How do Amazonian Tropical Forest Systems Photosynthesize under Seasonal Climatic Variability: Insights from Tropical Phenology
Ecology & Evolutionary Biology
AdvisorSaleska, Scott R.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractAmazonian evergreen forests are of broad interest, attributable to their ecological, economic, aesthetic, and cultural importance. However, their fate under climate change remains uncertain, largely due to unclear mechanisms in regulating tropical photosynthetic metabolism. Understanding mechanistic controls on these dynamics across time scales (e.g. hours to years) is essential and a prerequisite for realistically predicting tropical forest responses to inter-annual and longer-term climate variation and change. Tropical forest photosynthesis can be conceptualized as being driven by two interacting causes: variation due to changes in environmental drivers (e.g. solar radiation, diffuse light fraction, and vapor pressure deficit) interacting with model parameters that govern photosynthetic behavior, and variation in photosynthetic capacity (PC) due to changes in the parameters themselves. In this thesis, I aim to reveal photosynthetic controls by addressing three fundamental but complementary questions: (1) What are the mechanisms by which the subtle tropical phenology exert controls on tropical photosynthetic seasonality? (2) How do the extrinsic and intrinsic controls regulate the photosynthesis processes at hourly to interannual time scales in an Amazonian evergreen forest? (3) Are there sufficiently consistent relations among leaf traits, ages, and spectra that allow a single model predict the leaf aging process of Amazonian evergreen trees? To address question 1, I firstly show that seasonal change in ecosystem-scale photosynthetic capacity (PC), rather than environmental drivers, is the primary driver of seasonality of gross primary productivity (GPP) at four Amazonian evergreen forests spanning gradients in rainfall seasonality, forest composition, and flux seasonality. Using novel near-surface camera-detected leaf phenology to drive a simple leaf-cohort canopy model at two of these sites, I further show that leaf ontogeny and demography explain the changes in ecosystem photosynthetic capacity. The coordination of new leaf growth and old leaf divestment (litterfall) during the dry season shifts canopy composition towards younger leaves with higher photosynthetic capacity, driving large seasonal increases (~27%) in ecosystem photosynthetic capacity. To address question 2, I used the 7-year eddy covariance (EC) measurements in an Amazonian tropical evergreen forest. I used a statistical model to partition the variability of 7-year EC-derived GPP into two main causes: variation due to changes in extrinsic environmental drivers and variation in intrinsic PC. The fitted model well predicts variability in EC-derived GPP at hourly (R²=0.71) to interannaul (R²=0.81) timescales. Attributing model predictions to causal factors at different timescales, I find that ~92% of the variability in modeled hourly GPP could be attributed to environmental driver variability, and ~5% to variability in PC. When aggregating the modeled GPP into the annual time-step, the attribution is reversed (only ~4% to environment and ~91% to PC). These results challenge conventional approaches for modeling evergreen forests, which neglect intrinsic controls on PC and assume that the primary photosynthetic control at both long and short timescales is due to changes in the hourly-to-diurnal environment on the physiological phenotype. This work thus highlights the importance of accounting for differential regulation of different components of GPP at different timescales, and of identifying the underlying feedbacks and adaptive mechanisms which regulate them. To address question 3, I explored the potential for a general spectrally based leaf age model across tropical sites and within the vertical canopy profiles using a phenological dataset of 1831 leaves collected at two lowland Amazonian forests in Peru (12 species) and Brazil (11 species). This work shows that a simple model (parameterized using only Peruvian canopy leaves) successfully predicts ages of canopy leaves from both Peru (R²=0.83) and Brazil (R²=0.77), but ages for Brazilian understory leaves with significantly different growth environment and leaf trait values have lower prediction accuracy (R²=0.48). Prediction accuracy for all Brazilian samples is improved when information on growth environment and leaf traits were added into the model (5% R² increase; R²=0.69), or when leaves from the full range of trait values are used to parameterize the model (15% R² increase; R²=0.79). This work shows that fundamental ecophysiological rules constrain leaf traits and spectra to develop consistently across species and growth environment, providing a basis for a general model associating leaf age with spectra in tropical forests. In sum, in this thesis, I (1) conceptualize photosynthesis as being driven by two interacting dynamics, extrinsic and intrinsic, (2) propose and validate a model for biological mechanisms that mediate seasonal dynamics of tropical forest photosynthesis, (3) assess and quantify the factors controlling tropical forest photosynthesis on timescales from hourly to interannual, and (4) develop a general model for monitoring leaf aging processes of tropical trees across sites and growth environments. The revealed mechanisms (and proposed models) in this thesis greatly improve our mechanistic understanding of the photosynthetic and phenological processes in tropical evergreen forests. Strategic incorporation of these mechanisms will improve ecological, evolutionary and earth system theories describing tropical forests structure and function, allowing more accurate representation of forest dynamics and feedbacks to climate in earth system models.
Degree ProgramGraduate College
Ecology & Evolutionary Biology