Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.777348
Title: Carbon allocation and trait optimality drives Amazon forest response to changing water availability
Author: Flack-Prain, Sophie
ISNI:       0000 0004 7963 2456
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2019
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Abstract:
Climate change induced shifts in precipitation threaten the future carbon balance of Amazon rainforests. Our understanding of water constraints to photosynthesis is largely limited to physiology-climate effects. Less is known about the effects of carbon allocation and trait shifts in response to water availability. Mechanisms linking carbon allocation and trait responses are not well represented in current ecosystem models, causing uncertainty in predicted carbon dynamics under future climates. We ask (i) What drives the coupling between photosynthesis and precipitation is it canopy structure (leaf area index; LAI), leaf traits, or solely a physiology-climate response? (ii) Why do LAI and leaf traits vary with precipitation? (iii) What is the role of water availability and plant traits in driving carbon allocation between leaves and roots? Process based modelling allows the links between photosynthesis, water availability, carbon allocation and traits to be quantified explicitly, exploring interaction space not available to insitu experiments. We calibrated the Soil Plant Atmosphere model (SPA) to eight permanent sample plots across an Amazon mean annual precipitation gradient (1400-2800mm), as part of the Global Ecosystems Monitoring network. The model's representation of local carbon fluxes was evaluated against biometric estimates. We then conducted a series of model experiments to quantify the principal drivers of photosynthesis across the precipitation gradient and explore mechanisms of LAI, leaf trait and carbon allocation responses to water availability. LAI increased with precipitation (R2=0.42, p=0.08), and was the principal driver of differences in photosynthesis across the gradient, accounting for 36% of observed variation. Differences in leaf traits accounted for 20% of variance and physiology-climate interactions accounted for a further 12%. Spatial variance in LAI was underpinned by carbon economics, and best predicted by an optimality approach that maximised net canopy carbon export (R2=0.87, p < 0.001). Across the precipitation gradient, leaf trait strategies shifted from fast to slow as water availability increased (where fast leaf traits are a cohort of high photosynthetic capacitance, high metabolic rate, high nitrogen content, low LMA and short lifespan and vice versa for slow leaf traits). Leaf traits had a determinate effect on LAI optimality, and higher leaf areas at wet plots were supported by longer leaf lifespans rather than an increase in leaf net primary production (NPP). At dry plots, short leaf lifespans, inherent of fast leaf trait cohorts, effected lower LAI. However, fast leaf trait strategies did prove optimal at dry plots, as carbon losses during the dry season were minimised, whilst photosynthesis during the wet season was maximised. Field estimates showed that leaf NPP was highest at dry plots and declined with increasing precipitation, whilst root NPP was highest at wet plots, converse to optimal partitioning theory, which suggests prioritisation of roots under moisture stress and leaves under light limitation. Yet model results show that leaf:root NPP across the precipitation gradient was optimal, and was similarly best predicted by the maximisation of net canopy carbon export (R2=0.60, p=0.02). Optimality was supported by concurrent shifts in leaf and root traits, which together accounted for 63% of variation in optimal leaf:root NPP. Our findings demonstrate that optimality approaches can be used to successfully predict spatial variation in LAI, leaf:root NPP and leaf traits across an Amazon precipitation gradient. Leaf traits fundamentally shaped modelled optimal responses, ultimately determining carbon assimilation. The response of Amazon forests to increased moisture stress is therefore dependent on the current spatial distribution of leaf traits, their plasticity and the likelihood of future shifts in floristic and functional trait composition. Future work should expand on the findings presented by exploring the responses of carbon allocation and traits to water availability over different timescales.
Supervisor: Williams, Mathew ; Meir, Patrick Sponsor: Natural Environment Research Council (NERC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.777348  DOI: Not available
Keywords: climate change ; photosynthesis ; Amazon rainforest ; rainfall ; Amazon rainfall gradient ; leaf surface area ; optimal growth
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