Observer-oriented approach improves species distribution models from citizen science data.

Pietro Milanesi,Emiliano Mori,Mattia Menchetti

doi:10.1002/ece3.6832

Abstract

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.

Highlights

Monitoring biodiversity is fundamental for conservation and sustainable use of natural resources but governmental, non-governmental organizations (NGOs), and scientific agencies often lack financial resources to support long-term biodiversity assessment by professional scientists and volunteers (Bland et al, 2015; Kelling et al, 2018)
Instead of using random pseudo-absences, our approach consists of using records of species of other than the target species collected by the observers of the target species as pseudo-absences and adding proxies of sampling effort as additional predictors in species distribution models (SDMs)
We develop two sets of SDMs, alternatively using (a) totally random pseudo-absences and (b) observer-oriented approach

Summary

Introduction

Monitoring biodiversity is fundamental for conservation and sustainable use of natural resources but governmental, non-governmental organizations (NGOs), and scientific agencies often lack financial resources to support long-term biodiversity assessment by professional scientists and volunteers (Bland et al, 2015; Kelling et al, 2018). | 12105 which led scientists to adapt to a period of limited availability of research funds (Cagnacci et al, 2012) In this context, citizen science represents a powerful cost-effective strategy to collect baseline scientific data by engaging common, that is, non-professional, people, leveraging the growing public “environmental awareness” and the increase worldwide in wildlife enthusiasts (e.g., McCafferty, 2016; Silvertown et al, 2011; Willemen et al, 2015). Citizen science data often result in a high number of occurrences recorded over large areas (i.e., countries or continents), and time spans and at relatively low costs (Hobson et al, 2017; Mori, et al, 2017; Paul et al, 2014; Willemen et al, 2015). Citizen science is playing an important role in improving conservation biology, including natural resource management and environmental preservation (Devictor et al, 2010; McKinley et al, 2017; Van der Wal et al, 2015)

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Conclusion