Seagrass meadows shape fish assemblages across estuarine seascapes

Seagrasses – a group of foundation species in coastal ecosystems – provide key habitat for diverse and abundant faunal assemblages and support numerous ecosystem functions and services. However, whether the habitat role of seagrasses is influenced by seagrass diversity, by dominant species or both, remains unclear. To that end, we sought to investigate the specific seagrass characteristics (e.g., species diversity, seagrass traits) that influence tropical fish assemblages, and place this in the context of small-scale fishery use. We surveyed seagrass variables at 55 plots, nested within 12 sites around Zanzibar (Tanzania) in the Western Indian Ocean, and used Baited Remote Underwater Video (BRUV) systems to assess fish assemblages across plots. Using linear mixed models, we reveal that seagrass structural complexity and depth were the best predictors of fish abundance, with higher abundance occurring in deeper meadows or meadows with high canopy, leaf length and number of leaves per shoot. Moreover, an interaction between seagrass cover and land-use was the best predictor of fish species richness, where sites closer to human impacts were less affected by cover than sites with lower human impact. Overall, models with seagrass species richness or functional diversity as predictors poorly explained fish assemblages. Fish taxa that were important for small-scale fishery sectors (e.g., emperors, snappers, rabbitfish, and parrotfish) were primarily driven by seagrass structural complexity. Our results provide a unique analysis of the relationship between seagrass habitat and its associated fish assemblages in that we show that seagrass species diversity had little effect on seagrass fish assemblages, which instead appear driven by specific seagrass traits and seagrass cover. If conserving high value species that support adjacent fisheries is the priority for protecting seagrass meadows, then seagrass areas should be chosen with high cover and structural complexity that are in deeper waters. Any conservation measures also need to balance the needs of fishers that use the resources supported by seagrasses.

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Automating the analysis of fish abundance using object detection: optimising animal ecology with deep learning

10.1101/805796 ◽

2019 ◽

Cited By ~ 2

Author(s):

Ellen M. Ditria ◽

Sebastian Lopez-Marcano ◽

Michael K. Sievers ◽

Eric L. Jinks ◽

Christopher J. Brown ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Fish Assemblages ◽

Aquatic Ecology ◽

Video Data ◽

Fish Abundance ◽

Data Sets ◽

Seagrass Meadows ◽

Learning Techniques ◽

Better Than

AbstractAquatic ecologists routinely count animals to provide critical information for conservation and management. Increased accessibility to underwater recording equipment such as cameras and unmanned underwater devices have allowed footage to be captured efficiently and safely. It has, however, led to immense volumes of data being collected that require manual processing, and thus significant time, labour and money. The use of deep learning to automate image processing has substantial benefits, but has rarely been adopted within the field of aquatic ecology. To test its efficacy and utility, we compared the accuracy and speed of deep learning techniques against human counterparts for quantifying fish abundance in underwater images and video footage. We collected footage of fish assemblages in seagrass meadows in Queensland, Australia. We produced three models using a MaskR-CNN object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata). Our models were trained on three randomised 80:20 ratios of training:validation data-sets from a total of 6,080 annotations. The computer accurately determined abundance from videos with high performance using unseen footage from the same estuary as the training data (F1 = 92.4%, mAP50 = 92.5%), and from novel footage collected from a different estuary (F1 = 92.3%, mAP50 = 93.4%). The computer’s performance in determining MaxN was 7.1% better than human marine experts, and 13.4% better than citizen scientists in single image test data-sets, and 1.5% and 7.8% higher in video data-sets, respectively. We show that deep learning is a more accurate tool than humans at determining abundance, and that results are consistent and transferable across survey locations. Deep learning methods provide a faster, cheaper and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance. Deep learning techniques have much to offer the field of aquatic ecology.

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Tropical seaweed beds as important habitats for juvenile fish

Marine and Freshwater Research ◽

10.1071/mf16153 ◽

2017 ◽

Vol 68 (10) ◽

pp. 1921 ◽

Cited By ~ 22

Author(s):

S. A. Tano ◽

M. Eggertsen ◽

S. A. Wikström ◽

C. Berkström ◽

A. S. Buriyo ◽

...

