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Inventory statistics meet big data: complications for estimating numbers of species.


ABSTRACT: We point out complications inherent in biodiversity inventory metrics when applied to large-scale datasets. The number of units of inventory effort (e.g., days of inventory effort) in which a species is detected saturates, such that crucial numbers of detections of rare species approach zero. Any rare errors can then come to dominate species richness estimates, creating upward biases in estimates of species numbers. We document the problem via simulations of sampling from virtual biotas, illustrate its potential using a large empirical dataset (bird records from Cape May, NJ, USA), and outline the circumstances under which these problems may be expected to emerge.

SUBMITTER: Khalighifar A 

PROVIDER: S-EPMC7229767 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Inventory statistics meet big data: complications for estimating numbers of species.

Khalighifar Ali A   Jiménez Laura L   Nuñez-Penichet Claudia C   Freeman Benedictus B   Ingenloff Kate K   Jiménez-García Daniel D   Peterson Town T  

PeerJ 20200513


We point out complications inherent in biodiversity inventory metrics when applied to large-scale datasets. The number of units of inventory effort (e.g., days of inventory effort) in which a species is detected saturates, such that crucial numbers of detections of rare species approach zero. Any rare errors can then come to dominate species richness estimates, creating upward biases in estimates of species numbers. We document the problem via simulations of sampling from virtual biotas, illustr  ...[more]

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