RESUMO
Understanding how evolutionary history and the coordination between trait trade-off axes shape the drought tolerance of trees is crucial to predict forest dynamics under climate change. Here, we compiled traits related to drought tolerance and the fast-slow and stature-recruitment trade-off axes in 601 tropical woody species to explore their covariations and phylogenetic signals. We found that xylem resistance to embolism (P50) determines the risk of hydraulic failure, while the functional significance of leaf turgor loss point (TLP) relies on its coordination with water use strategies. P50 and TLP exhibit weak phylogenetic signals and substantial variation within genera. TLP is closely associated with the fast-slow trait axis: slow species maintain leaf functioning under higher water stress. P50 is associated with both the fast-slow and stature-recruitment trait axes: slow and small species exhibit more resistant xylem. Lower leaf phosphorus concentration is associated with more resistant xylem, which suggests a (nutrient and drought) stress-tolerance syndrome in the tropics. Overall, our results imply that (1) drought tolerance is under strong selective pressure in tropical forests, and TLP and P50 result from the repeated evolutionary adaptation of closely related taxa, and (2) drought tolerance is coordinated with the ecological strategies governing tropical forest demography. These findings provide a physiological basis to interpret the drought-induced shift toward slow-growing, smaller, denser-wooded trees observed in the tropics, with implications for forest restoration programmes.
Assuntos
Secas , Xilema , Florestas , Filogenia , Folhas de Planta/fisiologia , Clima Tropical , MadeiraRESUMO
Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5-300 m radii) surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT) and Maximum Entropy Species Distribution Modelling (MaxEnt). The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided 'outstanding' model predictions (AUCâ=â>0.9) for three of five fish species. MaxEnt provided 'outstanding' model predictions for two of five species, with the remaining three models considered 'excellent' (AUCâ=â0.8-0.9). In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy) than BRT (68% map accuracy). We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support conservation prioritization in marine protected area design, zoning in marine spatial planning, and ecosystem-based fisheries management.