Keyword(s):

Fish Species ◽

Fish Assemblages ◽

Fish Habitat ◽

Juvenile Fish ◽

Visual Census ◽

Seagrass Meadows ◽

Commercially Important ◽

Fish Habitats ◽

Habitat Variables

Seaweed beds within tropical seascapes have received little attention as potential fish habitat, despite other vegetated habitats, such as seagrass meadows and mangroves, commonly being recognised as important nurseries for numerous fish species. In addition, studies of vegetated habitats rarely investigate fish assemblages across different macrophyte communities. Therefore, the aim of the present study was to investigate the role of tropical seaweed beds as fish habitat, particularly for juvenile fish, by comparing their fish assemblages with those of closely situated seagrass beds. Fish assemblages were assessed by visual census in belt transects, where fish were identified and their length estimated, and habitat variables were estimated for each transect. The abundance of juvenile fish in seaweed beds was twice as high as that in seagrass meadows, whereas there was no difference in total, subadult or adult fish abundance. In addition, the abundance of commercially important and coral reef-associated juveniles was higher in seaweed beds, as was fish species richness. Fish assemblages differed between habitats, with siganids being more common in seagrass meadows and juvenile Labridae and Serranidae more common in seaweed beds. These results highlight that tropical seaweed beds are important juvenile fish habitats and underscore the need to widen the view of the shallow tropical seascape.

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Complex yet fauna-deficient seagrass ecosystems at risk in southern Myanmar

Botanica Marina ◽

10.1515/bot-2017-0082 ◽

2018 ◽

Vol 61 (3) ◽

pp. 193-203 ◽

Cited By ~ 2

Author(s):

Benjamin L. Jones ◽

Leanne C. Cullen-Unsworth ◽

Robert Howard ◽

Richard K.F. Unsworth

Keyword(s):

Fish Assemblages ◽

Marine Ecosystems ◽

Global Studies ◽

Baseline Assessment ◽

Underwater Video ◽

Associated Fauna ◽

Seagrass Meadows ◽

Marine Park ◽

Video Systems ◽

Seagrass Ecosystems

AbstractDependence on seafood across Southeast Asia is extensive. Myanmar is no exception, but the country’s provisioning marine ecosystems are threatened. Seagrass is one habitat that is frequently overlooked in management as an important fisheries resource, despite its nursery function. In Myanmar, research on seagrass habitats is particularly sparse, and as a result, our understanding of seagrass exploitation remains limited. In this study, we provide a baseline assessment of the seagrass-associated fish assemblages at four locations in the Myeik Archipelago in southern Myanmar using mono Baited Remote Underwater Video systems. Across the sites surveyed only 12 taxa of motile fauna were recorded. Relative to other regional and global studies, this figure is meagre. Our data adds to a growing literature suggesting that the marine ecosystems of Myanmar are in a worrying state. Despite the lack of recorded seagrass associated fauna, our study revealed minimal impacts to seagrass meadows from eutrophication or sedimentation, and the meadows included appeared to be healthy. The sites with the highest number of motile fauna were within Myanmar’s only National Marine Park offering some optimism for the effectiveness of protection, but further assessments are required to allow targeted management of Myanmar’s seagrass meadows.

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Demersal Fish Assemblages in NE Atlantic Seagrass and Kelp

Diversity ◽

10.3390/d12100366 ◽

2020 ◽

Vol 12 (10) ◽

pp. 366

Author(s):

Evie Furness ◽

Richard K.F. Unsworth

Keyword(s):

Species Richness ◽

Fish Assemblages ◽

Demersal Fish ◽

Kelp Forests ◽

Evidence Based ◽

Ecological Value ◽

Commercial Fish ◽

Seagrass Meadows ◽

The North ◽

Commercial Species

Global fisheries are in decline, calling for urgent evidence-based action. One such action is the identification and protection of fishery-associated habitats such as seagrass meadows and kelp forests, both of which have suffered long-term loss and degradation in the North Atlantic region. Direct comparisons of the value of seagrass and kelp in supporting demersal fish assemblages are largely absent from the literature. Here, we address this knowledge gap. Demersal fish were sampled using a baited camera to test for differences between habitats in (1) the species composition of the fish assemblages, (2) the total abundance and species richness of fishes, and (3) the abundances of major commercial species. Seagrass and kelp-associated fish assemblages formed two significantly distinct groupings, which were driven by increased whiting (Merlangius merlangus) and dogfish (Scyliorhinus canicula) presence in seagrass and higher abundances of pollock (Pollachius pollachius) and goby (Gobiusculus flavescens) in kelp. The abundance, diversity, and species richness did not change significantly between the two habitats. We conclude that seagrass and kelp do support unique demersal fish assemblages, providing evidence that they have different ecological value through their differing support of commercial fish species. Thus, this study improves the foundation for evidence-based policy changes.

