Publications by authors named "David R Galbraith"

Tropical forests face increasing climate risk, yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, [Formula: see text]) and hydraulic safety margins (for example, HSM) are important predictors of drought-induced mortality risk, little is known about how these vary across Earth's largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation.

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Premise: Understanding tree species' responses to drought is critical for predicting the future of tropical forests, especially in regions where the climate is changing rapidly.

Methods: We compared anatomical and functional traits of the dominant tree species of two tropical forests in southern Amazonia, one on deep, well-drained soils (cerradão [CD]) and one in a riparian environment (gallery forest [GF]), to examine potential anatomical indicators of resistance or vulnerability to drought.

Results: Leaves of CD species generally had a thicker cuticle, upper epidermis, and mesophyll than those of GF species, traits that are indicative of adaptation to water deficit.

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The forests of Amazonia are among the most biodiverse plant communities on Earth. Given the immediate threats posed by climate and land-use change, an improved understanding of how this extraordinary biodiversity is spatially organized is urgently required to develop effective conservation strategies. Most Amazonian tree species are extremely rare but a few are common across the region.

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Higher levels of taxonomic and evolutionary diversity are expected to maximize ecosystem function, yet their relative importance in driving variation in ecosystem function at large scales in diverse forests is unknown. Using 90 inventory plots across intact, lowland, terra firme, Amazonian forests and a new phylogeny including 526 angiosperm genera, we investigated the association between taxonomic and evolutionary metrics of diversity and two key measures of ecosystem function: aboveground wood productivity and biomass storage. While taxonomic and phylogenetic diversity were not important predictors of variation in biomass, both emerged as independent predictors of wood productivity.

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Given anticipated climate changes, it is crucial to understand controls on leaf temperatures including variation between species in diverse ecosystems. In the first study of leaf energy balance in tropical montane forests, we observed current leaf temperature patterns on 3 tree species in the Atlantic forest, Brazil, over a 10-day period and assessed whether and why patterns may vary among species. We found large leaf-to-air temperature differences (maximum 18.

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Lineages tend to retain ecological characteristics of their ancestors through time. However, for some traits, selection during evolutionary history may have also played a role in determining trait values. To address the relative importance of these processes requires large-scale quantification of traits and evolutionary relationships among species.

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Many tropical rain forest regions are at risk of increased future drought. The net effects of drought on forest ecosystem functioning will be substantial if important ecological thresholds are passed. However, understanding and predicting these effects is challenging using observational studies alone.

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There is considerable interest in understanding the fate of the Amazon over the coming century in the face of climate change, rising atmospheric CO levels, ongoing land transformation, and changing fire regimes within the region. In this analysis, we explore the fate of Amazonian ecosystems under the combined impact of these four environmental forcings using three terrestrial biosphere models (ED2, IBIS, and JULES) forced by three bias-corrected IPCC AR4 climate projections (PCM1, CCSM3, and HadCM3) under two land-use change scenarios. We assess the relative roles of climate change, CO fertilization, land-use change, and fire in driving the projected changes in Amazonian biomass and forest extent.

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Article Synopsis
  • Understanding how photosynthesis, plant growth, and energy use work together is important for figuring out how forests will change with climate change.
  • The study looked at ten different forest sites in the Amazon and found that while water affects how much plants grow, it doesn't always explain why some forests are more productive than others.
  • The research suggests that other factors, like how long trees live and how much carbon they use, are even more important for understanding forest growth and carbon storage.
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Considerable uncertainty surrounds the fate of Amazon rainforests in response to climate change. Here, carbon (C) flux predictions of five terrestrial biosphere models (Community Land Model version 3.5 (CLM3.

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A mosaic of protected areas, including indigenous lands, sustainable-use production forests and reserves and strictly protected forests is the cornerstone of conservation in the Amazon, with almost 50 per cent of the region now protected. However, recent research indicates that isolation from direct deforestation or degradation may not be sufficient to maintain the ecological integrity of Amazon forests over the next several decades. Large-scale changes in fire and drought regimes occurring as a result of deforestation and greenhouse gas increases may result in forest degradation, regardless of protected status.

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A better understanding of the mechanisms controlling the magnitude and sign of carbon components in tropical forest ecosystems is important for reliable estimation of this important regional component of the global carbon cycle. We used the JULES vegetation model to simulate all components of the carbon balance at six sites along an Andes-Amazon transect across Peru and Brazil and compared the results to published field measurements. In the upper montane zone the model predicted a lack of forest vegetation, indicating a need for better parameterization of the responses of cloud forest vegetation within the model.

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