Marine Biology

, 165:163 | Cite as

Habitat with small inter-structural spaces promotes mussel survival and reef generation

  • Camilla BertoliniEmail author
  • W. I. Montgomery
  • Nessa E. O’Connor
Open Access
Original paper


Spatially complex habitats provide refuge for prey and mediate many predator–prey interactions. Increasing anthropogenic pressures are eroding such habitats, reducing their complexity and potentially altering ecosystem stability on a global scale. Yet, we have only a rudimentary understanding of how structurally complex habitats create ecological refuges for most ecosystems. Better informed management decisions require an understanding of the mechanisms underpinning the provision of physical refuge and this may be linked to prey size, predator size and predator identity in priority habitats. We tested each of these factors empirically in a model biogenic reef system. Specifically, we tested whether mortality rates of blue mussels (Mytilus edulis) of different sizes differed among: (i) different forms of reef structural distribution (represented as ‘clumped’, ‘patchy’ and ‘sparse’); (ii) predator species identity (shore crab, Carcinus maenas and starfish, Asterias rubens); and (iii) predator size. The survival rate of small mussels was greatest in the clumped experimental habitat and larger predators generally consumed more prey regardless of the structural organisation of treatment. Small mussels were protected from larger A. rubens but not from larger C. maenas in the clumped habitats. The distribution pattern of structural objects, therefore, may be considered a useful proxy for reef complexity when assessing predator–prey interactions, and optimal organisations should be considered based on both prey and predator sizes. These findings are essential to understand ecological processes underpinning predation rates in structurally complex habitats and to inform future restoration and ecological engineering practices.


Habitat complexity plays a key role in mediating biotic interactions, such as predator–prey relationships (Heck and Crowder 1991; Warfe and Barmuta 2004; Klecka and Boukal 2014). Structurally complex habitats may provide refuge space for prey (Křivan 1998; O’Connor and Crowe 2008), thus modifying predator–prey dynamics (Beck 1995; Barrios-O’Neill et al. 2015), and lead to a cascade of indirect effects on multiple trophic levels (Grabowski and Kimbro 2005; Grabowski et al. 2008; O’Connor et al. 2013). Spatial refuges within complex habitats can be of particular importance for smaller individuals (Hacker and Steneck 1990; Strain et al. 2017), including recent recruits and juveniles, which are usually more vulnerable to predation than larger individuals (Gosselin and Chia 1995). Studies of habitat complexity often use different definitions of complexity or confound complexity with other habitat characteristics, such as surface area or heterogeneity (Beck 2000; Frost et al. 2005; Kovalenko et al. 2012; Loke et al. 2015), which can lead to misuses of these metrics for management purposes (Wedding et al. 2011). Habitat complexity per se is often used as an over-arching term that encompasses variation in several habitat ‘components’, e.g. density of specific habitat component such as pits, pneumatophores or crevices (McCoy and Bell 1991), which limits the application of the results of studies using generic or obtuse terminology. A more useful approach is to use only specific metrics of individual habitat components (Beck 2000).

Many structurally important habitats, e.g. rainforests, saltmarshes, and aquatic biogenic reefs, are under threat from anthropogenic disturbances (Ellison et al. 2005; Airoldi et al. 2008; Silliman et al. 2009; Newbold et al. 2014; Firth et al. 2015). Biogenic reefs formed by bivalves play an essential role as ecosystem engineers (Geraldi et al. 2017) by: (i) promoting higher levels of biodiversity than surrounding local environments (Gutierrez et al. 2003; O’Connor and Crowe 2007); (ii) providing habitat that acts as a nursery for commercially important species (Kent et al. 2016, 2017); (iii) stabilising sediments (Meadows et al. 1998); (iv) acting as natural wave barriers and protecting soft coastal habitat (Stone et al. 2005); and (v) contributing substantially to nutrient cycling (Kellogg et al. 2013). The loss of such biogenic habitat following disturbance events can lead to changes in many biotic interactions, which can impede the recovery of a system following further disturbances (Lotze et al. 2006; Bertness et al. 2015; Mrowicki et al. 2016). It is often assumed that biogenic reefs have a self-sustaining mechanism, such that once a reef is established, its complex structure provides refuge from predation, which facilitates recruitment (Bertness and Grosholz 1985; Nestlerode et al. 2007; Walles et al. 2015, 2016) and maintains a healthy and stable reef system. When a reef is damaged, however, this process will be diminished (Lenihan 1999), which could de-stabilise a reef-dominated system by reducing the establishment of new recruits and subsequent reef re-formation (Barrios-O’Neill et al. 2017; Fariñas-Franco et al. 2018; Fariñas‐Franco and Roberts 2018), potentially leading to an alternative stable state which may be represented as a ‘degraded’ system lacking in complexity (Petraitis and Dudgeon 2004).

It is essential to understand how predator–prey relationships interact with spatial complexity (Warfe et al. 2008; Hesterberg et al. 2017) so that we can comprehend how these processes are linked to refuge availability, which underpins biogenic reef formation and persistence. The density of reef-forming species can be used as a proxy to estimate the organisation (or spatial arrangement) of vertical objects in horizontal space (Bell et al. 1991). Density is a tractable measure that can be quantified and manipulated experimentally and, thus provides useful insights into the recruitment dynamics of reef-forming species (Carroll et al. 2015). The density of reef-formers, however, is often confounded with other factors, such as volume or abundance of individuals (e.g. Humphries et al. 2011a, b). The aim of our study was to test whether different, but typical, spatial arrangements of habitat structure within an experimental reef system affected the survival of small mussels. Specifically, by ensuring that other components of habitat structure were constant and manipulating only density, we tested directly whether the size of interstitial spaces available affected overall mussel survival, and whether different spatial arrangements of this habitat provided better protection for small-sized mussels from differently sized predators (Toscano and Griffen 2014; Bartholomew et al. 2016). Additionally, we tested whether refuge efficacy differed between species of common predators with distinct methods of catching and killing prey (O’Connor et al. 2008; Farina et al. 2014). Small interstitial spaces which are typical of mussel beds may be beneficial for the smaller mussels, whilst being of limited use for larger mussels, which may become more vulnerable to predation from larger or different predator species (Enderlein et al. 2003; Calderwood et al. 2015b).

