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.

Wildlife corridors aim to promote species’ persistence by connecting habitat patches across fragmented landscapes. Their implementation is limited by patterns of land ownership and complicated by differences in the jurisdictional and regulatory authorities under which lands are managed. Terrestrial corridor conservation requires coordination across jurisdictions and sectors subject to site‐specific overlapping sources of legal authority. Mapping spatial patterns of legal authority concurrent with habitat condition can illustrate opportunities to build or leverage capacity for connectivity conservation. Streamside areas provide pragmatic opportunities to leverage existing policy mechanisms for riverine and terrestrial habitat connectivity across boundaries. Conservation planners and practitioners can make use of these opportunities by harmonizing actions for multiple conservation outcomes. We formulated an integrative, data‐driven method for mapping multiple sources of legal authority weighted by capacity for coordinating terrestrial habitat conservation along streams. We generated a map of capacity to coordinate streamside corridor protections across a wildlife habitat gap to demonstrate this approach. We combined values representing coordination capacity and naturalness to generate an integrated legal‐ecological resistance map for connectivity modeling. We then computed least‐cost corridors across the integrated map, masking the terrestrial landscape to focus on streamside areas. Streamside least‐cost corridors in the integrated, local‐scale model diverged (∼25 km) from national‐scale least‐cost corridors based on naturalness. Spatial categories comparing legal‐ and naturalness‐based resistance values by stream reach highlighted potential locations for building or leveraging existing capacity through spatial coordination of policy mechanisms or restoration actions. Agencies or nongovernmental organizations intending to restore or maintain habitat connectivity across fragmented landscapes can use this approach to inform spatial prioritization and build coordination capacity. Article impact statement : Combined mapping of legal authority and habitat condition reveals capacity to coordinate actions along streams for clean water and wildlife.

Timely, policy-relevant monitoring data are essential for evaluating the effectiveness of environmental policies and conservation measures. Satellite and aerial imagery can fill data gaps at low cost but are often underused for ongoing environmental monitoring. Barriers include a lack of expertise or computational resources and the lag time between image acquisition and information delivery. Online image repositories and cloud computing platforms are increasingly used by researchers because they offer near-real-time, centralized access to local-to-global-scale data sets and analytics with minimal in-house computational requirements. We aim to broaden knowledge of these open access resources for biologists whose work routinely informs policy and management. To illustrate potential applications of cloud-based environmental monitoring (CBEM), we developed an adaptable approach to detect changes in natural vegetative cover in an agricultural watershed. The steps we describe can be applied to identify opportunities and caveats for applying CBEM in a wide variety of monitoring programs.

Remote sensing is a well-established tool for detecting forest disturbances. The increased availability of uncrewed aerial systems (drones) and advances in computer algorithms have prompted numerous studies of forest insects using drones. To date, most studies have used height information from three-dimensional (3D) point clouds to segment individual trees and two-dimensional multispectral images to identify tree damage. Here, we describe a novel approach to classifying the multispectral reflectances assigned to the 3D point cloud into damaged and healthy classes, retaining the height information for the assessment of the vertical distribution of damage within a tree. Drone images were acquired in a 27-ha study area in the Northern Rocky Mountains that experienced recent damage from insects and then processed to produce a point cloud. Using the multispectral data assigned to the points on the point cloud (based on depth maps from individual multispectral images), a random forest (RF) classification model was developed, which had an overall accuracy (OA) of 98.6%, and when applied across the study area, it classified 77.0% of the points with probabilities greater than 75.0%. Based on the classified points and segmented trees, we developed and evaluated algorithms to separate healthy from damaged trees. For damaged trees, we identified the damage severity of each tree based on the percentages of red and gray points and identified top-kill based on the length of continuous damage from the treetop. Healthy and damaged trees were separated with a high accuracy (OA: 93.5%). The remaining damaged trees were separated into different damage severities with moderate accuracy (OA: 70.1%), consistent with the accuracies reported in similar studies. A subsequent algorithm identified top-kill on damaged trees with a high accuracy (OA: 91.8%). The damage severity algorithm classified most trees in the study area as healthy (78.3%), and most of the damaged trees in the study area exhibited some amount of top-kill (78.9%). Aggregating tree-level damage metrics to 30 m grid cells revealed several hot spots of damage and severe top-kill across the study area, illustrating the potential of this methodology to integrate with data products from space-based remote sensing platforms such as Landsat. Our results demonstrate the utility of drone-collected data for monitoring the vertical structure of tree damage from forest insects and diseases.

