Governing Ecological Connectivity in Cross-Scale Dependent Systems.

Ecosystem management and governance of cross-scale dependent systems require integrating knowledge about ecological connectivity in its multiple forms and scales. Although scientists, managers, and policymakers are increasingly recognizing the importance of connectivity, governmental organizations may not be currently equipped to manage ecosystems with strong cross-boundary dependencies. Managing the different aspects of connectivity requires building social connectivity to increase the flow of information, as well as the capacity to coordinate planning, funding, and actions among both formal and informal governance bodies. We use estuaries in particular the San Francisco Estuary, in California, in the United States, as examples of cross-scale dependent systems affected by many intertwined aspects of connectivity. We describe the different types of estuarine connectivity observed in both natural and human-affected states and discuss the human dimensions of restoring beneficial physical and ecological processes. Finally, we provide recommendations for policy, practice, and research on how to restore functional connectivity to estuaries.

Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat.

Highly mobile species, such as migratory birds, respond to seasonal and interannual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species-distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16 days), and 17-year long-term average surface water data. Using abundance-weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. Although modeling with both real-time and long-term average data provided good fit to withheld validation data (the area under the receiver operating characteristic curve, or AUC, averaged between 0.79 and 0.89 for all taxa), there were small differences in model performance. The best models incorporated long-term average conditions and spatial pattern information for real-time flooding (e.g., perimeter-area ratio of real-time water bodies). There was not a substantial difference in the performance of real-time and long-term average data models within time periods when real-time surface water differed substantially from the long-term average (specifically during drought years 2013-2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real-time data may ultimately be best for guiding dynamic, adaptive conservation actions, whereas models based on long-term averages may be more helpful for guiding permanent wetland protection and restoration.

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds.

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.

Effects of drought on the abundance and distribution of non-breeding shorebirds in central California, USA.

Conservation of migratory species requires anticipating the potential impacts of extreme climatic events, such as extreme drought. During drought, reduced habitat availability for shorebirds creates the potential for changes in their abundance and distribution, in part because many species are highly mobile and rely on networks of interior and coastal habitats. Understanding how shorebirds responded to a recent drought cycle that peaked from 2013 to 2015 in central California, USA, will help optimize management of wetlands and fresh water for wildlife. In the Central Valley, a vast interior region that is characterized by a mosaic of wetlands and agricultural lands, we found 22% and 29% decreases in the annual abundance of shorebirds during periods of 3-year drought (2013-2015) and 2-year extreme drought (2014-2015), respectively, when compared to non-drought years. Lower abundance of shorebirds coincided with significant decreases in the mean proportion flooded of survey units (7% and 9%, respectively) that were reliant on fresh water. Drought was associated with lower abundance within both the interior Central Valley and coastal San Francisco Bay for greater and lesser yellowlegs (Tringa melanoleuca and T. flavipes) and long- and short-billed dowitchers (Limnodromus scolopaceus and L. griseus). Only dunlins (Calidris alpina) had patterns of abundance that suggested substantial shifts in distribution between the Central Valley and coastal regions of San Francisco Bay and Point Reyes. Our results indicate that drought has the potential to reduce, at least temporally, shorebird populations and flooded habitat in the Central Valley, and the ability to respond to drought by taking advantage of nearby coastal habitats may limit the long-term effects of drought on some species. Successful conservation strategies must balance the impacts of reduced habitat availability at interior sites with the ability of some migratory shorebirds to adapt rapidly to shifting distributions of resources.