In two separate experiments, the effects of predation of two common benthic predators (the shore crab, Carcinus maenas, and the starfish, Asterias rubens), on their shared prey (mussels) was quantified using artificial reefs that were designed to represent three different forms of habitat organisation. The size of the predators and of prey was also manipulated to test explicitly for size-dependent effects and to identify mechanisms that underpin predation in this system. Specifically, both experiments tested the hypotheses that: predation rates on mussels are dependent upon habitat organisation (‘sparse’, ‘patchy’ and ‘clumped’) with (1) mortality rate of small mussels being lowest in the ‘clumped’ habitat organisation; whereas (2) a ‘patchy organisation’, with heterogeneous sizes of refugia available, will provide generally more options for refuge, thus decreasing the mortality of larger individuals; and that (3) the size of predators will affect the mortality rates of their prey, in relation to accessibility to the interstitial spaces (Fig. 1), while (4) the ratio of small mussel mortality compared to total mortality will only differ with habitat spatial organisation.
Fig. 1

Hypothetically predicted predator–prey relationships in ‘sparse density’ (dotted line), ‘patchy organisation’ (dashed line) and ‘clumped density’ (black line) treatments with predators of increasing size for: a total mussel mortality, and b small mussels

Materials and methods

Experimental design

The experiments were conducted in outdoor, flow-through mesocosms at Queen’s University Marine Laboratory, Portaferry, Northern Ireland. Mesocosms consisted of opaque plastic boxes (55.5 × 35.5 × 22 cm) arranged on shallow tables and supplied independently with sand-filtered, seawater from the adjacent Strangford Lough cascading from the top on all of the tanks (further detail in Mrowicki and O’Connor 2015).

The first experiment quantified predation rates of crabs on mussels based on: (i) habitat organisation as a fixed factor (with three levels: ‘sparse’, ‘patchy’, and ‘clumped’); (ii) crab size (as a continuous variable); and (iii) trial (3 levels), which was included as a random factor. The second experiment, quantified predation rates of starfish on mussel beds based on: (i) habitat organisation as a fixed factor (with three levels: ‘sparse’, ‘patchy’, and ‘clumped’); (ii) starfish size as a fixed factor (with two levels: small, large) given the lack of intermediate-sized individuals; and, (iii) trial (2 levels), which was included as a random factor.

Artificial reefs were designed to manipulate interstitial space size whilst keeping overall area constant in all treatments. Artificial reefs were constructed from 30 × 30 cm Perspex plates (Fig. 2) each containing 9 mimics made from white PVC pipe (diameter 2.5 cm, height 3.5 cm). The experimental treatments were designed to represent reefs with ‘sparse’, ‘patchy’ and ‘clumped’ distributions based observations of local mussel abundance patterns. For example, in the sparse distribution treatment, individual mussel mimics were each placed 8.5 cm apart, which is representative of a degraded habitat of isolated mussels (Fariñas-Franco et al. 2014). This sparse treatment was hypothesised to provide limited or no physical refuge available. In the patchy distribution treatment, three mussel mimics were placed 8.5 cm apart, another three were 3.5 cm apart and another three were 1 cm apart, representing a patchy mussel organisation. This patchy treatment was hypothesised to provide several refugia for mussels of various sizes. In the clumped distribution treatment, the distance between all nine mussel mimics per plate was 1 cm, representing a densely packed and uniformly distributed mussel reef. This clumped treatment was hypothesised to provide refuges for small mussels.
Fig. 2

Top view (dorsal) of the design of artificial reefs with three different spatial organisations (experimental treatments). a ‘Sparse’, b ‘clumped’, c ‘patchy’. Squares represent perspex tile, circles represent PVC pipe

Experiment 1: Carcinus maenas predation

This experiment ran from December 2015 to January 2016 with three trials comprising all treatments running over this time period. Each of the three experimental treatment (sparse, patchy and clumped) were replicated eight times in each trial yielding a total of 72 experimental units. New crabs and mussels were collected for each experimental trial.

Mussel sizes used in the experiment were designed to mimic two common sizes available on mussel patches at local intertidal, soft-sediments shores: one small and able to find refuge and a larger one which would not be able to hide in small interstitial spaces. The specific abundance of the two classes was chosen to mimic natural conditions (mean abundance ± SE: small mussels 6.4 ± 1, large 4.8 ± 1.2/900 cm2), thus ten mussels (five mussels of two size classes, mean shell length ± SE: small, 11.2 ± 0.4 mm and large 22.7 ± 0.5 mm) were added to each experimental plate described above, which was then allocated randomly to a mesocosm. All mussels selected had clean shells, were collected from a local shore (54°29′15.96″N, 5°32′24.74″W) and were within the recorded feeding range of C. maenas (Mascaró and Seed 2000; Enderlein et al. 2003; Calderwood et al. 2016). Following pilot studies, mussels were allowed to acclimatise for 24 h prior to the addition of the predator (C. maenas), allowing them to find a suitable space within the model reef and to form a byssal attachment to the structure. All mussels attached to the habitat provided by the artificial structures, but no mussel-to-mussel clumping was observed during the experiment.

Shore crabs, C. maenas, were collected from a local shore (54°23′26.6424″N, 5°34′19.5126″W) and to standardise motivation to feed, only male individuals without any visible signs of damage were selected for use in this study. Crab carapaces were measured with digital calipers. Only crabs with carapace width > 30 mm were selected because crabs of this size are known to have M. edulis as a large component of their diet (Ropes 1968). All crabs were transferred into three holding tanks (35 L) and acclimatised for 24 h with ad libitum food supply of M. edulis. Crabs were then starved for 48 h prior to the beginning of the experiment to standardise hunger level (Elner and Hughes 1978). One crab was assigned randomly to each mesocosm and left to forage overnight for 18 h from 16.00 to 10.00 h (Ropes 1968, Calderwood et al. 2016). At the end of each trial, crabs were removed and the number of surviving small and large mussels was recorded. First, we analysed total mussel mortality and small mussel mortality. Then to assess whether the consumption of small mussels differed among experimental treatments and across the range of predator sizes we further analysed the small mussels consumed as a proportion of all mussels consumed.

