HOW IS A FAMILY OF SEDENTARY MARINE FISHES SHAPED BY ITS HABITATS, PREY, AND
PREDATORS?
by
Clayton Garin Manning
B.Sc. (First-Class Hons), University of Calgary, 2012
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
In
THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES
(Zoology)
THE UNIVERSITY OF BRITISH COLUMBIA
(Vancouver)
August 2017
©Clayton Garin Manning, 2017
i Abstract
Overall, this thesis expands on our ecological understanding of a group of biologically diverse marine fishes by investigating how they are shaped by their habitats, prey, and predators. In my first data chapter, I used the seahorse Hippocampus whitei as a case study for investigating the ecological correlates of syngnathid abundance and distributions. Expanding on research that had looked at how either their habitats, prey, or predators affected their populations, I considered all three components in a single holistic approach. I investigated these correlations at two scales: among different seagrass beds
(200-6000 m apart), and within a single seagrass bed (<100 m in size). I found that habitat, prey, and predator variables all correlated with seahorse density or height distributions to varying extents, depending on the scale of the study. Total predators was negatively associated with seahorse density across seagrass beds, the only ecological variable that was correlated with seahorses across beds. Within seagrass beds, seahorse locations correlated with greater depth, denser seagrass, more prey types, and fewer predators. In my second data chapter, I reviewed the diets and feeding behaviour of syngnathids, bringing together, summarizing, and providing new insights on a large amount of fragmented information on the topic. I answered three central questions. 1. How do syngnathids eat? 2. How does feeding and diet vary across a morphogically diverse family of fishes? 3. How does feeding and diet vary across a family of fishes that lives in a three- dimensional space? I answered question 1 by summarizing a number of different studies on the morphologies and kinematics of syngnathid feeding events. I answered questions 2 and 3 using a meta-analysis on syngnathid diets found the literature. Overall, I found there to be a large amount of variation in syngnathid diets that I hypothesize is caused by large
ii differences in prey availability. Of the explained variation, I found their diets were most strongly correlated with their relative snout lengths and gape sizes. These feeding morphologies also had high phylogenetic signal, suggesting that dietary differences across genera were largely explained by how they differed with respect to these morphologies.
iii Lay Summary
Overall, this thesis investigates how syngnathids—a family of fish that includes seahorses and pipefishes—are affected by their habitats, prey, and predators. First, I looked at how seahorse abundance was affected by habitat, prey, and predators in eastern
Australia. I found that each of these variables affected seahorse density or distributions to varying extents, depending on the scale of the study. Next, I reviewed syngnathid feeding, and provided a detailed summary on how syngnathids eat, and also analyzed what they eat. Overall, I found a lot of variation in what syngnathids eat, and suspect this is because there is a lot of variation in what is available to them in their environments. I also found their diets were best explained by the size and shapes of their snouts—highly advanced body parts that have evolved to help these fish feed on fast prey.
iv Preface
The research questions and methodological design of my thesis were developed in collaboration with my co-supervisors, Drs. Amanda Vincent and Sarah Foster. I collected all of the data used for Chapter 2 with the help of my host supervisor in Australia Dr. Dave
Harasti, and my research assistants. I collected the data and information used in Chapter 3 from peer-reviewed and grey literature. I carried out all analyses, and prepared all manuscripts in this thesis, with substantial input from Amanda Vincent and Sarah Foster.
A version of Chapter 2 is in the final stages of preparation for publication. As per our target journal’s requirements, the writing is in passive voice. I am the lead author, along with my co-supervisors Drs. Amanda Vincent and Sarah Foster, and my host-supervisor Dr.
Dave Harasti, who provided guidance, logistical support, and assisted with fieldwork. I conducted all of the research for Chapter 2, and wrote the paper in collaboration with Drs.
Sarah Foster and Amanda Vincent. Alistair Poore (University of New South Wales) helped train my assistants and me in crustacean identification, helped mold the study design, and provided statistical help. Meagan Abele and Natalie Scadden, my research assistants, were vital to the data collection and safety management of my project.
A version of Chapter 3 is in the final stages of preparation for publication. I am the lead author, along with my co-supervisors Drs. Amanda Vincent and Sarah Foster. I conducted the analysis for Chapter 3 and wrote the paper with Drs. Sarah Foster and
Amanda Vincent. I collected and tabulated the data used for Chapter 3 from primary and grey literature.
v All field research undertaken in Chapter 2 was done in accordance with the
University of British Columbia's Animal Care Committee permit A12-0288 and the NSW
DPI Animal Care and Ethics Committee permit 15/01.
vi Table of Contents
Abstract ...... ii
Lay Summary ...... iv
Preface ...... v
Table of Contents...... vii
List of Tables ...... xi
List of Figures ...... xiv
Acknowledgements ...... xvii
Chapter 1 Introduction ...... 1
1.1 Rationale ...... 1
1.2 Background ...... 1
1.3 Research objectives and thesis outline ...... 6
Chapter 2 Ecological correlates of White's seahorse (Hippocampus whitei) abundance and size distributions at different spatial scales ...... 8
2.1 Introduction ...... 8
2.2 Materials & Methods ...... 13
2.2.1 Study species ...... 13
2.2.2 Study locations & design ...... 13
2.2.3 Seahorse surveys ...... 17
2.2.4 Predator surveys ...... 18
2.2.5 Seagrass surveys ...... 19
vii 2.2.6 Prey surveys ...... 19
2.2.7 Predicting covariates within Little Beach ...... 20
2.2.7.1 Seagrass and prey ...... 20
2.2.7.2 Predators ...... 21
2.2.8 Statistical analyses ...... 22
2.3 Results ...... 25
2.3.1 Study scale: among all seagrass beds ...... 25
2.3.1.1 Seahorse survey summary statistics ...... 25
2.3.1.2 Correlates of seahorse density & seahorse height ...... 26
2.3.2 Study scale: within Little Beach seagrass bed ...... 26
2.3.3 Overall associations between seahorses and ecological correlates ...... 26
2.4 Discussion ...... 27
Chapter 3 Review paper: the diet and feeding behaviours of a family of biologically diverse marine fishes (Family Syngnathidae) ...... 48
3.1 Introduction ...... 48
3.2 Methods ...... 52
3.2.1 Literature review ...... 52
3.2.2 Diet taxonomy ...... 53
3.2.3 Syngnathid characteristics ...... 54
3.2.4 Statistical analyses ...... 55
3.2.4.1 Associations between syngnathid characteristics and their diets ...... 55
3.2.4.2 Phylogenetic signal of syngnathid characteristics ...... 57
3.3 Results ...... 58
viii 3.3.1 How do syngnathids eat? ...... 58
3.3.1.1 Head morphology & mechanics of a feeding event ...... 58
3.3.1.2 Stages of a feeding event ...... 59
3.3.1.3 Energetics of feeding ...... 61
3.3.1.4 Diurnal timing of feeding ...... 62
3.3.2 How does feeding and diet vary across a speciose marine fish family that is
morphologically diverse? ...... 63
3.3.2.1 Prey items by genus and species ...... 63
3.3.2.1.1 Entire family ...... 63
3.3.2.1.2 Hippocampus (seahorses) ...... 64
3.3.2.1.3 Syngnathus pipefishes ...... 64
3.3.2.1.4 Seadragons (Phyllopteryx) and other pipefishes ...... 65
3.3.2.2 Morphology ...... 65
3.3.2.2.1 Phylogenetic signal of syngnathid morphological characteristics ...... 65
3.3.2.2.2 Body form & orientation ...... 66
3.3.2.2.3 Snout shape ...... 66
3.3.2.2.4 Gape size ...... 68
3.3.2.2.5 Ontogenetics: changes in snout shape & gape size ...... 69
3.3.2.3 Sex & reproductive status ...... 71
3.3.3 How does feeding and diet vary across a marine fish family that lives in a three-
dimensional space? ...... 72
3.3.3.1 Variability ...... 72
3.3.3.2 Tail morphology & foraging strategies ...... 73
ix 3.4 Discussion ...... 76
Chapter 4 Conclusions ...... 100
4.1 Associations with habitats ...... 101
4.2 Associations with prey ...... 101
4.3 Associations with predators ...... 103
4.4 How this thesis fits in to the literature ...... 105
Bibliography ...... 108
Appendices ...... 141
Appendix A: The determination of seahorse sex, maturity, and reproductive status.
...... 141
Appendix B: Tables to support methods and results in Chapter 2 ...... 143
Appendix C: Tables to support methods and results in Chapter 3 ...... 146
x List of Tables
Table 2.1 Summary statistics for seahorse (SH) surveys at seven plots in the Port Stephens
estuary, NSW, Australia, during the November (Nov) and February (Feb) sampling
campaigns. SE is the standard error of the mean. Overall density represents the total
number of seahorses divided by the total area searched. For adult height, total
represents the mean of all adults pooled across plots. February Little Beach physical
maturity ratios do not add to total number of seahorses found because the sex of one
individual was not determined, and this individual was not included in any other
calculations. Plots are listed from smallest to largest distance from the estuary mouth.
...... 35
Table 2.2 Summary of the model-averaged statistics for the top models predicting: (a)
seahorse density among seagrass beds, (b) adult height among seagrass beds, (c) and
resource selection function within the Little Beach seagrass bed. LL = log-likelihood,
AICc = corrected Akaike information criterion, ΔAICc = difference in model AICc with
that of the top model, wi = Akaike weight, df = number of model parameters including
intercepts and residuals. The following abbreviations have been made: DPTH = depth,
DENS = seagrass density, HGHT = seagrass height, TPT = prey types, TPI = prey
density, FLNG = fouling, PRED = total predators, and TPRED = types of predators...... 36
Table 2.3 Model-averaged parameter estimates, standard error (SE) of the parameter,
correlate relative importance, the upper and lower 90% parameter confidence
intervals (CI) for variables predicting resource selection function within the Little
Beach seagrass bed...... 37
xi Table 2.4 Summary of relationships between seagrass, prey, and predator covariates with
(a) seahorse (SH) density and (b) adult height among seagrass beds, (c) resource
selection function (RSF) within the Little Beach seagrass bed and (d) SH height within
Little Beach, among SH with different sexes and reproductive statuses. RA =
reproductively active...... 38
Table 3.1 Relative importance of syngnathid diets. a The approximate area of sampling. For
comparative purposes, studies in close proximity (within 50 km of each other) have
the same location. b Bulk dietary studies include relative values that each food item
contributes to the total volume (%V), weight (%W), or area (%A) of dietary contents
collected, numeric dietary studies include relative values that each food item
contributes to the total number of food items (%N) collected, and frequency of
occurrence (%FO) studies include the relative number of stomach samples that a
particular food item occurs in. Each row represents a particular species in a particular
area of a particular study. c This table includes additive data, so if a taxon is centered
above other taxa it includes those numbers in its total. Numbers are added to columns
on the left (e.g. Crustacea includes Paracarida and Eucarida). Sample sizes of two and
under were not considered in statistical analyses. Blank cells represent missing data.
Shaded cells (for %FO data only) indicate the value of that cell was not provided in the
literature, and is a minimum value based on dietary items that were included at a
lower taxonomic resolution (see text)...... 84
Table 3.2 Measures of phylogenetic signal for syngnathid morphological characteristics.
Statistical significance indicated in bold (P < 0.05). N indicates the number of
syngnathid species that were used to assess the phylogenetic signal...... 94
xii Table 3.3 Results of the redundancy analyses that measured the associations between
multiple independent variables (syngnathid morphological characteristics) and
multiple dependent variables (dietary categories, e.g. amphipods). Models were based
on three different dietary metrics: bulk, numeric, and frequency of occurrence dietary
data. Statistical significance indicated in bold (P < 0.05)...... 95
xiii List of Figures
Figure 2.1 Location of the seven study plots along the southern coast of the Port Stephens
estuary, on the eastern coast of New South Wales, Australia. Plot names: (1) Shoal Bay,
(2) Little Beach, (3) Fly Point, (4) Seahorse Gardens 1, (5) Seahorse Gardens 2, (6)
Pipeline, (7) Dutchies. Shore Data: OpenStreetMap (and) contributors, CC-BY-SA...... 39
Figure 2.2 Map of the Little Beach seagrass beds showing an example of the stratified
random sampling design used at each of the seven study plots (for study among
seagrass beds). Light grey represents P. australis. White background includes all other
benthic substrate types, predominantly sand and Zostera sp. The 8 m length of the
stratum was calculated by dividing the 24 m length of the seagrass bed into thirds.
Shore Data: OpenStreetMap (and) contributors, CC-BY-SA...... 41
Figure 2.3 Mean (± SE) number of (a) total predators, and (b) different predator species
observed at fixed transects at Little Beach, measured 19-21 January 2016. Hashed
polynomial lines of best fit are included: (a) y = 0.0029x2 - 0.3473x + 11.024 (r2 =
0.87), (b) y = 0.0009x2 - 0.0849x + 2.3039 (r2 = 0.96)...... 42
Figure 2.4 (a) Mean seahorse density (± SE), (b) mean adult seahorse height, (c)
proportion of seahorses that were physically mature (adult), and (d) proportion of
adults that were male, by plot and by November and December survey campaigns.
Values at the base of bars indicate the number of adult seahorses (a, d) and the total
number of seahorses (c). Bars sharing a common letter do not differ significantly
(Tukey HSD, P > 0.05) ...... 43
Figure 2.5 Model-averaged effect sizes with 90% confidence intervals (CI) for predictor
variables of seahorse (a) density and (b) adult height, among seagrass beds. Outputs
xiv based on results of mixed-effects models. Parameter estimates are indicated to the
right of the CIs. P-values are indicated as follows: superscript a = P > 0.10, * = 0.10 < P
< 0.05, ** = P < 0.05...... 45
Figure 2.6 Model-averaged effect sizes with 90% confidence intervals (CI) for predictor
variables of seahorse height within the Little Beach seagrass bed, among (a) all
seahorses, (b) all reproductively active (RA) seahorses, (c) females, (d) RA females, (e)
males, (f) RA males. Outputs based on results of mixed-effects models. Parameter
estimates are indicated to the right of the CIs. P-values are indicated as follows:
superscript a = P > 0.10, * = 0.10 < P < 0.05, ** = P < 0.05. Total N = 328; includes 168
female, 148 male, and 12 juvenile (reproductively inactive) observations...... 47
Figure 3.1 Proportion of (a) bulk dietary data variance, (b) numeric dietary data variance,
and (c) frequency of occurrence dietary variance explained by syngnathid body traits
[component a; Relative fin size + Max. standard length (StL) + Snout depth (SnD) +
HL:SnL + SnL:SnD], genus [component c], covariance between syngnathid body traits
and genus [component b], and unexplained residuals [component d]...... 96
Figure 3.2 Figure 1 from © Van Wassenbergh, S., Strother, J. A., Flammang, B. E., Ferry-
Graham, L. A. & Aerts, P., Extremely fast prey capture in pipefish is powered by elastic
recoil, Journal of the Royal Society Interface, 2008, 5, 20, page 286, by permission of
the Royal Society. A schematic of a syngnathid body (Syngnathus leptorhynchus)
during a feeding strike. Specialized sternohyoideus muscles run along the dorsal and
ventral sides of the pipefish. When contracted, they pull the hyoid arch towards the
body. The neurocranium (including the snout) then rotates away from the body,
towards the prey...... 97
xv Figure 3.3 Biplot of the first two axes of the redundancy analyses (RDA) performed on
bulk dietary data. Points represent the diet of a particular species of syngnathid in a
particular area, as reported by a particular study. Points are coloured based on the
genus. Environmental vectors for syngnathid body characteristics are fit onto the
ordination, and the direction and strength of the gradient is represented by the length
of the arrow...... 98
Figure 3.4 Biplot of the first two axes of the redundancy analyses (RDA) performed on
bulk dietary data. Points represent the diet of a particular species of syngnathid in a
particular area, as reported by a particular study. Points are coloured based on the
genus. Environmental vectors for syngnathid body characteristics are fit onto the
ordination, and the direction and strength of the gradient is represented by the length
of the arrow...... 99
xvi Acknowledgements
First and foremost, I would like to thank my super-supportive and uber-helpful supervisors, Drs. Amanda Vincent and Sarah Foster. I appreciate the belief you had in my abilities, and for giving me the incredible opportunity to operate my own field season in the most beautiful field-site possible, Australia. It was a once in a lifetime experience that was challenging, rewarding, and a lot of fun. I would also like to thank Drs. Diane Srivastava and
John Richardson, my supervisory committee, for the constructive discussions that led to my project, the amazing statistical advice that turned it from a bunch of numbers into useful data, and for helping me refine these numbers into a manuscript.
My field season would not have even happened were it not for Dr. Dave Harasti, who invited me to his field station in Australia, provided me with everything I needed to succeed, helped with fieldwork, and taught me everything there is to know about running a field study. Also, a huge thanks to colleagues at the NSW Department of Primary Industries, who provided logistical assistance and excellent hospitality in Australia. My work in
Australia would not have been possible without my fellow Team Sea Stallion members,
Meagan Abele, and Natalie Scadden. Their tremendous assistance in the water kept me safe, their upbeat attitudes kept me sane, and their tireless work ethic allowed me to collect as much data as physically possible. I would like to acknowledge Alistair Poore for help with the study design and statistical methods, and Tyler Armitage for GIS assistance.
This thesis is a contribution from Project Seahorse. The fieldwork was financed by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Point
Defiance Zoo & Aquarium, and Guylian Chocolates Belgium through its partnership for marine conservation with Project Seahorse.
xvii The past three years have been a crazy journey, and made better by the people who supported me, and laughed with me, day in and day out. Kyle Gillespie, I can’t believe we managed to pay off a blimp AND build a world-famous brewery. Officelympics champion
(disputed), Ally Stocks, your ability to brighten a day is unmatched. Ravi Maharaj, thank you for teaching me how to play (football) and hanging out with me deep into nights of working late. To the rest of the PS family during my tenure—Lindsay Aylesworth, Julia
Lawson, Ting-Chun Kuo, Tanvi Vaidyanathan, Scott Finestone, Gina Bestbier, Tyler Stiem,
Riley Pollom, Jenny Selgrath, Xong Zhang, Emilie Stump, Iwao Fuji, and Lily Stanton—thank you for always bringing a smile to my face, even when dealing with daunting challenges.
Lastly (but definitely not leastly), I would like to thank my friends and family for the support through three incredible years, and one of the most difficult of my life. Mom, Dad,
Jenay and Rylan—you are my backbone, and my rock. I don’t know what I did to deserve you guys, but I’m damn lucky, and a far better person because of it. To my everyone else that I met and made me laugh—either at Zoology Beers, rehearsing for Huts, or somewhere in-between—thanks.
xviii
To my parents, Kirk and Marilynne
xix Chapter 1 Introduction
1.1 Rationale
In this thesis, I explore ecological relationships of an unusually sedentary family of marine fishes—the syngnathids. All animals shape and are shaped by their interactions with their habitats, prey, and predators they encounter during their lifespan, as they try to survive, grow, and reproduce. The specific interactions that an animal experiences can vary
—but as a whole they will affect the animal’s abundance and distribution (Sih et al., 1985;
Abrams, 2000; Krebs, 2009).
1.2 Background
An animal is shaped by its experiences with biological and physical challenges, and the costs and benefits to its survival. Perhaps an animal’s most important interaction is with its habitat, which is a direct and indirect source of resources, and can mediate the interactions it has with other members of community (Orth et al., 1984; Heck & Crowder,
1991; Canion & Heck, 2009). Seagrasses, for example, provide a direct source of food for herbivorous invertebrates and fish (Jones et al., 1994, 1997; Bruno & Bertness, 2001). In response, herbivores modify their distributions according to the distribution of seagrasses
(Fretwell & Lucas, 1970; Kennedy et al., 1993), and in doing so, can become a concentrated source of food for predatory fish (Stoner, 1980; Bell & Westoby, 1986; Jenkins et al., 2002).
In this way habitat complexity brings different species together, thereby facilitating interactions (i.e. predator-prey, competition and parasitism) among the organisms it supports (Briand & Cohen, 1987; Cohen et al., 1990; Cohen, 1994; Vander Zanden & Fetzer,
1 2007). In addition to facilitating interactions, habitats can also mediate interactions among community members—such as providing some members with refuge from predation, and others with a physical structure from which to ambush prey (Heck & Crowder, 1991; Jones et al., 1994, 1997; Bruno & Bertness, 2001). Overall, habitats create and maintain environmental conditions that are more favorable to life, increasing the fitness of associated species and allowing for greater species diversity (Jones et al., 1997; Bruno &
Bertness, 2001). In doing so, habitats affect the distribution and population size of both prey and predators alike (Heck & Crowder, 1991; Jones et al., 1994, 1997; Bruno &
Bertness, 2001).
In their efforts to survive, grow, and reproduce, animals can affect the abundance and distribution of other species (Sih et al., 1985; Abrams, 2000). All predators depend on their prey as an energy source, with prey physically shaping the morphology and physiology of predators (Schoener, 1971; West et al., 1991; Norton, 1991). In return, of course, prey are shaped by their predators through selection on traits that reduce capture.
In exploiting a food source for their own needs, predators are a direct source of mortality on the prey population, and reduce prey abundance in the process (Sih et al., 1985). In addition, predators may indirectly shape the distribution of their prey by inducing behavioural changes, as the prey try to find ways of reducing predation pressure (Sih,
1984; Lima & Dill, 1990; Lima, 1998; Werner & Peacor, 2003). Predators and prey are in an ever-evolving arms race with consequences for both. Predation can bring about the evolution of defensive traits among the prey population, such as the development of defensive toxins, morphology that aids in rapid predator evasion, or behavioural changes
(Sih, 1984; Lima & Dill, 1990; Lima, 1998; Werner & Peacor, 2003). Each of these changes
2 has consequences for the predators, who must find ways to counter them by changing their search, capture, and consumption of prey (Schoener, 1971; West et al., 1991; Norton,
1991). Predators might be expected to select their prey according to the energetic costs and benefits associated with consuming them.
Sedentary predators could be expected to have particularly strong relationships with their habitats, prey, and predators because they are long-term residents in one geographic area. The more sedentary an animal, the less likely it is to disperse to locations with more favourable conditions, and the more dependent it therefore becomes on local environmental conditions (Steffan-Dewenter et al., 2002; Öckinger et al., 2009). For example, while more mobile predators are able to seek out their preferred prey availability, sedentary ambush predators are dependent on the local suite of prey available to them. If local prey conditions are more stable, predators may become reliant on a certain group of prey, an effect that may be reinforced by the evolution of specialized traits that aid in their exploit (Schoener, 1971; Dawkins & Krebs, 1979; Abrams, 2000). Alternatively, conditions where the number and suite of prey available is under flux may favour the development of a more generalist palate (Bommarco et al., 2010). Sedentary species have similar limitations when it comes to poor habitat or predator conditions, and it may be that these species have to make the best of the local situation.
The marine environment is so highly connected—with so much movement of organisms and so many fluid influences on habitats, along with lower costs of movement— that sedentary marine species might have looser relationships with their environments than terrestrial species. Aquatic environments are defined by the liquid medium, which provides organisms with physical structure to survive, grow, and reproduce (Thorne-
3 Miller, 1999). As a result, aquatic environments are able to support more trophic levels and longer food chains than two-dimensional terrestrial habitats (Briand & Cohen, 1987;
Vander Zanden & Fetzer, 2007). Overall, marine animals interact with a greater number of species—both within and among trophic levels—than those in terrestrial or freshwater habitats (Cohen et al., 1990; Cohen, 1994). For sedentary marine predators, these interactions may not necessarily be with other resident species. Marine currents are responsible for high rates of flux, dispersing organisms, energy, and nutrients from habitat to habitat (Hixon & Menge, 1991; Palmer et al., 1996; Cowen & Sponaugle, 2009; McManus
& Woodson, 2012). Planktonic crustaceans, for example—an important food source for numerous marine fish—move largely by means of marine currents (Palmer et al., 1996;
McManus & Woodson, 2012). Such processes can result in large prey or nutrient differences at small spatial or temporal scales (Menge & Olson, 1990; Palmer et al., 1996).
The result is that sedentary marine predators may be fairly generalist, being better suited to handle a large number of conditions than more mobile species.
Syngnathids are a diverse family of sedentary marine predators whose relationships with predators, prey and habitats are poorly understood. Overall, no work has explored the simultaneous influences of habitats, prey, and predators on their abundance and distribution. Research on associations between syngnathids and their habitats has primarily focused on habitat preferences (e.g. Howard & Koehn, 1985; Rosa et al., 2007;
Harasti et al., 2014a), or has not considered habitat variables together with other potential factors, such as their communities (Curtis & Vincent, 2005; Caldwell & Vincent, 2012;
Aylesworth et al., 2015). While much is known about where, when, and on what syngnathids feed (e.g. Howard & Koehn, 1985; Bergert & Wainwright, 1997; Kendrick &
4 Hyndes, 2005), the role that prey have on where we find syngnathids, and in what abundance, is not. Likewise, we know very little about the effects that predators actually have on populations of syngnathids. Despite two recent studies that have identified some marine animals that consume syngnathids (Kleiber et al., 2011; Harasti et al., 2014b), no study has looked at how predators matter to syngnathids in the context of a complex marine environment, with numerous confounding variables that need to be considered.
Given that syngnathids show strong site fidelity (habitat), and are also at the nexus of bottom-up resource (prey) effects and top-down predatory effects (predators) – we must consider all sides of the story to fully understand what matters to syngnathid populations.
To date, there has been no work on general feeding patterns among syngnathids across genera, locations, and studies. This is despite a number of studies looking at syngnathid diets in the wild (e.g. Steffe et al., 1989; Franzoi et al., 2004; Kendrick & Hyndes,
2005; Gurkan et al., 2011a, 2011b), and many others that have contributed to an understanding of their feeding kinematics (e.g. Bergert & Wainwright, 1997; Van
Wassenbergh et al., 2009, 2011) and foraging tactics (e.g. Howard & Koehn, 1985; Ryer,
1988; Tipton & Bell, 1988). Syngnathids exhibit large variations in feeding-related morphology and behaviour across genera (Howard & Koehn, 1985; Kendrick & Hyndes,
2005), which better equip them to capture the prey that they are more likely to encounter
(Van Wassenbergh et al., 2008, 2009; Roos et al., 2009b). These morphological and behavioural adaptations offer a distinct opportunity to investigate the function that syngnathids’ characteristics have on their foraging and diets.
In my thesis, I seek to draw an unusually broad picture of syngnathid ecology. I was motivated to understand what components of a syngnathid’s world—including their
5 habitats, prey, and predators—affect their abundance and distributions. My aim was to build on previous seahorse habitat work that has investigated the associations between seahorses and either habitat, prey, or predator variables—and approach it from a holistic perspective—considering elements of all three groups of variables at once. I also wanted to compare and contrast these associations at different scales, to see if the variables that correlate with syngnathids within a site also correlate among different plots. While conducting a literature review to prepare myself for this work, I noticed that there were a large number of interesting, yet very different studies related to syngnathid feeding—and that this information had not been summarized, or reviewed in any way. I became keen to seek generalities (through comparative analyses) about syngnathid feeding across genera, locations, and studies. Specifically, I was interested in understanding the associations between syngnathid characteristics (in particular, morphology) and their foraging strategies, and diets. My interest was heightened by the opportunity to compare across 300 species and 57 genera, in a family with diverse morphologies and habitats.
1.3 Research objectives and thesis outline
My research focused on answering five questions:
1. What habitat, prey, and predator variables are associated with seahorse abundance
and size distributions among different seagrass beds in an Australian estuary?
(Chapter 2)
2. What habitat, prey, and predator variables are correlated with seahorse distribution
in a single seagrass bed? (Chapter 2)
3. How do syngnathids eat? (Chapter 3)
6 4. How does feeding and diet vary across a speciose marine fish family that is
morphologically diverse? (Chapter 3)
5. How does feeding and diet vary across a marine fish family that lives in a three-
dimensional space? (Chapter 3)
Chapter 2 expands on previous seahorse habitat studies by quantifying seahorse densities and size distributions and looking at correlations between these response variables and habitat, prey, and predator variables. I look at these relationships both at a medium scale (0.2-6 km; among different seagrass beds) and a small scale (> 100 m; within a single seagrass bed). My data came from my own field work on the east coast of Australia.
Chapter 3 presents the first synthetic overview of syngnathid diet and feeding.
Although much has been published on the diets, kinematics and foraging of syngnathids, there has been no effort to synthesize this information into a useful resource, or identify potential patterns. First, I synthesize information on the mechanisms and morphologies involved in syngnathid feeding strikes. Next, I scoured peer-reviewed and grey literature and performed a meta-analysis that investigates the associations between syngnathid diets and their feeding morphologies, and habitat associations. I present this meta-analyses in the context of what is known about syngnathid diets and feeding behaviours to date.
Finally, I end with a general discussion on my findings, and how they help answer my research questions (Chapter 4).
7 Chapter 2 Ecological correlates of White's seahorse (Hippocampus whitei) abundance and size distributions at different spatial scales
2.1 Introduction
Numerous living and non-living variables can shape where organisms can be found, and in what abundance. For example, extreme abiotic conditions (e.g. current velocity, water chemistry, turbidity) or poor resource availability in an area can exclude organisms
(Whittaker et al., 1973; Bruno & Bertness, 2001). Likewise, physical components of an organism's habitat, such as the size of refuges or degree of habitat complexity can determine if a prey species is able to persist under the threat of predation (Orth et al.,
1984; Heck & Crowder, 1991; Canion & Heck, 2009). Interactions among members of a community (i.e. predation, competition and parasitism) also have direct and indirect impacts on the survival of multiple species (Sih et al., 1985; Pace et al., 1999). Beyond this, organisms can indirectly affect the distribution of each other by inducing behavioural changes (e.g. to escape detection by predators; Lima & Dill, 1990; Werner & Peacor, 2003;
Preisser et al., 2005). The way that these relationships play out is determined by characteristics of the organisms involved, the mediating effect of the habitat in which they live, and the interactions among these biotic and abiotic variables. Research on animal abundances and distributions to date, however, has often focused on habitat, prey, or predator variables individually, rather than components of all three.
Habitats play a direct role in the life of an organism by providing the physical structure, and indirectly supplying resources that are needed for survival, growth and reproduction. Many habitats and their associated communities are structured by the presence of one or more foundation species—organisms that often form, and have strong
8 effects on the functioning of a community (Dayton, 1972; Bruno & Bertness, 2001).
Foundation species can indirectly provide necessary resources, can be a direct source of food themselves, can provide community members with refuge from predation, and can reduce the physiological stress for other species (Jones et al., 1994, 1997; Bruno &
Bertness, 2001). Habitats with greater structural complexity have been shown to reduce predator foraging success (Orth et al., 1984; Heck & Crowder, 1991; Canion & Heck, 2009) and increase prey populations (Heck & Orth, 2006), such that many species use complex habitats for their reproduction (Kroon et al., 2000; Sancho et al., 2000; Gladstone, 2007) and nursery grounds (Heck et al., 2003). In a global meta-analysis, juvenile fish and aquatic invertebrates experienced greater survival and growth rates in complex habitats, such as seagrass beds, than in those with less structural complexity (Heck et al., 2003). While much research has looked at how habitat variables affect species abundances and distributions, those species’ relationships with their communities are more complex, and less understood.
