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The environmental impact of the production and consumption of food is seldom depicted to consumers. The footprint of food products provides a means for consumers to compare environmental impacts across and within product groups. In this study we apply carbon, nitrogen, and water footprints in tandem and present food labels that could help inform consumers about the environmental impacts of individual food products. The footprint factors used in this study are specific to the United States, but the concept can be applied elsewhere. We propose three methods of footprint calculations: footprint weight, sustainability measures, and % daily value. We apply the three footprint calculation methods to four example labels (stars label, stoplight label, nutrition label add-on, and a detailed comparison label) that vary in design and the amount of detail provided. The stars label is simple and easily understood but provides minimal detail about the footprints. At the other end of the spectrum, the detailed comparison label gives context in relative terms (e.g., carbon emissions for equivalent distance driven) for the food product. Implementing environmental impact food labels requires additional understanding of how consumers use footprint labels, and label suitability may vary for government organizations, retail and local grocers, and farmers.

SignificanceLarge changes can occur when ecosystems cross certain thresholds. Crossing such thresholds poses a challenge to ecosystem management because the positions of the threshold are uncertain and change over time. However, as an ecosystem approaches a threshold its resilience declines, resulting in changes in system dynamics that increase variance and autocorrelation. Calculating these statistics requires frequent and sustained sampling efforts. Our study detected an approaching threshold by computing the statistical indicators from data collected by automated sensors, which are far less labor-intensive than comparable manual methods. Thus it may be feasible to monitor for approaching ecosystem thresholds using automated methods. This finding highlights a powerful use of modern sensor technology.

The dynamics of shallow-water coastal environments are controlled by external drivers—sea level rise, storms, and sediment and nutrient supplies—and by internal feedbacks. Interactions of biotic processes (vegetation growth, trophic dynamics) and abiotic drivers can lead to nonlinear responses to changing conditions and to the emergence of thresholds, hysteresis, and alternative stable states. We develop a conceptual framework for studying interactions between the dynamics of marshes and habitats in shallow coastal bays with unconsolidated sediments (seagrass, oyster reefs). Using examples primarily from the Virginia Coast Reserve Long Term Ecological Research site, we show that in the subtidal part of the landscape, two alternative stable states can exist—one dominated by seagrass up to a certain depth that represents a tipping point to the second, unvegetated stable state. The depth limit of the seagrass stable state is influenced by (1) the positive feedback of vegetation on reducing sediment suspension and improving the light environment for growth, (2) climate (e.g., temperature), and (3) water quality. Two stable states are also present in intertidal areas, with salt marshes lying above mean sea level and tidal flats below mean sea level. State transitions are driven by sediment availability, sea level rise, the relative strength of wind waves with respect to tidal currents, and the biotic feedback of vegetation on sediment stabilization and accretion. State-change dynamics in one system may propagate to adjacent systems, and this coupling may influence the landscape-scale response to environmental change. Seagrass meadows and oyster reefs affect adjacent marshes both positively (wave attenuation) and negatively (reduced sediment supply), and marsh-edge erosion could negatively influence the light environment for seagrass growth. Forecasting the resilience of coastal ecosystems and the landscape-scale response to environmental change in the next century requires an understanding of nonlinear dynamics, including the possibility of multiple stable states, the coupled evolution of adjacent systems, and potential early warning signs of thresholds of change.