Using modelled prey to predict the distribution of a highly mobile marine mammal

Potential feeding sites for seabirds and marine mammals reveal large overlap with offshore wind energy development worldwide

Offshore wind energy is experiencing accelerated growth worldwide to support global net zero ambitions. To ensure responsible development and to protect the natural environment, it is essential to understand and mitigate the potential impacts on wildlife, particularly on seabirds and marine mammals. However, fully understanding the effects of offshore wind energy production requires characterising its global geographic occurrence and its potential overlap with marine species. This study aims to generate risk maps of interaction between offshore and seabirds and marine mammals based on the distribution of their potential foraging areas. These maps will allow visualisation of the spatial occurrence of risk and its severity for both groups. To achieve it, we built a structural equation model of three levels (plankton, fish, and top predators) to predict small-ranged seabirds and marine mammal spatial richness as a proxy of potential feeding sites. Later, we overlapped these maps with global wind density (as a proxy of potential offshore development areas) to identify risk areas. Our results pointed to simplified trophic chain models that effectively explained the richness of small-ranged seabirds and marine mammals. Our risk maps reveal a high overlap with potential offshore wind development. Low-risk areas were located mainly in so-called Global North countries, suggesting vast knowledge gaps and potential hidden risks in these areas. Importantly, the highest risk values were found outside the Marine Protected Areas for both groups, underscoring the necessity for strategic planning and the expansion of renewable energy sources to avert potential conservation challenges in the future.

Climate change drives fish communities: Changing multiple facets of fish biodiversity in the Northwest Pacific Ocean

Global marine biodiversity is experiencing significant alterations due to climate change. Incorporating phylogenetic and functional diversity may provide novel insights into these impacts. This study used an ensemble model approach (random forest and boosted regression tree), to predict the habitat distribution of 47 fish species in the Northwestern Pacific under contemporary (2000-2014) and future scenarios (2040-2050, 2090-2100). We first examined the relationship between eleven functional traits and habitat changes, predicting the spatial distribution of functional traits within fish communities. A significant correlation was observed between temperature preference and habitat changes, highlighting the vulnerability of cold-water species and potential advantages for warm-water species in the future. Moreover, fish communities exhibited a spatial gradient distribution with southern regions characterized by shorter-lived and earlier maturity, contrasting with longer-lived and later maturity species in the north. Secondly, to assess the impact of climate change on marine biodiversity, we explored the taxonomic, phylogenetic, and functional diversity under contemporary and future scenarios, revealing higher indices in the East China Sea (ECS) and the coastal sea of Japan, with the Taiwan Strait emerging as a contemporary biodiversity hotspot. In future scenarios, the three biodiversity indices would decline in the Yellow Sea and ECS, but increase in the sea beyond the continental shelf, coastal sea of Hokkaido, and Sea of Okhotsk. Lastly, we explored processes and mechanisms in the change of community composition. By quantifying β-diversity, we identified species loss (nestedness) as the primary driver of fish community change by 2040-2050, with species replacement (turnover) predicted to become dominant in the far future. Our results explore the potential changes in multiple facets of fish biodiversity, providing crucial insights for policymakers aiming to protect fish resources and biodiversity.

Testing spatial transferability of species distribution models reveals differing habitat preferences for an endangered delphinid (Cephalorhynchus hectori) in Aotearoa, New Zealand

