The Roles of Sex, Mass and Individual Specialisation in Partitioning Foraging-Depth Niches of a Pursuit-Diving Predator

Abstract

Intra-specific foraging niche partitioning can arise due to gender differences or individual specialisation in behaviour or prey selection. These may in turn be related to sexual size dimorphism or individual variation in body size through allometry. These variables are often inter-related and challenging to separate statistically. We present a case study in which the effects of sex, body mass and individual specialisation on the dive depths of the South Georgia shag on Bird Island, South Georgia are investigated simultaneously using a linear mixed model. The nested random effects of trip within individual explained a highly significant amount of the variance. The effects of sex and body mass were both significant independently but could not be separated statistically owing to them being strongly interrelated. Variance components analysis revealed that 45.5% of the variation occurred among individuals, 22.6% among trips and 31.8% among Dives, while R2 approximations showed gender explained 31.4% and body mass 55.9% of the variation among individuals. Male dive depths were more variable than those of females at the levels of individual, trip and dive. The effect of body mass on individual dive depths was only marginally significant within sexes. The percentage of individual variation in dive depths explained by mass was trivial in males (0.8%) but substantial in females (24.1%), suggesting that differences in dive depths among males was largely due to them adopting different behavioural strategies whereas in females allometry played an additional role. Niche partitioning in the study population therefore appears to be achieved through the interactive effects of individual specialisation and gender upon vertical foraging patch selection, and has the potential to interact in complex ways with other axes of the niche hypervolume such as foraging locations, timing of foraging and diet.

Intra- and inter-individual variation in the foraging ecology of a generalist subantarctic seabird, the gentoo penguin

“…For all models, backward-stepwise model selection was used to select the most parsimonious model (Ratcliffe et al 2013). First, the most appropriate random effects structure was identified with the restricted maximum likelihood (REML), then the best fixed effects structure was determined using maximum likelihood (ML) after models were compared with the ANOVA function, and the most parsimonious models were found based on their Akaike's Information Criteria.…”

“…In order to quantify the intra-individual variation in diving behaviour and spatial use, we used the R package ape (Paradis et al 2004) to perform a variance component analysis. This method calculates the variance, standard deviation and proportion of total variance occurring at the levels of individual, and trip within individual when multiple observations per trip were obtained, as well as the residual variation (Ratcliffe et al 2013, Harris et al 2014). An estimate of individual specialisation is given by the proportion of variance explained by the individual variance component (Bolnick et al 2003, Dingemanse & Dochtermann 2013, Ratcliffe et al 2013).…”

“…Ideally, the use of complementary techniques that represent different timescales and resolutions should be implemented to accurately describe individual specialisations and their persistence (Kernaléguen et al 2016). Seabirds are suitable models to study individual specialisations, as most species nest in large colonies that allow for easy access to individuals that use the same environment, are strongly constrained during breeding as central place foragers and may compete for the same resources (Ratcliffe et al 2013).…”

Methods for detecting and quantifying individual specialisation in movement and foraging strategies of marine predators

“…For the non-breeding season, the analyses are often of migration schedules (timing and duration of events; Croxall et al 2005, Dias et al 2011, Yamamoto et al 2014, or the total distance travelled during the migration (Muller et al 2014). Other 1-dimensional data used in studies of individual specialisation include dive characteristics and activity metrics (Laidre et al 2002, Staniland et al 2004, Cook et al 2006, Ratcliffe et al 2013, Patrick et al 2014, Potier et al 2015, Wakefield et al 2015.…”

Plasticity in foraging behaviour and diet buffers effects of inter-annual environmental differences on chick growth and survival in southern rockhopper penguins Eudyptes chrysocome chrysocome

“…We included bird identity as random factor in the model for maximum 282 distance and trip nested in bird identity as random effect in the model for dive depth. We 283 followed Ratcliffe et al (2013) and conducted backwards-stepwise model selection, at first 284 identifying the best random-effects model structure by comparing models with and without 285 trip effect (nested in bird identity -only in the model for dive depth) that were fitted with 286 restricted maximum likelihood (REML). We did not test for the performance of a model 287 without bird identity, as removal would have violated the premise of independent data.…”