Effects of sea temperature and stratification changes on seabird breeding success

Abstract

Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. conditions. We use mixed models to consider how SST, the potential energy anomaly 47 (indicating density stratification strength) and the timing of seasonal stratification influence 48 kittiwake productivity. Across all colonies, higher breeding success was associated with 49 weaker stratification before breeding and lower SSTs during the breeding season. Eight 50 colonies with sufficient data were modelled individually: higher productivity was associated 51 with later stratification at three colonies, weaker stratification at two, and lower SSTs at one, 52 whilst two colonies showed no significant relationships. Hence, key drivers of productivity 53 varied among colonies. Climate change projections, made using fitted models, indicated that 54 breeding success could decline by 21 -43% between 1961-90 and 2070-99. Climate change 55 therefore poses a longer-term threat to kittiwakes, but as this will be mediated via availability 56 of key prey species, other marine apex predators could also face similar threats. 57 58 4 INTRODUCTION 59Ecological impacts of climate change are increasingly well-understood, with changes in 60 species' ranges and phenology predicted and observed in both terrestrial and marine 61

Kittiwake breeding success in the southern North Sea correlates with prior sandeel fishing mortality

“…However, no negative SST effect was seen for kittiwake breeding success. Given that kittiwakes may not show a simple negative relationship with SST in all areas (Carroll et al, ; Eerkes‐Medrano et al, ), further research on oceanographic influences on kittiwake food webs is required to understand the implications of rising temperatures under climate change.…”

“…To examine relationships with breeding success, generalized linear mixed models (GLMMs) were fitted, with binomial error and logit link function. The response variable was the number of fledged chicks (taken directly from the data) and failed chicks (estimated based on the assumption of two eggs per nest) each year, thus accounting for varying numbers of nests surveyed, and representing breeding success as ‘fledging probability per egg’ (Carroll et al, ; Cook et al, ). To aid model convergence, fixed effects were scaled by subtracting the mean and dividing by the standard deviation.…”

“…(b) 95% kernel density estimates of foraging locations for kittiwakes tracked in the focal region (Filey: Thick, dotted line; circle to indicate colony. Flamborough: Thin, dashed line; square to indicate colony), in the 2010 to 2015 breeding seasons (updated from Carroll et al (); for description of methods see Appendix S1)…”

Circumpolar dynamics of a marine top‐predator track ocean warming rates

“…We focused on the spring SST as this environmental parameter is a good proxy of prey availability during the breeding or prebreeding season and affects kittiwake reproduction, hence population size, through bottom‐up effects (Carroll et al., ; Moe et al., ; Murphy, Springer, & Roseneau, ). Colder spring SST may indeed be associated with higher fish abundance in spring and summer, earlier kittiwake breeding and higher kittiwake productivity (Shultz, Piatt, Harding, Kettle, & Van Pelt, ).…”

Linking oceanographic conditions, migratory schedules and foraging behaviour during the non‐breeding season to reproductive performance in a long‐lived seabird

“…However, investigating responses to variability in oceanographic conditions is an important step to predict the consequences of climate on population dynamics of marine species. Climate change impacts marine species mostly indirectly (Sydeman et al., ) by operating primarily via changes in the availability of feeding and breeding habitats (Emmerson & Southwell, ; Fuentes, Limpus, Hamann, & Dawson, ; Hsieh, Kim, Watson, Di Lorenzo, & Sugihara, ; Idjadi & Edmunds, ; Jones, McCormick, Srinivasan, & Eagle, ) and in the productivity and structure of food webs (Carroll et al., ; Edwards & Richardson, ; Forcada, Trathan, Reid, & Murphy, ; Greene, Pershing, Kenney, & Jossi, ; Jenouvrier, Péron, & Weimerskirch, ; Ramírez et al., ; Richardson & Schoeman, ), although direct impacts, such as physiological impacts, have also been reported (Reid et al., ).…”