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Quantifying feral pig movements and understanding the fine-scale ecological drivers of feral pig landscape use are important factors for optimising pest management programs. We tracked 59 GPS-collared feral pigs at four sites in eastern Australia between 2017 and 2021, for a mean of 375 ± 277 (s.d.) days. The mean number of successful GPS fixes was 15 577 ± 11 833 (s.d.) and these were recorded at 30-min intervals. We calculated mean hourly and daily distances travelled to determine feral pig activity and investigated the influence of sex, site, season and time of day on this activity. We also investigated the proximity of highly active sites to habitat covariates, along with intensity and frequency of site use. Male daily movement, 4.9 km (95% CI = 4.2, 5.6 km), was significantly greater than it was for females, 3.6 km (95% CI = 3.0, 4.1 km) and males maintained a high level of activity all night, while female activity was predominantly crepuscular. Study site was a significant determinant of daily movement, but season was not, across either sex or site. Highly-visited site selection was negatively associated with distance from creeks, dams, cultivation, open herbaceous vegetation and medium woody vegetation. Both medium woody vegetation and dam sites had the longest duration of use (3 and 2.7 h respectively) and the shortest time between visitations (14.5 and 13 h respectively). Quantifying feral pig activity and key habitat feature preference are important steps in improving management programs. Better prediction of feral pig movement and behaviour allows for more targeted placement of control tools, potentially increasing encounter rates. Understanding feral pig activity and habitat selection at fine scales will enable refinements in pest management programs. By tracking feral pigs at four study sites in Australia, we found that sex, site and time of day influence activity, while season does not. Highly-revisited sites were negatively correlated with distance to water, cultivation, open herbaceous vegetation and medium woody vegetation Targeting such areas with monitoring or control tools may improve feral pig encounter rates. [ABSTRACT FROM AUTHOR]

Globally, marine animal distributions are shifting in response to a changing climate. These shifts are usually considered at the species level, but individuals are likely to differ in how they respond to the changing conditions. Here, we investigate how movement behaviour and, therefore, redistribution, would differ by sex and maturation class in a wide-ranging marine predator. We tracked 115 tiger sharks (Galeocerdo cuvier) from 2002 to 2020 and forecast class-specific distributions through to 2030, including environmental factors and predicted occurrence of potential prey. Generalised Linear and Additive Models revealed that water temperature change, particularly at higher latitudes, was the factor most associated with shark movements. Females dispersed southwards during periods of warming temperatures, and while juvenile females preferred a narrow thermal range between 22 and 23 °C, adult female and juvenile male presence was correlated with either lower (< 22 °C) or higher (> 23 °C) temperatures. During La Niña, sharks moved towards higher latitudes and used shallower isobaths. Inclusion of predicted distribution of their putative prey significantly improved projections of suitable habitats for all shark classes, compared to simpler models using temperature alone. Tiger shark range off the east coast of Australia is predicted to extend ~ 3.5° south towards the east coast of Tasmania, particularly for juvenile males. Our framework highlights the importance of combining long-term movement data with multi-factor habitat projections to identify heterogeneity within species when predicting consequences of climate change. Recognising intraspecific variability will improve conservation and management strategies and help anticipate broader ecosystem consequences of species redistribution due to ocean warming. [ABSTRACT FROM AUTHOR]

Aim: To assess the efficacy of marine reserves in Australia for shy albatross, using long‐term tracking data. Location: Albatross Island, Tasmania, and south Australian waters. Methods: We integrated a tracking dataset consisting of 111 individuals collected over 23 years and generated Brownian bridge kernel density estimations to identify important habitat. We quantified the overlap between the foraging distribution of early incubating adults and post‐fledgling juveniles with management boundaries and marine reserves. We compared the extent of coverage of albatross foraging areas by Marine Protection Areas (MPAs) relative to a randomly designed network of the same size to determine whether the spatial protection measures are likely to be effective. Results: Incubating adults consistently foraged in waters to the northwest of Tasmania while post‐fledglings occupied shelf waters around Tasmania and South Australia. We show that our sample of 99 incubating adults adequately represented the population but that our sample of 12 post‐fledgling birds was insufficient, thereby limiting the confidence in our results for this life stage. The Commonwealth Government has the majority of management responsibility for shy albatross at‐sea, containing 88% and 90% of the area occupied most intensively by adult and post‐fledgling shy albatross, respectively. Randomly designed reserve networks outperformed the current MPA network for both life stages, such that the mean protection by a random reserve system was 30% and 12% higher than the actual protection for adults and juveniles in Commonwealth waters. Main conclusions: Important foraging habitat of shy albatross from Albatross Island is mostly within Commonwealth‐managed waters. The current MPA network, the only spatial protection measure for shy albatross, provides less coverage for this species than a randomly placed network. An increase in the representation of productive shelf waters in MPA networks would benefit the conservation of shy albatross through reducing fisheries interactions and protecting habitat in these regions. [ABSTRACT FROM AUTHOR]