Abstract
Abstract Many long‐term wildlife population monitoring programmes rely on citizen scientists for data collection. This can offer several benefits over traditional monitoring practices as it is a cost‐effective, large‐scale approach capable of providing long time series data and raising public environmental awareness. Whilst there is a debate about the quality of citizen science data, a standardised sampling design can allow citizen science data to be of a similar quality to those collected by professionals. However, many programmes use subjective, opportunistic selection of monitoring sites and this introduces several types of bias, which are not well understood. Using bat roost counts as a case study, we took a ‘virtual ecologist’ approach to simulate the effect of opportunistic site selection and uneven observer retention on our ability to accurately detect abundance trends. We simulated populations with different levels of temporal variability and site fidelity. Our simulations reveal that opportunistic site selection and low observer retention can result in biased trends and that these biases are magnified when monitored populations exhibit high levels of inter‐annual variation and low site fidelity. These results show that the synergistic effects of observer behaviour, site selection, and population dynamics lead to biased abundance trends in monitoring programmes. This study highlights the value of engaging and retaining citizen science observers, a standardised sampling design, and the collection of metadata. We conclude that monitoring programmes need to be aware of their focal species' temporal variability and site fidelity to adequately assess the potential bias caused by opportunistic site selection and low observer retention. Synthesis and applications. Accurate data on population changes are key for conservation success. Therefore, it is important that citizen science monitoring programmes assess and potentially quantify the biases present in their data. We demonstrate the applicability of an established simulation framework to assess the effect of biases on our ability to correctly detect abundance trends. Our findings highlight that monitoring programmes need to be aware of their study species’ temporal variability and site fidelity to assess and account for the effects of biased site selection and observer retention.
Highlights
A fundamental tool of conservation biology is the establishment of long-term monitoring programmes, which allow policy-makers to evaluate the extent of biodiversity changes and the effectiveness of conservation strategies (Buckland & Johnston, 2017; Magurran et al, 2010; Yoccoz, Nichols, & Boulinier, 2001)
These results show that the synergistic effects of observer behaviour, site selection, and population dynamics lead to biased abundance trends in monitoring programmes
We conclude that monitoring programmes need to be aware of their focal species' temporal variability and site fidelity to adequately assess the potential bias caused by opportunistic site selection and low observer retention
Summary
A fundamental tool of conservation biology is the establishment of long-term monitoring programmes, which allow policy-makers to evaluate the extent of biodiversity changes and the effectiveness of conservation strategies (Buckland & Johnston, 2017; Magurran et al, 2010; Yoccoz, Nichols, & Boulinier, 2001). Monitoring biodiversity through data collected by volunteer observers has a long-standing history (e.g. the Audubon Society's Christmas Bird Count, the British Breeding Bird Survey, NABU Vogelzählung), and in recent decades has become the standard for long-term monitoring programmes (Burgess et al, 2017; Callaghan, Rowley, Cornwell, Poore, & Major, 2019; Schmeller et al, 2009) Such citizen science programmes offer many benefits compared with traditional monitoring approaches. Whilst many citizen science monitoring programmes follow a structured sampling design (Monitoring Häufige Brutvögel, UK National Plant Monitoring Scheme, Streamwatch Australia), others use a subjective and/or opportunistic selection of monitoring sites, such as FrogID or NABU Insektensommer This can create a biased dataset for a number of reasons. We highlight circumstances that monitoring programmes need to be aware of, and approaches that could be employed, in order to avoid negatively biased abundance estimates
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