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Hindi Multi Resolution Species Distributional Information Analyzing Macroecological Patterns
*Corresponding Author:
Copyright: © 2021 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
In this brief report, a simple scaling algorithm was developed for reconstructing range cells upon the original pixellevel distributional points of species. The algorithm could generate the distributional cells of species that covers all the original distributional points of species, but being with varying spatial resolutions (i.e., different sizes of the smallest operative area unit). As such, I could quantitatively evaluate the macroecological patterns (including richness, rarity hotspots, and the influence of spatial autocorrelation on structuring species’ richness and rarity patterns) on the basis of these varying-resolution species distributional layers. Resultant diversity patterns thus could be compared to the true patterns directly derived from species’ original distributional points. The present theoretical results showed that, the macroecological patterns identified from varying-resolution data could be basically consistent to those from the true distributional data, as long as there were not many multi-scaling distributional layers inside the whole dataset. However, the estimated macroecological patterns would be far departed from the true ones when there were a remarkable number of multi-scaling layers inside. Thus, I argued that the varying-resolution data could be utilized but with some cautions so as to accurately reveal ecological patterns and interpret the relationship between species diversity, distribution and environment.