The relationships among species in a community greatly affect their abundances and distributions (Paine, 1966). The relative abundance of predators and prey are intimately linked, as predators kill prey and convert a portion of their biomass into new predators (Arditi & Ginzburg, 1989; McCauley et al., 1993; Abrams & Ginzburg, 2000).
Without prey biomass to consume, however, predator numbers will decrease (Arditi &
Ginzburg, 1989; Abrams & Ginzburg, 2000). Predator-prey relationships affect the distributions of both species, as each adjusts its behaviours to improve its own fitness (Sih et al., 1985; Lima & Dill, 1990; Abrams, 2000). For example, as a result of favourable abiotic conditions, habitat edges frequently have higher concentrations of herbivores than habitat
9 interiors (Ries et al., 2004). This can lead to higher predator abundances as they adjust their distribution to match their prey (Fretwell & Lucas, 1970; Kennedy et al., 1993; Ries et al., 2004). In response, prey make life-history trade-offs and may sacrifice the improved resources and relocate to refuges or alternative locations to avoid predation (Sih, 1984;
Lima & Dill, 1990; Lima, 1998; Werner & Peacor, 2003). When investigating organisms such as sedentary species, which both consume others and are consumed themselves, it is particularly vital to consider both their prey and their predators.
Sedentary species might be expected to have especially strong relationships with their particular habitat and community, being shaped and adapted to them. Species that are sedentary and rely on crypsis are largely limited to habitats that provide suitable refuge or substrate for cryptic opportunities (Ruxton et al., 2004), as leaving a safe space to forage or mate greatly increases the risk of being preyed upon (Sih, 1992). Sedentary species that are ambush predators are also dependent on local prey characteristics as they feed on nearby prey (Gerritsen, 1984; Sih & Moore, 1990). When animals engage in reproductive activities such as building nests for their young, or parental care, they are more vulnerable to predation (Magnhagen, 1991). The effect of these habitat characteristics is exacerbated when animals have small home ranges and are unlikely to disperse to more favourable conditions (Roberts & Hawkins, 1999). The distribution of these animals might therefore be expected to be shaped by their habitat microstructure and by fine-scale differences in prey and predator availability. Yet few, if any, studies have evaluated the relative effects of habitat, prey, and predator variables on the abundance or distribution of a sedentary animal species.
10 The relationship between a community and any particular species is particularly complex in the three-dimensional structure of the ocean. Three-dimensional habitats provide organisms with more physical opportunities to survive, grow, and reproduce
(Crowder & Norse, 2008). In aquatic environments, this structure is provided by the liquid medium itself, and by foundation species such as seagrasses, coral, macrophytes, and mangroves (Jones et al., 1994; Thorne-Miller, 1999). These habitats are able to support more trophic levels and longer food chains than two-dimensional habitats (Briand & Cohen,
1987; Vander Zanden & Fetzer, 2007). Food webs in marine habitats are particularly intricate, as animals in these environments interact with a greater number of species both within and among trophic levels as compared to terrestrial or freshwater habitats (Cohen et al., 1990; Cohen, 1994). Marine animals are therefore more intimately connected with their communities, which have the ability to affect species’ abundance and distribution
(Werner & Peacor, 2003).
Seahorses (Hippocampus spp.) are a genus of marine fishes with complex life histories whose relationships with their habitats and communities are poorly understood.
Life-history traits indicate that seahorse populations should be greatly influenced by the characteristics of their habitats and communities. These are sedentary, ambush predators with small home ranges and intensive male parental care (Vincent & Sadler, 1995; Foster &
Vincent, 2004; Vincent et al., 2005). They are poor swimmers, and spend most of their time waiting among benthic substrates (e.g. seagrass, coral) for prey to enter their strike zone
(Howard & Koehn, 1985; James & Heck, 1994; Kendrick & Hyndes, 2005). To anchor themselves for feeding strikes, and to remain cryptic to predators, seahorses grasp marine substrates with their prehensile tail (Foster & Vincent, 2004). Reproduction is also tied to
11 the habitat, as pairs within many species maintain long-term bonds that are reinforced by daily dances across their home ranges (Vincent et al., 1992; Vincent, 1995; Vincent &
Sadler, 1995). These characteristics suggest intimate relationships with both habitats and communities, but these have not been well identified in the literature. Most in-situ habitat studies on seahorses to date have examined aspects of their habitat such as holdfast preferences (Bell et al., 2003; Dias & Rosa, 2003; Martin-Smith & Vincent, 2005; Morgan &
Vincent, 2007; Rosa et al., 2007; Harasti et al., 2014a), and habitat correlates such as depth, temperature, and water velocity (Curtis & Vincent, 2005; Caldwell & Vincent, 2012;
Aylesworth et al., 2015). Those studies that examined biotic variables either did not consider abiotic variables (Harasti et al., 2014b), or did not differentiate between seahorse prey and predators (Curtis & Vincent, 2005). To improve our ecological understanding of seahorses, a study that considers habitat, prey, and predator variables together is needed.
This study is the first to explicitly investigate the relative importance of habitat characteristics and community correlates (prey and predators) to seahorse populations in the wild, as a model sedentary marine species. This study investigates the associations between ecological parameters and the densities and size distributions of White's seahorse
(Hippocampus whitei Bleeker 1895) in a New South Wales estuary. The goal of this study was to better understand how these characteristics are correlated with seahorse abundances and size distributions at different scales. To do this, seagrass, prey and predator correlates were compared to seahorse abundance and size data, both among different seagrass beds in an estuary (denoted ‘medium-scale study’ in the text), and within a single seagrass bed (denoted ‘small-scale study’ in the text).
12 2.2 Materials & Methods
2.2.1 Study species
Hippocampus whitei is a medium-sized seahorse species (mean height ± SD was 107
± 27 mm; this study) found in coastal waters along the east coast of Australia (Lourie et al.,
2016). They live among a variety of marine substrates, including soft coral
(Dendronephthya australis), various branching macroalgae (e.g. Sargassum sp., Codium sp.;
Harasti et al., 2014a), seagrasses (e.g. Zostera muelleri subsp. capricorni, Posidonia australis.; Vincent et al., 2005; Harasti et al., 2014a) and artificial structures such as protective swimming net enclosures (Clynick, 2008; Harasti et al., 2010). Juvenile H. whitei are more cryptic and prefer more complex habitats than adults (Harasti et al., 2014a).
Hippocampus whitei adults have small home-ranges (Vincent et al., 2005), and form faithful pair-bonds during the breeding season, which extends from September to February
(austral summer; Vincent & Sadler, 1995; Harasti et al., 2012).
2.2.2 Study locations & design
Study scale: among seagrass beds
The among seagrass beds study was carried out along a 6-km stretch of the southern coastline of the Port Stephens marine estuary in New South Wales (NSW),
Australia (Figure 2.1). The study was limited to the Eastern Port of the estuary, where H. whitei were known to occur (Harasti et al., 2012). The benthic substrate of this region is a mixture of Posidonia australis (Hooker 1858) seagrass, Zostera muelleri subsp. capricorni seagrass, sand, soft coral (Dendronephthya australis), various branching macroalgae (e.g.
Sargassum sp., Calerpa sp.), and sponges (Davis et al., 2015). Posidonia australis is the most
13 abundant holdfast type in shallow coastal areas of the estuary (to a depth of approximately
5 m; Davis et al., 2015). Seven P. australis-dominated seagrass beds, located between 200 m and 6000 m apart from one another, were the focus of this study (Figure 2.1). In all beds, P. australis occurred at high-densities, usually 150-200 shoots m-2, with leaves reaching 30-
50 cm (Table B1). The other benthic substrates listed above were occasionally found within the beds. Any generic mention of 'seagrass' in this paper refers specifically to P. australis.
The aim of the among seagrass beds study was to determine how seahorse density and body size correlated with (i) seagrass characteristics, (ii) availability of prey and (iii) presence of predators. To meet this aim, two sampling campaigns were conducted at each of the seven seagrass beds during the 2015-2016 austral summer: (1) 10 November - 23
December 2015; and (2) 3-25 February 2016. Surveys to characterize seahorses, seagrass, prey, and predators are described in separate sections below.
The locations of the plots were determined by randomly selecting a specific point along the seaward edge of each seagrass bed. From each of these seven points, a square sampling area was established that measured 30 m parallel to (width), and 30 m perpendicular (length) from the shoreline (Figure 2.2). However, the shape of the seagrass beds, and sometimes discontinuous nature of the seagrass, meant the sample areas never reached 900 m2, and instead ranged from 111 to 752 m2, with a mean of 551 m2 across the seven plots (Table 2.1).
The seven plots were sampled using a random-stratified design. The area of seagrass at each plot was divided into three strata by distance from the seaward edge of the seagrass patch (shallow, medium, deep). Each stratum was of equal length, measured perpendicular to shore (Figure 2.2). If the length of the seagrass bed did not reach 30 m, it
14 was still divided into thirds. The outline of each stratum was traced by slowly swimming its perimeter while towing a GPS device attached to a safety float (as described in Poulos et al.,
2013). The data were then uploaded to Google Earth Pro as point data, from which the area of each stratum was calculated.
Study scale: within Little Beach seagrass bed
One seagrass bed, Little Beach, had a much higher abundance of seahorses than the other six beds. Little Beach was therefore used as the area to study the ecological correlates of seahorse abundance and size distributions within a single bed. The Little Beach seagrass bed is bisected by a 3 m wide jetty (Figure 2.2). To the west of the jetty, is the smaller 350 m2 'West Patch', and to the east of the jetty, the larger 1248 m2 'Main Patch'. Taken together, the Little Beach seagrass bed is 90 m wide and 25 m long (Figure 2.2). The seaward edge of the bed was approximately 3.5 m deep. Despite having a relatively higher number of seahorses, the P. australis density and substrate characteristics were similar to the other plots across the estuary (Table B1).
The aim of the within Little Beach study was to determine how (1) seahorse distribution and (2) body size correlated with seagrass characteristics, availability of prey and presence of predators within a single seagrass bed.
(1) The goal of this part of the study was to determine if seahorses were found at locations (0.25 m2 areas of seagrass) within Little Beach that had seagrass, prey, and predator characteristics proportionate to their availability, or if seahorses were preferentially selecting locations with particular characteristics. A use-availability study (as defined by Johnson et al., 2006) was carried out at the Little Beach site in January 2016,
15 during which seagrass, prey, and predator characteristics were measured where seahorses were found ('used' locations, those adjacent to every fifth H. whitei found during a seahorse survey, see below), and at random locations available to seahorses but not necessarily being used by them ('available' locations, sampled at haphazard locations during free swims through Little Beach). Using a resource selection function model (RSF), the seagrass, prey, and predator characteristics were then compared between ‘used’ and ‘available’ locations to determine if seahorse were selecting for particular characteristics (Boyce et al.,
2002; Manly et al., 2002; Lele & Keim, 2006; McDonald, 2013; Northrup et al., 2013).
Surveys to characterise seahorses, seagrass and prey are described below. Predator data for 'used' and 'available' locations within Little Beach were predicted, as described below.
To determine the number of ‘used’ and ‘available’ samples required to encompass a representative proportion of the population, the seahorse population of Little Beach was estimated using a mark-resight model as has been done for H. whitei populations in Port
Stephens in the past (Harasti et al., 2012). This mark-resight model consisted of eight x 60- minute free swims across the entire site over a 5-day period (15-19 January 2016). Each seahorse was tagged using unique combinations of three small visual implant fluorescent elastomer tags (VIFE; Northwest Marine Technologies, USA). VIFE has been shown to have minimal impact on the mortality and behaviour of H. whitei and other seahorse species
(Woods & Martin-Smith, 2004; Woods, 2005; Harasti et al., 2012). The population was assumed closed during this short time period (i.e. no immigration, emigration, births or deaths; Seber, 1986). Using a closed capture estimate in the program NOREMARK (White,
1996), the population was estimated to be 312 individuals (0.195 seahorses m-2; 95% CI =
222-495). After factoring in the time taken to process the samples taken at a single
16 location, it was determined that roughly 20% of the population (‘used’ locations), and the same number of ‘available’ locations, could be sampled for ecological correlates. Therefore, the ecological correlates associated with every fifth seahorse were sampled. A total of 66
‘used’ and 71 ‘available’ samples were collected from 11-30 January 2016.
(2) The associations between ecological correlates (seagrass, prey, and predator characteristics) and the body size of seahorses within Little Beach were also investigated.
Seahorses observed in the (i) November and (ii) February sampling campaigns at Little
Beach as a part of the study among seagrass beds, as well as (iii) in the use-availability study within Little Beach were used to investigate seahorse size distributions within a single seagrass bed. Seagrass, prey, and predator data were predicted, as described below.
2.2.3 Seahorse surveys
For the medium-scale study, during the November and February sampling campaigns, the entirety of each plot was searched for H. whitei. A transect tape was laid 1 m in from the seaward edge of the shallow stratum, parallel to shore. The length of the transect was searched for seahorses by two divers using SCUBA; a 1 m strip on either side of the transect tape was slowly searched by each diver with the assistance of a 30 cm metal stick. The transect was then moved 2 m closer to shore and the survey was repeated. This continued until the entirety of each stratum in each of the seven survey plots was searched.
All seahorses were measured underwater (see below), and then returned to the holdfast from which they were taken. To prevent duplicate counting, each seahorse was marked using VIFEs as described above. The location of each tagged seahorse was recorded using a towed GPS. The body size, sex and reproductive status of each seahorse was recorded.
17 Body size was measured as height—the length from the tip of the coronet to the end of the outstretched tail (Lourie et al., 2004). The sex, maturity, and reproductive status of each individual was then determined using a combination of height and brood pouch cues, in combination with height at maturity models (Appendix A).
For the small-scale study, seahorse surveys consisted of 2 m wide transects along the length of the site, perpendicular to shore, starting on the most western edge. Transects were searched by two divers, and seahorses processed as described above—but in this case divers only sampled every fifth seahorse they encountered.
2.2.4 Predator surveys
For the medium-scale study, underwater visual censuses (UVC) were used to survey potential H. whitei predators. Within each stratum, one diver swam along a single haphazardly located transect (max 30 m in length) laid parallel to shore, at a constant speed of roughly 4 m min-1. As the transect was being laid, all fish observed within 2 m of the tape (both sides) and to a height of 4 m above the seafloor were recorded (to the lowest taxonomic level possible). As the transect was being retrieved, the seagrass along its length was searched for sedentary fish and crabs by two divers; a 1 m width on either side of the transect tape was searched by each diver. Predators in this paper includes fish and octopus species known to eat H. whitei—including dusky flathead Platycephalus fuscus, red rock cod
Scorpaena jacksoninsis, striped anglerfish Antennarius striatus, Sydney octopus Octopus tetricus, and blue-lined octopus Hapalochlaena fasciata (Harasti et al., 2014b). It also includes species that might eat fish similar to seahorses (e.g. yellowfin pike Dinolestes lewini; based on Froese & Pauly, 2017). Predator variables included 'total predators'
18 (number of individual predators along a transect) and 'predator types' (number of predator species observed along a transect).
For the small-scale study, predators were surveyed at Little Beach on three consecutive days, 19-21 January 2016, along 25 m transects laid perpendicular to shore, at eight fixed locations—0, 20, 35, 50, 65, and 80 m from the most western part of 'West
Patch'. Predators were surveyed using UVC, as described above.
2.2.5 Seagrass surveys
In the medium-scale study, seagrass was quantified in three quadrats placed at random along each predator transect (see above). The seagrass variables used in analyses were the mean seagrass density and seagrass height within a stratum. For the small-scale study focused on Little Beach, quadrats were either centred around a seahorse (‘used’ locations) or placed haphazardly (‘available’ locations). Quadrats of 50 cm x 50 cm (0.25 m2) were used to characterize seagrass (West, 1990). The number of P. australis shoots in each quadrat was counted ('seagrass density') and the longest blade in each of five haphazardly selected shoots in the quadrat was measured ('seagrass height'; Orth et al.,
2002; Ceccherelli et al., 2007).
2.2.6 Prey surveys
To determine prey availability in both the among and within seagrass bed studies, 2-
3 blades of a haphazardly selected P. australis shoot were collected within each 0.25 m2 seagrass quadrat. Seagrass blades were sealed in plastic bags, taken to the surface, and immediately preserved in 5% formalin for later processing.
19 In the lab, seagrass samples were rinsed through a 50 μm sieve to collect all small
(<1 cm) epifauna on the blade. All potential invertebrate prey were counted and identified to broad functional categories under a microscope (e.g. amphipods, copepods, polychaete worms; Horinouchi & Sano, 2000; Yip et al., 2015; Valladares et al., 2016). The determination of potential prey was based on the diets of other seahorse species as very limited information was available on the diet of H. whitei specifically (Burchmore et al.,
1984). Epiphytic growth was scraped off the seagrass blades, and both the epiphytic growth and seagrass leaves were dabbed dry and weighed to the nearest 0.01 g. Prey variables were quantified as 'Prey Types’ (number of functional prey categories in a sample) and 'Prey Density' (number of individual prey items divided by the mass of the P. australis sample). 'Fouling density' was calculated as the mass of epiphytic growth divided by the mass of the P. australis on which it grew.
Prey types, prey density and fouling density values were averaged (as a mean) among the three quadrats for each stratum in the among seagrass bed study. For the Little
Beach study, these values were enumerated for all used and available samples.
2.2.7 Predicting covariates within Little Beach
2.2.7.1 Seagrass and prey
Ideally, all seahorse observations at Little Beach would have been used in the analysis exploring the association between ecological variables and seahorse height (N =
328). However, seagrass and prey information was only available for seahorses observed during the use-availability study (N = 66). In order to generate associated seagrass and
20 prey information for the other 262 seahorse observations, the ordinary kriging function in
ArcMap's Geostatistical Analyst 10.2 was used to generate a series of interpolated spatial maps for seagrass and prey characteristics. These maps were subsequently used to predict the seagrass and prey characteristics for every seahorse observation based on their location within the interpolated spatial maps. The following parameters were predicted: depth, seagrass density, seagrass height, fouling density and prey types. The variability in prey density prevented reliable predictions, and was therefore excluded from this analysis.
These interpolations were used in analyses of seahorse height within Little Beach.
2.2.7.2 Predators
Because the only predator information for Little Beach was from fixed transects, predator data had to be predicted for all ‘used’ and ‘available’ locations in the RSF, and all seahorse observations in the height analysis. The total numbers of predators, and predator types, were regressed against the distance from the most western part of 'West Patch' using polynomial equations (Figure 2.3; see caption for model outputs). The equations were subsequently used to predict the total number of predators, and predator types, for all ‘used’ and ‘available’ locations in the RSF, and for all seahorse observations in the analysis of seahorse height within Little Beach.
21 2.2.8 Statistical analyses
Study scale: among seagrass beds
Generalized linear mixed effects models were used to determine the extent to which seagrass, prey, and predator characteristics explained differences in H. whitei density and height among plots. Using Cleveland dotplots, all seagrass, prey, and predator covariates were evaluated for the presence of outliers (Zuur et al., 2010). In both the seahorse density and height models, the effect of outliers in fouling density data was removed by a log- transformation. Variance Inflation Factor (VIF) analysis was used to determine and deal with multicollinearity in two steps, following the protocol described in (Zuur et al., 2010).
First, VIF was applied to independent variables within the same group (seagrass, prey, and predator covariates), such that the variables contributing the most variance (i.e. the highest
VIF) were sequentially removed until the VIF scores of all covariates within each group was less than 3. Second, the process was repeated for the remaining variables across groups.
Although a VIF of 3 is generally a strict cut-off for covariate inclusion (Zuur et al., 2010), the global models were constrained by degrees of freedom and required greater selectivity.
Resulting independent variables used in the global model to predict H. whitei density were: depth, seagrass density, prey types, prey density, and total predators; and for seahorse height were: seagrass density, seagrass height, prey types, prey density, total predators, and predator types.
Seahorse density fit a log-normal distribution, and the untransformed density values were fit using a generalized linear mixed effects model (GLMM) with a Gaussian distribution and a log-link function. Mean seahorse height was fit using a normally distributed linear mixed effects model. To account for spatial autocorrelation and site-level
22 effects in both models, stratum nested within plot was included as a random effect (Bolker et al., 2009). To account for temporal autocorrelation, a time-level random effect was used
(Bolker et al., 2009). The models were run using the lme4 package, version 1.1-12 in R 3.3.1
(www.r-project.org).
An information-criterion approach was used to select a group of models that best describe the data, but individually may not be distinguishable in their ability to model the data (Grueber et al., 2011). All potential models were first ranked by their AICc score, and then parameter estimates were averaged (with weight-adjustment) over models that were within 4 AICc units of the best model (Table 2.2; Grueber et al., 2011).
Study scale: within Little Beach seagrass bed
An exponential logistic RSF was applied to the use-availability design to compare characteristics measured at locations adjacent to seahorses to those measured at available locations. The RSF of a particular location is proportional to the probability of H. whitei selecting that particular location, given the set of seagrass, prey, and predator characteristics measured there. By comparing the RSFs of used and available locations, it can be determined if seahorses were selecting locations with particular ecological characteristics. The model was run using the ResourceSelection package version 0.3-0 in R
(www.r-project.org). Prior to running the model, outliers within, and multicollinearity among covariates were evaluated as in the among seagrass bed study. The global model had the following independent variables: depth, seagrass density, seagrass height, fouling density, prey types, prey density, and total predators (Table 2.2). An information-criterion, model-averaging approach was used to estimate the parameters as outlined in the among
23 seagrass bed study (Grueber et al., 2011).
Seahorse height data were modelled using a series of linear mixed effects models for the following groups of H. whitei: all seahorses, all reproductively active (RA) seahorses, females, RA females, males, and RA males. Outliers were evaluated as in the among seagrass bed study. Multicollinearity was evaluated as in the among seagrass bed study; however, a VIF cut-off of 4 was used because there were a high number of observations and lower number of candidate variables, so the models were not constrained by degrees of freedom (Zuur et al., 2010). Predator and interpolated seagrass and prey data were included as independent variables. Global models had the following independent variables: depth, seagrass height, prey types, total predators, and predator types. Because 91 of the
328 sightings represented the same individuals (sighted in both November and February), autocorrelation that would result from multiple sightings of the same individual was accounted for by including an individual-level random effect (Bolker et al., 2009). To account for temporal autocorrelation, a time-level random effect was included in the model. The model was run using the lme4 package, version 1.1-12 in R (www.r- project.org). Information-criterion, model-averaging approaches were used to estimate parameters for each seahorse reproductive group, as outlined in the among seagrass bed study (Table B2; Grueber et al., 2011).
All research undertaken in this project was done in accordance with the University of British Columbia's Animal Care Committee permit A12-0288 and the NSW DPI Animal
Care and Ethics Committee permit 15/01.
24 2.3 Results
2.3.1 Study scale: among all seagrass beds
2.3.1.1 Seahorse survey summary statistics
Among the plots sampled along the Port Stephens estuary, the number of seahorses found varied across both space and time (Figure 2.4; Table 2.1). During both sampling campaigns, Little Beach and Pipeline had the highest overall seahorse densities (Figure
2.4a). Seahorses found in plots other than Little Beach accounted for just 19% of individuals found in November, but nearly half of those found in February (48%). More seahorses were found in February than November in all plots except Little Beach and
Pipeline. A total of nine seahorses were found in November across Seahorse Gardens 1,
Seahorse Gardens 2 and Dutchies, but this increased to 73 in February; the increase in these three plots was largely due to an increased number of encountered females (2 to 27), and juveniles (6 to 34). In contrast, the number of seahorses encountered at Little Beach and Pipeline decreased only slightly from November to February (Table 2.1).
The height of adult seahorses did not differ significantly between months (ANOVA,
F1,257 = 0.83, P > 0.05), but did differ among plots (Figure 2.4b; ANOVA, F5,257= 15.20, P <
0.001). Nearly half (48%) of all adults were male in November, and just under half (42%) were male in February (Table 2.1). A higher percentage of seahorses were physically mature in November (95%) than February (74%; Table 2.1). The total number of observed adults was the same in November and February, but the number of juveniles was more than six times greater in February (Table 2.1). Little Beach and Pipeline also had the highest proportion of individuals that were physically mature (with the exception of Fly
Point in November, which had only three seahorses; Figure 2.4c).
25 2.3.1.2 Correlates of seahorse density & seahorse height
Among seagrass beds, seahorse density was significantly negatively associated with total predators (Figure 2.5a). None of the seagrass, prey, or predator variables was significantly associated with seahorse height among seagrass beds (Figure 2.5b).
2.3.2 Study scale: within Little Beach seagrass bed
Within Little Beach, seahorses were more likely to be found in deeper locations with denser seagrass, a greater number of prey types, and fewer total predators (Table 2.3).
Seagrass height, fouling density and prey density were not significant determinants of where seahorses were found (Table 2.3). Within the Little Beach seagrass bed, seahorse height increased significantly with depth for all seahorses, as well as subsets of the population: all RA seahorses (Figure 2.6a), females (Figure 2.6c), RA females (Figure 2.6d) and RA males (Figure 2.6f). Seahorse height also increased significantly with total predators for all RA seahorses (Figure 2.6b), and females (Figure 2.6c; Table 2.3). Seagrass height, prey types, and predator types did not correlate significantly with seahorse height in any subset of the population (Figure 2.6), and none of the independent variables correlated with male height (Figure 2.6e).
2.3.3 Overall associations between seahorses and ecological correlates
Although seahorses selected deeper locations, such locations were also associated with smaller seahorses (except males; Table 2.4). Seahorses selected locations with denser seagrass (Table 2.4). Seagrass height and fouling density were not correlated with any seahorse parameter within or among seagrass beds (Table 2.4). Seahorses selected
26 locations with more prey types (Table 2.4). Prey density was not correlated with any seahorse parameters both within or among seagrass beds (Table 2.4). Seahorses selected locations with fewer predators within Little Beach, and predators were negatively associated with seahorses among different seagrass beds (Table 2.4). Additionally, there was a positive association between predators and seahorse height (among RA seahorses and females; Table 2.4). Predator types were not associated with any seahorse parameters both in or among seagrass beds (Table 2.4).
2.4 Discussion
Habitat, prey, and predator variables all correlated with seahorse density or height distributions to varying extents, depending on the scale of the study. In general, habitat variables predicted seahorse distribution better within seagrass beds than across them, consistent with passive habitat selection at large scales and active habitat selection at the level of the seagrass bed, perhaps hinting that seahorses take what opportunities they can in the bed where they find themselves. Seahorses also show a negative association with predators once all other variables are accounted for, either because of direct mortality or behavioural changes among seahorses. Within a seagrass bed, this sedentary seahorse species was associated with denser seagrass and deeper habitat. It may be that such habitat offered more food and less detection, especially of their relatively visible courtship and mating. That said there was no clear association between the distribution of seahorses and the abundance of their epiphytic prey. All of the results of this study must, however, be considered in the context of a population in transition. Human activities have led to tremendous habitat loss, which may explain why seahorses are now found in seagrass beds
27 and not in their previous coral and sponge habitats. It is thus possible that relationships between seahorses and their abiotic environment and biotic community are still in flux.
Seahorses showed a stronger association with environmental variables within a single seagrass bed than among seagrass beds, indicating that they may have to make the best of the habitat in which they settle. Although most species are planktonic after birth— likely dispersed by currents—they settle at about 2–8 weeks, after which they are relatively sedentary (Foster & Vincent, 2004). While adults of some seahorse species make seasonal migrations to deeper waters, most species studied to date maintain small home- ranges for prolonged periods, likely because of their poor swimming ability, sit-and-wait predatory style, and stable social structures (Foster & Vincent, 2004). In fact, H. whitei shows particularly strong site associations across seasons and years (Vincent et al., 2005;
Harasti et al., 2014a). For example, none of the 1100 H. whitei tagged by Harasti (2016) between 2007 and 2009 was ever found at a different site along the estuary. Movement among different seagrass beds would likely be costly in terms of risk of exposure to predators, strong currents, and the risk of not finding a mate. In contrast, seagrass beds reduce flow within their canopies (Hendriks et al., 2008), and provide refuge from predators (Orth et al., 1984; Heck & Crowder, 1991; Canion & Heck, 2009). Seahorses are therefore able to move within seagrass beds without being exposed to the strong tidal currents of the Port Stephens estuary (Davis et al., 2015), while also maintaining crypsis.
Given that seahorses show strong fidelity with small areas, it is not surprising that this study found H. whitei to be associated with many ecological correlates within a seagrass bed, and very few among seagrass beds.
28 The negative association between seahorse and predator abundance may arise from changes in seahorse behaviour rather than direct mortality. Observation and gut-content studies suggest relatively low predation on seahorses (Kleiber et al., 2011). Indeed the most intense evaluation of predation on seahorses to date, involving hundreds of hours of observation of more than 2000 individual seahorses, recorded only 13 predation events
(Harasti et al., 2014b). This should not be that surprising as seahorses and their syngnathid relatives are highly cryptic fish with the ability to adjust coloration and to grow body filaments to camouflage with their seagrass habitats (Foster & Vincent, 2004). For predators that are able to find syngnathids, they are a poor meal; they are bony, difficult to digest, and a low energy food source (Harris et al., 2008). In most cases, predators that feed on syngnathids are opportunistic, generalist feeders (Kleiber et al., 2011). Taken together, this suggests that the negative association of seahorses and predators may instead reflect a predator-induced change in behaviour (Werner & Peacor, 2003; Preisser et al., 2005), with seahorses redistributing themselves to avoid predation, a common strategy among fish
(Milinski, 1993). Predator-avoidance behaviours may be particularly pronounced during H. whitei's breeding season, which is when this study was conducted. Seahorses are more vulnerable to predation during breeding as they engage in risky courtship behaviours and have lengthy parental care (Magnhagen, 1991; Foster & Vincent, 2004). While the present study is not the first to show a negative association between seahorses and their predators
(Harasti et al., 2014b), it is the first to show this association is independent of other ecological variables.
Hippocampus whitei may have selected denser seagrass locations because the increased habitat complexity improved its success as an ambush predator. Previous
29 research has suggested preference for certain habitat characteristics is not consistent among seahorse species, perhaps because of differences in foraging strategies. Relatively sedentary seahorse species may favour dense seagrass where the increased habitat complexity has been shown to either increase or have no effect on the success of ambush feeding (James & Heck, 1994; Flynn & Ritz, 1999). In contrast, since habitat complexity impedes the foraging success for more active fishes, more active seahorse species may prefer more open landscapes (Orth et al., 1984; Heck & Crowder, 1991; Canion & Heck,
2009). The sedentary seahorse Hippocampus guttulatus was found in more densely vegetated areas than its more active congener, Hippocampus hippocampus, probably because it preferred greater complexity for feeding purposes (Curtis & Vincent, 2005).
Likewise, the ambush predator Hippocampus erectus was shown to change from an ambush
(sit-and-wait) foraging strategy to one that was more active when moved to less dense seagrass habitats, presumably since they lost their cryptic advantage in areas with less seagrass (James & Heck, 1994). The finding that H. whitei selected denser seagrass is therefore consistent with its extremely sedentary nature (C. Manning, personal observation; Vincent et al., 2005; Harasti et al., 2014a), and may reflect a preference for better foraging prospects in these areas.
