| Space has been considered as the last frontier for ecological theory since all mechanisms and processes that affect the performance of plants during their lifespan must have left certain spatial signature in their spatial distribution in a community. Such features endow explicit maps of individual tree location with great potential to decipher various underlying mechanisms and processes. In this study, I focused on the application of species distribution for studying biodiversity maintenance mechanisms and conservation.At first, I applied data from a fully mapped 50-ha subtropical forest plot in Heishiding (HSD), China to conduct a community-wide assessment of the type and frequency of spatial association of species pairs. By analyzing the spatial association and attributes of the species pairs, I found that 55% of all species pairs showed negative bivariate association, which occurred less frequently among species sharing the same habitat association. In contrast, positive associations occurred more frequently among species with similar habitat requirements. The observation that species with similar habitat preference tend to aggregate suggests the vital role of environmental filtering in shaping community structure of this subtropical forest.Secondly, I developed a new method to estimate species abundance from species presence/absence maps. Existing methods on this subject either overlook inherent spatial autocorrelations of species distribution, thus leading to underestimation, or they demand extra information besides presence/absence maps. This study develops a simple method that takes account of spatial autocorrelation and only requires occurrence maps. The performance of this new method was compared against four other major methods (e.g., random placement model and negative binomial model) using both simulated and empirical data. The results showed that the performance of the new method is comparable with other methods but requires less and readily obtained input data, a property important for real applications.Finally, I used species distribution data to develop new concepts and models for identifying habitat loss thresholds. There is mounting evidence that many taxa respond in non-linear ways to perturbation and many statistical, physical and ecological methods have been developed to detect the critical points of perturbation. The majority of these methods define thresholds as the perturbation points causing abrupt species response, but in reality most species or ecosystems do not show a break point response but more gradual transitional change to perturbation. I developed a new method which delineates thresholds as a region in which the slope of the relationship between ecological response (y) and perturbation (x; e.g. habitat loss) is larger than 1:|dy|dx|≥1, where both x and y-axes are scaled to (0,1) range. The lower end of threshold zones so defined is of particular ecological interest because it is the smallest x that may trigger impending catastrophic response to a small change in x. I derived two landscape models (edge length and the number of patches of species distribution) and two biodiversity models (endemics-area relationship and half-population curve) to test this method. I applied the zonal thresholding method to these four models fit to empirical data of two forest plots to detect thresholds of species distribution to habitat loss. The two landscape metric models show that no species could tolerate more than 40% of habitat loss. The half-population model leads to a similar level of 40% habitat loss. Together I suggest 40% be the maximum permissible habitat loss threshold for biodiversity conservation.Overall, studying the relationship between spatial pattern of plants and species attributes provide significant insights into processes and mechanisms that maintain biodiversity. The simple methods I developed for both abundance estimation and habitat loss threshold detection are useful in biodiversity conservation. |
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