The importance of data quality for generating reliable distribution models for rare, elusive, and cryptic species

Abstract

The availability of spatially referenced environmental data and species occurrence records in online databases enable practitioners to easily generate species distribution models (SDMs) for a broad array of taxa. Such databases often include occurrence records of unknown reliability, yet little information is available on the influence of data quality on SDMs generated for rare, elusive, and cryptic species that are prone to misidentification in the field. We investigated this question for the fisher (Pekania pennanti), a forest carnivore of conservation concern in the Pacific States that is often confused with the more common Pacific marten (Martes caurina). Fisher occurrence records supported by physical evidence (verifiable records) were available from a limited area, whereas occurrence records of unknown quality (unscreened records) were available from throughout the fisher’s historical range. We reserved 20% of the verifiable records to use as a test sample for both models and generated SDMs with each dataset using Maxent. The verifiable model performed substantially better than the unscreened model based on multiple metrics including AUCtest values (0.78 and 0.62, respectively), evaluation of training and test gains, and statistical tests of how well each model predicted test localities. In addition, the verifiable model was consistent with our knowledge of the fisher’s habitat relations and potential distribution, whereas the unscreened model indicated a much broader area of high-quality habitat (indices > 0.5) that included large expanses of high-elevation habitat that fishers do not occupy. Because Pacific martens remain relatively common in upper elevation habitats in the Cascade Range and Sierra Nevada, the SDM based on unscreened records likely reflects primarily a conflation of marten and fisher habitat. Consequently, accurate identifications are far more important than the spatial extent of occurrence records for generating reliable SDMs for the fisher in this region. We strongly recommend that practitioners avoid using anecdotal occurrence records to build SDMs but, if such data are used, the validity of resulting models should be tested with verifiable occurrence records.

Predicting distributions, habitat preferences and associated conservation implications for a genus of rare fishes, seahorses (Hippocampus spp.)

“…MC are usually the most data‐rich source for many organisms (Ponder, Carter, Flemons, & Chapman, ), as is also shown in our seahorses. MC have been commonly used in SDM research (Newbold, ), although we demonstrate, compared with RG, MC may derive lower quality models with quite different predictions (Aubry et al., ). This is also true for citizen science.…”

“…Our study highlights that applying SDMs can derive useful geographical information for the assessment of conservation status for rare species (Aubry et al, 2017). Rare species (especially habitat specialists) that have low population density are often patchily distributed along with their key habitats (Marcer et al, 2013).…”

“…Given the difficulty in studying rare organisms, scientists usually need to collate distribution data from quality-unknown sources (e.g., citizen science) to supplement the limited high-accuracy data (e.g., research data). Applying unknown-quality data can be problematic and might, through errors, generate different results than quality data (Aubry et al, 2017;Graham, Ferrier, Huettman, Moritz, & Peterson, 2004). Studies comparing different data sources are rare in the literature (Jackson, Gergel, & Martin, 2015;Zhang & Vincent, 2017).…”

Outstanding Challenges in the Transferability of Ecological Models

“…More accurate and/or precise data should result in better transfers on theoretical grounds, with evidence showing that the accuracy of species records can be more important for transferability than their spatial extent [26]. Data of unverifiable quality (e.g., anecdotal reports of easily misidentified species) should therefore be avoided, even if available over broader geographical areas.…”

Seeking efficiency with carnivore survey methods: A case study with elusive martens

“…Obtaining reliable distribution estimates and population size of rare, wide‐ranging species is often an urgent conservation challenge because land managers frequently have obligations to conserve rare species for ethical, ecological, and legal reasons (Marcot and Molina ). Anecdotal observations are insufficient for describing rare species occurrence and distribution because they are unreliable and unrepeatable, and can provide conflicting results with scientific surveys (McKelvey et al , Aubry et al ). For rare species, quantifying occurrence and population parameters in a repeatable and consistent manner is a challenge for 2 main reasons.…”