Experiment 2: Asterias rubens predation

Two experimental trials were run in February and May 2016. Each of the three experimental treatments (sparse, patchy and clumped) was replicated four times in per each starfish size class per trial yielding a total of 48 experimental units. New starfish and new mussels were collected prior to the start of each experimental trial. To keep mussel density similar to the previous experiment and to avoid confounding effects of clumping while investigating habitat use, nine mussels (three mussels of three size classes: small, mean shell length ± SE 9.5 ± 0.4 mm, medium 20 ± 0.5 mm, and large 32.4 ± 0.6 mm) were added to each plate, which was then randomly allocated to a mesocosm. Clean shelled M. edulis were also collected at low tide from a local shore (54°29′15.96″N, 5°32′24.74″W), and sorted into the three size classes: all mussels were chosen to be sizes normally consumed by starfish (Hummel et al. 2011), with the small mussels able to seek refuge in the ‘clumped’ reef organisation. In contrast, medium and large mussels were chosen since they were excluded from the refuge space, with medium-sized mussels accessible by smaller starfish and large mussels possibly chosen by larger starfish (Hummel et al. 2011; Calderwood et al. 2015b). Mussels were then left for 24 h to acclimatise, move to a suitable space and form a byssal attachment to the structure. Again all mussels attached to the habitat provided by the artificial structures, but no mussel-to-mussel clumping was observed during the experiment.

Undamaged starfish were collected manually from a local shallow subtidal shore (54°23′29.9214″N, 5°34′29.301″W). Owing to the starfish sizes available locally, starfish were sorted by size with small (arm length 24.2 ± 1.7 mm) and large (96.02 ± 0.45 mm) individuals transferred into separate holding tanks (volume 330 L) where they were left to acclimatise for 24 h with ad libitum food supply (M. edulis). Starfish were then starved for 7 days prior to the beginning of experiment to standardise hunger levels. At the start of each trial, a small or large starfish was assigned randomly to a mesocosm and left to forage for 4 days. At the end of the foraging period, starfish were removed and the number of surviving mussels was recorded. Percentage mortality of all mussels and percentage mortality of smaller mussels was used for statistical analyses. To differentiate whether the consumption of the small class changed with organisation or predator size we further considered the proportion of small mussels consumed in relation to total mortality, expressed as a percentage.

Statistical analysis

All analyses were carried out using R (R Development Core Team 2015). Data were tested for homogeneity of variances using Levene’s test in the car package. For both experiments, linear mixed models fitted by maximum likelihood t tests using Satterthwaite (1946) approximations of degrees of freedom were chosen to incorporate the random effect of ‘trial’ (Bates 2005; package lme4). The initial model included fixed terms (‘habitat organisation’, ‘predator size’), their interactions and the random factor (‘trial’). Where the interactions were not significant they were removed from the model. The models with and without interactions were then compared based on AIC score and that with a lowest score was chosen. Moreover, the random effect ‘trial’ always explained < 5% of the total variance. Therefore, mixed linear models were compared to linear models that did not include the random factor using AIC scores. AIC were lower for linear models in all cases. The residuals were tested for normality with Shapiro–Wilk test and the model was validated. Type II ANOVA tables using the Anova function in the package car was then used to generate p values and test for significance. If terms were significant, pairwise comparisons between levels were carried out using least means squares estimates based on Tukey adjustments (lsmeans and multcomp packages).


Experiment 1: Carcinus maeanas predation

Small mussel (ca. 10 mm in shell length) mortality was significantly lower in the ‘clumped density’ treatment (F2,68 = 5.9, p < 0.01, Table 1, Fig. 3d–f). A ‘patchy’ organisation did not contribute to a decreased mortality (p > 0.05). The lowest total mussel mortality was in the ‘clumped density’ organisation (F2,68 = 5.88, p < 0.01, Fig. 3a–c, Table 1) and increased with increasing crab size (F1,68 = 5.9, p < 0.001, Fig. 3a–c). There was no interaction between organisation and crab size, however, both small and total mussels mortality was found to increase with increasing size of crabs (F1,68 = 5.9, p < 0.05, Fig. 3d–f). The proportion of small to total mussel mortality did not change with different organisations, however, this ratio decreased with increasing crab sizes (F1,63 = 25.1, p < 0.0001, Fig. 3g–i).
Table 1

Pairwise comparisons using least squares means estimates between complexity treatments for all mussel mortality in the presence of a crab



Std. error

t ratio


Total mussel mortality
















Small mussel mortality
















Significant differences in bold

Fig. 3

The relationship between mussel mortality and crab size in “sparse density” (squares, dotted line, a, d, g), “patchy” (circles, dashed line, b, e, h) and “clumped density” (triangles, continuous line, c, f, i) organisations based on: total mussel mortality (panels ac), small mussel mortality (df), and proportion of small mussels mortality compared to total expressed as percentage (gi). Lines and shading represent 95% confidence intervals extrapolated from linear models

Experiment 2: Asterias rubens predation

Small mussel (ca. 10 mm in shell length) mortality was significantly lower in the ‘clumped density’ (F2,44 = 6.2, p < 0.01, Table 2, Fig. 4d–f), however, this did not differ among starfish sizes, nor were there interactions between habitat organisation and predator size (p > 0.05).
Table 2

Pairwise comparisons using least squares means estimates between complexity treatments for small mussel mortality in the presence of a starfish



Std. error

t value

















Significant differences in bold

Fig. 4

Boxplot with overlying raw data representing medians and interquartile ranges of percentage mortality of mussels exposed to different starfish sizes in “sparse density” (squares, a, d, g), “patchy” (circles, b, e, h) and “clumped density” (triangles, c, f, i) organisations based on: total mussel mortality (ac), small mussel mortality (df), and proportion of small mussels mortality compared to total expressed as percentage (gi)

A ‘patchy’ organisation did not contribute to a decreased mortality (p > 0.05).

