The use of socio-economy in species distribution modelling: Features of rural societies improve predictions of barn owl occurrence

Parrots in the wild in Polish cities

Amateur breeding of parrots as pets has contributed to many species being found in areas where they never occurred in the wild, particularly in warmer regions, but also in the temperate climates of North America and Europe. Climate change is likely to exacerbate this process. We hypothesised that parrots occurred in the wild in the cities of Poland, especially the rose-ringed parakeet, as there are reports of breeding sites in the literature. Using information on lost, found and sighted parrots posted on social media, we have analysed the extent of parrot emergence in Poland. In a period of less than two years (from October 2018, through 2019 and from June to the end of December 2021), 2,675 parrot specimens of 49 species were found in the wild. The most frequently observed species were cockatiel Nymphicus hollandicus (N = 962), budgerigar Melopsittacus undulatus (N = 884) and rose-ringed parakeet Psittacula krameri (N = 182). Parrots were most frequently observed in urban areas, in regions characterized by a higher population size, a higher income and a higher proportion of people with a university degree. Our study shows that the occurrence of parrots, especially rose-ringed parakeets in the wild, needs to be monitored as new breeding sites may appear.

Socio-economic variables improve accuracy and change spatial predictions in species distribution models

In an era marked by increasing anthropogenic pressure, understanding the relations between human activities and wildlife is crucial for understanding ecological patterns, effective conservation, and management strategies. Here, we explore the potential and usefulness of socio-economic variables in species distribution modelling (SDM), focusing on their impact on the occurrence of wild mammals in Poland. Beyond the environmental factors commonly considered in SDM, like land-use, the study tests the importance of socio-economic characteristics of local human societies, such as age, income, working sector, gender, education, and village characteristics for explaining distribution of diverse mammalian groups, including carnivores, ungulates, rodents, soricids, and bats. The study revealed that incorporating socio-economic variables enhances the predictive power for >60 % of species and overall for most groups, with the exception being carnivores. For all the species combined, among the 10 predictors with highest predictive power, 6 belong to socio-economic group, while for specific species groups, socio-economic variables had similar predictive power as environmental variables. Furthermore, spatial predictions of species occurrence underwent changes when socio-economic variables were included in the model, resulting in a substantial mismatch in spatial predictions of species occurrence between environment-only models and models containing socio-economic variables. We conclude that socio-economic data has potential as useful predictors which increase prediction accuracy of wildlife occurrence and recommend its wider usage. Further, to our knowledge this is a first study on such a big scale for terrestrial mammals which evaluates performance based on presence or absence of socio-economic predictors in the model. We recognise the need for a more comprehensive approach in SDMs and that bridging the gap between human socio-economic dynamics and ecological processes may contribute to the understanding of the factors influencing biodiversity.