Milanesi Pietro, Mori Emiliano, Menchetti Mattia
Swiss Ornithological Institute Sempach Switzerland.
Istituto di Ricerca sugli Ecosistemi Terrestri Consiglio Nazionale delle Ricerche Sesto Fiorentino Firenze Italy.
Ecol Evol. 2020 Sep 26;10(21):12104-12114. doi: 10.1002/ece3.6832. eCollection 2020 Nov.
Citizen science platforms are increasingly growing, and, storing a huge amount of data on species locations, they provide researchers with essential information to develop sound strategies for species conservation. However, the lack of information on surveyed sites (i.e., where the observers did not record the target species) and sampling effort (e.g., the number of surveys at a given site, by how many observers, and for how much time) strongly limit the use of citizen science data. Thus, we examined the advantage of using an observer-oriented approach (i.e., considering occurrences of species other than the target species collected by the observers of the target species as pseudo-absences and additional predictors relative to the total number of observations, observers, and days in which locations were collected in a given sampling unit, as proxies of sampling effort) to develop species distribution models. Specifically, we considered 15 mammal species occurring in Italy and compared the predictive accuracy of the ensemble predictions of nine species distribution models carried out considering random pseudo-absences versus observer-oriented approach. Through cross-validations, we found that the observer-oriented approach improved species distribution models, providing a higher predictive accuracy than random pseudo-absences. Our results showed that species distribution modeling developed using pseudo-absences derived citizen science data outperform those carried out using random pseudo-absences and thus improve the capacity of species distribution models to accurately predict the geographic range of species when deriving robust surrogate of sampling effort.
公民科学平台日益增多，它们存储了大量关于物种分布地点的数据，为研究人员制定合理的物种保护策略提供了重要信息。然而，缺乏关于调查地点（即观察者未记录到目标物种的地点）和采样力度（例如，给定地点的调查次数、参与调查的观察者数量以及调查时长）的信息，极大地限制了公民科学数据的使用。因此，我们研究了采用一种以观察者为导向的方法（即把目标物种的观察者所收集到的除目标物种之外的其他物种出现情况视为伪缺失，并将其作为相对于给定采样单元中观测总数、观察者数量以及收集地点的天数的额外预测因子，以此作为采样力度的代理变量）来构建物种分布模型的优势。具体而言，我们考虑了意大利境内出现的15种哺乳动物，并比较了在考虑随机伪缺失与以观察者为导向的方法的情况下，九种物种分布模型的集成预测的预测准确性。通过交叉验证，我们发现以观察者为导向的方法改进了物种分布模型，其预测准确性高于随机伪缺失。我们的结果表明，利用源自公民科学数据的伪缺失构建的物种分布模型优于使用随机伪缺失构建的模型，因此在推导稳健的采样力度替代指标时，提高了物种分布模型准确预测物种地理分布范围的能力。
