Kuczynski, L. | Search bioRxiv

bioRxiv2025

Is passive dispersal informed? - Experimental evidence for decision-making in phytophagous arthropods

Animals must acquire and decode information to make the right decisions. While active dispersers can evaluate habitats en route, passive dispersers can only control their departure timing. Although the passive strategy is ubiquitous among arthropods, the mechanisms behind their take-off decisions remain poorly understood. We tested whether host niche breadth shapes passive dispersal in phytophagous mites by exposing them to host-derived kairomones and measuring departure rates. Using experimentally evolved specialist and generalist lineages, we found that dispersal depends more on the context in which cues are encountered that on the kairomones themselves. Host specialisation strongly shaped responses: mites left plants more readily when exposed to unfamiliar hosts, with generalists dispersing over twice as often as specialists. Increased number of unfamiliar kairomones strongly inhibited generalists dispersal but barely affected specialists. This suggests specialists use environmental novelty to trigger exploration, whereas generalists need multiple cues to confirm host suitability, revealing a trade-off between host range and environmental sensitivity.

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↗