DOI: 10.5061/dryad.vdncjsxtf
title: Fear effects and group size interact to shape herbivory on coral reefs

description: 1. Fear of predators (‘fear effects’) are an important determinant of
 foraging decisions by consumers across a range of ecosystems. Group size
 is one of the main behavioural mechanisms for mitigating fear effects but
 also provides foraging benefits to group members. Within coral reef
 ecosystems, fear effects have been shown to influence the feeding rates of
 herbivorous fishes (i.e. browsers), a key functional group that prevent
 macroalgal overgrowth. Yet, how fear effects and group size interact to
 shape macroalgal removal on coral reefs remain unclear. 2. Here, we
 conducted field-based experiments using models of a common piscivorous
 fish, the leopard coral grouper (Plectropomus leopardus), and a series of
 macroalgal (Sargassum ilicifolium) assays positioned at increasing
 distances from the models (1, 2, 3 and 4 m) on two Singaporean coral reefs
 to investigate how acute fear effects shape the intensity of herbivory,
 and whether these effects were influenced by variation in the group size
 of herbivorous fishes feeding on the assays. 3. We found acute fear
 effects strongly influenced the foraging behaviour of herbivorous fishes
 over small spatial scales. Rates of Sargassum biomass removal, feeding
 rates and the total number of individual feeding events were all lower
 near the predator model. These effects dissipated rapidly with increasing
 distance from the predator model, and were undetectable at a distance of 4
 m. We also found generally larger group sizes of herbivorous fishes
 further from the predator model presumably reflecting decreased risk.
 Further, the number of individual bites-event-1 increased significantly
 with increasing group size for two common browsing fishes, Siganus
 virgatus and Siganus javus. 4. Our findings highlight that acute fear
 effects influence the distribution and intensity of herbivory over small
 spatial scales. Fear effects also interacted with herbivore group size
 resulting in changes in the number of individual feeding events and bite
 rates that collectively shape the realised ecosystem function of
 macroalgal removal on coral reefs. Group size is an important
 context-dependent factor that should be considered when examining fear
 effects on coral reefs.
description: We
 conducted field-based experiments between September and October 2016 on
 Pulau Satumu and Kusu, two off-shore islands in Singapore with
 well-developed fringing reefs. Both reefs have a clearly defined reef
 crest at 3–4 m depth, and have the highest coral cover, the lowest
 macroalgal cover, and highest rates of herbivory in Singapore . Each
 experimental replicate consisted of a series of individual
 <i>Sargassum ilicifolium</i> assays positioned at increasing
 distances (1, 2, 3 and 4 m) from models of the piscivorous leopard coral
 grouper (<i>Plectropomus leopardus</i>, 53 cm total length,
 TL) to simulate different levels of acute predation risk, together with
 two experimental controls (i.e. object control and herbivore exclusion).
 <i>Plectropomus leopardus</i> was selected because this
 species is common on both Pulau Satumu and Kusu and have broad diets that
 include herbivorous fishes. The size of the models (53 cm TL) was selected
 to represent the maximum size of serranids (including <i>P.
 leopardus</i>) observed on Singaporean reefs. <i>Sargassum
 ilicifolium </i>was selected because it is the most abundant and
 widespread <i>Sargassum </i>species on Singapore reefs.
 Sargassum ilicifolium thalli
 of similar heights (ca. 40 cm) were collected daily from a nearby shallow
 reef flat on Pulau Hantu, Singapore. Individual thalli were spun in a
 salad spinner for ~20 s to remove excess water and the wet weight recorded
 to the nearest 0.1 g. The initial mass (mean ± se) of each thalli was 44.7
 ± 8.4 g. For each experimental replicate, six Sargassum assays were
 allocated randomly to one of three treatments: a predator model treatment
 (four assays positioned 1, 2, 3 and 4 m away from the predator model), one
 object control treatment (53 cm length of PVC pipe, 8 cm in diameter) with
 one assay positioned 1 m away where the largest effect on browsing was
 theorised to occur, and a herbivore exclusion treatment (one assay placed
 inside a 30 cm radius, 100 cm height, 0.5 cm plastic mesh cage). The
 object control was used to account for the effect of introducing a novel
 object in the water while the herbivore exclusion cage was used to account
 for the autogenic losses due to handling and translocation. A negative
 control treatment (i.e. a series of four assays separated by 1 m without a
 predator or novel object) was not included in this study because, with
 replication, there was no conceivable reason why browsing would
 consistently vary within a 4 m scale in the absence of any
 object. Each morning (09:30–10:30) we transplanted two
 replicates of six Sargassum assays (total of 12 assays) haphazardly along
 the reef crest at ~3–4  m depth at one site (i.e. either Pulau Satumu or
 Kusu). Predator models were secured ~50 cm above the reef substratum.
