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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., 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.

Human exploitation has profoundly depleted animal populations in the ocean, leading to declines in ecosystem productivity, resilience, and contributions to people1,2. However, it remains unclear how size structure of fish populations varies across marine habitat and levels of human exploitation while simultaneously underpinning food web architecture and ecosystem functioning3. Here, we process 6,701 pelagic and 10,710 benthic stereo-video deployments across all the oceans to extract body size of over 800,000 individual fish, spanning 6 orders of magnitude from zooplankton size-classes (1,000 kg), in order to ultimately assess vulnerability to exploitation and responses to protection status. Size-spectra analysis reveals peaks in body size distributions which differ in their position between pelagic and benthic habitats. Typical body size of relatively small and large individuals is greater in strictly protected areas remote from human markets, than in unprotected areas close to markets. Pelagic size-spectra are more sensitive to market remoteness and respond more strongly to protection than their benthic counterparts which are more species-rich, and therefore more likely to provide trophic replacements4 under exploitation, owing to body size redundancy5. This contrasted result suggests that fully protected areas can partly mitigate human impacts close to markets in benthic habitats while in pelagic habitats effective protection requires remoteness from humans. Thus, the restoration of fish body-size spectra, including some of the world’s largest and most endangered vertebrates6, can be promoted by implementing fully protected areas in pelagic habitats, particularly in remote locations.

Overfishing and habitat degradation are major threats to marine megafauna worldwide. Marine protected areas (MPAs) are effective spatial conservation tools for reducing anthropogenic pressures on threatened species but their benefits for megafauna are still debated. While the effects of MPAs on species abundances are widely reported, few studies have simultaneously investigated the confounding effect of habitat. This study aimed at disentangling the effects of coral reef habitat and spatial protection on megafauna densities in a shallow lagoon partly covered by a no-take MPA in New Caledonia (South-west Pacific). Twenty replicates of aerial-video surveys (representing 17 h of videos) were conducted during a 5-month period to estimate and map the densities of five megafauna taxa (dugongs, sea turtles, sharks, Dasyatidae rays and Myliobatidae rays). A permutational multivariate analysis of variance was then applied to assess and disentangle the effects of coral reef habitat obtained from high-resolution satellite imagery and spatial protection on megafauna taxa densities. The analysis revealed a significant effect of protection for sharks and Myliobatidae, with observed densities respectively 9 and 3 times higher inside the MPA compared with outside. The results also highlighted a significant combined effect of habitat and protection for dugongs and Dasyatidae, as well as a significant effect of habitat alone for Dasyatidae. In contrast, no significant effect of habitat or protection was detected for sea turtles. In conclusion, this study revealed positive effects of protection (alone or combined with habitat) for four of the five studied megafauna taxa, confirming the effectiveness of the current MPA. Future studies should be conducted over broader spatial and temporal scales to examine whether detected effects hold beyond the surveyed period and area.

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 database of 26 publicly available bottom-trawl surveys conducted in national waters of 18 countries that are standardized and pre-processed, covering a total of 2,162 sampled fish taxa and 232,800 hauls collected from 1963 to 2020. We describe the processing steps to create the database, flags, and standardization methods that we developed to assist users in conducting spatio-temporal analyses with stable regional survey footprints. The aim of this database is to support research, marine conservation, and management in the context of global change.

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.

WOS:000630817600001; International audience; The advent of big data and machine learning offers great promise for addressing conservation and management questions in the oceans. Yet, few applications of machine learning exist to mitigate the overexploitation of marine resources. Tropical tuna purse seine fisheries (TTPSF) are distributed worldwide and account for two thirds of the global tuna catch. In these fisheries, the use of Drifting Fish Aggregating Devices (DFADs)? n-made floating objects massively deployed by fishers to increase their tuna catches?results in the incidental catch of non-target species, termed bycatch. We explored the possibility of applying machine learning on echosounder buoys attached to DFADs, representing an unprecedented source of big data, for identifying high bycatch risk at DFADs. We trained random forests algorithms to differentiate between high and low bycatch occurrence based on matched echosounder and onboard observer data for the same DFADs (representing sample sizes of 838 and 2144 in the Atlantic and the Indian Ocean, respectively). Algorithms showed a better performance in the Atlantic Ocean (accuracy of 0.66 versus 0.58 in the Indian Ocean) and were best at detecting the ?high bycatch? occurrence class. These results unravel the potential of machine learning applied to fishers? buoys data for bycatch reduction and improved selectivity in one of the largest fisheries worldwide.

