How sampling scheme and species-area scale contribute to select the best statistical functions for upscaling plant species richness

Zahra Veiskarami, Babak Pilehvar and Gholam Hassan Veiskarami

Ecological Modelling, 2021, vol. 457, issue C

Abstract: Species–area curve is widely used to describe biological diversity across spatial scales. However, there are major ecological and statistical limitations in its application and inference, such as sampling methods, species-area scales, and statistical functions that must be considered in diversity assessment. This study aimed to explain relations among sampling methods, species-area curve scales and statistical functions in the Perk forested catchment, Zagros forests, western Iran. We studied plant diversity during the peak of the growing season, from early April to late May, 2019. The stratified random sampling was applied to collect species richness data on the same sized plots (4m2). The mean of data from noncontiguous plots was used to construct Scheiner's type IIIB curve. A comprehensive set of species-area models including five convex and three sigmoid models were tested. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to choose the best-fitted function model. Furthermore, the Log Error of Extrapolation (LEE) was used to assess the reliability of extrapolation. Both AIC and BIC results suggested that the Lomolino and Cumulative Weibull functions as the best-fit curves for extrapolating species richness beyond sampling area. However, the LEE showed the superiority of the Rational function. This discrepancy between LEE and both AIC and BIC introduced models originates from the LEE limitation of application to tenfold of the sampling area. This study showed that the sampling scheme and the study scale are the two important factors in the shape of statistical function (convex or sigmoid) which is using in constructing species-area curves. In contrast to the prevailing use of power function, the results showed that in a broader scale like the landscape scale, the Lomolino and Cumulative Weibull functions are superior in estimating species richness in Zagros forests, western Iran.

Keywords: Curve fitting; Species–area functions; Species richness; Zagros forest; Coppice oak forest (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:457:y:2021:i:c:s0304380021002507

DOI: 10.1016/j.ecolmodel.2021.109694

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