Year-round distribution of Northeast Atlantic seabird populations: applications for population management and marine spatial planning

Abstract

Tracking data of marine predators are increasingly used in marine spatial management. We developed a spatial data set with estimates of the monthly distribution of 6 pelagic seabird species breeding in the Northeast Atlantic. The data set was based on year-round global location sensor (GLS) tracking data of 2356 adult seabirds from 2006-2019 from a network of seabird colonies, data describing the physical environment and data on seabird population sizes. Tracking and environmental data were combined in monthly species distribution models (SDMs). Cross-validations were used to assess the transferability of models between years and breeding locations. The analyses showed that birds from colonies close to each other (<500 km apart) used the same nonbreeding habitats, while birds from distant colonies (>1000 km) used colony-specific and, in many cases, non-overlapping habitats. Based on these results, the SDM from the nearest model colony was used to predict the distribution of all seabird colonies lying within a species-specific cut-off distance (400-500 km). Uncertainties in the predictions were estimated by cluster bootstrap sampling. The resulting data set consisted of 4692 map layers, each layer predicting the densities of birds from a given species, colony and month across the North Atlantic. This data set represents the annual distribution of 23.5 million adult pelagic seabirds, or 87% of the Northeast Atlantic breeding population of the study species. We show how the data set can be used in population and spatial management applications, including the detection of population-specific nonbreeding habitats and identifying populations influenced by marine protected areas.

Tracking the movements of North Atlantic seabirds: steps towards a better understanding of population dynamics and marine ecosystem conservation

“…If the niche is constant across space, the estimated relationship can be used to predict the spatial distribution of the species in areas where the environmental variables are known (Guisan & Thuiller 2005, Elith & Leathwick 2009. Accordingly, SDMs could be used to predict the distribution of seabirds from colonies not covered by tracking data, and could therefore be used to predict the regional distribution of the species (Wakefield et al 2017, Hindell et al 2020, Fauchald et al 2021…”

“…Clearly, these methods could be useful in periods when the birds are strongly constrained by the location of the breeding colony. However, during the non-breeding period, when the birds are free to roam over larger areas (see Amélineau et al 2021, Merkel et al 2021b, the available habitat needs to be defined less strictly (Fauchald et al 2021). Accordingly, Fauchald et al (2021) defined the available habitat as an area that included all recorded occurrences of the species in the dataset and included distance to colony as a covariate in the SDMs.…”

“…However, during the non-breeding period, when the birds are free to roam over larger areas (see Amélineau et al 2021, Merkel et al 2021b, the available habitat needs to be defined less strictly (Fauchald et al 2021). Accordingly, Fauchald et al (2021) defined the available habitat as an area that included all recorded occurrences of the species in the dataset and included distance to colony as a covariate in the SDMs. The non-breeding habitat utilized by pelagic seabirds in the North Atlantic is highly complex and colony-specific (Amélineau et al 2021, Fauchald et al 2021, Merkel et al 2021b.…”

“…Accordingly, Fauchald et al (2021) defined the available habitat as an area that included all recorded occurrences of the species in the dataset and included distance to colony as a covariate in the SDMs. The non-breeding habitat utilized by pelagic seabirds in the North Atlantic is highly complex and colony-specific (Amélineau et al 2021, Fauchald et al 2021, Merkel et al 2021b. In line with this, Torres et al (2015) found that colonyspecific SDMs of the grey petrel Procellaria cinerea in the Southern Ocean performed well for each population but had poor transferability to an out-of-sample colony, suggesting that the populations had contrasting habitat preferences.…”

“…In line with this, Torres et al (2015) found that colonyspecific SDMs of the grey petrel Procellaria cinerea in the Southern Ocean performed well for each population but had poor transferability to an out-of-sample colony, suggesting that the populations had contrasting habitat preferences. Thus, the transferability of SDMs for pelagic seabirds seems to be low, and any model predicting the distribution of birds from colonies not covered by tracking data should assess the transferability of the model, or how well the model predicts the distribution of birds from other colonies (Torres et al 2015, Péron et al 2018, Fauchald et al 2021. A multi-colony design with respect to tracking data is therefore needed.…”