Frontiers in Ecosystem Ecology from a Community Perspective: The Future is Boundless and Bright

A synergistic future for AI and ecology

Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change. These challenges include understanding the unpredictability of systems-level phenomena and resilience dynamics on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a convergence research paradigm between ecology and AI. Ecological systems are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behaviors that may inspire new, robust AI architectures and methodologies. We share examples of how challenges in ecological systems modeling would benefit from advances in AI techniques that are themselves inspired by the systems they seek to model. Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. We emphasize the need for more purposeful synergy to accelerate the understanding of ecological resilience whilst building the resilience currently lacking in modern AI systems, which have been shown to fail at times because of poor generalization in different contexts. Persistent epistemic barriers would benefit from attention in both disciplines. The implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence-they are critical for both persisting and thriving in an uncertain future.

OUP accepted manuscript

Extreme events have increased in frequency globally, with a simultaneous surge in scientific interest about their ecological responses, particularly in sensitive freshwater, coastal, and marine ecosystems. We synthesized observational studies of extreme events in these aquatic ecosystems, finding that many studies do not use consistent definitions of extreme events. Furthermore, many studies do not capture ecological responses across the full spatial scale of the events. In contrast, sampling often extends across longer temporal scales than the event itself, highlighting the usefulness of long-term monitoring. Many ecological studies of extreme events measure biological responses but exclude chemical and physical responses, underscoring the need for integrative and multidisciplinary approaches. To advance extreme event research, we suggest prioritizing pre- and postevent data collection, including leveraging long-term monitoring; making intersite and cross-scale comparisons; adopting novel empirical and statistical approaches; and developing funding streams to support flexible and responsive data collection.

Leveraging public data to offer online inquiry opportunities

Inquiry activities have become increasingly common in Ecology and Evolution courses, but the rapid shift to remote instruction for many faculty members in response to the COVID-19 pandemic has created new challenges for maintaining these student-centered activities in a distance learning format. Moving forward, many instructors will be asked to create flexible course structures that allow for a mix of different teaching modalities and will be looking for resources to support student inquiry in both online and in-person settings. Here, we propose the use of data-driven inquiry activities as a flexible option for offering students experiences to build career-relevant skills and learn fundamental ecological concepts. We share lessons learned from our experiences teaching a two-semester course-based research experience in global change ecology that leverages publicly available datasets to engage students in broadly relevant scientific inquiry.

Constructing Causal Understanding in Complex Systems: Epistemic Strategies Used by Ecosystem Scientists

Moving from a correlational to a causal account involves epistemological assumptions in any discipline. It presents particular challenges when phenomena involve multiple causes, time lags, feedback loops, or thresholds, as is the case in ecosystem science. Although reductionist approaches may contribute to explanatory efforts, investigation in ecosystems science requires a systems perspective. Understanding how ecosystem scientists arrive at causal accounts—and importantly, that they do—is critical to public understanding of science. Interviews with ten ecosystem scientists revealed the strategies and habits of mind that ecosystem scientists bring to examining complex systems. The scientists described challenges in conducting experiments at relevant scales and the epistemic strategies employed in response. The themes included constructing a body of evidence using multiple approaches, integrating results through statistical and process-based models, measuring and describing variability, conducting experiments in context, thinking across levels, considering the limits to generalizability, and exercising epistemic fluency. We discuss implications for K–20 education.

Measuring soil sustainability via soil resilience

Soils are the nexus of water, energy and food, which illustrates the need for a holistic approach in sustainable soil management. The present study therefore aimed at identifying a bioindicator for the evaluation of soil management sustainability in a cross-disciplinary approach between soil science and multi-omics research. For this purpose we first discuss the remaining problems and challenges of evaluating sustainability and consequently suggest one measurable bioindicator for soil management sustainability. In this concept, we define soil sustainability as the maintenance of soil functional integrity. The potential to recover functional and structural integrity after a disturbance is generally defined as resilience. This potential is a product of the past and the present soil management, and at the same time prospect of possible soil responses to future disturbances. Additionally, it is correlated with the multiple soil functions and hence reflecting the multifunctionality of the soil system. Consequently, resilience can serve as a bioindicator for soil sustainability. The measurable part of soil resilience is the response diversity, calculated from the systematic contrasting of multi-omic markers for genetic potential and functional activity, and referred to as potential Maximum Ecological Performance (MEPpot) in this study. Calculating MEPpot will allow to determine the thresholds of resistance and resilience and potential tipping points for a regime shift towards irreversible or permanent unfavorable soil states for each individual soil considered. The calculation of such ecosystem thresholds is to our opinion the current global cross-disciplinary challenge.

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.

Automatic High Frequency Monitoring for Improved Lake and Reservoir Management

Recent technological developments have increased the number of variables being monitored in lakes and reservoirs using automatic high frequency monitoring (AHFM). However, design of AHFM systems and posterior data handling and interpretation are currently being developed on a site-by-site and issue-by-issue basis with minimal standardization of protocols or knowledge sharing. As a result, many deployments become short-lived or underutilized, and many new scientific developments that are potentially useful for water management and environmental legislation remain underexplored. This Critical Review bridges scientific uses of AHFM with their applications by providing an overview of the current AHFM capabilities, together with examples of successful applications. We review the use of AHFM for maximizing the provision of ecosystem services supplied by lakes and reservoirs (consumptive and non consumptive uses, food production, and recreation), and for reporting lake status in the EU Water Framework Directive. We also highlight critical issues to enhance the application of AHFM, and suggest the establishment of appropriate networks to facilitate knowledge sharing and technological transfer between potential users. Finally, we give advice on how modern sensor technology can successfully be applied on a larger scale to the management of lakes and reservoirs and maximize the ecosystem services they provide.