An autoregressive model for global vertebrate richness rankings: long-distance dispersers may have stronger spatial structures

Abstract

Background: Spatial autocorrelations are one of the most prevalent natural phenomena in ecological data. It is generally assumed that short-distance dispersers are spatially limited and thus have stronger spatial autocorrelation patterns than do long-distance dispersers. To test this hypothesis, I quantified and compared spatial autocorrelation patterns of global richness rankings of amphibians, mammals, and birds using an autoregressive model. A species richness ranking was used as a proxy of species richness, which was obtained through a digital image processing method from published world maps of species richness. Results: The results showed that the spatial structure could explain the highest variance involved in global richness rankings of mammals (intermediate-distance dispersers), followed by birds (long-distance dispersers). In contrast, amphibians, representing short-distance dispersers, had the lowest degree of spatial autocorrelation patterns. Thus, the present results do not support the abovementioned hypothesis. Conclusions: In conclusion, a complex relationship exists between an animal's dispersal ability and its spatial autocorrelation pattern. The dispersal abilities of species can be negatively correlated with spatial autocorrelation patterns.

Global environment- and space-richness ranking relationships: The effects of interaction and high-order terms of explanatory variables

“…To avoid artifacts, I also consider different richness groups using k-means clustering. However, I found the results from the 10, 30, and 50 classes of richness for different taxonomies were basically identical to those for the 15-class of species richness orderings, after removing some classes that only contained grid cells which were located in islands and edges of territory [9].…”

“…Extraction of species richness at each grid cell is very hard since the digitized map is not accurate due to limited resolution and the colors for representing different species richness are difficult to match. Thus, I must consider an alternative option, which is the very reason why I used richness ranking as the proximity to study the relationship of environment and diversity [8,9].…”

“…k-means clustering method was employed to obtain 15 classes of species richness based on the digitized colors [9]. Each class was then checked carefully by eyes to obtain the richness rankings when compared the clustered grid cells with those present in the published literature [7].…”

Latitudinal concordance between biogeographic regionalization, community structure, and richness patterns: a study on the reptiles of China

“…The purpose of utilizing CCA analysis here is that we are interested in the explanation of the latitudinal community structure patterns of reptiles of China. The purpose of utilizing spatial regression analysis here is to avoid the statistical artifacts (inflation of significance of the regression model) caused by spatial autocorrelation at macrospatial scales (Chen 2013;Costa et al 2007;Huang et al 2011;Rangel et al 2006Rangel et al , 2010 when studying latitudinal richness patterns of reptiles. Because SAR requires geographic weights among the sites as input, we utilized the geographic centroids of each latitudinal band within the territory of China to compute the geographic weights (whether two latitudinal bands are adjacent from each other or not).…”

“…Because SAR requires geographic weights among the sites as input, we utilized the geographic centroids of each latitudinal band within the territory of China to compute the geographic weights (whether two latitudinal bands are adjacent from each other or not). To check whether spatial autocorrelation was controlled after SAR model was implemented, we calculated Moran's I index on the residuals calculated from the fitted SAR model (Chen 2013(Chen , 2014.…”