Dula, M. | Search bioRxiv

bioRxiv2026

Wolf microevolution in the melting pot: range expansion, population sympatry, dynamic mosaic admixture zone and asymmetric gene flow

Here we describe substantial range shifts of historically differentiated wolf populations and formation of novel sympatric zones during the recent decade in Central Europe. This region provides a natural laboratory for testing alternative scenarios of population interactions from continued isolation or restricted gene flow to progressive population fusion, while prompting a reassessment of their geographic ranges. Based on sampling across the Czech Republic and Slovakia obtained from large-scale monitoring programmes over five wolf years (2020/21_2024/25), complemented by comparative material from neighbouring regions, we analysed mitochondrial haplotypes, autosomal microsatellite genotypes and sex-linked loci. The Central European population with Baltic ancestry predominated across large parts of Central Europe including the Bohemian Massif with enclaves in the Western Carpathians. The Carpathian population was predominant in Slovakia, with a smaller satellite occurrence in the northern part of the Bohemian Massif. Alpine population was centred in the Alps but extended into southern parts of Bohemian Massif and Central German Uplands. Following the sporadic occurrence of admixed individuals, broad mosaic and dynamic sympatric zones have formed in the Czech Republic and Slovakia in the last decade. These scenarios could be facilitated by the presence of intermediate habitats and isolation of the Bohemian Massif structural basin, framed by a massive ring fault system. Recent-immigration estimates are asymmetric, with the largest mean contributions from the Alpine to the Central European population and from the Central European to the Carpathian population, with the second case potentially linked to source-sink dynamics driven by the hunting pressure within the Carpathian population (whereas the others are protected year-round). Whether increasing admixture will enhance viability of populations (that currently have small effective sizes) through genetic rescue or carry risks of outbreeding depression remains uncertain, highlighting the need for continued transboundary monitoring within conservation biology framework.

genetics↗

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↗