The degree to which variation in plant community composition (beta-diversity) is predictable from environmental variation, relative to other spatial processes, is of considerable current interest. We addressed this question in Costa Rican rain forest pteridophytes (1,045 plots, 127 species). We also tested the effect of data quality on the results, which has largely been overlooked in earlier studies. To do so, we compared two alternative spatial models [polynomial vs. principal coordinates of neighbour matrices (PCNM)] and ten alternative environmental models (all available environmental variables vs. four subsets, and including their polynomials vs. not). Of the environmental data types, soil chemistry contributed most to explaining pteridophyte community variation, followed in decreasing order of contribution by topography, soil type and forest structure. Environmentally explained variation increased moderately when polynomials of the environmental variables were included. Spatially explained variation increased substantially when the multi-scale PCNM spatial model was used instead of the traditional, broad-scale polynomial spatial model. The best model combination (PCNM spatial model and full environmental model including polynomials) explained 32% of pteridophyte community variation, after correcting for the number of sampling sites and explanatory variables. Overall evidence for environmental control of beta-diversity was strong, and the main floristic gradients detected were correlated with environmental variation at all scales encompassed by the study (c. 100-2,000 m). Depending on model choice, however, total explained variation differed more than fourfold, and the apparent relative importance of space and environment could be reversed. Therefore, we advocate a broader recognition of the impacts that data quality has on analysis results. A general understanding of the relative contributions of spatial and environmental processes to species distributions and beta-diversity requires that methodological artefacts are separated from real ecological differences.

[1] Much of the 191.8 Pg C in the upper 1 m of Arctic soil of Arctic soil organic mater is, or is at risk of, being released to the atmosphere as CO 2 and/or CH 4 . Global warming will further alter the rate of emission of these gases to the atmosphere. Here we quantify the effect of major environmental variables affected by global climate change on CH 4 fluxes in the Alaskan Arctic. Soil temperature best predicts CH 4 fluxes and explained 89% of the variability in CH 4 emissions. Water table depth has a nonlinear impact on CH 4 efflux. Increasing water table height above the surface retards CH 4 efflux. Decreasing water table depth below the surface has a minor effect on CH 4 release once an aerobic layer is formed at the surface. In contrast with several other studies, we found that CH 4 emissions are not driven by net ecosystem exchange (NEE) and are not limited by labile carbon supply.

Summary1 Field studies to evaluate the roles of environmental variation and random dispersal in explaining variation in the floristic composition of rain forest plants at landscape to regional scales have yet to reach a consensus. Moreover, only one study has focused on scales below 10 km 2 , where the effects of dispersal limitation are expected to be easiest to observe. 2 In the present study, we estimate the importance of differences in some key environmental variables (describing canopy openness, soils and topography) relative to the geographical distances between sample plots as determinants of differences in pteridophyte (ferns and fern allies) species composition between plots within a c . 5.7 km 2 lowland rain forest site in Costa Rica. 3 To assess the relative importance of environmental vs. geographical distances in relation to the length of environmental gradient covered, we compared the results obtained over the full range of soil types, including swamps, with those for upland soils alone. 4 Environmental variability was found to be a far stronger predictor of changes in floristic differences than the geographical distance between sample plots. In particular, differences in soil nutrient content, drainage and canopy openness correlated with floristic differences. 5 The decline in mean floristic similarity with increasing geographical distance was stronger than proposed by the random dispersal model over short distances (up to c . 100 m), which is probably attributable to both dispersal limitation and environmental changes. The scatter around the mean was large at all distances. 6 Our initial expectation was that the effects of dispersal limitation (represented by geographical distance) on observed patterns of floristic similarity would be stronger, and those of environmental differences weaker, than at broader spatial scales. Instead, these results suggest that the niche assembly view is a more accurate representation of pteridophyte communities at local to mesoscales than the dispersal assembly view.

Leaf Area Index (leaf area per unit ground area, LAI) is a key driver of forest productivity but has never previously been measured directly at the landscape scale in tropical rain forest (TRF). We used a modular tower and stratified random sampling to harvest all foliage from forest floor to canopy top in 55 vertical transects (4.6 m(2)) across 500 ha of old growth in Costa Rica. Landscape LAI was 6.00 +/- 0.32 SEM. Trees, palms and lianas accounted for 89% of the total, and trees and lianas were 95% of the upper canopy. All vertical transects were organized into quantitatively defined strata, partially resolving the long-standing controversy over canopy stratification in TRF. Total LAI was strongly correlated with forest height up to 21 m, while the number of canopy strata increased with forest height across the full height range. These data are a benchmark for understanding the structure and functional composition of TRF canopies at landscape scales, and also provide insights for improving ecosystem models and remote sensing validation.

Populations occurring at species' range edges can be locally adapted to unique environmental conditions. From a species' perspective, range‐edge environments generally have higher severity and frequency of extreme climatic events relative to the range core. Under future climates, extreme climatic events are predicted to become increasingly important in defining species' distributions. Therefore, range‐edge genotypes that are better adapted to extreme climates relative to core populations may be essential to species' persistence during periods of rapid climate change. We use relatively simple conceptual models to highlight the importance of locally adapted range‐edge populations (leading and trailing edges) for determining the ability of species to persist under future climates. Using trees as an example, we show how locally adapted populations at species' range edges may expand under future climate change and become more common relative to range‐core populations. We also highlight how large‐scale habitat destruction occurring in some geographic areas where many species range edge converge, such as biome boundaries and ecotones (e.g., the arc of deforestation along the rainforest‐cerrado ecotone in the southern Amazonia), can have major implications for global biodiversity. As climate changes, range‐edge populations will play key roles in helping species to maintain or expand their geographic distributions. The loss of these locally adapted range‐edge populations through anthropogenic disturbance is therefore hypothesized to reduce the ability of species to persist in the face of rapid future climate change.

[1] The Arctic stores close to 14% of the global soil carbon, most of which is in a poorly decomposed state as a result of water-saturated soils and low temperatures. Climate change is expected to increase soil temperature, affecting soil moisture and the carbon storage and sink potential of many Arctic ecosystems. Additionally, increased temperatures can increase thermokarst erosion and flooding in some areas. Our goal was to determine the effects that water table shifts would have on the CO 2 sink potential of the Alaskan Coastal Plain tundra. To evaluate the effects of different water regimes, we used a large hydrological manipulation at Barrow, Alaska, where we maintained flooded, drained, and intermediate water levels in a naturally drained thaw lake basin over a period of three seasons: one pretreatment (2006) and two treatment (2007)(2008) seasons. To assess CO 2 flux components, we used 24 h chamber-based measurements done on a weekly basis. Increased water table strongly lowered ecosystem respiration (ER) by reducing soil oxygen availability. Flooding decreased gross primary productivity (GPP), most likely by submerging mosses and graminoid photosynthetic leaf area. A decrease in water table increased GPP and ER; however, the increase in root and microbial activity was greater than the increase in photosynthesis, negatively affecting net ecosystem exchange. In the short term, ER is the CO 2 flux component that responds most strongly to changes in water availability. Our results suggest that drying of the Alaskan Coastal Plain tundra in the short term could double ER rates, shifting the historic role of some Arctic ecosystems from a sink to a source of CO 2 .

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