Scott C. Stark

A general integrative model for scaling plant growth, carbon flux, and functional trait spectra

Linking functional traits to plant growth is critical for scaling attributes of organisms to the dynamics of ecosystems and for understanding how selection shapes integrated botanical phenotypes. However, a general mechanistic theory showing how traits specifically influence carbon and biomass flux within and across plants is needed. Building on foundational work on relative growth rate, recent work on functional trait spectra, and metabolic scaling theory, here we derive a generalized trait-based model of plant growth. In agreement with a wide variety of empirical data, our model uniquely predicts how key functional traits interact to regulate variation in relative growth rate, the allometric growth normalizations for both angiosperms and gymnosperms, and the quantitative form of several functional trait spectra relationships. The model also provides a general quantitative framework to incorporate additional leaf-level trait scaling relationships and hence to unite functional trait spectra with theories of relative growth rate, and metabolic scaling. We apply the model to calculate carbon use efficiency. This often ignored trait, which may influence variation in relative growth rate, appears to vary directionally across geographic gradients. Together, our results show how both quantitative plant traits and the geometry of vascular transport networks can be merged into a common scaling theory. Our model provides a framework for predicting not only how traits covary within an integrated allometric phenotype but also how trait variation mechanistically influences plant growth and carbon flux within and across diverse ecosystems.

Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests.

Leaf seasonality in Amazon forests Models assume that lower precipitation in tropical forests means less plant-available water and less photosynthesis. Direct measurements in the Amazon, however, show that production remains constant or increases in the dry season. To investigate this mismatch, Wu et al. use tower-based cameras to detect the phenology (i.e., the seasonal patterns) of leaf dynamics in tropical tree crowns in Amazonia, Brazil, and relate this to patterns of CO2 flux. Accounting for age-dependent variation among individual leaves and crowns is necessary for understanding the seasonal dynamics of photosynthesis in the entire ecosystem. Leaf phenology regulates seasonality of the carbon flux in tropical forests across a gradient of climate zones. Science, this issue p. 972 Camera recordings of the age distribution of leaves coupled with carbon dioxide flux data show the phenological basis of photosynthesis. In evergreen tropical forests, the extent, magnitude, and controls on photosynthetic seasonality are poorly resolved and inadequately represented in Earth system models. Combining camera observations with ecosystem carbon dioxide fluxes at forests across rainfall gradients in Amazônia, we show that aggregate canopy phenology, not seasonality of climate drivers, is the primary cause of photosynthetic seasonality in these forests. Specifically, synchronization of new leaf growth with dry season litterfall shifts canopy composition toward younger, more light-use efficient leaves, explaining large seasonal increases (~27%) in ecosystem photosynthesis. Coordinated leaf development and demography thus reconcile seemingly disparate observations at different scales and indicate that accounting for leaf-level phenology is critical for accurately simulating ecosystem-scale responses to climate change.

Toward accounting for ecoclimate teleconnections: intra- and inter-continental consequences of altered energy balance after vegetation change

ContextVegetation is projected to continue to undergo major structural changes in coming decades due to land conversion and climate change, including widespread forest die-offs. These vegetation changes are important not only for their local or regional climatic effects, but also because they can affect climate and subsequently vegetation in other regions or continents through “ecoclimate teleconnections”.ObjectivesWe propose that ecoclimate teleconnections are a fundamental link among regions within and across continents, and are central to advancing large-scale macrosystems ecology.Methods and resultsWe illustrate potential ecoclimate teleconnections in a bounding simulation that assumes complete tree cover loss in western North America due to tree die-off, and which predicts subsequent drying and reduced net primary productivity in other areas of North America, the Amazon and elsewhere. Central to accurately modeling such ecoclimate teleconnections is characterizing how vegetation change alters albedo and other components of the land-surface energy balance and then scales up to impact the climate system. We introduce a framework for rapid field-based characterization of vegetation structure and energy balance to help address this challenge.ConclusionsEcoclimate teleconnections are likely a fundamental aspect of macrosystems ecology needed to account for alterations to large-scale atmospheric-ecological couplings in response to vegetation change, including deforestation, afforestation and die-off.

