Open Advanced Search
Get 20M+ Full-Text Papers For Less Than $1.50/day.
Start a 14-Day Trial for You or Your Team.
Learn More →
Habitat Provision and Erosion Are Influenced by Seagrass Meadow Complexity: A Seascape Perspective
Habitat Provision and Erosion Are Influenced by Seagrass Meadow Complexity: A Seascape Perspective
Ferretto, Giulia;Vergés, Adriana;Poore, Alistair G. B.;Glasby, Tim M.;Griffin, Kingsley J.
diversity Article Habitat Provision and Erosion Are Inﬂuenced by Seagrass Meadow Complexity: A Seascape Perspective 1 , 2 , 1 , 3 1 , 3 1 , 4 1 , 2 Giulia Ferretto * , Adriana Vergés , Alistair G. B. Poore , Tim M. Glasby and Kingsley J. Grifﬁn Centre for Marine Science and Innovation, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia School of Biological Sciences & Oceans Institute, University of Western Australia, Perth, WA 6009, Australia Sydney Institute of Marine Science, Mosman, NSW 2088, Australia NSW Department of Primary Industries, Port Stephens Fisheries Institute, Port Stephens, NSW 2315, Australia * Correspondence: firstname.lastname@example.org Abstract: Habitat complexity plays a critical role in shaping biotic assemblages and ecosystem processes. While the impacts of large differences in habitat complexity are often well understood, we know less about how subtle differences in structure affect key ecosystem functions or properties such as biodiversity and biomass. The late-successional seagrass Posidonia australis creates vital habitat for diverse fauna in temperate Australia. Long-term human impacts have led to the decline of P. australis in some estuaries of eastern Australia, where it is now classiﬁed as an endangered ecological community. We examined the inﬂuence of P. australis structural complexity at small (seagrass density) and large (meadow fragmentation) spatial scales on ﬁsh and epifauna communities, predation and sediment erosion. Fine-scale spatially balanced sampling was evenly distributed across a suite of environmental covariates within six estuaries in eastern Australia using the Generalised Random Tessellation Structures approach. We found reduced erosion in areas with higher P. australis density, greater abundance of ﬁsh in more fragmented areas and higher ﬁsh richness in vegetated areas further from patch edges. The abundance of epifauna and ﬁsh, and ﬁsh species richness were Citation: Ferretto, G.; Vergés, A.; higher in areas with lower seagrass density (seagrass density did not correlate with distance to patch Poore, A.G.B.; Glasby, T.M.; Grifﬁn, edge). These ﬁndings can inform seagrass restoration efforts by identifying meadow characteristics K.J. Habitat Provision and Erosion that inﬂuence ecological functions and processes. Are Inﬂuenced by Seagrass Meadow Complexity: A Seascape Perspective. Keywords: ecosystem function; seascape ecology; endangered seagrass; seagrass restoration; Diversity 2023, 15, 125. https:// doi.org/10.3390/d15020125 Posidonia australis Academic Editors: Lina Mtwana Nordlund, Jonathan S. Lefcheck, Salomão Bandeira, Stacey 1. Introduction M. Trevathan-Tackett Seascape habitat structure and complexity (hereafter referred to as habitat complexity) and Michael Wink can strongly inﬂuence biotic assemblages, ecosystem functioning and processes . For Received: 18 November 2022 example, more complex habitats generally host higher richness and abundance of asso- Revised: 16 December 2022 ciated species because they provide a greater variety of niches . Differences in habitat Accepted: 28 December 2022 complexity can also inﬂuence predator-prey interactions [3,4], for example by altering Published: 17 January 2023 availability of shelter for prey and access to predators . Seascape habitat complexity incorporates small-scale structural complexity (e.g., shoot density of plants) and large-scale variables related to the spatial conﬁguration and fragmen- tation of habitats . Our understanding of how ecosystem functions and processes are Copyright: © 2023 by the authors. impacted by different components of habitat complexity is limited and at times, conﬂicting. Licensee MDPI, Basel, Switzerland. For example, several studies suggest fragmentation leads to declines in carbon stocks and This article is an open access article biodiversity [7–9]. In contrast, Fahrig  reports positive effects of fragmentation ‘per se’ distributed under the terms and on biodiversity and habitat functioning. conditions of the Creative Commons The growth of human populations, overexploitation of natural resources and climate Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ change are modifying ecosystems globally, increasing habitat degradation and unpre- 4.0/). dictably altering habitat complexity [11–13], resulting in impacts on biodiversity , Diversity 2023, 15, 125. https://doi.org/10.3390/d15020125 https://www.mdpi.com/journal/diversity Diversity 2023, 15, 125 2 of 19 ecosystem functioning [15,16] and ecosystems services [17–19]. The decline in foundation species (i.e., species that support biodiversity and deﬁne the structure of a community) can have dramatic consequences for habitat complexity and dependent species, leading to disproportionate effects on ecosystem functions [14,20,21]. Marine ecosystems are heavily impacted by human activities globally  and recent decades have seen extensive changes in the overall abundance and habitat complexity of habitats such as kelp forests  and seagrass meadows [23,24]. Seagrasses are marine ﬂowering plants that form extensive coastal habitats that support high biodiversity and provide a range of ecosystem functions . Some seagrasses can capture and store carbon more efﬁciently than terrestrial plants [26,27], create a dense habitat where a wide variety of fauna can live, ﬁnd protection and/or forage [28,29], and they prevent coastal erosion by stabilising sediment . Seagrass habitats support higher biodiversity and perform a range of ecosystem functions beyond unvegetated habitats [31–33] but we have limited information on how differences in habitat complexity are impacting these relationships. Habitat complexity can inﬂuence the composition of seagrass associated macroinver- tebrates  and ﬁsh [35–37], and impact ecological processes like predation . This has been investigated by isolating individual aspects of habitat complexity, such as patch size and distance to meadow edge [32,38–40]. Fish assemblages across estuarine seascapes are also shaped by seagrass meadows  and by landscape patterns at different spatial scales, including habitat composition and conﬁguration . Differences in habitat complexity can alter sediment movement  for example by altering hydrodynamic ﬂow at within- patch  and meadow scales . However, our understanding of how seagrass habitat complexity at multiple spatial scales inﬂuences ecosystem functions is limited and having this information can guide conservation and restoration approaches. In this study we examine how habitat complexity relates to ecosystem functions (ﬁsh and epifauna composition, predation and sediment erosion rates) at several spatial scales to inform conservation and restoration of the threatened seagrass, Posidonia australis (Hook.f.). This seagrass is endemic to the southern half of Australia, where it creates dense meadows in sheltered and shallow bays. Some economically important species of ﬁsh and invertebrates depend on P. australis complex habitat during their juvenile stages or during their whole life [45,46]. There is evidence that some ﬁsh species respond to small-scale differences in P. australis habitat complexity  and macrofauna abundance may be driven by habitat preference  but little is known about the consequences that differences in habitat complexity may have on seagrass-associated species assemblages. Long-term human impacts have led to the decline of P. australis in some estuaries of eastern Australia. Six populations of P. australis in south-eastern Australia being listed as endangered under NSW Government legislation in 2012 (Fisheries Management Act 1994 (NSW), Australia) and an additional three estuaries were also listed as threatened ecological communities under the Commonwealth legislation in 2015 (Environment Protection and Biodiversity Conservation Act 1999 (Cth), Australia (EPBC Act)). Some populations are still declining despite protection [49,50], due to multiple impacts including boat moorings , dredging and construction . P. australis can take decades to recover after disturbances due to slow growth rates . Small-scale restoration projects using innovative techniques to revegetate areas where P. australis has declined are having success  but the challenge now, in the UN Decade of Ecosystem Restoration, is to scale-up restoration efforts . Mitigation approaches such as conservation and restoration projects are becoming essential tools to reverse the decline of foundation species such as seagrasses. Thus, quantifying the relationships between habitat complexity and ecosystem functions better informs restoration strategies and outcomes (e.g., by helping select targets for restoration projects). This is particularly relevant for the recovery of ecological communities associated with endangered and slow-growing species like P. australis. Diversity 2023, 15, 125 3 of 19 Working across six estuaries that include some of the most impacted P. australis meadows in south-eastern Australia, we quantify how habitat complexity (seagrass density, meadow fragmentation and distance to meadow edge) is related to: (1) ﬁsh abundance and species composition, (2) mobile epifauna abundance, (3) ﬁsh predation rates and (4) erosion rates. As marine fauna and processes can respond to complexity at different spatial scales , we used a seascape approach that incorporates variability at different levels including sub-patch (e.g., seagrass shoot scale; ), within-patch e.g., distance to meadow edge; ) and among-patch (connectivity) or seascape scale . Seascape ecology is a growing ﬁeld of marine science that brings spatial approaches common in terrestrial landscape ecology into marine ecosystems to help resolve spatial patterns [59,60]. Including a seascape approach allows a deeper understanding of heterogenous marine ecosystems and their connections, incorporating the natural interconnectivity of seascapes, which are shaped by patterns and processes that operate at multiple spatial scales [6,59]. 