Abstract
Species distribution models (SDM) have been increasingly developed in recent years, but their validity is questioned. Their assessment can be improved by the use of independent data, but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability.We used opportunistic presence‐only data along with presence–absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models’ performance by (1) cross‐validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent datasets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork.Cross‐validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity, and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data were strongly filtered.Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer's participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation.
Highlights
In the current context of biodiversity loss, a stronger relationship between conservation science and citizen participation could help to make conservation actions more effective (Ahmadi et al, 2017; Lewandowski & Oberhauser, 2017)
This data has great potential because (i) large quantities of data can be collected over large areas, which would be difficult and expensive for researchers to collect; (ii) data may be collected over long time periods, which is especially useful for studying the effects of climate and landscape changes on population dynamics at large scales; (iii) citizens are involved in the research process, thereby gaining knowledge, and their involvement might lead to improved implementation of biodiversity conservation action (Dickinson, Zuckerberg & Bonter, 2010; McKinley et al, 2017)
The median area under the curve (AUC) was higher with internal validation than external validation for all three pseudo-absence selection strategies (s1, s2 and s3), both for GAM and Random Forest with a delta-AUC ranging from 0.05 (T. marmoratus) to 0.21 (B. spinosus)
Summary
In the current context of biodiversity loss, a stronger relationship between conservation science and citizen participation could help to make conservation actions more effective (Ahmadi et al, 2017; Lewandowski & Oberhauser, 2017). Researchers have been skeptical about the value of datasets from citizen science, recent publications show that some could be as valid as data collected by professional scientists (Kosmala et al, 2016). This is conditional on such data being judged in context (i.e. according to the sampling methods used, program objectives and applications) on the use of rigorous data sorting and analyses (Isaac et al, 2014; Steen, Elphick & Tingley, 2019; Robinson et al, 2020)
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