Conservation prioritization requires knowledge about organism distribution and density. This information is often inferred from models that estimate the probability of species occurrence rather than from models that estimate species abundance, because abundance data are harder to obtain and model. However, occurrence and abundance may not display similar patterns and therefore development of robust, scalable, abundance models is critical to ensuring that scarce conservation resources are applied where they can have the greatest benefits. Motivated by a dynamic land conservation program, we develop and assess a general method for modeling relative abundance using citizen science monitoring data. Weekly estimates of relative abundance and occurrence were compared for prioritizing times and locations of conservation actions for migratory waterbird species in California, USA. We found that abundance estimates consistently provided better rankings of observed counts than occurrence estimates. Additionally, the relationship between abundance and occurrence was nonlinear and varied by species and season. Across species, locations prioritized by occurrence models had only 10-58% overlap with locations prioritized by abundance models, highlighting that occurrence models will not typically identify the locations of highest abundance that are vital for conservation of populations.

Citizen science, big data, and a habitat marketplace enable dynamic habitat for migratory birds in California’s Central Valley.

As human impacts to the environment accelerate, disparities in the distribution of damages between rich and poor nations mount. Globally, environmental change is dramatically affecting the flow of ecosystem services, but the distribution of ecological damages and their driving forces has not been estimated. Here, we conservatively estimate the environmental costs of human activities over 1961-2000 in six major categories (climate change, stratospheric ozone depletion, agricultural intensification and expansion, deforestation, overfishing, and mangrove conversion), quantitatively connecting costs borne by poor, middle-income, and rich nations to specific activities by each of these groups. Adjusting impact valuations for different standards of living across the groups as commonly practiced, we find striking imbalances. Climate change and ozone depletion impacts predicted for low-income nations have been overwhelmingly driven by emissions from the other two groups, a pattern also observed for overfishing damages indirectly driven by the consumption of fishery products. Indeed, through disproportionate emissions of greenhouse gases alone, the rich group may have imposed climate damages on the poor group greater than the latter's current foreign debt. Our analysis provides prima facie evidence for an uneven distribution pattern of damages across income groups. Moreover, our estimates of each group's share in various damaging activities are independent from controversies in environmental valuation methods. In a world increasingly connected ecologically and economically, our analysis is thus an early step toward reframing issues of environmental responsibility, development, and globalization in accordance with ecological costs. ecological degradation ͉ ecosystem change ͉ ecosystem services ͉ external cost

Conservation organizations seeking to reduce over-fishing and promote better fishing practices have increasingly turned to market-based mechanisms such as environmental sustainability labels (eco-labels) in order to shift patterns of household consumption. This paper presents an analysis of consumer response to an advisory for sustainable seafood adopted by a regional supermarket in the United States. The advisory consisted of a label in which one of three traffic light colors was placed on each fresh seafood product to inform consumers about its relative environmental sustainability. Green meant "best" choice, yellow meant "proceed with caution," and red meant "worst choice". Using a unique product-level panel scanner data set of weekly sales and taking advantage of the random phase-in of the advisory by the retailer, we apply a differencein-differences identification strategy to estimate the effect of the advisory on overall seafood sales as well as the heterogeneous impact of the advisory by label color and whether the seafood met additional health-related criteria. We find evidence that the advisory led to a statistically significant 15.3% decline in overall seafood sales, a statistically significant 34.9% decline in the sale of yellow labeled seafood, and a statistically significant 41.3% decline in the sale of yellow labeled seafood on a mercury safe list. We find no statistically significant difference in sales of green or red labeled seafood.