2. Materials and Methods 2.1. Study Estuaries and a-Priori Site Selection Fish and epifauna composition, sediment erosion and predation rates were quantiﬁed in Posidonia australis meadows in six different estuaries in New South Wales, Australia (Figure 1). These included three estuaries in which P. australis is classiﬁed as endangered under the NSW and Commonwealth Government legislation: Lake Macquarie ( 33.049637, 151.647302), Pittwater ( 33.591091,151.318788) and Botany Bay ( 34.006181, 151.193384), and three estuaries where P. australis is not endangered under NSW Government legislation: Port Stephens ( 32.718373, 152.125055), Jervis Bay ( 35.040582, 150.784482) and St Georges Basin ( 35.140870, 150.638630; Figure 1). The P. australis ecological community in Port Stephens is listed as endangered under the Commonwealth legislation. P. australis is con- ﬁned to only 17 of the 121 NSW estuaries known to contain seagrass  and only grows in three geomorphic types of estuaries, speciﬁcally ocean embayments (Botany Bay and Jervis Bay), tide-dominated estuaries (Port Stephens and Pittwater) and wave-dominated estuaries (Lake Macquarie and St Georges Basin) see  for details on the characterises of these estuaries. Although the six estuaries display some different environmental charac- teristics (Table S1), the sampled areas included here are characterised by broadly similar oceanographic regimes that enable Posidonia australis to occur, including high salinity, low nutrients levels and relatively stable environmental conditions . P. australis distribution was initially identiﬁed using the high-quality imagery program NearMap Australia. High-resolution spatial layers describing the area and extent of P. australis were obtained using the latest available seagrass mapping in each estuary (NSW Fisheries Spatial Data Portal) . We selected a single meadow within each estuary, outside of any Marine Park Sanctuary Zones (present in Port Stephens and Jervis Bay) and away from boat moorings, to avoid potentially confounding processes. Each selected 2 2 meadow has an area ranging from 200,000 m to 600,000 m . Traditional sampling in a stratiﬁed random pattern (e.g., with repeated samples taken from inside or outside habitat patches) produces clusters of sites in similar spatial and environmental settings, and corresponding gaps in sampling effort. Spatially balanced sam- pling aims to resolve the clustering issue by selecting sites a priori that are evenly dispersed across space and a set of landscape-related covariates. These methods are particularly useful for landscape (or seascape) studies  and are now used in marine systems , facilitated by an increase in the availability of marine spatial data. Within each meadow, spe- ciﬁc GPS sampling sites (thereafter, sites) were selected a priori using Generalised Random Tessellation Structures (GRTS; ). The area covered was approximately 90,000 m per meadow. The GRTS algorithm pre-selected 15 sites algorithmically to ensure that they were spatially balanced, i.e., evenly separated along both spatial and predictor scales (level of fragmentation, area of seagrass, and distance to patch edge; see Table S2 for complete data). Site depth ranges are as follows: 1.0–4.1 m in Port Stephens, 0.7–3.5 m in Lake Macquarie, 1.3–3.3 m in Pittwater, 1.0–4.0 m in Botany Bay, 1.5–4.9 m in Jervis Bay and 1.2–3.7 m in Diversity 2023, 15, 125 4 of 19 St Georges Basin (Table S2). Minimum distance between sites was 20 m and a site could be on a bare patch or on seagrass. Gridded spatial layers were generated at each site to represent the level of meadow fragmentation, area of seagrass, and distance to patch edge, based on the seagrass mapping data described above. Seagrass area and fragmentation (perimeter-area ratio, Figure 2) were calculated in a 50 m, 250 m and 500 m radius from each site (noting that multiple methods exist to measure fragmentation; noting that multiple methods exist to measure fragmentation; [65,66]). In this study, the term ‘fragmentation’ refers to the level of patchiness of the seagrass on a continuum from a continuous meadow conﬁguration to a more heterogeneous seascape of bare patches within the meadow to a conﬁguration made up of patches of seagrass in a matrix of bare sand (Figure 2). Distance to patch edge was derived from the same data and was estimated as the distance from the site to the nearest edge of seagrass (Figure 2). These steps in the sampling selection process were completed in the R statistical environment (R Core Team 2021). Packages ‘rgdal’ , ‘raster ’  and ‘geosphere’  were used to manipulate the spatial data, and ‘SDMTools’ was used to calculate the fragmentation statistics (now superseded by the ‘landscapemetrics’ package). 2.2. Sampling Sampling took place by free diving during the late spring/summer months (November–January) in all estuaries, to avoid seasonal variations that might influence epifauna  and fish communities . Botany Bay and Port Stephens were visited during 2019–2020 summer, while the remaining estuaries were sampled during 2020–2021 summer due to COVID-19 travel restrictions that came into place in early 2020. At each of the 15 preselected sites per meadow, we recorded in situ P. australis shoot density (where seagrass was present) by counting individual shoots present in a 25 25 cm (0.0625 m ) quadrat. All the in situ measurements were taken within 2 m of the GPS sampling site. 2.3. Variation in Abundance of Epifauna with Habitat Complexity The mobile epifaunal community was quantiﬁed at each preselected site using artiﬁcial seagrass made to mimic P. australis (Figure S1a,b). Artiﬁcial seagrass was used instead of sampling real shoots to standardise sampling (including age and size of ‘shoots’) and to avoid collecting shoots of an endangered seagrass. Each unit was individually tagged and contained one wooden pole covered by a green plastic material and six pieces of partially frayed brown rope, one at the top, four at mid-height and one at the base. One unit was deployed in each site (n = 15 per meadow). Individual artiﬁcial seagrass units were collected after 4 to 5 weeks using a 1 L plastic jar underwater. The plastic material and the pieces of ropes were pulled out as a unit from the pole and quickly placed into the container to retain all invertebrates. The container was immediately closed to minimise loss of epifauna. Samples were stored in 5% formaldehyde solution with marine water. Prior to sorting, samples were rinsed in freshwater and contents were passed through a 500 m sieve. Invertebrates of 10 sites from each estuary (except in Pittwater where we could only ﬁnd and recover 8 artiﬁcial seagrass units) were sorted to morphospecies under a dissecting microscope, counted and stored in 70% ethanol. Samples that had been highly colonised by epifauna were carefully subsampled using a Folsom Plankton Splitter in order to sort approximately the same amount of invertebrates in each sample. Diversity 2023, 15, 125 5 of 19 Diversity 2023, 15, x FOR PEER REVIEW 4 of 19 Figure 1. The coastline sampled along the east coast of Australia is highlighted by the red box (A). Figure 1. The coastline sampled along the east coast of Australia is highlighted by the red box (A). Map of the six estuaries sampled in New South Wales (B). Map credits: Jordan Gacutan. Aerial im- Map of the six estuaries sampled in New South Wales (B). Map credits: Jordan Gacutan. Aerial agery of the six meadows sampled for this study with sampling sites in yellow: Port Stephens, Lake imagery Macqua of rie, P the isix ttwater, Botany meadows sampled Bay, Jervi for s Bay this and St G study with eorg sampling es Basin. sites Imagin ery yellow: collected fro Port Stephens, m Google Lake Earth (2021). Under the NSW Government legislation P. australis is classified as non-endangered in Macquarie, Pittwater, Botany Bay, Jervis Bay and St Georges Basin. Imagery collected from Google Port Stephens, Jervis Bay, St Georges Basin, while it is classified as endangered in Lake Macquarie, Earth (2021). Under the NSW Government legislation P. australis is classiﬁed as non-endangered in Pittwater and Botany Bay. Port Stephens, Lake Macquarie, Pittwater and Botany Bay are also listed Port Stephens, Jervis Bay, St Georges Basin, while it is classiﬁed as endangered in Lake Macquarie, as endangered ecological communities under the Commonwealth legislation. Pittwater and Botany Bay. Port Stephens, Lake Macquarie, Pittwater and Botany Bay are also listed as endangered ecological communities under the Commonwealth legislation. P. australis distribution was initially identified using the high-quality imagery pro- gram NearMap Australia. High-resolution spatial layers describing