Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models

Abstract

AimInformation on species’ habitat associations and distributions, across a wide range of spatial and temporal scales, is a fundamental source of ecological knowledge. However, collecting information at relevant scales is often cost prohibitive, although it is essential for framing the broader context of more focused research and conservation efforts. Citizen science has been signalled as an increasingly important source to fill in data gaps where information is needed to make comprehensive and robust inferences on species distributions. However, there are perceived trade‐offs of combining highly structured, scientific survey data with largely un‐structured, citizen science data.MethodsWe explore these trade‐offs by applying a simplified approach of filtering citizen science data to resemble structured survey data and analyse both sources of data under a common framework. To accomplish this, we integrated high‐resolution survey data on shorebirds in the northern Central Valley of California with observations in eBird for the entire region that were filtered to improve their quality.ResultsThe integration of survey data with the filtered citizen science data resulted in improved inference and increased the extent and accuracy of distribution models on shorebirds for the Central Valley. The structured surveys improved the overall accuracy of ecological inference over models using citizen science data only by increasing the representation of data collected from high‐quality habitats for shorebirds.Main conclusionsThe practical approach we have shown for data integration can also be used to improve the efficiency of designing biological surveys in the context of larger, citizen science monitoring efforts, ultimately reducing the financial and time expenditures typically required of monitoring programs and focused research. The simple method we present can be used to integrate other types of data with more localized efforts, ultimately improving our ecological knowledge on the distribution and habitat associations of species of conservation concern worldwide.

Regional Assessment of the Conservation Status of Snubfin Dolphins (Orcaella heinsohni) in the Kimberley Region, Western Australia

“…A simpler alternative is to assess ambiguous sightings in terms of their similarity to locations where the species has been documented to occur, for example by down-weighting (or filtering) records associated with environmental conditions that deviate from those encountered in verified datasets collected by professionals (Allouche et al, 2008;Lin et al, 2017;Fletcher et al, 2019;Robinson et al, 2020). We did this for snubfins using an adaptation of the 2D smoothing protocol described by Tarjan and Tinker (2016).…”

A ‘How-to’ Guide for Interpreting Parameters in Habitat-Selection Analyses

“…New technologies (e.g., improved Global Positioning System [GPS] collars) and advances in remote sensing have made it possible to collect animal location data on unprecedented spatial and temporal scales [1,2], which in turn has fueled the development of new methods for modeling animal movement and for linking individuals to their environments [3,4]. Two of the most popular approaches for analyzing telemetry data, resource-selection and step-selection analyses, compare environmental covariates at locations visited by an animal ("used locations") to environmental covariates at a set of locations assumed available to the animal ("available locations") using logistic and conditional logistic regression, respectively [5][6][7].…”

A ‘How to’ guide for interpreting parameters in habitat‐selection analyses

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New technologies (e.g. improved Global Positioning System [GPS] collars) and advances in remote sensing have made it possible to collect animal location data on unprecedented spatial and temporal scales (Kays et al, 2015;Robinson et al, 2020), which in turn has fuelled the development of new methods for modelling animal movement and for linking individuals to their environments (Guisan et al, 2017;Hooten et al, 2017). Two of the most popular approaches for analysing telemetry data, habitat-selection functions (HSFs; Box 1) and step-selection functions (SSFs), compare environmental covariates at locations visited by an animal ('used locations') to environmental covariates at a set of locations assumed available to the animal ('available locations') using logistic and conditional logistic regression re-

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