Biology subjects

Bartak, V.

Publications and source records attributed to Bartak, V..

3 recordsLinked to original sources

bioRxiv2026

Segmentation and profile-based classification of movement strategies from animal tracking data

O_LIClassifying animal movement strategies from GPS tracking data is essential for understanding space use, population dynamics and conservation planning. However, existing approaches either require strong parametric assumptions about trajectory shape, large labelled datasets (i.e. expert-annotated) for machine learning, or lack formal uncertainty quantification. These limitations create barriers for researchers working with novel species or limited sample sizes. C_LIO_LIWe present a profile-based classification framework consisting of three steps. First, trajectories are segmented using breakpoint detection applied to Net Squared Displacement (NSD) time series. Movement metrics are then extracted from each segment and classified by comparing them to empirically derived behavioural profiles via Z-score distances transformed to softmax probabilities. Bootstrap resampling quantifies uncertainty in the resulting classifications from both training and test data. We validated the framework through simulation experiments and applied it to GPS tracking data from two ecologically contrasting species: gray wolf (Canis lupus;43 individuals) and northern lapwing (Vanellus vanellus;15 individuals). C_LIO_LISimulations showed that 5-10 training segments per movement strategy suffice for reliable classification, with overall accuracy of 91.1%across residential, floating and dispersal strategies. Segment duration of 30-60 days was required for confident discrimination of residential and floating behaviour. For wolves, the framework clearly distinguished residency, floating or dispersal (91.2%of segments classified with >50%probability). For lapwings, migration was identified with high confidence, while residential-floating discrimination reflected genuine ecological ambiguity confirmed by domain experts, with bootstrap confidence intervals transparently flagging uncertain cases. C_LIO_LIThe profile-based framework provides an accessible, interpretable alternative to parametric NSD fitting and machine learning approach, requiring modest training data while delivering probabilistic classifications with honest uncertainty estimates. An R package (moveprofile) implementing the complete workflow is freely available. The framework is applicable to any tracked species where distinct movement strategies can be identified by experts knowledge. C_LI

ecology↗

bioRxiv2025

Spatial autocorrelation of species diversity and distributions in time and across spatial scales

AimSpatial autocorrelation (SAC), also known as aggregation, is a notable property of species distributions and diversity; it reflects species niche and dispersal, has conservation significance, and affects ecological models. Yet, we know little about spatial and temporal patterns of SAC in empirical data. Here, we assess SAC in both observed species distributions and species richness, quantifying its magnitude and prevalence over large extents and across spatial resolutions. We also assess its dynamics over the past 50 years. LocationCzechia, Europe, New York State, Japan Time period1972 - 2017 Major taxa studiedBirds MethodsWe analyzed four temporally replicated gridded bird atlases, each aggregated to multiple grain sizes. To measure SAC in species distributions, we used the Join count statistic (JC) and its deviation from the expectation under a random distribution. We assessed temporal changes in JC and their relationship with changes in occupancy, given their close association. We used Morans I to measure SAC in species richness. ResultsBoth species distributions and diversity were positively autocorrelated across all regions, periods, and grains, and the magnitude of autocorrelation mostly decreased with increasing grain. We found that the temporal change of JC varied across species and regions, with zero average trends in Morans I, JC, and occupancy. However, when JC and occupancy were considered jointly, we found systematic temporal shifts: contracting species became more aggregated (compact) while expanding species became more fragmented (disjoint). Main conclusionsStronger SAC at finer grains suggests greater predictability of diversity and distributions at these scales. Despite zero average change in occupancy or SAC, their coupled shifts highlight the importance of considering both jointly. We found long-distance dispersal (rather than advancing edge) and vulnerability of isolated populations to extinction as the major drivers of range dynamics in temperate birds.

ecology↗

bioRxiv2024

Should regional species loss be faster, or slower, than local loss? It depends on density-dependent rate of death

1Assessment of the rate of species loss, which we also label extinction, is an urgent task. However, the rate depends on spatial grain (average area A) over which it is assessed--local species loss can be on average faster, or slower, than regional or global loss. Ecological mechanisms behind this discrepancy are unclear. We propose that the relationship between extinction rate and A is driven by two classical ecological phenomena: the Allee effect and the Janzen-Connell effect. Specifically, we hypothesize that (i) when per-individual probability of death (Pdeath) decreases with population density N (as in Allee effects), per-species extinction rate (Px) should be high at regional grains, and low locally. (ii) In contrast, when Pdeath increases with N (as in Janzen-Connell effects), Px should be low regionally, but high locally. (iii) Total counts of extinct species (Ex) should follow a more complex relationship with A, as they also depend on drivers of the species-area relationship (SAR) prior to extinctions, such as intraspecific aggregation, species pools, and species-abundance distributions. We tested these hypotheses using simulation experiments, the first based on point patterns, the second on a system of generalized Lotka-Volterra equations. In both experiments, we used a single continuous parameter that moved between the Allee effect, no relationship between Pdeath and N, and the Janzen-Connell effect. We found support for our hypotheses, but only when regional species-abundance distributions were uneven enough to provide sufficiently rare or common species for Allee or Janzen-Connell to act on. In all, we have theoretically demonstrated a mechanism behind different rates of biodiversity change at different spatial grains which has been observed in empirical data.

ecology↗