the area and extent of P. australis were obtained using the latest available seagrass mapping in each estuary (NSW Fisheries Spatial Data Portal) . We selected a single meadow within each estu- ary, outside of any Marine Park Sanctuary Zones (present in Port Stephens and Jervis Bay) Diversity 2023, 15, x FOR PEER REVIEW 5 of 19 and away from boat moorings, to avoid potentially confounding processes. Each selected 2 2 meadow has an area ranging from 200,000 m to 600,000 m . Traditional sampling in a stratified random pattern (e.g., with repeated samples taken from inside or outside habitat patches) produces clusters of sites in similar spatial and environmental settings, and corresponding gaps in sampling effort. Spatially bal- anced sampling aims to resolve the clustering issue by selecting sites a priori that are evenly dispersed across space and a set of landscape-related covariates. These methods are particularly useful for landscape (or seascape) studies  and are now used in marine systems , facilitated by an increase in the availability of marine spatial data. Within each meadow, specific GPS sampling sites (thereafter, sites) were selected a priori using Generalised Random Tessellation Structures (GRTS; GRTS; ). The area covered was approximately 90,000 m per meadow. The GRTS algorithm pre-selected 15 sites algorith- mically to ensure that they were spatially balanced, i.e., evenly separated along both spa- tial and predictor scales (level of fragmentation, area of seagrass, and distance to patch edge; see Table S2 for complete data). Site depth ranges are as follows: 1.0–4.1 m in Port Stephens, 0.7–3.5 m in Lake Macquarie, 1.3–3.3 m in Pittwater, 1.0–4.0 m in Botany Bay, 1.5–4.9 m in Jervis Bay and 1.2–3.7 m in St Georges Basin (Table S2). Minimum distance between sites was 20 m and a site could be on a bare patch or on seagrass. Gridded spatial layers were generated at each site to represent the level of meadow fragmentation, area of seagrass, and distance to patch edge, based on the seagrass mapping data described above. Seagrass area and fragmentation (perimeter-area ratio, Figure 2) were calculated in a 50 m, 250 m and 500 m radius from each site (noting that multiple methods exist to measure fragmentation; noting that multiple methods exist to measure fragmentation; [65,66]). In this study, the term ‘fragmentation’ refers to the level of patchiness of the seagrass on a continuum from a continuous meadow configuration to a more heterogene- ous seascape of bare patches within the meadow to a configuration made up of patches of seagrass in a matrix of bare sand (Figure 2). Distance to patch edge was derived from the same data and was estimated as the distance from the site to the nearest edge of seagrass (Figure 2). These steps in the sampling selection process were completed in the R statistical environment (R Core Team 2021). Packages ‘rgdal’ , ‘raster’  and ‘geosphere’  Diversity 2023, 15, 125 6 of 19 were used to manipulate the spatial data, and ‘SDMTools’ was used to calculate the frag- mentation statistics (now superseded by the ‘landscapemetrics’ package). Figure 2. Description of the variables used to measure seagrass complexity and included in the analyses. The dot represents a GPS sampling site. Fragmentation (seagrass perimeter-area ratio in a 500 m radius from each GPS sampling site, dashed lines), distance to patch edge (double arrows) and P. australis shoot density. 2.4. Variation in Fish Community with Habitat Complexity The ﬁsh community was quantiﬁed at each preselected site with remote underwater video by deploying one GoPro Hero 4 (Figure S1c). Using stereo cameras was considered but logistically not feasible and instead we opted to sample more sites simultaneously. Sampling took place at high tide from 9 am to 2 pm on sunny days. Cameras were attached to an adjustable metal stand (ranging from 30 to 70 cm height) such that they were at the top of the seagrass canopy (Figure S1c), with a ﬂoat on the surface to identify the location and assist retrieval. Visibility was assessed on site and sampling only proceeded if visibility was more than 1.5 m. The 15 cameras in each meadow were left recording simultaneously. After excluding the ﬁrst 5 min from each video to eliminate deployment disturbance, 40 simultaneous minutes per estuary were selected and analysed using the EventMeasure software (version 5.41, created by Jim Seager, Bacchus Marsh, Victoria, Australia; SeaGis Pty. Ltd., www.seagis.com.au). To characterise the ﬁsh community in each video, we calculated the species richness (number of species) and MaxN (the maximum number of ﬁsh of each species recorded in a single frame). MaxN is a commonly used conservative method that avoids re-counting of the same ﬁsh individuals [71,72]. The sum of each species’ MaxN gave the total relative abundance at each sampling site. Data on ﬁsh functional traits (feeding information, Table S2) were collected to under- stand whether the ﬁsh functional traits could explain the relationship between the ﬁsh abundance and the predictors. The feeding information for each ﬁsh species was extracted from the online resource FishBase (ﬁshbase.se) . Fish species were classiﬁed into the following four groups: carnivore (piscivorous and non-piscivorous ﬁsh), planktivore, her- bivore (eating mostly macroalgae/seagrass), omnivore (eating some macroalgae/seagrass). For species lacking ecological information on FishBase, we gathered trait data from the liter- ature where possible and remaining species data were extracted from the online resources Fishes of Australia  and The Australian Museum  (https://ﬁshesofaustralia.net.au, https://australianmuseum.net.au, accessed on 28 April 2022; see Table S2 for complete table and data sources). Diversity 2023, 15, 125 7 of 19 2.5. Variation in Predation Rates with Habitat Complexity Predation rates were investigated using the standardised ‘squid-pop’ method . Equally sized pieces of dried squid (2 2 cm) were secured to the top of a pole using ﬁshing line. One pole was deployed at each preselected GPS site, with the squid at the top of the seagrass canopy. Squid-pops were visually checked after 1 h of deployment and removed if the bait was eaten. Remaining squid-pops were checked after 24 h and then were all removed. Squid-pop loss was recorded as ‘1’ where the entire bait was eaten, or ‘0’ where the whole or part of the bait remained. 2.6. Variation in Erosion with Habitat Complexity Erosion was measured using the depth of disturbance (DOD) rod method . One DOD rod was placed at each preselected site and measured after 4 to 5 weeks. A DOD rod consists of a stainless-steel rod (5 mm diameter and 1.2 m length) which we positioned protruding 49 cm above the sediment, with a loosely ﬁtted washer on the rod laying on the seabed. When the sediment is eroded the washer sinks and the maximum erosion is given by the difference between the ﬁnal and the initial elevation of the washer. 2.7. Statistical Analysis We tested for correlation among predictors using the R function ggpairs in the package GGally and ggcorplot in ggplot2 to ensure variables were not highly correlated. Using a cutoff of r > 0.45, seagrass area and fragmentation within 50 m and 250 m radius and seagrass area at 500 m were not included in the models due to high correlation (Figure S2). Hereafter ‘fragmentation’ refers to fragmentation at 500 m. We used generalised linear mixed models (GLMMs) to test the inﬂuence of the predictor variables on each of the response variables, with ‘estuary’ as a random effect. The predictor variables considered were all continuous measurements: ‘P. australis shoot density’, ‘meadow fragmentation’ and ‘distance to patch edge’. There was a separate model for each response variable: ‘sediment erosion’, ‘epifauna abundance’, ‘relative abundance of ﬁsh’ (using the negative binomial family), ‘relative abundance of ﬁsh per feeding group’ (using the negative binomial family), ‘ﬁsh richness’ (Poisson family), ‘predation after 1 h’ and ‘predation after 24 h’ (binomial family). GLMMs were ﬁtted using the glmmTMB function in the glmmTMB package . Statistical analyses and graphs were performed using the software R (version 4.0.2; R Core Team 2020) and relied on the tidyverse workﬂow  and ggplot2 . 3. Results 3.1. Variation in Abundance of Epifauna with Habitat Complexity A total of 55,224 individuals of mobile epifauna were counted across the six meadows, with abundance ranging from 23,272 invertebrates collected at Pittwater and 17,752 at Botany Bay to only 1692 invertebrates collected at Lake Macquarie. Amphipod crustaceans accounted for 82% of all individuals collected, with 10% from the family Caprellidae, fol- lowed by polychaete worms (5.4%). Total abundance of epifauna was signiﬁcantly higher in areas with lower seagrass density (p < 0.001, Figure 3b, Table 1), where seagrass density was the only signiﬁcant predictor. 