Abstract
As human activity accelerates the global crisis facing wildlife populations, private land conservation provides an example of wildlife management challenges in social-ecological systems. This study reports on the research phase of 'WildTracker' - a co-created citizen science project, involving 160 landholders across three Tasmanian regions. This was a transdisciplinary collaboration between an environmental organisation, university researchers, and local landholders. Focusing on mammal and bird species, the project integrated diverse data types and technologies: social surveys, quantitative ecology, motion sensor cameras, acoustic recorders, and advanced machine-learning analytics. An iterative analytical methodology encompassed Pearson and point-biserial correlation for interrelationships, Non-Metric Multidimensional Scaling (NMDS) for clustering, and Random Forest machine learning for variable importance and prediction. Taken together, these analyses revealed complex relationships between wildlife populations and a suite of ecological, socio-economic, and land management variables. Both site-scale habitat characteristics and landscape-scale vegetation patterns were useful predictors of mammal and bird activity, but these relationships were different for mammals and birds. Four focal mammal species showed variation in their response to ecological and land management drivers. Unexpectedly, threatened species, such as the eastern quoll (Dasyurus viverrinus), favoured locations where habitat was substantially modified by human activities. The research provides actionable insights for landowners, and highlights the importance of 'messy,' ecologically heterogeneous, mixed agricultural landscapes for wildlife conservation. The identification of thresholds in habitat fragmentation reinforced the importance of collaboration across private landscapes. Participatory research models such as WildTracker can complement efforts to address the wicked problem of wildlife conservation in the Anthropocene.