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Widespread global declines in shellfish reefs (ecosystem‐forming bivalves such as oysters and mussels) have led to growing interest in their restoration and protection. With restoration projects now occurring on four continents and in at least seven countries, global restoration guidelines for these ecosystems have been developed based on experience over the past two decades. The following key elements of the guidelines are outlined: (a) the case for shellfish reef restoration and securing financial resources; (b) planning, feasibility, and goal setting; (c) biosecurity and permitting; (d) restoration in practice; (e) scaling up from pilot to larger scale restoration, (f) monitoring, (g) restoration beyond oyster reefs (specifically mussels), and (h) successful communication for shellfish reef restoration projects.

Resource selection functions (RSFs) have been widely applied to animal tracking data to examine relative habitat selection and to help guide management and conservation strategies. While readily used in terrestrial ecology, RSFs have yet to be extensively used within marine systems. As acoustic telemetry continues to be a pervasive approach within marine environments, incorporation of RSFs can provide new insights to help prioritize habitat protection and restoration to meet conservation goals. To overcome statistical hurdles and achieve high prediction accuracy, machine learning algorithms could be paired with RSFs to predict relative habitat selection for a species within and even outside the monitoring range of acoustic receiver arrays, making this a valuable tool for marine ecologists and resource managers. Here, we apply RSFs using machine learning to an acoustic telemetry dataset of four shark species to explore and predict species-specific habitat selection within a marine protected area. In addition, we also apply this RSF-machine learning approach to investigate predator-prey relationships by comparing and averaging tiger shark relative selection values with the relative selection values derived for eight potential prey-species. We provide methodological considerations along with a framework and flexible approach to apply RSFs with machine learning algorithms to acoustic telemetry data and suggest marine ecologists and resource managers consider adopting such tools to help guide both conservation and management strategies.

Much of the United States’ critical infrastructure is either aging or requires significant repair, leaving U.S. communities and the economy vulnerable. Outdated and dilapidated infrastructure places coastal communities, in particular, at risk from the increasingly frequent and intense coastal storm events and rising sea levels. Therefore, investments in coastal infrastructure are urgently needed to ensure community safety and prosperity; however, these investments should not jeopardize the ecosystems and natural resources that underlie economic wealth and human well-being. Over the past 50 years, efforts have been made to integrate built infrastructure with natural landscape features, often termed “green” infrastructure, in order to sustain and restore valuable ecosystem functions and services. For example, significant advances have been made in implementing green infrastructure approaches for stormwater management, wastewater treatment, and drinking water conservation and delivery. However, the implementation of natural and nature-based infrastructure (NNBI) aimed at flood prevention and coastal erosion protection is lagging. There is an opportunity now, as the U.S. government reacts to the recent, unprecedented flooding and hurricane damage and considers greater infrastructure investments, to incorporate NNBI into coastal infrastructure projects. Doing so will increase resilience and provide critical services to local communities in a cost-effective manner and thereby help to sustain a growing economy.

Many shark species exhibit complex spatial ecology throughout their life histories, posing a challenge for conservation and management. Although most marine protected areas (MPAs) were originally established to protect less mobile organisms, protection of shark species from fishing and other impacts is possible if individuals exhibit high residency and site fidelity within the MPA boundaries. For this study, we used a fixed acoustic telemetry array to study the residency, habitat use, and interspecific space use among 4 shark species in Buck Island Reef National Monument (BIRNM), an MPA in St. Croix, US Virgin Islands. From June 2013 to May 2017, 11 nurse sharks Ginglymostoma cirratum, 6 lemon sharks Negaprion brevirostris, 13 Caribbean reef sharks Carcharhinus perezi, and 6 tiger sharks Galeocerdo cuvier were monitored in the array. Overall, residency was high for all species, with a mean residency index of 0.52 or higher for each species. Network analysis revealed complex inter- and intraspecific spatial associations among individuals. Community detection algorithms showed that G. cirratum and N. brevirostris frequently used the same areas in BIRNM, selecting for shallow sand and seagrass habitats near linear reefs, while G. cuvier and C. perezi had more individualized space use. C. perezi also exhibited ontogenetic shifts, developing individual territories and using deeper water with increasing body size. This work emphasizes the importance of MPA size, placement, and habitat composition when aiming to protect highly mobile species with potentially large home ranges and shifting space use throughout their life histories.

In the United States, extensive investments have been made to restore the ecological function and services of coastal marine habitats. Despite a growing body of science supporting coastal restoration, few studies have addressed the suite of societally enabling conditions that helped facilitate successful restoration and recovery efforts that occurred at meaningful ecological (i.e., ecosystem) scales, and where restoration efforts were sustained for longer (i.e., several years to decades) periods. Here, we examined three case studies involving large-scale and long-term restoration efforts including the seagrass restoration effort in Tampa Bay, Florida, the oyster restoration effort in the Chesapeake Bay in Maryland and Virginia, and the tidal marsh restoration effort in San Francisco Bay, California. The ecological systems and the specifics of the ecological restoration were not the focus of our study. Rather, we focused on the underlying social and political contexts of each case study and found common themes of the factors of restoration which appear to be important for maintaining support for large-scale restoration efforts. Four critical elements for sustaining public and/or political support for large-scale restoration include: (1) resources should be invested in building public support prior to significant investments into ecological restoration; (2) building political support provides a level of significance to the recovery planning efforts and creates motivation to set and achieve meaningful recovery goals; (3) recovery plans need to be science-based with clear, measurable goals that resonate with the public; and (4) the accountability of progress toward reaching goals needs to be communicated frequently and in a way that the general public comprehends. These conclusions may help other communities move away from repetitive, single, and seemingly unconnected restoration projects towards more large-scale, bigger impact, and coordinated restoration efforts.