Species distribution models (SDMs) can be used to predict distributions in novel times or space (termed transferability) and fill knowledge gaps for areas that are data poor. In conservation, this can be used to determine the extent of spatial protection required. To understand how well a model transfers spatially, it needs to be independently tested, using data from novel habitats. Here, we test the transferability of SDMs for Hector's dolphin (Cephalorhynchus hectori), a culturally important (taonga) and endangered, coastal delphinid, endemic to Aotearoa New Zealand. We collected summer distribution data from three populations from 2021 to 2023. Using Generalised Additive Models, we built presence/absence SDMs for each population and validated the predictive ability of the top models (with TSS and AUC). Then, we tested the transferability of each top model by predicting the distribution of the remaining two populations. SDMs for two populations showed useful performance within their respective areas (Banks Peninsula and Otago), but when used to predict the two areas outside the models' source data, performance declined markedly. SDMs from the third area (Timaru) performed poorly, both for prediction within the source area and when transferred spatially. When data for model building were combined from two areas, results were mixed. Model interpolation was better when presence/absence data from Otago, an area of low density, were combined with data from areas of higher density, but was otherwise poor. The overall poor transferability of SDMs suggests that habitat preferences of Hector's dolphins vary between areas. For these dolphins, population-specific distribution data should be used for conservation planning. More generally, we demonstrate that a one model fits all approach is not always suitable. When SDMs are used to predict distribution in data-poor areas an assessment of performance in the new habitat is required, and results should be interpreted with caution.

Vulnerability to climate change of United States marine mammal stocks in the western North Atlantic, Gulf of Mexico, and Caribbean

Climate change and climate variability are affecting marine mammal species and these impacts are projected to continue in the coming decades. Vulnerability assessments provide a framework for evaluating climate impacts over a broad range of species using currently available information. We conducted a trait-based climate vulnerability assessment using expert elicitation for 108 marine mammal stocks and stock groups in the western North Atlantic, Gulf of Mexico, and Caribbean Sea. Our approach combined the exposure (projected change in environmental conditions) and sensitivity (ability to tolerate and adapt to changing conditions) of marine mammal stocks to estimate vulnerability to climate change, and categorize stocks with a vulnerability index. The climate vulnerability score was very high for 44% (n = 47) of these stocks, high for 29% (n = 31), moderate for 20% (n = 22), and low for 7% (n = 8). The majority of stocks (n = 78; 72%) scored very high exposure, whereas 24% (n = 26) scored high, and 4% (n = 4) scored moderate. The sensitivity score was very high for 33% (n = 36) of these stocks, high for 18% (n = 19), moderate for 34% (n = 37), and low for 15% (n = 16). Vulnerability results were summarized for stocks in five taxonomic groups: pinnipeds (n = 4; 25% high, 75% moderate), mysticetes (n = 7; 29% very high, 57% high, 14% moderate), ziphiids (n = 8; 13% very high, 50% high, 38% moderate), delphinids (n = 84; 52% very high, 23% high, 15% moderate, 10% low), and other odontocetes (n = 5; 60% high, 40% moderate). Factors including temperature, ocean pH, and dissolved oxygen were the primary drivers of high climate exposure, with effects mediated through prey and habitat parameters. We quantified sources of uncertainty by bootstrapping vulnerability scores, conducting leave-one-out analyses of individual attributes and individual scorers, and through scoring data quality for each attribute. These results provide information for researchers, managers, and the public on marine mammal responses to climate change to enhance the development of more effective marine mammal management, restoration, and conservation activities that address current and future environmental variation and biological responses due to climate change.

Estimating energetic intake for marine mammal bioenergetic models

Bioenergetics is the study of how animals achieve energetic balance. Energetic balance results from the energetic expenditure of an individual and the energy they extract from their environment. Ingested energy depends on several extrinsic (e.g prey species, nutritional value and composition, prey density and availability) and intrinsic factors (e.g. foraging effort, success at catching prey, digestive processes and associated energy losses, and digestive capacity). While the focus in bioenergetic modelling is often on the energetic costs an animal incurs, the robust estimation of an individual's energy intake is equally critical for producing meaningful predictions. Here, we review the components and processes that affect energy intake from ingested gross energy to biologically useful net energy (NE). The current state of knowledge of each parameter is reviewed, shedding light on research gaps to advance this field. The review highlighted that the foraging behaviour of many marine mammals is relatively well studied via biologging tags, with estimates of success rate typically assumed for most species. However, actual prey capture success rates are often only assumed, although we note studies that provide approaches for its estimation using current techniques. A comprehensive collation of the nutritional content of marine mammal prey species revealed a robust foundation from which prey quality (comprising prey species, size and energy density) can be assessed, though data remain unavailable for many prey species. Empirical information on various energy losses following ingestion of prey was unbalanced among marine mammal species, with considerably more literature available for pinnipeds. An increased understanding and accurate estimate of each of the components that comprise a species NE intake are an integral part of bioenergetics. Such models provide a key tool to investigate the effects of disturbance on marine mammals at an individual and population level and to support effective conservation and management.