Since seahorses change feeding strategies as they age, it is surprising that seahorse size was not correlated with prey variables, whether with or among seagrass beds. As seahorses grow, they are able to feed on a greater variety of prey types and eat a greater quantity of food (Flynn & Ritz, 1999; Castro et al., 2008). This type of ontogenetic shift in diet is also seen in other syngnathids (Bennett, 1989; Rosa et al., 2007), and teleost fishes in general (Wootton, 2012). Even if seahorses are unlikely to move among seagrass beds
30 after they settle, it might be expected that larger seahorses would aggregate within seagrass beds in areas with more prey types and higher prey densities. It is possible that the method of prey sampling used in this study, in which prey items were collected from the surface of seagrass blades, did not represent the prey used by different age classes of seahorses. Although prey associated with the surface of plants are a major source of food for seahorses (Horinouchi & Sano, 2000; Teixeira & Musick, 2001; Kendrick & Hyndes,
2005; Kitsos et al., 2008; Storero & Gonzalez, 2008; Gurkan et al., 2011b), planktonic and epibenthic prey - not considered in this study - are important dietary items for smaller seahorses (Teixeira & Musick, 1995; Kanou & Kohno, 2001; Castro et al., 2008). For the scale of prey sampling in the present study (based on 0.25m2 quadrats), however, the collection of planktonic prey was impractical. A goal of future studies that aim to better understand the full-scope of relationships between seahorses and their prey should be to include planktonic and epibenthic species.
This is the first study to empirically show that larger seahorses aggregate at shallower depths within a habitat patch possibly because of spatial differences in feeding and reproductive opportunities. A different result was reported by Harasti et al. (2014a), who found no difference in the depths at which adults and juveniles were found. Within
Little Beach, greater depth is associated with the seaward edge of the P. australis bed.
Seagrass edges are a physical barrier to nutrient flow and have the ability to intercept zooplankton carried to seagrass beds in the water column (Jenkins & Sutherland, 1997;
Hendriks et al., 2008), concentrating these resources along the seaward edge. Since planktonic crustaceans (not considered in this study) are important dietary items for smaller seahorses, smaller individuals may benefit from foraging in these areas (Teixeira &
31 Musick, 1995; Castro et al., 2008). Habitat edges could also benefit smaller seahorses by allowing them to occasionally forage for planktonic prey over open sand, while remaining close to their protective vegetative habitat (Orth et al., 1984). In addition, adult seahorses may have preferred shallower depths because the seagrass provides better refuge from predation while engaging in risky reproductive activities such as courtship and parental care (Magnhagen, 1991).
The seahorse densities found in this study among all seagrass beds in the Port
Stephens seagrass beds may reflect a mass habitat shift of the population in response to habitat loss at greater depths. Notable seahorse population declines have been reported in
Port Stephens in the last decade, linked to a loss of the soft coral and sponge habitats preferred by seahorses, apparently as a result of mooring installation, anchor damage, and sand inundation (Harasti, 2016). A multi-year survey of Port Stephens (2006-2009) found seahorses concentrated in deeper soft coral and sponges, with very few in the seagrass patches that were surveyed in the present study (Harasti et al., 2014a; Harasti, 2016).
Nearly ten years later, this study estimated a seahorse population at one seagrass bed
(Little Beach) at a density almost twice that previously recorded in in the deeper habitats
(Harasti, 2016). The corollary is that seahorses were very difficult to find in the deeper soft coral and sponge habitats, unlike Harasti (2016). It is unclear from this study whether seagrass is a preferred habitat type for this species, or is being used because preferred types have been lost locally (Harasti et al., 2014a). While seahorse surveys conducted during this study suggest that the population decline was not as bad as initially suspected
(Harasti, 2016), the loss of preferred habitat at greater depths places pressure on H. whitei
(Foster & Vincent, 2004). In the context of substantial declines in soft coral and sponge
32 habitat at depth (Harasti, 2016), seagrass may now be playing an important role in H. whitei ecology.
This study should be considered in decisions about closing areas to fishing. Some research indicates that spatial bans on recreational fishing can result in more predatory fish (Jouvenel & Pollard, 2001; Schroeder & Love, 2002), leading to declines in populations of smaller fish, including cryptic species (Willis & Anderson, 2003). This mechanism has also been argued to apply to seahorses in Port Stephens. In two separate comparisons,
Harasti et al. (2014b) found significantly more predators and significantly fewer seahorses within two sites that banned recreational fishing (sanctuary zone) compared to two sites that allowed it (habitat protection zone). In this study, however, the highest densities of seahorses were found inside the sanctuary zone (Little Beach). This is compatible with a single study in the Philippines that showed no reserve effect for seahorse abundance, although seahorses inside the MPAs were somewhat larger than outside (Yasué et al.,
2012). More research is necessary to understand how closing areas to fishing might affect seahorses in areas with and without predators.
In summary, this study demonstrates the importance of a holistic approach that considers an integration of habitat, prey, and predator variables in animal studies. Animals do not live in isolation; they are involved in trophic interactions with other organisms
(Paine, 1966; Polis & Strong, 1995), and often have intimate relationships with their habitats (Jones et al., 1994, 1997; Bruno & Bertness, 2001). This makes it crucial that both their communities and their habitats are considered, especially when they are sedentary, or involved in both sides of the predator-prey dynamic. Using seahorses as an example, research to date has generally looked at the relationship between animals and either their
33 habitat (Bell et al., 2003; Dias & Rosa, 2003; Curtis & Vincent, 2005; Martin-Smith &
Vincent, 2005; Rosa et al., 2007; Caldwell & Vincent, 2012; Harasti et al., 2014a; Aylesworth et al., 2015), or a component of their community (Harasti et al., 2014b). This study is therefore unique among seahorse studies in evaluating seahorse abundance and distribution in models that consider habitat, prey, and predator correlates together. This study’s results, showing that habitat, prey, and predator variables were all important correlates of seahorse abundance at various scales, suggests that more such studies would be valuable.
34 Table 2.1 Summary statistics for seahorse (SH) surveys at seven plots in the Port Stephens estuary, NSW, Australia, during the November
(Nov) and February (Feb) sampling campaigns. SE is the standard error of the mean. Overall density represents the total number of seahorses divided by the total area searched. For adult height, total represents the mean of all adults pooled across plots. February Little Beach physical maturity ratios do not add to total number of seahorses found because the sex of one individual was not determined, and this individual was not included in any other calculations. Plots are listed from smallest to largest distance from the estuary mouth.
Abundances Adult Height (mm) Sex Ratios Physical Maturity Ratios Total SH Density (SH 100m-2) November February November February November February Plot Area (m2) Nov Feb Nov Feb Mean SE Mean SE Male Female Male Female Adult Juvenile Adult Juvenile Shoal Bay 625 0 0 0.0 0.0 - - - - 0 0 0 0 0 0 0 0 Little Beach 600 112 92 18.7 15.3 119 1 124 2 55 56 38 46 111 1 84 7 Fly Point 604 3 3 0.5 0.5 99 2 - - 2 1 0 0 3 0 0 3 SHG 1 561 5 39 0.9 7.0 125 13 95 3 1 1 8 15 2 3 23 16 SHG 2 605 1 21 0.2 3.5 - - 103 6 0 0 3 9 0 1 12 9 Pipeline 111 15 10 13.5 9.0 120 3 116 8 6 9 5 4 15 0 9 1 Dutchies 752 3 13 0.4 1.7 131 - 94 8 0 1 1 3 1 2 4 9 Overall 3858 139 178 3.6 4.6 119 1 115 2 64 68 55 77 132 7 132 45
35 Table 2.2 Summary of the model-averaged statistics for the top models predicting: (a) seahorse density among seagrass beds, (b) adult height among seagrass beds, (c) and resource selection function within the Little Beach seagrass bed. LL = log-likelihood, AICc = corrected Akaike information criterion, ΔAICc = difference in model AICc with that of the top model, wi = Akaike weight, df = number of model parameters including intercepts and residuals. The following abbreviations have been made: DPTH = depth, DENS = seagrass density, HGHT = seagrass height, TPT = prey types, TPI = prey density, FLNG = fouling, PRED
= total predators, and TPRED = types of predators.
Model and parameters included LL AICc ΔAICc wi df Among seagrass beds (a) Seahorse density PRED -104.80 224.3 0.00 0.47 6 TPI, PRED -104.56 226.86 2.56 0.13 7 TPT, PRED -104.62 226.97 2.67 0.12 7 DPTH, PRED -104.75 227.22 2.92 0.11 7 DENS, PRED -104.79 227.31 3.01 0.1 7 TPI -106.76 228.23 3.93 0.07 6 (b) Adult seahorse height DENS, HGHT, TPT, TPI, PRED, TPRED (global model) -94.10 229.05 0.00 0.25 11 HGHT, TPT, TPI, PRED, TPRED -97.55 229.77 0.72 0.17 10 DENS, TPT, TPI, PRED, TPRED -97.60 229.87 0.81 0.17 10 DENS, HGHT, TPT, PRED, TPRED -97.76 230.18 1.12 0.14 10 DENS, HGHT, TPT, TPI, TPRED -97.76 230.19 1.14 0.14 10 DENS, HGHT, TPT, TPI, PRED -97.83 230.32 1.27 0.13 10 Within Little Beach seagrass bed (c) Resource Selection Function DPTH, DENS, TPT, TPI, PRED -225.47 462.04 0.00 0.32 5 DPTH, DENS, FLNG, TPT, TPI, PRED -224.97 463.53 1.48 0.15 6 DPTH, DENS, HGHT, TPT, TPI, PRED -225.26 464.1 2.05 0.12 6 DPTH, TPT, TPI, PRED -227.89 464.52 2.47 0.09 4 DPTH, DENS, PRED -229.16 464.74 2.70 0.08 3 DPTH, DENS, FLNG, TPT, PRED -226.88 464.87 2.83 0.08 5 DPTH, DENS, TPT, PRED -228.40 465.53 3.48 0.06 4 DPTH, DENS, HGHT, FLNG, TPT, TPI, PRED (global model) -224.85 465.86 3.82 0.05 7 DPTH, DENS, HGHT, PRED -228.57 465.88 3.83 0.05 4
36
Table 2.3 Model-averaged parameter estimates, standard error (SE) of the parameter, correlate relative importance, the upper and lower
90% parameter confidence intervals (CI) for variables predicting resource selection function within the Little Beach seagrass bed.
Variable Parameter estimate SE Relative importance 5% CI 95% CI Depth 0.856** 0.255 1.00 0.437 1.276 Seagrass density 0.031** 0.015 0.91 0.006 0.056 Seagrass height 0.026a 0.045 0.21 -0.048 0.101 Fouling (log) -3.095a 3.121 0.28 -8.229 2.039 Prey types 0.207* 0.113 0.87 0.021 0.393 Prey density (log) -1.099a 0.682 0.73 -2.220 0.022 Total predators -0.805** 0.179 1.00 -1.100 -0.511 a, P > 0.10 * 0.10 > P > 0.05 ** P < 0.05
37 Table 2.4 Summary of relationships between seagrass, prey, and predator covariates with (a) seahorse (SH) density and (b) adult height among seagrass beds, (c) resource selection function (RSF) within the Little Beach seagrass bed and (d) SH height within Little Beach, among
SH with different sexes and reproductive statuses. RA = reproductively active.
Among seagrass beds Within Little Beach seagrass bed (a) SH (b) Adult (c) RSF (d) SH Height Density SH Height Parameter All SH RA SH Females RA Females Males RA Males Seagrass covariates Depth 0 + ------0 - Seagrass density 0 0 + Seagrass height 0 0 0 0 0 0 0 0 Fouling 0 0 Prey covariates Prey types 0 0 + 0 0 0 0 0 Prey density 0 0 0 Predator covariates Total predators - - 0 - - 0 + + + 0 0 0 Predators types 0 0 0 0 0 0 0
+ +, positive relationship, P < 0.05 +, positive relationship, 0.10 > P > 0.05 0, no significant relationship, P > 0.10 - -, negative relationship, P < 0.05 -, negative relationship, 0.10 > P > 0.05 blank cell, not included in final model
38
Figure 2.1 Location of the seven study plots along the southern coast of the Port Stephens estuary, on the eastern coast of New South Wales, Australia. Plot names: (1) Shoal Bay, (2) Little Beach, (3) Fly Point, (4)
Seahorse Gardens 1, (5) Seahorse Gardens 2, (6) Pipeline, (7) Dutchies. Shore Data: OpenStreetMap (and) contributors, CC-BY-SA.
39
40 Figure 2.2 Map of the Little Beach seagrass beds showing an example of the stratified random sampling design used at each of the seven study plots (for study among seagrass beds). Light grey represents P. australis. White background includes all other benthic substrate types, predominantly sand and Zostera sp. The 8 m length of the stratum was calculated by dividing the 24 m length of the seagrass bed into thirds.
Shore Data: OpenStreetMap (and) contributors, CC-BY-SA.
41
Figure 2.3 Mean (± SE) number of (a) total predators, and (b) different predator species observed at fixed transects at Little Beach, measured 19-21 January 2016. Hashed polynomial lines of best fit are included: (a) y = 0.0029x2 - 0.3473x + 11.024 (r2 = 0.87), (b) y = 0.0009x2 - 0.0849x + 2.3039 (r2 = 0.96).
42
Figure 2.4 (a) Mean seahorse density (± SE), (b) mean adult seahorse height, (c) proportion of seahorses that were physically mature (adult), and (d) proportion of adults that were male, by plot and by November and December survey campaigns. Values at the base of bars indicate the number of adult seahorses (a, d) and the total number of seahorses (c). Bars sharing a common letter do not differ significantly (Tukey HSD, P >
0.05) 43
44 Figure 2.5 Model-averaged effect sizes with 90% confidence intervals (CI) for predictor variables of seahorse (a) density and (b) adult height, among seagrass beds. Outputs based on results of mixed-effects models. Parameter estimates are indicated to the right of the CIs. P-values are indicated as follows: superscript a = P > 0.10, * = 0.10 < P < 0.05, ** = P < 0.05.
45
46
Figure 2.6 Model-averaged effect sizes with 90% confidence intervals (CI) for predictor variables of seahorse height within the Little Beach seagrass bed, among (a) all seahorses, (b) all reproductively active
(RA) seahorses, (c) females, (d) RA females, (e) males, (f) RA males. Outputs based on results of mixed- effects models. Parameter estimates are indicated to the right of the CIs. P-values are indicated as follows: superscript a = P > 0.10, * = 0.10 < P < 0.05, ** = P < 0.05. Total N = 328; includes 168 female, 148 male, and
12 juvenile (reproductively inactive) observations.
47 Chapter 3 Review paper: the diet and feeding behaviours of a family of biologically diverse marine fishes (Family Syngnathidae)
3.1 Introduction
An animal’s ability to meet the energetic demands of growth and reproduction are directly related to its ability to capture and consume prey. For predators, the energy and nutrients required for biological functions comes exclusively from their prey (Paine, 1966;
Pimm, 1982). Those that fail to capture and consume enough food to meet their energetic and nutritional demands will either die, or will be unable to allocate energy towards growth and reproduction (Schoener, 1971; Hislop et al., 1978; Chambers & Trippel, 1997;
Lester et al., 2004; Kooijman, 2010). However, predators must first find, catch, and handle prey before they are able to consume them (Schoener, 1971; Pyke et al., 1977; Gendron &
Staddon, 1983). Each of these steps expends energy and increases the chances of a failed attack, forcing predators to optimize their foraging and make difficult prey selection decisions that net them the greatest energy benefit (Pyke et al., 1977). Many animals, for example, will only eat larger prey because they are more profitable, and do not require any more handling time than smaller prey (Richardson & Verbeek, 1986; Costa, 2009).
Because of the high fitness consequences of failing to meet energy demands, predators evolve morphologies and behaviours that enable them to exploit their prey resources better (Schoener, 1971; Dawkins & Krebs, 1979). Predation is a huge selective pressure for both predators and their prey, and can result in an “arms race”: the rapid co- evolution of traits selected to either aid in the capture of prey, or in the avoidance of predators (Dawkins & Krebs, 1979; Abrams, 2000). Predators evolve energetically expensive offensive tactics that can influence development, morphology, or behavioural
48 traits (Dawkins & Krebs, 1979; Abrams, 2000). Examples include the evolution of morphologies that help predators seek (Warrant & Locket, 2004), capture (Norton, 1991) and consume (West et al., 1991) prey, as well as specialized search behaviours (Schoener,
1971), and physiological resistance to defensive neurotoxins excreted by prey (Brodie &
Brodie, 1999).
In marine ecosystems particularly, the intimate coupling of predators and their prey is mediated by their three-dimensional habitats (Rose, 2000). The vast majority of marine species can move in three dimensions, allowing organisms more physical space in which to live (Crowder & Norse, 2008). In marine environments, this structure is provided by the liquid medium itself, and by foundation species (e.g. seagrasses, coral, macrophytes) that dramatically increase the structural complexity of a landscape (Jones et al., 1994; Thorne-
Miller, 1999; Bruno & Bertness, 2001). As in terrestrial environments, habitat complexity provides prey with refuge from predation (Jones et al., 1994, 1997; Bruno & Bertness,
2001) while providing predators with a backdrop from which to ambush prey that cannot see them (Orth & Heck, 1980; James & Heck, 1994). Unlike in most terrestrial environments, however, marine predators must pursue prey in three dimensions. Even some of the most sedentary, benthic marine crustaceans are capable of swimming away from predators (Main, 1987; Orav-Kotta & Kotta, 2004; Zamzow et al., 2010). To capture prey that are able to both hide and move in three dimensions, some marine predators have evolved unique prey capture techniques.
Syngnathid fishes (Family Syngnathidae) are a family of marine predators that ambush small prey items in complex habitats. The 300 described species—within 57 genera—live in seagrass, coral reefs, mangroves, macrophytes, and artificial structures in
49 tropical and temperate coastal waters around the world (Froese & Pauly, 2017). They are notable for their extensive male parental care, which varies by genus from simple ventral gluing of the eggs through a series of pouch enhancements to the fully sealed brood pouch of the seahorses. Syngnathids have highly advanced prey-capture techniques that aid them in the capture of prey (Howard & Koehn, 1985; Kendrick & Hyndes, 2005). All species use pivot feeding, the rapid propulsion of the mouth towards a prey item (de Lussanet &
Muller, 2007; Van Wassenbergh et al., 2011, 2014). This technique is faster than the reaction time of even their fastest prey items (Gemmell et al., 2013), and is a large reason they are successful marine predators. Syngnathids also differ greatly in their feeding- related morphologies, as adult body sizes range 30 to 600 mm (standard length) and they have snouts that vary dramatically in shape and size (Dawson 1982, 1985).
We have considerable amounts of fragmented information about syngnathid diets and feeding behaviours. Syngnathid feeding mechanics have been the focus of numerous studies that investigated their structure, kinematics and evolutionary development (e.g.
Bergert & Wainwright 1997; Van Wassenbergh et al., 2009, 2011, 2014, Roos et al., 2010,
2011). Other studies have investigated the role that different variables have on syngnathid feeding, including, but not limited to, morphology (e.g. Howard & Koehn 1985; Kendrick &
Hyndes 2005), sex and reproductive status (e.g. D’Entremont, 2002; Berglund et al., 2006;
Kitsos et al., 2008), ontogenetics (e.g. Brown, 1972; Castro et al., 2008), habitat structure
(e.g. Howard & Koehn, 1985; Curtis & Vincent, 2005; Kendrick & Hyndes, 2005), and diurnal/seasonal variability (e.g. Ryer & Orth, 1987; Woods, 2002; Uncumusaoglu et al.,
2017). In addition, dozens of studies have tabulated syngnathid diets in the wild (e.g. Steffe
50 et al., 1989; Gaughan & Potter 1997; Woods, 2002; Kendrick & Hyndes, 2005; Kitsos et al.,
2008; Smith et al., 2011a).
While numerous studies have looked at the diets and foraging behaviours of syngnathids, no work has summarized general patterns in the mechanics and morphologies involved in syngnathid feeding events. Also, no study has explored how syngnathid diets and foraging behaviours vary across genera, locations, and studies within this morphologically diverse family of fishes, which lives in a variable three-dimensional space.
Our first goal is to provide a resource that summarizes important ecological knowledge about syngnathid feeding in the wild. Our second goal is to identify general patterns in syngnathid diets and discern how they are associated with body characteristics. Here we test the hypothesis that syngnathid feeding morphologies—which differ considerably within the family—are correlated with their diets, across genera, locations, and studies.
Specifically, we look at syngnathid morphologies that are directly related to the size and speed of prey they can capture (i.e. snouts, gapes), and those that can indirectly affect where syngnathids feed (i.e. overall body size, and relative fin sizes). We expect there to be large variation in the diets of syngnathids in these analyses due to differences in prey availability. Additionally, we expect that snout morphologies—which have been shown to affect syngnathid diets in the past (Kendrick & Hyndes, 2005)—will be correlated with what they eat. In this review we synthesize information about the diets and feeding behaviours of syngnathids in the wild, with a focus on exploring the associations between syngnathid morphologies and their diets.
51 3.2 Methods
We conducted a meta-analysis of peer-reviewed literature, unpublished university dissertations, and government reports on the diet and feeding of syngnathids to summarize the known information. The emphasis of this study was to evaluate how, what, where and when syngnathids eat, and what affects their diets and feeding behaviours. We provide a review of the literature, and perform comparative analyses to see if there are patterns in syngnathid diets across genera, locations, and studies.
3.2.1 Literature review
We used information from and sources cited in FishBase (Froese & Pauly, 2017), and materials found during searches on Google Scholar up until June 2017. We divided search terms into two groups. (i) taxa: syngnathid*, pipefish, seahorse, seadragon, and all
57 syngnathid genera names (e.g. Hippocampus, Syngnathus), and (ii) diet: diet, feed*, forag*, prey-capture. We then searched all pairwise combinations using one term from each group. Our goal was to obtain the broadest possible understanding of syngnathid feeding in the wild. Our narrative preferentially included in-situ studies, and supplemented them with ex-situ studies if they included novel information not already found on syngnathids in the wild. Our statistical analyses were based on a subset of the diet studies, including studies conducted on syngnathids in the wild which involved more than two individuals.
52 3.2.2 Diet taxonomy
We categorized all prey items reported quantitatively in in-situ diet studies using the World Register of Marine Species (www.marinespecies.org). Converting all cited species to current taxonomic classifications made the diet studies consistent and comparable. Dietary data were reported as either bulk data (the relative contribution that a food item made to volume, weight, or area of the stomach contents), numeric data (the relative contribution that a food item made to the total number of food items in stomach contents) or frequency of occurrence (FO; the number of stomachs that contained a certain food item). To compare the greatest number of studies at similar taxonomic resolutions, we summed all additive dietary information (bulk and numeric data) from lower into higher taxonomic categories when not explicitly calculated by the authors. For example, although most studies did not report the volume of Crustacea (subphylum) explicitly, they did report volumes of lower taxonomic levels (i.e. Peracarida and Eucarida) that encompass Crustacea, such that the volume of crustaceans could be inferred. We also evaluated non-additive information (FO), but these data could not be summed to higher taxonomic levels. If the FO of a taxonomic level was not explicitly reported, we estimated its value by recording the maximum value of taxonomic levels below, and within it. For example, if the frequency of Crustacea was not reported, we estimated it by recording the maximum value of the taxonomic groups that were within it. If Peracarida FO was 55%,
Eucarida 45%, and Copepoda 15%, Crustacea would be estimated as 55%. Since we do not know which stomachs had which items (the basis for FO data), we can only say “at least
55% of stomachs had Crustacea".
53 3.2.3 Syngnathid characteristics
For all syngnathid species with in-situ dietary data, we recorded maximum standard length (StL), and ratios of standard length to head length (StL:HL), head length to snout length (HL:SnL), and snout length to snout depth (SnL:SnD). Standard length was recorded from FishBase (Froese & Pauly, 2017). Ratio information was recorded from species descriptions reported in field guides, measured from specimens at the Australian Museum in Sydney, Australia, and measured from photos (Table C1). For consistency, we also preferentially selected information in that order. We selected mean ratios when available, but took the median value if reported as a range. We then used these ratios to estimate head length, snout length, and snout depth from StL for each species. We were forced to standardize syngnathid sizes in this way because studies rarely reported the specific sizes of syngnathids used in their studies (and almost never reported HL, SnL, or SnD). We measured the relative size of caudal fins (relative fin size) by measuring the area of the caudal fin and dividing it by the area of the syngnathid’s body, calculated from their profile view. For consistency, we preferentially selected adult females and did not include pectoral or anal fins in our calculations. We preferentially analyzed photos over sketches (Table C1), and because of the time required to process each image and the lack of quality profile images available for most species, we calculated all areas on one image per species in
ImageJ version 1.59m9m (National Institutes of Health, Bethesda, MD).
54 3.2.4 Statistical analyses
3.2.4.1 Associations between syngnathid characteristics and their diets
To determine what body characteristics are correlated with the diets of syngnathids, we executed redundancy analyses (RDA; Van Den Wollenberg, 1977; McArdle & Anderson,
2001) on bulk (volume, weight, and area data), numeric, and frequency of occurrence (FO) dietary data. In total, we included 48 diets in our analysis of bulk dietary data (across 14 genera, 34 species, and 28 studies), 33 diets for numeric data (across 5 genera, 19 species, and 16 studies) and 40 diets for FO data (across 12 genera, 30 species, and 18 studies). The justification for the (i) dependent and (ii) independent, and (iii) possible conditioning variables included in this model is explained below.
First, based on our additive calculations (see above), we considered dietary response variables using a blend of taxonomy and biological knowledge. Dietary data are often reported within functional groups that do not align perfectly with taxonomic groupings. For example, many studies report the volume of amphipods and copepods in the same list, although Amphipoda is an order and Copepoda is a subclass. We therefore considered the lowest level of taxonomic resolution that could be calculated for all studies, within these functional groups. As another example, because we cannot calculate the values for individual orders of Copepoda (i.e. calanoid, cyclopoid and harpacticoids) for some studies (those that only report to the level of Copepoda), the lowest taxonomic resolution across all studies within this functional group is to the level of Copepoda. The following ten response variables (prey items) were therefore considered: amphipods, copepods, decapods, gastropods, isopods, mysids, ostracods, polychaetes, tanaids, total eggs (sum of eggs from all prey taxa), and total larvae (sum of larvae from all prey taxa). To conserve
55 degrees of freedom in our model, we then removed food items that had a mean of less than
5% across all samples. For bulk data, we retained the following five prey items: amphipods, copepods, mysids, decapods, and total eggs. For numeric data, we retained five prey items: amphipods, copepods, mysids, decapods, and ostracods. For FO data, we retained seven prey items: amphipods, copepods, mysids, decapods, ostracods, total larvae, and ‘other’
(which included various items such as algae, sediment, insects, etc.).
Second, as candidate independent variables, we included relative fin size, maximum standard length (StL), head length (HL), snout length (SnL), snout depth (SnD), the ratio of head length to snout length (HL:SnL), and the ratio of snout length to snout depth
(SnL:SnD) of syngnathid species. We used Variance Inflation Factor (VIF) to determine and deal with multicollinearity. We applied VIF to all predictor variables and removed the variables contributing the most variance (i.e. the highest VIF) until the VIF scores of all predictors was less than 5 (Zuur et al., 2010). HL and SnL were removed from all three models. StL:HL was not considered as a candidate variable because it inflated the VIF of all models.
Third, we wanted to determine if it was necessary to control for a certain variable in our model, by partialing out its variance. Therefore, to determine the relative ability different components of the model had in explaining the variation in syngnathid diets, we used an RDA to partial out the variance into syngnathid body characteristics and genus (see
Figure 3.1). If syngnathid body characteristics were to explain a large proportion of the variance in their diets, the relative variation explained purely by the body characteristics, independent of any covariance with genus, would be high [component a]. A similar trend would be seen if a large contribution of the variation was explained by the pure effects of
56 their genus [component c]. However, diet may also depend on the covariance between body characteristics and genus [component b]. Some variance in diet will remain unexplained [component d]. As is evident, very little of the variance in bulk diet is explained by the pure effects of genus (component c; Figure 3.1a). A much higher proportion of variance is explained by the pure effects of body characteristics [component a; Figure 3.1a] and the covariance between syngnathid body characteristics and genus
(component b; Figure 3.1a). For numeric data, no variance was explained by genus alone
(component c; Figure 3.1b), and little variance was explained by the covariance between syngnathid body traits and genus (component b; Figure 3.1b). For FO data, only 7% of the variance was explained by genus alone (component c; Figure 3.1c). This information, in conjunction with the high phylogenetic signals of these characteristics (Table 3.1), provides justification for removing the variance explained by genus alone in our three models. As a result, no conditional terms were considered in either RDA.
To test the significance that syngnathid body characteristics had on bulk and numeric syngnathid diets, we executed a permuational multivariate analysis of variance
(PERMANOVA) with 999 permutations. We did all variance partitioning and RDA analyses using the vegan package, version 2.4-3 in R 3.4.1 (www.r-project.org).
3.2.4.2 Phylogenetic signal of syngnathid characteristics
We estimated the phylogenetic signal of syngnathid body characteristics using
Bloomberg’s K statistic. Phylogenetic signal measures the similarity of related species (with respect to a trait), as compared to species drawn randomly from a phylogenetic tree
57 (Blomberg & Garland, 2002). A K statistic of zero indicates that the trait values are randomly distributed within the phylogeny, and a value approaching 1 indicates that the trait values are evolving as expected by Brownian motion models (Blomberg et al., 2003). A value over 1 means the trait has more phylogenetic signal than expected from evolutionary models. We used the most recent and comprehensive phylogenetic tree for syngnathids
(Hamilton et al., 2017), and estimated the phylogenetic signals using the phytools package, version 0.6-00 in R 3.4.1 (www.r-project.org).
3.3 Results
3.3.1 How do syngnathids eat?
3.3.1.1 Head morphology & mechanics of a feeding event
Although syngnathid feeding mechanisms vary across the family (Van Wassenbergh, et al., 2011), species share a number of general characteristics. Syngnathids have jaws that are tilted slightly upwards at the end of an elongated snout and lack teeth (Figure 3.2;
Bergert & Wainwright 1997; Flammang et al., 2009). Syngnathid snouts are rigid, tubular structures made of specialized neurocranial and suspensorial bones (Bergert &
Wainwright, 1997; Flammang et al., 2009). Syngnathid feeding involves a coupling between hyoid rotation and neurocranial elevation and is powered by elastic energy generated by specialized muscle groups (Figure 3.2; Muller & Osse, 1984; Bergert & Wainwright ,1997;
Van Wassenbergh et al., 2008; Flammang et al., 2009; Roos et al., 2009a). The hyoid is a V- shaped bone complex on the bottom side of the snout, running parallel to the snout when in a resting position. The expansive phase (see next paragraph) of a feeding event is
58 initiated by hyoid rotation, and occurs about half a second before the start of neurocranial elevation (Table C2; Roos et al., 2009b). By contracting the sternohyoideus muscles, which run along the top and bottom side of syngnathids (Figure 3.2; Van Wassenbergh et al.,
2008, 2014), the hyoid arch is rotated away from the snout (Muller & Osse, 1984; Bergert &
Wainwright, 1997). The bones involved in hyoid rotation cause suspensorial bones of the snout to be driven away from the body, towards their prey (Flammang et al., 2009; Van
Wassenbergh et al., 2013). As the skull expands laterally during head rotation, it causes a build-up of negative pressure within the head that is used to inhale the prey item (Bergert
& Wainwright, 1997; Van Wassenbergh et al., 2013).
