Divergent responses of pelagic and benthic fish body-size structure to remoteness and protection from humans.

Animal body-size variation influences multiple processes in marine ecosystems, but habitat heterogeneity has prevented a comprehensive assessment of size across pelagic (midwater) and benthic (seabed) systems along anthropic gradients. In this work, we derive fish size indicators from 17,411 stereo baited-video deployments to test for differences between pelagic and benthic responses to remoteness from human pressures and effectiveness of marine protected areas (MPAs). From records of 823,849 individual fish, we report divergent responses between systems, with pelagic size structure more profoundly eroded near human markets than benthic size structure, signifying greater vulnerability of pelagic systems to human pressure. Effective protection of benthic size structure can be achieved through MPAs placed near markets, thereby contributing to benthic habitat restoration and the recovery of associated fishes. By contrast, recovery of the world's largest and most endangered fishes in pelagic systems requires the creation of highly protected areas in remote locations, including on the High Seas, where protection efforts lag.

FISHGLOB_data: an integrated dataset of fish biodiversity sampled with scientific bottom-trawl surveys.

Scientific bottom-trawl surveys are ecological observation programs conducted along continental shelves and slopes of seas and oceans that sample marine communities associated with the seafloor. These surveys report taxa occurrence, abundance and/or weight in space and time, and contribute to fisheries management as well as population and biodiversity research. Bottom-trawl surveys are conducted all over the world and represent a unique opportunity to understand ocean biogeography, macroecology, and global change. However, combining these data together for cross-ecosystem analyses remains challenging. Here, we present an integrated dataset of 29 publicly available bottom-trawl surveys conducted in national waters of 18 countries that are standardized and pre-processed, covering a total of 2,170 sampled fish taxa and 216,548 hauls collected from 1963 to 2021. We describe the processing steps to create the dataset, flags, and standardization methods that we developed to assist users in conducting spatio-temporal analyses with stable regional survey footprints. The aim of this dataset is to support research, marine conservation, and management in the context of global change.

Contrasting ecological information content in whaling archives with modern cetacean surveys for conservation planning and identification of historical distribution changes.

Many species are restricted to a marginal or suboptimal fraction of their historical range due to anthropogenic impacts, making it hard to interpret their ecological preferences from modern-day data alone. However, inferring past ecological states is limited by the availability of robust data and biases in historical archives, posing a challenge for policy makers . To highlight how historical records can be used to understand the ecological requirements of threatened species and inform conservation, we investigated sperm whale (Physeter macrocephalus) distribution in the Western Indian Ocean. We assessed differences in information content and habitat suitability predictions based on whale occurrence data from Yankee whaling logs (1792-1912) and from modern cetacean surveys (1995-2020). We built maximum entropy habitat suitability models containing static (bathymetry-derived) variables to compare models comprising historical-only and modern-only data. Using both historical and modern habitat suitability predictions  we assessed marine protected area (MPA) placement by contrasting suitability in- and outside MPAs. The historical model predicted high habitat suitability in shelf and coastal regions near continents and islands, whereas the modern model predicted a less coastal distribution with high habitat suitability more restricted to areas of steep topography. The proportion of high habitat suitability inside versus outside MPAs was higher when applying the historical predictions than the modern predictions, suggesting that different marine spatial planning optimums can be reached from either data sources. Moreover, differences in relative habitat suitability predictions between eras were consistent with the historical depletion of sperm whales from coastal regions, which were easily accessed and targeted by whalers, resulting in a modern distribution limited more to steep continental margins and remote oceanic ridges. The use of historical data can provide important new insights and, through cautious interpretation, inform conservation planning and policy, for example, by identifying refugee species and regions of anticipated population recovery.

