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 xanthophyll cycle and NPQ in diverse desert and aquatic green algae.

It has long been suspected that photoprotective mechanisms in green algae are similar to those in seed plants. However, exceptions have recently surfaced among aquatic and marine green algae in several taxonomic classes. Green algae are highly diverse genetically, falling into 13 named classes, and they are diverse ecologically, with many lineages including members from freshwater, marine, and terrestrial habitats. Genetically similar species living in dramatically different environments are potentially a rich source of information about variations in photoprotective function. Using aquatic and desert-derived species from three classes of green algae, we examined the induction of photoprotection under high light, exploring the relationship between nonphotochemical quenching and the xanthophyll cycle. In liquid culture, behavior of aquatic Entransia fimbriata (Klebsormidiophyceae) generally matched patterns observed in seed plants. Nonphotochemical quenching was lowest after overnight dark adaptation, increased with light intensity, and the extent of nonphotochemical quenching correlated with the extent of deepoxidation of xanthophyll cycle pigments. In contrast, overnight dark adaptation did not minimize nonphotochemical quenching in the other species studied: desert Klebsormidium sp. (Klebsormidiophyceae), desert and aquatic Cylindrocystis sp. (Zygnematophyceae), and desert Stichococcus sp. (Trebouxiophyceae). Instead, exposure to low light reduced nonphotochemical quenching below dark-adapted levels. De-epoxidation of xanthophyll cycle pigments paralleled light-induced changes in nonphotochemical quenching for species within Klebsormidiophyceae and Trebouxiophyceae, but not Zygnematophyceae. Inhibition of violaxanthin-zeaxanthin conversion by dithiothreitol reduced high-light-associated nonphotochemical quenching in all species (Zygnematophyceae the least), indicating that zeaxanthin can contribute to photoprotection as in seed plants but to different extents depending on taxon or lineage.

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.