Douglas Sheil

Conservation of mountain ecosystems can benefit from knowledge of habitats and their distribution patterns. This benefit is particularly true for diverse ecosystems with high conservation values such as the “Afromontane” rainforests. We mapped the vegetation of one such forest: the rugged Bwindi Impenetrable Forest, Uganda—a World Heritage Site known for its many restricted‐range plants and animal taxa including several iconic species. Given variation in elevation, terrain and human impacts across Bwindi, we hypothesized that these factors influence the composition and distribution of tree species. To test this, detailed surveys were carried out using stratified random sampling. We established 289 georeferenced sample sites (each with 15 trees ≥20 cm dbh) ranging from 1320 to 2467 m a.s.l. and measured 4335 trees comprising 89 species that occurred in four or more sample sites. These data were analyzed against 21 digitally mapped biophysical variables using various analytical techniques including nonmetric multidimensional scaling (NMDS) and random forests. We identified six tree species assemblages with distinct compositions. Among the biophysical variables, elevation had the strongest correlation with the ordination ( r 2 = 0.5; p < 0.001). The “out‐of‐bag” (OOB) estimate of the error rate for the best final model was 50.7% meaning that nearly half of the variation was accounted for using a limited set of variables. We demonstrate that it is possible to predict the spatial pattern of such a forest based on sampling across a highly complex landscape. Such methods offer accurate mapping of composition that can guide conservation.

The terrestrial water cycle links the soil and atmosphere moisture reservoirs through four fluxes: precipitation, evaporation, runoff, and atmospheric moisture convergence (net import of water vapor to balance runoff). Each of these processes is essential for sustaining human and ecosystem well‐being. Predicting how the water cycle responds to changes in vegetation cover remains a challenge. Recently, changes in plant transpiration across the Amazon basin were shown to be associated disproportionately with changes in rainfall, suggesting that even small declines in transpiration (e.g., from deforestation) would lead to much larger declines in rainfall. Here, constraining these findings by the law of mass conservation, we show that in a sufficiently wet atmosphere, forest transpiration can control atmospheric moisture convergence such that increased transpiration enhances atmospheric moisture import and results in water yield. Conversely, in a sufficiently dry atmosphere increased transpiration reduces atmospheric moisture convergence and water yield. This previously unrecognized dichotomy can explain the otherwise mixed observations of how water yield responds to re‐greening, as we illustrate with examples from China's Loess Plateau. Our analysis indicates that any additional precipitation recycling due to additional vegetation increases precipitation but decreases local water yield and steady‐state runoff. Therefore, in the drier regions/periods and early stages of ecological restoration, the role of vegetation can be confined to precipitation recycling, while once a wetter stage is achieved, additional vegetation enhances atmospheric moisture convergence and water yield. Recent analyses indicate that the latter regime dominates the global response of the terrestrial water cycle to re‐greening. Evaluating the transition between regimes, and recognizing the potential of vegetation for enhancing moisture convergence, are crucial for characterizing the consequences of deforestation as well as for motivating and guiding ecological restoration.