3.3.1.2 Stages of a feeding event
Syngnathids are among the fastest feeders of all fish, capturing prey between 3 and
8 ms after initiating an attack (Table C2; Bergert & Wainwright, 1997; Van Wassenbergh et al., 2011a). Syngnathid feeding events share similarities with other suction feeders, involving preparation, expansion and recovery phases (Lauder, 1985; Bergert &
Wainwright, 1997). During the preparatory phase, syngnathids visually scan their environment in search of prey. For a syngnathid attached to a holdfast, this involves using its cryptic ability and waiting for a prey item to move into its area (James & Heck, 1994). If unattached, the syngnathid may actively swim through the water column in search of prey
(James & Heck, 1994). Syngnathids preferentially select oddly positioned or coloured prey in swarms because they are more easily tracked, and overall syngnathids are more successful at capturing prey from smaller groups (Ocken & Ritz, 1994). Once prey are
59 located, syngnathids fix their independently moving eyes on it (James & Heck, 1994; Ocken
& Ritz, 1994). A syngnathid must then successfully orient its mouth close enough to a prey that the mouth can reach the prey with a pivot of its head (Muller & Osse, 1984). This approach may involve extending their body closer to the prey if the syngnathid is attached to holdfast, or swimming towards it if unattached (Ryer, 1988; Felicio et al., 2006). Slower approaches are more successful than faster ones, as they are less likely to elicit a prey escape response (Hippocampus zosterae; Gemmell et al., 2013). Syngnathids have evolved a head morphology that limits fluid disturbance during a feeding strike, reducing the chances of being detected by prey (Gemmell et al., 2013). Feeding events are accompanied by a
'clicking' sound (Bergert & Wainwright, 1997; Colson et al., 1998; Ripley & Foran, 2007;
Haris et al., 2014).
After positioning its body so the snout is within a few millimeters of their prey
(Table C2), a syngnathid snaps its mouth towards its prey by very rapidly rotating its head away from its body (see detailed description in previous paragraph; Bergert & Wainwright,
1997; Flammang et al., 2009; Roos et al., 2009a). Its mouth is propelled along an arc towards its prey in less than 10 ms (Table C2; Van Wassenbergh et al., 2011a), much faster than the reaction latency of planktonic copepods, themselves among the fastest syngnathid prey items (Gemmell et al., 2013). The explosive head rotation allows the syngnathid to maintain a constant body position, unlike most teleosts which lunge when feeding (Bergert
& Wainwright, 1997). Following a feeding strike, during the recovery phase, the syngnathid head and hyoid return to resting positions, and the snout volume returns to normal (Muller
& Osse, 1984; Bergert & Wainwright, 1997). Recovery generally takes about one second
(Bergert & Wainwright, 1997).
60 3.3.1.3 Energetics of feeding
Syngnathids have low metabolic rates, and eat numerous low-energy prey items in the attempt to meet their energetic demands (Dunham, 2010). As a result of having short, rudimentary guts, and no differentiated stomachs, syngnathids are relatively inefficient at extracting energy from their food (Dunham, 2010). Syngnathids also have relatively inefficient gills which reduce their ability to metabolize the food they do eat (Prein &
Kunzmann, 1987). This effect is exacerbated because syngnathids feed on small crustaceans with high surface area-volume ratios (i.e. relatively low energy prey), meaning that they must eat proportionally more frequently in order to acquire the energy that could be extracted from larger prey (Kooijman, 2000). At times when prey availability is not limiting, gut volume and rates of gut evacuation may limit feeding rates of seahorses
(Sheng et al., 2006) and pipefishes (Ryer & Boehlert, 1983). In contrast, at times of low prey availability, syngnathids may reduce the gut evacuation rate, and increase assimilation efficiency to maximize energy gains (Ryer & Boehlert, 1983).
Research on captive feeding studies suggests seahorses eat proportionally more than pipefishes. Pipefish have been reported to feed at a rate of between 3.8 and 4.5% of their body mass per day (Ryer & Boehlert, 1983; Bennett & Branch, 1990), while seahorses consume as much as 23.4% of their body mass per day (Do et al., 1996; Lin et al., 2007).
Juvenile seahorses have been reported to consume 214-360 copepod larvae per hour
(Herald & Rakowicz, 1951; Payne & Rippingale, 2000). Temperatures deviating from birth- temperature, and high water velocities have been shown to decrease seahorse feeding rates (Sheng et al., 2006; Qin et al., 2014). Low temperatures restrict seahorse eating physiologically, as gut evacuation rates increase with temperature (Ryer & Boehlert, 1983).
61 3.3.1.4 Diurnal timing of feeding
Syngnathids are visual feeders with diurnal feeding cycles that are dictated by light availability. A comparison by Kendrick (2002) between daytime and nighttime stomach contents among twelve syngnathid species suggested most fed predominantly during the day. This study looked at Stigmatopora argus and Stigmatopora nigra feeding in greater detail, showing a similar pattern between gut fullness and the percentage of undigested prey in pipefish stomachs (Kendrick, 2002). For both species, gut fullness and undigested prey increased sharply after sunrise, continued to increase until shortly after noon, decreased from after noon until shortly before midnight, and remained low until the next sunrise (Kendrick, 2002). Other pipefishes have shown similar patterns, eating exclusively during daylight hours (Syngnathus acus; Bennett & Branch, 1990). At night, at least some seahorses attached themselves to holdfasts where they remained mostly inactive
(Hippocampus reidi; Felicio et al., 2006), with empty guts or guts that contained only highly digested prey (Hippocampus histrix and Hippocampus trimaculatus; Do et al., 1996). To date only two species of seahorses have been reported feeding at night; Hippocampus comes were found foraging early in the morning, possibly as a result of fishing pressure (Perante et al., 2002), and Hippocampus erectus fed more actively at night in Sweetings Pond,
Bahamas (S. Foster, personal observation). Likewise, the only evidence of nocturnal pipefish feeding was among Syngnathus folletti—however even they consumed more prey and on more prey types during the day (Garcia et al., 2005).
Captive seahorses are able to feed in low-light conditions, but will rarely feed in complete darkness (James & Heck, 1994; Perante et al., 2002; Felicio et al., 2006; Garcia et al., 2012). Juvenile seahorses were found to move and feed continuously if provided
62 constant light (Hippocampus trimaculatus; Sheng et al., 2006). When provided with a simulated 24-hour day, peak seahorse feeding occurred in the morning between 06:00 and
08:00 (Hippocampus kuda; Do et al., 1996), their greatest gut fullness was at 08:00 (Truong
& Nga, 1995), and they did not feed at all between 20:00 and 06:00 (Truong & Nga, 1995).
3.3.2 How does feeding and diet vary across a speciose marine fish family that is morphologically diverse?
3.3.2.1 Prey items by genus and species
The sources and citations for this entire section are found in Table 3.1. We explore all syngnathids before singling out the Hippocampus (all seahorses) and the Syngnathus pipefishes, as they are the most frequently cited genera. In this section only, the species names in brackets relate to the syngnathids and not the prey.
3.3.2.1.1 Entire family
Overall, syngnathids primarily eat small crustaceans. Crustaceans are responsible for more than 75% of the bulk and 75% of the numeric prey items of most syngnathid diets. Across studies the types of crustaceans in diets varies quite considerably. However, amphipods and copepods are most often the dominant prey types in both bulk and numeric data. Although they usually account for a relatively low percentage of bulk and numeric diets, in some studies mysids and decapods are important to syngnathid diets. Amphipods and copepods are also found in most syngnathid diets (FO). Although to a lesser extent, mysids and decapods are also frequently (FO) found in many syngnathid stomachs.
63 3.3.2.1.2 Hippocampus (seahorses)
Amphipods are the main bulk dietary item for a number of seahorse species including Hippocampus breviceps, Hippocampus coronatus, Hippocampus erectus and
Hippocampus whitei. Other primary bulk items include copepods (Hippocampus zosterae) and total eggs (Hippocampus hippocampus). The primary numeric prey items include amphipods (Hippocampus guttulatus and Hippocampus hippocampus), copepods
(Hippocampus mohnikei, and Hippocampus zosterae), and decapods (Hippocampus patagonicus). Amphipods also occur in more stomachs (FO) than any other prey item for
Hippocampus breviceps, Hippocampus erectus, Hippocampus guttulatus, Hippocampus patagonicus, and Hippocampus subelongatus. Decapods and copepods occur in a large number of stomachs (FO) for many seahorse species, mysids are eaten by many
Hippocampus guttulatus, and tanaids by Hippocampus histrix.
3.3.2.1.3 Syngnathus pipefishes
Copepods are the major bulk prey item for Syngnathus abaster and Syngnathus acus.
Other primary bulk prey items include decapods (Syngnathus floridae, Syngnathus typhle), isopods (Syngnathus folletti), and ostracods (Syngnathus louisianae). Copepods are the most numerous dietary item for Syngnathus abaster, Syngnathus taenionotus, and
Syngnathus typhle. Amphipods, decapods, copepods and mysids occur in a large number of stomachs (FO).
64 3.3.2.1.4 Seadragons (Phyllopteryx) and other pipefishes
Amphipods are the dominant bulk prey item for Filicampus tigris, Histiogamphelus cristatus, Pugnaso curtirostris, and Vanacampus phillipi, and are a major contributor to diets of Lissocampus caudalis, Mitotichys semistriatus, and Nerophis ophidion. Other primary dietary items include copepods (Stigmatopora spp., Lissocampus caudalis, Mitotichthys meraculus, Urocampus carinirostris), mysids (Mitotichthys meraculus, Phyllopteryx taeniolatus, Vanacampus poecilolaemus), and decapods (Syngnathoides biaculeatus). For numeric data, primary diet contributors are copepods (Anarchopterus criniger, Nerophis lumbriciformis, Urocampus carinirostris). For frequency data, primary contributors are amphipods (Filicampus tigris, Histiogamphelus cristatus, Vanacampus phillipi), mysids
(Mitotichthys meraculus, Phyllopteryx taeniolatus, Vanacampus poecilolaemus), and copepods (Stigmatopora spp.).
3.3.2.2 Morphology
3.3.2.2.1 Phylogenetic signal of syngnathid morphological characteristics
Across syngnathids, feeding morphologies were more similar among closely-related species that they were among randomly-selected species from the clade. Relative fin size, max standard length (StL), head length (HL), snout length (SnL), snout depth (SnD), StL:HL,
HL:SnL, and SnL:SnD all showed significant phylogenetic signal (Table 3.2). All measured traits had K values over 0.60, and all but two traits had K values over 0.80. As well, HL and
SnL had K values slightly over 1, indicating that related species were more similar to each other—with respect to these traits—than would be expected from evolution.
65 3.3.2.2.2 Body form & orientation
During feeding events, syngnathid body-orientation is optimized to maximize either strike distance, or mouth velocity (Van Wassenbergh et al., 2011a). When compared proportionally to head length, the curved body-form of seahorses increases the distance from which it can capture prey, and the straight body form of pipefishes allows for greater mouth velocity for some genera (including Doryrhamphus spp. and Syngnathus spp.; Van
Wassenbergh et al., 2011b). In addition to the vertical mouth movement associated with all syngnathid feeding strikes, seahorses also move their mouths forward, towards prey (Van
Wassenbergh et al., 2011b). In contrast, many syngnathids that feed from a straight-body orientation move their mouths vertically, and even backwards in some cases (Van
Wassenbergh et al., 2011b). However, the angled, seahorse-like head orientation and strike kinematics of the pipefish Corythoichthys intestinalis displayed an intermediate feeding body orientation (Van Wassenbergh et al., 2011b). Like all pipefish, Corythoichthys intestinalis has a straight-body form with no angle between its head and trunk (Leysen et al., 2011), yet this syngnathid bends its head and moves its mouth forward during a feeding event, much like a seahorse.
3.3.2.2.3 Snout shape
Across genera, locations, and studies, the relative length of syngnathid snouts was significantly correlated with the bulk diets of syngnathids, and the frequency that prey items occurred in syngnathid diets (Table 3.3). Syngnathids with relatively longer snouts
(low HL:SnL) tended to feed on decapods (e.g. Syngnathoides biaculates), mysids (e.g.
66 Phyllopteryx taeniolatus), total eggs (e.g. Syngnathoides biaculates), copepods (e.g.
Stigmatopora spp.) or decapods (e.g. Syngnathoides biaculates) in bulk (Figure 3.3), and on
‘other’ dietary items (e.g. Mitotichthys meraculus), mysids (e.g. Vanacampus poecilolaemus), decapods (e.g. Syngnathus floridae) and total larvae (e.g. Syngnathus acus) in frequency (FO data; Figure 3.4). In contrast, syngnathids with relatively shorter snouts tended to feed on amphipods (e.g. Hippocampus breviceps) in bulk (Figure 3.3), and on ostracods (e.g.
Lissocampus caudalis), amphipods (e.g. Pugnaso curtirostris), and copepods (e.g.
Lissocampus caudalis) in frequency (Figure 3.4). Snout length and relative snout length did not correlate with numeric diet data (Table 3.3).
Snout shape appears to dictate from how far away a syngnathid can attack a prey item, and the speed with which it can capture the item (Roos et al., 2010, 2011; Van
Wassenbergh et al., 2011a). A major difference between pipefish and seahorse head shape can be attributed to differences in snout shape, as many pipefish species have snouts that are relatively longer and narrower than seahorses (Leysen et al., 2011). Longer-snouted syngnathids can attack prey from further away as the angle to reach prey is reduced compared to shorter-snouted relatives (de Lussanet & Muller, 2007). If snout width increased proportionally with snout length, the head rotation velocity of longer-snouted syngnathids would be compromised as a result of the increased drag (de Lussanet &
Muller, 2007; Leysen et al., 2011). To compensate, species of pipefishes that have relatively longer snouts tend to have relatively narrower snouts too, allowing for faster head rotation
(de Lussanet & Muller, 2007; Leysen et al., 2011). Snout shape has therefore been linked to the mobility of prey consumed, with longer snouts being better suited to capturing more mobile prey types (Kendrick & Hyndes, 2005). In a lagoon off western Sicily, sympatric
67 pipefishes maintained different food niches, purportedly as a result of snout length differences (Campolmi et al., 1996). The longer-snouted Syngnathus typhle was better suited at catching fast pelagic organisms while the broader-snouted Syngnathus abaster caught sessile, benthic prey (Campolmi et al., 1996). Syngnathids with longer snouts are more specialized than those with shorter snouts, with shorter snouted individuals eating a wider range of prey (Kendrick & Hyndes, 2005).
The effect that snout shape has on the suction ability of syngnathids is unclear from the literature. According to Roos et al., (2011), smaller snouts (both in length and width) can inhale prey more quickly (Roos et al., 2011). Muller and Osse (1984) disagree, suggesting that the velocity and acceleration of suction increases with greater snout length.
3.3.2.2.4 Gape size
Across genera, locations, and studies, the relative size of syngnathid snout depths was significantly correlated with the bulk diets of syngnathids, and the frequency that prey items occurred in syngnathid diets (Table 3.3). Syngnathids with relatively deeper snouts
(low SnL:SnD) tended to feed on amphipods (e.g. Hippocampus erectus) in bulk (Figure 3.3), and on mysids, ‘other’ items, amphipods, decapods, and total larvae in frequency (Figure
3.4). On the other hand, those with relatively smaller snout depths tended to feed on copepods (e.g. Stigmatopora spp.) and mysids (e.g. Phyllopteryx taeniolatus) in bulk (Figure
3.3), and copepods (e.g. Stigmatopora spp.) in frequency (Figure 3.4). Snout depth and relative snout depth did not correlate with numeric diet data (Table 3.3).
Gape size limits the size of prey that syngnathids can consume (Mercer, 1973; Ryer,
68 1988). Capture success decreases if a syngnathid attempts to consume prey that is proportionately too large for its gape (Mercer, 1973; Ryer, 1988). Among syngnathids with similar snout lengths, gape size dictates which types of mobile prey items they consume — with smaller-gaped syngnathids eating smaller prey (Kendrick & Hyndes, 2005). The size of prey items consumed by syngnathids often match their gape size quite well Garcia et al.,
2005), being between half and three quarters the width of the mouth (Gaughan & Potter,
1997; Celino et al., 2012). The size of prey items consumed by Urocampus carinirostris, for example, was limited by the disproportionately small gape size to body size (Gaughan &
Potter, 1997).
3.3.2.2.5 Ontogenetics: changes in snout shape & gape size
Syngnathids produce juveniles that are not only equipped with fully developed prey-capture abilities—itself a rarity among fish—but in some cases also possess record- breaking abilities. Juvenile Hippocampus reidi have been recorded rotating their hyoids over 90° at speed of 80 000° s-1, among the fastest velocities ever recorded among fish prey-capture systems (Table C2; Van Wassenbergh et al., 2009). Likewise, juvenile head rotation was recorded at 30 000° s-1, more than three times faster than adults (Table C2;
Roos et al., 2009b; Van Wassenbergh et al., 2009). It is likely that the exceptionally fast juvenile cranial rotation abilities allow them to reach their prey more quickly than adults.
This is particularly impressive given the greater angle over which they must attack.
Juveniles are also able to inhale proportionally more water and at a greater speeds than
69 adults (Roos et al., 2011). Together, it is likely that the lightning-quick feeding events that syngnathids are famous for is even faster in juveniles.
As individuals grow, so do their gapes and snouts. Syngnathids are born with relatively shorter and broader snouts that lengthen and narrow as they age (Roos et al.,
2010, 2011). Across all ontogenetic stages, seahorse snout length is optimized to reach prey as quickly as possible (Roos et al., 2010). These animals are susceptible to the varying kinematic constrains associated with differing snout morphologies throughout their lives.
Their snout morphology therefore dictates the size of prey that they are capable of inhaling. Changes in snout shape and gape size are largely responsible for ontogenetic prey choice differences (Oliveira et al., 2007; Sakurai et al., 2009). As a result, syngnathids start as specialist feeders as juveniles, feeding on a limited suite of small prey and then become more successful, generalist feeders as adults, feeding on a wider range of prey (Ryer &
Orth, 1987; Gaughan & Potter, 1997; Flynn & Ritz, 1999; Oliveira et al., 2007; Castro et al.,
2008; Taskavak et al., 2010).
While we did not find any across-genera correlations between maximum standard length and the bulk, numeric, or FO diets of syngnathids (Table 3.3), a number of studies have shown this trend within species. For example, mean prey item weight increased with seahorse size for Hippocampus guttulatus and Hippocampus hippocampus, with small-to- intermediate sized Hippocampus guttulatus feeding primarily on decapod larvae and larger individuals eating more mysids (Gurkan et al., 2011b). Among Hippocampus abdominalis, larger individuals consumed proportionally more caridean shrimp than smaller individuals, which preferred smaller prey such as amphipods (Woods, 2002). Other seahorse species were found to prey primarily on copepods as juveniles and then transition
70 to amphipods (Hippocampus erectus: Teixeira & Musick, 2001; Hippocampus kuda: Truong and Nga 1995) and decapods (Hippocampus reidi: Castro et al., 2008; Hippocampus mohnikei: Kanou and Kohno 2001) as adults. Syngnathus spp. experience similar ontogenetic changes with individuals shifting towards larger prey types at larger sizes
(Livingston, 1982, 1984; Ryer & Orth, 1987; Tipton & Bell, 1988; Franzoi et al., 1993;
Teixeira & Musick, 1995). Pipefish diets shifted from copepods in smaller individuals to larger prey such as amphipods (Brown, 1972; Bennett, 1989), mysids, and caridean shrimp
(Brown, 1972; Oliveira et al., 2007) at larger sizes. Smaller Syngnathus acus ate copepods and larger individuals ate decapod eggs and larvae at larger sizes (Taskavak et al., 2010).
Likewise, the large prey item, caridean shrimp, were found to be the only prey in the largest specimens of Syngnathus floridae (Brook, 1977).
Contrary to the above studies, a few reports indicate no ontogenetic changes in syngnathid diets (Nerophis ophidion: Lyons & Dunne, 2004; Hippocampus patagonicus:
Storero & Gonzalez, 2008; Syngnathoides biaculeatus: Horinouchi et al., 2012).
3.3.2.3 Sex & reproductive status
Among seahorses, the relationship between sex and reproductive status and feeding has been inconsistent, with some studies showing that females eat more than males (Kitsos et al., 2008), and others showing no difference (Woods, 2002; Felicio et al., 2006; Storero &
Gonzalez, 2008; Gurkan et al., 2011b). Among Hippocampus reidi, adult males changed their diets and fed on smaller prey when they became reproductively active (Castro et al., 2008).
However, among Hippocampus guttulatus, neither sex nor reproductive status affected
71 seahorse diets of (D’Entremont, 2002).
The effect of sex and reproductive status on feeding has also been variable among pipefishes, although a number of studies have suggested females eat more than males.
Compared to males, female Syngnathus fuscus and Syngnathus floridae ate more (Teixeira &
Musick, 1995), and female Stigmatopora nigra, Stigmatopora argus (Steffe et al., 1989) and
Syngnathus typhle (Oliveira et al., 2007) had fuller guts. Similarly, Nerophis lumbriciformis females consumed a greater quantity and diversity of prey than non-reproductive males
(Lyons & Dunne, 2004). Female Syngnathus folletti fed on a larger range of prey sizes than males who preyed primarily on small isopods and copepods (Garcia et al., 2005). The trend also translates to ex-situ studies on Syngnathus typhle, where reproductively active females ate more and larger prey than reproductively active males (Svensson, 1988). In contrast to these studies, Berglund et al. (2006) found that Syngnathus typhle males invested more time into feeding than females, and Lyons and Dunne (2004) found that among Nerophis lumbriciformis males, egg-bearing individuals ate more prey than non-reproductive males.
Diets of males and female did not differ for Syngnathus typhle, Stigmatopora nigra,
Urocampus carinirostris, Vanacapus phillpi and Mitotichthys semistriatus (Howard & Koehn,
1985).
3.3.3 How does feeding and diet vary across a marine fish family that lives in a three-dimensional space?
3.3.3.1 Variability
Syngnathid diets have been shown to vary seasonally and geographically, largely as a result of differences in the abundance and composition of prey. For example, the diet of
72 an Indo-Pacific seahorse was comprised of 37% and 7% (by volume) of eucarids and peracarids, respectively, at one site, and 0% and 67% on the opposite side of the narrow
Malaysian Peninsula (Hippocampus trimaculatus; Yip et al., 2015). A similar effect was found among Hippocampus patagonicus diets in Argentina, which showed large variation between two sites located less than 5 km from each other (Storero & Gonzalez, 2008).
Likewise, despite some studies that found no seasonal effect (Huh & Kitting, 1985; Tipton &
Bell, 1988; Teixeira & Musick, 1995), the majority of work has shown that syngnathid diets change seasonally (Brown, 1972; Livingston, 1982; Ryer & Orth, 1987; Franzoi et al., 1993;
Motta et al., 1995; Woods, 2002; Oliveira et al., 2007; Taskavak et al., 2010; Gurkan et al.,
2011a; Horinouchi et al., 2012).
3.3.3.2 Tail morphology & foraging strategies
Across genera, locations, and studies, there was no correlation between the relative size of syngnathid caudal fins and the bulk diets of syngnathids, numeric diets, or the frequency that prey items occurred in syngnathid diets (Table 3.3).
Syngnathid tail morphology—including its grasping ability and the relative size of a potential caudal fin—is thought to affect their foraging strategy, and in turn, what types of prey they eat. Syngnathid tail morphology can broadly be broken down into three groups
(based on even broader categories in Neutens et al. (2014): (1) syngnathids that lack caudal fins, but have prehensile tails that can grasp holdfasts (including seahorses), (2) pipefishes that have caudal fins but lack prehensile abilities (including most pipefish genera), and (3) seadragons, which lack both caudal fins and prehensile abilities.
73 Syngnathids belonging to group 1, including seahorses and certain genera of pipefishes
(e.g. Nerophis spp., Stigmatopora spp., Urocampus spp.) rely on rapid dorsal and pectoral fin oscillations for movement (Consi et al., 2001). Without caudal fin propulsion, this group of syngnathids swims comparatively slowly, but with good maneuverability (Consi et al.,
2001). Instead, these syngnathids prefer to remain attached to a holdfast, adopting a sit- and-wait feeding strategy (Howard & Koehn, 1985; James & Heck, 1994; Ocken & Ritz,
1994; Kendrick & Hyndes, 2005). In this position, syngnathids are difficult for prey to detect. Seahorses are able to adjust their colouration to match their background, and some grow long skin filaments to improve crypsis (Foster & Vincent, 2004). Likewise, some pipefishes belonging to this group—with a greenish-brown body colour, and a slow, rhythmic body movement—resemble eelgrass leaves (Howard & Koehn, 1985). While attached to a holdfast, these sedentary ambush predators feed from the water column or from the surfaces of plants (Howard & Koehn, 1985). In contrast, pipefishes belonging to group 2 are typically more mobile, active feeders (Howard & Koehn, 1985; Kendrick &
Hyndes, 2005). Among these pipefishes there are genera that are associated with the vegetation, often resting horizontally within the canopy (e.g. Mitotichthys spp.), and others that lay near or on the substrate (e.g. Vanacampus spp.; Howard & Koehn, 1985, Kendrick &
Hyndes, 2005). The latter group are often camouflaged brown and resemble mud (Howard
& Koehn, 1985; Kendrick & Hyndes, 2005). Although syngnathids with caudal fins will occasionally feed from their sitting positions, they routinely swim clear of the vegetation to capture prey (Howard & Koehn, 1985; Kendrick & Hyndes, 2005). These pipefish genera typically feed on a wider range of epibenthic and planktonic prey types (Howard & Koehn,
1985; Kendrick & Hyndes, 2005). Seadragons (Group 3; Phyllopteryx spp. and Phycodurus
74 sp.) —which resemble drifting algae and lack both prehensile tails and caudal fins—swim above the vegetation and feed on prey in the open water (Kendrick & Hyndes, 2005).
Syngnathids have been shown to switch to a more sedentary strategy in areas of greater habitat complexity, leading to diets with fewer mobile prey. Niche partitioning may drive some syngnathids to use more active feeding strategies. For example, Hippocampus hippocampus was found to swim and hunt prey in more open, unvegetated areas than its congener, Hippocampus guttulatus, which conformed to the traditional seahorse sit-and- wait strategy in more vegetated areas (Curtis & Vincent, 2005). Additionally, some have found that higher habitat complexity promoted seahorses to adopt a more sedentary feeding strategy, presumably because of greater ambush opportunities (James & Heck,
1994; Curtis & Vincent, 2005; Felicio et al., 2006). In aquaria with limited vegetation, seahorses abandoned the typical sit-and-wait strategy and would instead search for and consume prey while swimming (Felicio et al., 2006). A similar change in strategy was found with Syngnathus fuscus, which abandoned their post to chase prey in low vegetation aquaria, but rarely did so in those with greater vegetation densities (Ryer, 1988). Greater habitat complexity may also promote a switch from planktonic to epibenthic prey types among some syngnathids. For example, with increased seagrass density, mid-sized
Syngnathus scovelli shifted their diets from more mobile to less mobile copepods (Krejci,
2012), and Stigmatopora nigra shifted from planktonic copepods to benthic copepods
(Smith et al., 2011a). Overall, research on the impacts of habitat complexity on syngnathid feeding and success have had mixed results. For example, some research on seahorses has found increased capture success in more complex habitats (Flynn & Ritz, 1999), and others have found no effect (James & Heck, 1994). Likewise, increased habitat complexity was
75 associated with greater gut fullness among Stigmatopora argus, but not among its congener, Stigmatopora nigra (Steffe et al., 1989; Smith et al., 2011a).
In addition, syngnathids may change their foraging strategy based on their satiation level, switching to more active foraging when hungry (Ocken & Ritz, 1994). Satiated seahorses are more likely to conserve energy, attach to a holdfast and attack easier prey whereas hungrier seahorses will actively swim and become less selective (Ocken & Ritz,
1994). Unattached seahorses have been shown to have greater capture success than those associated with a holdfast (Ocken & Ritz, 1994). In this way, the need for a rapid acquisition of energy makes active swimming more important than the higher costs associated with it
(Ocken & Ritz, 1994).
3.4 Discussion
While all syngnathids feed predominantly on combinations of epibenthic and planktonic crustaceans, we deduce that the relative amounts and specific types of prey are dictated by a combination of prey availability and syngnathid morphology. First, research on syngnathid diets has shown that studies in different areas and season often yield very different results—likely because of prey availability. We found large unexplained diet variation, perhaps because of vast differences in prey availability across the global locations and many seasons included in our analyses. Second, we were surprised to find that no metric of body size was related to what they ate—despite that the literature repeatedly showing that larger syngnathids ate larger prey. Instead, we showed that across genera, locations, and studies, the relative snout morphologies of syngnathids correlated
76 with their diet. These snout morphologies may also explain the relatively generalist feeding strategies among certain species, and may better explain the capture of prey with differing mobility than their own swimming abilities. Our phylogenetic results show that among syngnathids, more closely related species are more similar in feeding morphologies than less-related species (Blomberg et al., 2003; Revell et al., 2008). Differences in diet observed among different genera probably arises, therefore, because of differences in important feeding morphologies.
Our results show considerable variation in syngnathid diets suggesting that adult syngnathids are relatively generalist feeders and that their diet depends largely on prey availability. In all three dietary RDA models (bulk, numeric, FO) the majority of the variation was neither explained by genus, nor morphological characteristics. Our need to standardize the size of syngnathids in our across-genera comparisons—to maximum adult sizes—may have obscured some variation in syngnathid diets. Adults of many syngnathids have relatively generalist diets compared to juveniles (Ryer & Orth, 1987; Gaughan &
Potter, 1997; Flynn & Ritz, 1999; Oliveira et al., 2007; Castro et al., 2008; Taskavak et al.,
2010). Adults are physically able to feed on a wider range of prey because they can find, capture, and consume more types of prey (Flynn & Ritz, 1999), they extract energy and nutrients from their prey more efficiently than juveniles (Brown & Maurer, 1989), and their feeding morphologies (e.g. mouth) are larger, and less size restrictive (Scharf et al.,
2000). Many studies have suggested that differing prey availability among locations and seasons, in combination with this generalist ability of adult syngnathids, is responsible for the large diet differences recorded among locations and seasons (e.g. Storero & Gonzalez,
2008; Yip et al., 2015). It is therefore quite likely that much of our unexplained diet
77 variation was due to differences in prey availability among the various study locations included in our analyses, as is the case with other fishes (Griffiths, 1973, 1975). To date, no syngnathid study has explicitly compared diet to the prey that are available to the fish. It could be that adults are actually selecting for certain prey when their distributions overlap, but its effect may be masked by other samples in areas where the overlap does not occur
(Griffiths, 1973, 1975).