 Individual Sargassum assays were subsequently attached to the reef
 substratum at increasing distances (1, 2, 3 and 4 m) from the predator
 model. The two additional assays were positioned approximately 20 m
 (object control) and 30 m (herbivore exclusion control) away from the
 predator models within the same habitat (i.e. ~3–4 depth along the reef
 crest). Within each site,  experimental replicates were separated by a
 minimum of 30 m to facilitate independence. This procedure was replicated
 over four non-consecutive days on each reef (n = 8 experimental
 replicates, with n = 4 per reef). To identify
 herbivorous fish species feeding on the Sargassum assays, a small video
 camera (GoPro) mounted on a dive weight (2kg) was positioned approximately
 1m from each of the assays in the predator exposure treatment (i.e. 1, 2,
 3 and 4 m from the predator model). Filming commenced immediately after
 the assays and predator models were deployed, with a small scale bar (10
 cm) placed adjacent to each assay for 10 s to allow calibration of fish
 sizes on the videos. All cameras, macroalgal assays and predator models
 were collected after 4.5 h. Thus on each day of the experiment there were
 8 cameras per reef, resulting in 144 h of video observations for each reef
 (288 h in total). Following retrieval, each individual
 Sargassum thalli was spun and re-weighed as above to calculate biomass
 loss per thallus (section 2.3). To minimize potential diver interference
 the first 20 min and last 10 min of each video were discarded. From the
 video footage, we recorded the total number of bites, species and
 estimated TL to the nearest cm for each fish feeding, group size per
 feeding event, and total bites per feeding event. Size estimates for each
 fish were converted to biomass using published length-weight
 relationships. A feeding event was recorded every time a fish entered the
 video frame and fed on Sargassum, and the bites from each individual fish
 were counted until each fish left the video frame. If other fishes entered
 during the feeding event, bites taken by those individuals were counted
 and included within the same feeding event. Group feeding was defined as 2
 or more fishes feeding simultaneously feeding during an event. To account
 for variation in the feeding impact of individual fishes related to body
 size, mass-standardized bite impact was calculated as the product of the
 number of bites and the estimated body mass (kg) for each individual
 following Hoey & Bellwood (2009). Individual assays
 positions within each predator exposure treatment replicate (i.e., at 1,
 2, 3 and 4 m) were considered non-independent due to their close
 proximity, and hence potential exposure to the same individual herbivorous
 fishes. To account for non-independence, we used a Bayesian mixed
 modelling approach employing Markov chain Monte Carlo (MCMC) methods for
 fitting generalized linear mixed models (Hadfield, 2010) with experimental
 replicate defined as the random effect. To examine the response of
 herbivorous fishes to the predator model, we compared: i) changes in
 <i>Sargassum </i>biomass at each position away from the
 predator model and the object control, ii) herbivorous fish species from
 the video footage feeding at each assay position from the predator model.
