Accounting for imperfect detection in data from museums and herbaria when modeling species distributions: combining and contrasting data‐level versus model‐level bias correction

Abstract

The digitization of museum collections as well as an explosion in citizen science initiatives has resulted in a wealth of data that can be useful for understanding the global distribution of biodiversity, provided that the well‐documented biases inherent in unstructured opportunistic data are accounted for. While traditionally used to model imperfect detection using structured data from systematic surveys of wildlife, occupancy models provide a framework for modelling the imperfect collection process that results in digital specimen data. In this study, we explore methods for adapting occupancy models for use with biased opportunistic occurrence data from museum specimens and citizen science platforms using seven species of Anacardiaceae in Florida as a case study. We explored two methods of incorporating information about collection effort to inform our uncertainty around species presence: 1) filtering the data to exclude collectors unlikely to collect the focal species and 2) incorporating collection covariates (collection type, time of collection and history of previous detections) into a model of collection probability. We found that the best models incorporated both the background data filtration step as well as collector covariates. Month, method of collection and whether a collector had previously collected the focal species were important predictors of collection probability. Efforts to standardize meta‐data associated with data collection will improve efforts for modeling the spatial distribution of a variety of species.

Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth

“…Thus, an environmental datum identified with NEP might be “nearest” to a given centroid, yet still possess combinations of variables very different from those preferred by a species (for this reason, when analysing Asclepias , we discarded imprecise records locatable only to areas larger than San Bernardino county, the largest “county” in North America; Supporting Information Appendix S3). Finally, like precise records, imprecise records might be collected in a biased manner, requiring corrective modelling methods (Erickson & Smith, 2021).…”

Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth

“…(For this reason, when analyzing Asclepias , we discarded imprecise records locatable only to areas larger than San Bernardino county, the largest “county” in North America; Appendix S3.). Finally, like precise records, imprecise records may be collected in a biased manner, requiring corrective modeling methods (Erickson & Smith 2021).…”

Challenges and opportunities for using natural history collections to estimate insect population trends

“…Model‐level approaches are arguably more robust than data‐level approaches because they explicitly account for collection biases and changes in sampling effort over time via random effects structures, covariates, or an explicit observation process model component (e.g. Erickson & Smith, 2021). Random effects (e.g.…”

“…Combining data‐ and model‐level approaches has been shown to improve accuracy for modelling species' distributions using NHC data (e.g. Erickson & Smith, 2021), but has yet to be explored for estimating population trends and still does not help overcome challenges associated with large data gaps across space and time.…”