It was surprising that we were not able to detect a correlation between syngnathid body size and diet across genera, locations, and studies. The ecological principle that larger predators feed on larger prey has been shown before within syngnathid species
(Livingston, 1982, 1984; Ryer & Orth, 1987; Tipton & Bell, 1988; Franzoi et al., 1993;
Truong & Nga, 1995; Teixeira & Musick, 1995, 2001; Kanou & Kohno, 2001; Woods, 2005;
Castro et al., 2008; Gurkan et al., 2011a; 2011b), and among various clades of marine fishes
(Scharf et al., 2000; Costa, 2009; Barnes et al., 2010). It is generally understood that this may be the result of two factors. First, larger predators have larger feeding morphologies
(e.g. mouths), and are therefore better suited, physically, to handling larger prey (Scharf et al., 2000). Second, larger predators have greater energy demands which are more easily met if they optimize their foraging, and feed on larger, more energetically beneficial prey
(Costa, 2009). Therefore, we expect we would have found correlations between syngnathid diets and either their body size or non-relative values of feeding morphologies including snout length or snout depths. Instead, however, we found correlations between their diets and relative snout lengths and depths.
Our results, which complement other studies, explicitly show—for the first time—
78 that differences in syngnathid diets across genera, locations, and studies can largely be attributed to the relative shape and sizes of their snouts. In general, pipefishes have relatively longer snouts than seahorses (Table C1). Our results expand on those of Kendrick
& Hyndes (2005), as we show here that relative snout length has an effect on the diets of syngnathids across genera, locations, and studies. The relatively long snouts typical of pipefishes are often used to capture more mobile prey, such as copepods and mysids. To make up for increased drag, longer snouts are usually quite narrow, too (Table C1; de
Lussanet & Muller 2007; Leysen et al., 2011). Syngnathids with long, narrow snouts have less angle to cover during head rotation and less drag to overcome, and some have hypothesized that longer, narrower snouts would result in the fastest prey-capture times
(de Lussanet & Muller, 2007; Van Wassenbergh et al., 2011a). While this snout shape allows for speed, it comes at the expense of reduced gape size (Table C1; de Lussanet &
Muller 2007; Leysen et al., 2011). This is important, and our study was the first to show that it is relative gape size that matters to syngnathid diets. We found that syngnathids with relatively small gapes had limited diets, and were presumably forced to feed on very small copepods. Stigmatopora spp. —a genus of pipefishes with the longest snouts and smallest gapes (relative measurements; Table C1) of all syngnathids in this study—had diets that were equally extreme, composed almost entirely of copepods (in bulk and FO).
This genus may represent one end of the spectrum in syngnathid snout morphology, in fact, as this pattern was consistent among three studies (Steffe et al., 1989; Kendrick & Hyndes,
2005; Smith et al., 2011a). In contrast to most pipefishes are seahorses, having notably short and broad snouts. Relatively broad snouts allow syngnathids to fit more types of prey in their gape, so it is not surprising that most prey types ended up in the stomachs of
79 species with relatively larger gapes more frequently (FO) than those with smaller gapes.
While broad snouts increased the breadth of prey diversity they were able to eat, the bulk of their diets was primarily taken up by larger, slower prey like amphipods.
Our findings that the relative amounts of mobile and sedentary prey in the diets of syngnathids is influenced more by snout morphology than syngnathid mobility are at odds with studies of other fish predators. It has been argued that mobile predators feed on sedentary prey more frequently than sedentary predators do because they encounter these prey types more often (Cooper et al., 1985). Such a pattern had been suggested for syngnathids, too, as a reason for the difference in diets between syngnathids that are free- swimming versus those attached to a holdfast (Howard & Koehn, 1985). Syngnathid foraging mobility has been partially explained by tail morphology, which ranges from the grasping, prehensile tails of sedentary seahorses to the massive caudal fins of mobile flagtail pipefishes (Neutens et al., 2014). In contrast to this hypothesis, however, studies on pipefishes suggest that less active syngnathids may instead feed on less active prey (Smith et al., 2011b; Krejci, 2012). It is possible that there is a two-fold reason for greater numbers of less-mobile prey in the diets of syngnathids feeding in more complex habitats. First, the increasing seagrass density promotes a sit and wait strategy where syngnathids feed from among the surfaces of seagrass blades, where less mobile prey typically reside (James &
Heck, 1994; Curtis & Vincent, 2005; Felicio et al., 2006). Second, densities of less mobile prey increase with greater seagrass density (Stoner, 1980; Bell & Westoby, 1986; Jenkins et al., 2002). Therefore, if seagrass density is great enough, sedentary syngnathids may actually encounter more sedentary prey than mobile syngnathids, despite the latter’s active search behaviours. In addition, the propensity of seahorses to feed on relatively sedentary
80 prey—while being a sedentary predator themselves—seems to suggest an alternative explanation. Perhaps general trends in the mobility of syngnathid prey are more a consequence of their snout morphology than their mobility. In support of this view, we were not able to detect an association between diet and relative caudal fin size, a proxy of mobility, in this this study.
Overall, our results show that syngnathid diet was better explained by body characteristics than by genus alone. We showed that seahorse and pipefish diets can overlap considerably because of overlaps in feeding-dependent morphologies. For example, Pugnaso curtirostris is a pipefish with a relatively short and narrow snout characteristic similar to seahorses. And, like seahorses, this pipefish feeds primarily on amphipods and decapods. The opposite trend can also be true; despite feeding while attached to a holdfast, like seahorses, Stigmatopora spp. have very little diet overlap with most seahorses. Instead, they have very long and narrow snouts which are likely responsible for their copepod-dominated diets. Syngnathid diet may be a function of the species’ body characteristics, with any differences among genera arising because of differences in those morphologies among genera. Our phylogenetic results support this, demonstrating that among syngnathids, more closely related species are more similar in feeding morphologies than are less-related species (Blomberg et al., 2003; Revell et al.,
2008). It is therefore reasonable that we see differences in diet among different genera, as they are statistically less similar to each other for important feeding morphologies than species within a genus.
Our study shows that meta-analyses can cut across inevitable variability across genera, locations, and methods to identify new broad taxonomic patterns and to expand
81 previous generalities to a broader scale. Review studies also serve to expose gaps in the literature and prompt future studies. For example, to fully understand prey selectivity among syngnathids, we suggest the need for a quantitative comparison between their prey consumption and the availability of these prey. The results of our study suggest that across genera, locations, and studies, syngnathids forage in a way that has the greatest overall energy benefit, but is constrained by prey availability and their feeding morphologies.
Syngnathids which live in denser habitats more often adopt a sedentary foraging strategy, presumably because of greater ambush opportunities (James & Heck, 1994; Curtis &
Vincent, 2005; Felicio et al., 2006). Since all syngnathids must eat constantly, on account of their rudimentary digestive tract—and sedentary syngnathids must feed on what is around them—it makes energetic sense for more sedentary types to be less picky (Dunham, 2010).
More sedentary syngnathids may have therefore evolved relatively larger gapes that allow them to feed on a larger suite of prey, including larger, more energetically beneficial types, as we found in this study. Syngnathids with relatively larger gapes also have relatively shorter snouts (de Lussanet & Muller, 2007; Leysen et al., 2011). This may be an evolutionary adaptation to the greater densities of sedentary prey types found in highly complex habitats (Stoner, 1980; Bell & Westoby, 1986; Jenkins et al., 2002), or the result of a morphological trade-off with snout length (de Lussanet & Muller, 2007; Leysen et al.,
2011). Either way, shorter, broader snouts are likely beneficial to more sedentary syngnathids which benefit from feeding on a wider range of prey—including the large, sedentary types most often found in their complex habitats. In the same way, longer, narrower snouts are more beneficial to those feeding on smaller, faster prey that are common in the water column. This may be why we often see more mobile pipefish with
82 these types of snouts. Unfortunately, very few studies have compared the home range or mobility of species with their dietary breadth. In one study, however, more specialized monkeys were found to have larger home ranges, purportedly because they needed to travel further to find their preferred food types (Clutton-Brock, 1975). Our understanding of syngnathid prey choices and their ability to meet their energy demands would benefit from a study that looked at the energy benefits of different prey types, and the energy costs of different foraging strategy.
83 Table 3.1 Relative importance of syngnathid diets. a The approximate area of sampling. For comparative purposes, studies in close proximity
(within 50 km of each other) have the same location. b Bulk dietary studies include relative values that each food item contributes to the total volume (%V), weight (%W), or area (%A) of dietary contents collected, numeric dietary studies include relative values that each food item contributes to the total number of food items (%N) collected, and frequency of occurrence (%FO) studies include the relative number of stomach samples that a particular food item occurs in. Each row represents a particular species in a particular area of a particular study. c This table includes additive data, so if a taxon is centered above other taxa it includes those numbers in its total. Numbers are added to columns on the left (e.g. Crustacea includes Paracarida and Eucarida). Sample sizes of two and under were not considered in statistical analyses. Blank cells represent missing data. Shaded cells (for %FO data only) indicate the value of that cell was not provided in the literature, and is a minimum value based on dietary items that were included at a lower taxonomic resolution (see text). Abbreviated locations; PoF = Port of Frematle.
84 Taxon Subph ylum Crustacea Subcla ss Copepoda Super order Peracarida Eucarida Amp Tan hipo Mys Isop aida Deca Calan Cyclop Harpacti Order da ida oda cea poda oida oida coida Location Diet Genus Species of Study N Metric Reference Kendrick & PoF, Hydnes Filicampus tigris Australia 10 %V ++++ ++++ ++++ + - - + + + - - + (2005) Lim, Pulau unpublishe Tinggi, d data Hippichthys cyanospilus Malaysia 25 %A ++ + + + - - + + + (2015) Trang, Horinouchi Hippichthys cyanospilus Thailand 16 %V ++++ ++ ++ - - - ++ ++ + et al. (2012) Wellingt on Harbour, Woods Hippocampus abdominalis NZ 89 %V +++ ++ + + + + ++ ++ + - + + (2002) Kendrick & PoF, Hydnes Hippocampus breviceps Australia 67 %V ++++ ++++ +++ + + + - - + + + + (2005) Horinouchi Tokyo, & Sano Hippocampus coronatus Japan 60 %V ++++ ++++ +++ + - - - - + - (2000) Tampa Dunham Hippocampus erectus Bay, FL NA %V ++++ +++ +++ - + - + + + (2010) Aegean Sea, Gurkan et Hippocampus guttulatus Turkey 16 %W ++ ++ + ++ + - + - + + - - al. (2011b) Aegean Sea, Gurkan et Hippocampus hippocampus Turkey 21 %W + + + + - - - - + - - + al. (2011b) Mamang uape estuary, Castro et al. Hippocampus reidi Brazil 280 %A +++ + + - + - ++ ++ ++ + + + (2008) Hippocampus spiniosissimus East 29 %V ++ + + + - - ++ ++ + - - + Yip et al. 85 Coast, (2015) Malaysia West Coast, Yip et al. Hippocampus spiniosissimus Malaysia 4 %V +++ +++ ++ ++ - - - - + - (2015) Kendrick & PoF, Hydnes Hippocampus subelongatus Australia 22 %V ++++ +++ ++ + - - ++ ++ - - - - (2005) East Coast, Yip et al. Hippocampus trimaculatus Malaysia 36 %V +++ + + + - - ++ ++ - - - - (2015) West Coast, Yip et al. Hippocampus trimaculatus Malaysia 16 %V ++++ + + - - - + + +++ + (2015) Sydney, Burchmore Hippocampus whitei Australia 8 %V ++++ ++++ +++ ++ - - + + + et al. (1984) Tampa Tipton & Hippocampus zosterae Bay, FL 87 %W ++++ + + - - - + + ++++ + + ++++ Bell (1988) Kendrick & Histiogamphe PoF, Hydnes lus cristatus Australia 58 %V ++++ ++++ +++ + - - + + + + + + (2005) Kendrick & PoF, Hydnes Lissocampus caudalis Australia 8 %V ++++ ++ ++ - - - - - +++ + - ++ (2005) Kendrick & PoF, +++ Hydnes Mitotichthys meraculus Australia 16 %V ++++ ++++ + + ------(2005) Melbour Howard & ne, Koehn Mitotichthys semistriatus Australia 8 %V ++++ ++ ++ - - - - - +++ ++ - - (1985) Aegean Sea, Gurkan et Nerophis ophidion Turkey 43 %W ++++ ++ ++ - - - - - ++ - + + al. (2011a) Kendrick & PoF, +++ Hydnes Phyllopteryx taeniolatus Australia 29 %V ++++ ++++ + + - - + + + + - - (2005) Kendrick & PoF, Hydnes Pugnaso curtirostris Australia 39 %V ++++ +++ +++ + + + + + ++ + + ++ (2005) Kendrick & PoF, Hydnes Stigmatopora argus Australia 165 %V ++++ + + - - - + + ++++ +++ + + (2005) Sydney, Steffe et al. Stigmatopora argus Australia 40 %V ++++ + + + + - - - ++++ (1989) Stigmatopora argus Sydney, 1 %V +++ ------+ Burchmore 86 Australia et al. (1984) Melbour ne, Smith et al. Stigmatopora nigra Australia 350 %W ++++ + + - + + - - ++++ + (2011a) Kendrick & PoF, Hydnes Stigmatopora nigra Australia 144 %V ++++ + + + - - + + ++++ +++ + + (2005) Sydney, Steffe et al. Stigmatopora nigra Australia 40 %V ++++ + + + - - - - ++++ (1989) Syngnathiode Trang, Horinouchi s biaculeatus Thailand 88 %V +++ + + + - - ++ ++ - - - - et al. (2012) Amitori Syngnathoide Bay, Nakamura s biaculeatus Japan 15 %V ++++ + - + - - +++ +++ + - et al. (2003) Bot Bennett & estuary, Branch Syngnathus acus SA 77 %W ++++ ++ + - + + - - +++ ++ + (1990) Chesape Teixeira & ake Bay, 129 Musick Syngnathus floridae USA 5 %W ++++ ++ + + + - ++ ++ + (1995) Patos lagoon, Garcia et al. Syngnathus folletti Brazil 108 %A ++++ +++ + - +++ - - - + (2005) Chesape Teixeira & ake Bay, 331 Musick Syngnathus fuscus USA 1 %W ++++ ++++ ++++ + + - + + + (1995) Chesape ake Bay, Ryer & Orth Syngnathus fuscus USA 136 %W ++++ +++ ++ + ++ - - - + + - - (1987) Cape Hatteras, +++ Bowman et Syngnathus fuscus NC 13 %W ++++ ++++ + + ------al. (2000) Kwangy Huh & ang Bay, 134 Kwak, Syngnathus schlegali Korea 7 %W ++++ +++ +++ + - + + + ++ ++ - - (1997) Ishikari, Sakurai et Syngnathus schlegali Japan 51 %W ++++ ++++ ++ ++ + - - - + + + + al. (2009)
Horinouchi Tokyo, & Sano Syngnathus schlegali Japan 41 %V ++++ + + + - - - - +++ - (2000) Tampa Tipton & Syngnathus scovelli Bay, FL 178 %W +++ + + - - - + + ++ + + ++ Bell (1988) Indian Krejci Syngnathus scovelli River 101 %V +++ + + - - - + + + + - + (2012) 87 Lagoon, FL Tampa Motta et al. Syngnathus scovelli Bay, FL 30 %W ++++ ++ ++ - + - ++ ++ + (1995) Ria Formosa , Oliveira et Syngnathus typhle Portugal 411 %W +++ + + + + - +++ +++ + al. (2007) Melbour Howard & ne, Koehn Urocampus carinirostris Australia 17 %V ++++ + + - - - - - ++++ + +++ + (1985) Kendrick & PoF, Hydnes Vanacampus phillipi Australia 26 %V ++++ +++ ++ + + + + + + + + + (2005) Melbour Howard & ne, Koehn Vanacampus phillipi Australia 13 %V +++ ++ ++ - + - - - ++ ++ - + (1985) Kendrick & PoF, Hydnes Vanacampus poecilolaemus Australia 67 %V ++++ +++ + +++ + - + + + + - + (2005)
Anarchopteru Cedar Brown s criniger Key, FL 27 %N ++++ ++ + + + - + + +++ (1972) Chesape Teixeira & ake Bay, Musick Hippocampus erectus USA 136 %N ++++ ++++ ++++ - + - + + + (2001) Rhodes Island, Kitsos et al. Hippocampus guttulatus Greece 279 %N ++++ +++ ++ + + + ++ ++ + (2008) Aegean Sea, Gurkan et Hippocampus guttulatus Turkey 16 %N +++ +++ + ++ + - + - + + - - al. (2011b) Aegean Sea, Gurkan et Hippocampus hippocampus Turkey 21 %N +++ +++ + ++ - - - - + - - + al. (2011b) Rhodes Island, Kitsos et al. Hippocampus hippocampus Greece 19 %N ++++ +++ ++ + - - + + - (2008) Kanou & Tokyo, Kohno Hippocampus mohnikei Japan 64 %N ++++ ------++++ + +++ - (2001) San Antonio Storero & Bay, Gonzalez Hippocampus patagonicus Argentin 32 %N ++++ ++ ++ - - - +++ +++ - - - - (2008) 88 a Tampa Tipton & Hippocampus zosterae Bay, FL 87 %N ++++ + + - - - + + ++++ + + +++ Bell (1988) Galway Lyons & lumbriciformi Bay, Dunne Nerophis s Ireland 220 %N ++++ + + - + - - - ++++ + + +++ (2004) Sacca di Scardov Franzoi et Syngnathus abaster ari, Italy 180 %N ++++ + + + + - + + ++++ + + ++++ al. (1993) Stagnon e di Marsala lagoon, Campolmi Syngnathus abaster Italy 87 %N ++++ ++ + + + + - - +++ - - +++ et al. (1996) Aegean Sea, Taskavak et Syngnathus acus Turkey 95 %N ++++ + + + + - + + ++ + + ++ al. (2010) Kromme Hanekom & estuary, Baird, Syngnathus acus SA 2 %N ++++ - - - - - ++++ ++++ - - - - (1984) Chesape ake Bay, 129 Mercer Syngnathus floridae USA 5 %N ++++ ++++ ++ ++ + - - - + + - - (1973) Chesape Teixeira & ake Bay, 129 Musick Syngnathus floridae USA 5 %N ++++ + + + + - + + +++ (1995) Ecofina Livingston Syngnathus floridae River, FL 875 %N ++++ + + + - - ++ ++ + - - - (1984) Cedar Brown Syngnathus floridae Key, FL 140 %N ++++ + + + + - + + + (1972) Patos lagoon, Garcia et al. Syngnathus folletti Brazil 108 %N ++++ +++ + - +++ + + + ++ (2005) Chesape Teixeira & ake Bay, 331 Musick Syngnathus fuscus USA 1 %N ++++ ++ ++ + + - + + +++ (1995) Chesape ake Bay, 129 Mercer Syngnathus fuscus USA 5 %N ++++ ++++ +++ + + - - - + + - - (1973) Cedar Brown Syngnathus louisianae Key, FL 18 %N ++++ + + + + - + + + (1972) Kwangy Huh & ang Bay, 134 Kwak Syngnathus schlegali Korea 7 %N ++++ +++ ++ + - + + + ++ ++ - - (1997)
89 Ecofina Livingston Syngnathus scovelli River, FL 291 %N ++++ ++ ++ + + - + + + - - + (1984) Tampa Tipton & Syngnathus scovelli Bay, FL 178 %N ++++ + + - - - + + ++++ + + ++ Bell (1988) Cedar Brown Syngnathus scovelli Key, FL 150 %N ++++ + + + + - + + ++ (1972) Indian River Lagoon, Krejci Syngnathus scovelli FL 101 %N ++++ + + - - - + + +++ + - +++ (2012) Tampa Motta et al. Syngnathus scovelli Bay, FL 30 %N +++ ++ ++ - + - + + + (1995) Sacca di Scardov Franzoi et Syngnathus taenionotus ari, Italy 137 %N +++ + + + + - + + +++ ++ + ++ al. (1993) Ria Formosa , Oliveira et Syngnathus typhle Portugal 411 %N ++++ + + + + - + + ++++ al. (2007) Aegean Uncumusao Sea, glu et al. Syngnathus typhle Turkey 95 %N ++ - - - - - + + ++ + - ++ (2017) Stagnon e di Marsala lagoon, +++ Campolmi Syngnathus typhle Italy 94 %N ++++ ++++ + + ------et al. (1996) Wilson Gaughan & Inlet, Potter Urocampus carinirostris Australia 268 %N +++ ------+++ + ++ - (1997)
Kendrick & PoF, Hydnes Filicampus tigris Australia 10 %FO ++++ ++++ ++++ +++ - - + + + - - + (2005) Lim, Pulau unpublishe Tinggi, d data Hippichthys cyanospilus Malaysia 25 %FO ++ ++ ++ + - - + + ++ (2015) Wellingt on Harbour, Woods Hippocampus abdominalis NZ 59 %FO ++++ ++ ++ + + + +++ +++ + + + (2002) Kendrick & PoF, Hydnes Hippocampus breviceps Australia 67 %FO ++++ ++++ ++++ + ++ + - - +++ + + +++ (2005) 90 Chesape Teixeira & ake Bay, Musick Hippocampus erectus USA 136 %FO ++ ++ ++ + + - + + + (2001) Rhodes Island, Kitsos et al. Hippocampus guttulatus Greece 279 %FO ++++ ++++ ++++ ++ + + +++ +++ + (2008) Aegean Sea, +++ Gurkan et Hippocampus guttulatus Turkey 16 %FO ++++ ++++ ++ + + - + - + + - - al. (2011b) Aegean Sea, Gurkan et Hippocampus hippocampus Turkey 21 %FO + + + + - - - - + - - + al. (2011b) Rhodes Island, Kitsos et al. Hippocampus hippocampus Greece 19 %FO +++ +++ +++ ++ - - + + - (2008) Khanh Hoa, Do et al. Hippocampus histrix Vietnam 15 %FO +++ +++ +++ + - +++ ++ ++ - - - - (1996) San Antonio Bay, Storero & Argentin Gonzalez Hippocampus patagonicus a 23 %FO ++++ ++++ ++++ - - - +++ +++ - - - - (2008) San Antonio Bay, Storero & Argentin Gonzalez Hippocampus patagonicus a 9 %FO ++++ ++++ ++++ ------(2008) Mamang uape estuary, Castro et al. Hippocampus reidi Brazil 280 %FO ++++ + + - + - + + ++++ ++ ++ ++++ (2008) East Coast, Yip et al. Hippocampus spiniosissimus Malaysia 29 %FO + + + + - - + + + - - + (2015) West Coast, Yip et al. Hippocampus spiniosissimus Malaysia 4 %FO + + + + - - - - + - (2015) Kendrick & PoF, Hydnes Hippocampus subelongatus Australia 22 %FO ++++ +++ +++ ++ - - ++ ++ + - - + (2005) East Coast, Yip et al. Hippocampus trimaculatus Malaysia 36 %FO ++ + + + - - ++ ++ + - (2015) Hippocampus trimaculatus Khanh 19 %FO +++ +++ +++ + - ++ + + - - - - Do et al. 91 Hoa, (1996) Vietnam West Coast, Yip et al. Hippocampus trimaculatus Malaysia 16 %FO + + + - - - + + + + (2015) Tampa Tipton & Hippocampus zosterae Bay, FL 87 %FO ++++ ++ ++ - - - + + ++++ ++++ ++ ++++ Bell (1988) Kendrick & Histiogamphe PoF, Hydnes lus cristatus Australia 58 %FO ++++ ++++ ++++ ++ - - + + + + + + (2005) Kendrick & PoF, Hydnes Lissocampus caudalis Australia 8 %FO ++++ ++++ ++++ - - - - - ++++ ++++ - ++++ (2005) Anarchopteru Cedar Brown s criniger Key, FL 27 %FO ++++ +++ +++ + + - + + ++++ (1972) Kendrick & PoF, +++ Hydnes Mitotichthys meraculus Australia 16 %FO ++++ ++++ ++ + ------(2005) Kendrick & PoF, +++ Hydnes Phyllopteryx taeniolatus Australia 29 %FO ++++ ++++ + + - - ++ ++ + + - + (2005) Kendrick & PoF, Hydnes Pugnaso curtirostris Australia 39 %FO ++++ ++++ ++++ + + + + + ++++ ++ + ++++ (2005) Kendrick & PoF, Hydnes Stigmatopora argus Australia 165 %FO ++++ + + + - + + + ++++ ++++ ++++ ++++ (2005) Melbour ne, Smith et al. Stigmatopora nigra Australia 350 %FO ++++ +++ +++ - + + - - ++++ ++++ (2011a) Kendrick & PoF, Hydnes Stigmatopora nigra Australia 144 %FO ++++ + + + - - + + ++++ ++++ ++++ ++++ (2005) Aegean Sea, Tackavak et Syngnathus acus Turkey 95 %FO ++++ ++++ ++++ + + - ++ ++ ++++ + ++ ++++ al. (2010) Cedar +++ Brown Syngnathus floridae Key, FL 140 %FO ++++ ++++ ++ + + - ++++ ++++ ++ (1972) Cedar +++ Brown Syngnathus louisianae Key, FL 18 %FO ++++ ++++ ++++ + + - ++++ ++++ +++ (1972) Kwangy Huh & ang Bay, 134 Kwak Syngnathus schlegali Korea 7 %FO ++ ++ ++ + - + + + ++ ++ - - (1997) Tampa Tipton & Syngnathus scovelli Bay, FL 178 %FO ++++ +++ +++ - - - + + ++++ ++++ +++ ++++ Bell (1988) 92 Cedar Brown Syngnathus scovelli Key, FL 150 %FO ++++ ++++ ++++ ++ + - ++ ++ +++ (1972) Tampa Motta et al. Syngnathus scovelli Bay, FL 30 %FO ++++ ++++ ++++ - + - +++ +++ + (1995) Ria Formosa , Oliveira et Syngnathus typhle Portugal 411 %FO ++ + + + + - ++ ++ + al. (2007) Aegean Uncumusao Sea, glu et al. Syngnathus typhle Turkey 95 %FO + - - - - - + + + + - + (2017) Kendrick & PoF, Hydnes Vanacampus phillipi Australia 26 %FO ++++ ++++ ++++ ++ ++ + + + +++ + + +++ (2005) Kendrick & PoF, +++ Hydnes Vanacampus poecilolaemus Australia 67 %FO ++++ ++++ + + + - ++ ++ + + - + (2005)
– =0% + >0 to 25% ++ >25 to 50% +++ >50 to 75% ++++ >75%
93 Table 3.2 Measures of phylogenetic signal for syngnathid morphological characteristics.
Statistical significance indicated in bold (P < 0.05). N indicates the number of syngnathid species that were used to assess the phylogenetic signal.
Bloomberg's K Characteristic N K P-value Relative fin size 27 0.93 0.006 Max. Standard length (StL) 67 0.96 0.001 Head length (HL) 67 1.02 0.001 Snout length (SnL) 67 1.02 0.001 Snout depth (SnD) 67 0.84 0.001 StL:HL 67 0.95 0.001 HL:SnL 67 0.67 0.001 SnL:SnD 67 0.72 0.001
94 Table 3.3 Results of the redundancy analyses that measured the associations between multiple independent variables (syngnathid morphological characteristics) and multiple dependent variables (dietary categories, e.g. amphipods). Models were based on three different dietary metrics: bulk, numeric, and frequency of occurrence dietary data.
Statistical significance indicated in bold (P < 0.05).
Model Variable F P-value Bulk dietary data Relative fin size 0.29 0.870 Max Standard length (StL) 1.46 0.223 Snout length (SnL) 2.42 0.068 Snout depth (SnD) 1.14 0.329 HL:SnL 4.44 0.010 SnL:SnD 5.93 0.003 Numeric dietary data Relative fin size 0.10 0.985 Max Standard length (StL) 0.41 0.748 Snout depth (SnD) 1.46 0.221 HL:SnL 0.47 0.702 SnL:SnD 0.27 0.850 Frequency of occurrence data Relative fin size 1.57 0.160 Max Standard length (StL) 1.96 0.081 Snout length (SnL) 1.21 0.301 Snout depth (SnD) 1.12 0.347 HL:SnL 2.29 0.044 SnL:SnD 1.58 0.154
95
Figure 3.1 Proportion of (a) bulk dietary data variance, (b) numeric dietary data variance, and (c) frequency of occurrence dietary variance explained by syngnathid body traits
[component a; Relative fin size + Max. standard length (StL) + Snout depth (SnD) + HL:SnL
+ SnL:SnD], genus [component c], covariance between syngnathid body traits and genus
[component b], and unexplained residuals [component d].
96
Figure 3.2 Figure 1 from © Van Wassenbergh, S., Strother, J. A., Flammang, B. E., Ferry-
Graham, L. A. & Aerts, P., Extremely fast prey capture in pipefish is powered by elastic recoil, Journal of the Royal Society Interface, 2008, 5, 20, page 286, by permission of the
Royal Society. A schematic of a syngnathid body (Syngnathus leptorhynchus) during a feeding strike. Specialized sternohyoideus muscles run along the dorsal and ventral sides of the pipefish. When contracted, they pull the hyoid arch towards the body. The neurocranium (including the snout) then rotates away from the body, towards the prey.
97 Filicampus Hippichthys Hippocampus Histiogamphelus Lissocampus Mitotichthys Nerophis Amphipoda Phyllopteryx Pugnaso Stigmatopora Syngnathiodes Syngnathus Urocampus Vanacampus
HL.SnL Finratio RDA2
MaxSL Copepoda
SnDTotalEggs
Mysida SnL.SnD
Decapoda 10 1 5 0 5 10 − −
0 5 10
RDA1
Figure 3.3 Triplot of the first two axes of the redundancy analyses (RDA) performed on bulk dietary data. Points represent the diet of a particular species of syngnathid in a particular area, as reported by a particular study. Points are coloured based on the genus.
Environmental vectors for syngnathid body characteristics are fit onto the ordination, and the direction and strength of the gradient is represented by the length of the arrow.
98 Anarchopterus Filicampus Hippichthys Hippocampus Histiogamphelus Lissocampus Mitotichthys Phyllopteryx Pugnaso Stigmatopora Syngnathus Vanacampus
SnL.SnD
HL.SnL SnD RDA2 Copepoda Ostracoda TotalLarvae 20 2 4 6 −
Decapoda Finratio MaxSL
4 Other −
Mysida 6 − 10 1 − Amphipoda
−10 −50 5
RDA1
Figure 3.4 Triplot of the first two axes of the redundancy analyses (RDA) performed on bulk dietary data. Points represent the diet of a particular species of syngnathid in a particular area, as reported by a particular study. Points are coloured based on the genus.
Environmental vectors for syngnathid body characteristics are fit onto the ordination, and the direction and strength of the gradient is represented by the length of the arrow.
