Site‐Occupancy Distribution Modeling to Correct Population‐Trend Estimates Derived from Opportunistic Observations

Abstract

Species' assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection-nondetection records) are generated. Within-season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectability means correcting for observation effort. Second, site-occupancy models are applied directly to the detection-history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site-occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen-science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered when extracting trend information from haphazard observations. We expect our method is widely applicable for global biodiversity monitoring and modeling of species distributions.

Modest recovery of biodiversity in a western European country: The Living Planet Index for the Netherlands

“…A change in observer effort will result in a change in the probability to detect the presence of a species and may lead to spurious signals of change. Occupancy models are being considered as the best tools currently available to avoid this problem while analysing opportunistic data (Kéry et al, 2010;Van Strien et al, 2013;Isaac et al, 2014). Occupancy models separate the estimation of occupancy (the presence of a species in a site) from detection (the observation of a species in that site) when analysing field data and thereby enable correction of any changes in observer efforts over space and time.…”

Citizen science and smartphones take roadkill monitoring to the next level

“…The pitfalls of using roving records are numerous and concern, among others, problems related to search effort (in population trend analyses), detectability and observer expertise. However, many of these problems can be addressed during the analyses if good knowledge of the limitations of the data are available or if the roving records are combined with other data sources (Kéry et al 2010, Yu et al 2010, Snäll et al 2011, Sardà-Palomera et al 2012. The growing number of publications based on roving records shows that the careful use of this kind of data is now becoming accepted in the scientific world (Silvertown 2009).…”

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“…Newman et al (2011) provide an approach to increase data standardization with their cyber-infrastructure support tool, CitSci.org, which allows fledgling citizen science programs to use data entry forms that: (1) include a common core of required location and attribute information to ensure a minimum comparability of data, and (2) can be customized according to the needs of individual programs. For data that has already been collected, recent modeling efforts are beginning to allow researchers to account for sampling and data collection biases (e.g., spatiotemporal exploratory models to account for geographic biases, Sullivan et al, 2014; modeling approaches to handle opportunistically collected data, Kery et al, 2010;Snäll et al, 2011).…”