In the starfish experiment (Fig. 4) total mussel mortality rate did not differ among habitat organisations, however, total mussel mortality increased when starfish were larger (F1,44 = 25.5, p < 0.001, Fig. 4a–c). The proportion of small to total mussel mortality decreased with increasing complexity of organisation from sparse to clumped (F2,38 = 3.5, p < 0.05), and was lower when starfish were larger (F1,38 = 8.8, p < 0.001, Fig. 4g–i).


In agreement with our initial hypotheses (Fig. 1), we found that in the presence of crabs, different habitat organisations affected mussel predation rates differently, and that mussel survival was greatest in the clumped density treatment. In contrast, we did not identify any effects of different habitat organisation on total mussel mortality in the presence of starfish. However, when predation effects were examined for small mussels only, clumped habitat organisation significantly increased survival of small mussel classes independent of predator species. There was no effect of organisation on the ratio of small mussels consumed compared to total mortality when crabs were the predators, while there was a slight decrease in this ratio with increasing organisation density when starfish were predators.

In the present study, habitat organisation was manipulated only with regard to horizontal space by manipulating object (mussel mimic) density. Despite this simplification of variability in reef structure, which did not consider variations in three dimensions (Hesterberg et al. 2017), and used habitat mimics, it was a highly suitable proxy to test for effects of different interstitial space sizes among objects (Bartholomew and Burt 2015; Bartholomew et al. 2016) and was a suitable mediator of predation rates. Where habitat organisation was clumped, prey mortality, in general, was lower, suggesting that altering habitat organisation even in two dimensions reveals useful mechanistic insights with regard to refuge availability and refuge efficacy. The reduction in mortality owing to predation in habitats with clumped density organisation could be driven by the ability of prey to hide from predators and/or the inability of predators to reach inside the refuge to access prey (Klecka and Boukal 2014), rendering the task too difficult or energetically prohibitive (Dolmer 1998).

Variability in the efficacy of refugia was prey size specific. Small mussels benefitted from the clumped-density organisation, where they found refuge. Mortality for this size class was lower in the clumped compared to the lower density organisation treatments, suggesting an effect of habitat organisation and not general prey size preference per se. These findings suggest that promoting a habitat with small available refuge spaces can significantly increase survival of small mussels in the presence of predators, and thus enhance their potential to increase reef sustainability and growth (van de Koppel et al. 2005; Commito et al. 2014; Folmer et al. 2014; Bertolini et al. 2017).

The size of the predators affected mussel mortality positively and the lack of interactions showed that this occurred independently of habitat organisation. This result was in contrast to our third and fourth hypotheses, as we found that in general consumption rate, particularly the consumption of larger mussels, increased with predator size. Smaller predators in both experiments were small enough to use the interstitial spaces in the ‘clumped density’ organisation, and were observed doing so. In contrast to predictions, larger crabs were able to feed on small mussels in the clumped organisation. This did not occur when starfish were present because predator size did not affect their predation rates on mussels in the clumped organisation. This could be because of their different physical feeding methods, with crabs having strong claws (Vermeij 1977) and able to reach into a refuge space and pull a mussel out. Starfish might not be able to reach and secure a small mussel in a small space because of the bulk of their arms and relative weakness of a hydrostatic tube foot system leading to a poor capacity to grip prey (Dolmer 1998). Moreover, predators with different prey-detecting strategies (visual vs chemical vs tactile cue) may have different rates of prey encounter in complex habitats (Farina et al. 2014; Klecka and Boukal 2014).

Larger predators consumed more mussels and tended to prefer larger sizes, whilst smaller predators were limited to consumption of smaller mussels, suggesting that larger mussels may be able to escape predation from smaller predators in all of the organisations tested here. This was consistent with results from studies of crab predation on oyster reefs where the presence of large crabs was the major determinant of mussel and oyster mortality (Toscano and Griffen 2012; Pickering et al. 2017). Thus, mussels that are excluded from refuge space and are of edible size may suffer from high mortality rates. While other experiments found that, A. rubens (Hummel et al. 2011) and C. maenas (Smallegange and Van Der Meer 2003) often prefer small prey items. We found that overall small mussel mortality also increased with increasing predator size, and the ratio of small mussels mortality compared to total mortality decreased with increasing predator size, suggesting that consumption of larger size classes is important for larger predator sizes. In natural reefs, refugia may be highly variable in size with suitable but limited refuge space for all size classes, however, for damaged reefs to recover it is important that refugia for small mussels is present so they can grow to regenerate a self-sustaining reef.

Different predator species were found to have generally similar effects on mortality rates, but some differences were highlighted. For example, larger crabs were found to eat small mussels in the clumped density organisation. This was not observed in trials involving large starfish. The importance of considering predator assemblage composition has been highlighted for commercial mussel seeding operations (Calderwood et al. 2015a) and this is reinforced by the present study. Clumped mussel habitat can also be beneficial for smaller predators which may hide therein (Thiel and Dernedde 1994), highlighting the importance of considering the ontogenetic and behavioural responses of predators (Pirtle et al. 2012). It is known, for example, that mussel reefs are nursery grounds for whelks (Kent et al. 2016) and crabs (Lindsey et al. 2006) and the size of small predators used in this experiment may spend more time sheltering from larger predators in refuge space afforded by a reef than actively feeding. This should be tested empirically.

