The climatic feedbacks from vegetation, particularly from tropical forests, can alter climate through land‐atmospheric interactions. Expected shifts in species composition can alter these interactions with profound effects on climate and terrestrial ecosystem dynamics. Ecosystem demographic (ED) models can explicitly represent vegetation dynamics and are a key component of next‐generation Earth System Models (ESMs). Although ED models exhibit greater fidelity and allow more direct comparisons with observations, their interacting parameters can be more difficult to calibrate due to the complex interactions among vegetation groups and physical processes. In addition, while representation of forest successional coexistence in ESMs is necessary to accurately capture forest‐climate interactions, few models can simulate forest coexistence and few studies have calibrated coexisted forest species. Furthermore, although both vegetation characteristics and soil properties affect vegetation dynamics, few studies have paid attention to jointly calibrating parameters related to these two processes. In this study, we develop a computationally‐efficient and physical model structure‐based framework that uses a parallel surrogate global optimization algorithm to calibrate ED models. We calibrate two typically coexisted tropical tree species, early and late successional plants, in a state‐of‐the‐art ED model that is capable of simulating successional diversity in forests. We concurrently calibrate vegetation and soil parameters and validate results against carbon, energy, and water cycle measurements collected in Barro Colorado Island, Panama. The framework can find optimal solutions within 4–12 iterations for 19‐dimensional problems. The calibration for tropical forests has important implications for predicting land‐atmospheric interactions and responses of tropical forests to environmental changes.
In tropical forests, both vegetation characteristics and soil properties are important not only for controlling energy, water, and gas exchanges directly but also determining the competition among species, successional dynamics, forest structure and composition. However, the joint effects of the two factors have received limited attention in Earth system model development. Here we use a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM‐FATES, to explore how plant traits and soil properties affect tropical forest growth and composition concurrently. A large ensemble of simulations with perturbed vegetation and soil hydrological parameters is conducted at the Barro Colorado Island, Panama. The simulations are compared against observed carbon, energy, and water fluxes. We find that soil hydrological parameters, particularly the scaling exponent of the soil retention curve (
- Award ID(s):
- 2017804
- NSF-PAR ID:
- 10362298
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 1942-2466
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Identifying factors controlling forest productivity is critical to understanding forest‐climate change feedbacks, modelling vegetation dynamics and carbon finance schemes. However, little research has focused on productivity in regenerating tropical forests which are expanding in their fraction of global area have an order of magnitude larger carbon uptake rates relative to older forest.
We examined above‐ground net primary productivity (ANPP) and its components (wood production and litterfall) over 10 years in forest plots that vary in successional age, soil characteristics and species composition using band dendrometers and litterfall traps in regenerating seasonally dry tropical forests in northwestern Costa Rica.
We show that the components of ANPP are differentially driven by age and annual rainfall and that local soil variation is important. Total ANPP was explained by a combination of age, annual rainfall and soil variation. Wood production comprised 35% of ANPP on average across sites and years, and was explained by annual rainfall but not forest age. Conversely, litterfall increased with forest age and soil fertility yet was not affected by annual rainfall. In this region, edaphic variability is highly correlated with plant community composition. Thus, variation in ecosystem processes explained by soil may also be partially explained by species composition.
These results suggest that future changes in annual rainfall can alter the secondary forest carbon sink, but this effect will be buffered by the litterfall flux which varies little among years. In determining the long‐term strength of the secondary forest carbon sink, both rainfall and forest age will be critical variables to track. We also conclude that detailed understanding of local site variation in soils and plant community may be required to accurately predict the impact of changing rainfall on forest carbon uptake.
Synthesis . We show that in seasonally dry tropical forest, annual rainfall has a positive relationship with the growth of above‐ground woody tissues of trees and that droughts lead to significant reductions in above‐ground productivity. These results provide evidence for climate change—carbon cycle feedbacks in the seasonal tropics and highlight the value of longitudinal data on forest regeneration. -
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Abstract Soil resource partitioning and dispersal limitation have been shown to shape the tree community structure of mature tropical forests, but are poorly studied in the context of forest succession. We examined the relative contributions of both ecological processes to the variation in the species composition of young tropical secondary forests at different spatial scales, and if the relative importance of these two ecological processes changed during succession. At the species level, we examined if the association between species abundances and soil fertility differed between early and late successional species and/or changed over the course of succession.
We used vegetation and soil data from 47 secondary forest sites with two plots each in a tropical agricultural landscape. A distance‐based redundancy analysis and variation partitioning were employed to examine the relative importance of spatial distance (proxy for dispersal limitation) and heterogeneity in soil nutrients (proxy for soil nutrient partitioning) at the landscape scale, and a linear regression to test their effects at the local scale. We examined interspecific variation in species’ responses to successional age and soil nutrients with a joint species distribution model.
