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Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway

ABSTRACT: Abstract Many publications make use of opportunistic data, such as citizen science observation data, to infer large‐scale properties of species’ distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species’ home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS‐telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species’ ecology. Models naïvely using opportunistic observations in habitat‐use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species’ RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat‐use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat‐use studies. We provide a novel method to use citizen science data for fine‐scale studies.

SUBMITTER: Cretois B

PROVIDER: S-EPMC8571602 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Wild penguins are facing increased threats to their populations and their welfare as a consequence of human activities. Understanding the perception of animal welfare is essential to identify ethical concerns related to the negative impact of anthropogenic factors on wild species and to guide conservation efforts that reflect societal values. Since penguin conservation is of general interest, we examined the human dimension of welfare assessment across a range of interest groups concerned with penguins, seabird biology and wildlife conservation. We provided participants with a Penguin Welfare Assessment Tool (PWAT) based on the five domains model. The PWAT supports consideration of the impact of four physical aspects on welfare-relevant mental states. Bibliometric analysis of keywords from 347 scientific articles indicated that penguins around the world face five main types (themes) of anthropogenic factors and we then developed five hypothetical scenarios, each related to one theme. Seventy-five participants scored the overall impact of the events described in the scenarios on penguin welfare as negative using the PWAT. Participants rated short-duration, high-intensity events (i.e., being trapped in a ghost fishing net) as having a significantly more severe impact on penguin welfare than low-intensity, long-duration events (P < 0.0001). Scores provided by participants for each domain for each scenario were largely as expected and we found good correlation (all P < 0.0001) between the physical domains and "mental state" for all scenarios, indicating that the tool was facilitating the participants' assessment of welfare. No evidence was found that experience of working or studying penguins, or indeed any other demographic factor investigated, influenced the assessments of welfare. We found little agreement between participants in the scores provided (unalike scores mostly between 0.7 and 0.8), and agreement between participants with experience of working with penguins was no better than between participants without such experience. We discuss the possibility that low agreement within different interest groups may be improved by providing more scientific information to support the evaluation of penguin welfare. We conclude that scientific knowledge of penguin biological responses to anthropogenic factors is vital to support the evaluation of wild penguin welfare by the public and other stakeholders.

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Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway

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