Michael C. Dietze

Nonstructural Carbon in Woody Plants

Nonstructural carbon (NSC) provides the carbon and energy for plant growth and survival. In woody plants, fundamental questions about NSC remain unresolved: Is NSC storage an active or passive process? Do older NSC reserves remain accessible to the plant? How is NSC depletion related to mortality risk? Herein we review conceptual and mathematical models of NSC dynamics, recent observations and experiments at the organismal scale, and advances in plant physiology that have provided a better understanding of the dynamics of woody plant NSC. Plants preferentially use new carbon but can access decade-old carbon when the plant is stressed or physically damaged. In addition to serving as a carbon and energy source, NSC plays important roles in phloem transport, osmoregulation, and cold tolerance, but how plants regulate these competing roles and NSC depletion remains elusive. Moving forward requires greater synthesis of models and data and integration across scales from -omics to ecology.

Evaluation of 11 terrestrial carbon-nitrogen cycle models against observations from two temperate Free-Air CO2 Enrichment studies

We analysed the responses of 11 ecosystem models to elevated atmospheric [CO2] (eCO2) at two temperate forest ecosystems (Duke and Oak Ridge National Laboratory (ORNL) Free-Air CO2 Enrichment (FACE) experiments) to test alternative representations of carbon (C)–nitrogen (N) cycle processes. We decomposed the model responses into component processes affecting the response to eCO2 and confronted these with observations from the FACE experiments. Most of the models reproduced the observed initial enhancement of net primary production (NPP) at both sites, but none was able to simulate both the sustained 10-yr enhancement at Duke and the declining response at ORNL: models generally showed signs of progressive N limitation as a result of lower than observed plant N uptake. Nonetheless, many models showed qualitative agreement with observed component processes. The results suggest that improved representation of above-ground–below-ground interactions and better constraints on plant stoichiometry are important for a predictive understanding of eCO2 effects. Improved accuracy of soil organic matter inventories is pivotal to reduce uncertainty in the observed C–N budgets. The two FACE experiments are insufficient to fully constrain terrestrial responses to eCO2, given the complexity of factors leading to the observed diverging trends, and the consequential inability of the models to explain these trends. Nevertheless, the ecosystem models were able to capture important features of the experiments, lending some support to their projections.

PREDICTING BIODIVERSITY CHANGE: OUTSIDE THE CLIMATE ENVELOPE, BEYOND THE SPECIES–AREA CURVE

Efforts to anticipate threats to biodiversity take the form of species richness predictions (SRPs) based on simple correlations with current climate and habitat area. We review the major approaches that have been used for SRP, species-area curves and climate envelopes, and suggest that alternative research efforts may provide more understanding and guidance for management. Extinction prediction suffers from a number of limitations related to data and the novelty of future environments. We suggest additional attention to (1) identification of variables related to biodiversity that are diagnostic and potentially more predictable than extinction, (2) constraints on species dispersal and reproduction that will determine population persistence and range shifts, including limited sources or potential immigrants for many regions, and (3) changes in biotic interactions and phenology. We suggest combinations of observational and experimental approaches within a framework available for ingesting heterogeneous data sources. Together, these recommendations amount to a shift in emphasis from prediction of extinction numbers to identification of vulnerabilities and leading indicators of change, as well as suggestions for surveillance tools needed to evaluate important variables and the experiments likely to provide most insight.