It is concluded that refuge efficacy in reducing mortality from predation is greatest in clumped density habitat organisation but is strongly dependent on prey size rather than predator size. This contrasts with previous research, which found that space size relative to predator width (Sp/Pr) was one of the most important predictors of survivorship, with prey survivorship decreasing sigmoidally with increasing Sp/Pr (Bartholomew et al. 2000). We recommend that refuge size should be evaluated in relation to prey size (Sp/Py) (Hacker and Steneck 1990; Bartholomew and Shine 2008; Bartholomew 2012), that habitats containing multi-sized interstitial spaces should be promoted to offer refuge to multiple size classes, and ultimately should be context-specific with regards to the identity of predators present in the system.

Our findings demonstrate the use of spatial organisation as a measure of habitat complexity to explain the effects of predator–prey interactions (Almany 2004; Carroll et al. 2015; Hesterberg et al. 2017). Also, the density and size of mussels may be critical in off-setting the effects of one or more predator species each able to access and exploit different components of the mussel population (Garner and Litvaitis 2013). These findings have important consequences for ecological engineering projects (Firth et al. 2016) and the management of structures when the aim is to aid the reintroduction of species (e.g. canopy algae, Susini et al. 2007; Perkol-Finkel et al. 2012; or native oysters, Strain et al. 2017) while keeping in mind the role of biotic interactions (Ferrario et al. 2016; Gianni et al. 2018) to promote self-sustainability after initial restoration efforts.



We are thankful to the staff of Queen’s University Marine Laboratory and to professors Mark Emmerson and Steve Widdicombe for useful comments on previous drafts. This work was founded as part of CB PhD studentship funded by Northern Ireland Environment Agency and Queen’s University Belfast.


This study was part of Ph.D. scholarship obtained by Camilla Bertolini, funded by Queen’s University Belfast and Northern Ireland Environment Agency.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interests.

Human and animal rights

Institutional guidelines for the use and care of animals involved in this study were followed.


