Search

There is still much uncertainty about ecological controls on the rate and direction of net C[O.sub.2] exchange by tropical rain forests, in spite of their importance to global C cycling. These controls are thought to arise from hydrologic and nutrient constraints to C[O.sub.2] fixation caused by seasonality of precipitation and adverse chemical properties of some major tropical soil types. Using the ecosystem model ecosys, we show that water uptake to a depth of 8 m avoids constraints to C[O.sub.2] and energy exchange from soil drying during five-month dry seasons typical for eastern Amazonian forests. This avoidance in the model was tested with eddy covariance (EC) measurements of C[O.sub.2] and energy fluxes during 2003 and 2004 over an old-growth forest on an acidic, nutrient-poor oxisol in the Tapajds National Forest (TNF) in Para, Brazil. Modeled C[O.sub.2] fixation was strongly constrained by slow phosphorus (P) uptake caused by low soil pH. Daytime C[O.sub.2] influxes in the model were in close agreement with EC measurements ([R.sup.2] > 0.8) during both wet and dry seasons. Both modeled and measured fluxes indicated that seasonality of precipitation affected C[O.sub.2] and energy exchange more through its effect on radiation and air temperature than on soil water content. When aggregated to a yearly scale, modeled and gap-filled EC C[O.sub.2] fluxes indicated that old-growth forest stands in the TNF remained within 100 g C*[m.sup.-2]*[yr.sup.-1] of C neutrality in the absence of major disturbance. Annual C transformations in ecosys were further corroborated by extensive biometric measurements taken in the TNF and elsewhere in the Amazon basin, which also indicated that old-growth forests were either small C sources or small C sinks. Long-term model runs suggested that rain forests could be substantial C sinks for several decades while regenerating after stand-replacing disturbances, but would gradually decline toward C neutrality thereafter. The time course of net ecosystem productivity (NEP) in the model depended upon annual rates of herbivory and tree mortality, which were based on site observations as affected by weather (e.g., El Nino Southern Oscillation [ENSO] events). This dependence suggests that rain forest NEP is strongly controlled by disturbance as well as by weather. Key words: Brazil; C[O.sub.2] exchange; disturbance; ecosys; ecosystem modeling; Her ecosystem productivity; rain forest.

Despite its widespread occurrence globally, wildfire preferentially occupies an environmental middle ground and is significantly less prevalent in biomes characterized by environmental extremes (e.g., tundra, rain forests, and deserts). We evaluated the biophysical 'environmental space' of wildfire from regional to subcontinental extents, with methods widely used for modeling habitat distributions. This approach is particularly suitable for the biogeographic study of wildfire, because it simultaneously considers patterns in multiple factors controlling wildfire suitability over large areas. We used the Maxent and boosted regression tree algorithms to assess wildfire-environment relationships for three levels of complexity (in terms of inclusion of variables) at three spatial scales: the conterminous United States, the state of California, and five wildfire-prone ecoregions of California. The resulting models were projected geographically to obtain spatial predictions of wildfire suitability and were also applied to other regions to assess their generality and spatial transferability. Predictions of the potential range of wildfire had high classification accuracy; they also highlighted areas where wildfires had not recently been observed, indicating the potential (or past) suitability of these areas. The models identified several key variables that were not suspected to be important in the large-scale control of wildfires, but which might indirectly affect control by influencing the presence of flammable vegetation. Models transferred to different areas were useful only when they overlapped appreciably with the target area's environmental space. This approach should allow exploration of the potential shifts in wildfire range in a changing climate, the potential for restoration of wildfire where it has been 'extirpated,' and, conversely, the 'invasiveness' of wildfire after changes in plant species composition. Our study demonstrates that habitat distribution models and related concepts can be used to characterize environmental controls on a natural disturbance process, but also that future work is needed to refine our understanding of the direct causal factors controlling wildfire at multiple spatial scales. Key words: boosted regression trees; disturbance ecology; environmental space; fire regime controls; habitat distribution models; Maxent algorithm; spatial fire prediction; wildfire.