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.
Highlights
Biodiversity measurements have important implications for conservation efforts (Sousa-Baena, Garcia & Peterson, 2014)
We have shown that any errors in the data, even at very minor frequencies, can end up dominating the estimation process with the common and long-used nonparametric estimators, such as Chao2; the older species accumulation curve approach would clearly overestimate numbers, given that “error” species would appear as species documented in the inventory
These biodiversity inventory statistics are important, offering crucial additional information to the process of biotic inventories; updating and amending these approaches to approaches that are less vulnerable to bias, or at least being cognizant of the potential for problems in estimation for big(ger) datasets, is important
Summary
Biodiversity measurements have important implications for conservation efforts (Sousa-Baena, Garcia & Peterson, 2014). Biodiversity metrics provide information about community composition, numbers of species, and similarity or dissimilarity of species composition among sites (Colwell & Coddington, 1994), and can allow researchers to separate well-inventoried sites from partially-inventoried sites for macroecological analyses (Lobo et al, 2018). Tracking species richness in biodiversity inventories was originally achieved via visual assessment of asymptotic behavior of species accumulation curves (Karr, 1980), and with the quantitative assist of non-linear regressions (Clench, 1979; Soberón & Llorente, 1993). For the past 20+ years, non-parametric estimators of numbers of species have been used to estimate species richness, a set of estimators based on sampling theory (Chao, 1987). Diverse data origins and variable data quality pose significant challenges for such analyses, when data are drawn from publicly
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