  1. Airoldi L, Balata D, Beck MW (2008) The gray zone: relationships between habitat loss and marine diversity and their applications in conservation. J Exp Mar Biol Ecol 366:8–15. CrossRefGoogle Scholar
  2. Almany GR (2004) Does increased habitat complexity reduce predation and competition in coral reef fish assemblages? Oikos 106:275–284. CrossRefGoogle Scholar
  3. Barrios-O’Neill D, Dick JT, Emmerson MC, Ricciardi A, MacIsaac HJ (2015) Predator-free space, functional responses and biological invasions. Funct Ecol 29:377–384. CrossRefGoogle Scholar
  4. Barrios-O’Neill D, Bertolini C, Collins PC (2017) Trophic cascades and the transient keystone concept. Biol Conserv 212:191–195. CrossRefGoogle Scholar
  5. Bartholomew A (2012) Space size relative to prey width and total cover in an area both influence the habitat choices of freshwater angelfish Pterophyllum scalare in mesocosms. Mar Freshw Behav Physiol 45:29–43. CrossRefGoogle Scholar
  6. Bartholomew A, Burt JA (2015) Both decreasing interstructural space size and increasing total cover increase shrimp abundance on artificial structures deployed in a UAE seagrass bed. Mar Freshw Behav Physiol 48:213–220. CrossRefGoogle Scholar
  7. Bartholomew A, Shine R (2008) Space size relative to prey width (Sp/Py) influences macrofaunal colonization of artificial structures. Mar Ecol Prog Ser 358:95–102. CrossRefGoogle Scholar
  8. Bartholomew A, Diaz RJ, Cicchetti G (2000) New dimensionless indices of structural habitat complexity: predicted and actual effects on a predator’s foraging success. Mar Ecol Prog Ser 206:45–58CrossRefGoogle Scholar
  9. Bartholomew A, Hafezi SA, Karimi S (2016) Effects of habitat complexity on the abundance, species richness and size of darkling beetles (Tenebrionidae) in artificial vegetation. J Arid Environ 129:35–41. CrossRefGoogle Scholar
  10. Bates D (2005) Fitting linear mixed models in R. R News 5:27–30. CrossRefGoogle Scholar
  11. Beck MW (1995) Size-specific shelter limitation in stone crabs: a test of the demographic bottleneck hypothesis. Ecology 76:968–980. CrossRefGoogle Scholar
  12. Beck MW (2000) Separating the elements of habitat structure: independent effects of habitat complexity and structural components on rocky intertidal gastropods. J Exp Mar Biol Ecol 249:29–49CrossRefGoogle Scholar
  13. Bell SS, McCoy E, Mushinsky H (1991) Habitat structure: the physical arrangement of objects in space. Springer, DordrechtCrossRefGoogle Scholar
  14. Bertness MD, Grosholz E (1985) Population dynamics of the ribbed mussel, Geukensia demissa: the costs and benefits of an aggregated distribution. Oecologia 67:192–204. CrossRefPubMedGoogle Scholar
  15. Bertness MD, Brisson CP, Crotty SM (2015) Indirect human impacts turn off reciprocal feedbacks and decrease ecosystem resilience. Oecologia 178:231–237. CrossRefPubMedGoogle Scholar
  16. Bertolini C, Geraldi NR, Montgomery WI, Connor NEO (2017) Substratum type and conspecific density as drivers of mussel patch formation. J Sea Res 121:24–32. CrossRefGoogle Scholar
  17. Calderwood J, O’Connor NE, Roberts D (2015a) Effects of baited crab pots on cultivated mussel (Mytilus edulis) survival rates. ICES J Mar Sci 71:236–240. CrossRefGoogle Scholar
  18. Calderwood J, O’Connor NE, Roberts D (2015b) The effects of transportation stress and barnacle fouling on predation rates of starfish (Asterias rubens) on mussels (Mytilus edulis). Aquaculture 444:108–113. CrossRefGoogle Scholar
  19. Calderwood J, O’Connor NE, Roberts D (2016) Breaking and entering: examining the role of stress and aerial exposure in predator–prey relationships between the common shore crab (Carcinus maenas) and cultivated blue mussels (Mytilus edulis). Aquaculture 452:217–223. CrossRefGoogle Scholar
  20. Carroll JM, Jackson LJ, Peterson BJ (2015) The effect of increasing habitat complexity on bay scallop survival in the presence of different decapod crustacean predators. Estuaries Coasts 38:1569–1579. CrossRefGoogle Scholar
  21. Commito JA, Commito AE, Platt RV, Grupe BM, Dow Piniak WE, Gownaris NJG, Reeves KA, Vissichelli AM (2014) Recruitment facilitation and spatial pattern formation in soft-bottom mussel beds. Ecosphere 5:1–26CrossRefGoogle Scholar
  22. Dolmer P (1998) The interactions between bed structure of Mytilus edulis L. and the predator Asterias rubens L. J Exp Mar Biol Ecol 228:137–150. CrossRefGoogle Scholar
  23. Ellison AM, Bank MS, Clinton BD, Colburn EA, Elliott K, Ford CR, Foster DR, Kloeppel BD, Knoepp JD, Lovett GM, Mohan J, Orwig DA, Rodenhouse NL, Sobczak WV, Stinson KA, Stone JK, Swan CM, Thompson J, Von Holle B, Webster JR (2005) Loss of foundation species: consequences for the structure and dynamics of forested ecosystems. Front Ecol Environ 3:479–486.;2 CrossRefGoogle Scholar
  24. Elner R, Hughes RN (1978) Energy maximization in the diet of the shore crab, Carcinus maenas. J Anim Ecol 47:103–116CrossRefGoogle Scholar
  25. Enderlein P, Moorthi S, Rhrscheidt H, Wahl M (2003) Optimal foraging versus shared doom effects: interactive influence of mussel size and epibiosis on predator preference. J Exp Mar Biol Ecol 292:231–242. CrossRefGoogle Scholar
  26. Farina S, Arthur R, Pags JF, Prado P, Romero J, Verges A, Hyndes G, Heck KL, Glenos S, Alcoverro T (2014) Differences in predator composition alter the direction of structure-mediated predation risk in macrophyte communities. Oikos 123:1311–1322. CrossRefGoogle Scholar
  27. Fariñas-Franco JM, Roberts D (2018) The relevance of reproduction and recruitment to the conservation and restoration of keystone marine invertebrates: a case study of sublittoral Modiolus modiolus reefs impacted by demersal fishing. Aquat Conserv 28:672–689CrossRefGoogle Scholar
  28. Fariñas-Franco JM, Pearce B, Porter JS, Harries D, Mair J, Woolmer A, Sanderson WG (2014) Marine strategy framework directive indicators for biogenic reefs formed by Modiolus modiolus, Mytilus edulis and Sabellaria spinulosa: Part 1: Defining and validating the indicators. JNCC, Peterborough (ISSN 096) Google Scholar
  29. Fariñas-Franco JM, Allcock L, Roberts D (2018) Protection alone may not promote natural recovery of biogenic habitats of high biodiversity damaged by mobile fishing gears. Mar Environ Res 135:18–28CrossRefGoogle Scholar
  30. Ferrario F, Iveša L, Jaklin A, Perkol-Finkel S, Airoldi L (2016) The overlooked role of biotic factors in controlling the ecological performance of artificial marine habitats. J Appl Ecol 53:16–24. CrossRefGoogle Scholar
