Quantitative predictions are ubiquitous in ecology, yet there is limited discussion on the nature of prediction in this field. Herein I derive a general quantitative framework for analyzing and ...partitioning the sources of uncertainty that control predictability. The implications of this framework are assessed conceptually and linked to classic questions in ecology, such as the relative importance of endogenous (density-dependent) vs. exogenous factors, stability vs. drift, and the spatial scaling of processes. The framework is used to make a number of novel predictions and reframe approaches to experimental design, model selection, and hypothesis testing. Next, the quantitative application of the framework to partitioning uncertainties is illustrated using a short-term forecast of net ecosystem exchange. Finally, I advocate for a new comparative approach to studying predictability across different ecological systems and processes and lay out a number of hypotheses about what limits predictability and how these limits should scale in space and time.
Most tree roots on Earth form a symbiosis with either ecto‐ or arbuscular mycorrhizal fungi. Nitrogen fertilization is hypothesized to favor arbuscular mycorrhizal tree species at the expense of ...ectomycorrhizal species due to differences in fungal nitrogen acquisition strategies, and this may alter soil carbon balance, as differences in forest mycorrhizal associations are linked to differences in soil carbon pools. Combining nitrogen deposition data with continental‐scale US forest data, we show that nitrogen pollution is spatially associated with a decline in ectomycorrhizal vs. arbuscular mycorrhizal trees. Furthermore, nitrogen deposition has contrasting effects on arbuscular vs. ectomycorrhizal demographic processes, favoring arbuscular mycorrhizal trees at the expense of ectomycorrhizal trees, and is spatially correlated with reduced soil carbon stocks. This implies future changes in nitrogen deposition may alter the capacity of forests to sequester carbon and offset climate change via interactions with the forest microbiome.
Responses of arbuscular and ectomycorrhizal trees to atmospheric nitrogen deposition.
Iterative near-term ecological forecasting Dietze, Michael C.; Fox, Andrew; Beck-Johnson, Lindsay M. ...
Proceedings of the National Academy of Sciences - PNAS,
02/2018, Letnik:
115, Številka:
7
Journal Article
Recenzirano
Odprti dostop
Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering ...these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfra-structure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.
Nonstructural carbon in woody plants Dietze, Michael C; Sala, Anna; Carbone, Mariah S ...
Annual review of plant biology,
01/2014, Letnik:
65
Journal Article
Recenzirano
Odprti dostop
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.
Mycorrhizal fungi are critical members of the plant microbiome, forming a symbiosis with the roots of most plants on Earth. Most plant species partner with either arbuscular or ectomycorrhizal fungi, ...and these symbioses are thought to represent plant adaptations to fast and slow soil nutrient cycling rates. This generates a second hypothesis, that arbuscular and ectomycorrhizal plant species traits complement and reinforce these fungal strategies, resulting in nutrient acquisitive vs. conservative plant trait profiles. Here we analyzed 17,764 species level trait observations from 2,940 woody plant species to show that mycorrhizal plants differ systematically in nitrogen and phosphorus economic traits. Differences were clearest in temperate latitudes, where ectomycorrhizal plant species are more nitrogen use- and phosphorus use-conservative than arbuscular mycorrhizal species. This difference is reflected in both aboveground and belowground plant traits and is robust to controlling for evolutionary history, nitrogen fixation ability, deciduousness, latitude, and species climate niche. Furthermore, mycorrhizal effects are large and frequently similar to or greater in magnitude than the influence of plant nitrogen fixation ability or deciduous vs. evergreen leaf habit. Ectomycorrhizal plants are also more nitrogen conservative than arbuscular plants in boreal and tropical ecosystems, although differences in phosphorus use are less apparent outside temperate latitudes. Our findings bolster current theories of ecosystems rooted in mycorrhizal ecology and support the hypothesis that plant mycorrhizal association is linked to the evolution of plant nutrient economic strategies.
Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an ...important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real‐world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first‐generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter‐disciplinary communication.
In this review, we summarize in detail the development of vegetation demographics models as components of Earth System Models. We particularly highlight the ecological uncertainties around the strength of growth‐resource acquisition feedbacks that are common across model developments, and illustrate the myriad new opportunities for ecological‐scale data streams to inform and validate these new model structures.
Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around ...model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints. However, the Markov chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally demanding models and large datasets. We employ an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity. Analysis involved informative priors constructed from a meta-analysis of the primary literature and specification of both model and data uncertainties, and it introduced novel approaches to autocorrelation corrections on multiple data streams and emulating the sufficient statistics surface. We report the integration of this method within an ecological workflow management software, Predictive Ecosystem Analyzer (PEcAn), and its application and validation with two process-based terrestrial ecosystem models: SIPNET and ED2. In a test against a synthetic dataset, the emulator was able to retrieve the true parameter values. A comparison of the emulator approach to standard brute-force MCMC involving multiple data constraints showed that the emulator method was able to constrain the faster and simpler SIPNET model's parameters with comparable performance to the brute-force approach but reduced computation time by more than 2 orders of magnitude. The emulator was then applied to calibration of the ED2 model, whose complexity precludes standard (brute-force) Bayesian data assimilation techniques. Both models are constrained after assimilation of the observational data with the emulator method, reducing the uncertainty around their predictions. Performance metrics showed increased agreement between model predictions and data. Our study furthers efforts toward reducing model uncertainties, showing that the emulator method makes it possible to efficiently calibrate complex models.
Substantial uncertainty surrounds how forest ecosystems will respond to the simultaneous impacts of multiple global change drivers. Long‐term forest dynamics are sensitive to changes in tree ...mortality rates; however, we lack an understanding of the relative importance of the factors that affect tree mortality across different spatial and temporal scales. We used the US Forest Service Forest Inventory and Analysis database to evaluate the drivers of tree mortality for eastern temperate forest at the individual‐level across spatial scales from tree to landscape to region. We investigated 13 covariates in four categories: climate, air pollutants, topography, and stand characteristics. Overall, we found that tree mortality was most sensitive to stand characteristics and air pollutants. Different functional groups also varied considerably in their sensitivity to environmental drivers. This research highlights the importance of considering the interactions among multiple global change agents in shaping forest ecosystems.
Forest biogeochemistry in response to drought Schlesinger, William H.; Dietze, Michael C.; Jackson, Robert B. ...
Global change biology,
July 2016, Letnik:
22, Številka:
7
Journal Article
Recenzirano
Trees alter their use and allocation of nutrients in response to drought, and changes in soil nutrient cycling and trace gas flux (N2O and CH4) are observed when experimental drought is imposed on ...forests. In extreme droughts, trees are increasingly susceptible to attack by pests and pathogens, which can lead to major changes in nutrient flux to the soil. Extreme droughts often lead to more common and more intense forest fires, causing dramatic changes in the nutrient storage and loss from forest ecosystems. Changes in the future manifestation of drought will affect carbon uptake and storage in forests, leading to feedbacks to the Earth's climate system. We must improve the recognition of drought in nature, our ability to manage our forests in the face of drought, and the parameterization of drought in earth system models for improved predictions of carbon uptake and storage in the world's forests.
Monitoring leaf phenology tracks the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Networks of finite-scale remote sensing, such as the ...PhenoCam network, provide valuable information on phenological state at high temporal resolution, but they have limited coverage. Satellite-based data with lower temporal resolution have primarily been used to more broadly measure phenology (e.g., 16 d MODIS normalized difference vegetation index (NDVI) product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and GOES-17) can monitor NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we begin to examine the current capacity of these new data to measure the phenology of deciduous broadleaf forests for the first 2 full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the transition dates of the start, middle, and end of the season to those obtained from PhenoCam and MODIS 16 d NDVI and enhanced vegetation index (EVI) products. Compared to these MODIS products, GOES was more correlated with PhenoCam at the start and middle of spring but had a larger bias (3.35 ± 0.03 d later than PhenoCam) at the end of spring. Satellite-based autumn transition dates were mostly uncorrelated with those of PhenoCam. PhenoCam data produced significantly more certain (all p values ≤0.013) estimates of all transition dates than any of the satellite sources did. GOES transition date uncertainties were significantly smaller than those of MODIS EVI for all transition dates (all p values ≤0.026), but they were only smaller (based on p value <0.05) than those from MODIS NDVI for the estimates of the beginning and middle of spring. GOES will improve the monitoring of phenology at large spatial coverages and provides real-time indicators of phenological change even when the entire spring transition period occurs within the 16 d resolution of these MODIS products.
PDF
