Opportunities and challenges of citizen science for monitoring a
recolonizing large herbivore
Abstract
Monitoring is a prerequisite for evidence-based wildlife management, yet
conventional monitoring approaches are often ineffective for species
occurring at low densities. However, some species such as large mammals
are often observed by lay people and this information can be leveraged
through citizen science monitoring schemes. Assessing the quantity,
quality, and potential biases of such data sources is crucial before
making inferences at scale. For Eurasian moose (Alces alces), a species
currently reoccurring in north-eastern Germany in low numbers, we
compared three different citizen science tools: a mail/email report
system, a smartphone application, and a webpage. Among these monitoring
tools, the mail/email report system yielded the greatest number of moose
reports in absolute and in standardized (corrected for time effort)
terms. The reported moose were predominantly identified as single,
adult, male individuals, and reports occurred mostly during late summer.
Overlaying citizen science data with independently generated habitat
suitability and connectivity maps showed that members of the public
detected moose in suitable habitats but not necessarily in movement
corridors. Also, moose detections were often recorded near roads,
suggestive of spatial bias in sampling effort. Our results suggest that
citizen science-based data collection can be facilitated by brief,
intuitive digital reporting systems. However, inference from the
resulting data can be limited due to unquantified and possibly biased
sampling effort. To overcome these challenges, we offer specific
recommendations such as engaging outdoor enthusiasts in suitable moose
habitats, for improving quantity, quality and analysis of citizen
science-based data for making robust inferences about wildlife
populations.