Passive acoustic monitoring is increasingly being used as a cost‐effective way to study wildlife populations, especially those that are difficult to census using conventional methods. Burrow‐nesting seabirds are among the most threatened birds globally, but they are also one of the most challenging taxa to census, making them prime candidates for research into such automated monitoring platforms. Passive acoustic monitoring has the potential to determine presence/absence or quantify burrow‐nesting populations, but its effectiveness remains unclear. We compared passive acoustic monitoring, tape‐playbacks and GPS tracking data to investigate the ability of passive acoustic monitoring to capture unbiased estimates of within‐colony variation in nest density for the Manx Shearwater Puffinus puffinus. Variation in acoustic activity across 12 study plots on an island colony was examined in relation to burrow density and environmental factors across 2 years. As predicted fewer calls were recorded when wind speed was high, and on moon‐lit nights, but there was no correlation between acoustic activity and the density of breeding birds within the plots as determined by tape‐playback surveys. Instead, acoustic indices correlated positively with spatial variation in the in‐colony flight activity of breeding individuals detected by GPS. Although passive acoustic monitoring has enormous potential in avian conservation, our results highlight the importance of understanding behaviour when using passive acoustic monitoring to estimate density and distribution.
Marine Protected Areas (MPAs) are an important tool for the conservation of seabirds. However, mapping seabird distributions using at-sea surveys or tracking data to inform the designation of MPAs is costly and time-consuming, particularly for far-ranging pelagic species. Here we explore the potential for using predictive distribution models to examine the effectiveness of current MPAs for the conservation of seabirds, using Britain and Ireland as a case study. A distance-weighted foraging radius approach was used to project distributions at sea for an entire seabird community during the breeding season, identifying hotspots of highest density and species richness. The percentage overlap between distributions at sea and MPAs was calculated at the level of individual species, family group, foraging range group (coastal or pelagic foragers), and conservation status. On average, 32.5% of coastal populations and 13.2% of pelagic populations overlapped with MPAs, indicating that pelagic species (many of which are threatened) are likely to have significantly less coverage from protected areas. We suggest that a foraging radius approach provides a pragmatic and rapid method of assessing overlap with MPA networks for central place foragers. It can also act as an initial tool to identify important areas for potential designation. This would be particularly useful for regions throughout the world with limited data on seabird distributions at sea and limited resources to collect this data. Future assessment for marine conservation management should account for the disparity between coastal and pelagic foraging species to ensure that wider-ranging seabirds are afforded adequate levels of protection.
Relatively simple foraging radius models have the potential to generate predictive distributions for a large number of species rapidly, thus providing a cost-effective alternative to large-scale surveys or complex modelling approaches. Their effectiveness, however, remains largely untested. Here we compare foraging radius distribution models for all breeding seabirds in Ireland, to distributions of empirical data collected from tracking studies and aerial surveys. At the local/colony level, we compared foraging radius distributions to GPS tracking data from seabirds with short (Atlantic puffin Fratercula arctica, and razorbill Alca torda) and long (Manx shearwater Puffinus puffinus, and European storm-petrel Hydrobates pelagicus) foraging ranges. At the regional/national level, we compared foraging radius distributions to extensive aerial surveys conducted over a two-year period. Foraging radius distributions were significantly positively correlated with tracking data for all species except Manx shearwater. Correlations between foraging radius distributions and aerial survey data were also significant, but generally weaker than those for tracking data. Correlations between foraging radius distributions and aerial survey data were benchmarked against generalised additive models (GAMs) of the aerial survey data that included a range of environmental covariates. While GAM distributions had slightly higher correlations with aerial survey data, the results highlight that the foraging radius approach can be a useful and pragmatic approach for assessing breeding distributions for many seabird species. The approach is likely to have acceptable utility in complex, temporally variable ecosystems and when logistic and financial resources are limited.
Predicting the distribution and behaviour of animals is a fundamental objective in ecology and a cornerstone of conservation biology. Modelling the distribution of ocean-faring species like seabirds remains a significant challenge due to ocean dynamics, colony-specific effects and the vast ranges seabirds can cover. We used a spatial and behavioural approach to model the distribution of the Manx shearwater Puffinus puffinus, a pelagic, central-place forager that can cover great distances while foraging. GPS data from birds tagged in 2 colonies over 3 yr were modelled with a range of environmental predictors of marine productivity. For both colonies, transitions to foraging behaviour correlated with chlorophyll a, and the distribution of foraging behaviour was also associated with areas of high chlorophyll a concentration in coastal but not offshore areas for one colony. Furthermore, there was evidence for colony differences in habitat use, prevalence of nocturnal foraging, and for some competitive exclusion on foraging grounds, even though the colonies were 170 km apart. Despite the extensive dataset, our models had modest predictive power, which we suggest can probably only be improved by including biotic interactions, including more direct measures of food resource distribution. Our results highlight the importance of including spatial complexity and data from multiple sites when predicting the distribution of wide-ranging predators, because patterns of distribution and habitat use likely differ across the range of a population.
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