3.2. Variation in Fish Community with Habitat Complexity A total of 52 species of ﬁsh were observed across the six meadows (Table S3), ranging from 31 species in Jervis Bay to 20 species in St Georges Basin and Pittwater. Fish richness declined with increasing seagrass density (p < 0.05, Figure 4b, Table 1) and increasing distance to patch edge (p < 0.001, Figure 4a, Table 1) and was not signiﬁcantly associated with seagrass fragmentation (Figure 4c, Table 1). Diversity 2023, 15, x FOR PEER REVIEW 8 of 19 Diversity 2023, 15, 125 8 of 19 Figure 3. The relationships between abundance of epifauna and (a) distance to patch edge, (b) P. Figure 3. The relationships between abundance of epifauna and (a) distance to patch edge, (b) P. aus- australis shoot density/0.0625 m and (c) seagrass fragmentation. Sites on bare sediment have a neg- tralis shoot density/0.0625 m and (c) seagrass fragmentation. Sites on bare sediment have a negative ative value for distance to edge. Each point represents a sampling site, coloured by estuary, with the value for distance to edge. Each point represents a sampling site, coloured by estuary, with the points jittered to avoid overplotting. Fitted lines are predictions ± 95% Coefficient Intervals from points jittered to avoid overplotting. Fitted lines are predictions 95% Coefﬁcient Intervals from generalised linear mixed models. generalised linear mixed models. Table 1. Model outputs for each response variable after performing generalised linear mixed models Table 1. Model outputs for each response variable after performing generalised linear mixed models including the 3 predictors as fixed factors and estuary as a random effect. Asterisks indicate signif- including icant p-va thel3 ues ( predictors * for p-value as ﬁxed ≤ 0.05, ** for factors and p-va estuary lue ≤ 0. as 01, *** for a random p-value effect.≤Asterisks 0.001). indicate signiﬁcant p-values (* for p-value 0.05, ** for p-value 0.01, *** for p-value 0.001). Response Variables Predictor Variables Estimate p-Value Relative abundance of fish Distance to meadow edge 0.002 0.32 Response Variables Predictor Variables Estimate p-Value Fragmentation 1.61 0.004 ** Distance to meadow edge 0.002 0.32 Shoot density −0.037 0.001 ** Fragmentation 1.61 0.004 ** Relative abundance of ﬁsh Fish richness Distance to meadow edge 0.004 <0.001 *** Shoot density 0.037 0.001 ** Fragmentation 0.44 0.34 Distance Shoot density to meadow edge 0.004 −0.01 0 <0.001.03 * *** Epifauna abundance Distance to meadow edge −0.003 0.17 Fragmentation 0.44 0.34 Fish richness Fragmentation −0.63 0.75 Shoot density 0.01 0.03 * Distance to meadow edge 0.003 0.17 Epifauna abundance Fragmentation 0.63 0.75 Shoot density 0.06 0.0004 *** Diversity 2023, 15, x FOR PEER REVIEW 9 of 19 Shoot density −0.06 0.0004 *** Predation after 1 h Distance to meadow edge −0.01 0.08 Fragmentation −4.65 0.05 Diversity 2023, 15, 125 9 of 19 Shoot density 0.02 0.55 Predation after 24 h Distance to meadow edge −0.009 0.31 Fragmentation 0.29 0.94 Table 1. Cont. Shoot density 0.01 0.72 Response Variables Predictor Variables Estimate p-Value Sediment erosion Distance to meadow edge 0.005 0.07 Distance to meadow edge 0.01 0.08 Fragmentation −0.89 0.15 Shoot density 0.037 0.004 ** Fragmentation 4.65 0.05 Predation after 1 h Shoot density 0.02 0.55 3.2. Variation in Fish Community with Habitat Complexity Distance to meadow edge 0.009 0.31 A total of 52 species of fish were observed across the six meadows (Table S3), ranging Fragmentation 0.29 0.94 Predation after 24 h from 31 species in Jervis Bay to 20 species in St Georges Basin and Pittwater. Fish richness Shoot density 0.01 0.72 declined with increasing seagrass density (p < 0.05, Figure 4b, Table 1) and increasing dis- Distance to meadow edge 0.005 0.07 tance to patch edge (p < 0.001, Figure 4a, Table 1) and was not significantly associated with seagrass fragmentation (Figure 4c, Table 1). Fragmentation 0.89 0.15 Sediment erosion Shoot density 0.037 0.004 ** Figure 4. The relationships between species richness of ﬁsh and (a) distance to seagrass edge, (b) P. australis shoot density/0.0625 m and (c) seagrass fragmentation. Color patterns and ﬁgure details are described in Figure 3. Diversity 2023, 15, x FOR PEER REVIEW 10 of 19 Figure 4. The relationships between species richness of fish and (a) distance to seagrass edge, (b) P. australis shoot density/0.0625 m and (c) seagrass fragmentation. Color patterns and figure details are described in Figure 3. A total of 2385 fish (sum of MaxN) were observed during the study across the six meadows, ranging from 265 in Port Stephens to 663 in St Georges Basin. The total relative abundance of fish decreased with increasing seagrass density (p < 0.01, Figure 5b, Table 1). Total relative abundance of fish also increased with increasing seagrass fragmentation (p < 0.01, Figure 5c, Table 1) but did not vary with distance to edge of a patch (Figure 5a, Table 1). Most fish species were carnivorous (n = 30), 16 were omnivores, four were plankti- Diversity 2023, 15, 125 10 of 19 vores and only two were herbivores (Table S1). Total relative abundance of carnivorous fishes declined with increasing seagrass density (p < 0.001), increased with increasing seagrass fragmentation (p < 0.001) and did not vary with distance to edge. Total relative A total of 2385 ﬁsh (sum of MaxN) were observed during the study across the six abundance of herbivores only declined with increased fragmentation (p < 0.05). The rela- meadows, ranging from 265 in Port Stephens to 663 in St Georges Basin. The total relative tive abundance of the other groups of fish (omnivores and planktivores) was not signifi- abundance of ﬁsh decreased with increasing seagrass density (p < 0.01, Figure 5b, Table 1). cantly associated with any of the predictor variables. Total relative abundance of ﬁsh also increased with increasing seagrass fragmentation (p < 0.01, Figure 5c, Table 1) but did not vary with distance to edge of a patch (Figure 5a, Table 1). Figure 5. Relationships between total relative abundance (MaxN) of ﬁsh and (a) distance to seagrass edge, (b) P. australis shoot density/0.0625 m and (c) seagrass fragmentation. Color patterns and ﬁgure details are described in Figure 3. Most ﬁsh species were carnivorous (n = 30), 16 were omnivores, four were planktivores and only two were herbivores (Table S1). Total relative abundance of carnivorous ﬁshes declined with increasing seagrass density (p < 0.001), increased with increasing seagrass fragmentation (p < 0.001) and did not vary with distance to edge. Total relative abundance of herbivores only declined with increased fragmentation (p < 0.05). The relative abundance of the other groups of ﬁsh (omnivores and planktivores) was not signiﬁcantly associated with any of the predictor variables. Diversity 2023, 15, 125 11 of 19 3.3. Variation in Erosion with Habitat Complexity Sediment erosion (ranging from 0 to 16 cm over the entire sampling time) varied with seagrass density (p < 0.01, Table 1), with denser P. australis having the least erosion. No other predictor variables were associated with changes in erosion. 3.4. Variation in Predation Rates with Habitat Complexity On average, 31% (10%) of squid pops were eaten after 1 h and 71% were eaten after 24 h (13%) across the six estuaries. Predation rates had no signiﬁcant relationship with any of the measured biotic variables (Table 1). 4. Discussion This study adds to our understanding of the relationships between structural attributes of seagrass meadows and their associated biotic assemblages and functional processes. We tested how a range of habitat complexity measures (at within-patch to seascape scales) inﬂuenced functional habitat provisioning for ﬁsh and invertebrate communities, along with rates of predation and sediment erosion in six seagrass meadows. Fish were more abundant in areas with high levels of habitat fragmentation and both ﬁsh and epifauna were less abundant where seagrass density was greatest. We found lower ﬁsh species richness in areas with denser seagrass, but richness was higher in vegetated areas further from patch edges (seagrass density did not correlate with distance to patch edge). Similar to other studies, sediment erosion was reduced in densely vegetated areas . These ﬁndings highlight the complexity of the relationship between habitat spatial conﬁguration and functional habitat provision. 4.1. Habitat Use and Predation by Fish While there were some consistent patterns in habitat use and predation responses to habitat structure across the six estuaries, these patterns did not always match the general patterns in the literature. In contrast to previous studies [29,36,81]; but see , we found that ﬁsh abundance and richness declined with increases in seagrass density, which represented the smallest scale at which habitat complexity was measured. Accordingly, there was no indication of a threshold level of shoot density that ensured the use of seagrass meadows by ﬁsh. However, we acknowledge that the environmental differences among the estuaries may have inﬂuenced some of the results (Table S1; ). The sampled meadows displayed variable ranges of shoot density (as per ): the seagrass in St Georges Basin 2 2 reached 50 shoots/0.0625 m (~800 shoots/m ) while the meadows in all other estuaries had 2 2 between 10–30 shoots/0.0625 m (~160–480 shoots/m ). This is not entirely unexpected, as species assemblages often differ between low and high densities of seagrass  and not all ﬁsh species respond to seagrass density . The majority of the ﬁsh observed were classiﬁed as carnivores and they were the only ones inﬂuenced by seagrass density (less ﬁsh in denser seagrass), suggesting that most ﬁsh observed here are not driven by the need of ﬁnding refuge but perhaps by presence of other smaller ﬁsh as prey. While not addressed within this study, some of the ﬁsh sampled here may be residents of the meadow , and thus may be more affected by seagrass density than transient species. Detecting ﬁsh where vegetation is particularly dense may also require more detailed methods than remote cameras [84–86]. This sampling method mostly detected the supra-canopy ﬁshes associated