CHANGING THE GAP DYNAMICS PARADIGM: VEGETATIVE REGENERATION CONTROL ON FOREST RESPONSE TO DISTURBANCE

Understanding the manner in which changes in disturbance regimes will affect forest biodiversity is an important goal of global change research. Prevailing theories of recruitment after disturbance center on the role of pioneer species; predictions of forest biodiversity focus almost exclusively on dispersal and shade tolerance while vegetative reproduction is virtually omitted from models and serious discussions of the topic. However, the persistence of live damaged trees increases understory shade, generates fine-scale environmental heterogeneity, and moderates ecosystem responses to damage, while the sprouting of damaged trees offers a shortcut to reestablishment of the canopy. While a number of studies document snapshots of post-disturbance vegetative reproduction, we lack an understanding of the underlying demographic processes needed in order to both comprehend and predict observed patterns. In this study we quantify the abundance, competitive ability, and interspecific variability of vegetative reproduction in 18 replicated experimental gaps in the southern Appalachians and Carolina Piedmont, USA, in order to assess the potential role of sprouting in driving gap dynamics. Annual rates of damaged adult survival, sprout initiation, growth, and mortality were monitored over four years and compared to the performance of gap-regenerating saplings. Recruitment from sprouts was found to constitute 26-87% of early gap regeneration and forms the dominant pathway of regeneration for some species. Sprouts from recently damaged trees also grow significantly faster than the saplings with which they compete. For all measured demographic rates (damaged tree survival, sprout initiation, number, growth, and survival) differences among species are large and consistent across sites, suggesting that vegetative reproduction is an important and non-neutral process in shaping community composition. Sprouting ability does not correlate strongly with other life-history trade-offs, thus sprouting potentially provides an alternate trait axis in promoting diversity.

High‐dimensional coexistence based on individual variation: a synthesis of evidence

High biodiversity of forests is not predicted by traditional models, and evidence for trade-offs those models require is limited. High-dimensional regulation (e.g., N factors to regulate N species) has long been recognized as a possible alternative explanation, but it has not be been seriously pursued, because only a few limiting resources are evident for trees, and analysis of multiple interactions is challenging. We develop a hierarchical model that allows us to synthesize data from long-term, experimental, data sets with processes that control growth, maturation, fecundity, and survival. We allow for uncertainty at all stages and variation among 26 000 individuals and over time, including 268 000 tree years, for dozens of tree species. We estimate population-level parameters that apply at the species level and the interactions among latent states, i.e., the demographic rates for each individual, every year. The former show that the traditional trade-offs used to explain diversity are not present. Demographic rates overlap among species, and they do not show trends consistent with maintenance of diversity by simple mechanisms (negative correlations and limiting similarity). However, estimates of latent states at the level of individuals and years demonstrate that species partition environmental variation. Correlations between responses to variation in time are high for individuals of the same species, but not for individuals of different species. We demonstrate that these relationships are pervasive, providing strong evidence that high- dimensional regulation is critical for biodiversity regulation.

Where does the carbon go? A model-data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites.

Elevated atmospheric CO2 concentration (eCO2) has the potential to increase vegetation carbon storage if increased net primary production causes increased long-lived biomass. Model predictions of eCO2 effects on vegetation carbon storage depend on how allocation and turnover processes are represented. We used data from two temperate forest free-air CO2 enrichment (FACE) experiments to evaluate representations of allocation and turnover in 11 ecosystem models. Observed eCO2 effects on allocation were dynamic. Allocation schemes based on functional relationships among biomass fractions that vary with resource availability were best able to capture the general features of the observations. Allocation schemes based on constant fractions or resource limitations performed less well, with some models having unintended outcomes. Few models represent turnover processes mechanistically and there was wide variation in predictions of tissue lifespan. Consequently, models did not perform well at predicting eCO2 effects on vegetation carbon storage. Our recommendations to reduce uncertainty include: use of allocation schemes constrained by biomass fractions; careful testing of allocation schemes; and synthesis of allocation and turnover data in terms of model parameters. Data from intensively studied ecosystem manipulation experiments are invaluable for constraining models and we recommend that such experiments should attempt to fully quantify carbon, water and nutrient budgets.

COEXISTENCE: HOW TO IDENTIFY TROPHIC TRADE-OFFS

Analyses of growth response to resource availability are the basis for inter- preting whether trophic trade-offs contribute to diversity. If different species respond most to resources that are limiting at different times, then those differences may trade off with other trophic or life-history traits that, together, help to maintain diversity. The statistical models used to infer trophic differences do not accommodate uncertainty in resources and variability in how individuals use resources. We provide hierarchical models for resource- growth responses that accommodate stochasticity in parameters and in data, despite the fact that causes are typically unknown. A complex joint posterior distribution taken over > 102 parameters is readily integrated to provide a comprehensive accounting of uncertainty in the growth response, together with a small number of hyperparameters that summarize the population response. An application involving seedling growth response to light avail- ability shows that large trophic differences among species suggested by traditional models can be an artifact of the assumption that all individuals respond identically. The hierarchical analysis indicates broad trophic overlap, with the implication that slow dynamics play a more important role in preserving diversity than is widely believed.

