Conservation science in NOAA’s National Marine Sanctuaries: description and recent accomplishments

This report describes cases relating to the management of national marine sanctuaries in which certain scientific information was required so managers could make decisions that effectively protected trust resources. The cases presented represent only a fraction of difficult issues that marine sanctuary managers deal with daily. They include, among others, problems related to wildlife disturbance, vessel routing, marine reserve placement, watershed management, oil spill response, and habitat restoration. Scientific approaches to address these problems vary significantly, and include literature surveys, data mining, field studies (monitoring, mapping, observations, and measurement), geospatial and biogeographic analysis, and modeling. In most cases there is also an element of expert consultation and collaboration among multiple partners, agencies with resource protection responsibilities, and other users and stakeholders. The resulting management responses may involve direct intervention (e.g., for spill response or habitat restoration issues), proposal of boundary alternatives for marine sanctuaries or reserves, changes in agency policy or regulations, making recommendations to other agencies with resource protection responsibilities, proposing changes to international or domestic shipping rules, or development of new education or outreach programs. (PDF contains 37 pages.

Launching the Grand Challenges for Ocean Conservation

The ten most pressing Grand Challenges in Oceans Conservation were identified at the Oceans Big Think and described in a detailed working document:A Blue Revolution for Oceans: Reengineering Aquaculture for SustainabilityEnding and Recovering from Marine DebrisTransparency and Traceability from Sea to Shore:  Ending OverfishingProtecting Critical Ocean Habitats: New Tools for Marine ProtectionEngineering Ecological Resilience in Near Shore and Coastal AreasReducing the Ecological Footprint of Fishing through Smarter GearArresting the Alien Invasion: Combating Invasive SpeciesCombatting the Effects of Ocean AcidificationEnding Marine Wildlife TraffickingReviving Dead Zones: Combating Ocean Deoxygenation and Nutrient Runof

Landscape-Level Long-Term Biological Research and Monitoring Plan for the Crane Trust

Our obligation is to make sure we are effectively utilizing science to meet the objectives of the Platte River Whooping Crane Maintenance Trust (1981) laid out in its charter “to rehabilitate and preserve a portion of the habitat for Whooping Cranes and other migratory birds in the Big Bend reach of the Platte River between Overton and Chapman (i.e., Central Platte River Valley), Nebraska”. The original declaration is aimed at maintaining “the physical, hydrological, and biological integrity of the Big Bend area as a life-support system for the Whooping Crane and other migratory species that utilize it.” It was clear from the institution’s founding that to accomplish this goal it was necessary to study the effectiveness of land conservation and management actions in providing habitat for Whooping Cranes and other migratory bird species. Quality habitat necessarily comprises all the components that Whooping Cranes and other migratory bird life require to complete their migrations –food and shelter– including nutrient rich diet items such as invertebrates, vascular plants, herpetofauna, fish, and small mammals as well as suitable roosting and foraging locations including wide braided rivers and undisturbed wet meadows (Allen 1952; Steenhof et al. 1988; Geluso 2013; Caven et al. 2019, 2021). Article “A” of the Crane Trust’s (1981) declaration is “to establish a written habitat monitoring plan which can be used to describe change in…[habitat] within the Big Bend of the Platte River…utilized by Sandhill Cranes and Whooping Cranes….” Following initial inventories including avian (Hay and Lingle 1982), vegetation (Kolstad 1981; Nagel 1981), small mammals (Springer 1981), herpetofauna (Jones et al. 1981), insects (Ratcliffe 1981), and fish (Cochar and Jenson 1981), a variety of excellent research has continued at the Crane Trust (https://cranetrust.org/conservation-research/publications/). However, despite the clarity of the Trust’s original declaration, long-term habitat monitoring has not progressed unabated throughout the history of the Crane Trust.https://digitalcommons.unl.edu/zeabook/1130/thumbnail.jp

Urban wetland parks in Finland: improving water quality and creating endangered habitats

Urbanization changes water balance, degrades water quality and disrupts habitats. Wetlands offer storm water volume and flow control, water pollution mitigation, and rich land–water interphase habitats. In the present case study, urban wetlands were designed and implemented to provide multiple functions, including water quality improvement and the establishment of critically endangered clay stream habitat, along a revived urban stream within the Baltic Sea watershed in Southern Finland. The primary water quality concern in the recipient lake is algal bloom controlling and clay particle-carried phosphorus. Wetlands were monitored for functioning over five calendar years. At a wetland monitored for 5 years, herbaceous vegetation was well self-established in the second year, and reached 102 species, of which 97% were native, in the fifth growing season. Successful breeding of amphibians and water birds occurred right after construction. Continuous water quality monitoring over the fourth year at this wetland, with 0.1% area of its watershed, revealed seasonal and event-based differences: for total phosphorus, an annual 10% average with lower removal rates outside, and up to 71% event reductions during the growing season, while highest load reductions occurred during heavy rain and snowmelt events outside the growing season. The created wetlands provided critical habitat and beneficial functions and thus compensated partly for urbanization.Peer reviewe

Sensing Through the Continent: Towards Monitoring Migratory Birds Using Cellular Sensor Networks

