Biology subjects

Kuczynski, L.

Publications and source records attributed to Kuczynski, L..

5 recordsLinked to original sources

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↗

bioRxiv2026

Biotic niche expansion constrains the fundamental abiotic niche: Evidence from experimental evolution

In an ever-changing world, organisms are subject to selective pressures that shape their ecological niches. Niche theory predicts that environmental heterogeneity selects for niche expansion, yet niches are inherently multidimensional, and expansion in one dimension may impose severe constrains on others. In this study, we employed rigorous experimental evolution to investigate these cross-dimensional trade-offs in the wheat curl mite, Aceria tosichella. By adapting replicated lineages to either stable (single-host) or alternating (two-host) environments for hundreds of generations, we successfully expanded the mites fundamental biotic host niche, enabling lineages to exploit diverse host species, including those unencountered during their evolutionary history. Crucially, however, this biotic generalization incurred a significant cost in the abiotic niche dimension. Lineages adapted to alternating hosts exhibited significantly reduced thermal tolerance compared to host specialists, which maintained superior performance across a wider thermal range. This trade-off appears to be driven by a combination of genetically based metabolic constraints and behavioral dispersal strategies. Our results provide compelling experimental evidence for the "Jack-of-all-trades is master of none" hypothesis across niche dimensions. We demonstrate that physiological trade-offs between biotic versatility and abiotic resilience strictly constrain the evolution of the multidimensional niche, with critical implications for forecasting species distributions and invasion potential under climate change.

ecology↗

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

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↗