How expected increases in climate variability will affect species diversity depends on the role of such variability in regulating the coexistence of competing species. Despite theory linking temporal environmental fluctuations with the maintenance of diversity, the importance of climate variability for stabilizing coexistence remains unknown because of a lack of appropriate long-term observations. Here, we analyze three decades of demographic data from a Kansas prairie to demonstrate that interannual climate variability promotes the coexistence of three common grass species. Specifically, we show that (i) the dynamics of the three species satisfy all requirements of ''storage effect'' theory based on recruitment variability with overlapping generations, (ii) climate variables are correlated with interannual variation in species performance, and (iii) temporal variability increases low-density growth rates, buffering these species against competitive exclusion. Given that environmental fluctuations are ubiquitous in natural systems, our results suggest that coexistence based on the storage effect may be underappreciated and could provide an important alternative to recent neutral theories of diversity. Field evidence for positive effects of variability on coexistence also emphasizes the need to consider changes in both climate means and variances when forecasting the effects of global change on species diversity.climate change ͉ competition ͉ grassland ͉ plant community ͉ population dynamics S trong climate variability characterizes ecosystems worldwide, and in many regions this variability is predicted to increase over the next century because of higher frequencies of severe storms and droughts (1, 2). The ecological impacts of increased climate variability are poorly understood (3), especially in comparison with threats posed by increasing mean temperatures (4,5). This gap in empirical research contrasts sharply with a considerable body of theory examining the effects of environmental fluctuations on the maintenance of species diversity (6-9).''Storage effect'' theory derives the conditions under which climate variability will have stabilizing or destabilizing effects on species coexistence (10). The temporal storage effect described in refs. 6 and 9 requires that three conditions be met. To satisfy condition 1, species must have long lifespans to buffer their populations against unfavorable years. For condition 2, species must differ in their response to climate variation. These speciesspecific responses to climate cause each species to experience relatively more intraspecific competition during its favorable years and more interspecific competition during its unfavorable years. Condition 3 requires that the effect of competition on each species must be more severe in years favorable for that species than in unfavorable years. When condition 2 is present, intraspecific competition will be more severe than interspecific competition. As a result, climate variability gives species an advantage when they become ra...

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A general-purpose parallel raster processing programming library test application using a geographic cellular automata model" (2010 Department of Geography, University of California, Santa Barbara, Santa Barbara, CA, USA Abstract A general-purpose parallel raster processing programming library (pRPL) was developed and applied to speed up a commonly used cellular automaton model with known tractability limitations. The library is suitable for use by geographic information scientists with basic programming skills, but who lack knowledge and experience of parallel computing and programming. pRPL is a general-purpose programming library that provides generic support for raster processing, including local-scope, neighborhood-scope, regional-scope, and global-scope algorithms as long as they are parallelizable. The library also supports multilayer algorithms. Besides the standard data domain decomposition methods, pRPL provides a spatially adaptive quad-tree-based decomposition to produce more evenly distributed workloads among processors. Data parallelism and task parallelism are supported, with both static and dynamic load-balancing. By grouping processors, pRPL also supports data-task hybrid parallelism, i.e., data parallelism within a processor group and task parallelism among processor groups. pSLEUTH, a parallel version of a well-known cellular automata model for simulating urban land-use change (SLEUTH), was developed to demonstrate full utilization of the advanced features of pRPL. Experiments with real-world data sets were conducted and the performance of pSLEUTH measured. We conclude not only that pRPL greatly reduces the development complexity of implementing a parallel raster-processing algorithm, it also greatly reduces the computing time of computationally intensive raster-processing algorithms, as demonstrated with pSLEUTH.

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