Fear effects and group size interact to shape herbivory on coral reefs

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.

Methods
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 Sargassum ilicifolium assays positioned at increasing distances (1, 2, 3 and 4 m) from models of the piscivorous leopard coral grouper (Plectropomus leopardus, 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). Plectropomus leopardus 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 P. leopardus) observed on Singaporean reefs. Sargassum ilicifolium was selected because it is the most abundant and widespread Sargassum 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 Sargassum 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: [(Ho x Cf/Co) - Hf] where Ho and Hf were the initial and final wet weights, respectively, of the macroalgal assay exposed to browsing, and Co and Cf were the initial and final masses of the corresponding assays from the herbivore exclusion treatments. Changes in Sargassum 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). 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-1), (ii) feeding rates (mass-standardized bites.hour-1 hereafter ‘ms-bites’) and (iii) group size per feeding event (group-size.event-1). Bites.event-1 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-1 were modelled for the four most common herbivores (Siganus virgatus, Kyphosus vagiensis, Scarus rivulatus, and Siganus javus) using a Poisson error structure. Group sizes >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 S. virgatus because this species was responsible for most of the feeding (see Results). 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-1) was performed for the entire dataset, including group sizes >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.

1. 捕食者恐惧效应（fear effects）是多种生态系统中消费者觅食决策的重要决定因素。群体规模是缓解恐惧效应的主要行为机制之一，同时也能为群体成员带来觅食收益。在珊瑚礁生态系统中，已有研究表明恐惧效应会影响植食性鱼类（herbivorous fishes）的摄食率——这类关键功能群可防止大型藻类（macroalgal）过度繁殖。然而，恐惧效应与群体规模如何共同作用，塑造珊瑚礁上大型藻类的移除速率，目前仍不明确。
2. 本研究通过野外实验，以新加坡两处珊瑚礁上的常见食鱼鱼类（piscivorous fish）——豹纹鳃棘鲈（Plectropomus leopardus）的模型为对象，设置距离模型梯度为1、2、3、4米的大型藻类（Sargassum ilicifolium）实验样块，探究急性恐惧效应（acute fear effects）如何改变植食性鱼类的啃食强度，以及这类效应是否会随摄食实验样块的植食性鱼类群体规模变化而改变。
3. 结果显示，急性恐惧效应在小空间尺度上强烈影响植食性鱼类的觅食行为。在捕食者模型附近，Sargassum ilicifolium的生物量移除速率、摄食率以及单次摄食事件的总次数均显著降低。这类效应随与捕食者模型的距离增加而快速消退，在4米距离处已无法检测到。同时我们发现，远离捕食者模型的区域，植食性鱼类的群体规模普遍更大，这大概率反映了捕食风险的降低。此外，对于两种常见的啃食性鱼类：Siganus virgatus与Siganus javus，单次摄食事件的咬食次数随群体规模增加而显著上升。
4. 本研究结果表明，急性恐惧效应会在小空间尺度上影响植食性啃食作用的分布与强度。恐惧效应还会与植食性鱼类的群体规模产生交互作用，改变单次摄食事件的次数与咬食速率，最终共同塑造珊瑚礁上大型藻类移除这一已实现的生态系统功能。在探究珊瑚礁上的恐惧效应时，群体规模是一项重要的情境依赖性因素，应予以纳入考量。

