelled its relationships with lapwing density, hooded crow (Corvus cornix) abundance, habitat composition, and winter climate using generalised additive mixed models. Spatial organisation showed density dependence, but this relationship weakened progressively with increasing crow abundance. At low crow abundance, increasing lapwing density was associated with greater spatial dispersion, whereas the relationship approached zero at high crow abundance. Although it became slightly negative at the upper end of the crow-abundance gradient, uncertainty provided no clear evidence of a reversal to density-dependent aggregation. Broad-scale habitat composition and winter precipitation were not supported as predictors, whereas warmer winter temperatures were associated with greater aggregation. Our results show that predator context can attenuate the spatial expression of density dependence, suggesting that spatial organisation may be an overlooked component of variation in density-dependent population processes.

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↗