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.

The importance of lake-specific characteristics for water quality across the continental United States.

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agency's 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to-volume ratio) were important for explaining water quality (54-60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28-39% variance explained). Basin-scale land use and land cover explained between 45-62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.

Phosphorus speciation in a eutrophic lake by ³¹P NMR spectroscopy.

For eutrophic lakes, patterns of phosphorus (P) measured by standard methods are well documented but provide little information about the components comprising standard operational definitions. Dissolved P (DP) and particulate P (PP) represents important but rarely characterized nutrient pools. Samples from Lake Mendota, Wisconsin, USA were characterized using 31-phosphorus nuclear magnetic resonance spectroscopy ((31)P NMR) during the open water season of 2011 in this unmatched temporal study of aquatic P dynamics. A suite of organic and inorganic P forms was detected in both dissolved and particulate fractions: orthophosphate, orthophosphate monoesters, orthophosphate diesters, pyrophosphate, polyphosphate, and phosphonates. Through time, phytoplankton biomass, temperature, dissolved oxygen, and water clarity were correlated with changes in the relative proportion of P fractions. Particulate P can be used as a proxy for phytoplankton-bound P, and in this study, a high proportion of polyphosphate within particulate samples suggested P should not be a limiting factor for the dominant primary producers, cyanobacteria. Hypolimnetic particulate P samples were more variable in composition than surface samples, potentially due to varying production and transport of sinking particles. Surface dissolved samples contained less P than particulate samples, and were typically dominated by orthophosphate, but also contained monoester, diester, polyphosphate, pyrophosphate, and phosphonate. Hydrologic inflows to the lake contained more orthophosphate and orthophosphate monoesters than in-lake samples, indicating transformation of P from inflowing waters. This time series explores trends of a highly regulated nutrient in the context of other water quality metrics (chlorophyll, mixing regime, and clarity), and gives insight on the variability of the structure and occurrence of P-containing compounds in light of the phosphorus-limited paradigm.

Microbial contributions to phosphorus cycling in eutrophic lakes and wastewater.

Phosphorus is a key element controlling the productivity of freshwater ecosystems, and microbes drive most of its relevant biogeochemistry. Eutrophic lakes are generally dominated by cyanobacteria that compete fiercely with algae and heterotrophs for the element. In wastewater treatment, engineers select for specialized bacteria capable of sequestering phosphorus from the water, to protect surface waters from further loading. The intracellular storage molecule polyphosphate plays an important role in both systems, allowing key taxa to control phosphorus availability. The importance of dissolved organic phosphorus in eutrophic lakes and mineralization mechanisms is still underappreciated and understudied. The need for functional redundancy through biological diversity in wastewater treatment plants is also clear. In both systems, a holistic ecosystems biology approach is needed to understand the molecular mechanisms controlling phosphorus metabolism and the ecological interactions and factors controlling ecosystem-level process rates.