Contrastes del contenido de información ecológica entre los archivos de la caza de ballenas y los censos actuales de cetáceos para la planeación de la conservación y la identificación de cambios en la distribución histórica Resumen Muchas especies están restringidas a una fracción marginal o subóptima de su área de distribución histórica debido a impactos antropogénicos. Esto dificulta interpretar sus preferencias ecológicas con sólo usar los datos actuales. Sin embargo, la inferencia de estados ecológicos pasados está limitada a la disponibilidad de datos sólidos y a los sesgos de los archivos históricos, lo que plantea un reto para la conservación y los responsables de las políticas. Analizamos la distribución del cachalote (Physeter macrocephalus) en el Océano Índico occidental para resaltar cómo pueden utilizarse los registros históricos para comprender los requisitos ecológicos de las especies amenazadas y direccionar su conservación. Evaluamos las diferencias en el contenido de la información y las predicciones de idoneidad del hábitat basadas en los datos de presencia de ballenas de los registros balleneros Yanquis (1792-1912) y de los estudios actuales sobre cetáceos (1995-2020). Construimos modelos de idoneidad de hábitat con máxima entropía que incluían variables estáticas (derivadas de la batimetría) para comparar los modelos que abarcan datos históricos y actuales. Evaluamos la ubicación de las áreas marinas protegidas (AMP) contrastando las predicciones dentro y fuera de ellas con los modelos históricos y actuales de la idoneidad del hábitat. El modelo histórico predijo una alta idoneidad del hábitat en las regiones costeras y de la plataforma continental cercanas a los continentes e islas, mientras que el modelo moderno predijo una distribución menos costera con una alta idoneidad del hábitat más restringida a las zonas de topografía escarpada. La proporción de hábitats de alta idoneidad dentro y fuera de las AMP fue mayor con la aplicación de las predicciones históricas que con la de las modernas, lo que sugiere que se pueden alcanzar diferentes niveles óptimos de ordenación del espacio marino a partir de ambas fuentes de datos. Además, las diferencias entre los periodos en las predicciones relacionadas con la idoneidad del hábitat fueron coherentes con la reducción histórica de los cachalotes en las regiones costeras, las cuales eran fácilmente accesibles para los balleneros, lo que resultó en una distribución actual más limitada a los márgenes continentales escarpados y a las crestas oceánicas remotas. El uso de datos históricos puede aportar nuevos e importantes conocimientos e informar, mediante una interpretación prudente, a la planificación y la política de conservación; por ejemplo, con la identificación de especies refugiadas y regiones de recuperación poblacional.

Leveraging social media and deep learning to detect rare megafauna in video surveys.

Deep learning has become a key tool for the automated monitoring of animal populations with video surveys. However, obtaining large numbers of images to train such models is a major challenge for rare and elusive species because field video surveys provide few sightings. We designed a method that takes advantage of videos accumulated on social media for training deep-learning models to detect rare megafauna species in the field. We trained convolutional neural networks (CNNs) with social media images and tested them on images collected from field surveys. We applied our method to aerial video surveys of dugongs (Dugong dugon) in New Caledonia (southwestern Pacific). CNNs trained with 1303 social media images yielded 25% false positives and 38% false negatives when tested on independent field video surveys. Incorporating a small number of images from New Caledonia (equivalent to 12% of social media images) in the training data set resulted in a nearly 50% decrease in false negatives. Our results highlight how and the extent to which images collected on social media can offer a solid basis for training deep-learning models for rare megafauna detection and that the incorporation of a few images from the study site further boosts detection accuracy. Our method provides a new generation of deep-learning models that can be used to rapidly and accurately process field video surveys for the monitoring of rare megafauna.

