1. The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored.2. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF).3. We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology.4. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data.

1. The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and * led to the collection of ever-increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyze tracking data, however many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the R software alone, we listed 58 packages created to deal with tracking data or 'tracking packages'.2. Here we reviewed and described each tracking package based on a workflow centered around tracking data (i.e. spatio-temporal locations (x, y, t)), broken down into three stages: pre-processing, post-processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others. Supporting documentation is key to render a package accessible for users.Based on a user survey, we reviewed the quality of packages' documentation, and identified 11 packages with good or excellent documentation. 4. Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the R movement-ecology programming community.5. Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community.2 Keywords biologging, movement ecology, R project for statistical computing, spatial, tracking data

The identification of geographic areas where the densities of animals are highest across their annual cycles is a crucial step in conservation planning. In marine environments, however, it can be particularly difficult to map the distribution of species, and the methods used are usually biased towards adults, neglecting the distribution of other life‐history stages even though they can represent a substantial proportion of the total population. Here we develop a methodological framework for estimating population‐level density distributions of seabirds, integrating tracking data across the main life‐history stages (adult breeders and non‐breeders, juveniles and immatures). We incorporate demographic information (adult and juvenile/immature survival, breeding frequency and success, age at first breeding) and phenological data (average timing of breeding and migration) to weight distribution maps according to the proportion of the population represented by each life‐history stage. We demonstrate the utility of this framework by applying it to 22 species of albatrosses and petrels that are of conservation concern due to interactions with fisheries. Because juveniles, immatures and non‐breeding adults account for 47%–81% of all individuals of the populations analysed, ignoring the distributions of birds in these stages leads to biased estimates of overlap with threats, and may misdirect management and conservation efforts. Population‐level distribution maps using only adult distributions underestimated exposure to longline fishing effort by 18%–42%, compared with overlap scores based on data from all life‐history stages. Synthesis and applications. Our framework synthesizes and improves on previous approaches to estimate seabird densities at sea, is applicable for data‐poor situations, and provides a standard and repeatable method that can be easily updated as new tracking and demographic data become available. We provide scripts in the R language and a Shiny app to facilitate future applications of our approach. We recommend that where sufficient tracking data are available, this framework be used to assess overlap of seabirds with at‐sea threats such as overharvesting, fisheries bycatch, shipping, offshore industry and pollutants. Based on such an analysis, conservation interventions could be directed towards areas where they have the greatest impact on populations.

1. Incidental mortality (bycatch) in fisheries remains the greatest threat to many large marine vertebrates and is a major barrier to fisheries sustainability. Robust assessments of bycatch risk are crucial for informing effective mitigation strategies, but are hampered by missing information on the distributions of key life-history stages (adult breeders and non-breeders, immatures and juveniles).2. Using a comprehensive biologging dataset (1,692 tracks, 788 individuals) spanning all major life-history stages, we assessed spatial overlap of four threatened seabird populations from South Georgia, with longline and trawl fisheries in the Southern Ocean. We generated monthly population-level distributions, weighting each lifehistory stage according to population age structure based on demographic models. Specifically, we determined where and when birds were at greatest potential bycatch risk, and from which fleets.3. Overlap with both pelagic and demersal longline fisheries was highest for blackbrowed albatrosses, then white-chinned petrels, wandering and grey-headed albatrosses, whereas overlap with trawl fisheries was highest for white-chinned petrels.4. Hotspots of fisheries overlap occurred in all major ocean basins, but particularly the south-east and south-west Atlantic Ocean (longline and trawl) and south-west Indian Ocean (pelagic longline). Overlap was greatest with pelagic longline fleets in May-September, when fishing effort south of 25°S is highest, and with demersal and trawl fisheries in January-June. Overlap scores were dominated by particular fleets: pelagic longline-Japan, Taiwan; demersal longline and trawl-Argentina, Namibia, Falklands, South Africa; demersal longline-Convention for Conservation of Antarctic Marine Living Resources (CCAMLR) waters, Chile, New Zealand. Synthesis and applications.We provide a framework for calculating appropriately weighted population-level distributions from biologging data, which we | 1883Journal of Applied Ecology CLAY et AL.