Intrinsic and extrinsic factors drive ontogeny of early-life at-sea behaviour in a marine top predator

Young animals must learn to forage effectively to survive the transition from parental provisioning to independent feeding. Rapid development of successful foraging strategies is particularly important for capital breeders that do not receive parental guidance after weaning. The intrinsic and extrinsic drivers of variation in ontogeny of foraging are poorly understood for many species. Grey seals (Halichoerus grypus) are typical capital breeders; pups are abandoned on the natal site after a brief suckling phase, and must develop foraging skills without external input. We collected location and dive data from recently-weaned grey seal pups from two regions of the United Kingdom (the North Sea and the Celtic and Irish Seas) using animal-borne telemetry devices during their first months of independence at sea. Dive duration, depth, bottom time, and benthic diving increased over the first 40 days. The shape and magnitude of changes differed between regions. Females consistently had longer bottom times, and in the Celtic and Irish Seas they used shallower water than males. Regional sex differences suggest that extrinsic factors, such as water depth, contribute to behavioural sexual segregation. We recommend that conservation strategies consider movements of young naïve animals in addition to those of adults to account for developmental behavioural changes

Habitat partitioning in sympatric delphinids around the Falkland Islands : predicting distributions based on a limited data set

Funding: The field work was funded by the Darwin Initiative UK Overseas Territories Challenge Fund Project “Inshore Cetaceans of the Falkland Islands” (Project Ref: EIDCF019, administered jointly by Falklands Conservation & Mr Grant Munro), and Darwin Plus: Overseas Territories Environment and Climate Fund Project “Dolphins of the kelp: Data priorities for Falkland’s inshore cetaceans” (Project Ref: DPLUS042, administered by SAERI).Spatial modelling based on line transect data is a standard method for characterising marine mammal distributions and habitat preference. However, collecting the data required is costly and may be difficult in remote areas. Models based on habitat variables offer the potential to predict where the species will occur in areas outside the area of a localised survey. This has important implications for spatial management where decisions have to be made that affect wide areas over which comprehensive survey efforts may not be feasible. This study demonstrates that it is possible, using a spatially limited data set, to characterise habitat use and predict the distribution of two poorly known sympatric delphinids around the Falkland (Malvinas) Islands (FI), Commerson’s dolphins (Cephalorhynchus commersonii) and Peale’s dolphins (Lagenorhynchus australis). We used a Hurdle model approach to investigate the relationship between dolphin sightings (from a spatially restricted boat-based line transect survey) and environmental covariates. We then used the modelled relationships to predict the distribution and relative abundance of Commerson’s and Peale’s dolphins over the entire FI inshore waters. We compared the predicted distribution maps to independent sightings from a subsequent island-wide aerial line transect survey, and found a close match between predicted and observed distributions. Commerson’s dolphins preferred nearshore waters with strong tidal mixing and were most numerous close to river mouths and in upper inlets or channels. In contrast, Peale’s dolphins preferred deeper, well-stratified areas further from shore as well as nearshore waters with extensive kelp beds. While the two dolphin species are often considered sympatric, our results indicate fine-scale habitat partitioning based on specific habitat preferences, which is important to consider in further studies and marine spatial planning. We provide several methodological refinements to prepare transect sighting data for spatial analysis and implement Hurdle models more easily using the new “dshm” R-package. We also show the usefulness of such refinements applied to a carefully chosen spatially limited dataset as a cost-effective approach to elucidating species distribution patterns. Our methodology and software implementations can be easily applied to transect survey data of other marine and terrestrial taxa.Publisher PDFPeer reviewe

Animal Borne Ocean Sensors – AniBOS – An Essential Component of the Global Ocean Observing System

