Does adding community science observations to museum records improve distribution modeling of a rare endemic plant?

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Ecosphere

سال: 2023

ISSN: ['2150-8925']

DOI: https://doi.org/10.1002/ecs2.4419