No abstract

Satellite measurements of surface water offer promise for understanding wetland habitat availability at broad spatial and temporal scales; reliable habitat is crucial for the persistence of migratory shorebirds that depend on wetland networks. We analyzed water extent dynamics within wetland habitats at a globally important shorebird stopover site for a 1983-2015 Landsat time series, and evaluated the effect of climate on water extent. A range of methods can detect open water from imagery, including supervised classification approaches and thresholds for spectral bands and indices. Thresholds provide a time advantage; however, there is no universally superior index, nor single best threshold for all instances. We used random forest to model the presence or absence of water from >6200 reference pixels, and derived an optimal water probability threshold for our study area using receiver operating characteristic curves. An optimized mid-infrared (1.5-1.7 μm) threshold identified open water in the Sacramento Valley of California at 30-m resolution with an average of 90% producer's accuracy, comparable to approaches that require more intensive user input. SLC-off Landsat 7 imagery was integrated by applying a customized interpolation that mapped water in missing data gaps with 99% user's accuracy. On average we detected open water on ~26000 ha (~3% of the study area) in early April at the peak of shorebird migration, while water extent increased five-fold after the migration rush. Over the last three decades, late March water extent declined by ~1300 ha per year, primarily due to changes in the extent and timing of agricultural flood-irrigation. Water within shorebird habitats was significantly associated with an index of water availability at the peak of migration. Our approach can be used to optimize thresholds for time series analysis and near-real-time mapping in other regions, and requires only marginally more time than generating a confusion matrix.

Context Animal movements are inherently linked to landscape structure. Understanding this relationship for highly-mobile species requires documenting their responses to spatiotemporal variability of resources. To that end, characterizing movement behaviors and resource distributions using the principles of habitat connectivity facilitates coordinated landscape planning efforts within highly modified landscapes. Objectives and methods We tracked locations and movements for 156 dunlin (Calidris alpina) and 109 long-billed dowitchers (Limnodromus scolopaceus) overwintering in two regions with distinct water distributions in California's Central Valley. We then compared residency rates, functional connectivity to other regions, and associations between movement distances and average habitat availability and structural connectivity of habitat at multiple temporal and spatial scales. Results A widespread yet highly variable regional water distribution was associated with lower residency rates and substantially higher functional connectivity to nearby regions when compared to a stable regional water distribution characterized by a large, contiguous wetland complex. Longer movements were associated with decreasing average availability and spatial aggregation of surface water. Movement models suggested shorebirds primarily responded to habitat availability at smaller scales (\ 10 km) and structural connectivity at larger scales (C 10 km). Conclusions Differences in movement behaviors suggested that wintering shorebirds will avoid long distance movements and remain resident within a wetland region when possible. Conservation and management efforts should reliably flood individual wetlands and agricultural lands from November to April and prioritize locations that maximize structural wetland connectivity and limit spatiotemporal variability of surface water throughout the Central Valley.

Human-induced climate change is bringing warmer conditions to the Southwestern United States. More extreme urban heat island (UHI) effects are not distributed equally, and often impact socioeconomically vulnerable populations the most. This study aims to quantify how land surface temperature (LST) changes with increasing green vegetation landscapes, identify disparities in urban warming exposure, and provide a method for developing evidence-based mitigation options. ECOSTRESS LST products, detailed land use and land cover (LULC) classes, and socioeconomic variables were used to facilitate the analysis. We examined the relationship between LST and the fractions of LULC and socioeconomic factors in the city of Phoenix, Arizona. A machine learning approach (Random Forest) was used to model LST changes by taking the LULC fractions (scenario-based approaches) as the explanatory variables. We found that vegetation features—trees, grass, and shrubs—were the most important factors mitigating UHI effects during the summer daytime. Trees tended to lower surface temperature more effectively, whereas we observed elevated daytime LST most often near roads. Meanwhile, higher summer daytime temperatures were observed on land with unmanaged soil compared to the built environment. We found that affluent neighborhoods experienced lower temperatures, while low-income communities experienced higher temperatures. Scenario analyses suggest that replacing 50% of unmanaged soil with trees could reduce average summer daytime temperatures by 1.97°C if the intervention was implemented across all of Phoenix and by 1.43°C if implemented within the urban core only. We suggest that native trees requiring little to no additional water other than rainfall should be considered. We quantify mitigation options for urban warming effect under vegetation management interventions, and our results provide some vital insight into existing disparities in UHI impacts. Future UHI mitigation strategies seriously need to consider low-income communities to improve environmental justice. These can be used to guide the development of sustainable and equitable policies for vegetation management to mitigate heat exposure impacts on communities.