Predicting population- and ecosystem-level benefits of habitat restoration minimally requires an understanding of the link between the trophic ecology of a species and their use of a habitat. This study combined novel, non-lethal natural tracers of trophic ecology with acoustic tagging techniques to examine spatial and temporal patterns of habitat use of spotted seatrout Cynoscion nebulosus on Half Moon Reef (HMR), a recently restored oyster reef in Matagorda Bay, Texas. Forty-one spotted seatrout (408 ± 25 mm total length) were captured at HMR, surgically implanted with acoustic transmitters, and monitored by an array of underwater listening stations from December 2015 to August 2016. Patterns of presence-absence on HMR were strongly influenced by water temperature, and to a lesser extent, salinity and tidal height. Overall, spotted seatrout residency to HMR was low, with fish being present on the reef 24% of days. When present, individual fish exhibited strong site-attachment to small portions of the reef. Residency to HMR increased significantly with size, while scale stable isotope analysis revealed fish exhibiting high residency to HMR occupied significantly smaller isotopie niches. If indeed smaller fish with decreased residency rely upon a wider range of prey items across multiple habitats than larger, more resident individuals, restored oyster reef habitat may be expected to primarily benefit larger spotted seatrout.

Little is known of the diversity, demography, and essential fish habitat of sharks within the United States Virgin Islands (USVI) marine ecosystem. To examine species diversity and the relative abundance of elasmobranchs in this region, bottom-longline and hand-gear sampling was conducted in Fish Bay, St. John, USVI, from June 2004 to December 2005. In the 8 sampling trips during this period, 54 standardized longline sets caught 174 elasmobranchs comprising 5 species of sharks and 1 batoid. Overall catch per unit effort [ln(CPUE + 1) ± SE] was 1.83 ± 0.16 elasmobranchs 100 hooks–1h–1. Lemon sharksNegaprion brevirostrishad the highest relative abundance based on log-transformed CPUE data (0.98 ± 0.15), followed by blacktip sharksCarcharhinus limbatus(0.91 ± 0.18), southern stingraysDasyatis americana(0.28 ± 0.08), nurse sharksGinglymostoma cirratum(0.08 ± 0.05), blacknose sharksCarcharhinus acronotus(0.06 ± 0.04) and the Caribbean sharpnoseRhizoprionodon porosus(0.03 ± 0.03). The relative abundance of all species was significantly higher in the summer (2.6 ± 0.2) than during the winter (1.1 ± 0.2). For the blacktip (N = 89 captures of 74 individuals) and lemon (N = 66, 48 individuals) sharks, which comprised the bulk of the catch, mean fork length (± SE) was 51.9 ± 0.63 cm and 59.9 ± 1.2 cm, respectively, representing primarily neonatal and young-of-the-year life stages. The recapture rates for blacktip and lemon sharks were 21% and 29%, respectively, and nearly all recaptures occurred within the bay, indicating a high degree of site fidelity. Capture information and limited acoustic tracking provided evidence of spatial and temporal habitat partitioning by these 2 shark species within the bay. Although the CPUE of both species was highest over shallow (<1 m) seagrass substrate, lemon sharks were found and tracked exclusively on shallow, mangrove-fringed seagrass habitat, while blacktip sharks utilized a wider area of the bay. Fish Bay was determined to provide important nursery habitat for young juvenile lemon and blacktip sharks in the USVI.

Using simulations performed with 24 coupled atmosphere–ocean global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), projections of Northern Hemisphere daily snowfall events under the RCP8.5 emissions scenario are analyzed for the periods of 2021–50 and 2071–2100 and compared to the historical period of 1971–2000. The overall frequency of daily snowfall events is simulated to decrease across much of the Northern Hemisphere, except at the highest latitudes such as northern Canada, northern Siberia, and Greenland. Seasonal redistributions of daily snowfall event frequency and average daily snowfall are also projected to occur in some regions. For example, large portions of the Northern Hemisphere, including much of Canada, Tibet, northern Scandinavia, northern Siberia, and Greenland, are projected to experience increases in average daily snowfall and event frequency in midwinter. But in warmer months, the regions with increased snowfall become fewer in number and are limited to northern Canada, northern Siberia, and Greenland. These simulations also show changes in the frequency distribution of daily snowfall event intensity, including an increase in heavier snowfall events even in some regions where the overall snowfall decreases. The projected changes in daily snowfall event frequency exhibit some dependence on the temperature biases of the individual models in certain regions and times of the year, with colder models typically toward the positive end of the distribution of event frequency changes and warmer models toward the negative end, particularly in regions near the transition zone between increasing and decreasing snowfall.

The spread of infectious diseases can be unpredictable. With the emergence of antibiotic resistance and worrying new viruses, and with ambitious plans for global eradication of polio and the elimination of malaria, the stakes have never been higher. Anticipation and measurement of the multiple factors involved in infectious disease can be greatly assisted by mathematical methods. In particular, modeling techniques can help to compensate for imperfect knowledge, gathered from large populations and under difficult prevailing circumstances. Heesterbeek et al. review the development of mathematical models used in epidemiology and how these can be harnessed to develop successful control strategies and inform public health policy. Science , this issue 10.1126/science.aaa4339 Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.