Engelbrecht, BJ, Comita, LS, Condit, R, Kursar, TA, Tyree, MT, Turner, BL, & Hubbell, SP 2007, ‘Drought sensitivity shapes species distribution patterns in tropical forests’, Nature, vol. 447, no. 7140, pp. 80-82
DOI: 10.1038/nature05747
Investigation into the mechanisms behind tree species distribution along an environmental gradient. Differential drought sensitivity shapes plant distribution in tropical forests at both regional and local scales. 48 species were evaluated on their local and regional distribution within a network of122 inventory sites spanning a rainfall gradient along central Panama. The results suggest that niche differentiation with respect to soil water availability is a direct determinant of both local and regional scale distribution of tropical trees. However, global climate change and forest fragmentation can alter soil moisture availability, causing a change in tree species distribution which will be important to monitor for future studies. Regional species distribution – presence/absence and density of species were collected from sampling plots that were situated on both the wet (Caribbean) and dry (Pacific) sides of the Isthmus of Panama. An index of dry season response for 44 of the species was based on the fitted probability of occurrence toward the dry end and toward the wet end of the gradient. Drought sensitivity was a significant predictor of the probability of occurrence of the species on the dry relative to the wet side. At the local scale – species density was collected from sampling plots on wet and dry slopes within Barro Colorado Island. Local associations were analyzed of the tree species with dry and wet habitats. The paper further addresses interactions between environmental conditions that may or may not indirectly influence drought resistance (i.e. water availability) which is suspected to be a major driver of tree species distribution. Linear regressions were used to examine whether species reactions to drought or light were significant predictors of their densities in dry vs. wet sites. Knowledge of these more specific influential factors can help predict what impacts climate change and deforestation can have on future distributions of the species. Instead of using linear regression to detect significant predictor variables and this way determine species distribution, this project could have also benefitted from using a machine-learning algorithm of an ecological niche model. Because all of the elements were available, such as species presence/absence and environmental data it would have been interesting to compare their results with an ENM and determine which methods works best.