Key questions in marine mammal bioenergetics

Bioenergetic approaches are increasingly used to understand how marine mammal populations could be affected by a changing and disturbed aquatic environment. There remain considerable gaps in our knowledge of marine mammal bioenergetics, which hinder the application of bioenergetic studies to inform policy decisions. We conducted a priority-setting exercise to identify high-priority unanswered questions in marine mammal bioenergetics, with an emphasis on questions relevant to conservation and management. Electronic communication and a virtual workshop were used to solicit and collate potential research questions from the marine mammal bioenergetic community. From a final list of 39 questions, 11 were identified as 'key' questions because they received votes from at least 50% of survey participants. Key questions included those related to energy intake (prey landscapes, exposure to human activities) and expenditure (field metabolic rate, exposure to human activities, lactation, time-activity budgets), energy allocation priorities, metrics of body condition and relationships with survival and reproductive success and extrapolation of data from one species to another. Existing tools to address key questions include labelled water, animal-borne sensors, mark-resight data from long-term research programs, environmental DNA and unmanned vehicles. Further validation of existing approaches and development of new methodologies are needed to comprehensively address some key questions, particularly for cetaceans. The identification of these key questions can provide a guiding framework to set research priorities, which ultimately may yield more accurate information to inform policies and better conserve marine mammal populations.

Mapping the potential for offshore aquaculture of salmonids in the Yellow Sea

Mariculture has been one of the fastest-growing global food production sectors over the past three decades. With the congestion of space and deterioration of the environment in coastal regions, offshore aquaculture has gained increasing attention. Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss) are two important aquaculture species and contribute to 6.1% of world aquaculture production of finfish. In the present study, we established species distribution models (SDMs) to identify the potential areas for offshore aquaculture of these two cold-water fish species considering the mesoscale spatio-temporal thermal heterogeneity of the Yellow Sea. The values of the area under the curve (AUC) and the true skill statistic (TSS) showed good model performance. The suitability index (SI), which was used in this study to quantitatively assess potential offshore aquaculture sites, was highly dynamic at the surface water layer. However, high SI values occurred throughout the year at deeper water layers. The potential aquaculture areas for S. salar and O. mykiss in the Yellow Sea were estimated as 52,270 ± 3275 (95% confidence interval, CI) and 146,831 ± 15,023 km2, respectively. Our results highlighted the use of SDMs in identifying potential aquaculture areas based on environmental variables. Considering the thermal heterogeneity of the environment, this study suggested that offshore aquaculture for Atlantic salmon and rainbow trout was feasible in the Yellow Sea by adopting new technologies (e.g., sinking cages into deep water) to avoid damage from high temperatures in summer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-022-00141-2.

A Review of Modeling Approaches for Understanding and Monitoring the Environmental Effects of Marine Renewable Energy

Understanding the environmental effects of marine energy (ME) devices is fundamental for their sustainable development and efficient regulation. However, measuring effects is difficult given the limited number of operational devices currently deployed. Numerical modeling is a powerful tool for estimating environmental effects and quantifying risks. It is most effective when informed by empirical data and coordinated with the development and implementation of monitoring protocols. We reviewed modeling techniques and information needs for six environmental stressor–receptor interactions related to ME: changes in oceanographic systems, underwater noise, electromagnetic fields (EMFs), changes in habitat, collision risk, and displacement of marine animals. This review considers the effects of tidal, wave, and ocean current energy converters. We summarized the availability and maturity of models for each stressor–receptor interaction and provide examples involving ME devices when available and analogous examples otherwise. Models for oceanographic systems and underwater noise were widely available and sometimes applied to ME, but need validation in real-world settings. Many methods are available for modeling habitat change and displacement of marine animals, but few examples related to ME exist. Models of collision risk and species response to EMFs are still in stages of theory development and need more observational data, particularly about species behavior near devices, to be effective. We conclude by synthesizing model status, commonalities between models, and overlapping monitoring needs that can be exploited to develop a coordinated and efficient set of protocols for predicting and monitoring the environmental effects of ME.

