Mammal Research Institute, Polish Academy of Sciences, Stoczek 1, 17-230 Białowieża, Poland.
Mammal Research Institute, Polish Academy of Sciences, Stoczek 1, 17-230 Białowieża, Poland.
Sci Total Environ. 2024 May 10;924:171588. doi: 10.1016/j.scitotenv.2024.171588. Epub 2024 Mar 8.
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.
在人类活动导致的压力不断增加的时代，了解人类活动与野生动物之间的关系对于理解生态模式、有效保护和管理策略至关重要。在这里，我们探讨了社会经济变量在物种分布模型（SDM）中的潜力和有用性，重点研究了它们对波兰野生动物出现的影响。除了 SDM 中通常考虑的环境因素（如土地利用）外，该研究还测试了当地人类社会的社会经济特征的重要性，例如年龄、收入、工作部门、性别、教育和村庄特征，以解释不同哺乳动物群体（包括食肉动物、有蹄类动物、啮齿动物、鼩鼱科动物和蝙蝠）的分布情况。研究表明，纳入社会经济变量可以提高 >60%的物种和大多数群体的预测能力，食肉动物除外。对于所有组合的物种，在具有最高预测能力的 10 个预测因子中，有 6 个属于社会经济组，而对于特定的物种群体，社会经济变量与环境变量具有相似的预测能力。此外，当将社会经济变量纳入模型中时，物种出现的空间预测发生了变化，导致仅包含环境变量的模型和包含社会经济变量的模型之间在物种出现的空间预测上存在很大差异。我们得出的结论是，社会经济数据具有作为有用预测因子的潜力，可以提高野生动物出现的预测准确性，并建议更广泛地使用它。此外，据我们所知，这是首次针对如此大规模的陆生哺乳动物进行的此类研究，该研究根据模型中是否存在社会经济预测因子来评估性能。我们认识到在 SDM 中需要更全面的方法，并且弥合人类社会经济动态与生态过程之间的差距可能有助于理解影响生物多样性的因素。