This work was funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, managed by the U.S. Navy’s Living Marine Resources program under Contract No. N39430-17-C-1982. 1. Forecasting the responses of biodiversity to global change has never been more important. However, many ecologists faced with limited sample sizes and shoestring budgets often resort to extrapolating predictive models beyond the range of their data to support management actions in data‐deficient contexts. This can lead to error‐prone inference that has the potential to misdirect conservation interventions and undermine decision‐making. Despite the perils associated with extrapolation, little guidance exists on the best way to identify it when it occurs, leaving users questioning how much credence they should place in model outputs. To address this, we present dsmextra, a new R package for measuring, summarizing and visualizing extrapolation in multivariate environmental space. 2. dsmextra automates the process of conducting quantitative, spatially explicit assessments of extrapolation on the basis of two established metrics: the Extrapolation Detection (ExDet) tool and the percentage of data nearby (%N). The package provides user‐friendly functions to (a) calculate these metrics, (b) create tabular and graphical summaries, (c) explore combinations of covariate sets as a means of informing covariate selection and (d) produce visual displays in the form of interactive html maps. 3. dsmextra implements a model‐agnostic approach to extrapolation detection that is applicable across taxonomic groups, modelling techniques and datasets. We present a case study fitting a density surface model to visual detections of pantropical spotted dolphins Stenella attenuata in the Gulf of Mexico. 4. Predictive modelling seeks to deliver actionable information about the states and trajectories of ecological systems, yet model performance can be strongly impaired out of sample. By assessing conditions under which models are likely to fail or succeed in extrapolating, ecologists may gain a better understanding of biological patterns and their underlying drivers. Critical to this is a concerted effort to standardize best practice in model evaluation, with an emphasis on extrapolative capacity. Publisher PDF

Place: Hoboken Publisher: Wiley WOS:000531110000001; International audience; The distributions of highly mobile marine species such as cetaceans are increasingly modeled at basin scale by combining data from multiple regions. However, these basin-wide models often overlook geographical variations in species habitat relationships between regions. We tested for geographical variations in habitat relationships for a suite of cetacean taxa between the two sides of the North Atlantic basin. Using cetacean visual survey data and remote sensing data from the western and eastern basin in summer, we related the probability of presence of twelve cetacean taxa from three guilds to seafloor depth, sea surface temperature and primary productivity. In a generalized additive model framework, we fitted 1) basin-wide (BW) models, assuming a single global relationship, 2) region-specific intercepts (RI) models, assuming relationships with the same shape in both regions, but allowing a region-specific intercept and 3) region-specific shape (RS) models, assuming relationships with different shapes between regions. RS models mostly yielded significantly better fits than BW models, indicating cetacean occurrences were better modeled with region-specific than with global relationships. The better fits of RS models over RI models further provided statistical evidence for differences in the shapes of region-specific relationships. Baleen whales showed striking differences in both the shapes of relationships and their mean presence probabilities between regions. Deep diving whales and delphinoids showed contrasting relationships between regions with few exceptions (e.g. non-statistically different shapes of region-specific relationships for harbor porpoise and beaked whales with depth). Our findings stress the need to account for geographical differences in habitat relationships between regions when modeling species distributions from combined data at the basin scale. Our proposed hypotheses offer a roadmap for understanding why habitat relationships may geographically vary in cetaceans and other highly mobile marine species.

Aim: Deep‐diving cetaceans are oceanic species exposed to multiple anthropogenic pressures including high intensity underwater noise, and knowledge of their distribution is crucial to manage their conservation. Due to intrinsic low densities, wide distribution ranges and limited presence at the sea surface, these species are rarely sighted. Pooling data from multiple visual surveys sharing a common linetransect methodology can increase sightings but requires accounting for heterogeneity in protocols and platforms. Location: North Atlantic Ocean and Mediterranean Sea. Time period: 1998 to 2015. Major taxa: Ziphiidae; Physeteriidae; Kogiidae. Methods: About 1,240,000 km of pooled effort provided 630 sightings of ziphiids, 836 of physeteriids and 106 of kogiids. For each taxon, we built a hierarchical model to estimate the effective strip width depending on observation conditions and survey types. We then modelled relative densities in a generalized additive modelling framework. Geographical predictions were limited to interpolations identified with a gap analysis of environmental space coverage. Results: Deeper areas of the North Atlantic gyre were mostly environmental extrapolation in the predictions, thereby highlighting gaps in sampling across the different surveys. For the three species groups, the highest relative densities were predicted along continental slopes, particularly in the western North Atlantic Ocean where the Gulf Stream creates dynamic frontal zones and eddies. Main conclusions: Pooling a large number of surveys provided the first basin‐wide models of distribution for deep‐diving cetaceans, including several data‐deficient taxa, across the North Atlantic Ocean and the Mediterranean Sea. These models can help the conservation of elusive and poorly known marine megafauna.

