Path analysis reveals combined winter climate and pollution effects on the survival of a marine top predator
Creators: name: Layton-Matthews, Kate
name: Erikstad, Kjell Einar
name: Sandvik, Hanno
name: Ballesteros, Manuel
name: Hodges, Kevin
name: Mesquita, Michel
name: Reiertsen, Tone
name: Yoccoz, Nigel
name: Bustnes, Jan Ove
Publisher: name: Zenodo
Year: 2024
Subjects: Great black-backed gull
Larus marinus
path analysis
Bayesian inference
capture-mark-recapture
adult survival
Climate change
Persistent organic pollutants

description: Marine ecosystems are experiencing growing pressure from multiple threats caused by human activities, with far-reaching consequences for marine food webs. Determining the effects of multiple stressors is complex, in part, as they can affect different aspects of biological organisation (behaviour, individual traits, demographic rates). Determining the combined effects of stressors, through different biological pathways, is key to predicting the consequences for the viability of populations threatened by global change. Due to their position in the food chain, top predators such as seabirds are considered more sensitive to environmental changes. Climate change is affecting the prey resources available for seabirds, through bottom-up effects, while organic pollutants can bioaccumulate in food chains with the greatest impacts on top predators. However, knowledge of their combined effects on population dynamics is scarce. Using a path analysis, we quantify the effects of climate change and pollution on the survival of adult great black-backed gulls, both directly and through the effects of individuals' body mass. Warmer ocean temperatures in gulls' winter foraging areas in the North Sea were correlated with higher survival, potentially explained by shifts in prey availability associated with global climate change. We also found support for the indirect negative effects of organochlorines, highly toxic pollutants to seabirds, on survival which acted, in part, through a negative effect on body mass. The results from this path analysis highlight how, even for such long-lived species where variance in survival tends to be limited, two stressors still have had a marked influence on adult survival and illustrate the potential of path models to improve predictions of population variability under multiple stressors.
description: Funding provided by: The Research Council of NorwayROR ID: https://ror.org/00epmv149Award Number: 160022/F40
Funding provided by: The FRAM CentreROR ID: https://ror.org/05x7v6y85Award Number:
description: Capture-mark-resight data

We used individual-level capture-mark-resight (CMR) data collected between 2001 and 2017. Only mark-recapture data from individuals with known levels of OCs (N=158), i.e., caught and sampled in either 2001 or 2002, were included in the analysis. During the first capture, birds were marked with a unique numbered metal ring and individually coded colour ring for future resighting, which is possible with a telescope or binoculars. Each year, visual searches were made for marked birds, predominantly at the same colony where initial capture and ring-marking took place.

Biometric measures (weight)

Breeding adults were caught using nest traps during the incubation period (May–June) in 2001 and 2002. Once caught, birds were weighed, and the head and bill length were measured. GBB gulls were sexed using measurements of head and bill length as they are size dimorphic (see Bustnes et al. 2008a for details). Individuals' body mass (weight) was used as a measure of individual quality.

Pollutants

Blood concentrations of several organochlorines (OCs) were measured from 158 individuals during field campaigns in 2001 and 2002.

Climate covariates

We selected climate variables reflecting non-breeding season conditions that may affect survival rates, which were available at different spatial scales; North Atlantic Oscillation, Atlantic Water inflow, land temperature, sea surface temperature, and mean sea level pressure. North Atlantic Oscillation (NAO) and Atlantic Water inflow (AW) were available as monthly values. The NAO index reflects large-scale weather patterns specifically cyclone activity in the North Atlantic and is widely used as a proxy for indirect effects of climate conditions on seabirds via changes in prey distributions and abundances (Stenseth et al. 2003). Annual monthly values of the NAO index are openly available from www.cpc.ncep.noaa.gov. In the Barents Sea, ocean conditions are largely determined by fluctuations in the inflow of warm and saline Atlantic water (AW; Loeng 1991; Ingvaldsen, Asplin & Loeng 2004), again affecting seabirds through changes in prey availability (Barrett, Erikstad & Reiertsen 2017). Cyclones, in part, modulate the influx of Atlantic water and so this can be related to NAO (Heukamp et al. 2023). Data of AW were available at a monthly scale from www.thredds.met.no/thredds/catalog/nansen-legacy-ocean/SVIM/catalog.html. NAO and AW were aggregated for two periods: winter (November–February) and spring (March–April). Land temperature (airT), sea surface temperature (SST), and mean sea level pressure (MSLP) were available as gridded data and were extracted from within seabirds' seasonal foraging ranges (Appendix S2, Fig. S2) and were available from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim Project ('ERA-Interim'). ERA-Interim is a gridded model dataset at a resolution of 0.70° or approximately 79km based on data assimilation of meteorological station data, and satellite data, among others (Dee et al., 2011; Mesquita et al., 2015). Values of MSLP, SST and airT were extracted from areas reflecting the utilisation distributions, for the relevant seasonal periods (Supplementary Information S2 Figure S3). Annual covariates were aggregated to four periods: Autumn (September–October, Aut), early Winter (November–December, Ewin), late Winter (January–February, Lwin), and Spring (March–April, Spr), for the years 2001–2017. Values were extracted from the North Sea region for Autumn, early Winter, and late Winter (NNS/SNS, Figure S3). For spring, values were extracted from around the breeding colony at Hornøya (HRN, Figure S3). See Supplementary information S2 Figure S4 for plots of the annual time series.