This paper presents CraneTracker, a novel sensor platform for monitoring migratory birds. The platform is designed to monitor Whooping Cranes, an endangered species that conducts an annual migration of 4, 000 km between southern Texas and north-central Canada. CraneTracker includes a rich set of sensors, a multi-modal radio, and power control circuitry for sustainable, continental-scale information delivery during migration. The need for large-scale connectivity motivates the use of cellular technology in low-cost sensor platforms augmented by a low-power transceiver for ad-hoc connectivity. This platform leads to a new class of cellular sensor networks (CSNs) for time-critical and mobile sensing applications. The CraneTracker is evaluated via field tests on Wild Turkeys, Siberian Cranes, and an on-going alpha deployment with wild Sandhill Cranes. Experimental evaluations demonstrate the potential of energy-harvesting CSNs for wildlife monitoring in large geographical areas, and reveal important insights into the movements and behaviors of migratory animals. In addition to benefiting ecological research, the developed platform is expected to extend the application domain of sensor networks and enable future research applications

Great Bay Estuary Restoration Compendium

Single species approaches to natural resource conservation and management are now viewed as antiquated and oversimplified for dealing with complex systems. Scientists and managers who work in estuaries and other marine systems have urged adoption of ecosystem based approaches to management for nearly a decade, yet practitioners are still struggling to translate the ideas into practice. Similarly, ecological restoration projects in coastal systems have typically addressed one species or habitat. In recent years, efforts to focus on multiple species and habitats have increased. Our project developed an integrated ecosystem approach to identify multi-habitat restoration opportunities in the Great Bay estuary, New Hampshire. We created a conceptual site selection model based on a comparison of historic and modern distribution and abundance data, current environmental conditions, and expert review. Restoration targets included oysters and softshell clams, salt marshes, eelgrass beds, and seven diadromous fish species. Spatial data showing the historical and present day distributions for multiple species and habitats were compiled and integrated into a geographic information system. A matrix of habitat interactions was developed to identify potential for synergy and subsequent restoration efficiency. Output from the site selection models was considered within this framework to identify ecosystem restoration landscapes. The final products of these efforts include a series of maps detailing multi-habitat restoration opportunities extending from upland freshwater fish habitat down to the bay bottom. A companion guidance document was created to present project methods and a review of restoration methods. The authors hope that this work will help to stimulate and inform new restoration projects within the Great Bay estuarine system, and that it will serve as a foundation to be updated and improved as more information is collected

Integrating Technology Into Wildlife Surveys

Technology is rapidly improving and being incorporated into field biology, with survey methods such as machine learning and uncrewed aircraft systems (UAS) headlining efforts. UAS paired with machine learning algorithms have been used to detect caribou, nesting waterfowl and seabirds, marine mammals, white-tailed deer, and more in over 19 studies within the last decade alone. Simultaneously, UAS and machine learning have also been implemented for infrastructure monitoring at wind energy facilities as wind energy construction and use has skyrocketed globally. As part of both pre-construction and regulatory compliance of newly constructed wind energy facilities, monitoring of impacts to wildlife is assessed through ground surveys following the USFWS Land-based Wind Energy Guidelines. To streamline efforts at wind energy facilities and improve efficiency, safety, and accuracy in data collection, UAS platforms may be leveraged to not only monitor infrastructure, but also impacts to wildlife in the form of both pre- and post-construction surveys. In this study, we train, validate, and test a machine learning approach, a convolutional neural network (CNN), in the detection and classification of bird and bat carcasses. Further, we compare the trained CNN to the currently accepted and widely used method of human ground surveyors in a simulated post-construction monitoring scenario. Last, we establish a baseline comparison of manual image review of waterfowl pair surveys with currently used ground surveyors that could inform both pre-construction efforts at energy facilities, along with long-standing federal and state breeding waterfowl surveys. For the initial training of the CNN, we collected 1,807 images of bird and bat carcasses that were split into 80.0% training and 20.0% validation image sets. Overall detection was extremely high at 98.7%. We further explored the dataset by evaluating the trained CNN’s ability to identify species and the variables that impacted identification. Classification of species was successful in 90.5% of images and was associated with sun angle and wind speed. Next, we performed a proof of concept to determine the utility of the trained CNN against ground surveyors in ground covers and with species that were both used in the initial training of the model and novel. Ground surveyors performed similar to those surveying at wind energy facilities with 63.2% detection, while the trained CNN fell short at 28.9%. Ground surveyor detection was weakly associated with carcass density within a plot and strongly with carcass size. Similarly, detection by the CNN was associated with carcass size, ground cover type, visual obstruction of vegetation, and weakly with carcass density within a plot. Finally, we examined differences in breeding waterfowl counts between ground surveyors and UAS image reviewers and found that manual review of UAS imagery yielded similar to slightly higher counts of waterfowl. Significant training, testing, and repeated validation of novel image data sets should be performed prior to implementing survey methods reliant upon machine learning algorithms. Additionally, further research is needed to determine potential biases of counting live waterfowl in aerial imagery, such as bird movement and double counting. While our initial results show that UAS imagery and machine learning can improve upon current techniques, extensive follow-up is strongly recommended in the form of proof-of-concept studies and additional validation to confirm the utility of the application in new environments with new species that allow models to be generalized. Remotely sensed imagery paired with machine learning algorithms have the potential to expedite and standardize monitoring of wildlife at wind energy facilities and beyond, improving data streams and potentially reducing costs for the benefit of both conservation agencies and the energy industry