Dispersal limitation and soil niche partitioning drove considerable variation in the composition of plant communities at local and landscape scales. The relative contribution of these two ecological processes changed with scale (local vs. landscape) and topography (lower slope vs. upper slope plots). At the species level, significant abundance–soil fertility associations were mostly positive. Most species became less responsive to soil nutrients over the first few decades of tropical forest succession, probably because light became the main limiting resource in older forests.
Synthesis. Our key finding is that spatial heterogeneity in soil resources and spatial distance jointly drive compositional variation within and across early successional forests. Our results highlight that a network of forest fragments enhances the resilience of ecological processes and the potential of secondary forests to restore and preserve biodiversity in human‐modified landscapes. To advance our understanding of ecological succession, we need to move beyond single‐factor and local‐scale studies and examine the effects of multiple variables on succession at different spatial scales. -
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Abstract Secondary forest regrowth shapes community succession and biogeochemistry for decades, including in the Upper Great Lakes region. Vegetation models encapsulate our understanding of forest function, and whether models can reproduce multi‐decadal succession patterns is an indication of our ability to predict forest responses to future change. We test the ability of a vegetation model to simulate C cycling and community composition during 100 years of forest regrowth following stand‐replacing disturbance, asking (a) Which processes and parameters are most important to accurately model Upper Midwest forest succession? (b) What is the relative importance of model structure versus parameter values to these predictions? We ran ensembles of the Ecosystem Demography model v2.2 with different representations of processes important to competition for light. We compared the magnitude of structural and parameter uncertainty and assessed which sub‐model–parameter combinations best reproduced observed C fluxes and community composition. On average, our simulations underestimated observed net primary productivity (NPP) and leaf area index (LAI) after 100 years and predicted complete dominance by a single plant functional type (PFT). Out of 4,000 simulations, only nine fell within the observed range of both NPP and LAI, but these predicted unrealistically complete dominance by either early hardwood or pine PFTs. A different set of seven simulations were ecologically plausible but under‐predicted observed NPP and LAI. Parameter uncertainty was large; NPP and LAI ranged from ~0% to >200% of their mean value, and any PFT could become dominant. The two parameters that contributed most to uncertainty in predicted NPP were plant–soil water conductance and growth respiration, both unobservable empirical coefficients. We conclude that (a) parameter uncertainty is more important than structural uncertainty, at least for ED‐2.2 in Upper Midwest forests and (b) simulating both productivity and plant community composition accurately without physically unrealistic parameters remains challenging for demographic vegetation models.
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Abstract Despite their low contribution to forest carbon stocks, lianas (woody vines) play an important role in the carbon dynamics of tropical forests. As structural parasites, they hinder tree survival, growth and fecundity; hence, they negatively impact net ecosystem productivity and long‐term carbon sequestration.
Competition (for water and light) drives various forest processes and depends on the local abundance of resources over time. However, evaluating the relative role of resource availability on the interactions between lianas and trees from empirical observations is particularly challenging. Previous approaches have used labour‐intensive and ecosystem‐scale manipulation experiments, which are infeasible in most situations.
We propose to circumvent this challenge by evaluating the uncertainty of water and light capture processes of a process‐based vegetation model (ED2) including the liana growth form. We further developed the liana plant functional type in ED2 to mechanistically simulate water uptake and transport from roots to leaves, and start the model from prescribed initial conditions. We then used the PEcAn bioinformatics platform to constrain liana parameters and run uncertainty analyses.
Baseline runs successfully reproduced ecosystem gas exchange fluxes (gross primary productivity and latent heat) and forest structural features (leaf area index, aboveground biomass) in two sites (Barro Colorado Island, Panama and Paracou, French Guiana) characterized by different rainfall regimes and levels of liana abundance.
Model uncertainty analyses revealed that water limitation was the factor driving the competition between trees and lianas at the drier site (BCI), and during the relatively short dry season of the wetter site (Paracou). In young patches, light competition dominated in Paracou but alternated with water competition between the wet and the dry season on BCI according to the model simulations.
The modelling workflow also identified key liana traits (photosynthetic quantum efficiency, stomatal regulation parameters, allometric relationships) and processes (water use, respiration, climbing) driving the model uncertainty. They should be considered as priorities for future data acquisition and model development to improve predictions of the carbon dynamics of liana‐infested forests.
Synthesis . Competition for water plays a larger role in the interaction between lianas and trees than previously hypothesized, as demonstrated by simulations from a process‐based vegetation model.