99 Chapter 4 Conclusions
My thesis helps reveal, for the first time, the interplay between habitats, prey, and predators in shaping the ecology of fishes in the family Syngnathidae. In Chapter 2, I used the seahorse Hippocampus whitei as a case study, exploring ecological correlates of abundance and distributions. Expanding on previous work that had investigated how either their habitats or their prey or their predators affected their populations, I considered all three components of their environment together, using a holistic approach. I also investigated how the correlations differed at two scales: among different seagrass beds
(200-6000 m apart), and within a single seagrass bed (<100 m in size). I found that habitat, prey, and predator variables all correlated with seahorse density or their size distributions to varying extents, depending on the scale of the study. In Chapter 3, I then probed syngnathid foraging, synthesizing a large amount of fragmented information on their feeding and diets. My focus was to relate their diets to their relatively diverse feeding morphologies and habitat use across the family. In this—the first study to compare syngnathid diets across genera, locations, and studies—I found their diets were best explained by their snout morphologies. These morphologies also had high phylogenetic signal, suggesting that dietary differences across genera were largely explained by how these diverse fishes differed with respect to these morphologies. Ecological studies of this family are of considerable interest because the unusually intimate and proximate relationship between these mostly sedentary species and their environment.
100 4.1 Associations with habitats
The results of Chapter 2 suggest that Hippocampus whitei tolerate the habitats in which they settle, but then make the best of the hand they have been dealt. I found that seahorse distribution was correlated with more ecological variables within a single bed than among different beds. This is reasonable, as these seahorses are slow swimmers
(Consi et al., 2001), and travel to new patches (200 to 6000 m away in my study) would require that they cross patches of open sand where they would be more exposed to predators and strong currents (Brown, 1999; Hendriks et al., 2008; Canion & Heck, 2009).
While there is evidence that some seahorses migrate to deeper waters after the breeding season (Vincent & Sadler, 1995), doing so during the breeding season could be risky. After all, seahorses often live in low densities within patchy distributions (Foster & Vincent,
2004). Instead, seahorses are well suited to living stationary lives in complex habitats, thanks to excellent maneuverability and camouflage. Within those habitats, the data from
Little Beach suggests they choose areas with fewer predators, more prey options, and denser canopies—locations that presumably improved their prospects of surviving, growing, and reproducing.
4.2 Associations with prey
My results indicate that many sedentary syngnathids are generalist feeders, and select locations that have a diverse range of food options. Presumably, this is because it is energetically beneficial for a slow-moving animal to be less selective, consuming what they encounter (Gerritsen, 1984; Sih & Moore, 1990). Our work revealed no relationship
101 between prey metrics and abundance and distribution of the sedentary seahorse, H. whitei, among seagrass beds (Chapter 2). Syngnathids that have very small home ranges (e.g. seahorses) are unlikely to make risky long-distance movements for food options (Vincent et al., 2005; Dunham, 2010). If they do move to improve their feeding prospects, it is probably at small scales. For example—although I did not monitor their actual movement within a seagrass bed—I found that seahorses selected locations within a seagrass bed with greater numbers of prey types. It may be that sedentary syngnathids relocate short distances, within the habitat patch in which they are already settled, to locations that have a greater number of prey types to select from. These options could be energetically beneficial, allowing syngnathids to change the food they eat depending on their current energy needs, such as during the energetically expensive breeding season (Dunham, 2010).
Although this thesis suggests that syngnathid diet is driven largely by prey availability, a quantitative comparison between syngnathid diets and what is available to them is badly needed to assess selectivity. As evident in our review, diet is a function of many factors including the relative sizes of predators and their prey, predator capture success—and the relative distributions of predators and their prey. In the absence of any work that has empirically compared syngnathid feeding to the number and types of prey available to them, however, I was left to look for patterns in diet across genera and speculate on the feeding selectivity of different syngnathids. A study that compared syngnathid diets to prey availability would disentangle the potential variables that could lead to erroneous conclusions that can occur in both directions. For example, one might wrongfully conclude that syngnathids were selecting for a particular prey type—if they were concentrated in their stomachs—when they were simply feeding at random in an
102 area with a notably high concentration of that prey type (Griffiths, 1973). Alternatively, a stomach with many prey types of equal volumes could mislead some to believe that a syngnathid fed randomly, when in reality their preferred prey types were simply not present in the area (Griffiths, 1973). A study that looks at syngnathid feeding in relation to availability would greatly add to our understanding—and one that did it an area with many different syngnathids would allow for analyses that could compare diet selectivity to morphology, behaviour and phylogeny. As an example of one such location, Kendrick &
Hyndes (2005) found 12 species of syngnathid within 9 genera in a network of seagrasses in southwestern Australia.
4.3 Associations with predators
While it would be valuable to know the relative effects of direct mortality and predator evasion on seahorse distributions, SCUBA-assisted observational studies are likely not the answer. In Chapter 2, I concluded that the negative association between seahorses and predators was likely the result of predator-evasion tactics among seahorses, and not the result of direct mortality. This hypothesis was largely born from anecdotal evidence, and work that showed predation on seahorses to be rare. As an example, only 13 predation events were recorded in the most thorough evaluation of predation on seahorses to date, involving thousands of hours of observation and 2000 individuals (Harasti et al.,
2014b). It may very well be that syngnathids are only eaten on rare occasion by opportunistic, generalist feeders (Kleiber et al., 2011) because of their unpalatable and energy-poor morphologies (Harris et al., 2008). Or, perhaps they are depredated, but we are not looking in the correct ways. Using SCUBA can strongly influence what fish we see
103 underwater, and their behaviours when we do see them (Dickens et al., 2011). To avoid these diver effects, tethering experiment have been used in observational studies to monitor predation events (Aronson & Heck, 1995). Since many syngnathids show strong site fidelity, researchers can monitor syngnathids without having to sacrifice them. Recent work, for example, has shown that GoPro cameras can be set up facing seahorses in the wild and effectively monitor their behaviours—including when they are attacked by predators (Claasens, 2017). Indeed the use of GoPro cameras in remote observation studies is becoming more and more common in marine sciences (Struthers et al., 2015). If adapted correctly, we may be able to measure relative frequency with which syngnathids are consumed by their predators, and better understand how syngnathid distributions are shaped by their predators.
Future work on syngnathid predators should focus on discerning the impact of predation on mortality of newborn juveniles. While conducting my fieldwork, one of the most memorable moments was witnessing a seahorse birth in the wild. I was struck by how helpless the newborn seahorses seemed as they drifted towards the surface—at the mercy of the ocean currents. This was not an uncommon occurrence as many seahorse species have a planktonic phase in their first few weeks of existence (Foster & Vincent,
2004). That said, we know very little about the fate of newborn syngnathids (Vincent &
Giles, 2003), and a large percentage may end up as food for piscivorous fish (Foster &
Vincent, 2004). Newborn syngnathids are extremely small (2-20 mm; Foster & Vincent,
2004) and could conceivably fit into the gapes of most piscivorous fish. So despite the poor nutritional quality of syngnathids (Harris et al., 2008)—which has been credited as a reason for the low rates of predation on adults (Kleiber et al., 2011)—opportunistic feeders
104 would likely not hesitate to eat newborns (Gerking, 1994). In fact, spikes in the newborn syngnathid populations (i.e. during breeding seasons) can lead to higher rates of predation on syngnathids (Kleiber et al., 2011). It may be that opportunistic predators—which rarely feed on adults syngnathids—have a large role in the very high mortality rate among newborn syngnathids. As such, a study that tracked the fate of newborn syngnathids could shed light on the effect of predators in juvenile recruitment.
4.4 How this thesis fits in to the literature
My discoveries in this thesis can make a contribution to applied questions of seahorse conservation—many species are threatened—and management. Prior to this thesis, we often knew where seahorses lived and what kinds of habitats they occupied, but were less aware of how this mattered with respect to their populations. This thesis identified that at the scale of 200 m to 6000 m, that total predators was the only variable that was correlated with seahorse abundance. The association between seahorses and predators continued within a single habitat patch (<100 m), showing that seahorses occurred in areas where predator numbers were lower. Such knowledge matters in designing marine management and particularly in placing MPAs. One study has argued that that MPAs, by fostering an increase in the number of predatory fishes, could actually add to the pressures on seahorses (Harasti et al., 2014b). The results of this thesis do not align with that hypothesis, however, as I found our highest seahorse densities in a sanctuary zone that banned recreational fishing. While fish known to eat seahorses are highly desirable fishing targets (Harasti et al., 2014b), it could be that recreational fishing does
105 not remove the predatory pressure to a large enough extent to have a tangible effect on seahorse numbers. MPAs are most likely to protect seahorses where seahorse extraction is prohibited or where destructive fishing that damages habitats is banned (Vincent et al.,
2011). Overall our understanding of how MPAs affect these flagship fishes is still at an elementary stage (Yasué et al., 2012). To provide conservationists with the tools to best select the locations for MPAs, we need to better understand how they affect seahorses, both directly and indirectly.
Using syngnathids as a case study, my thesis improves on our understanding of how animals are shaped by their world—including their habitats, their prey, and their predators. I used syngnathids as a case study—a group of morphologically diverse marine fishes which have evolved to live in certain habitats, eat certain prey, and hide from certain predators. To best understand how syngnathids interact with their environments—my thesis shows that it is important to consider all three components. First, syngnathids are shaped by their habitats. Syngnathids are found in all of the world’s oceans, living amongst many different types of coastal habitats—including coral reefs, macroalgae, mangroves, seagrasses, sponges and artificial structures (Dawson, 1982, 1985; Foster & Vincent, 2004).
While some species have evolved to live in specific habitat types, many syngnathids are found living in more than one type. In this thesis, I found H. whitei distributions were shaped by specific components of their habitats, including depth and density. This was potentially an example of the mediating effect that habitats can have on predator-prey interactions, as more complex locations may improve the feeding prospects of sedentary syngnathids such as H. whitei. Second, syngnathids are also shaped by their prey. In my thesis, I showed that seahorses selected locations with more prey types, presumably
106 because more prey options would be energetically beneficial. I also reviewed how syngnathids have been physically shaped by their prey. Syngnathids have evolved highly- advanced prey-capture morphologies that aid in the capture of elusive prey (Van
Wassenbergh et al., 2008, 2009; Roos et al., 2009b; Gemmell et al., 2013). These morphologies vary across genera, and I showed that across genera, locations, and studies, feeding morphologies—and in particular, their snout shape—are correlated with what they eat. Third, syngnathids are also shaped by their predators. In response to predation, syngnathids demonstrate various forms of behavioural and physiological crypsis. Some pipefish species mimic the rhythmic movements of seagrass, while seahorses are able to change colour to match their backgrounds, and grow long skin filaments (Howard & Koehn,
1985; Foster & Vincent, 2004). This was the first study to demonstrate that syngnathid abundance and distributions correlates with presence of their predators, after controlling for other facets of their environment. Overall, I show that syngnathid abundances and distributions are affected by their habitats, prey, and predators, to some degree. To best understand how an animal interacts with—and is shaped by—its environment, my thesis demonstrates that animal studies should focus on investigating these relationships from multiple angles.
107 Bibliography
Abrams, P. A. (2000). The evolution of predator-prey interactions: theory and evidence.
Annual Review of Ecological Systems 31, 79–105. doi: 10.1146/annurev.ecolsys.31.1.79
Abrams, P. A. & Ginzburg, L. R. (2000). The nature of predation: prey dependent, ratio
dependent or neither? Trends in Ecology and Evolution 15, 337–341. doi:
10.1016/S0169-5347(00)01908-X
Arditi, R. & Ginzburg, L. R. (1989). Coupling in predator-prey dynamics: ratio-dependence.
Journal of Theoretical Biology 139, 311–326. doi: 10.1016/S0022-5193(89)80211-5
Aronson, R. B. & Heck, K. L. (1995). Tethering experiments and hypothesis testing in
ecology. Marine Ecology Progress Series 121, 307–309.
Aylesworth, L. A., Xavier, J. H. A., Oliveira, T. P. R., Tenorio, G. D., Diniz, A. F. & Rosa, I. L.
(2015). Regional-scale patterns of habitat preference for the seahorse Hippocampus
reidi in the tropical estuarine environment. Aquatic Ecology 49, 499–512. doi:
10.1007/s10452-015-9542-3
Barnes, C., Maxwell, D., Reuman, D. C., Jennings, S., Barnes, C., Maxwell, D., Reuman, D. C. &
Jennings, S. (2010). Global patterns in predator — prey size relationships reveal size
dependency of trophic transfer efficiency. Ecology 91, 222–232.
Bell, E. M., Lockyear, J. F., McPherson, J. M., Marsden, A. D. & Vincent, A. C. J. (2003). First
field studies of an endangered South African seahorse, Hippocampus capensis.
Environmental Biology of Fishes 67, 35–46. doi: 10.1023/A:1024440717162
Bell, J. D. & Westoby, M. (1986). Abundance of macrofauna in dense seagrass is due to
108 habitat preference, not predation. Oecologia 68, 205–209.
Bennett, B. A. (1989). The diets of fish in three south-western Cape estuarine systems.
South African Journal of Zoology 24, 163–177. doi: 10.1080/02541858.1989.11448149
Bennett, B. A. & Branch, G. M. (1990). Relationships between production and consumption
of prey species by resident fish in the Bot, a cool temperate South African estuary.
Estuarine, Coastal and Shelf Science 31, 139–155. doi: 10.1016/0272-7714(90)90043-
Q
Bergert, B. A. & Wainwright, P. C. (1997). Morphology and kinematics of prey capture in the
sygnathid fishes Hippocampus erectus and Sygnathus floridae. Marine Biology 127,
563–570.
Berglund, A., Rosenqvist, G. & Robinson-Wolrath, S. (2006). Food or sex—males and
females in a sex role reversed pipefish have different interests. Behavioral Ecology and
Sociobiology 60, 281–287. doi: 10.1007/s00265-006-0166-4
Blomberg, S. P. & Garland, T. (2002). Tempo and mode in evolution: phylogenetic inertia,
adaptation and comparative methods. Journal of Evolutionary Biology 15, 899–910.
doi: 10.1046/j.1420-9101.2002.00472.x
Blomberg, S. P., Garland, T. & Ives, A. R. (2003). Testing for phylogenetic signal in
comparative data: behavioral traits are more labile. Evolution 57, 717. doi:
10.1554/0014-3820(2003)057[0717:TFPSIC]2.0.CO;2
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H. & White,
J.-S. S. (2009). Generalized linear mixed models: a practical guide for ecology and
109 evolution. Trends in Ecology and Evolution 24, 127–135. doi:
10.1016/j.tree.2008.10.008
Bommarco, R., Biesmeijer, J. C., Meyer, B., Stuart, P., Potts, S. G., Po, J., Roberts, M., Steffan-
Dewenter, I. & Ockinger, E. (2010). Dispersal capacity and diet breadth modify the
response of wild bees to habitat loss. Proceedings of the Royal Society of London B. doi:
10.1098/rspb.2009.2221
Bowman, R. E., Stillwell, C. E., Michaels, W. L. & Grosslein, M. D. (2000). Food of Northwest
Atlantic Fishes and Two Common Species of Squid. U.S. Department of Commerce
National Oceanic and Atmospheric Administration Nation Marine Fisheries Service
Northeast Region. Retrieved from
http://nefsc.noaa.gov/nefsc/publications/tm/tm155/tm155.pdf
Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. (2002). Evaluating resource
selection functions. Ecological Modelling 157, 281–300. doi: 10.1016/S0304-
3800(02)00200-4
Briand, F. & Cohen, J. E. (1987). Environmental correlates of food chain length. Science 238,
956–960. doi: 10.1126/science.3672136
Brodie, E. D. & Brodie, E. D. (1999). Costs of exploiting poisonous prey: evolutionary trade-
offs in a predator-prey arms race. Evolution 53, 626–631.
Brook, I. M. (1977). Trophic relationships in a seagrass community (Thalassia testudinum),
in Card Sound, Florida. Fish dies in relation to macrobenthic and cryptic faunal
abundance. Transactions of the American Fisheries Society 106, 219–229.
110 Brown, J. D. (1972). A comparative life history study of four species of pipefishes (family
Syngnathidae) in Florida. University of Florida.
Brown, J. H. & Maurer, B. A. (1989). Macroecology: the division of food and space among
species on continents. Science, New Series 243, 1145–1150.
Brown, J. S. (1999). Vigilance, patch use and habitat selection: foraging under predation
risk. Evolutionary Ecology Research 1, 49–71.
Bruno, J. F. & Bertness, M. D. (2001). Habitat modification and facilitation in benthic marine
communities. In: Marine Community Ecology (Bertness, M. D., Gaines, S. D. & Hay, M. E.,
eds), pp. 201–218. Sunderland, MA: Sinauer Associates.
Burchmore, J. J., Pollard, D. A. & Bell, J. D. (1984). Community structure and trophic
relationships of the fish fauna of an estuarine Posidonia australis seagrass habitat in
Port Hacking, New South Wales. Aquatic Botany 18, 71–87. doi: 10.1016/0304-
3770(84)90081-0
Caldwell, I. R. & Vincent, A. C. J. (2012). Revisiting two sympatric European seahorse
species: apparent decline in the absence of exploitation. Aquatic Conservation: Marine
and Freshwater Ecosystems 22, 427–435. doi: 10.1002/aqc.2238
Campolmi, M., Franzoi, P. & Mazzola, A. (1996). Observations on pipefish (Syngnathidae)
biology in the Stagnone lagoon (west Sicily). Publications Especial de I’ Institut Espana
de Oceanografie 21, 205–209.
Canion, C. R. & Heck, K. L. (2009). Effect of habitat complexity on predation success: re-
evaluating the current paradigm in seagrass beds. Marine Ecology Progress Series 393,
111 37–46. doi: 10.3354/meps08272
Castro, A. L. C., Diniz, A. F., Martins, I. Z., Vendel, A. L., Oliveira, T. P. R. & Rosa, I. L. (2008).
Assessing diet composition of seahorses in the wild using a nondestructive method:
Hippocampus reidi (Teleostei: Syngnathidae) as a study-case. Neotropical Ichthyology
6, 637–644. doi: 10.1590/S1679-62252008000400012
Ceccherelli, G., Campo, D. & Milazzo, M. (2007). Short-term response of the slow growing
seagrass Posidonia oceanica to simulated anchor impact. Marine Environmental
Research 63, 341–349. doi: 10.1016/j.marenvres.2006.10.004
Celino, F. T., Hilomen-Garcia, G. V. & del Norte-Campos, A. G. C. (2012). Feeding selectivity
of the seahorse, Hippocampus kuda (Bleeker), juveniles under laboratory conditions.
Aquaculture Research 43, 1804–1815. doi: 10.1111/j.1365-2109.2011.02988.x
Chambers, R. C. & Trippel, E. A. (1997). Early Life History and Recuritment in Fish
Populations. London: Chapman & Hall.
Claasens, L. (2017). Big brother is watching: gaining insights into seahorse behaviour using
GoPro cameras. In: SyngBio 2017 - Fantastical Fishes: Seahorses, Pipefishes and
Seadragons into the Future. Tampa, FL.
Clutton-Brock, T. H. (1975). Feeding behaviour of red colobus and black and white colobus
in East Africa. Folia Primatologica 23, 165–207.
Clynick, B. G. (2008). Harbour swimming nets: a novel habitat for seahorses. Aquatic
Conservation: Marine and Freshwater Ecosystems 18, 483–492. doi: 10.1002/aqc
Cohen, J. E. (1994). Marine and continental food webs: three paradoxes? Philosophical
112 Transactions of the Royal Society B 343, 57–69. doi: 10.1098/rstb.1994.0008
Cohen, J. E., Braind, F. & Newman, C. M. (1990). Community Food Webs: Data and Theory.
New York: Springer-Verlag.
Colson, D. J., Patek, S. N., Brainerd, E. L. & Lewis, S. M. (1998). Sound production during
feeding in Hippocampus seahorses (Syngnathidae). Environmental Biology of Fishes
221–229.
Consi, T. R., Seifert, P. A., Triantafyllou, M. S. & Edelman, E. R. (2001). The dorsal fin engine
of the seahorse (Hippocampus sp.). Journal of Morphology 97, 80–97.
Cooper, S. D., Smith, D. W. & Bence, J. R. (1985). Prey selection by freshwater predators with
different foraging strategies. Canadian Journal of Fish and Aquatic Sciences 42, 1720–
1732.
Costa, G. C. (2009). Predator size, prey size, and dietary niche breadth relationships in
marine predators. Ecology 90, 2014–2019.
Cowen, R. K. & Sponaugle, S. (2009). Larval dispersal and marine population connectivity.
Annual Review of Marine Sciences 1, 443–466. doi:
10.1146/annurev.marine.010908.163757
Crowder, L. B. & Norse, E. (2008). Essential ecological insights for marine ecosystem-based
management and marine spatial planning. Marine Policy 32, 772–778. doi:
10.1016/j.marpol.2008.03.012
Curtis, J. M. R. & Vincent, A. C. J. (2005). Distribution of sympatric seahorse species along a
gradient of habitat complexity in a seagrass-dominated community. Marine Ecology
113 Progress Series 291, 81–91. doi: 10.3354/meps291081
D’Entremont, J. (2002). Sex-differences in feeding behaviour and diet in Hippocampus
guttulatus. In: Life History and Ecology of Seahorses: Implications for Conservation and
Management (Foster, S. J. & Vincent, A. C. J., eds).
Davis, T. R., Gallen, C., Laird, R. & Harasti, D. (2015). Mapping sub-tidal habitats in the
Eastern Port of Port Stephens. Retrieved from
http://hunter.lls.nsw.gov.au/__data/assets/pdf_file/0008/572291/portstephenshabit
atmappingreport.pdf
Dawkins, R. & Krebs, J. R. (1979). Arms races between and within species. Proceedings of
the Royal Society of London B 205, 489–511.
Dawson, C. E. (1982). Fishes of the Western North Atlantic, Part 8. (eds Cohen, D. M.,
Marshall, N. B., Ebeling, A. W., et al.). New Haven, CT: Yale University.
Dawson, C. E. (1985). Indo-Pacific Pipefishes, Red Sea to the Americas. Ocean Springs, MS:
The Gulf Coast Research Laboratory.
Dayton, P. (1972). Toward an understanding of resilience and the potential effects of
enrichments to the benthos at McMurdo Sound, Antarctica. In: Proceedings of the
Colloquium of Conservation Problems in Antarctica, pp. 81–96. Lawrence, KS: Allen
Press. de Lussanet, M. H. E. & Muller, M. (2007). The smaller your mouth, the longer your snout:
predicting the snout length of Syngnathus acus, Centriscus scutatus and other pipette
feeders. Journal of the Royal Society, Interface / the Royal Society 4, 561–573. doi:
114 10.1098/rsif.2006.0201
Dias, T. L. P. & Rosa, I. L. (2003). Habitat preferences of a seahorse species, Hippocampus
reidi (Teleostei: Syngnathidae) in Brazil. Aqua Journal of Ichthyology and Aquatic
Biology 6, 165–176.
Dickens, L. C., Goatley, C. H. R., Tanner, J. K. & Bellwood, D. R. (2011). Quantifying relative
diver effects in underwater visual censuses. PLoS ONE 6, e18965. doi:
10.1371/journal.pone.0018965
Do, H. H., Truong, S. K. & Ho, T. H. (1996). Feeding behaviour and food of seahorses in
Vietnam. In: Proceedings of the Third International Conference on the Marine Biology of
the South China Sea. Hong Kong: Hong Kong University Press.
Dunham, N. M. (2010). The life history and energy budget of Hippocampus erectus in Tampa
Bay, Florida. University of South Florida. Retrieved from
http://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1345&context=sph_epide
miology_biostatistics_facpub
Felicio, A. K. C., Rosa, I. L., Souto, A. & Freitas, R. H. A. (2006). Feeding behavior of the
longsnout seahorse Hippocampus reidi Ginsburg, 1933. Journal of Ethology 24, 219–
225. doi: 10.1007/s10164-005-0189-8
Flammang, B. E., Ferry-Graham, L. A., Rinewalt, C., Ardizzone, D., Davis, C. & Trejo, T. (2009).
Prey capture kinematics and four-bar linkages in the bay pipefish, Syngnathus
leptorhynchus. Zoology 112, 86–96. doi: 10.1016/j.zool.2008.04.003
Flynn, A. J. & Ritz, D. A. (1999). Effect of habitat complexity and predatory style on the
115 capture success of fish feeding on aggregated prey. Journal of the Marine Biological
Association of the UK 79, 487–494. doi: 10.1017/S0025315498000617
Foster, S. J. & Vincent, A. C. J. (2004). Life history and ecology of seahorses: implications for
conservation and management. Journal of Fish Biology 65, 1–61. doi: 10.1111/j.1095-
8649.2004.00429.x
Franzoi, P., Maccagnani, R., Rossi, R. & Ceccherelli, V. U. (1993). Life cycles and feeding
habits of Syngnathus taenionotus and S. abaster (Pisces, Syngnathidae) in a brackish
bay of the Po River Delta (Adriatic Sea). Marine Ecology-Progress Series 97, 71–81.
Franzoi, P., Riccato, F., Franco, A. & Torricelli, P. (2004). Dietary differences in three
pipefish species (Osteichthyes, Syngnathidae) related to snout morphology. Biologia
Marina Mediterranea 11, 592–594.
Fretwell, S. D. & Lucas, H. L. (1970). On territorial behaviour and other factors influencing
habitat distribution in birds. Acta Biotheoretica 19, 16–36. doi: 10.1007/BF01601953
Froese, R. & Pauly, D. (2017). FishBase. Retrieved November 1, 2016, from
www.fishbase.org
Garcia, A. M., Geraldi, R. M. & Vieira, J. P. (2005). Diet composition and feeding strategy of
the southern pipefish. Neotropical Ichthyology 3, 427–432.
Garcia, L. M. B., Hilomen-Garcia, G. V., Celino, F. T., Gonzales, T. T. & Maliao, R. J. (2012). Diet
composition and feeding periodicity of the seahorse Hippocampus barbouri reared in
illuminated sea cages. Aquaculture 358–359, 1–5. doi:
10.1016/j.aquaculture.2012.06.013
116 Gaughan, D. J. & Potter, I. C. (1997). Analysis of diet and feeding strategies within an
assemblage of estuarine larval fish and an objective assessment of dietary niche
overlap. Fishery Bulletin 95, 722–731.
Gemmell, B. J., Sheng, J. & Buskey, E. J. (2013). Morphology of seahorse head
hydrodynamically aids in capture of evasive prey. Nature Communications 4, 2840. doi:
10.1038/ncomms3840
Gendron, R. P. & Staddon, J. E. R. (1983). Searching for cryptic prey: the effect of search rate.
The American Naturalist 121, 172–186.
Gerking, S. D. (1994). The Feeding Ecology of Fish. San Diego: Academic Press, Inc.
Gerritsen, J. (1984). Size efficiency reconsidered: a general foraging model for free-
swimming aquatic animals. The American Naturalist 123, 450–467. doi:
10.1086/284216
Gladstone, W. (2007). Selection of a spawning aggregation site by Chromis hypsilepis
(Pisces: Pomacentridae): habitat structure, transport potential, and food availability.
Marine Ecology Progress Series 351, 235–247. doi: 10.3354/meps07108
Griffiths, D. (1973). The food of animals in an acid moorland pond. Journal of Animal
Ecology 42, 285–293.
Griffiths, D. (1975). Prey availability and the food of predators. Ecology 56, 1209–1214.
Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. (2011). Multimodel inference in
ecology and evolution: challenges and solutions. Journal of Evolutionary Biology 24,
699–711. doi: 10.1111/j.1420-9101.2010.02210.x
117 Gurkan, S., Sever, T. M. & Taskavak, E. (2011a). Seasonal food composition and prey-length
relationships of pipefish Nerophis ophidion (Linnaeus, 1758) inhabiting the Aegean
Sea. Acta Adriatica 52, 5–14.
Gurkan, S., Taskavak, E., Sever, T. M. & Akalin, S. (2011b). Gut contents of two European
seahorses Hippocampus hippocampus and Hippocampus guttulatus in the Aegean Sea,
coasts of Turkey. Pakistan Journal of Zoology 43, 1197–1201. doi: 0030-
9923/2011/0006-1197
Hamilton, H., Saarman, N., Short, G., Sellas, A. B., Moore, B., Hoang, T., Grace, C. L., Gomon, M.,
Crow, K. & Brian Simison, W. (2017). Molecular phylogeny and patterns of
diversification in syngnathid fishes. Molecular Phylogenetics and Evolution 107, 388–
403. doi: 10.1016/j.ympev.2016.10.003
Hanekom, N. & Baird, D. (1984). Fish community structures in Zostera and non-Zostera
regions of the Kromme estuary, St Francis Bay. South African Journal of Zoology 19,
295–301.
Harasti, D. (2016). Declining seahorse populations linked to loss of essential marine
habitats. Marine Ecology Progress Series 546, 173–181. doi: 10.3354/meps11619
Harasti, D., Glasby, T. M. & Martin-Smith, K. M. (2010). Striking a balance between retaining
populations of protected seahorses and maintaining swimming nets. Aquatic
Conservation: Marine and Freshwater Ecosystems 20, 159–166. doi: 10.1002/aqc
Harasti, D., Martin-Smith, K. M. & Gladstone, W. (2012). Population dynamics and life
history of a geographically restricted seahorse, Hippocampus whitei. Journal of Fish
Biology 81, 1297–1314. doi: 10.1111/j.1095-8649.2012.03406.x
118 Harasti, D., Martin-Smith, K. M. & Gladstone, W. (2014a). Ontogenetic and sex-based
differences in habitat preferences and site fidelity of White’s seahorse Hippocampus
whitei. Journal of Fish Biology 85, 1413–1428. doi: 10.1111/jfb.12492
Harasti, D., Martin-Smith, K. M. & Gladstone, W. (2014b). Does a no-take marine protected
area benefit seahorses? PLoS ONE 9. doi: 10.1371/journal.pone.0105462
Haris, K., Chakraborty, B., Menezes, A., Sreepada, R. A. & Fernandes, W. A. (2014).
Multifractal detrended fluctuation analysis to characterize phase couplings in seahorse
(Hippocampus kuda) feeding clicks. Journal of the Acoustical Society of America 136,
1972–1981. doi: 10.1121/1.4895713
Harris, M. P., Newell, M., Daunt, F., Speakman, J. R. & Wanless, S. (2008). Snake Pipefish
Entelurus aequoreus are poor food for seabirds. Ibis 150, 413–415. doi:
10.1111/j.1474-919X.2007.00780.x
Heck, K. L. & Crowder, L. B. (1991). Habitat structure and predator-prey interactions in
vegetated aquatic systems. In: Habitat Structure (Bell, S. S., McCoy, E. D. & Mushinsky,
H. R., eds), pp. 281–299. Dordrecht: Springer Netherlands. doi: 10.1007/978-94-011-
3076-9_14
Heck, K. L., Hays, G. & Orth, R. J. (2003). Critical evaluation of nursery hypothesis for
seagrasses. Marine Ecology Progress Series 253, 123–136. doi: 10.3354/meps253123
Heck, K. L. & Orth, R. J. (2006). Predation in Seagrass Beds. In: Seagrasses: Biology, Ecology
and Conservation (Larkum, A., Orth, R. J. & Duarte, C., eds), pp. 537–550. Dordrecht:
Springer Netherlands. doi: 10.1007/1-4020-2983-7_22
119 Hendriks, I. E., Sintes, T., Bouma, T. J. & Duarte, C. M. (2008). Experimental assessment and
modeling evaluation of the effects of the seagrass Posidonia oceanica on flow and
particle trapping. Marine Ecology Progress Series 356, 163–173. doi:
10.3354/meps07316
Herald, E. S. & Rakowicz, M. (1951). Stable requirements for raising seahorses. Aquarium
Journal 22, 234–242.