with the meadow whereas methods such as visual census may be more appropriate for sampling within-canopy species . Habitat fragmentation appears to be an inconsistent but important driver of ﬁsh species richness and abundance at seascape scales. In this study, ﬁsh abundance increased with fragmentation, supporting that seascape-scale spatial arrangement of habitats inﬂu- enced seagrass ﬁsh communities . This relationship was driven by the ﬁsh species belonging to the carnivore functional group and may be related to a predatory behaviour that is facilitated in a more fragmented habitat. In contrast, herbivorous ﬁsh were less abundant in more fragmented areas. Fragmented seagrass beds may create a more di- Diversity 2023, 15, 125 12 of 19 verse habitat, with seagrass interspersed with bare sediments, attracting ﬁsh with different habitat preferences . There was not, however, evidence of a relationship between ﬁsh species richness and fragmentation in this study. The greater ﬁsh richness observed in vegetated sites toward the middle of the meadow in this study may suggest that many ﬁsh we observed may be utilising the seagrass meadows both as a refuge and for forag- ing . However, there was no evidence that the abundance of any functional groups was signiﬁcantly inﬂuenced by distance to edge. Fish communities can be more abundant and diverse at seagrass edges [89,90] and responses to edges are often species-speciﬁc [89,91] or depend on patch size . In contrast, this study did not detect positive edge effects on ﬁsh communities. This result may be explained by the variability of responses by the species sampled as some may have a stronger association with interior areas, others with edges or with both habitats [39,92]. Many individuals observed in this study were juveniles (pers. obs.), providing support for the utility of seagrass beds as a preferred nursery area [45,93]. In this study, predation was not influenced by any variables. Predation success and foraging are often greater in more fragmented areas [94,95] or at edges , however, presence of top-level predators (not targeted in this study) may alter this relation- ship [97,98] by inducing a predator avoidance behaviour. Although the squid-pop technique is commonly used worldwide , it may be targeting only a limited range of fish species . The high rate of squid pop consumption overnight may be explained by diel migration of some species of fish between vegetated and bare habitat . This can often be the reason of a greater abundance of fish in seagrass beds at night [99,100]. 4.2. Use of Seagrass Habitats by Invertebrate Epifauna We observed greater numbers of invertebrates in less dense seagrass areas. This is in contrast with some previous results, that showed more abundant epifauna in areas with higher P. australis densities due to animals preference for more complex habitat . Predation success may be reduced in dense vegetation [93,101], however, there may be exceptions related to the predatory behaviours, as ambush predators are not be negatively impacted by vegetation [102,103]. Mobile invertebrates are often more abundant in complex habitats with high levels of epiphytes , rather than greater macrophyte complexity [70,104]. This may explain, beyond the effect of the large range of shoot density sampled here, the more abundant epifauna found where epiphytic algae grew or drifted on the deployed artificial seagrass (Botany Bay and Pittwater, pers. obs.). Epiphyte abundance and composition could also be driven by the presence of seagrass detritus, with some species using it as food source or as physical habitat . Previous studies have found that proximity to the edge of seagrass meadows strongly inﬂuenced epifauna abundance, with different patterns depending on the epifauna group e.g., cumaceans increase at edges, while amphipods decline from seagrass to sand; ). We did not ﬁnd any edge-effect, with this study adding to those that have found variable responses of epifauna to edges, including those with a greater abundance of inverte- brates [40,107] or those ﬁnding that habitat centres have more abundant invertebrates . 4.3. Variation in Erosion with Seagrass Density Sediment erosion was reduced in denser areas. This supports the critical function of seagrasses at reducing erosion and sediment movement by trapping and stabilising sediments [30,109,110]. Coastal erosion is affecting coasts worldwide and is often combatted through shore nourishment (i.e., deliberate placement of sand to restore a beach) or coastal hard constructions (e.g., groynes and breakwaters; ). These solutions are, however, usually temporary and not very cost-efﬁcient at a long term and can further alter local hydrodynamic conditions [109,111]. On the other hand, protecting and restoring vegetated beach foreshore habitats helps stabilise sediments and creates a natural self-sustaining system . Diversity 2023, 15, 125 13 of 19 4.4. Future Directions A key consideration after this study is that undertaking a “seascape” approach may provide highly valuable insights to understand faunal assemblages associated with seagrass. The application of techniques developed in landscape ecology can help unravel what drives the communities that inhabit and utilise marine habitats [6,56,59,113]. Understanding how processes and faunal communities respond to different components of a habitat is critical for modern conservation and restoration, as critical foundation species continue to decline globally [114,115]. Therefore, incorporating landscape-scale approach into site selection can improve restoration success . Habitat connectivity is a major driver for the distribution of fauna in marine sys- tems [34,87,117] and, although not directly measured in this study, it needs to be mentioned as the sampled areas displayed some different environmental characteristics (Table S1, Figure 1; ). Greater connectivity may reduce the impacts of urbanisation for more resilient species . The types of surrounding habitats, their complexity and their spatial connections can inﬂuence marine communities [119–121] for example by shaping ﬁsh move- ments. Individual ﬁsh can rely on different habitats, move among them with the tides, time of day and during different stages of their life . The presence of different surrounding habitats may enhance ﬁsh abundance and species  as they tend to prefer habitats with high diversity and high connectivity . A more detailed interrogation including seascape variables such as distance from natural reefs, mangroves and saltmarshes may contribute to explaining some of the patterns. Differences in environmental variables among sites/estuaries may have inﬂuenced some of the outcomes of this study, including the types of species present in the meadows of P. australis. Meadows in Port Stephens and in St Georges Basin were particularly narrow as the depth dropped very quickly into bare sediment, which may explain the high fragmentation and the small range in distance to edge. Port Stephens, Lake Macquarie, Pittwater and Botany Bay are located in catchments with a large human population density relative to Jervis Bay and St Georges Basin, and human activities could inﬂuence the estuaries differently. Different ﬁshing pressure among estuaries may have also played a role in the variability among sites, for example commercial ﬁshing is no longer permitted in Lake Macquarie, Botany Bay or St Georges Basin. Although Port Stephens and Jervis Bay estuaries are within marine parks, the speciﬁc sites sampled in these estuaries were not in ﬁshing exclusion (sanctuary) zones. The morphological characterises of the estuaries and position of each site relative to the mouth of the estuary also differed, which may affect water exchange and perhaps ﬁsh or invertebrate assemblages. However, estuaries were similar enough to enable P. australis to grow (such characteristics are found in only 17 of 121 seagrass estuaries in NSW ). Future studies could beneﬁt from sampling multiple meadows (with different levels of fragmentation) in each estuary. This study advanced understanding about what structural characteristics of P. australis meadows drive biotic assemblages and can inform strategies to manage endangered sea- grass habitats by informing decisions for future restorations, e.g., identifying speciﬁc meadow structural traits that may be targeted during conservation or restoration projects. For example, to reduce coastal erosion, the restoration target would include higher seagrass densities, supporting the thesis that seagrasses are critical at stabilising coastlines , while to ensure high species richness of ﬁsh, it may be preferable to protect vegetated areas further from the edge. On the other hand, if the target is to enhance biodiversity and improve habitat provision, these results showed no evidence of a seagrass density thresh- old, meaning that beneﬁts of restoration can be achieved without necessarily restoring meadows to the highest natural densities observed (e.g., ﬁsh were supported across a wide range of shoot densities). In conclusion, despite observing high variation in relationships with habitat provisioning, there is clear value in considering habitat spatial patterns at multiple scales in seagrass systems. Diversity 2023, 15, 125 14 of 19 Supplementary Materials: The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/d15020125/s1, Table S1: Environmental characteristics and aspects of the ecology of the six estuaries sampled in this study, based on [61,122,123]. NA—no commercial ﬁshing in these estuaries. Table S2: List of the sampling sites and variables obtained using Generalised Random Tessellation Structures (GRTS). Site ‘ps7 was not included in the analyses because too deep and not representative of a seagrass habitat. Table S3: list of ﬁsh species observed in the videos, including species functional traits (feeding information) [29,46,61,122–126]; Figure S1: example of (a) an artiﬁcial Posidonia unit in a seagrass patch and (b) in a bare area and (c) of a supra-canopy GoPro set up; Figure S2: correlation plots among variables: distance to patch edge, seagrass shoot density, area and level of fragmentation at 50 m, 250 m and 500 m of radius. Author Contributions: Conceptualization, G.F., A.G.B.P., A.V., T.M.G. and K.J.G.; Methodology, G.F., A.G.B.P., A.V., T.M.G. and K.J.G.; Formal Analysis, G.F., A.G.B.P. and K.J.G.; Investigation, G.F. and K.J.G.; Writing—Original Draft Preparation, G.F.; Writing—Review and Editing, G.F., A.G.B.P., A.V., T.M.G. and K.J.G.; Visualization, G.F. and A.G.B.P.; Funding Acquisition, G.F., A.G.B.P., A.V. and T.M.G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Sea World Research and Rescue Foundation, grant num- ber SWR/8/2019. Institutional Review Board Statement: Work was carried out under NSW DPI Fisheries Section 37 permit (P13/0007-2.0), a NSW Marine Parks Permit and Animal Ethics ACEC 19/9B. