Does adding community science observations to museum records improve distribution modeling of a rare endemic plant?. Issue 3 (23rd March 2023)

• Record Type: Journal Article
• Authors: Gaier, Andrew G.; Resasco, Julian
• Is Part Of: Ecosphere. Volume 14:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-23
• Subjects: alpine botany -- biogeography -- Chasmophyte -- ecological niche model -- iNaturalist -- Rocky Mountains -- Saxifragaceae -- species distribution model; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.4419
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: Understanding the ranges of rare and endangered species is central to conserving biodiversity in the Anthropocene. Species distribution models (SDMs) have become a common and powerful tool for analyzing species–environment relationships across geographic space. Although evaluating the distribution of rare species is integral to their conservation, this can be difficult when limited distribution data are available. Community science platforms, such as iNaturalist, have emerged as alternative sources for species occurrence data. Although these observations are often thought to be of lower quality than those of natural history collections, they may have potential for improving SDMs for species with few occurrence records from collections. Here, we investigate the utility of iNaturalist data for developing SDMs for a rare high‐elevation plant, Telesonix jamesii . Because methods for modeling rare species are limited in the literature, five different modeling techniques were considered, including profile methods, statistical models, and machine learning algorithms. The inclusion of iNaturalist data doubled the number of usable records for T. jamesii. We found that a random forest (RF) model using ensemble training data performed the highest of any model (area under curve = 0.98). We then compared the performance of RF models that use only natural history training data and those that use a combination of natural history (herbarium specimens) and iNaturalist training data. All models heavily relied on climate data (mean temperature of driest quarter, and precipitation of the warmest quarter), indicating that this species is under threat as climate continues to change. Validation datasets affected model fits as well. Models using only herbarium data performed slightly poorer when evaluated with cross‐validation than when validated externally with iNaturalist data. This study can serve as a model for future SDM studies of species with similar data limitations.