Synthesizing evidence in sustainability science through harmonized experiments: Community monitoring in common pool resources

Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference

Causal effects of biodiversity on ecosystem functions can be estimated using experimental or observational designs - designs that pose a tradeoff between drawing credible causal inferences from correlations and drawing generalizable inferences. Here, we develop a design that reduces this tradeoff and revisits the question of how plant species diversity affects productivity. Our design leverages longitudinal data from 43 grasslands in 11 countries and approaches borrowed from fields outside of ecology to draw causal inferences from observational data. Contrary to many prior studies, we estimate that increases in plot-level species richness caused productivity to decline: a 10% increase in richness decreased productivity by 2.4%, 95% CI [-4.1, -0.74]. This contradiction stems from two sources. First, prior observational studies incompletely control for confounding factors. Second, most experiments plant fewer rare and non-native species than exist in nature. Although increases in native, dominant species increased productivity, increases in rare and non-native species decreased productivity, making the average effect negative in our study. By reducing the tradeoff between experimental and observational designs, our study demonstrates how observational studies can complement prior ecological experiments and inform future ones.

Modeling fine-grained spatio-temporal pollution maps with low-cost sensors

The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.

A strategic monitoring approach for learning to improve natural infrastructure

Natural infrastructure (NI) development, including ecosystem restoration, is an increasingly popular approach to leverage ecosystem services for sustainable development, climate resilience, and biodiversity conservation goals. Although implementation and planning for these tools is accelerating, there is a critical need for effective post-implementation monitoring to accumulate performance data and evidence for best practices. The complexity and longer time scales associated with NI, compounded by differing disciplinary definitions and concepts of monitoring necessitate a deliberate and strategic approach to monitoring that encompasses different timeframes and objectives. This paper outlines a typology of monitoring classes differentiated by temporal scale, purpose of data collection, the information benefits of monitoring, and the responsible party. Next, we provide a framework and practical guidelines for designing monitoring plans for NI around learning objectives. In particular, we emphasize conducting research and development monitoring, which provides scientifically rigorous evidence for methodological improvement beyond the project scale. Wherever feasible, and where NI tools are relatively new and untested, such monitoring should avoid wasted effort and ensure progress and refinement of methodology and practice over time. Finally, we propose institutional changes that would promote greater adoption of research and development monitoring to increase the evidence base for NI implementation at larger scales.

Causal assumptions and causal inference in ecological experiments

Causal inferences from experimental data are often justified based on treatment randomization. However, inferring causality from data also requires complementary causal assumptions, which have been formalized by scholars of causality but not widely discussed in ecology. While ecologists have recognized challenges to inferring causal relationships in experiments and developed solutions, they lack a general framework to identify and address them. We review four assumptions required to infer causality from experiments and provide design-based and statistically based solutions for when these assumptions are violated. We conclude that there is no clear demarcation between experimental and non-experimental designs. This insight can help ecologists design better experiments and remove barriers between experimental and observational scholarship in ecology.

On design-based empirical research and its interpretation and ethics in sustainability science

Generating credible answers to key policy questions is crucial but difficult in most coupled human and natural systems because complex feedback mechanisms can confound identification of the causal mechanisms behind observed phenomena. By using explicit research designs intended to isolate the causal effects of specific interventions on community monitoring of common property resources, and on the well-being of those resources and their human neighbors, the papers in this Special Feature offer an important advance in empirical sustainability science research. Like earlier advances in my own field of development economics, however, they suffer some avoidable interpretive and ethical errors. This essay celebrates the powerful potential of design-based sustainability science studies, much of it admirably reflected in this set of papers, while simultaneously flagging opportunities to improve future work in this tradition.