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Acknowledgments: For assistance with ﬁeld work we thank Derrick Cruz, Madelaine Langley, Javier Jimenez, Paula Sgarlatta, Chris Roberts, Sam Nolan, Olavo Bueno and Barbara Sanna Godoi. We also thank UNSW volunteers for their help in sorting epifauna in the lab: Alexandra Laitly, Chloe Brant, Morna McGuire, Hadi Esmaeil, Kathy Liu, Itasca Motter, Alex Ingall, Kiki Liang, Daphne Willemsen, Rosie Dowsett, Tom Chaffey, Melissa Abdallah, Jessica Nguyen, Cameron Suen. Daniel Swadling and Emma Jackson provided useful feedback on the manuscript. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. Kovalenko, K.E.; Thomaz, S.M.; Warfe, D.M. Habitat complexity: Approaches and future directions. Hydrobiologia 2012, 685, 1–17. [CrossRef] 2. MacArthur, R.H.; MacArthur, J.W. On Bird Species Diversity. Ecology 1961, 42, 594–598. [CrossRef] 3. Denno, R.F.; Finke, D.L.; Langellotto, G.A. Direct and indirect effects of vegetation structure and habitat complexity on predator- prey and predator-predator interactions. In Ecology of Predator-Prey Interactions; Oxford University: Oxford, UK, 2005; pp. 211–239. 4. Warfe, D.M.; Barmuta, L. Habitat structural complexity mediates the foraging success of multiple predator species. Oecologia 2004, 141, 171–178. [CrossRef] 5. Farina, S.; Arthur, R.; Pagès, J.; Prado, P.; Romero, J.; Verges, A.; Hyndes, G.; Heck, K.L.; Glenos, S.; Alcoverro, T. Differences in predator composition alter the direction of structure-mediated predation risk in macrophyte communities. Oikos 2014, 123, 1311–1322. [CrossRef] 6. Boström, C.; Pittman, S.; Simenstad, C.; Kneib, R. Seascape ecology of coastal biogenic habitats: Advances, gaps, and challenges. Mar. Ecol. Prog. Ser. 2011, 427, 191–217. [CrossRef] 7. Haddad, N.M.; Brudvig, L.A.; Clobert, J.; Davies, K.F.; Gonzalez, A.; Holt, R.D.; Lovejoy, T.E.; Sexton, J.O.; Austin, M.P.; Collins, C.D.; et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 2015, 1, e1500052. [CrossRef] [PubMed] 8. Wilson, M.C.; Chen, X.-Y.; Corlett, R.T.; Didham, R.K.; Ding, P.; Holt, R.D.; Holyoak, M.; Hu, G.; Hughes, A.C.; Jiang, L.; et al. Habitat fragmentation and biodiversity conservation: Key ﬁndings and future challenges. Landsc. Ecol. 2016, 33, 341–352. 9. Bustamante, M.M.C.; Roitman, I.; Aide, T.M.; Alencar, A.; Anderson, L.O.; Aragão, L.; Asner, G.P.; Barlow, J.; Berenguer, E.; Chambers, J.; et al. Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity. Glob. Chang. Biol. 2016, 22, 92–109. [CrossRef] [PubMed] 10. Fahrig, L. Ecological Responses to Habitat Fragmentation Per Se. Annu. Rev. Ecol. Evol. Syst. 2017, 48, 1–23. [CrossRef] 11. Halpern, B.S.; Walbridge, S.; Selkoe, K.A.; Kappel, C.V.; Micheli, F.; D’Agrosa, C.; Bruno, J.F.; Casey, K.S.; Ebert, C.; Fox, H.E.; et al. A global map of human impact on marine ecosystems. Science 2008, 319, 948–952. [CrossRef] Diversity 2023, 15, 125 15 of 19 12. Berger-Tal, O.; Saltz, D. Invisible barriers: Anthropogenic impacts on inter- and intra-speciﬁc interactions as drivers of landscape- independent fragmentation. Philos. Trans. R. Soc. B Biol. Sci. 2019, 374, 20180049. [CrossRef] 13. Delarue, E.M.P.; Kerr, S.E.; Rymer, T.L. Habitat complexity, environmental change and personality: A tropical perspective. Behav. Process. 2015, 120, 101–110. [CrossRef] 14. Hooper, D.U.; Adair, E.C.; Cardinale, B.J.; Byrnes, J.E.K.; Hungate, B.A.; Matulich, K.L.; Gonzalez, A.; Duffy, J.E.; Gamfeldt, L.; O’Connor, M.I. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 2012, 486, 105–108. [CrossRef] 15. Hooper, D.U.; Chapin, F.S., III; Ewel, J.J.; Hector, A.; Inchausti, P.; Lavorel, S.; Lawton, J.H.; Lodge, D.M.; Loreau, M.; Naeem, S.; et al. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 2005, 75, 3–35. [CrossRef] 16. Thompson, I.; Mackey, B.; McNulty, S.; Mosseler, A. Forest Resilience, Biodiversity, and Climate Change; Technical Series no. 43; Secretariat of the Convention on Biological Diversity: Montreal, QC, Canada, 2009; pp. 1–67. 17. Duffy, J.E.; Godwin, C.M.; Cardinale, B.J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 2017, 549, 261–264. [CrossRef] 18. Cardinale, B.J.; Duffy, J.E.; Gonzalez, A.; Hooper, D.U.; Perrings, C.; Venail, P.; Narwani, A.; Mace, G.M.; Tilman, D.; Wardle, D.A.; et al. Biodiversity loss and its impact on humanity. Nature 2012, 486, 59–67. [CrossRef] 19. Sintayehu, D.W. Impact of climate change on biodiversity and associated key ecosystem services in Africa: A systematic review. Ecosyst. Health Sustain. 2018, 4, 225–239. [CrossRef] 20. Ellison, A.M.; Bank, M.S.; Clinton, B.D.; Colburn, E.A.; Elliott, K.; Ford, C.R.; Foster, D.R.; Kloeppel, B.D.; Knoepp, J.D.; Lovett, G.M.; et al. Loss of foundation species: Consequences for the structure and dynamics of forested ecosystems. Front. Ecol. Environ. 2005, 3, 479–486. [CrossRef] 21. Toenies, M.J.; Miller, D.A.W.; Marshall, M.R.; Stauffer, G.E. Shifts in vegetation and avian community structure following the decline of a foundational forest species, the eastern hemlock. Ornithol. Appl. 2018, 120, 489–506. [CrossRef] 22. Wernberg, T.; Krumhansl, K.; Filbee-Dexter, K.; Pedersen, M.F. Status and trends for the world’s kelp forests. In World Seas: An Environmental Evaluation; Elsevier: Amsterdam, The Netherlands, 2019; pp. 57–78. 23. Burgos, E.; Montefalcone, M.; Ferrari, M.; Paoli, C.; Vassallo, P.; Morri, C.; Bianchi, C.N. Ecosystem functions and economic wealth: Trajectories of change in seagrass meadows. J. Clean. Prod. 2017, 168, 1108–1119. [CrossRef] 24. Dunic, J.C.; Brown, C.J.; Connolly, R.M.; Turschwell, M.P.; Côté, I.M. Long-term declines and recovery of meadow area across the world’s seagrass bioregions. Glob. Change Biol. 2021, 27, 4096–4109. [CrossRef] [PubMed] 25. Duffy, J. Biodiversity and the functioning of seagrass ecosystems. Mar. Ecol. Prog. Ser. 2006, 311, 233–250. [CrossRef] 26. Lavery, P.S.; Mateo, M.; Serrano, O.; Rozaimi, M. Variability in the Carbon Storage of Seagrass Habitats and Its Implications for Global Estimates of Blue Carbon Ecosystem Service. PLoS ONE 2013, 8, e73748. [CrossRef] [PubMed] 27. Ricart, A.M.; York, P.H.; Bryant, C.V.; Rasheed, M.A.; Ierodiaconou, D.; Macreadie, P.I. High variability of Blue Carbon storage in seagrass meadows at the estuary scale. Sci. Rep. 2020, 10, 5865. [CrossRef] [PubMed] 28. Jackson, E.L.; Rowden, A.A.; Attrill, M.J.; Bossey, S.J.; Jones, M.B. The importance of seagrass beds as a habitat for ﬁshery species. Oceanogr. Mar. Biol. 2001, 39, 269–304. 29. Gillanders, B.M. Seagrasses, ﬁsh, and ﬁsheries. In Seagrasses: Biology, Ecology and Conservation; Springer: Berlin/Heidelberg, Germany, 2007; pp. 503–505. 30. Potouroglou, M.; Bull, J.C.; Krauss, K.W.; Kennedy, H.A.; Fusi, M.; Daffonchio, D.; Mangora, M.M.; Githaiga, M.N.; Diele, K.; Huxham, M. Measuring the role of seagrasses in regulating sediment surface elevation. Sci. Rep. 2017, 7, 11917. [CrossRef] 31. Gray, C.; McElligott, D.; Chick, R.; Chick, R. Intra- and inter-estuary differences in assemblages of ﬁshes associated with shallow seagrass and bare sand. Mar. Freshw. Res. 1996, 47, 723–735. [CrossRef] 32. Ferrell, D.; Bell, J. Differences among assemblages of ﬁsh associated with Zostera capricorni and bare sand over a large spatial scale. Mar. Ecol. Prog. Ser. 1991, 72, 15–24. [CrossRef] 33. Jackson, E.L.; Rowden, A.A.; Attrill, M.J.; Bossy, S.F.; Jones, M.B. Comparison of ﬁsh and mobile macroinvertebrates associated with seagrass and adjacent sand at St. Catherine Bay, Jersey (English Channel): Emphasis on commercial species. Bull. Mar. Sci. 2002, 71, 1333–1341. 