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Influence of habitat complexity on fish density and species richness in structurally simple forereef seagrass meadows

Botanica Marina ◽

10.1515/bot-2017-0115 ◽

2018 ◽

Vol 61 (6) ◽

pp. 547-557 ◽

Cited By ~ 1

Author(s):

Nina Ann Jin Ho ◽

Jillian Lean Sim Ooi ◽

Yang Amri Affendi ◽

Ving Ching Chong

Keyword(s):

Species Richness ◽

Habitat Complexity ◽

Fish Assemblages ◽

Fish Habitat ◽

Relative Size ◽

Shoot Density ◽

Fish Density ◽

Percent Cover ◽

Seagrass Meadows ◽

Fish Habitats

Abstract Seagrass meadows are highly productive habitats that support commercially and ecologically important fishes and invertebrates. However, evidence of fish-habitat relationships are seldom drawn from forereef seagrass meadows that are structurally simple in terms of species richness and relative size and, consequently, their role as habitats is unclear. In this study, fish-habitat relationships were examined in such meadows in the forereef seagrasses of Tinggi Island and Babi Besar Island, Malaysia, by documenting habitat complexity attributes (canopy height, shoot density and percent cover), distance to adjacent coral reefs, and water depth within 2×2 m quadrats. Fish assemblages were recorded using the Remote Underwater Video Station method. A total of 1166 individuals from 86 taxa were found and enumerated. This study showed that (1) fish density and species richness were significantly associated with seagrass percent cover alone; (2) commercially important carnivores formed the majority of the fish populations in the meadows, and (3) fish density and species richness were positively correlated with distance to the nearest adjacent coral reef, indicating the need to account for the effects of neighboring habitats in studies of this nature. Thus, structurally simple seagrass meadows are still important fish habitats, especially as feeding grounds for commercially significant carnivores.

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Fish Assemblages in Seagrass (Zostera marina L.) Meadows and Mussel Reefs (Mytilus edulis): Implications for Coastal Fisheries, Restoration and Marine Spatial Planning

Water ◽

10.3390/w13223268 ◽

2021 ◽

Vol 13 (22) ◽

pp. 3268

Author(s):

Georgios A. Orfanidis ◽

Konstantinos Touloumis ◽

Claus Stenberg ◽

Patrizio Mariani ◽

Josianne Gatt Støttrup ◽

...

Keyword(s):

Spatial Planning ◽

Fish Assemblages ◽

Current Speed ◽

Water Current ◽

Marine Biodiversity ◽

Marine Spatial Planning ◽

Fish Assemblage Structure ◽

Coastal Fisheries ◽

Seagrass Meadows ◽

Fish Abundances

Seagrass meadows and mussel reefs provide favorable habitats for many fish species, but few studies have compared the associated fish assemblages directly and examined the influence of environmental variables. Knowledge of fish assemblages associated with disparate habitats is needed for the conservation of coastal fisheries and marine spatial planning. Catch per unit effort data derived from fyke nets showed similar species richness and diversity in seagrass meadows and mussel reefs, suggesting that both habitats support elevated marine biodiversity of mobile fauna. However, it was shown that fish assemblage structure differed between those habitats, and also fish abundance in seagrass meadows was significantly higher than in mussel reefs by comparing the data with a multivariate extension of Generalized Linear Models (GLM). Furthermore, employing underwater video recordings to compare fish abundances in high and low water current speed mussel reefs with a Generalized Linear Mixed Model with negative binomial distribution, data revealed similar fish abundances (in terms of the MaxN metric) despite the variation in current speed, probably because the mussel formations provide sufficient shelter, even from high water currents. The commercially important species Atlantic cod (G. morhua), however, was significantly more abundant in the low water current mussel reef. Therefore, restoration efforts targeting G. morhua could benefit from restoring low current mussel reefs. Our study provides input for the conservation of coastal recreational and commercial fisheries, habitat restoration and marine spatial planning where certain habitats may be prioritized.

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