Hislop, J. R. G., Robb, A. P. & Gauld, J. A. (1978). Observations on effects of feeding level on
growth and reproduction in haddock, Melanogrammus aeglefinue (L.) in captivity.
Journal of Fish Biology 13, 85–98.
Hixon, M. A. & Menge, B. A. (1991). Species interactive diversity: prey refuges modify the
effects of predation and competition. Theoretical Population Biology 39, 178–200.
Horinouchi, M. & Sano, M. (2000). Food habits of fishes in a Zostera marina bed at
Aburatsubo, central Japan. Ichthyological Research 47, 163–173. doi:
10.1007/BF02684237
Horinouchi, M., Tongnunui, P., Furumitsu, K., Nakamura, Y., Kanou, K., Yamaguchi, A.,
Okamoto, K. & Sano, M. (2012). Food habits of small fishes in seagrass habitats in
Trang, southern Thailand. Fisheries Science 78, 577–587. doi: 10.1007/s12562-012-
0485-5
Howard, R. K. & Koehn, J. D. (1985). Population dynamics and feeding ecology of pipefish
(Syngnathidae) associated with eelgrass beds of Western Port, Victoria. Marine and
Freshwater Research 36, 361–370. doi: 10.1071/MF9850361
120 Huh, S.-H. & Kitting, C. L. (1985). Trophic relationships among concentrated populations of
small fishes in segrass meadows. Journal of Experimental Marine Biology and Ecology
92, 29–43.
Huh, S.-H. & Kwak, S. N. (1997). Feeding habits of Syngnathus schlegeli in eelgrass (Zostera
marina) bed in Kwangyang Bay. Journal of the Korean Fisheries Socieity 30, 896–902.
James, P. L. & Heck, K. L. (1994). The effects of habitat complexity and light intensity on
ambush predation within a smulated seagrass habitat. Journal of Experimental Marine
Biology and Ecology 176, 187–200. doi: 10.1016/0022-0981(94)90184-8
Jenkins, G. P. & Sutherland, C. R. (1997). The influence of habitat structure on nearshore
fish assemblages in a southern Australian embayment: colonisation and turnover rate
of fishes associated with artificial macrophyte beds of varying physical structure.
Journal of Experimental Marine Biology and Ecology 218, 103–125. doi:
10.1016/S0022-0981(97)00071-3
Jenkins, G. P., Walker-Smith, G. K. & Hamer, P. A. (2002). Elements of habitat complexity
that influence harpacticoid copepod associated with seagrass beds in a temperate bay.
Oecologia 131, 598–605.
Johnson, C. J., Nielsen, S. E., Merrill, E. H., Trent, L. & Boyce, M. S. (2006). Resource selection
functions based on use–availability data: theoretical motivation and evaluation
methods. Journal of Wildlife Management 70, 347–357. doi: 10.2193/0022-
541X(2006)70
Jones, C. G., Lawton, J. H. & Shachak, M. (1994). Organisms as ecosystem engineers. Oikos
69, 373–386. doi: 10.2307/3545850
121 Jones, C. G., Lawton, J. H. & Shachak, M. (1997). Positive and negative effects of organisms as
physical ecosystem engineers. Ecology 78, 1946–1957. doi: 10.1890/0012-
9658(1997)078[1946:PANEOO]2.0.CO;2
Jouvenel, J.-Y. & Pollard, D. A. (2001). Some effects of marine reserve protection on the
population structure of two spearfishing target-fish species, Dicentrarchus labrax
(Moronidae) and Sparus aurata (Sparidae), in shallow inshore waters, along a rocky
coast in the northwestern Mediterranean . Aquatic Conservation: Marine and
Freshwater Ecosystems 11, 1–9. doi: 10.1002/aqc.424
Kanou, K. & Kohno, H. (2001). Early life history of a seahorse, Hippocampus mohnikei, in
Tokyo Bay, Japan. Ichthyological Research 48, 361–368. doi: 10.1007/s10228-001-
8159-9
Kendrick, A. J. (2002). Resource utilisation and reproductive biology of syngnathid fishes in a
seagrass-dominated marine environment in south-western Australia. Murdoch
University.
Kendrick, A. J. & Hyndes, G. A. (2005). Variations in the dietary compositions of
morphologically diverse syngnathid fishes. Environmental Biology of Fishes 72, 415–
427. doi: 10.1007/s10641-004-2597-y
Kennedy, M., Gray, R. D., Kennedy, M. & Gray, R. D. (1993). Can ecological theory predict the
distribution of foraging animals? A critical analysis of experiments on the ideal free
distribution. Oikos 68, 158–166. doi: 10.2307/3545322
King, J. (2007). Fisheries Biology, Assessment and Management. Oxford, UK: John Wiley &
Sons. doi: 10.1002/9781118688038
122 Kitsos, M. S., Tzomos, T., Anagnostopoulou, L. & Koukouras, A. (2008). Diet composition of
the seahorses, Hippocampus guttulatus Cuvier, 1829 and Hippocampus hippocampus
(L., 1758) (Teleostei, Syngnathidae) in the Aegean Sea. Journal of Fish Biology 72,
1259–1267. doi: 10.1111/j.1095-8649.2007.01789.x
Kleiber, D., Blight, L. K., Caldwell, I. R. & Vincent, A. C. J. (2011). The importance of seahorses
and pipefishes in the diet of marine animals. Reviews in Fish Biology and Fisheries 21,
205–223. doi: 10.1007/s11160-010-9167-5
Kooijman, S. A. L. M. (2010). Dynamic Energy Budget Theory: For Metabolic Organization.
New York: Cambridge University Press.
Krebs, C. J. (2009). Ecology: The Experimental Analysis of Distribution and Abundance, 6th
ed. San Francisco, CA: Benjamin Cummings.
Krejci, S. E. (2012). Habitat preferences and the effects of seagrass density on population
demographics and feeding ecology of pipefish in the Indian River Lagoon, FL. Florida
Institute of Technology.
Kroon, F. J., De Graaf, M. & Liley, N. R. (2000). Social organisation and competition for
refuges and nest sites in Coryphopterus nicholsii (Gobiidae), a temperate protogynous
reef fish. Environmental Biology of Fishes 57, 401–411. doi:
10.1023/A:1007600728267
Kuiter, R. H. (2000). Seahorses, Pipefishes and Their Relatives: A Comprehensive Guide to
Syngnathiformes. Chorleywood, UK: TMC Publishing.
Lauder, G. V. (1985). Aquatic feeding in lower vertebrates. In: Functional Vertebrate
123 Morphology (Hildebrand, M., Bramble, D. M., Liem, K. F., et al., eds), pp. 210–229.
Cambridge, MA.
Lele, S. R. & Keim, J. L. (2006). Weighted distributions and estimation of resource selection
probability functions. Management 87, 3021–3028. doi: 10.1890/0012-
9658(2006)87[3021:WDAEOR]2.0.CO;2
Lester, N. P., Shuter, B. J. & Abrams, P. A. (2004). Interpreting the von Bertalanffy model of
somatic growth in fishes: the cost of reproduction 1625–1631. doi:
10.1098/rspb.2004.2778
Leysen, H., Roos, G. & Adriaens, D. (2011). Morphological variation in head shape of
pipefishes and seahorses in relation to snout length and developmental growth.
Journal of Morphology 272, 1259–1270. doi: 10.1002/jmor.10982
Lima, S. L. (1998). Nonlethal effects in the ecology of predator-prey interactions. BioScience
48, 25–34. doi: 10.2307/1313225
Lima, S. L. & Dill, L. M. (1990). Behavioral decisions made under the risk of predation: a
review and prospectus. Canadian Journal of Zoology 68, 619–640. doi: 10.1139/z90-
092
Lin, Q., Gao, Y., Sheng, J., Chen, Q., Zhang, B. & Lu, J. (2007). The effects of food and the sum
of effective temperature on the embryonic development of the seahorse, Hippocampus
kuda Bleeker. Aquaculture 262, 481–492. doi: 10.1016/j.aquaculture.2006.11.011
Livingston, R. J. (1982). Trophic organization of fishes in a coastal seagrass system. Marine
Ecology Progress Series 7, 1–12. doi: 10.3354/meps007001
124 Livingston, R. J. (1984). Trophic responses of fishes to habitat variability in coastal seagrass
systems. Ecology 65, 1258–1275.
Lourie, S. A., Foster, S. J., Cooper, E. W. T. & Vincent, A. C. J. (2004). A Guide to the
Identification of Seahorses. Washington D.C.: University of British Columbia and World
Wildlife Fund. Retrieved from http://www.traffic.org/species-
reports/traffic_species_fish29.pdf
Lourie, S. A., Pollom, R. A. & Foster, S. J. (2016). A global revision of the Seahorses
Hippocampus Rafinesque 1810 (Actinopterygii: Syngnathiformes): Taxonomy and
biogeography with recommendations for further research. Zootaxa 4146, 1–66. doi:
10.11646/zootaxa.4146.1.1
Lourie, S. A., Pritchard, J. C., Casey, S. P., Truong, S. K., Hall, H. J. & Vincent, A. C. J. (1999). The
taxonomy of Vietnam’s exploited seahorses (family Syngnathidae). Biological Journal
of the Linnean Society 66, 231–256. doi: 10.1111/j.1095-8312.1999.tb01886.x
Lyons, D. O. & Dunne, J. J. (2004). Inter- and intra-gender analyses of feeding ecology of the
worm pipefish (Nerophis lumbriciformis). Journal of the Marine Biological Association of
the UK 84, 461–464. doi: 10.1017/S0025315404009452h
Magnhagen, C. (1991). Predation risk as a cost of reproduction. Trends in Ecology and
Evolution 6, 183–186. doi: 10.1016/0169-5347(91)90210-O
Main, K. L. (1987). Predator avoidance in seagrass meadows: prey behavior, microhabitat
selection, and cryptic coloration. Ecology 68, 170–180.
Manly, B. F. J., McDonald, L. L., Thomas, D. L., McDonald, T. L. & Erickson, W. P. (2002).
125 Resource Selection by Animals: Statistical Design and Analysis for Field Studies. doi:
10.1007/0-306-48151-0
Martin-Smith, K. M. & Vincent, A. C. J. (2005). Seahorse declines in the Derwent estuary,
Tasmania in the absence of fishing pressure. Biological Conservation 123, 533–545.
doi: 10.1016/j.biocon.2005.01.003
McArdle, B. H. & Anderson, M. J. (2001). Fitting multivariate models to community data: a
comment on distance-based redundancy models. Ecology 82, 290–297.
McCauley, E., Wilson, W. G. & de Roos, A. M. (1993). Dynamics of age-structured and
spatially structured predator-prey interactions: individual-based models and
population-level formulations. The American Naturalist 142, 412–442. doi:
10.1086/285547
McDonald, T. L. (2013). The point process use-availability or presence-only likelihood and
comments on analysis. Journal of Animal Ecology 82, 1174–1182. doi: 10.1111/1365-
2656.12132
McManus, M. A. & Woodson, C. B. (2012). Plankton distribution and ocean dispersal. The
Journal of Experimental Biology 215, 1008–1016. doi: 10.1242/jeb.059014
Menge, B. A. & Olson, A. M. (1990). Role of scale and environmental factors in regulation of
community structure. Trends in Ecology and Evolution 5.
Mercer, L. P. (1973). The comparative ecology of two species of pipefish (Syngnathidae) in the
York River, Virginia. The College of William and Mary in Virginia.
Milinski, M. (1993). Predation risk and feeding behaviour. In: Behaviour of Teleost Fishes
126 (Pitcher, T. J., ed), pp. 285–306. Dordrecht: Springer Netherlands.
Morgan, S. K. & Vincent, A. C. J. (2007). The ontogeny of habitat associations in the tropical
tiger tail seahorse Hippocampus comes Cantor, 1850. Journal of Fish Biology 71, 701–
724. doi: 10.1111/j.1095-8649.2007.01535.x
Morgan, S. K. & Vincent, A. C. J. (2013). Life-history reference points for management of an
exploited tropical seahorse. Marine and Freshwater Research 64, 185–200. doi:
10.1071/MF12171
Motta, P. J., Clifton, K. B., Hernandez, P., Eggold, B. T., Giordano, S. D., Wilcox, R. & Jenny, S.
(1995). Feeding relationships among nine species of seagrass fishes of Tampa Bay,
Florida. Bulletin of Marine Science 56, 185–200.
Muller, M. & Osse, J. W. M. (1984). Hydrodynamics of suction feeding in fish. Transactions of
the Zoological Society of London 37, 51–135. doi: 10.1111/j.1096-
3642.1984.tb00068.x
Nakamura, Y., Horinouchi, M., Nakai, T. & Sano, M. (2002). Food habits of fishes in a
seagrass bed on a fringing coral reef at Iriomote Island, southern Japan. Ichthyological
Research 50, 15–22. doi: 10.1007/s102280300002
Neutens, C., Adriaens, D., Christiaens, J., De Kegel, B., Dierick, M., Boistel, R. & Van
Hoorebeke, L. (2014). Grasping convergent evolution in syngnathids: A unique tale of
tails. Journal of Anatomy 224, 710–723. doi: 10.1111/joa.12181
Northrup, J. M., Hooten, M. B., Anderson, C. R. & Wittemyer, G. (2013). Practical guidance on
characterizing availability in resource selection functions under a use-availability
127 design. Ecology 94, 1456–1463. doi: 10.1890/12-1688.1
Norton, S. F. (1991). Capture success and diet of cottid fishes: the role of predator
morphology and attack kinematics. Ecology 72, 1807–1819.
Ocken, A. E. J. & Ritz, D. A. (1994). Prey capture techniques of the seahorse Hippocampus
abdominalis feeding on swarming prey. Zoology Department. University of Tasmania.
Öckinger, E., Franzén, M., Rundlöf, M. & Smith, H. G. (2009). Mobility-dependent effects on
species richness in fragmented landscapes. Basic and Applied Ecology 10, 573–578.
doi: 10.1016/j.baae.2008.12.002
Oliveira, F., Erzini, K. & Gonçalves, J. M. S. (2007). Feeding habits of the deep-snouted
pipefish Syngnathus typhle in a temperate coastal lagoon. Estuarine, Coastal and Shelf
Science 72, 337–347. doi: 10.1016/j.ecss.2006.11.003
Orav-Kotta, H. & Kotta, J. (2004). Food and habitat choice of the isopod Idotea baltica in the
northeastern Baltic Sea. Hydrobiologia 514, 79–85.
Orth, R. J. & Heck, K. L. (1980). Structural Components of eelgrass (Zostera marina)
meadows in the lower Chesapeake Bay - Fishes. Estuaries 3, 278–288.
Orth, R. J., Heck, K. L. & Tunbridge, D. J. (2002). Predation on seeds of the seagrass Posidonia
australis in Western Australia. Marine Ecology Progress Series 244, 81–88. doi:
10.3354/meps244081
Orth, R. J., Heck, K. L. & van Montfrans, J. (1984). Faunal communities in seagrass beds: a
review of the influence of plant structure and prey characteristics on predator-prey
relationships. Ecosystems 7, 339–350. doi: 10.2307/1351618
128 Pace, M. L., Cole, J. J., Carpenter, S. R. & Kitchell, J. F. (1999). Trophic cascades revealed in
diverse ecosystems. Trends in Ecology and Evolution 14, 483–488. doi:
10.1016/S0169-5347(99)01723-1
Paine, R. T. (1966). Food web complexity and species diversity. The American Naturalist
103, 91–93.
Palmer, M. A., Allan, D. & Butman, C. A. (1996). Dispersal as a regional process affecting the
local dynamics of marine and stream benthic invertebrates. Trends in Ecology and
Evolution 11, 322–326. doi: 10.1016/0169-5347(96)10038-0
Payne, M. F. & Rippingale, R. J. (2000). Rearing West Australian seahorse, Hippocampus
subelongatus, juveniles on copepod nauplii and enriched Artemia. Aquaculture 188,
353–361. doi: 10.1016/S0044-8486(00)00349-5
Perante, N. C., Pajaro, M. G., Meeuwig, J. J. & Vincent, A. C. J. (2002). Biology of a seahorse
species, Hippocampus comes in the central Philippines. Journal of Fish Biology 60, 821–
837. doi: 10.1111/j.1095-8649.2002.tb02412.x
Pimm, S. L. (1982). Food Webs. Dordrecht: Springer Netherlands.
Polis, G. A. & Strong, D. R. (1995). Food web complexity and community dynamics 147,
813–846. doi: 10.1086/285880
Poulos, D. E., Harasti, D., Gallen, C. & Booth, D. J. (2013). Biodiversity value of a
geographically restricted soft coral species within a temperate estuary. Aquatic
Conservation: Marine and Freshwater Ecosystems 23, 838–849. doi: 10.1002/aqc.2362
Prein, M. & Kunzmann, A. (1987). Structural organization of the gills of pipefish (Teleostei,
129 Syngnathidae). Zoomorphology 107, 161–168. Preisser, E. L., Bolnick, D. I. & Benard, M. F. (2005). Scared to death? The effects of
intimidation and consumption in predator–prey interactions. Ecology 86, 501–509.
doi: 10.1890/04-0719
Pyke, G. H., Pulliam, H. R. & Charnov, E. L. (1977). Optimal foraging: a selective review of
theory and tests. The Quarterly Review of Biology 52, 137–154.
Qin, G., Zhang, Y., Huang, L. & Lin, Q. (2014). Effects of water current on swimming
performance, ventilation frequency, and feeding behavior of young seahorses
(Hippocampus erectus). Journal of Experimental Marine Biology and Ecology 461, 337–
343. doi: 10.1016/j.jembe.2014.09.001
Revell, L. J., Harmon, L. J. & Collar, D. C. (2008). Phylogenetic signal, evolutionary process,
and rate. Systematic Biology 57, 591–601. doi: 10.1080/10635150802302427
Richardson, H. & Verbeek, N. A. M. (1986). Diet selection and optimization by northwestern
crows feeding on Japanese littleneck clams. Ecology 67, 1219–1226.
Ries, L., Fletcher, R. J., Battin, J. & Sisk, T. D. (2004). Ecological responses to habitat edges:
mechanisms, models, and variability explained. Annual Review of Ecology, Evolution
and Systematics 35, 491–522. doi: 10.1146/annurev.ecolsys.35.112202.130148
Ripley, J. L. & Foran, C. M. (2007). Influence of estuarine hypoxia on feeding and sound
production by two sympatric pipefish species (Syngnathidae). Marine Environmental
Research 63, 350–367. doi: 10.1016/j.marenvres.2006.10.003
Roberts, C. M. & Hawkins, J. P. (1999). Extinction risk in the sea. Trends in Ecology and
130 Evolution 14, 241–246. doi: 10.1016/S0169-5347(98)01584-5
Roos, G., Leysen, H., Van Wassenbergh, S., Herrel, A., Jacobs, P., Dierick, M., Aerts, P. &
Adriaens, D. (2009a). Linking morphology and motion: a test of a four-bar mechanism
in seahorses. Physiological and Biochemical Zoology: PBZ 82, 7–19. doi:
10.1086/589838
Roos, G., Van Wassenbergh, S., Herrel, A. & Aerts, P. (2009b). Kinematics of suction feeding
in the seahorse Hippocampus reidi. The Journal of Experimental Biology 212, 3490–8.
doi: 10.1242/jeb.033050
Roos, G., Van Wassenbergh, S., Aerts, P., Herrel, A. & Adriaens, D. (2011). Effects of snout
dimensions on the hydrodynamics of suction feeding in juvenile and adult seahorses.
Journal of Theoretical Biology 269, 307–317. doi: 10.1016/j.jtbi.2010.10.023
Roos, G., Van Wassenbergh, S., Herrel, A., Adriaens, D. & Aerts, P. (2010). Snout allometry in
seahorses: insights on optimisation of pivot feeding performance during ontogeny. The
Journal of Experimental Biology 213, 2184–93. doi: 10.1242/jeb.040972
Rosa, I. L., Oliveira, T. P. R., Castro, A. L. C., Moraes, L. E., Xavier, J. H. A., Nottingham, M. C.,
Dias, T. L. P., Bruto-Costa, L. V., Araújo, M. E., Birolo, A. B., Mai, A. C. G. & Monteiro-Neto,
C. (2007). Population characteristics, space use and habitat associations of the
seahorse Hippocampus reidi (Teleostei: Syngnathidade). Neotropical Ichthyology 5,
405–414. doi: 10.1590/S1679-62252007000300020
Rose, K. A. (2000). Why are quantitative relationships between environmental quality and
fish populations so elusive. Ecological Applications 10, 367–385.
131 Ruxton, G. D., Sherratt, T. N. & Speed, M. P. (2004). Avoiding Attack: The Evolutionary
Ecology of Crypsis, Warning Signals & Mimicry. Oxford, UK: Oxford University Press.
doi: 10.1093/acprof:oso/9780198528609.001.0001
Ryer, C. H. (1988). Pipefish foraging: effects of fish size, prey size and altered habitat
complexity. Marine Ecology Progress Series 48, 37–45. doi: 10.3354/meps048037
Ryer, C. H. & Boehlert, G. W. (1983). Feeding chronology, daily ration, and the effects of
temperature upon gastric evacuation in the pipefish, Syngnathus fuscus. Environmental
Biology of Fishes 9, 301–306. doi: 10.1007/BF00692379
Ryer, C. H. & Orth, R. J. (1987). Feeding ecology of the northern pipefish, Syngnathus fuscus,
in a seagrass community of the Lower Chesapeake Bay. Estuaries 10, 330–336. doi:
10.1007/BF02689864
Sakurai, I., Kaneta, T., Nakayama, T., Fukuda, H. & Kaneko, T. (2009). Food habits of fish
communities in a Sargassum confusum bed off the coast of Ishikari, Hokkaido, Japan.
Nippon Suisan Gakkaishi 75, 365–375.
Sancho, G., Petersen, C. W. & Lobel, P. S. (2000). Predator-prey relations at a spawning
aggregation site of coral reef fishes. Marine Ecology Progress Series 203, 275–288. doi:
10.3354/meps203275
Scharf, F. S., Juanes, F. & Rountree, R. A. (2000). Predator size - prey size relationships of
marine fish predators: interspecific variation and effects of ontogeny and body size on
trophic-niche breadth. Marine Ecology Progress Series 208, 229–248. doi:
10.3354/meps208229
132 Schoener, T. W. (1971). Theory of feeding strategies. Annual Review of Ecology and
Systematics 2, 369–404.
Schroeder, D. M. & Love, M. S. (2002). Recreational fishing and marine fish populations in
California. California Cooperative Oceanic Fisheries Investigations 43, 182–190.
Seber, G. A. F. (1986). A review of estimating animal abundance. Biometrics 42, 267–292.
doi: 10.2307/2531049
Sheng, J., Lin, Q., Chen, Q., Gao, Y., Shen, L. & Lu, J. (2006). Effects of food, temperature and
light intensity on the feeding behavior of three-spot juvenile seahorses, Hippocampus
trimaculatus Leach. Aquaculture 256, 596–607. doi:
10.1016/j.aquaculture.2006.02.026
Sih, A. (1984). The behavioral response race between predator and prey. The American
Naturalist 123, 143–150. doi: 10.1086/284193
Sih, A. (1992). Prey uncertainty and the balancing of antipredator and feeding needs. The
American Naturalist 139, 1052–1069. doi: 10.1086/285372
Sih, A., Crowley, P., McPeek, M., Petranka, J. & Strohmeier, K. (1985). Predation,
competition, and prey communities: a review of field experiments. Annual Review of
Ecological Systems 16, 269–311. doi: 10.1146/annurev.es.16.110185.001413
Sih, A. & Moore, R. D. (1990). Interacting effects of predator and prey behavior in
determining diets. In: Behavioural Mechanisms of Food Selection (Huges, R. N., ed), pp.
771–795. Berlin: Springer. doi: 10.1007/978-3-642-75118-9_37
Smith, T. M., Hindell, J. S., Jenkins, G. P., Connolly, R. M. & Keough, M. J. (2011a). Fine-scale
133 spatial and temporal variations in diets of the pipefish Stigmatopora nigra within
seagrass patches. Journal of Fish Biology 78, 1824–1832. doi: 10.1111/j.1095-
8649.2011.02977.x
Smith, T. M., Hindell, J. S., Jenkins, G. P., Connolly, R. M. & Keough, M. J. (2011b). Edge effects
in patchy seagrass landscapes: the role of predation in determining fish distribution.
Journal of Experimental Marine Biology and Ecology 399, 8–16. doi:
10.1016/j.jembe.2011.01.010
Steffan-Dewenter, I., Munzenberg, U., Burger, C., Thies, C. & Tscharntke, T. (2002). Scale-
dependent effects of landscape context on three polinator guilds. Ecology 83, 1421–
1432.
Steffe, A. S., Westoby, M. & Bell, J. D. (1989). Habitat selection and diet in two species of
pipefish from seagrass: sex differences. Marine Ecology Progress Series 55, 23–30. doi:
10.3354/meps055023
Stoner, A. W. (1980). The role of seagrass biomass in the organization of benthic
macrofaunal assemblages. Bulletin of Marine Science 30, 537–551.
Storero, L. P. & Gonzalez, R. A. (2008). Feeding habits of the seahorse Hippocampus
patagonicus in San Antonio Bay (Patagonia, Argentina). Journal of the Marine Biological
Association of the United Kingdom 88, 1503–1508. doi: 10.1017/S0025315408002506
Struthers, D. P., Danylchuk, A. J., Wilson, A. D. M. & Steven, J. (2015). Action cameras:
bringing aquatic and fisheries research into view. Fisheries 40, 502–512. doi:
10.1080/03632415.2015.1082472
134 Svensson, I. (1988). Reproductive costs in two sex-role reversed pipefish species
(Syngnathidae). The Journal of Animal Ecology 57, 929–942. doi: 10.2307/5102
Taskavak, E., Gurkan, S., Sever, T. M., Akalin, S. & Ozaydin, O. (2010). Gut contents and
feeding habits of the great pipefish, Syngnathus acus Linnaeus, 1758, in Izmir Bay
(Aegean Sea, Turkey). Zoology in the Middle East 50, 75–82. doi:
10.1080/09397140.2010.10638414
Teixeira, R. L. & Musick, J. A. (1995). Trophic ecology of two congeneric pipefishes
(Syngnathidae) of the lower York River, Virginia. Environmental Biology of Fishes 43,
295–309. doi: 10.1007/BF00005862
Teixeira, R. L. & Musick, J. A. (2001). Reproduction and food habits of the lined seahorse,
Hippocampus erectus (Teleostei: Syngnathidae) of Chesapeake Bay, Virginia. Revista
Brasileira de Biologia 61, 79–90. doi: 10.1590/S0034-71082001000100011
Thorne-Miller, B. (1999). The Living Ocean: Understanding and Protecting Marine
Biodiversity. Washington D.C.: Island Press.
Tipton, K. & Bell, S. S. (1988). Foraging patterns of two syngnathid fishes: importance of
harpacticoid copepods. Marine Ecology Progress Series 47, 31–13. doi:
10.3354/meps047031
Truong, S. K. & Nga, T. N. M. (1995). Reproduction of two species of seahorses,
Hippocampus histrix and H. trimaculatus, in Binh Thuan waters. Bao Cao Khoa Hoc 27,
68.
Uncumusaoglu, A. A., Gurkan, S. & Taskavak, E. (2017). Seasonally prey composition of
135 Broad-nosed pipefish, Syngnathus typhle, distributed in the coasts of Aegean Sea,
Turkey 26, 2673–2677.
Valladares, S., Soto, D. X. & Planas, M. (2017). Dietary composition of endangered seahorses
determined by stable isotope analysis. Marine and Freshwater Research 68, 831–839.
doi: http://dx.doi.org/10.1071/MF16013
Van Den Wollenberg, A. N. (1977). Redundancy analysis an alternative for canonical
correlation analysis. Psychometrika 42, 207–219.
Van Wassenbergh, S., Dries, B. & Herrel, A. (2014). New insights into muscle function
during pivot feeding in seahorses. PLoS ONE 9. doi: 10.1371/journal.pone.0109068
Van Wassenbergh, S., Leysen, H., Adriaens, D. & Aerts, P. (2013). Mechanics of snout
expansion in suction-feeding seahorses: musculoskeletal force transmission. The
Journal of Experimental Biology 216, 407–17. doi: 10.1242/jeb.074658
Van Wassenbergh, S., Roos, G., Aerts, P., Herrel, A. & Adriaens, D. (2011a). Why the long
face? A comparative study of feeding kinematics of two pipefishes with different snout
lengths. Journal of Fish Biology 78, 1786–1798. doi: 10.1111/j.1095-
8649.2011.02991.x
Van Wassenbergh, S., Roos, G. & Ferry, L. (2011b). An adaptive explanation for the horse-
like shape of seahorses. Nature 2, 164. doi: 10.1038/ncomms1168
Van Wassenbergh, S., Roos, G., Genbrugge, A., Leysen, H., Aerts, P. & Herrel, A. (2009).
Suction is kid’s play: extremely fast suction in newborn seahorses. Biology Letters 5.
doi: 10.1098/rsbl.2008.0765
136 Van Wassenbergh, S., Strother, J. A., Flammang, B. E., Ferry-Graham, L. A. & Aerts, P. (2008).
Extremely fast prey capture in pipefish is powered by elastic recoil. Journal of the
Royal Society Interface 5, 285–296. doi: 10.1098/rsif.2007.1124
Vander Zanden, M. J. & Fetzer, W. W. (2007). Global patterns of aquatic food chain length.
Oikos 116, 1378–1388. doi: 10.1111/j.2007.0030-1299.16036.x
Vincent, A. C. J. (1995). A role for daily greetings in maintaining seahorse pair bonds.
Animal Behaviour 49, 258–260. doi: 10.1016/0003-3472(95)80178-2
Vincent, A. C. J., Ahnesjö, I., Berglund, A. & Rosenqvist, G. (1992). Pipefishes and seahorses:
are they all sex role reversed? Trends in Ecology and Evolution 7, 237–241. doi:
10.1016/0169-5347(92)90052-D
Vincent, A. C. J., Evans, K. L. & Marsden, A. D. (2005). Home range behaviour of the
monogamous Australian seahorse Hippocampus whitei. Environmental Biology of
Fishes 72, 1–12. doi: DOI 10.1007/s10641-004-4192-7
Vincent, A. C. J., Foster, S. J. & Koldewey, H. J. (2011). Conservation and management of
seahorses and other Syngnathidae. Journal of Fish Biology 78, 1681–1724. doi:
10.1111/j.1095-8649.2011.03003.x
Vincent, A. C. J. & Giles, B. G. (2003). Correlates of reproductive success in a wild population
of Hippocampus whitei. Journal of Fish Biology 63, 344–355. doi: 10.1046/j.1095-
8649.2003.00154.x
Vincent, A. C. J. & Sadler, L. M. (1995). Faithful pair bonds in wild seahorses, Hippocampus
whitei. Animal Behaviour 50, 1557–1569. doi: 10.1016/0003-3472(95)80011-5
137 Warrant, E. J. & Locket, N. A. (2004). Vision in the deep sea. Biological Reviews 79, 671–712.
doi: 10.1017/S1464793103006420
Werner, E. E. & Peacor, S. D. (2003). A review of trait-mediated indirect interactions in
ecological communities. Ecology 84, 1083–1100. doi: 10.1890/0012-
9658(2003)084[1083:AROTII]2.0.CO;2
West, K., Cohen, A. & Baron, M. (1991). Morphology and behavior of crabs and gastropods
from Lake Tanganyika, Africa: implications for lacustrine predator-prey evolution.
