Regional and global models of the terrestrial biosphere depend critically on models of photosynthesis when predicting impacts of global change. This paper focuses on identifying the primary data ...needs of these models, what scales drive uncertainty, and how to improve measurements. Overall, there is a need for an open, cross-discipline database on leaf-level photosynthesis in general, and response curves in particular. The parameters in photosynthetic models are not constant through time, space, or canopy position but there is a need for a better understanding of whether relationships with drivers, such as leaf nitrogen, are themselves scale dependent. Across time scales, as ecosystem models become more sophisticated in their representations of succession they needs to be able to approximate sunfleck responses to capture understory growth and survival. At both high and low latitudes, photosynthetic data are inadequate in general and there is a particular need to better understand thermal acclimation. Simple models of acclimation suggest that shifts in optimal temperature are important. However, there is little advantage to synoptic-scale responses and circadian rhythms may be more beneficial than acclimation over shorter timescales. At high latitudes, there is a need for a better understanding of low-temperature photosynthetic limits, while at low latitudes the need is for a better understanding of phosphorus limitations on photosynthesis. In terms of sampling, measuring multivariate photosynthetic response surfaces are potentially more efficient and more accurate than traditional univariate response curves. Finally, there is a need for greater community involvement in model validation and model-data synthesis.
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. Variance decomposition allows us to quantify the contribution of each parameter to model uncertainty; this information can be used to prioritize subsequent data collection. By streamlining the use of models and focusing efforts to identify and constrain the dominant sources of uncertainty in model output, the approach used by PEcAn can speed scientific progress.
We demonstrate PEcAn's ability to incorporate data to reduce uncertainty in productivity of a perennial grass monoculture (
Panicum virgatum
L.) modeled by the Ecosystem Demography model. Prior estimates were specified for 15 model parameters, and species-level data were available for seven of these. Meta-analysis of species-level data substantially reduced the contribution of three parameters (specific leaf area, maximum carboxylation rate, and stomatal slope) to overall model uncertainty. By contrast, root turnover rate, root respiration rate, and leaf width had little effect on model output; therefore trait data had little impact on model uncertainty.
For fine-root allocation, the decrease in parameter uncertainty was offset by an increase in model sensitivity. Remaining model uncertainty is driven by growth respiration, fine-root allocation, leaf turnover rater, and specific leaf area. By establishing robust channels of feedback between data collection and ecosystem modeling, PEcAn provides a framework for more efficient and integrative science.
Summary
Accurate representation of photosynthesis in terrestrial biosphere models (TBMs) is essential for robust projections of global change. However, current representations vary markedly between ...TBMs, contributing uncertainty to projections of global carbon fluxes. Here we compared the representation of photosynthesis in seven TBMs by examining leaf and canopy level responses of photosynthetic CO2 assimilation (A) to key environmental variables: light, temperature, CO2 concentration, vapor pressure deficit and soil water content. We identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBMs and research areas where data are urgently needed for model parameterization or evaluation. We provide a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and Earth system models.
Data integration is a statistical modeling approach that incorporates multiple data sources within a unified analytical framework. Macrosystems ecology – the study of ecological phenomena at broad ...scales, including interactions across scales – increasingly employs data integration techniques to expand the spatiotemporal scope of research and inferences, increase the precision of parameter estimates, and account for multiple sources of uncertainty in estimates of multiscale processes. We highlight four common analytical challenges to data integration in macrosystems ecology research: data scale mismatches, unbalanced data, sampling biases, and model development and assessment. We explain each problem, discuss current approaches to address the issue, and describe potential areas of research to overcome these hurdles. Use of data integration techniques has increased rapidly in recent years, and given the inferential value of such approaches, we expect continued development and wider application across ecological disciplines, especially in macrosystems ecology.
The potential for model‐data synthesis is growing in importance as we enter an era of ‘big data’, greater connectivity, and faster computation. Realizing this potential requires that the research ...community broaden its perspective about how and why they interact with models. We provide a review and perspective on the statistics and informatics of model‐data fusion in plant biology. Overall we promote a community‐based paradigm to model‐data synthesis and highlight some of the tools and techniques that facilitate this approach.
The potential for model–data synthesis is growing in importance as we enter an era of ‘big data’, greater connectivity and faster computation. Realizing this potential requires that the research community broaden its perspective about how and why they interact with models. Models can be viewed as scaffolds that allow data at different scales to inform each other through our understanding of underlying processes. Perceptions of relevance, accessibility and informatics are presented as the primary barriers to broader adoption of models by the community, while an inability to fully utilize the breadth of expertise and data from the community is a primary barrier to model improvement. Overall, we promote a community‐based paradigm to model–data synthesis and highlight some of the tools and techniques that facilitate this approach. Scientific workflows address critical informatics issues in transparency, repeatability and automation, while intuitive, flexible web‐based interfaces make running and visualizing models more accessible. Bayesian statistics provides powerful tools for assimilating a diversity of data types and for the analysis of uncertainty. Uncertainty analyses enable new measurements to target those processes most limiting our predictive ability. Moving forward, tools for information management and data assimilation need to be improved and made more accessible.
