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Item type:Publication, Quantifying productivity at landscape scale using remotely-sensed foliar traits and canopy structure(Research Square, 2020-11-02)Abstract Forests are integral to global carbon cycling but are threatened by anthropogenic degradation and climate change. Assessing this global threat has been hindered by a lack of clear, flexible, and easy-to-use productivity models along with a lack of functional trait and productivity data for parameterizing and testing those models. Current productivity models are either extremely complex requiring up to hundreds of parameters, many sub-models, and considerable computational expense or rely on statistical trait-growth relationships that can be hard to extrapolate to new systems or climates. Here we provide a simple alternative: a remote sensing canopy functional model (RS-CFM) that uses remotely-sensed foliar traits and canopy structure data to efficiently map productivity at high-resolution and large spatial scales. We test this model by quantifying net primary productivity (NPP) at 0.01-ha resolution in 30,040 hectares of Peruvian tropical rainforest along a 3,322-m Amazon-to-Andes elevation gradient. Our model predicts local NPP and elevational shifts in NPP much more accurately and in greater detail than a prominent alternative method—NASA’s MODIS NPP product. Furthermore, we show how NPP estimates depend on light competition and identify the appropriate spatial resolution for remote productivity estimation. Our framework opens up possibilities to fully harness remote sensing data and reliably scale up from traits to map regional or global productivity in a more direct, efficient, and cost-effective manner. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Global Ecosystems Monitoring network: monitoring ecosystem productivity and carbon cycling across the tropics(Elsevier, 2021-01-01)A rich understanding of the productivity, carbon and nutrient cycling of terrestrial ecosystems is essential in the context of understanding, modelling and managing the future response of the biosphere to global change. This need is particularly acute in tropical ecosystems, home to over 60% of global terrestrial productivity, over half of planetary biodiversity, and hotspots of anthropogenic pressure. In recent years there has been a surge of activity in collecting data on the carbon cycle, productivity, and plant functional traits of tropical ecosystems, most intensively through the Global Ecosystems Monitoring network (GEM). The GEM approach provides valuable insights by linking field-based ecosystem ecology with the needs of Earth system science. In this paper, we review and synthesize the context, history and recent scientific output from the GEM network. Key insights have emerged on the spatial and temporal variability of ecosystem productivity and on the role of temperature and drought stress on ecosystem function and resilience. New work across the network is now linking carbon cycling to nutrient cycling and plant functional traits, and subsequently to airborne remote sensing. We discuss some of the novel emerging patterns and practical and methodological challenges of this approach, and examine current and possible future directions, both within this network and as lessons for a more general terrestrial ecosystem observation scheme. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improving landscape-scale productivity estimates by integrating trait-based models and remotely-sensed foliar-trait and canopy-structural data(John Wiley and Sons Inc, 2022-08-01)Assessing the impacts of anthropogenic degradation and climate change on global carbon cycling is hindered by a lack of clear, flexible and easy-to-use productivity models along with scarce trait and productivity data for parameterizing and testing those models. We provide a simple solution: a mechanistic framework (RS-CFM) that combines remotely-sensed foliar-trait and canopy-structural data with trait-based metabolic theory to efficiently map productivity at large spatial scales. We test this framework by quantifying net primary productivity (NPP) at high-resolution (0.01-ha) in hyper-diverse Peruvian tropical forests (30040 hectares) along a 3322-m elevation gradient. Our analysis captures hotspots and elevational shifts in productivity more accurately and in greater detail than alternative empirical- and process-based models that use plant functional types. This result exposes how high-resolution, location-specific variation in traits and light competition drive variability in productivity, opening up possibilities to fully harness remote sensing data and reliably scale up from traits to map global productivity in a more direct, efficient and cost-effective manner. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Basin-Wide Variation in Tree Hydraulic Safety Margins Predicts the Carbon Balance of Amazon Forests(Nature Research, 2023-05-04)Tropical forests face increasing climate risk1,2, 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, Ψ50) and hydraulic safety margins (for example, HSM50) are important predictors of drought-induced mortality risk3–5, 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. Parameters Ψ50 and HSM50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both Ψ50 and HSM50 influence the biogeographical distribution of Amazon tree species. However, HSM50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM50 are gaining more biomass than are low HSM50 forests. We propose that this may be associated with a growth–mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM50 in the Amazon6,7, with strong implications for the Amazon carbon sink.