 For all analyses, assay position was considered an ordinal factor rather
 than a continuous covariate and the five positions were modelled for
 analyses of biomass removal (i.e. 1 m from the object control and 1, 2, 3,
 and 4 m from the predator model). To examine biomass
 (g) loss due to herbivory at each assay position, data were first
 standardized to control for autogenic loss during handling following
 Cronin & Hay (1996). For individual assays in each replicate, the
 reductions in macroalgal biomass attributed to herbivory was calculated
 using the following formula: [(H<sub>o </sub><i>x
 </i>C<sub>f</sub>/C<sub>o</sub>) -
 H<sub>f</sub>] where H<sub>o</sub> and
 H<sub>f</sub> were the initial and final wet weights,
 respectively, of the macroalgal assay exposed to browsing, and
 C<sub>o</sub> and C<sub>f</sub> were the initial
 and final masses of the corresponding assays from the herbivore exclusion
 treatments.<b><i> </i></b>Changes in
 <i>Sargassum </i>biomass were compared by modelling the
 absolute (g) and relative (proportion) reduction in biomass of replicate
 assays. In the latter case, proportions were logit transformed (Warton
 & Hui, 2011).<b> </b>Changes in biomass data were
 modelled using a Gaussian error structure with site, position and their
 interaction as fixed effects in initial models. 
 From the video feeding
 observations, we modelled the following three response variables: (i)
 counts of bites per feeding event (bites.event<sup>-1</sup>),
 (ii) feeding rates (mass-standardized bites.hour<sup>-1</sup>
 hereafter ‘ms-bites’) and (iii) group size per feeding event
 (group-size.event<sup>-1</sup>). Bites.event<sup>-1
 </sup>was modelled to assess whether individual foraging events were
 affected by distance to the predator models, whereas feeding rates
 indicated the overall effect of predator on macroalgal removal at each
 position. Bites.event<sup>-1</sup> were modelled for the four
 most common herbivores (<i>Siganus virgatus</i>,
 <i>Kyphosus vagiensis, Scarus rivulatus</i>, and
 <i>Siganus javus</i>) using a Poisson error structure. Group
 sizes &gt;4 were excluded from the analysis due to lack of cases
 across other explanatory variables. The initial model included the
 explanatory terms site, group size, assay position, their three-way
 interaction and pairwise two-way interactions and terms for species and
 species/group size interaction. Feeding rates were only analysed for
 <i>S. virgatus</i> because this species was responsible for
 most of the feeding (see Results).<i> </i>Feeding rates
 (ms-bites) were rounded to whole integers to employ a Poisson error
 structure, and site, assay position and their interaction were used as
 explanatory variables in the initial model. Analysis of group size
 (group-size.event<sup>-1</sup>) was performed for the entire
 dataset, including group sizes &gt;4, with the initial model including
 the explanatory terms site, assay position and species, with site/assay
 position and site/species interactions, using a Poisson error
 structure.
description: Models were fit using the
 MCMCglmm package, which provides parameter estimates, parameter 95%
 highest posterior density (HPD) credible intervals and a p-value (pMCMC)
 corresponding to the smaller of two times the probability that the MCMC
 parameter estimate is either &gt;0 or &lt;0 (Hatfield, 2010).
 Model terms were considered significant where one or more levels (for
 factors) or a co-variate had a pMCMC value &lt;0.05 and parameter
 estimate 95% HPD’s did not include zero. Diffuse, uninformative inverse
 gamma priors were used for variance components and default priors for
 fixed effects. Backward model selection was applied from initial models
 (defined above) by comparing reduced nested models with deviance
 information criterion (DIC) and model weight. Top ranking models were
 compared, and the most parsimonious selected as the simplest model with
 explanatory terms significant. Presented results are predicted posterior
 means and their 95% HPD intervals unless otherwise stated. Comparisons
 between factor levels in final models were considered statistically
 significant when prediction 95% HPD’s did not overlap prediction estimates
 of other factor levels. All models were fit using 130,000 iterations, a
 burn-in of 30,000 iterations and a thinning interval of 50, except for
 feeding rates and group size, which required longer iterations, burn-ins
 and thinning intervals due to poor mixing and unacceptably high
 autocorrelation between thinned samples in models with fewer iterations.
 Diagnostics were performed by visual inspection of trace plots and
 ensuring autocorrelation between thinning intervals was low (i.e. at least
 &lt;0.05). Model comparisons were made using the MuMIn package
 (Barton, 2015). All data were analysed in R (R Development Core Team,
 2017). The R code, full model selection details, parameter estimates, 95%
 HDP’s and pMCMC values are provided in electronic supplementary
 materials.