El aprendizaje profundo se ha convertido en una importante herramienta para el monitoreo automatizado de las poblaciones animales con video-censos. Sin embargo, la obtención de cantidades abundantes de imágenes para preparar dichos modelos es un reto primordial para las especies elusivas e infrecuentes porque los video-censos de campo proporcionan pocos avistamientos. Diseñamos un método que aprovecha los videos acumulados en las redes sociales para preparar a los modelos de aprendizaje profundo para detectar especies infrecuentes de megafauna en el campo. Preparamos algunas redes neurales convolucionales con imágenes tomadas de las redes sociales y las pusimos a prueba con imágenes tomadas en los censos de campo. Aplicamos nuestro método a los censos aéreos en video de dugongos (Dugong dugon) en Nueva Caledonia (Pacífico sudoccidental). Las redes neurales convolucionales preparadas con 1,303 imágenes de las redes sociales produjeron 25% de falsos positivos y 38% de falsos negativos cuando las probamos en video-censos de campo independientes. La incorporación de un número pequeño de imágenes tomadas en Nueva Caledonia (equivalente al 12% de las imágenes de las redes sociales) dentro del conjunto de datos usados en la preparación dio como resultado una disminución de casi el 50% en los falsos negativos. Nuestros resultados destacan cómo y a qué grado las imágenes recolectadas en las redes sociales pueden ofrecer una base sólida para la preparación de modelos de aprendizaje profundo para la detección de megafauna infrecuente. También resaltan que la incorporación de unas cuantas imágenes del sitio de estudio aumenta mucho más la certeza de detección. Nuestro método proporciona una nueva generación de modelos de aprendizaje profundo que pueden usarse para procesar rápida y acertadamente los video-censos de campo para el monitoreo de megafauna infrecuente.

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.

Outstanding Challenges in the Transferability of Ecological Models.

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.

Assessing cetacean surveys throughout the Mediterranean Sea: a gap analysis in environmental space.

Heterogeneous data collection in the marine environment has led to large gaps in our knowledge of marine species distributions. To fill these gaps, models calibrated on existing data may be used to predict species distributions in unsampled areas, given that available data are sufficiently representative. Our objective was to evaluate the feasibility of mapping cetacean densities across the entire Mediterranean Sea using models calibrated on available survey data and various environmental covariates. We aggregated 302,481 km of line transect survey effort conducted in the Mediterranean Sea within the past 20 years by many organisations. Survey coverage was highly heterogeneous geographically and seasonally: large data gaps were present in the eastern and southern Mediterranean and in non-summer months. We mapped the extent of interpolation versus extrapolation and the proportion of data nearby in environmental space when models calibrated on existing survey data were used for prediction across the entire Mediterranean Sea. Using model predictions to map cetacean densities in the eastern and southern Mediterranean, characterised by warmer, less productive waters, and more intense eddy activity, would lead to potentially unreliable extrapolations. We stress the need for systematic surveys of cetaceans in these environmentally unique Mediterranean waters, particularly in non-summer months.

Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas.

As human activities expand beyond national jurisdictions to the high seas, there is an increasing need to consider anthropogenic impacts to species inhabiting these waters. The current scarcity of scientific observations of cetaceans in the high seas impedes the assessment of population-level impacts of these activities. We developed plausible density estimates to facilitate a quantitative assessment of anthropogenic impacts on cetacean populations in these waters. Our study region extended from a well-surveyed region within the U.S. Exclusive Economic Zone into a large region of the western North Atlantic sparsely surveyed for cetaceans. We modeled densities of 15 cetacean taxa with available line transect survey data and habitat covariates and extrapolated predictions to sparsely surveyed regions. We formulated models to reduce the extent of extrapolation beyond covariate ranges, and constrained them to model simple and generalizable relationships. To evaluate confidence in the predictions, we mapped where predictions were made outside sampled covariate ranges, examined alternate models, and compared predicted densities with maps of sightings from sources that could not be integrated into our models. Confidence levels in model results depended on the taxon and geographic area and highlighted the need for additional surveying in environmentally distinct areas. With application of necessary caution, our density estimates can inform management needs in the high seas, such as the quantification of potential cetacean interactions with military training exercises, shipping, fisheries, and deep-sea mining and be used to delineate areas of special biological significance in international waters. Our approach is generally applicable to other marine taxa and geographic regions for which management will be implemented but data are sparse.

Habitat-based cetacean density models for the U.S. Atlantic and Gulf of Mexico.

