The importance of scale when investigating ecological patterns and processes is recognised across many species. In marine ecosystems, the processes that drive species distribution have a hierarchical structure over multiple nested spatial and temporal scales. Hence, multi-scale approaches should be considered when developing accurate distribution models to identify key habitats, particularly for populations of conservation concern. Here, we propose a modelling procedure to identify the best spatial and temporal scale for each modelled and remotely sensed oceanographic variable to model harbour porpoise (Phocoena phocoena) distribution. Harbour porpoise sightings were recorded during dedicated line-transect aerial surveys conducted in the summer of 2016, 2021 and 2022 in the northeast Atlantic. Binary generalised additive models were used to assess the relationships between porpoise presence and oceanographic variables at different spatial (5, 20 and 40 km) and temporal (daily, monthly and across survey period) scales. Selected variables included sea surface temperature, thermal fronts, chlorophyll-a, sea surface height, mixed layer depth and salinity. A total of 30,514 km was covered on-effort with 216 harbour porpoise sightings recorded. Overall, the best spatial scale corresponded to the coarsest resolution considered in this study (40 km), while porpoise presence showed stronger association with oceanographic variables summarised at a longer temporal scale (monthly and averaged over survey period). Habitat models including covariates at coarse spatial and temporal scales may better reflect the processes driving availability and abundance of prey resources at the large scales covered during the surveys. These findings support the hypothesis that a multi-scale approach should be applied when investigating species distribution. Identifying suitable spatial and temporal scale would improve the functional interpretation of the underlying relationships, particularly when studying how a small marine predator interacts with its environment and responds to climate and ecosystem changes.

Passive acoustic monitoring (PAM) is a cost-effective method for monitoring cetacean populations compared to techniques such as aerial and ship-based surveys. The C-POD (Cetacean POrpoise Detector) has become an integral tool in monitoring programmes globally for over a decade, providing standardised metrics of occurrence that can be compared across time and space. However, the phasing out of C-PODs following development of the new F-POD (Full waveform capture Pod) with increased sensitivity, improved train detection, and reduced false positive rates, represents an important methodological change in data collection, particularly when being introduced into existing monitoring programmes. Here, we compare the performance of the C-POD with that of its successor, the F-POD, co-deployed in a field setting for 15 months, to monitor harbour porpoise (Phocoena phocoena). While similar temporal trends in detections were found for both devices, the C-POD detected only 58% of the detection positive minutes (DPM), recorded by the F-POD. Differences in detection rates were not consistent through time making it difficult to apply a correction factor or directly compare results obtained from the two PODs. To test whether these differences in detection rates would have an effect on analyses of temporal patterns and environmental drivers of occurrence, generalised additive models (GAMs) were applied. No differences were found in seasonal patterns or the environmental correlates of porpoise occurrence (month, diel period, temperature, environmental noise, and tide). However, the C-POD failed to detect sufficient foraging buzzes to identify temporal patterns in foraging behaviour that were clearly shown by the F-POD. Our results suggest that the switch to F-PODs will have little effect on determining broad-scale seasonal patterns of occurrence, but may improve our understanding of fine-scale behaviours such as foraging. We highlight how care must be taken interpreting F-POD results as indicative of increased occurrence when used in time-series analysis.

Animal-borne telemetry devices provide essential insights into the life-history strategies of far-ranging species and allow us to understand how they interact with their environment. Many species in the seabird family Alcidae undergo a synchronous moult of all primary flight feathers during the non-breeding season, making them flightless and more susceptible to environmental stressors, including severe storms and prey shortages. However, the timing and location of moult remains largely unknown, with most information coming from studies on birds killed by storms or shot at sea. Using light-level geolocators with saltwater immersion loggers, we develop a method for determining flightless periods in the context of the annual cycle. Four Atlantic puffins (Fratercula arctica) were equipped with geolocator/immersion loggers on each leg to attempt to overcome issues of leg-tucking in plumage while sitting on the water, which confounds the interpretation of logger data. Light level and saltwater immersion time-series data were combined to correct for this issue. This approach was adapted and applied to 40 puffins equipped with the standard practice deployments of geolocators on one leg only. Flightless periods consistent with moult were identified in the dual-equipped birds, whereas moult identification in single-equipped birds was less definitive and should be treated with caution. Within the dual-equipped sample, we present evidence for two flightless moult periods per non-breeding season in two puffins that undertook more extensive migrations (> 2000km), and were flightless for up to 76 days in a single non-breeding season. A biannual flight feather moult is highly unusual among non-passerine birds, and may be unique to birds that undergo catastrophic moult, i.e. become flightless when moulting. Though our conclusions are based on a small sample, we have established a freely available methodological framework for future investigation of the moult patterns of this and other seabird species.

Many of the species in decline around the world are subject to different environmental stressors across their range, so replicated large-scale monitoring programmes, are necessary to disentangle the relative impacts of these threats. At the same time as funding for long-term monitoring is being cut, studies are increasingly being criticised for lacking statistical power. For those taxa or environments where a single vantage point can observe individuals or ecological processes, time-lapse cameras can provide a cost-effective way of collecting time series data replicated at large spatial scales that would otherwise be impossible. However, networks of time-lapse cameras needed to cover the range of species or processes create a problem in that the scale of data collection easily exceeds our ability to process the raw imagery manually. Citizen science and machine learning provide solutions to scaling up data extraction (such as locating all animals in an image). Crucially, citizen science, machine learning-derived classifiers, and the intersection between them, are key to understanding how to establish monitoring systems that are sensitive to – and sufficiently powerful to detect –changes in the study system. Citizen science works relatively ‘out of the box’, and we regard it as a first step for many systems until machine learning algorithms are sufficiently trained to automate the process. Using Penguin Watch (www.penguinwatch.org) data as a case study, we discuss a complete workflow from images to parameter estimation and interpretation: the use of citizen science and computer vision for image processing, and parameter estimation and individual recognition for investigating biological questions. We discuss which techniques are easily generalizable to a range of questions, and where more work is needed to supplement ‘out of the box’ tools. We conclude with a horizon scan of the advances in camera technology, such as on-board computer vision and decision making.

1. Understanding the animal-habitat relationship at local scale is crucial in ecology, particularly to develop strategies for wildlife management and conservation. As this relationship is governed by environmental features and intra and inter-specific interactions, habitat selection of a population may vary locally between its core and edges. 2. This is particularly true for central place foragers, such as grey and harbour seals, whose trends in numbers vary among different regions in the Northeast Atlantic. Here, we aimed at studying how foraging habitat selection may vary locally with the influence of population trends and physical habitat features 3. Using GPS/GSM tags deployed in grey and harbour seal colonies of contrasting sizes, we investigate spatial patterns and foraging habitat selection by comparing trip characteristics and home range similarities, and fitting GAMM to the seal distribution and environmental data respectively. 4. We show that grey seal foraging habitat selection and spatial patterns differed markedly between regions. Grey seals may select environmental characteristics for their foraging habitat accounting for local differences in prey consumed. Spatial patterns were different might depend on local seal density and regional productivity, located from inshore to offshore areas for the limit ranges and core population respectively. Our results on foraging habitat selection reflected the coastal and sedentary behaviour of harbour seals. We found no difference in spatial patterns between colonies, except for the Inner Hebrides where seals foraged further, potentially reflecting density dependence pressure, as the number in this colony is higher. 5. These results suggest that local conditions might have a strong influence on population spatial ecology, highlighting as well the relevance of studying foraging habitat selection based on foraging behaviour at fine geographical scale, particularly if species are managed within regional units.