Iterative Near-Term Ecological Forecasting: Needs, Opportunities, And Challenges

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 cyberinfrastructure. 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

Emissions pathways, climate change, and impacts on California

The magnitude of future climate change depends substantially on the greenhouse gas emission pathways we choose. Here we explore the implications of the highest and lowest Intergovernmental Panel on Climate Change emissions pathways for climate change and associated impacts in California. Based on climate projections from two state-of-the-art climate models with low and medium sensitivity (Parallel Climate Model and Hadley Centre Climate Model, version 3, respectively), we find that annual temperature increases nearly double from the lower B1 to the higher A1fi emissions scenario before 2100. Three of four simulations also show greater increases in summer temperatures as compared with winter. Extreme heat and the associated impacts on a range of temperature-sensitive sectors are substantially greater under the higher emissions scenario, with some interscenario differences apparent before midcentury. By the end of the century under the B1 scenario, heatwaves and extreme heat in Los Angeles quadruple in frequency while heat-related mortality increases two to three times; alpine subalpine forests are reduced by 50–75%; and Sierra snowpack is reduced 30–70%. Under A1fi, heatwaves in Los Angeles are six to eight times more frequent, with heat-related excess mortality increasing five to seven times; alpine subalpine forests are reduced by 75–90%; and snowpack declines 73–90%, with cascading impacts on runoff and streamflow that, combined with projected modest declines in winter precipitation, could fundamentally disrupt California’s water rights system. Although interscenario differences in climate impacts and costs of adaptation emerge mainly in the second half of the century, they are strongly dependent on emissions from preceding decades

Modeling long-term continuous stream metabolism across the continent: NEON opportunities and approaches

Background/Question/Methods Stream metabolism, the measurement of primary productivity (GPP) and ecosystem respiration (ER) in stream ecosystems, is an important measure of stream health and indicator of climatic and local environmental change. Long-term, continuous monitoring of stream metabolism can enable better understanding of the integrated and complex effects of large-scale change (e.g., land-use, climate, atmospheric deposition, invasive species, etc.) on stream ecosystem function. Similarly, parallel measurements of metabolism in streams across a broad range of ecosystems can inform understanding of local and regional controls over ecosystem function, and illustrate differing responses to shared climate changes. Long-term, continuous monitoring across a large number of sites creates challenges in both data management and modeling. A standardized framework is needed for error and uncertainty estimation and propagation, and for modeling approaches and parameterization. Challenges to modeling GPP and ER from long-term stream data include 1) obtaining unbiased, low-error estimates of daily fluxes, 2) interpreting GPP and ER estimates over extended time periods, and 3) developing an automated, standardized model. Results/Conclusions The National Ecological Observatory Network (NEON) is a national-scale research platform that will use consistent procedures and protocols to standardize measurements across the United States, providing long-term, high-quality, open-access data from a connected network to address large-scale change. NEON is currently preparing for the collection, processing, and delivery to the public of 30 years of continuous whole-ecosystem stream metabolism data from 29 stream and river sites across the US. In partnership with academic and government scientists, NEON is developing a Bayesian inverse modeling framework for partitioning metabolism into GPP and ER. Automating metrics of both data and model quality is a major goal. The model developed by NEON must be flexible enough to accommodate NEON’s diverse aquatic sites, and thus will be usable at other sites as well. The model itself will be made available to the community along with all associated data

Challenges and opportunities of long-term continuous metabolism

Background/Question/Methods Recent advances in dissolved oxygen sensing and modeling have made continuous measurements of whole-stream metabolism relatively easy to make, allowing ecologists to quantify and evaluate stream ecosystem health at expanded temporal and spatial scales. Long-term monitoring of continuous stream metabolism will enable a better understanding of the integrated and complex effects of large-scale change (e.g., land-use, climate, atmospheric deposition, invasive species, etc.) on stream ecosystem function. In addition to their value in the particular streams measured, information derived from long-term data will improve the ability to extrapolate from shorter-term data. With the need to better understand drivers and responses of whole-stream metabolism come difficulties in interpreting the results. Long-term trends will encompass physical changes in stream morphology and flow regime (e.g., variable flow conditions and changes in channel structure) combined with changes in biota. Additionally long-term data sets will require careful quantification of errors and uncertainties, as well as propagation of error as a result of the calculation of metabolism metrics. Parsing of continuous data and the choice of modeling approaches can also have a large influence on results and on error estimation. The two main modeling challenges include 1) obtaining unbiased, low-error daily estimates of gross primary production (GPP) and ecosystem respiration (ER), and 2) interpreting GPP and ER measurements over extended time periods. Results/Conclusions The National Ecological Observatory Network (NEON), in partnership with academic and government scientists, has begun to tackle several of these challenges as it prepares for the collection and calculation of 30 years of continuous whole-stream metabolism data. NEON is a national-scale research platform that will use consistent procedures and protocols to standardize measurements across the United States, providing long-term, high-quality, open-access data from a connected network to address large-scale change. NEON infrastructure will support 36 aquatic sites across 19 ecoclimatic domains. Sites include core sites, which remain for 30 years, and relocatable sites, which move to capture regional gradients. NEON will measure continuous whole-stream metabolism in conjunction with aquatic, terrestrial and airborne observations, allowing researchers to link stream ecosystem function with landscape and climatic drivers encompassing short to long time periods (i.e., decades)