34. Boström, C.; Jackson, E.L.; Simenstad, C.A. Seagrass landscapes and their effects on associated fauna: A review. Estuar. Coast. Shelf Sci. 2006, 68, 383–403. [CrossRef] 35. Curtis, J.; Vincent, A. Distribution of sympatric seahorse species along a gradient of habitat complexity in a seagrass-dominated community. Mar. Ecol. Prog. Ser. 2005, 291, 81–91. [CrossRef] 36. McCloskey, R.M.; Unsworth, R.K. Decreasing seagrass density negatively inﬂuences associated fauna. PeerJ 2015, 3, e1053. [CrossRef] [PubMed] 37. Staveley, T.A.B.; Perry, D.; Lindborg, R.; Gullström, M. Seascape structure and complexity inﬂuence temperate seagrass ﬁsh assemblage composition. Ecography 2016, 40, 936–946. [CrossRef] 38. Bologna, P.A.X.; Heck, K.L. Impact of habitat edges on density and secondary production of seagrass-associated fauna. Estuaries 2002, 25, 1033–1044. [CrossRef] 39. Smith, T.; Hindell, J.S.; Jenkins, G.P.; Connolly, R.M. Seagrass patch size affects ﬁsh responses to edges. J. Anim. Ecol. 2009, 79, 275–281. [CrossRef] Diversity 2023, 15, 125 16 of 19 40. Tanner, J.E. Edge effects on fauna in fragmented seagrass meadows. Austral Ecol. 2005, 30, 210–218. [CrossRef] 41. Gilby, B.; Olds, A.; Connolly, R.; Maxwell, P.; Henderson, C.; Schlacher, T. Seagrass meadows shape ﬁsh assemblages across estuarine seascapes. Mar. Ecol. Prog. Ser. 2018, 588, 179–189. [CrossRef] 42. Ricart, A.M. Insights into Seascape Ecology: Landscape Patterns as Drivers in Coastal Marine Ecosystems. Doctoral Dissertation, Universitat de Barcelona, Barcelona, Spain, 2016. 43. González-Ortiz, V.; Egea, L.G.; Ramos, R.J.; Moreno-Marín, F.; Perez-Llorens, J.L.; Bouma, T.J.; Brun, F.G. Interactions between Seagrass Complexity, Hydrodynamic Flow and Biomixing Alter Food Availability for Associated Filter-Feeding Organisms. PLoS ONE 2014, 9, e104949. [CrossRef] 44. Folkard, A.M. Hydrodynamics of model Posidonia oceanica patches in shallow water. Limnol. Oceanogr. 2005, 50, 1592–1600. [CrossRef] 45. Middleton, M.; Bell, J.; Burchmore, J.; Pollard, D.; Pease, B. Structural differences in the ﬁsh communities of Zostera capricorni and Posidonia australis seagrass meadows in Botany Bay, New South Wales. Aquat. Bot. 1984, 18, 89–109. [CrossRef] 46. Burchmore, J.; Pollard, D.; Bell, J. Community structure and trophic relationships of the ﬁsh fauna of an estuarine Posidonia Australis seagrass habitat in port hacking, new South Wales. Aquat. Bot. 1984, 18, 71–87. [CrossRef] 47. Bell, J.D.; Westoby, M. Variation in seagrass height and density over a wide spatial scale: Effects on common ﬁsh and decapods. J. Exp. Mar. Biol. Ecol. 1986, 104, 275–295. [CrossRef] 48. Bell, J.D.; Westoby, M. Abundance of macrofauna in dense seagrass is due to habitat preference, not predation. Oecologia 1986, 68, 205–209. [CrossRef] [PubMed] 49. Evans, S.M.; Grifﬁn, K.J.; Blick, R.A.J.; Poore, A.; Vergés, A. Seagrass on the brink: Decline of threatened seagrass Posidonia australis continues following protection. PLoS ONE 2018, 13, e0190370. [CrossRef] [PubMed] 50. West, G.J.; Glasby, T.M. Interpreting Long-Term Patterns of Seagrasses Abundance: How Seagrass Variability Is Dependent on Genus and Estuary Type. Estuaries Coasts 2022, 45, 1393–1408. [CrossRef] 51. Glasby, T.M.; West, G. Dragging the chain: Quantifying continued losses of seagrasses from boat moorings. Aquat. Conserv. Mar. Freshw. Ecosyst. 2018, 28, 383–394. [CrossRef] 52. EPBC Act. Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) (s266B). Approved Conservation Advice (Including Listing Advice) for Posidonia Australis Seagrass Meadows of the Manning-Hawkesbury Ecoregion Ecological Community; Ofﬁce of Legislative Drafting and Publishing: Canberra, Australia, 2015. 53. Meehan, A.J.; West, R.J. Recovery times for a damaged Posidonia australis bed in south eastern Australia. Aquat. Bot. 2000, 67, 161–167. [CrossRef] 54. Ferretto, G.; Glasby, T.M.; Poore, A.G.; Callaghan, C.T.; Houseﬁeld, G.P.; Langley, M.; Sinclair, E.A.; Statton, J.; Kendrick, G.A.; Vergés, A. Naturally-detached fragments of the endangered seagrass Posidonia australis collected by citizen scientists can be used to successfully restore fragmented meadows. Biol. Conserv. 2021, 262, 109308. [CrossRef] 55. Waltham, N.J.; Elliott, M.; Lee, S.Y.; Lovelock, C.; Duarte, C.M.; Buelow, C.; Simenstad, C.; Nagelkerken, I.; Claassens, L.; Wen, C.K.-C.; et al. UN Decade on Ecosystem Restoration 2021–2030—What chance for success in restoring coastal ecosystems? Front. Mar. Sci. 2020, 7, 71. [CrossRef] 56. Jackson, E.L.; Attrill, M.J.; Jones, M.B. Habitat characteristics and spatial arrangement affecting the diversity of ﬁsh and decapod assemblages of seagrass (Zostera marina) beds around the coast of Jersey (English Channel). Estuar. Coast. Shelf Sci. 2006, 68, 421–432. [CrossRef] 57. Boström, C.; Bonsdorff, E. Zoobenthic community establishment and habitat complexity the importance of seagrass shoot-density, morphology and physical disturbance for faunal recruitment. Mar. Ecol. Prog. Ser. 2000, 205, 123–138. [CrossRef] 58. Swadling, D.S.; Knott, N.A.; Rees, M.J.; Davis, A.R. Temperate zone coastal seascapes: Seascape patterning and adjacent seagrass habitat shape the distribution of rocky reef ﬁsh assemblages. Landsc. Ecol. 2019, 34, 2337–2352. [CrossRef] 59. Pittman, S.J. Seascape Ecology; John Wiley & Sons: Hoboken, NJ, USA, 2018. 60. Pittman, S.; Yates, K.; Bouchet, P.; Alvarez-Berastegui, D.; Andréfouët, S.; Bell, S.; Berkström, C.; Boström, C.; Brown, C.; Connolly, R.; et al. Seascape ecology: Identifying research priorities for an emerging ocean sustainability science. Mar. Ecol. Prog. Ser. 2021, 663, 1–29. [CrossRef] 61. Roy, P.; Williams, R.; Jones, A.; Yassini, I.; Gibbs, P.; Coates, B.; West, R.; Scanes, P.; Hudson, J.; Nichol, S. Structure and Function of South-east Australian Estuaries. Estuar. Coast. Shelf Sci. 2001, 53, 351–384. [CrossRef] 62. NSW Department of Primary Industries. Fisheries NSW Spatial Data Portal. Available online: https://webmap.industry.nsw.gov. au/Html5Viewer/index.html?viewer=Fisheries_Data_Portal (accessed on 11 February 2019). 63. Stevens, D.L., Jr.; Olsen, A.R. Spatially balanced sampling of natural resources. J. Am. Stat. Assoc. 2004, 99, 262–278. [CrossRef] 64. Rees, M.; Knott, N.A.; Hing, M.L.; Hammond, M.; Williams, J.; Neilson, J.; Swadling, D.S.; Jordan, A. Habitat and humans predict the distribution of juvenile and adult snapper (Sparidae: Chrysophrys auratus) along Australia’s most populated coastline. Estuar. Coast. Shelf Sci. 2021, 257, 107397. [CrossRef] 65. Sleeman, J.C.; Kendrick, G.; Boggs, G.; Hegge, B. Measuring fragmentation of seagrass landscapes: Which indices are most appropriate for detecting change? Mar. Freshw. Res. 2005, 56, 851–864. [CrossRef] 66. Santos, R.O.; Lirman, D.; Pittman, S.J. Long-term spatial dynamics in vegetated seascapes: Fragmentation and habitat loss in a human-impacted subtropical lagoon. Mar. Ecol. 2015, 37, 200–214. [CrossRef] Diversity 2023, 15, 125 17 of 19 67. Bivand, R.; Keitt, T.; Rowlingson, B.; Pebesma, E.; Sumner, M.; Hijmans, R.; Rouault, E.l. Package ‘Rgdal’. Bindings for the Geospatial Data Abstraction Library 2015. Available online: https://CRAN.R-project.org/package=rgdal (accessed on 11 February 2019). 68. Hijmans, R.J.; van Etten, J.; Sumner, M.; Cheng, J.; Baston, D.; Bevan, A.; Bivand, R.; Busetto, L.; Canty, M.; Fasoli, B.; et al. Package ‘raster ’: Geographic Data Analysis and Modeling. 2015. Available online: https://CRAN.R-project.org/package=raster (accessed on 11 February 2019). 69. Hijmans, R.J.; Karney, C.; Williams, E.; Vennes, C.; Hijmans, R.J. Package ‘geosphere’: Spherical Trigonometry 2017. Available online: https://CRAN.R-project.org/package=geosphere (accessed on 11 February 2019). 70. Martin-Smith, K.M. Abundance of mobile epifauna: The role of habitat complexity and predation by ﬁshes. J. Exp. Mar. Biol. Ecol. 1993, 174, 243–260. [CrossRef] 71. Cappo, M.; Harvey, E.; Malcolm, H.; Speare, P. Potential of Video Techniques to Monitor Diversity, Abundance and Size of Fish in Studies of Marine Protected Areas; Aquatic Protected Areas-what works best and how do we know; University of Queensland: Brisbane, Australia, 2003; pp. 455–464. 72. Willis, T.J.; Millar, R.B.; Babcock, R.C. Detection of spatial variability in relative density of ﬁshes: Comparison of visual census, angling, and baited underwater video. Mar. Ecol. Prog. Serles 2000, 198, 249–260. [CrossRef] 73. Froese, R.; Pauly, D. FishBase 2000: Concepts, Design and Data Sources; ICLARM: Los Baños, Laguna, Philippines, 2000; 344p. 74. Bray, D.J.; Gomon, M.F.; Fishes of Australia. Museums Victoria and OzFishNet. Available online: https://ﬁshesofaustralia.net.au/ (accessed on 28 April 2022). 75. McGrouther, M. Australian Museum Website. Available online: https://australian.museum/learn/animals/ﬁshes/ (accessed on 28 April 2022). 76. Duffy, J.E.; Ziegler, S.; Campbell, J.E.; Bippus, P.M.; Lefcheck, J. Squidpops: A Simple Tool to Crowdsource a Global Map of Marine Predation Intensity. PLoS ONE 2015, 10, e0142994. [CrossRef] [PubMed] 77. Vila-Concejo, A.; Harris, D.L.; Power, H.E.; Shannon, A.M.; Webster, J.M. Sediment transport and mixing depth on a coral reef sand apron. Geomorphology 2014, 222, 143–150. [CrossRef] 78. Brooks, M.E.; Kristensen, K.; van Benthem, K.J.; Magnusson, A.; Berg, C.W.; Nielsen, A.; Skaug, H.J.; Maechler, M.; Bolker, B.M. GlmmTMB Balances Speed and Flexibility Among Packages for Zero-inﬂated Generalized Linear Mixed Modeling. The R Journal 2017, 9, 378–400. [CrossRef] 79. Wickham, H.; Averick, M.; Bryan, J.; Chang, W.; D’Agostino McGowan, L.; François, R.; Grolemund, G.; Hayes, A.; Henry, L.; Hester, J.; et al. Welcome to the Tidyverse. J. Open Source Softw. 2019, 4, 1686. [CrossRef] 80. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis; Springer: Berlin/Heidelberg, Germany, 2016. 81. Heck, K.L., Jr.; Orth, R.J. Seagrass habitats: The roles of habitat complexity, competition and predation in structuring associated ﬁsh and motile macroinvertebrate assemblages. In Estuarine Perspectives; Elsevier: Amsterdam, The Netherlands, 1980; pp. 449–464. 