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

A practical guide to selecting models for exploration, inference, and prediction in ecology

Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions

Data from: Competition and coexistence in plant communities: intraspecific competition is stronger than interspecific competition

Theory predicts that intraspecific competition should be stronger than interspecific competition for any pair of stably coexisting species, yet previous literature reviews found little support for this pattern. We screened over 5400 publications and identified 39 studies that quantified phenomenological intraspecific and interspecific interactions in terrestrial plant communities. Of the 67% of species pairs in which both intra- and interspecific effects were negative (competition), intraspecific competition was, on average, four to five-fold stronger than interspecific competition. Of the remaining pairs, 93% featured intraspecific competition and interspecific facilitation, a situation that stabilizes coexistence. The difference between intra- and interspecific effects tended to be larger in observational than experimental data sets, in field than greenhouse studies, and in studies that quantified population growth over the full life cycle rather than single fitness components. Our results imply that processes promoting stable coexistence at local scales are common and consequential across terrestrial plant communities

Data from: Competition and coexistence in plant communities: intraspecific competition is stronger than interspecific competition

Theory predicts that intraspecific competition should be stronger than interspecific competition for any pair of stably coexisting species, yet previous literature reviews found little support for this pattern. We screened over 5400 publications and identified 39 studies that quantified phenomenological intraspecific and interspecific interactions in terrestrial plant communities. Of the 67% of species pairs in which both intra- and interspecific effects were negative (competition), intraspecific competition was, on average, four to five-fold stronger than interspecific competition. Of the remaining pairs, 93% featured intraspecific competition and interspecific facilitation, a situation that stabilizes coexistence. The difference between intra- and interspecific effects tended to be larger in observational than experimental data sets, in field than greenhouse studies, and in studies that quantified population growth over the full life cycle rather than single fitness components. Our results imply that processes promoting stable coexistence at local scales are common and consequential across terrestrial plant communities

3D-SoftChip: A Novel Architecture for Next-Generation Adaptive Computing Systems

<p/> <p>This paper introduces a novel architecture for next-generation adaptive computing systems, which we term 3D-SoftChip. The 3D-SoftChip is a 3-dimensional (3D) vertically integrated adaptive computing system combining state-of-the-art processing and 3D interconnection technology. It comprises the vertical integration of two chips (a configurable array processor and an intelligent configurable switch) through an indium bump interconnection array (IBIA). The configurable array processor (CAP) is an array of heterogeneous processing elements (PEs), while the intelligent configurable switch (ICS) comprises a switch block, 32-bit dedicated RISC processor for control, on-chip program/data memory, data frame buffer, along with a direct memory access (DMA) controller. This paper introduces the novel 3D-SoftChip architecture for real-time communication and multimedia signal processing as a next-generation computing system. The paper further describes the advanced HW/SW codesign and verification methodology, including high-level system modeling of the 3D-SoftChip using SystemC, being used to determine the optimum hardware specification in the early design stage.</p