International audienceMarine animals equipped with biological and physical electronic sensors have produced long-term data streams on key marine environmental variables, hydrography, animal behavior and ecology. These data are an essential component of the Global Ocean Observing System (GOOS). The Animal Borne Ocean Sensors (AniBOS) network aims to coordinate the long-term collection and delivery of marine data streams, providing a complementary capability to other GOOS networks that monitor Essential Ocean Variables (EOVs), essential climate variables (ECVs) and essential biodiversity variables (EBVs). AniBOS augments observations of temperature and salinity within the upper ocean, in areas that are under-sampled, providing information that is urgently needed for an improved understanding of climate and ocean variability and for forecasting. Additionally, measurements of chlorophyll fluorescence and dissolved oxygen concentrations are emerging. The observations AniBOS provides are used widely across the research, modeling and operational oceanographic communities. High latitude, shallow coastal shelves and tropical seas have historically been sampled poorly with traditional observing platforms for many reasons including sea ice presence, limited satellite coverage and logistical costs. Animal-borne sensors are helping to fill that gap by collecting and transmitting in near real time an average of 500 temperature-salinity-depth profiles per animal annually and, when instruments are recovered (∼30% of instruments deployed annually, n = 103 ± 34), up to 1,000 profiles per month in these regions. Increased observations from under-sampled regions greatly improve the accuracy and confidence in estimates of ocean state and improve studies of climate variability by delivering data that refine climate prediction estimates at regional and global scales. The GOOS Observations Coordination Group (OCG) reviews, advises on and coordinates activities across the global ocean observing networks to strengthen the effective implementation of the system. AniBOS was formally recognized in 2020 as a GOOS network. This improves our ability to observe the ocean’s structure and animals that live in them more comprehensively, concomitantly improving our understanding of global ocean and climate processes for societal benefit consistent with the UN Sustainability Goals 13 and 14: Climate and Life below Water. Working within the GOOS OCG framework ensures that AniBOS is an essential component of an integrated Global Ocean Observing System

Animal Borne Ocean Sensors – AniBOS – An Essential Component of the Global Ocean Observing System

International audienceMarine animals equipped with biological and physical electronic sensors have produced long-term data streams on key marine environmental variables, hydrography, animal behavior and ecology. These data are an essential component of the Global Ocean Observing System (GOOS). The Animal Borne Ocean Sensors (AniBOS) network aims to coordinate the long-term collection and delivery of marine data streams, providing a complementary capability to other GOOS networks that monitor Essential Ocean Variables (EOVs), essential climate variables (ECVs) and essential biodiversity variables (EBVs). AniBOS augments observations of temperature and salinity within the upper ocean, in areas that are under-sampled, providing information that is urgently needed for an improved understanding of climate and ocean variability and for forecasting. Additionally, measurements of chlorophyll fluorescence and dissolved oxygen concentrations are emerging. The observations AniBOS provides are used widely across the research, modeling and operational oceanographic communities. High latitude, shallow coastal shelves and tropical seas have historically been sampled poorly with traditional observing platforms for many reasons including sea ice presence, limited satellite coverage and logistical costs. Animal-borne sensors are helping to fill that gap by collecting and transmitting in near real time an average of 500 temperature-salinity-depth profiles per animal annually and, when instruments are recovered (∼30% of instruments deployed annually, n = 103 ± 34), up to 1,000 profiles per month in these regions. Increased observations from under-sampled regions greatly improve the accuracy and confidence in estimates of ocean state and improve studies of climate variability by delivering data that refine climate prediction estimates at regional and global scales. The GOOS Observations Coordination Group (OCG) reviews, advises on and coordinates activities across the global ocean observing networks to strengthen the effective implementation of the system. AniBOS was formally recognized in 2020 as a GOOS network. This improves our ability to observe the ocean’s structure and animals that live in them more comprehensively, concomitantly improving our understanding of global ocean and climate processes for societal benefit consistent with the UN Sustainability Goals 13 and 14: Climate and Life below Water. Working within the GOOS OCG framework ensures that AniBOS is an essential component of an integrated Global Ocean Observing System