3. Producción
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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, Variation of non-structural carbohydrates across the fast-slow continuum in Amazon forest canopy trees(Wiley, 2021-11-20)Tropical tree species span a range of life‐history strategies within a fast–slow continuum. The position of a species within this continuum is thought to reflect a negative relationship between growth and storage, with fast‐growing species allocating more carbon to growth and slow‐growing species investing more in storage. For tropical species, the relationship between storage and life‐history strategies has been largely studied on seedlings and less so in adult trees. We evaluated how stored non‐structural carbohydrates (NSC) vary across adult trees spanning the fast–slow continuum in the Peruvian Amazon by: (a) analysing whole‐tree NSC in two species of contrasting growth and (b) investigating the relationships with key life‐history traits across a broader set of species. Our results are consistent with a growth–storage trade‐off. The analysis of whole‐tree NSC revealed that the slow‐growing Eschweilera coriacea stored about 2.7 times as much NSC as the fast‐growing Bixa arborea due to markedly higher storage in woody stems and roots. B. arborea also had higher seasonality in NSC, reflecting its strong seasonality in stem growth. Across a range of species, stem starch was negatively related to species growth rate and positively related to wood density. Given the role of NSC in mediating plants' response to stress, our results suggest that slow‐growing species with greater storage reserves may be more resilient to drought than fast‐growing species. - 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.