Methods
实验方法
本研究于2016年9-10月在新加坡的两座离岸珊瑚岛——萨图穆岛（Pulau Satumu）和龟岛（Kusu）开展野外实验，两处海域均发育有完善的岸礁。两座礁体均在3-4米水深处拥有清晰的礁脊，且拥有新加坡海域最高的珊瑚覆盖率、最低的大型藻类覆盖率以及最高的植食性啃食速率。
每个实验重复组包含一系列Sargassum ilicifolium实验样块，以距离食鱼鱼类模型（豹纹鳃棘鲈Plectropomus leopardus，全长53cm，TL）递增的距离（1、2、3、4米）布置，用以模拟不同梯度的急性捕食风险；同时设置两组对照实验：物体对照与植食性鱼类排除对照。选择豹纹鳃棘鲈作为模型物种，是因为该物种在萨图穆岛与龟岛均较为常见，且食谱涵盖植食性鱼类。实验模型的尺寸（53cm TL）参考了新加坡礁体上观测到的鮨科鱼类（包括豹纹鳃棘鲈）的最大体型。选择Sargassum ilicifolium则是因为它是新加坡礁体上丰度最高、分布最广的马尾藻属物种。
每日从新加坡汉都岛（Pulau Hantu）附近的浅礁坪采集高度相近（约40cm）的Sargassum ilicifolium藻体，用沙拉甩干机甩干约20秒以去除多余水分，并称取湿重至0.1g精度。每块藻体的初始鲜重均值为44.7±8.4g。每个实验重复组中，随机分配6块Sargassum ilicifolium实验样块至三种处理组：①捕食者模型处理组：4块样块分别布置在距离模型1、2、3、4米处；②物体对照组：采用长度53cm、直径8cm的PVC管作为对照物体，设置1块样块于1米距离处（理论上啃食效应最强的位置）；③植食性排除对照组：将1块样块置于半径30cm、高100cm、网孔0.5cm的塑料网笼内。物体对照用于抵消引入新型水下物体带来的干扰效应，植食性排除笼则用于校正因搬运、转移导致的藻体自源性损失（autogenic losses）。本研究未设置空白对照组（即无捕食者或新型物体、以1米间隔布置的4块样块），因为在无任何物体的情况下，4米尺度内的啃食速率难以出现一致性的差异，且实验设置了重复，不存在此类假设的必要性。
每日上午09:30-10:30，我们在一处礁体（萨图穆岛或龟岛）的礁脊区域（水深约3-4米）随机布置两个重复组的6块Sargassum ilicifolium实验样块（每组共12块样块）。捕食者模型固定于礁底上方约50cm处，随后将各Sargassum ilicifolium样块以1、2、3、4米的间距布置在模型周围。另外两块样块分别布置在距离捕食者模型约20米（物体对照组）与30米（植食性排除对照组）的相同生境中（即礁脊水深3-4米区域）。每个实验位点间至少间隔30米以保证独立性。该实验流程在两座礁体上分别开展4个非连续日期的重复（每座礁体共8个实验重复组，每组4个）。
为识别取食Sargassum ilicifolium样块的植食性鱼类物种，我们在捕食者暴露处理组的每块样块（即距离模型1、2、3、4米处）附近1米位置，安装一台搭载在2kg潜水配重上的小型运动相机（GoPro）。相机在样块与模型部署完成后立即开始录制，同时在每块样块旁放置10cm的比例尺以校准视频中的鱼类体型，录制总时长为4.5小时。所有相机、实验样块与捕食者模型均在4.5小时后回收。因此每日实验中每座礁体部署8台相机，单座礁体的视频观测总时长为144小时，两座礁体总计288小时。
回收藻体后，再次用甩干机甩干并称取湿重，以计算每块藻体的生物量损失。为减少潜水员干扰的影响，我们舍弃每段视频的前20分钟与最后10分钟的片段。从视频片段中，我们记录每尾摄食鱼类的总咬食次数、物种、估算的全长（精确至1cm）、单次摄食事件的群体规模，以及单次摄食事件的总咬食次数。单次摄食事件定义为：鱼类进入视频帧并开始取食Sargassum，直至离开视频帧的过程；若在此期间有其他鱼类进入并取食，则将该个体的咬食次数计入同一次摄食事件。群体取食定义为单次事件中有2尾及以上鱼类同时取食。为校正个体鱼类体型对摄食影响的差异，我们参考Hoey & Bellwood (2009)的方法，计算质量标准化咬食影响：即单尾鱼类的咬食次数与其估算体质量（kg）的乘积。
由于捕食者暴露处理组中各位置的实验样块间距较近，可能被同一群植食性鱼类访问，因此各位置的样块数据被视为非独立样本。为校正非独立性，我们采用贝叶斯混合建模（Bayesian mixed modelling）方法，使用马尔可夫链蒙特卡洛（Markov chain Monte Carlo, MCMC）方法拟合广义线性混合模型（generalized linear mixed models, Hadfield, 2010），以实验重复组作为随机效应。为探究植食性鱼类对捕食者模型的响应，我们对比了：①各距离位置的Sargassum生物量变化与物体对照组；②各距离位置的实验样块上，视频记录到的植食性鱼类物种组成。所有分析中，样块位置被视为有序因子而非连续协变量，生物量移除分析共涵盖5个位置（物体对照组1米处，以及捕食者模型1、2、3、4米处）。
为校正各实验样块因搬运、转移导致的自源性损失，我们首先对各位置的植食性啃食导致的生物量（g）损失数据进行标准化处理（Cronin & Hay, 1996）。对于每个重复组中的样块，植食性啃食导致的大型藻类生物量减少量计算公式为：[(Ho × Cf/Co) - Hf]，其中Ho与Hf分别为接受啃食处理的藻体的初始与最终湿重，Co与Cf分别为对应植食性排除对照组藻体的初始与最终湿重。我们通过建模对比了实验样块的绝对（g）与相对（比例）生物量减少量；后者需进行对数变换（logit transformed, Warton & Hui, 2011）。生物量变化数据采用高斯误差结构建模，初始模型中包含位点、位置及其交互作用作为固定效应。
基于视频摄食观测数据，我们对以下三个响应变量进行建模：①单次摄食事件的咬食次数（bites·event⁻¹）；②摄食率（质量标准化咬食·小时⁻¹，下文简称“ms-bites”）；③单次摄食事件的群体规模（group-size·event⁻¹）。建模分析单次摄食事件的咬食次数，旨在探究个体觅食事件是否受与捕食者模型距离的影响，而摄食率则用于反映捕食者对各位置大型藻类移除的整体效应。我们针对四种最常见的植食性鱼类（Siganus virgatus、Kyphosus vagiensis、Scarus rivulatus与Siganus javus）的单次摄食咬食次数进行建模，采用泊松误差结构。由于群体规模大于4的样本量不足，我们排除了此类数据。初始模型包含的解释项有：位点、群体规模、样块位置、三者的三阶交互作用、各成对二阶交互作用，以及物种项与物种/群体规模交互项。摄食率仅针对S. virgatus开展分析，因为该物种贡献了绝大多数的摄食行为（详见结果部分）。摄食率（ms-bites）需取整以适配泊松误差结构，初始模型中包含位点、样块位置及其交互作用作为解释项。群体规模（group-size·event⁻¹）的分析涵盖全部数据集（包括群体规模大于4的样本），初始模型包含的解释项有：位点、样块位置、物种，以及位点/样块位置、位点/物种的交互作用，采用泊松误差结构。