While ecologists have long recognized the influence of spatial resolution on species distribution models (SDMs), they have given relatively little attention to the influence of temporal resolution. Considering temporal resolutions is critical in distribution modelling of highly mobile marine animals, as they interact with dynamic oceanographic processes that vary at time-scales from seconds to decades. We guide ecologists in selecting temporal resolutions that best match ecological questions and ecosystems, and managers in applying these models. We group the temporal resolutions of environmental variables used in SDMs into three classes: instantaneous, contemporaneous and climatological. We posit that animal associations with fine-scale and ephemeral features are best modelled with instantaneous covariates. Associations with large scale and persistent oceanographic features are best modelled with climatological covariates. Associations with mesoscale features are best modelled with instantaneous or contemporaneous covariates if ephemeral processes are present or interannual variability occurs, and climatological covariates if seasonal processes dominate and interannual variability is weak.

As human activities expand beyond national jurisdictions to the high seas, there is increasing need to consider anthropogenic impacts to species that inhabit these waters. The current scarcity of scientific observations of cetaceans in the high seas impedes the assessment of population-level impacts of these activities. This study is directed towards an important management need in the high seas—the development of plausible density estimates to facilitate a quantitative assessment of anthropogenic impacts on cetacean populations in these waters. Our study region extends from a well-surveyed region within the United States Exclusive Economic Zone into a large region of the western North Atlantic sparsely surveyed for cetaceans. We modeled densities of 15 cetacean taxa using available line transect survey data and habitat covariates and extrapolated predictions to sparsely surveyed regions. We formulated models carefully 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 performed several qualitative assessments, such as mapping where predictions were made outside sampled covariate ranges, and comparing them with maps of sightings from a variety of sources that could not be integrated into our models. Our study revealed a range of confidence levels for the model results depending on the taxon and geographic area, and highlights the need for additional surveying in environmentally distinct areas. Combined with their explicit confidence levels and necessary caution, our density estimates can inform a variety of management needs in the high seas, such as the quantification of potential cetacean interactions with military training exercises, shipping, fisheries, deep-sea mining, as well as delineation of 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. This article is protected by copyright. All rights reserved

International audience; 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 'trans-ferability') 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. Predicting the Unknown Predictions facilitate the formulation of quantitative, testable hypotheses that can be refined and validated empirically [1]. Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in many situations, severe data deficiencies preclude the development of specific models, and the collection of new data can be prohibitively costly or simply impossible [6]. It is in this context that interest in transferable models (i.e., those that can be legitimately projected beyond the spatial and temporal bounds of their underlying data [7]) has grown. Transferred models must balance the tradeoff between estimation and prediction bias and variance (homogenization versus nontransferability, sensu [8]). Ultimately, models that can Highlights Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions.

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.

Aim Our knowledge of cetacean distributions is impeded by large data-gaps worldwide, particularly at tropical latitudes. This study aims to (1) find generic relationships between cetaceans and their habitats in a range of tropical waters, and (2) extrapolate cetacean densities in a circumtropical belt extending far beyond surveyed regions. Location Pelagic, circumtropical. Methods Aerial surveys were conducted over three regions in the tropical Atlantic (132,000 km2), Indian (1.4 million km2) and Pacific (1.4 million km2) oceans. Three cetacean guilds were studied (Delphininae, Globicephalinae and sperm and beaked whales). For each guild, a generalized additive model was fitted using sightings recorded in all three regions and 14 candidate environmental predictors. Cetacean densities were tentatively extrapolated over a circumtropical belt, excluding waters where environmental characteristics departed from those encountered in the surveyed regions. Results Each cetacean guild exhibited a relationship with the primary production and depth of the minimum dissolved oxygen concentration. Delphininae also showed a relationship with the dominant phytoplankton group. The prediction envelopes were primarily constrained by water temperature. Circumtropical extrapolations of Delphininae and Globicephalinae were contrasted between ocean basins, with high densities predicted in the equatorial waters of the three ocean basins. The predicted densities of sperm and beaked whales were lower and more uniform across the circumtropical belt than for the other two guilds. Main conclusions Our modelling approach represents a good analytical solution to predicting cetacean population densities in poorly documented tropical waters. Future data collection should concentrate on areas where environmental characteristics were not encountered in our survey regions and where the predicted densities were the most uncertain. By highlighting cetacean hotspots far beyond waters under national jurisdiction, this study can provide guidance for the delimitation of Ecologically and Biologically Significant Marine Areas.