A quantitative review comparing the yield of switchgrass in monocultures and mixtures in relation to climate and management factors

Switchgrass (Panicum virgatum L.), a US Department of Energy model species, is widely considered for US biomass energy production. While previous studies have demonstrated the effect of climate and management factors on biomass yield and chemical characteristics of switchgrass monocultures, information is lacking on the yield of switchgrass grown in combination with other species for biomass energy. Therefore, the objective of this quantitative review is to compare the effect of climate and management factors on the yield of switchgrass monocultures, as well as on mixtures of switchgrass, and other species. We examined all peer‐reviewed articles describing productivity of switchgrass and extracted dry matter yields, stand age, nitrogen fertilization (N), temperature (growing degree days), and precipitation/irrigation. Switchgrass yield was greater when grown in monocultures (10.9 t ha−1, n=324) than when grown in mixtures (4.4 t ha−1, n=85); yield in monocultures was also greater than the total yield of all species in the mixtures (6.9 t ha−1, n=90). The presence of legume species in mixtures increased switchgrass yield from 3.1 t ha−1 (n=65) to 8.9 t ha−1 (n=20). Total yield of switchgrass‐dominated mixtures with legumes reached 9.9 t ha−1 (n=25), which was not significantly different from the monoculture yield. The results demonstrated the potential of switchgrass for use as a biomass energy crop in both monocultures and mixtures across a wide geographic range. Monocultures, but not mixtures, showed a significant positive response to N and precipitation. The response to N for monocultures was consistent for newly established (stand age <3 years) and mature stands (stand age ≥3 years) and for lowland and upland ecotypes. In conclusion, these results suggest that fertilization with N will increase yield in monocultures, but not mixtures. For monocultures, N treatment need not be changed based on ecotype and stand age; and for mixtures, legumes should be included as an alternative N source.

Facilitating feedbacks between field measurements and ecosystem models

Ecological models help us understand how ecosystems function, predict responses to global change, and identify future research needs. However, widespread use of models is limited by the technical challenges of model–data synthesis and information management. To address these challenges, we present an ecoinformatic workflow, the Predictive Ecosystem Analyzer (PEcAn), which facilitates model analysis. Herein we describe the PEcAn modules that synthesize plant trait data to estimate model parameters, propagate parameter uncertainties through to model output, and evaluate the contribution of each parameter to model uncertainty. We illustrate a comprehensive approach to the estimation of parameter values, starting with a statement of prior knowledge that is refined by species-level data using Bayesian meta-analysis; this is the first use of a rigorous meta-analysis to inform the parameters of a mechanistic ecosystem model. Parameter uncertainty is propagated using ensemble methods to estimate model uncertainty...

Bioenergy crop models: Descriptions, data requirements and future challenges

Field studies that address the production of lignocellulosic biomass as a source of renewable energy provide critical data for the development of bioenergy crop models. A literature survey revealed that 14 models have been used for simulating bioenergy crops including herbaceous and woody bioenergy crops, and for crassulacean acid metabolism (CAM) crops. These models simulate field‐scale production of biomass for switchgrass (ALMANAC, EPIC, and Agro‐BGC), miscanthus (MISCANFOR, MISCANMOD, and WIMOVAC), sugarcane (APSIM, AUSCANE, and CANEGRO), and poplar and willow (SECRETS and 3PG). Two models are adaptations of dynamic global vegetation models and simulate biomass yields of miscanthus and sugarcane at regional scales (Agro‐IBIS and LPJmL). Although it lacks the complexity of other bioenergy crop models, the environmental productivity index (EPI) is the only model used to estimate biomass production of CAM (Agave and Opuntia) plants. Except for the EPI model, all models include representations of leaf area dynamics, phenology, radiation interception and utilization, biomass production, and partitioning of biomass to roots and shoots. A few models simulate soil water, nutrient, and carbon cycle dynamics, making them especially useful for assessing the environmental consequences (e.g., erosion and nutrient losses) associated with the large‐scale deployment of bioenergy crops. The rapid increase in use of models for energy crop simulation is encouraging; however, detailed information on the influence of climate, soils, and crop management practices on biomass production is scarce. Thus considerable work remains regarding the parameterization and validation of process‐based models for bioenergy crops; generation and distribution of high‐quality field data for model development and validation; and implementation of an integrated framework for efficient, high‐resolution simulations of biomass production for use in planning sustainable bioenergy systems.