82. Worthington, D.; Ferrell, D.J.; McNeill, S.E.; Bell, J.D. Effects of the shoot density of seagrass on ﬁsh and decapods: Are correlation evident over larger spatial scales? Mar. Biol. 1992, 112, 139–146. [CrossRef] 83. Bell, J.D.; Westoby, M. Importance of local changes in leaf height and density to ﬁsh and decapods associated with seagrasses. J. Exp. Mar. Biol. Ecol. 1986, 104, 249–274. [CrossRef] 84. Kiggins, R.S.; Knott, N.A.; Davis, A.R. Miniature baited remote underwater video (mini-BRUV) reveals the response of cryptic ﬁshes to seagrass cover. J. Appl. Phycol. 2018, 101, 1717–1722. [CrossRef] 85. Swadling, D.S.; Knott, N.A.; Rees, M.J.; Pederson, H.; Adams, K.R.; Taylor, M.D.; Davis, A.R. Seagrass canopies and the performance of acoustic telemetry: Implications for the interpretation of ﬁsh movements. Anim. Biotelem. 2020, 8, 8. [CrossRef] 86. French, B.; Wilson, S.; Holmes, T.; Kendrick, A.; Rule, M.; Ryan, N. Comparing ﬁve methods for quantifying abundance and diversity of ﬁsh assemblages in seagrass habitat. Ecol. Indic. 2021, 124, 107415. [CrossRef] 87. Henderson, C.J.; Gilby, B.L.; Lee, S.Y.; Stevens, T. Contrasting effects of habitat complexity and connectivity on biodiversity in seagrass meadows. Mar. Biol. 2017, 164, 117. [CrossRef] 88. Horinouchi, M.; Tongnunui, P.; Nanjyo, K.; Nakamura, Y.; Sano, M.; Ogawa, H. Differences in ﬁsh assemblage structures between fragmented and continuous seagrass beds in Trang, southern Thailand. Fish. Sci. 2009, 75, 1409–1416. [CrossRef] 89. Smith, T.; Hindell, J.S.; Jenkins, G.P.; Connolly, R.M. Edge effects on ﬁsh associated with seagrass and sand patches. Mar. Ecol. Prog. Ser. 2008, 359, 203–213. [CrossRef] 90. Macreadie, P.I.; Hindell, J.S.; Jenkins, G.P.; Connolly, R.M.; Keough, M.J. Fish Responses to Experimental Fragmentation of Seagrass Habitat. Conserv. Biol. 2009, 23, 644–652. [CrossRef] [PubMed] 91. Macreadie, P.I.; Hindell, J.S.; Keough, M.J.; Jenkins, G.P.; Connolly, R.M. Resource distribution inﬂuences positive edge effects in a seagrass ﬁsh. Ecology 2010, 91, 2013–2021. [PubMed] 92. Bender, D.J.; Contreras, T.A.; Fahrig, L. Habitat loss and population decline: A meta-analysis of the patch size effect. Ecology 1998, 79, 517–533. [CrossRef] 93. Heck, K., Jr.; Hays, G.; Orth, R.J. Critical evaluation of the nursery role hypothesis for seagrass meadows. Mar. Ecol. Prog. Ser. 2003, 253, 123–136. [CrossRef] 94. Yarnall, A.H.; Fodrie, F.J. Predation patterns across states of landscape fragmentation can shift with seasonal transitions. Oecologia 2020, 193, 403–413. [CrossRef] Diversity 2023, 15, 125 18 of 19 95. Irlandi, E.A. Large- and small-scale effects of habitat structure on rates of predation: How percent coverage of seagrass affects rates of predation and siphon nipping on an infaunal bivalve. Oecologia 1994, 98, 176–183. [CrossRef] 96. Smith, T.M.; Hindell, J.S.; Jenkins, G.P.; Connolly, R.; Keough, M.J. Edge effects in patchy seagrass landscapes: The role of predation in determining ﬁsh distribution. J. Exp. Mar. Biol. Ecol. 2011, 399, 8–16. [CrossRef] 97. Hovel, K.A.; Regan, H.M. Using an individual-based model to examine the roles of habitat fragmentation and behavior on predator–prey relationships in seagrass landscapes. Landsc. Ecol. 2007, 23, 75–89. [CrossRef] 98. Lester, E.K.; Langlois, T.J.; Simpson, S.D.; McCormick, M.I.; Meekan, M.G. Reef-wide evidence that the presence of sharks modiﬁes behaviors of teleost mesopredators. Ecosphere 2021, 12, e03301. [CrossRef] 99. Lubbers, L.; Boynton, W.; Kemp, W. Variations in structure of estuarine ﬁsh communities in relation to abundance of submersed vascular plants. Mar. Ecol. Prog. Ser. 1990, 65, 1–14. [CrossRef] 100. Olney, J.; Boehlert, G. Nearshore ichthyoplankton associated with seagrass beds in the lower Chesapeake Bay. Mar. Ecol. Prog. Ser. 1988, 45, 33–43. [CrossRef] 101. Orth, R.J.; Heck, K.L.; van Montfrans, J. Faunal Communities in Seagrass Beds: A Review of the Inﬂuence of Plant Structure and Prey Characteristics on Predator: Prey Relationships. Estuaries 1984, 7, 339–350. [CrossRef] 102. James, P.L.; Heck, K.L. The effects of habitat complexity and light intensity on ambush predation within a simulated seagrass habitat. J. Exp. Mar. Biol. Ecol. 1994, 176, 187–200. [CrossRef] 103. Heck, K.L., Jr.; Orth, R.J. Predation in Seagrass Beds. In Seagrasses: Biology, Ecology and Conservation; Springer: Berlin/Heidelberg, Germany, 2006; pp. 537–550. 104. Edgar, G.J. Artificial algae as habitats for mobile epifauna: Factors affecting colonization in a Japanese Sargassum bed. Hydrobiologia 1991, 226, 111–118. [CrossRef] 105. Costa, V.; Chemello, R.; Iaciofano, D.; Brutto, S.L.; Rossi, F. Small-scale patches of detritus as habitat for invertebrates within a Zostera noltei meadow. Mar. Environ. Res. 2021, 171, 105474. [CrossRef] 106. Macreadie, P.I.; Connolly, R.M.; Jenkins, G.P.; Hindell, J.S.; Keough, M.J. Edge patterns in aquatic invertebrates explained by predictive models. Mar. Freshw. Res. 2010, 61, 214–218. [CrossRef] 107. Lanham, B.S.; Poore, A.G.; Gribben, P.E. Fine-scale responses of mobile invertebrates and mesopredatory ﬁsh to habitat conﬁguration. Mar. Environ. Res. 2021, 168, 105319. [CrossRef] 108. Moore, E.C.; Hovel, K.A. Relative inﬂuence of habitat complexity and proximity to patch edges on seagrass epifaunal communities. Oikos 2010, 119, 1299–1311. [CrossRef] 109. Paul, M. The protection of sandy shores—Can we afford to ignore the contribution of seagrass? Mar. Pollut. Bull. 2018, 134, 152–159. [CrossRef] [PubMed] 110. Maxwell, P.S.; Eklöf, J.S.; van Katwijk, M.M.; O’Brien, K.R.; de la Torre-Castro, M.; Boström, C.; Bouma, T.J.; Krause-Jensen, D.; Unsworth, R.K.F.; van Tussenbroek, B.I.; et al. The fundamental role of ecological feedback mechanisms for the adaptive management of seagrass ecosystems—A review. Biol. Rev. 2017, 92, 1521–1538. [CrossRef] [PubMed] 111. van Rijn, L. Coastal erosion and control. Ocean Coast. Manag. 2011, 54, 867–887. [CrossRef] 112. James, R.K.; Silva, R.; Van Tussenbroek, B.I.; Escudero-Castillo, M.; Mariño-Tapia, I.; Dijkstra, H.A.; Van Westen, R.M.; Pietrzak, J.D.; Candy, A.; Katsman, C.A.; et al. Maintaining Tropical Beaches with Seagrass and Algae: A Promising Alternative to Engineering Solutions. Bioscience 2019, 69, 136–142. [CrossRef] 113. Robbins, B.D.; Bell, S.S. Seagrass landscapes: A terrestrial approach to the marine subtidal environment. Trends Ecol. Evol. 1994, 9, 301–304. [CrossRef] [PubMed] 114. Hemminga, M.A.; Duarte, C.M. Seagrass Ecology; Cambridge University Press: Cambridge, UK, 2000. 115. Waycott, M.; Duarte, C.M.; Carruthers, T.J.B.; Orth, R.J.; Dennison, W.C.; Olyarnik, S.; Calladine, A.; Fourqurean, J.W.; Heck, K.L., Jr.; Hughes, A.R.; et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc. Natl. Acad. Sci. USA 2009, 106, 12377–12381. [CrossRef] 116. Gilby, B.L.; Olds, A.D.; Connolly, R.M.; Henderson, C.J.; Schlacher, T.A. Spatial Restoration Ecology: Placing Restoration in a Landscape Context. Bioscience 2018, 68, 1007–1019. [CrossRef] 117. Unsworth, R.; De León, P.; Garrard, S.; Jompa, J.; Smith, D.; Bell, J. High connectivity of Indo-Paciﬁc seagrass ﬁsh assemblages with mangrove and coral reef habitats. Mar. Ecol. Prog. Ser. 2008, 353, 213–224. [CrossRef] 118. Vargas-Fonseca, E.; Olds, A.D.; Gilby, B.L.; Connolly, R.M.; Schoeman, D.S.; Huijbers, C.M.; Hyndes, G.A.; Schlacher, T.A. Combined effects of urbanization and connectivity on iconic coastal ﬁshes. Divers. Distrib. 2016, 22, 1328–1341. [CrossRef] 119. Tanner, J.E. Landscape ecology of interactions between seagrass and mobile epifauna: The matrix matters. Estuar. Coast. Shelf Sci. 2006, 68, 404–412. [CrossRef] 120. Olds, A.; Connolly, R.; Pitt, K.; Maxwell, P. Primacy of seascape connectivity effects in structuring coral reef ﬁsh assemblages. Mar. Ecol. Prog. Ser. 2012, 462, 191–203. [CrossRef] 121. Whippo, R.; Knight, N.S.; Prentice, C.; Cristiani, J.; Siegle, M.R.; O’Connor, M.I. Epifaunal diversity patterns within and among seagrass meadows suggest landscape-scale biodiversity processes. Ecosphere 2018, 9, e02490. [CrossRef] 122. Roper, T.; Creese, B.; Scanes, P.; Stephens, K.; Williams, R.; Dela-Cruz, J.; Coade, G.; Coates, B.; Fraser, M. Assessing the condition of estuaries and coastal lake ecosystems in NSW, Monitoring, evaluation and reporting program. In Estuaries and Coastal Lakes; Technical Report Series; Ofﬁce of Environment and Heritage: Sydney, Australia, 2011. Diversity 2023, 15, 125 19 of 19 123. Creese, R.G.; Glasby, T.M.; West, G.; Gallen, C. Mapping the Habitats of NSW Estuaries; Fisheries Final Report Series No. 113; Industry & Investment NSW: Nelson Bay, NSW, Australia, 2009. 124. Truong, L.; Suthers, I.M.; Cruz, D.O.; Smith, J.A. Plankton supports the majority of ﬁsh biomass on temperate rocky reefs. Mar. Biol. 2017, 164, 1–12. [CrossRef] 125. Manjakasy, J.M.; Day, R.D.; Kemp, A.; Tibbetts, I.R. Functional morphology of digestion in the stomachless, piscivorous needleﬁshes Tylosurus gavialoides and Strongylura leiura ferox (Teleostei: Beloniformes). J. Morphol. 2009, 270, 1155. [CrossRef] 126. Champion, C.; Suthers, I.M.; Smith, J.A. Zooplanktivory is a key process for ﬁsh production on a coastal artiﬁcial reef. Mar. Ecol. Prog. Ser. 2015, 541, 1–14. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Multidisciplinary Digital Publishing Institute
Habitat Provision and Erosion Are Influenced by Seagrass Meadow Complexity: A Seascape Perspective
Poore, Alistair G. B.
Glasby, Tim M.
Griffin, Kingsley J.
, Volume 15 (2) –
Jan 17, 2023
Share Full Text for Free
Add to Folder
Web of Science