Adaptive governance requires institutional capacity to coordinate responses to environmental problems at appropriate scales and utilizes networks for information sharing. This implies a capability to translate successful governance experiments from one social-ecological setting to another. Yet, translating lessons learned from case studies in adaptive water governance to other settings is all but straightforward. Watershed condition is a cumulative result of upstream ecological factors as well as land use decision-making processes, which may involve diverse stakeholders and multiple, nested levels of government. The relationships between site-specific land management decisions and water-related ecosystem services not only vary by location, but are further complicated by biogeochemical flows, ecological interactions, and social-ecological trade-offs. We view this governance challenge from a biophysical science perspective, highlighting the need to focus on translatability of governance approaches such that land use decision-making processes can better fit the dynamic, multidimensional, spatially continuous nature of riverine networks. To learn from a previous attempt to translate a successful water governance experiment across social-ecological settings, we investigated a case study of riverside area management in Washington State, northwestern USA. As participants in an agency-led workshop, we observed particular challenges in coordinating riverside management recommendations across a spatially variable social-ecological landscape. To clarify potential steps for translating riverine policy experiments, we intersected ecological understanding with adaptive governance scholarship. Using the case study as an example of the challenges of translating a policy experiment, we reviewed the ecological, management, and adaptive governance literatures to identify four elements of translatability: (1) a cross-sectoral, multiscale understanding of the shared goals or future desired state of the system; (2) quantified functional relationships between measurable site-scale features and ecosystem functions related to the shared goals; (3) definition frameworks to relate ecological concepts to the levels of potentially networked governance; (4) mapping strategies to visualize emerging networked governance in spatial context. We reviewed definitions pertaining to riverside areas and arranged them along a concept-application spectrum to provide a framework to relate ecological knowledge to the levels of potentially networked riverine governance. We mapped the spatial footprints of related policies nested within areas of similar ecological landscape characteristics to show spatial patterns that could inform translation of governance experiments in empirical context. We then discussed the role of translatability in adaptive water governance. We conclude with recommendations for considering the translatability of adaptive water-governance experiments and identifying potential opportunities to leverage existing ecological and institutional relationships to improve cross-scale fit with ecosystems across heterogeneous landscapes.

The imperative for sustainable agriculture has led to the promotion of conservation tillage practices, which are critical for maintaining soil health and reducing negative environmental impacts. However, the ability to verify the adoption of these practices at a regional scale and with minimal costs remains a challenge, particularly in regions with diverse crop systems. This study presents a satellite-imagery-based random forest classifier that integrates crop class information and a customized importance weighting technique for field-level, regional-scale mapping of tillage practices in crop-diverse regions. We use eastern Washington State, U.S., with multiple grains, legumes, and canola as a case study. Using 577 field-level ground-truth data points across two counties, the tillage classifier achieved a median overall accuracy of 84%, representing a 15 percentage point improvement over the baseline median overall accuracy of 69%, driven by the inclusion of crop class and the application of importance weighting. Specific crop classes, including grains, legumes. and canola, showed accuracy improvements of 6, 42, and 13 percentage points, respectively. While the model showed promising results, limitations such as overprediction of no-tillage in non-grain crop classes were identified, pointing to the need for further refinement with additional data. The model was applied to map tillage practices across dryland fields in eastern Washington State for the years 2012, 2017, and 2022, and the predictions were generally congruent with the USDA Census of Agriculture county-level statistics. This research offers a scalable tool for the verification of conservation tillage practice adoption, supporting both regional and global monitoring efforts for sustainable agricultural practices.