Home‐range estimation is an important application of animal tracking data that is frequently complicated by autocorrelation, sampling irregularity, and small effective sample sizes. We introduce a novel, optimal weighting method that accounts for temporal sampling bias in autocorrelated tracking data. This method corrects for irregular and missing data, such that oversampled times are downweighted and undersampled times are upweighted to minimize error in the home‐range estimate. We also introduce computationally efficient algorithms that make this method feasible with large data sets. Generally speaking, there are three situations where weight optimization improves the accuracy of home‐range estimates: with marine data, where the sampling schedule is highly irregular, with duty cycled data, where the sampling schedule changes during the observation period, and when a small number of home‐range crossings are observed, making the beginning and end times more independent and informative than the intermediate times. Using both simulated data and empirical examples including reef manta ray, Mongolian gazelle, and African buffalo, optimal weighting is shown to reduce the error and increase the spatial resolution of home‐range estimates. With a conveniently packaged and computationally efficient software implementation, this method broadens the array of data sets with which accurate space‐use assessments can be made.

Knowledge of the three-dimensional movement patterns of elasmobranchs is vital to understand their ecological roles and exposure to anthropogenic pressures. To date, comparative studies among species at global scales have mostly focused on horizontal movements. Our study addresses the knowledge gap of vertical movements by compiling the first global synthesis of vertical habitat use by elasmobranchs from data obtained by deployment of 989 biotelemetry tags on 38 elasmobranch species. Elasmobranchs displayed high intra- and interspecific variability in vertical movement patterns. Substantial vertical overlap was observed for many epipelagic elasmobranchs, indicating an increased likelihood to display spatial overlap, biologically interact, and share similar risk to anthropogenic threats that vary on a vertical gradient. We highlight the critical next steps toward incorporating vertical movement into global management and monitoring strategies for elasmobranchs, emphasizing the need to address geographic and taxonomic biases in deployments and to concurrently consider both horizontal and vertical movements.

The recent Kunming-Montreal Global Biodiversity Framework (GBF) sets ambitious goals but no clear pathway for how zero loss of important biodiversity areas and halting human-induced extinction of threatened species will be achieved. We assembled a multi-taxa tracking dataset (11 million geopositions from 15,845 tracked individuals across 121 species) to provide a global assessment of space use of highly mobile marine megafauna, showing that 63% of the area that they cover is used 80% of the time as important migratory corridors or residence areas. The GBF 30% threshold (Target 3) will be insufficient for marine megafauna’s effective conservation, leaving important areas exposed to major anthropogenic threats. Coupling area protection with mitigation strategies (e.g., fishing regulation, wildlife-traffic separation) will be essential to reach international goals and conserve biodiversity.

Aim: Our aim was to collect sightings data on oceanic manta rays (Mobula birostris) within the Raja Ampat Archipelago to better understand their population dynamics within the region. These data were compared with environmental variables to seek correlates that may explain any variations in observed sightings frequency. Combined, it is hoped this knowledge will be used to aid effective management of this species in the region.Location: Raja Ampat Archipelago, West Papua, Indonesia.Methods: We collected and catalogued photo‐identification of individuals to create a sightings database. To generate estimates of abundance, survival, sighting probability and recruitment to the population, we used a POPAN mark–recapture model. We considered time‐varying and fixed values for each parameter and possible covariate relationships of the El Niño–Southern Oscillation (ENSO) and sex.Results: A total of 588 individuals were identified over six years, of which 72.4% were female, and 28.2% of individuals were resighted. There was an exponential increase in sightings during the 2015–2016 ENSO event despite constant effort; significant correlation was found between sightings and the multivariate ENSO index and with sea surface temperatures but not with chlorophyll‐a. Mark–recapture analysis shows a clear relationship between ENSO and entry probability, and the most parsimonious model estimated a superpopulation size N of 1875 individuals.Main conclusion: Oceanic manta ray distributions appear to be impacted by ENSO‐related climate phenomena. Our findings on the relationship of ENSO to manta sightings and distribution indicate that oceanic manta rays are likely sensitive to large‐scale climatic variability. This illustrates the potential impacts of climate change on oceanic manta populations and the need to consider climate impacts in developing management strategies. Continued photo‐ID, tagging and population genetics would greatly enhance knowledge and help develop management strategies that bolster conservation of the species.

This study explores the application of small, commercially available drones to determine morphometric the measurements and record key demographic parameters of reef manta rays (Mobula alfredi) in Raja Ampat, Indonesia. DJI Mavic 2 Pro drones were used to obtain videos of surface-feeding M. alfredi with a floating, known-length PVC pipe as a reference scale—thus avoiding the need to utilize altitude readings, which are known to be unreliable in small drones, in our photogrammetry approach. Three dimensions (disc length (DL), disc width (DW), and cranial width (CW)) from 86 different individuals were measured. A hierarchical multivariate model was used to estimate the true measurements of these three dimensions and their population-level multivariate distributions. The estimated true measurements of these dimensions were highly accurate and precise, with the measurement of CW more accurate than that of DL and, especially, of DW. Each pairing of these dimensions exhibited strong linear relationships, with estimated correlation coefficients ranging from 0.98–0.99. Given these, our model allows us to accurately calculate DW (as the standard measure of body size for mobulid rays) using the more accurate CW and DL measurements. We estimate that the smallest mature M. alfredi of each sex we measured were 274.8 cm (males, n = 30) and 323.5 cm DW (females, n = 8). We conclude that small drones are useful for providing an accurate “snapshot” of the size distribution of surface-feeding M. alfredi aggregations and for determining the sex and maturity of larger individuals, all with minimal impact on this vulnerable species.