  31. Firth LB, Mieszkowska N, Grant LM, Bush LE, Davies AJ, Frost MT, Moschella PS, Burrows MT, Cunningham PN, Dye SR, Hawkins SJ (2015) Historical comparisons reveal multiple drivers of decadal change of an ecosystem engineer at the range edge. Ecol Evol. CrossRefPubMedPubMedCentralGoogle Scholar
  32. Firth LB, Knights AM, Bridger D, Evans AJ, Mieszkowska N, Moore PJ, O’Connor NE, Sheehan EV, Thompson RC, Hawkins SJ (2016) Ocean sprawl: challenged and opportunities for biodiversity management in a changing world. Annu Rev 54:193–269Google Scholar
  33. Folmer EO, Drent J, Troost K, Büttger H, Dankers N, Jansen J, van Stralen M, Millat G, Herlyn M, Philippart CJM (2014) Large-scale spatial dynamics of intertidal mussel (Mytilus edulis L.) bed coverage in the German and Dutch Wadden Sea. Ecosystems 17:550–566. CrossRefGoogle Scholar
  34. Frost NJ, Burrows MT, Johnson MP, Hanley ME, Hawkins SJ (2005) Measuring surface complexity in ecological studies. Limnol Oceanogr Methods 3:203–210. CrossRefGoogle Scholar
  35. Garner YL, Litvaitis MK (2013) Effects of injured conspecifics and predators on byssogenesis, attachment strength and movement in the blue mussel, Mytilus edulis. J Exp Mar Biol Ecol 448:136–140. CrossRefGoogle Scholar
  36. Geraldi NR, Bertolini C, Emmerson MC, Roberts D, Sigwart JD, O’Connor NE (2017) Aggregations of brittle stars can provide similar ecological roles as mussel reefs. Mar Ecol Prog Ser 563:157–167. CrossRefGoogle Scholar
  37. Gianni F, Bartolini F, Airoldi L, Mangialajo L (2018) Reduction of herbivorous fish pressure can facilitate focal algal species forestation on artificial structures. Mar Environ Res 138:102–109CrossRefGoogle Scholar
  38. Gosselin LA, Chia F-S (1995) Characterizing temperate rocky shores from the perspective of an early juvenile snail: the main threats to survival of newly hatched Nucella emarginata. Mar Biol 122:625–635. CrossRefGoogle Scholar
  39. Grabowski JH, Kimbro DL (2005) Predator-avoidance behavior extends trophic cascades to refuge habitats. Ecology 86:1312–1319CrossRefGoogle Scholar
  40. Grabowski JH, Hughes AR, Kimbro DL (2008) Habitat complexity influences cascading effects of multiple predators. Ecology 89:3413–3422. CrossRefPubMedGoogle Scholar
  41. Gutierrez JL, Jones CG, Strayer DL, Iribarne OO (2003) Mollusks as ecosystem engineers: the role of shell production in aquatic habitats. Oikos 101:79–90CrossRefGoogle Scholar
  42. Hacker SD, Steneck RS (1990) Habitat architecture and the abundance and body size dependent habitat selection of a phytal amphipod. Ecology 71:2269–2285. CrossRefGoogle Scholar
  43. Heck KL, Crowder JLB (1991) Habitat structure and predator–prey interactions in vegetated aquatic systems. In: Habitat structure: the physical arrangement of objects in space, pp 281–299CrossRefGoogle Scholar
  44. Hesterberg SG, Duckett CC, Salewski EA, Bell SS (2017) Three-dimensional interstitial space mediates predator foraging success in different spatial arrangements. Ecology 98:1153–1162. CrossRefPubMedGoogle Scholar
  45. Hummel C, Honkoop P, van der Meer J (2011) Small is profitable: no support for the optimal foraging theory in sea stars Asterias rubens foraging on the blue edible mussel Mytilus edulis. Estuar Coast Shelf Sci 94:89–92. CrossRefGoogle Scholar
  46. Humphries AT, La Peyre MK, Kimball ME, Rozas LP (2011a) Testing the effect of habitat structure and complexity on nekton assemblages using experimental oyster reefs. J Exp Mar Biol Ecol 409:172–179. CrossRefGoogle Scholar
  47. Humphries AT, La Peyre MK, Decossas GA (2011b) The effect of structural complexity, prey density, and “predator-free space” on prey survivorship at created oyster reef mesocosms. PLoS One 6:e28339. CrossRefPubMedPubMedCentralGoogle Scholar
  48. Kellogg ML, Cornwell JC, Owens MS, Paynter KT (2013) Denitrification and nutrient assimilation on a restored oyster reef. Mar Ecol Prog Ser 480:1–19. CrossRefGoogle Scholar
  49. Kent FEA, Gray MJ, Last KS, Sanderson WG (2016) Horse mussel reef ecosystem services: evidence for a whelk nursery habitat supporting a shellfishery. Int J Biodivers Sci Ecosyst Serv Manag 12:172–180. CrossRefGoogle Scholar
  50. Kent FEA, Mair JM, Newton J, Lindenbaum C, Porter JS, Sanderson WG (2017) Commercially important species associated with horse mussel (Modiolus modiolus) biogenic reefs: a priority habitat for nature conservation and fisheries benefits. Mar Pollut Bull 118:71–78. CrossRefPubMedGoogle Scholar
  51. Klecka J, Boukal DS (2014) The effect of habitat structure on prey mortality depends on predator and prey microhabitat use. Oecologia 176:183–191. CrossRefPubMedGoogle Scholar
  52. Kovalenko KE, Thomaz SM, Warfe DM (2012) Habitat complexity: approaches and future directions. Hydrobiologia 685:1–17. CrossRefGoogle Scholar
  53. Křivan V (1998) Effects of optimal antipredator behavior of prey on predator–prey dynamics: the role of refuges. Theor Popul Biol 53:131–142. CrossRefPubMedGoogle Scholar
  54. Lenihan HS (1999) Physical–biological coupling on oyster reefs: how habitat structure influences individual performance. Ecol Monogr 69:251–275.;2 CrossRefGoogle Scholar
  55. Lindsey EL, Altieri AH, Witman JD (2006) Influence of biogenic habitat on the recruitment and distribution of a subtidal xanthid crab. Mar Ecol Prog Ser 306:223–231. CrossRefGoogle Scholar
  56. Loke LHL, Ladle RJ, Bouma TJ, Todd PA (2015) Creating complex habitats for restoration and reconciliation. Ecol Eng 77:307–313. CrossRefGoogle Scholar
  57. Lotze HK, Lenihan HS, Bourque BJ, Bradbury RH, Cooke RG, Kay MC, Kidwell SM, Kirby MX, Peterson CH, Jackson JBC, Bay M (2006) Depletion, degradation and recovery potential of estuaries and coastal seas. Science 80 312:1806–1809CrossRefGoogle Scholar
  58. Mascaró M, Seed R (2000) Choice of prey size and species in Carcinus maenas (L.) feeding on four bivalves of contrasting shell morphology. Hydrobiologia 449:159–170. CrossRefGoogle Scholar
  59. McCoy ED, Bell SS (1991) Habitat structure: the evolution and diversification of a complex topic. In: Bell SS, McCoy ED, Mushinsky HR (eds) Habitat structure. Springer, London, pp 3–27CrossRefGoogle Scholar
  60. Meadows PS, Meadows A, West FJC, Shand PS, Shaikh MA (1998) Mussels and mussel beds (Mytilus edulis) as stabilizers of sedimentary environments in the intertidal zone. Geol Soc Lond (Special Publ) 139:331–347. CrossRefGoogle Scholar