Anthropogenic climate change may result in novel disturbances to Arctic tundra ecosystems. Understanding the natural variability of tundra-fire regimes and their linkages to climate is essential in ...evaluating whether tundra burning has increased in recent years. Historical observations and charcoal records from lake sediments reveal a wide range of fire regimes in Arctic tundra, with fire-return intervals varying from decades to millennia. Analysis of historical data shows strong climate-fire relationships, with threshold effects of summer temperature and precipitation. Projections based on 21st-century climate scenarios suggest that annual area burned will approximately double in Alaskan tundra by the end of the century. Fires can release ancient carbon from tundra ecosystems and catalyze other biogeochemical and biophysical changes, with local to global consequences. Given the increased likelihood of tundra burning in coming decades, land managers and policy makers need to consider the ecological and socioeconomic impacts of fire in the Far North.
The leaf economic spectrum is a widely studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale, ...where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of plant functional types (PFTs). We investigated whether global leaf economic relationships are also present within the scale of plant functional types (PFTs) commonly used by Earth System models, and the extent to which this global-PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFTs and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (specific leaf area SLA, N, P) were the most robust within and across PFTs, suggesting that covariation in these traits is driven by universal leaf construction trade-offs and stoichiometry. Relationships among metabolic traits (dark respiration Rd, maximum RuBisCo carboxylation rate Vc,max, maximum electron transport rate Jmax) were slightly less consistent, reflecting in part their much sparser sampling (especially for high-latitude PFTs), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across-PFT differences and that within-PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, this covariance was an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships.
Noise pollution can reduce the ability of urban protected areas to provide a refuge for people and habitat for wildlife. Amidst an unprecedented global pandemic, it is unknown if the changes in human ...activity have significantly impacted noise pollution in metropolitan parks. We tested the hypothesis that reduced human activity associated with the COVID-19 pandemic lockdowns would lead to reduced sound levels in protected areas compared with non-pandemic times. We measured sound levels in three urban protected areas in metropolitan Boston, MA (USA) at three time periods: in the fall and summer before the pandemic, immediately after the government-imposed lockdown in March 2020 when the trees were leafless, and during the beginning of reopening in early June 2020 when the trees had leaves. At all time periods, sound levels were highest near major roads and demonstrated a logarithmic decrease further from roads. At the two protected areas closest to the city center, sound levels averaged 1–3 dB lower during the time of the pandemic lockdown. In contrast, at the third protected area, which is transected by a major highway, sound levels were 4–6 dB higher during the time of the pandemic, likely because reduced traffic allowed vehicles to travel faster and create more noise. This study demonstrates that altered human levels of activity, in this case associated with the COVID-19 pandemic, can have major, and in some cases unexpected, effects on the levels of noise pollution in protected areas.
•Sound in urban protected areas was monitored before and during COVID-19 lockdown.•Park soundscapes were modeled based on lockdown stage and distance from roads.•Sound levels decreased in two parks and increased in one park during lockdown.•Sound levels were highest near roads and decreased with distance from roads.•COVID-19 noise pollution is affected by traffic speeds and timing of tree leaf out.
Reducing uncertainties in Earth System Model predictions requires carefully evaluating core model processes. Here we examined how canopy radiative transfer model (RTM) parameter uncertainties, in ...combination with canopy structure, affect terrestrial carbon and energy projections in a demographic land-surface model, the Ecosystem Demography model (ED2). Our analyses focused on temperate deciduous forests and tested canopies of varying structural complexity. The results showed a strong sensitivity of tree productivity, albedo, and energy balance projections to RTM parameters. Impacts of radiative parameter uncertainty on stand-level canopy net primary productivity ranged from ~2 to > 20% and was most sensitive to canopy clumping and leaf reflectance/transmittance in the visible spectrum (~400-750 nm). ED2 canopy albedo varied by ~1 to ~10% and was most sensitive to near-infrared reflectance (~800-1200 nm). Bowen ratio, in turn, was most sensitive to wood optical properties parameterization; this was much larger than expected based on literature, suggesting model instabilities. In vertically and spatially complex canopies the model response to RTM parameterization may show an apparent reduced sensitivity when compared to simpler canopies, masking much larger changes occurring within the canopy. Our findings highlight both the importance of constraining canopy RTM parameters in models and valuating how the canopy structure responds to those parameter values. Finally, we advocate for more model evaluation, similar to this study, to highlight possible issues with model behavior or process representations, particularly models with demographic representations, and identify potential ways to inform and constrain model predictions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Forest insects and pathogens (FIPs) have enormous impacts on community dynamics, carbon storage and ecosystem services, however, ecosystem modelling of FIPs is limited due to their variability in ...severity and extent. We present a general framework for modelling FIP disturbances through their impacts on tree ecophysiology. Five pathways are identified as the basis for functional groupings: increases in leaf, stem and root turnover, and reductions in phloem and xylem transport. A simple ecophysiological model was used to explore the sensitivity of forest growth, mortality and ecosystem fluxes to varying outbreak severity. Across all pathways, low infection was associated with growth reduction but limited mortality. Moderate infection led to individual tree mortality, whereas high levels led to stand‐level die‐offs delayed over multiple years. Delayed mortality is consistent with observations and critical for capturing biophysical, biogeochemical and successional responses. This framework enables novel predictions under present and future global change scenarios.
PDF