Linking canopy leaf area and light environments with tree size distributions to explain Amazon forest demography

Forest biophysical structure - the arrangement and frequency of leaves and stems - emerges from growth, mortality and space filling dynamics, and may also influence those dynamics by structuring light environments. To investigate this interaction, we developed models that could use LiDAR remote sensing to link leaf area profiles with tree size distributions, comparing models which did not (metabolic scaling theory) and did allow light to influence this link. We found that a light environment-to-structure link was necessary to accurately simulate tree size distributions and canopy structure in two contrasting Amazon forests. Partitioning leaf area profiles into size-class components, we found that demographic rates were related to variation in light absorption, with mortality increasing relative to growth in higher light, consistent with a light environment feedback to size distributions. Combining LiDAR with models linking forest structure and demography offers a high-throughput approach to advance theory and investigate climate-relevant tropical forest change.

Synergistic Ecoclimate Teleconnections from Forest Loss in Different Regions Structure Global Ecological Responses

Forest loss in hotspots around the world impacts not only local climate where loss occurs, but also influences climate and vegetation in remote parts of the globe through ecoclimate teleconnections. The magnitude and mechanism of remote impacts likely depends on the location and distribution of forest loss hotspots, but the nature of these dependencies has not been investigated. We use global climate model simulations to estimate the distribution of ecologically-relevant climate changes resulting from forest loss in two hotspot regions: western North America (wNA), which is experiencing accelerated dieoff, and the Amazon basin, which is subject to high rates of deforestation. The remote climatic and ecological net effects of simultaneous forest loss in both regions differed from the combined effects of loss from the two regions simulated separately, as evident in three impacted areas. Eastern South American Gross Primary Productivity (GPP) increased due to changes in seasonal rainfall associated with Amazon forest loss and changes in temperature related to wNA forest loss. Eurasia’s GPP declined with wNA forest loss due to cooling temperatures increasing soil ice volume. Southeastern North American productivity increased with simultaneous forest loss, but declined with only wNA forest loss due to changes in VPD. Our results illustrate the need for a new generation of local-to-global scale analyses to identify potential ecoclimate teleconnections, their underlying mechanisms, and most importantly, their synergistic interactions, to predict the responses to increasing forest loss under future land use change and climate change.

Forest structure along a 600 km transect of natural disturbances and seasonality gradients in central-southern Amazonia

A negative relationship between stand biomass and the density of stems is expected to develop during the self-thinning process in resource-limited forests; this leads to a large proportion of the total biomass occurring in large trees. Nevertheless, frequent disturbance regimes can reduce self-thinning and the accumulation of large trees. We investigated size-density relationships and the contribution of large trees (dbh ≥ 70 cm) to stand biomass in 55 1-ha plots along a 600 km transect in central-southern Amazonia. The effects of natural-disturbance gradients (frequency of storms and soil characteristics) and seasonality on forest-structure components (density of stems and mean individual mass) and stand biomass were examined. Contrary to self-thinning predictions, stand biomass increased in forests with higher stem densities. Large trees contained only an average of 5% of stand biomass, and half of the stand biomass was represented by small trees with diameters

Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest

Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun-sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate-phenology relationships in the tropics.

Age‐dependent leaf physiology and consequences for crown‐scale carbon uptake during the dry season in an Amazon evergreen forest

Satellite and tower-based metrics of forest-scale photosynthesis generally increase with dry season progression across central Amazônia, but the underlying mechanisms lack consensus. We conducted demographic surveys of leaf age composition, and measured the age dependence of leaf physiology in broadleaf canopy trees of abundant species at a central eastern Amazon site. Using a novel leaf-to-branch scaling approach, we used these data to independently test the much-debated hypothesis - arising from satellite and tower-based observations - that leaf phenology could explain the forest-scale pattern of dry season photosynthesis. Stomatal conductance and biochemical parameters of photosynthesis were higher for recently mature leaves than for old leaves. Most branches had multiple leaf age categories simultaneously present, and the number of recently mature leaves increased as the dry season progressed because old leaves were exchanged for new leaves. These findings provide the first direct field evidence that branch-scale photosynthetic capacity increases during the dry season, with a magnitude consistent with increases in ecosystem-scale photosynthetic capacity derived from flux towers. Interactions between leaf age-dependent physiology and shifting leaf age-demographic composition are sufficient to explain the dry season photosynthetic capacity pattern at this site, and should be considered in vegetation models of tropical evergreen forests.