Evolution 45, 589–607.
West, R. J. (1990). Depth-related structural and morphological variations in an Australian
Posidonia seagrass bed. Aquatic Botany 36, 153–166. doi: 10.1016/0304-
3770(90)90079-Z
White, G. C. (1996). NOREMARK: population estimate from mark-resighting surveys.
Wildlife Society Bulletin 24, 50–52.
Whitehead, P., Bauchot, M., Hureau, J., Nielsen, J. & Tortonese, E. (1984). Fishes of the North-
Eastern Atlantic and the Mediterranean (Vol. 2). Paris: UNESCO.
Whittaker, R. H., Levin, S. A. & Root, R. B. (1973). Niche, habitat, and ecotope. The American
Naturalist 107, 321–338. doi: 10.1086/282837
Willis, T. J. & Anderson, M. J. (2003). Structure of cryptic reef fish assemblages:
relationships with habitat characteristics and predator density. Marine Ecology
Progress Series 257, 209–221. doi: 10.3354/meps257209
Woods, C. M. C. (2002). Natural diet of the seahorse Hippocampus abdominalis. New Zealand
138 Journal of Marine and Freshwater Research 36, 655–660. doi:
10.1080/00288330.2002.9517121
Woods, C. M. C. (2005). Evaluation of VI-alpha and PIT-tagging of the seahorse
Hippocampus abdominalis. Aquaculture International 13, 175–186. doi:
10.1007/s10499-004-1087-8
Woods, C. M. C. & Martin-Smith, K. M. (2004). Visible implant fluorescent elastomer tagging
of the big-bellied seahorse, Hippocampus abdominalis. Fisheries Research 66, 363–371.
doi: 10.1016/S0165-7836(03)00183-8
Wootton, R. (2012). Ecology of Teleost Fishes. Dordrecht: Springer Netherlands. doi:
10.1007/978-94-009-0829-1
Yasué, M., Nellas, A. & Vincent, A. C. J. (2012). Seahorses helped drive creation of marine
protected areas, so what did these protected areas do for the seahorses?
Environmental Conservation 39, 183–193. doi: 10.1017/S0376892911000622
Yip, M. Y., Lim, A. C. O., Chong, V. C., Lawson, J. M. & Foster, S. J. (2015). Food and feeding
habits of the seahorses Hippocampus spinosissimus and Hippocampus trimaculatus
(Malaysia). Journal of the Marine Biological Association of the United Kingdom 95,
1033–1040. doi: 10.1017/S0025315414001660
Zamzow, J. P., Amsler, C. D., Mcclintock, J. B. & Baker, B. J. (2010). Habitat choice and
predator avoidance by Antarctic amphipods: the roles of algal chemistry and
morphology. Marine Ecology Progress Series 400, 155–163. doi: 10.3354/meps08399
Zuur, A. F., Ieno, E. N. & Elphick, C. S. (2010). A protocol for data exploration to avoid
139 common statistical problems. Methods in Ecology and Evolution 1, 3–14. doi:
10.1111/j.2041-210X.2009.00001.x
140 Appendices
Appendix A: The determination of seahorse sex, maturity, and reproductive status.
Adult males were identified by the presence of a brood pouch, and older juvenile males based on a dark oval region where a brood pouch was developing. Individuals without a brood pouch or a dark oval region were considered to have 'unknown sex' and
'unknown reproductive status' until height at physical maturity (HTM) was determined (see below). The pregnancy states of adult males were assigned as follows: (1) pouch tight and empty - no recent pregnancy, (2) pouch round - currently pregnant, (3) pouch rotund – male about to release young, and (4) pouch flaccid – male released young recently ( Vincent
& Sadler, 1995; Perante et al., 2002; Harasti et al., 2012). Juvenile males were considered to be physically immature, with a pregnancy status of 0. Although having a fully developed brood pouch suggests physical maturity (Morgan & Vincent, 2013), only males that displayed evidence of engaging in reproduction (pregnancy states 2-4) were considered reproductively active (Harasti et al., 2012; Morgan & Vincent, 2013). Since female maturity can only be determined by killing individuals and dissecting their ovaries (Foster &
Vincent, 2004), females were necessarily assumed to mature (both physically and reproductively) at the same height as males (see next paragraph).
The height of physical and reproductive maturity for H. whitei across Port Stephens was determined using linear models fit to the height of males, with binary response variables. To model HTM, males were considered either (0) physically immature juveniles
(state 0), or (1) physically mature adults (states 1-4). To model the height of reproductive maturity (HTR), males were considered either (0) reproductively inactive (states 0-1), or
(1) reproductively active (states 2-4). HTM and HTR were determined by the 50% inflection
141 points in these models (King, 2007). Mean ± SE HTM was 72.3 ± 3.7 mm, and HTR was 91.2 ±
3.7 mm. In subsequent analyses, all seahorses smaller than the mean HTM were considered juveniles, and those larger were considered adults. Adult seahorses without a pouch were considered to be females. In the same way, mean HTR was the cut-off for reproductive maturity. HTM and HTR models were run in R 3.3.1 (www.r-project.org).
142 Appendix B: Tables to support methods and results in Chapter 2
Table B1 Seagrass and substrate summary statistics for the seven plots. Mean and standard error of the mean (SE) calculated from all quadrats at a specific plot, regardless of strata. Using a gridded-quadrat, the percent cover of P. australis and substrate was estimated by counting the number of grid intersections each substrate type accounted for. Substrate is the percentage cover that is not sand or rock. SHG = Seahorse Gardens.
Depth (m) Seagrass density (shoots 0.25m-2) Seagrass height (cm) % P. australis % Substrate Plot Mean SE Mean SE Mean SE Mean SE Mean SE Shoal Bay 0.3 0.1 68 25 32 2 74 8 88 4 Little Beach 1.0 0.2 41 7 38 2 70 9 80 5 Fly Point 1.1 0.3 50 6 48 3 80 5 90 3 SHG 1 1.7 0.3 24 4 55 6 61 9 70 10 SHG 2 1.7 0.2 26 4 48 7 67 8 79 4 Pipeline 2.0 0.1 26 6 41 2 40 10 86 4 Dutchies 1.5 0.2 38 6 41 2 84 4 90 3
143 Table B2 Summary of the model-averaged statistics for the top models predicting height within Little Beach for (a) all seahorses, (b) all reproductively active (RA) seahorses, (c) females, (d) RA females, (e) males, and (f) RA males. LL = log-likelihood, AICc = corrected
Akaike information criterion, ΔAICc = difference in model AICc with that of the top model, wi
= Akaike weight, df = number of model parameters including intercepts and residuals. The following abbreviations have been made: DPTH = depth, DENS = seagrass density, HGHT = seagrass height, TPT = prey types, TPI = prey density, FLNG = fouling, PRED = total predators, and TPRED = types of predators.
Model and parameters included LL AICc ΔAICc wi df (a) Height - All Seahorses DPTH, HGHT, TPT, PRED, TPRED (global model) -1347.57 2713.71 0.00 0.31 9 DPTH, TPT, PRED, TPRED -1349.37 2715.19 1.48 0.15 8 DPTH, HGHT, TPT, PRED -1349.43 2715.31 1.60 0.14 8 DPTH, HGHT, PRED, TPRED -1349.44 2715.34 1.63 0.14 8 DPTH, HGHT, PRED -1351.20 2716.75 3.04 0.07 7 DPTH, TPT, PRED -1351.21 2716.77 3.06 0.07 7 DPTH, PRED, TPRED -1351.24 2716.83 3.12 0.07 7 DPTH, HGHT, TPT, TPRED -1350.24 2716.93 3.22 0.06 8 (b) Height - RA Seahorses DPTH, HGHT, TPT, PRED, TPRED (global model) -1128.95 2276.51 0.00 0.27 9 DPTH, HGHT, PRED, TPRED -1130.50 2277.49 0.97 0.17 8 DPTH, HGHT, TPT, PRED -1130.53 2277.54 1.03 0.16 8 DPTH, TPT, PRED, TPRED -1130.84 2278.16 1.65 0.12 8 DPTH, HGHT, PRED -1132.02 2278.42 1.91 0.11 7 DPTH, PRED, TPRED -1132.39 2279.15 2.64 0.07 7 DPTH, TPT, PRED -1132.76 2279.9 3.39 0.05 7 DPTH, HGHT, TPT, TPRED -1131.76 2280.01 3.49 0.05 8 (c) Height - Females DPTH, HGHT, PRED, TPRED (global model) -630.13 1277.18 0.00 0.46 8 DPTH, HGHT, PRED -631.94 1278.57 1.40 0.23 7 DPTH, PRED, TPRED -632.08 1278.86 1.68 0.20 7 DPTH, PRED -633.78 1280.08 2.90 0.11 6 (d) Height - RA Females DPTH, HGHT, TPT, PRED, TPRED (global model) -576.81 1172.81 0.00 0.48 9 DPTH, TPT, PRED, TPRED -578.83 1174.61 1.80 0.20 8
144 DPTH, HGHT, TPT, PRED -578.94 1174.84 2.03 0.18 8 DPTH, HGHT, PRED, TPRED -579.82 1176.59 3.78 0.07 8 DPTH, HGHT, TPT, TPRED -579.86 1176.66 3.85 0.07 8 (e) Height - Males DPTH, HGHT, TPT, PRED, TPRED (global model) -557.14 1133.59 0.00 0.31 9 DPTH, HGHT, TPT, PRED -559.09 1135.22 1.63 0.14 8 DPTH, TPT, PRED, TPRED -559.21 1135.46 1.87 0.12 8 DPTH, HGHT, TPT, TPRED -559.29 1135.62 2.03 0.11 8 DPTH, HGHT, PRED, TPRED -559.45 1135.94 2.36 0.10 8 DPTH, TPT, PRED -560.95 1136.71 3.12 0.07 7 DPTH, HGHT, TPT -560.99 1136.78 3.19 0.06 7 DPTH, TPT, TPRED -561.23 1137.26 3.67 0.05 7 HGHT, TPT, PRED, TPRED -560.28 1137.59 4.00 0.04 8 (f) Height - RA Males DPTH, HGHT, TPT, PRED, TPRED (global model) -523.69 1066.71 0.00 0.35 9 DPTH, HGHT, TPT, PRED -525.55 1068.16 1.45 0.17 8 DPTH, TPT, PRED, TPRED -525.72 1068.49 1.78 0.14 8 DPTH, HGHT, TPT, TPRED -525.87 1068.81 2.10 0.12 8 DPTH, TPT, PRED -527.33 1069.48 2.77 0.09 7 DPTH, HGHT, PRED, TPRED -526.43 1069.92 3.21 0.07 8 DPTH, HGHT, TPT -527.86 1070.53 3.82 0.05 7
145 Appendix C: Tables to support methods and results in Chapter 3
Table C1 Summary of morphological characteristics for syngnathids used in this study, and the references used to generate the data. a Neutens et al. (2014). b Data from FishBase (Froese & Pauly, 2017) with the following exceptions: ^ Storero & Gonzalez, (2008), * Dawson (1982). If
mentioned, specimens were measured at the Australian Museum in Sydney, Australia.
Body form and tail traits Head length Snout length Snout depth a (HL) (SnL) (SnD) References Caudal fin: Body Body Caud Prehens area Max StL Ave. StL:H Ave. HL:Sn Ave. SnL:S For HL, SnL, SnD For caudal fin: body Genus Species form al fin ile tail ratio (mm) b (mm) L (mm) L (mm) nD measurements area measurements Acentron tentacul pipefi ura ata sh no yes 0.000 63 9.9 6.4 3.1 3.2 1.2 2.5 Dawson (1985) - Anarchop pipefi terus criniger sh yes no 0.025 100 9.1 11.0 2.7 3.4 1.6 1.7 Dawson (1982) Whitehead et al. (1984) Apterygo epinnula pipefi mpus tus sh yes no - 30 2.6 11.4 0.7 3.9 0.6 1.2 Dawson (1985) - pipefi Bryx dunckeri sh yes no - 75 6.9 10.9 2.1 3.3 1.2 1.7 Dawson (1982) - Choeroic brachys pipefi hthys oma sh yes no - 70 14.0 5.0 6.7 2.1 1.4 4.8 Dawson (1985) - Choeroic pipefi hthys sculptus sh yes no - 85 14.2 6.0 6.3 2.3 2.2 2.9 Dawson (1985) - Corythoic amplexu pipefi hthys s sh yes no - 100 9.8 10.2 4.0 2.5 0.9 4.6 Dawson (1985) - Corythoic intestina pipefi hthys lis sh yes no - 160 18.0 8.9 9.0 2.0 1.3 6.9 Dawson (1985) - Cosmoca pipefi mpus elucens sh yes no - 150 19.7 7.6 9.9 2.0 1.5 6.4 Dawson (1982) - Doryrha excisus pipefi mphus excisus sh yes no 0.095 70 15.9 4.4 7.2 2.2 1.3 5.5 Dawson (1985) Dawson (1985) Doryrha pipefi mphus janssi sh yes no - 140 29.8 4.7 18.1 1.7 1.8 10.1 Dawson (1985) - Dunckero pipefi campus baldwini sh yes no - 140 28.3 5.0 17.7 1.6 1.6 11.0 Dawson (1985) - Dunckero chapma pipefi campus ni sh yes no - 85 22.7 3.8 13.3 1.7 1.7 8.1 Dawson (1985) - Dunckero dactylio pipefi campus phorus sh yes no 0.054 190 45.8 4.2 28.6 1.6 1.8 15.7 Dawson (1985) Dawson (1985) 146 Dunckero pessulife pipefi campus rus sh yes no - 160 42.1 3.8 28.1 1.5 2.7 10.5 Dawson (1985) - Festucale pipefi x cinctus sh yes no - 130 14.9 8.7 7.0 2.2 1.6 4.5 Dawson (1985) - Festucale pipefi x scalaris sh yes no - 180 19.5 9.3 8.5 2.3 2.4 3.6 Dawson (1985) - Filicamp pipefi us tigris sh yes no 0.024 296 29.6 10.0 12.9 2.3 2.3 5.6 Dawson (1985) Dawson (1985) Halicamp pipefi us brocki sh yes no - 120 11.5 10.5 4.3 2.7 1.2 3.5 Dawson (1985) - Halicamp pipefi us dunckeri sh yes no - 150 12.3 12.2 3.3 3.7 1.4 2.5 Dawson (1985) - Halicamp macrorh pipefi us ynchus sh yes no - 180 36.7 4.9 22.3 1.7 1.8 12.6 Dawson (1985) - Halicamp pipefi us nitidus sh yes no - 73 7.7 9.5 2.3 3.3 1.0 2.3 Dawson (1985) - nocturn pipefi Heraldia a sh yes no - 92 18.4 5.0 7.8 2.4 2.4 3.3 Dawson (1985) - Hippichth heptago pipefi ys nus sh yes no - 150 14.1 10.7 6.0 2.4 1.7 3.5 Dawson (1985) - Hippichth penicillu pipefi ys s sh yes no - 180 26.5 6.8 14.3 1.9 2.6 5.6 Dawson (1985) - Hippichth cyanospi pipefi Measured from ys los sh yes no 0.038 160 18.7 8.6 8.7 2.2 2.5 3.5 specimens (N = 22) Kuiter (2000) Hippoca abdomin seaho Lourie, mpus alis rse no yes 0.000 350 60.2 5.8 23.0 2.6 5.9 3.9 unpublished data - Hippoca brevicep seaho Lourie, mpus s rse no yes 0.000 150 31.1 4.8 10.0 3.1 3.7 2.7 unpublished data - Hippoca coronat seaho Lourie, mpus us rse no yes 0.000 108 21.3 5.1 8.5 2.5 2.5 3.4 unpublished data - Hippoca seaho Lourie, mpus erectus rse no yes 0.000 190 40.4 4.7 14.8 2.7 5.0 2.9 unpublished data - Hippoca guttulat seaho Lourie, mpus us rse no yes 0.000 215 43.7 4.9 16.9 2.6 4.8 3.5 unpublished data - Hippoca hippoca seaho Lourie, mpus mpus rse no yes 0.000 150 27.5 5.5 9.2 3.0 3.7 2.5 unpublished data - Hippoca mohnike seaho mpus i rse no yes 0.000 80 14.1 5.7 3.8 3.7 2.3 1.6 Lourie et al. (1999) - Hippoca patagon seaho Lourie, mpus icus rse no yes 0.000 162^ 34.6 4.7 11.3 3.1 4.2 2.7 unpublished data - Hippoca seaho Lourie, mpus reidi rse no yes 0.000 175 40.2 4.4 18.4 2.2 4.5 4.1 unpublished data - Hippoca spinosiss seaho mpus imus rse no yes 0.000 172 33.9 5.1 15.1 2.2 3.7 4.1 Lourie et al. (1999) - Hippoca subelon seaho no yes 0.000 200 47.5 4.2 22.2 2.1 4.0 5.5 Lourie, - 147 mpus gatus rse unpublished data Hippoca trimacul seaho mpus atus rse no yes 0.000 220 40.5 5.4 18.0 2.2 3.7 4.9 Lourie et al. (1999) - Hippoca seaho Lourie, mpus whitei rse no yes 0.000 130 32.8 4.0 14.5 2.3 3.0 4.9 unpublished data - Hippoca seaho Lourie, mpus zosterae rse no yes 0.000 50 13.9 3.6 3.2 4.3 1.6 2.0 unpublished data - Hippoca seaho mpus kuda rse no yes 0.000 140 26.9 5.2 11.6 2.3 3.1 3.8 Lourie et al. (1999) - Histioga pipefi mphelus briggsii sh yes no - 225 24.9 9.1 11.3 2.2 5.1 2.2 Dawson (1985) - Histioga pipefi mphelus cristatus sh yes no 0.055 265 29.3 9.1 13.3 2.2 6.0 2.2 Dawson (1985) Kuiter (2000) Hypselog rostratu pipefi nathus s sh yes no - 305 50.4 6.1 34.8 1.5 3.6 9.7 Dawson (1985) - pipefi Kaupus costatus sh yes no - 129 14.7 8.8 5.2 2.8 2.0 2.7 Dawson (1985) - Leptoicht fistulari pipefi hys us sh yes no - 630 132.6 4.8 98.2 1.4 5.2 19.0 Dawson (1985) - Lissocam pipefi pus caudalis sh yes yes 0.009 100 7.5 13.4 2.3 3.2 1.7 1.4 Dawson (1985) Dawson (1985) Lissocam pipefi pus runa sh yes no - 94 7.4 12.8 2.4 3.1 1.5 1.6 Dawson (1985) - Maroubr perserra pipefi a ta sh yes no - 72 11.7 6.2 5.4 2.2 1.1 5.1 Dawson (1985) - Microgna anderso pipefi thus nii sh yes no - 85 9.2 9.3 3.0 3.1 1.4 2.2 Dawson (1985) - Microgna pipefi thus crinitus sh yes no - 150 14.6 10.3 4.7 3.1 2.1 2.2 Dawson (1982) - Microgna pipefi thus natans sh yes no - 60 8.2 7.4 3.5 2.4 1.0 3.5 Dawson (1985) - Microphi pipefi s deocata sh yes no - 150 20.1 7.5 11.8 1.7 1.6 7.2 Dawson (1985) - Microphi ocellatu pipefi s s sh yes no - 125 13.6 9.2 5.9 2.3 1.4 4.2 Dawson (1985) - Mitoticht meracul pipefi hys us sh yes no 0.009 222 28.1 7.9 14.8 1.9 3.1 4.8 Dawson (1985) Web photo Mitoticht semistri pipefi hys atus sh yes no 0.007 268 38.6 7.0 22.0 1.8 2.6 8.5 Dawson (1985) Web sketch Nannoca pipefi mpus pictus sh yes no - 100 8.2 12.2 2.7 3.0 1.6 1.7 Dawson (1985) - lumbrici pipefi Measured from Nerophis formis sh no yes 0.000 170 14.0 12.1 5.1 2.7 2.3 2.2 specimens (N = 9) - pipefi Measured from Nerophis ophidion sh no yes 0.000 300 17.9 16.8 7.3 2.5 2.5 3.0 specimens (N = 6) - 148 pipefi Oostethus jagorii sh yes no - 160 23.2 6.9 11.0 2.1 1.9 5.7 Dawson (1985) - Phoxoca diacanth pipefi mpus us sh yes no - 87 12.2 7.2 5.1 2.4 1.9 2.7 Dawson (1985) - Phycodur seadr us eques agon no no - 350 76.1 4.6 46.1 1.7 7.0 6.6 Dawson (1985) - Phyllopte taeniola seadr ryx tus agon no no 0.000 460 96.8 4.8 62.5 1.6 6.1 10.2 Dawson (1985) - curtirost pipefi Pugnaso ris sh yes no 0.007 182 17.1 10.7 6.2 2.8 2.4 2.6 Dawson (1985) Dawson (1985) Siokunich nigrolin pipefi thys eatus sh yes no - 80 5.1 15.6 1.4 3.7 1.3 1.1 Dawson (1985) - Solegnat hardwic pipefi hus kii sh no yes 0.000 400 56.3 7.1 33.1 1.7 4.1 8.1 Dawson (1985) - Solegnat spinosiss pipefi hus imus sh no yes 0.000 490 79.7 6.2 46.9 1.7 6.4 7.4 Dawson (1985) - Stigmato pipefi Measured from pora argus sh no yes 0.000 254 42.4 6.0 28.1 1.5 2.0 14.1 specimens (N = 53) - Stigmato pipefi Measured from pora nigra sh no yes 0.000 162 26.4 6.1 16.5 1.6 1.4 12.0 specimens (N = 54) - Stipecam pipefi pus cristatus sh yes no - 220 16.2 13.6 4.8 3.4 3.3 1.5 Dawson (1985) - Syngnath biaculea pipefi Measured from oides tus sh no yes 0.000 290 55.0 5.3 33.4 1.6 5.1 6.5 specimens (N = 12) - Syngnath pipefi Measured from us abaster sh yes no 0.026 210 29.5 7.1 14.9 2.0 3.3 4.5 specimens (N = 4) Web photo Syngnath pipefi us acus sh yes no 0.040 500 61.3 8.2 31.5 2.0 5.2 6.0 Dawson (1985) Dawson (1985) Syngnath californi pipefi us ensis sh yes no - 500 66.2 7.6 37.8 1.8 4.9 7.7 Dawson (1982) - Syngnath pipefi us floridae sh yes no 0.023 250 38.5 6.5 22.6 1.7 3.1 7.2 Dawson (1982) Web photo Syngnath pipefi us folletti sh yes no 0.029 200.5* 21.2 9.5 10.9 2.0 1.8 6.2 Dawson (1982) Web sketch Syngnath pipefi us fuscus sh yes no 0.043 330 39.8 8.3 18.9 2.1 3.7 5.1 Dawson (1982) Web photo Syngnath louisian pipefi us ae sh yes no 0.034 380 54.3 7.0 31.9 1.7 3.1 10.2 Dawson (1982) Web photo Syngnath pipefi us schlegeli sh yes no 0.039 300 36.6 8.2 20.3 1.8 2.4 8.5 Dawson (1985) Web photo Syngnath pipefi us scovelli sh yes no 0.043 183 24.1 7.6 10.5 2.3 2.8 3.7 Dawson (1982) Web photo Syngnath taeniono pipefi Measured from us tus sh yes no 0.042 190 26.7 7.1 13.0 2.1 2.2 5.9 photo (N = 1) Web photo Syngnath typhle pipefi yes no 0.032 350 59.3 5.9 34.4 1.7 6.3 5.4 Measured from Web photo 149 us sh specimens (N = 9) Trachyrh bicoarct pipefi amphus atus sh yes no - 400 34.9 11.5 20.0 1.8 2.4 8.2 Dawson (1985) - Urocamp cariniro pipefi us stris sh yes no 0.001 100 8.8 11.4 2.7 3.2 1.8 1.6 Dawson (1985) Dawson (1985) Vanacam margari pipefi pus tifer sh yes no - 159 19.2 8.3 9.3 2.1 1.5 6.2 Dawson (1985) - Vanacam pipefi Measured from pus phillipi sh yes no 0.006 184 22.4 8.2 10.1 2.2 2.4 4.2 specimens (N = 50) Web photo Vanacam pipefi pus vercoi sh yes no - 105 10.4 10.1 3.9 2.7 1.3 3.0 Dawson (1985) - Vanacam poecilol pipefi pus aemus sh yes no 0.007 261 35.5 7.4 18.7 1.9 2.4 7.7 Dawson (1985) Web photo
150
Table C2 Summary of syngnathid feeding kinematics in the literature. Time = 0 was calculated as being one frame before the onset of hyoid movement for all studies unless species name is denoted by ^, which indicates time = 0 was calculated as being one frame before onset of head rotation. *Measurements made on one individual.
Doryrhamphus Doryrhamphus Entelurus Hippocampus Hippocampu Species dactyliophorus melanopleura aequoreus erectus^ s reidi Hippocampus reidi Age 0-3 days No. of repetitions 20 20 25 14 Individuals 2 2 4 5 Approac h Mean approach speed (successful strike; mm/s) Mean approach speed (unsuccessful strike; mm/s) Onset of activity Time to start of epaxial muscle activity (ms) Time to start of hyoid rotation (ms) 0.5 Time to start of head rotation (ms) 0.64 Time to start of mouth opening (ms) 0.75 Intial prey orientation Initial prey distance (mm) 2.46 1.835 7.8* 6.71 Mean initial prey distance (successful strike; mm) Mean initial prey distance (unsuccessful strike; mm) Initial prey angle (deg) 117.425 113.2 Hyoid rotation Total hyoid rotation (deg) 68.45 96 Total hyoid rotation time (ms) 4.7 17.29 4.5 Max. hyoid rotation velocity (deg/s) 2985
151 Head rotation Total head rotation (deg) 9.65 9.8 29.1 31.1 42 Total head rotation time (ms) 6.65 6.5 6.5 18.46 2.5 Mean head velocity (deg/s) Max. head velocity (deg/s) 4250 4700 13880 Prey distance after head rotation (mm) 1.33 0.595 Prey angle after head rotation (deg) 177.35 160.55 Snout and mouth movement Total snout movement (mm) 2.45 1.685 Total snout rotation (deg) Total snout movement time (ms) 9.75 9.35 Max. linear snout velocity (m/s) 0.885 0.705 Max. gape opening (mm) 2.7* 2.7 Time to max. mouth opening (ms) 4.9 3.5 Body rotation Total body rotation (deg) 3.75 3.65 Total body rotation time (ms) 4.175 4.45 Max. body velocity (deg/s) 2000 1925 Prey capture To tal prey capture time (ms) 5.5 3.25 ~5 ms 5.8 5.5 Max. prey velocity (m/s) 0.195 0.13 0.27 Time to max. prey velocity (ms) 4 2.125 5.1
Van Wassenbergh et Van Wassenbergh et Muller & Osse Bergert & Roos et al. Van Wassenbergh et Reference al. (2011a) al. (2011a) (1984) Wainwright (1997) (2009a) al. (2009)
152 Table C2 (Continued)
Species Hippocampus reidi Hippocampus reidi Hippocampus zostera Age <1 week 1 week 2 weeks 3 weeks Adult No. of repetitions 10 13 13 14 12 10 15 Individuals 3 2 6 Approach Mean approach speed (successful strike; mm/s) 8.4 Mean approach speed (unsuccessful strike; mm/s) 14.1 Onset of activity Time to start of epaxial muscle activity (ms) Time to start of hyoid rotation (ms) Time to start of head rotation (ms) 0.4 Time to start of mouth opening (ms) 1.6 Intial prey orientation Initial prey distance (mm) Mean initial prey distance (successful strike; mm) 0.89 Mean initial prey distance (unsuccessful strike; mm) 1.12 Initial prey angle (deg) Hyoid rotation Total hyoid rotation (deg) 116 Total hyoid rotation time (ms) 20.25 Max. hyoid rotation velocity (deg/s) Head rotation Total head rotation (deg) 44 43 37.7 33.3 25 26.3 Total head rotation time (ms) 3.3 3.11 3.36 3.78 6.4 5.45 Mean head velocity (deg/s) Max. head velocity (deg/s) 32500 33000 26500 21900 15300 Prey distance after head rotation (mm) Prey angle after head rotation (deg) 124.3 Snout and mouth movement Total snout movement (mm) Total snout rotation (deg)
153 Total snout movement time (ms) Max. linear snout velocity (m/s) Max. gape opening (mm) 4.45 Time to max. mouth opening (ms) 3.6 Body rotation Total body rotation (deg) Total body rotation time (ms) Max. body velocity (deg/s) Prey capture Total prey capture time (ms) 5.75 <1 ms Max. prey velocity (m/s) Time to max. prey velocity (ms)
Reference Roos et al. (2010) Roos et al. (2009b) Gemmell et al. (2013)
154 Table C2 (Continued)
Syngnathus Syngnathus Species Sentriscus scutatus Syngnathus acus Syngnathus floridae^ leptorhynchus leptorhynchus^ Juven Age Adult ile No. of repetitions 3 2 1 7 19 27 Individuals 1 1 1 3 2 9 Approac h Mean approach speed (successful strike; mm/s) Mean approach speed (unsuccessful strike; mm/s) Onset of activity Time to start of epaxial muscle activity (ms) 271 Time to start of hyoid rotation (ms) Time to start of head rotation (ms) Time to start of mouth opening (ms) Intial prey orientation Initial prey distance (mm) 9.3* 7.9 Mean initial prey distance (successful strike; mm) Mean initial prey distance (unsuccessful strike; mm) Initial prey angle (deg) Hyoid r otation Total hyoid rotation (deg) ~128 Total hyoid rotation time (ms) ~8.8 ms 6.1 16 Max. hyoid rotation velocity (deg/s) Head rotation Total head rotation (deg) ~9.3 ~30.5 29.2 19.2 19.2 Total head rotation time (ms) ~8 ~25.5 7.5 5.1 17.4 Mean head velocity (deg/s) 2005 Max. head velocity (deg/s) 4010 8300 Prey distance after head rotation (mm)
155 Prey angle after head rotation (deg) Snout and mouth movement Total snout movement (mm) Total snout rotation (deg) 10.7 Total snout movement time (ms) 14.8 Max. linear snout velocity (m/s) Max. gape opening (mm) 3.2* 3.6 Time to max. mouth opening (ms) 6.8 Body rotation Total body rotation (deg) Total body rotation time (ms) Max. body velocity (deg/s) Prey capture Total prey capture time (ms) ~5.4 ~7.4 ~6.1 7.9 4.2 Max. prey velocity (m/s) Time to max. prey velocity (ms)
de Lussanet & de Lussanet & Bergert & Van Wassenbergh et Flammang et al. Reference Muller (2007) Muller (2007) Wainwright (1997) al. (2008) (2009)
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