Ponds are often identified by their small size and shallow depths, but the lack of a universal evidence-based definition hampers science and weakens legal protection. Here, we compile existing pond definitions, compare ecosystem metrics (e.g., metabolism, nutrient concentrations, and gas fluxes) among ponds, wetlands, and lakes, and propose an evidence-based pond definition. Compiled definitions often mentioned surface area and depth, but were largely qualitative and variable. Government legislation rarely defined ponds, despite commonly using the term. Ponds, as defined in published studies, varied in origin and hydroperiod and were often distinct from lakes and wetlands in water chemistry. We also compared how ecosystem metrics related to three variables often seen in waterbody definitions: waterbody size, maximum depth, and emergent vegetation cover. Most ecosystem metrics (e.g., water chemistry, gas fluxes, and metabolism) exhibited nonlinear relationships with these variables, with average threshold changes at 3.7 ± 1.8 ha (median: 1.5 ha) in surface area, 5.8 ± 2.5 m (median: 5.2 m) in depth, and 13.4 ± 6.3% (median: 8.2%) emergent vegetation cover. We use this evidence and prior definitions to define ponds as waterbodies that are small (< 5 ha), shallow (< 5 m), with < 30% emergent vegetation and we highlight areas for further study near these boundaries. This definition will inform the science, policy, and management of globally abundant and ecologically significant pond ecosystems.

Significance: The food system’s negative impact on biodiversity is increasing over time. Conserving biodiversity requires immediate and widespread action to reduce the biodiversity footprint of food consumption, but biodiversity has historically been neglected in sustainability assessments. We combine high-resolution estimates of the biodiversity footprint with food system scenario modeling to predict the consequences of two key food system sustainability actions in the United States: diet shifts and food waste reduction. Taking these actions may benefit biodiversity in some places and harm it in others. The results of this study can help decision makers understand the trade-offs we must navigate to balance human health, economics, and environmental sustainability and help consumers understand how their diets and food waste behaviors influence global biodiversity.

Significance: Flooding remains one of the costliest natural disasters globally. Perceptions of—and strategies for mitigating—riverine flooding risk vary both within and across communities, yet this is often overlooked in formal planning efforts. We fit a sociohydrologic model to 50 US metropolitan areas to understand divergent community risk behaviors. We identified two archetypes: “risk-enduring” communities with lower flood defenses and longer memory of past floods and “risk-averse” communities with higher defenses but shorter memory. Behaviors were correlated with streamflow conditions, local dam heights, and the proportion of White to non-White residents. Our findings highlight a potential awareness of local hydrology that may drive perceptions of risk as well as racial inequity in flood exposure and resilience within the United States.

Methane emissions from small freshwater ecosystems represent one of the largest components of uncertainty in the global methane budget. While these systems are known to produce large amounts of methane relative to their size, quantifying the timing, magnitude, and spatial extent of their emissions remains challenging. We begin to address this challenge in seasonally inundated forested mineral soil wetlands by (1) measuring wetland methane fluxes and hydrologic regime across both inundated and non-inundated soils, (2) characterizing how wetland hydrologic regime impacts the spatial extent of methane emission source areas, and (3) modeling average daily wetland-scale flux rates using four different upscaling techniques. Our results show that inundation extent and duration, but not frequency or depth, were major drivers of wetland methane emissions. Moreover, we found that methane fluxes were best described by the direction of water level change (i.e. rising or falling), where emissions were generally higher when water levels were falling. Once soils were inundated, subsequent changes in water level did not explain observed variability of methane concentrations in standing water. Finally, our spatial modeling suggests that representing inundation and associated methane source areas is a critical step in estimating local to regional scale methane emissions. Intermittently inundated soils alternated between being sources and sinks of methane depending on water level, soil moisture, and the direction of water level change. These results demonstrate that quantifying the hydrologic regime of seasonally inundated forested freshwater wetlands enables a more accurate estimation of methane emissions.

A major source of uncertainty in the global methane budget arises from quantifying the area of wetlands and other inland waters. This study addresses how the dynamics of surface water extent in forested wetlands affect the calculation of methane emissions. We used fine resolution satellite imagery acquired at sub‐weekly intervals together with a semiempirical methane emissions model to estimate daily surface water extent and diffusive methane fluxes for a low‐relief wetland‐rich watershed. Comparisons of surface water model predictions to field measurements showed agreement with the magnitude of changes in water extent, including for wetlands with surface area less than 1,000 m². Results of methane emission models showed that wetlands smaller than 1 hectare (10,000 m²) were responsible for a majority of emissions, and that considering dynamic inundation of forested wetlands resulted in 49%–62% lower emission totals compared to models using a single estimate for each wetland’s size.

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.