Data from: Non-invasive genetic monitoring involving citizen science enables reconstruction of current pack dynamics in a re-establishing wolf population
Granroth-Wilding, Hanna et al. (2017), Data from: Non-invasive genetic monitoring involving citizen science enables reconstruction of current pack dynamics in a re-establishing wolf population, Dryad, Dataset, https://doi.org/10.5061/dryad.76tr6
Background: Carnivores are re-establishing in many human-populated areas, where their presence is often contentious. Reaching consensus over management decisions is often hampered by a dispute about the size of the local carnivore population. Understanding the reproductive dynamics and individual movements of the carnivores can provide support for management decisions, but individual-level information can be difficult to obtain from elusive, wide-ranging species. Non-invasive genetic sampling can yield such information, but makes subsequent reconstruction of population history challenging due to incomplete population coverage and error-prone data. Here, we combine a collaborative, volunteer-based sampling scheme with Bayesian pedigree reconstruction to describe the pack dynamics of an establishing grey wolf (Canis lupus) population in south-west Finland, where wolf breeding was recorded in 2006 for the first time in over a century. Results: Using DNA extracted mainly from faeces collected since 2008, we identified 81 individual wolves and assigned credible full parentages to 70 of these and partial parentages to a further 9, revealing 7 breeding pairs. Individuals used a range of strategies to obtain breeding opportunities, including dispersal to established or new packs, long-distance migration and inheriting breeding roles. Gene flow occurred between all packs but inbreeding events were rare. Conclusions: These findings demonstrate that characterizing ongoing pack dynamics can provide detailed, locally-relevant insight into the ecology of contentious species such as the wolf. Involving various stakeholders in data collection makes these results more likely to be accepted as unbiased and hence reliable grounds for management decisions.