Cetaceans are protected worldwide but vulnerable to incidental harm from an expanding array of human activities at sea. Managing potential hazards to these highly-mobile populations increasingly requires a detailed understanding of their seasonal distributions and habitats. Pursuant to the urgent need for this knowledge for the U.S. Atlantic and Gulf of Mexico, we integrated 23 years of aerial and shipboard cetacean surveys, linked them to environmental covariates obtained from remote sensing and ocean models, and built habitat-based density models for 26 species and 3 multi-species guilds using distance sampling methodology. In the Atlantic, for 11 well-known species, model predictions resembled seasonal movement patterns previously suggested in the literature. For these we produced monthly mean density maps. For lesser-known taxa, and in the Gulf of Mexico, where seasonal movements were less well described, we produced year-round mean density maps. The results revealed high regional differences in small delphinoid densities, confirmed the importance of the continental slope to large delphinoids and of canyons and seamounts to beaked and sperm whales, and quantified seasonal shifts in the densities of migratory baleen whales. The density maps, freely available online, are the first for these regions to be published in the peer-reviewed literature.

Predicting cetacean habitats from their energetic needs and the distribution of their prey in two contrasted tropical regions.

To date, most habitat models of cetaceans have relied on static and oceanographic covariates, and very few have related cetaceans directly to the distribution of their prey, as a result of the limited availability of prey data. By simulating the distribution of six functional micronekton groups between the surface and ≃1,000 m deep, the SEAPODYM model provides valuable insights into prey distributions. We used SEAPODYM outputs to investigate the habitat of three cetacean guilds with increasing energy requirements: sperm and beaked whales, Globicephalinae and Delphininae. We expected High Energy Requirements cetaceans to preferentially forage in habitats of high prey biomass and/or production, where they might easily meet their high energetic needs, and Low Energy Requirements cetaceans to forage in habitats of either high or low prey biomass and/or production. Cetacean sightings were collected from dedicated aerial surveys in the South West Indian Ocean (SWIO) and French Polynesia (FP). We examined cetacean densities in relation to simulated distributions of their potential prey using Generalised Additive Models and predicted their habitats in both regions. Results supported their known diving abilities, with Delphininae mostly related to prey present in the upper layers of the water column, and Globicephalinae and sperm and beaked whales also related to prey present in deeper layers. Explained deviances ranged from 9% for sperm and beaked whales in the SWIO to 47% for Globicephalinae in FP. Delphininae and Globicephalinae appeared to select areas where high prey biomass and/or production were available at shallow depths. In contrast, sperm and beaked whales showed less clear habitat selection. Using simulated prey distributions as predictors in cetacean habitat models is crucial to understand their strategies of habitat selection in the three dimensions of the ocean.

Assessing the impact of bycatch on dolphin populations: the case of the common dolphin in the eastern North Atlantic.

Fisheries interactions have been implicated in the decline of many marine vertebrates worldwide. In the eastern North Atlantic, at least 1000 common dolphins (Delphinus delphis) are bycaught each year, particularly in pelagic pair-trawls. We have assessed the resulting impact of bycatch on this population using a demographic modeling approach. We relied on a sample of females stranded along the French Atlantic and western Channel coasts. Strandings represent an extensive source of demographic information to monitor our study population. Necropsy analysis provided an estimate of individual age and reproductive state. Then we estimated effective survivorship (including natural and human-induced mortality), age at first reproduction and pregnancy rates. Reproductive parameters were consistent with literature, but effective survivorship was unexpectedly low. Demographic parameters were then used as inputs in two models. A constant parameter matrix proposed an effective growth rate of -5.5±0.5%, corresponding to the current situation (including bycatch mortality). Subsequently, deterministic projections suggested that the population would be reduced to 20% of its current size in 30 years and would be extinct in 100 years. The demographic invariant model suggested a maximum growth rate of +4.5±0.09%, corresponding to the optimal demographic situation. Then, a risk analysis incorporating Potential Biological Removal (PBR), based on two plausible scenarii for stock structure suggested that bycatch level was unsustainable for the neritic population of the Bay of Biscay under a two-stock scenario. In depth assessment of stock structure and improved observer programs to provide scientifically robust bycatch estimates are needed. Effective conservation measures would be reducing bycatch to less than 50% of the current level in the neritic stock to reach PBR. Our approach provided indicators of the status and trajectory of the common dolphin population in the eastern North Atlantic and therefore proved to be a valuable tool for management, applicable to other dolphin populations.