International audience; Top predators need to develop optimal strategies of resources and habitats utilization in order to optimize their foraging success. At the individual scale, a predator has to maximize his intake of food while minimizing his cost of foraging to optimize his energetic gain. At the ecosystem scale, we hypothesized that foraging strategies of predators also respond to their general energetic constraints. Predators with energetically costly lifestyles may be constrained to select high quality habitats whereas more phlegmatic predators may occupy both low and high quality habitats. The objectives of this study were 1) to investigate predator responses to heterogeneity in habitat quality with reference to their energetic strategies and 2) to evaluate their responses to contemporaneous versus averaged habitat quality. We collected cetacean and seabird data from an aerial survey in the Southwest Indian Ocean, a region characterized by heterogeneous oceanographic conditions. We classified cetaceans and seabirds into energetic guilds and described their habitats using remotely sensed covariates at contemporaneous and time-averaged resolutions and static covariates. We used generalized additive models to predict their habitats at the regional scale. Strategies of habitat utilization appeared in accordance with predators energetic constraints. Cetaceans responded to the heterogeneity in habitat quality, with higher densities predicted in more productive areas. However, the costly Delphininae appeared to be more dependent on habitat quality (showing a 1-to-13 ratio between the lowest and highest density sectors) than the more phlegmatic sperm and beaked whales (showing only a 1-to-3 ratio). For seabirds, predictions primarily reflected colony locations, although the colony effect was stronger for costly seabirds. Moreover, our results suggest that predators may respond better to persistent oceanographic features. To provide a third dimension to habitat quality, cetacean strategies of utilization of the vertical habitat could be related to the distribution of micronekton in the water column.

International audience; Oligotrophic regions are expected to host low densities of top predators. Nevertheless, top predators with contrasting energetic costs might respond differently to the productivity of their habitats. Predators with high energetic demands might be constrained to select the most productive habitats to meet their high energetic requirements, whereas less active predators would be able to satisfy their needs by exploiting either high or low productivity habitats. Although situated in the core of the South Pacific oligotrophic gyre, French Polynesia is characterized by a fairly marked productivity gradient from the extremely oligotrophic Australs area to the more productive Marquesas area. The aim of this study was to investigate cetacean and seabird habitats in French Polynesia in light of their general energetic constraints. We collected cetacean and seabird sightings from an aerial survey across French Polynesian waters during the austral summer 2011. We classified cetaceans and seabirds into energetic guilds according to the literature. For each guild, we built generalized additive models along with static covariates and oceanographic covariates at the seasonal and climatological resolutions. We provided regional habitat predictions for Delphininae, Globicephalinae, sperm and beaked whales, tropicbirds, grey terns, noddies, white terns, boobies, petrels and shearwaters, sooty terns and frigatebirds. Explained deviances ranged from 5% to 30% for cetaceans and from 14% to 29% for seabirds. Cetaceans clearly responded to the productivity gradient, with the highest predicted densities around the productive waters of the Marquesas. However, Delphininae and Globicephalinae, characterized by higher energetic demands, depended more strongly on productivity, showing a ratio of 1-26 and 1-31 between their lowest and highest density areas respectively, compared to the less active sperm and beaked whales (showing only a ratio of 1-3.5 in predicted densities). In contrast, seabird distributions appeared more governed by the availability of nesting and roosting sites than by energetic constraints.

International audience; Productivity was shown to structure megavertebrate communities, particularly in tropical oceans where they may be under selective pressure to maintain their foraging success in these low productivity environments. However, overall oligotrophic tropical oceans may encompass contrasting ecosystems. In the western tropical Atlantic, the Antilles and Guiana belong to two different biogeographic provinces and surface chlorophyll concentration is on average 10 times higher in Guiana. The aim of this study was to document megavertebrate communities in terms of encounter rates, species composition, abundance and spatial distribution and to compare them between these two contrasting regions. As part of an aerial census of cetaceans and other megavertebrates, line transect surveys were conducted across the French Antilles and Guiana. Both distance sampling and geostatistics were used to estimate abundance and geostatistics also provided maps of local density. Cetacean encounter rate was 3 times higher in Guiana (1.8 sightings/100 km) compared to the Antilles (0.6 sightings/100 km). Moreover, small delphinids strongly dominated in Guiana (representing 83% of the odontocete sightings) with a high abundance of bottlenose dolphins (Tursiops truncatus) (34,965 individuals, CV=0.28). Seabird encounter rate was similar in both regions (4.7 sightings/100 km in the Antilles and 3.6 sightings/100 km in Guiana). Charadriiformes, mainly represented by terns, were abundant in both regions whereas Pelecaniformes were only abundant in the Antilles and represented by frigatebirds, tropicbirds and boobies. Additionally, cetaceans and seabirds were mainly distributed close to the islands in the Antilles and were more widely distributed in Guiana. This study provided new information about megavertebrates which are poorly documented in these waters and highlighted differences in communities between these contrasting ecosystems. In particular, odontocete communities appeared to respond to productivity and may be structured by a differential distribution of resources through the water column. (C) 2012 Elsevier B.V. All rights reserved.