  61. Mrowicki RJ, O’Connor NE (2015) Wave action modifies the effects of consumer diversity and warming on algal assemblages. Ecology 96:1020–1029. CrossRefPubMedGoogle Scholar
  62. Mrowicki RJ, O’Connor NE, Donohue I (2016) Temporal variability of a single population can determine the vulnerability of communities to perturbations. J Ecol 104:887–897. CrossRefGoogle Scholar
  63. Nestlerode JA, Luckenbach MW, Beirn FXO, O’Beirn FX (2007) Settlement and survival of the oyster Crassostrea virginica on created oyster reef habitats in Chesapeake Bay. Restor Ecol 15:273–283. CrossRefGoogle Scholar
  64. Newbold T, Hudson LN, Phillips HRP, Hill SLL, Contu S, Lysenko I, Blandon A, Butchart SHM, Booth HL, Day J, De Palma A, Harrison MLK, Kirkpatrick L, Pynegar E, Robinson A, Simpson J, Mace GM, Scharlemann JPW, Purvis A (2014) A global model of the response of tropical and sub-tropical forest biodiversity to anthropogenic pressures. Proc Biol Sci 281:20141371. CrossRefPubMedPubMedCentralGoogle Scholar
  65. O’Connor NE, Crowe TP (2007) Biodiversity among mussels: separating the influence of sizes of mussels from the ages of patches. J Mar Biol Assoc UK 87:551. CrossRefGoogle Scholar
  66. O’Connor NE, Crowe TP (2008) Do mussel patches provide a refuge for algae from grazing gastropods? J Molluscan Stud 74:75–78. CrossRefGoogle Scholar
  67. O’Connor NE, Grabowski JH, Ladwig LM, Bruno JF (2008) Simulated predator extinctions: predator identity affects survival and recruitment of oysters. Ecology 89:428–438. CrossRefPubMedGoogle Scholar
  68. O’Connor NE, Emmerson MC, Crowe TP, Donohue I (2013) Distinguishing between direct and indirect effects of predators in complex ecosystems. J Anim Ecol 82:438–448. CrossRefPubMedGoogle Scholar
  69. Perkol-Finkel S, Ferrario F, Nicotera V, Airoldi L (2012) Conservation challenges in urban seascapes: promoting the growth of threatened species on coastal infrastructures. J Appl Ecol 49:1457–1466. CrossRefGoogle Scholar
  70. Petraitis P, Dudgeon SR (2004) Detection of alternative stable states in marine communities. J Exp Mar Biol Ecol (Special Issue) 300:343–371CrossRefGoogle Scholar
  71. Pickering TR, Poirier LA, Barrett TJ, McKenna S, Davidson J, Quijn PA (2017) Non-indigenous predators threaten ecosystem engineers: interactive effects of green crab and oyster size on American oyster mortality. Mar Environ Res 127:24–31. CrossRefPubMedGoogle Scholar
  72. Pirtle J, Eckert G, Stoner A (2012) Habitat structure influences the survival and predator–prey interactions of early juvenile red king crab Paralithodes camtschaticus. Mar Ecol Prog Ser 465:169–184. CrossRefGoogle Scholar
  73. R Development Core Team R (2015) R: a language and environment for statistical computing. R Found Stat Comput 1:409Google Scholar
  74. Ropes JW (1968) The feeding habits of the green crab, Carcinus maenas (L.). Fish Bull 67:183–203Google Scholar
  75. Satterthwaite FE (1946) An approximate distribution of estimates of variance components. Biometrics 2:110–114. CrossRefPubMedGoogle Scholar
  76. Silliman BR, Grosholz E, Bertness MD, Bertness MD (2009) Human impacts on salt marshes : a global perspective. University of California Press, CaliforniaGoogle Scholar
  77. Smallegange IM, Van Der Meer J (2003) Why do shore crabs not prefer the most profitable mussels? J Anim Ecol 72:599–607. CrossRefGoogle Scholar
  78. Stone G, Zhang X, Sheremet A (2005) The role of barrier islands, muddy shelf and reefs in mitigating the wave field along coastal Louisiana. J Coast Res (sp. issue) 2005:40–55Google Scholar
  79. Strain EM, Morris RL, Coleman RA, Figueira WF, Steinberg PD, Johnston EL, Bishop MJ (2017) Increasing microhabitat complexity on seawalls can reduce fish predation on native oysters. Ecol Eng. CrossRefGoogle Scholar
  80. Susini ML, Mangialajo L, Thibaut T, Meinesz A (2007) Development of a transplantation technique of Cystoseira amentacea var. stricta and Cystoseira compressa. In: Biodiversity in enclosed seas and artificial marine habitats. Springer Netherlands, Dordrecht, pp 241–244Google Scholar
  81. Thiel M, Dernedde T (1994) Recruitment of shore crabs Carcinus maenas on tidal flats: mussel clumps as an important refuge for juveniles. Helgol Meeresunters 48:321–332. CrossRefGoogle Scholar
  82. Toscano BJ, Griffen BD (2012) Predatory crab size diversity and bivalve consumption in oyster reefs. Mar Ecol Prog Ser 445:65–74. CrossRefGoogle Scholar
  83. Toscano BJ, Griffen BD (2014) Trait-mediated functional responses: predator behavioural type mediates prey consumption. J Anim Ecol 83:1469–1477. CrossRefPubMedGoogle Scholar
  84. van de Koppel J, Rietkerk M, Dankers N, Herman PMJ (2005) Scale-dependent feedback and regular spatial patterns in young mussel beds. Am Nat 165:E66–E77CrossRefGoogle Scholar
  85. Vermeij G (1977) Patterns in crab claw size: the geography of crushing. Syst Biol 26:138–151CrossRefGoogle Scholar
  86. Walles B, Mann R, Ysebaert T, Troost K, Herman PMJ, Smaal AC (2015) Demography of the ecosystem engineer Crassostrea gigas, related to vertical reef accretion and reef persistence. Estuar Coast Shelf Sci 154:1–10. CrossRefGoogle Scholar
  87. Walles B, Troost K, van den Ende D, Nieuwhof S, Smaal AC, Ysebaert T (2016) From artificial structures to self-sustaining oyster reefs. J Sea Res 108:1–9. CrossRefGoogle Scholar
  88. Warfe DM, Barmuta LA (2004) Habitat structural complexity mediates the foraging success of multiple predator species. Oecologia 141:171–178. CrossRefPubMedGoogle Scholar
  89. Warfe DM, Barmuta LA, Wotherspoon S (2008) Quantifying habitat structure: surface convolution and living space for species in complex environments. Oikos 117:1764–1773. CrossRefGoogle Scholar
  90. Wedding LM, Lepczyk CA, Pittman SJ, Friedlander AM, Jorgensen S (2011) Quantifying seascape structure: extending terrestrial spatial pattern metrics to the marine realm. Mar Ecol Prog Ser 427:219–232CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.School of Biological SciencesQueen’s University of BelfastBelfastNorthern Ireland, UK
  2. 2.NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine and Delta SystemsUtrecht UniversityYersekeThe Netherlands
  3. 3.School of Natural Sciences, Zoology BuildingTrinity College DublinDublin 2Ireland

Personalised recommendations