The respiratory release of carbon dioxide (CO2) from soil is a major yet poorly understood flux in the global carbon cycle. Climatic warming is hypothesized to increase rates of soil respiration, potentially fueling further increases in global temperatures. However, despite considerable scientific attention in recent decades, the overall response of soil respiration to anticipated climatic warming remains unclear. We synthesize the largest global dataset to date of soil respiration, moisture, and temperature measurements, totaling >3,800 observations representing 27 temperature manipulation studies, spanning nine biomes and over 2 decades of warming. Our analysis reveals no significant differences in the temperature sensitivity of soil respiration between control and warmed plots in all biomes, with the exception of deserts and boreal forests. Thus, our data provide limited evidence of acclimation of soil respiration to experimental warming in several major biome types, contrary to the results from multiple single-site studies. Moreover, across all nondesert biomes, respiration rates with and without experimental warming follow a Gaussian response, increasing with soil temperature up to a threshold of ∼25 °C, above which respiration rates decrease with further increases in temperature. This consistent decrease in temperature sensitivity at higher temperatures demonstrates that rising global temperatures may result in regionally variable responses in soil respiration, with colder climates being considerably more responsive to increased ambient temperatures compared with warmer regions. Our analysis adds a unique cross-biome perspective on the temperature response of soil respiration, information critical to improving our mechanistic understanding of how soil carbon dynamics change with climatic warming.

As communities and populations become increasingly fragmented, much theoretical and some empirical research has focused on the dynamics of metapopulations. Many metapopulation models describe dynamics among populations in a region, yet the scale of the ''region'' to which different models apply often is undefined. Because the spatial scale is undefined, testing predictions and assumptions of these models is problematic. Our goal is to present two scaling concepts relevant to these models, distance scaling and organismal scaling, and to apply these scaling notions to patterns of species distribution. To determine distance effects, we analyzed patterns of distribution of four taxonomic groups in tallgrass prairie (grasshoppers, small mammals, vascular plants, and breeding birds) at two spatial scales. To assess organismal effects, we held spatial scale constant and we compared patterns of distribution and abundance among these taxonomic groups.Using long-term data from Konza Prairie, Kansas, there were significant differences in the pattern of distribution of grasshoppers, small mammals, vascular plants, and breeding birds within a single spatial scale. The number of core species (species occupying Ͼ90% of the sites in a region) of plants and birds was less than the number of satellite species (those occupying Ͻ10% of the sites in a region). The opposite was true for grasshoppers and small mammals. All four distribution patterns were significantly nonrandom, but only grasshoppers and small mammals were significantly bimodal at this scale. Plants and birds were unimodal. The patterns of distribution within these taxonomic groups at two spatial scales were significantly different as well. In all cases, the percentage of species in the core group declined, and the percentage of species in the satellite group increased as spatial scale increased. These results demonstrate the difficulty of testing theoretical models with only one taxonomic group at a single spatial scale. One should not accept or reject a model until the spatial domains of organismal and distance scaling have been properly evaluated.

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