Towards a better characterisation of deep-diving whales' distributions by using prey distribution model outputs?

In habitat modelling, environmental variables are assumed to be proxies of lower trophic levels distribution and by extension, of marine top predator distributions. More proximal variables, such as potential prey fields, could refine relationships between top predator distributions and their environment. In situ data on prey distributions are not available over large spatial scales but, a numerical model, the Spatial Ecosystem And POpulation DYnamics Model (SEAPODYM), provides simulations of the biomass and production of zooplankton and six functional groups of micronekton at the global scale. Here, we explored whether generalised additive models fitted to simulated prey distribution data better predicted deep-diver densities (here beaked whales Ziphiidae and sperm whales Physeter macrocephalus) than models fitted to environmental variables. We assessed whether the combination of environmental and prey distribution data would further improve model fit by comparing their explanatory power. For both taxa, results were suggestive of a preference for habitats associated with topographic features and thermal fronts but also for habitats with an extended euphotic zone and with large prey of the lower mesopelagic layer. For beaked whales, no SEAPODYM variable was selected in the best model that combined the two types of variables, possibly because SEAPODYM does not accurately simulate the organisms on which beaked whales feed on. For sperm whales, the increase model performance was only marginal. SEAPODYM outputs were at best weakly correlated with sightings of deep-diving cetaceans, suggesting SEAPODYM may not accurately predict the prey fields of these taxa. This study was a first investigation and mostly highlighted the importance of the physiographic variables to understand mechanisms that influence the distribution of deep-diving cetaceans. A more systematic use of SEAPODYM could allow to better define the limits of its use and a development of the model that would simulate larger prey beyond 1,000 m would probably better characterise the prey of deep-diving cetaceans.

Bowhead and beluga whale acoustic detections in the western Beaufort Sea 2008-2018

The Distributed Biological Observatory (DBO) was established to detect environmental changes in the Pacific Arctic by regular monitoring of biophysical responses in each of 8 DBO regions. Here we examine the occurrence of bowhead and beluga whale vocalizations in the western Beaufort Sea acquired by acoustic instruments deployed from September 2008-July 2014 and September 2016-October 2018 to examine inter-annual variability of these Arctic endemic species in DBO Region 6. Acoustic data were collected on an oceanographic mooring deployed in the Beaufort shelfbreak jet at ~71.4°N, 152.0°W. Spectrograms of acoustic data files were visually examined for the presence or absence of known signals of bowhead and beluga whales. Weekly averages of whale occurrence were compared with outputs of zooplankton, temperature and sea ice from the BIOMAS model to determine if any of these variables influenced whale occurrence. In addition, the dates of acoustic whale passage in the spring and fall were compared to annual sea ice melt-out and freeze-up dates to examine changes in phenology. Neither bowhead nor beluga whale migration times changed significantly in spring, but bowhead whales migrated significantly later in fall from 2008-2018. There were no clear relationships between bowhead whales and the environmental variables, suggesting that the DBO 6 region is a migratory corridor, but not a feeding hotspot, for this species. Surprisingly, beluga whale acoustic presence was related to zooplankton biomass near the mooring, but this is unlikely to be a direct relationship: there are likely interactions of environmental drivers that result in higher occurrence of both modeled zooplankton and belugas in the DBO 6 region. The environmental triggers that drive the migratory phenology of the two Arctic endemic cetacean species likely extend from Bering Sea transport of heat, nutrients and plankton through the Chukchi and into the Beaufort Sea.