Quantitative tools for perfecting species lists

Abstract

SUMMARYA substantial body of literature has accumulated on the topic of the estimation of species richness by extrapolation. However, most of these methods rely on an objective sampling of nature. This condition is dif®cult to meet and seldom achieved for large regions. Furthermore, scientists conducting biological surveys often already have preliminary but subjectively gathered species lists, and would like to assess the completeness of such lists, and/or to ®nd a way to perfect them. We propose several strategies for utilizing external data (such as might be obtained using GIS) to aid in the completion of species lists. These include: (i) using existing species lists to develop predictive models; (ii) using the uniqueness of the environment as a guide to ®nd underrepresented species; (iii) using spectral heterogeneity to locate environmentally heterogeneous regions; (iv) combining surveys with statistical model-building in an iterative manner. We demonstrate the potential of these approaches using simulation and case studies from Oklahoma.

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

“…Palmer, Earls, Hoagland, White, & Wohlgemuth, 2002;Rocchini, Balkenhol, Carter, et al, 2010) which relies on the assumption that spectral heterogeneity can be used to quantify (species) diversity. The obtained species diversity patterns are in turn believed to also be related to environmental (ecosystem) heterogeneity, based on the 'portfolio effect' (Rocchini et al, 2010).…”

Integrated Analysis of Productivity and Biodiversity in a Southern Alberta Prairie

“…Traditionally, three basic methods have been used to estimate biodiversity with remote sensing: (1) mapping habitat for key species; (2) mapping species distribution [27][28][29][30] or community composition [31]; and (3) assessing species richness [32][33][34], α-diversity [33] or β-diversity [35] through spatial variation in vegetation optical properties (optical diversity in space), sometimes referred to as "spectral heterogeneity" [36]. Using this latter approach, which we call "optical diversity," a variety of methods have been used to capture optical variation as a way to estimate biodiversity.…”

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

“…In terms of annual vegetation, even if some of this was not present in its active state during March, we were careful to identify the remaining dry parts of leaves, fruit, and flowers. A systematic sampling design of 60 square nested plots [28,67] separated by 200 m was considered. Each sampling point was determined randomly inside the 200 m grid design by discarding those plots with slopes greater than 45° and with strong accessibility problems due mainly to tangled vegetation (Figure 6b).…”

“…On the one hand, a number of studies have associated the biodiversity of different sites with the information obtained by passive remote sensors [23][24][25][26][27], correlating biological diversity directly with spectral reflectance values [28], with different spectral vegetation indexes [19,25,[29][30][31], and different types of feature extraction like principal components analysis (PCA) [32] or minimum noise fraction (MNF) [26]. Taking this into account, it is expected that hyperspectral sensors, which have great ability to detect characteristics associated with the biochemical, physiological, and structural spectral variability of the vegetation in the electromagnetic spectrum, will provide valuable information for the evaluation of biodiversity [33,34].…”