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

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.

Improved Accuracy for Automated Counting of a Fish in Baited Underwater Videos for Stock Assessment

The ongoing need to sustainably manage fishery resources can benefit from fishery-independent monitoring of fish stocks. Camera systems, particularly baited remote underwater video system (BRUVS), are a widely used and repeatable method for monitoring relative abundance, required for building stock assessment models. The potential for BRUVS-based monitoring is restricted, however, by the substantial costs of manual data extraction from videos. Computer vision, in particular deep learning (DL) models, are increasingly being used to automatically detect and count fish at low abundances in videos. One of the advantages of BRUVS is that bait attractants help to reliably detect species in relatively short deployments (e.g., 1 h). The high abundances of fish attracted to BRUVS, however, make computer vision more difficult, because fish often obscure other fish. We build upon existing DL methods for identifying and counting a target fisheries species across a wide range of fish abundances. Using BRUVS imagery targeting a recovering fishery species, Australasian snapper (Chrysophrys auratus), we tested combinations of three further mathematical steps likely to generate accurate, efficient automation: (1) varying confidence thresholds (CTs), (2) on/off use of sequential non-maximum suppression (Seq-NMS), and (3) statistical correction equations. Output from the DL model was more accurate at low abundances of snapper than at higher abundances (>15 fish per frame) where the model over-predicted counts by as much as 50%. The procedure providing the most accurate counts across all fish abundances, with counts either correct or within 1–2 of manual counts (R2 = 88%), used Seq-NMS, a 45% CT, and a cubic polynomial corrective equation. The optimised modelling provides an automated procedure offering an effective and efficient method for accurately identifying and counting snapper in the BRUV footage on which it was tested. Additional evaluation will be required to test and refine the procedure so that automated counts of snapper are accurate in the survey region over time, and to determine the applicability to other regions within the distributional range of this species. For monitoring stocks of fishery species more generally, the specific equations will differ but the procedure demonstrated here could help to increase the usefulness of BRUVS.

Seascape Context as a Driver of the Fish Community Structure of Posidonia oceanica Meadows in the Adriatic Sea

Marine underwater habitats dominated by seagrass Posidonia oceanica play an essential role in fish community assembly, affecting taxonomic and functional diversity, abundance and fish behavior. The value of seagrasses as habitat depends on the spatial arrangement of the seascape elements and the availability of alternative habitats. Little is known about the effect of the seascape context of P. oceanica meadows on fish assemblages in the Mediterranean Sea. To identify P. oceanica meadows’ relative importance as a habitat for fishes, fish communities in the Croatian Adriatic Sea were investigated, using SCUBA lure-assisted visual census. The results show a significant effect of different arrangements of P. oceanica meadows’ seascape elements and surrounding habitats on fish community structure. Fragmented mosaic meadows with P. oceanica growing directly on and between rocky-algal reefs/boulders had significantly higher fish abundances compared to both types of continuous meadows (bordering rock and bordering sand). Continuous meadows bordering sand harbored the highest number of unique species. Evidence that alternative structured habitats within proximity to seagrass beds may affect the community structure of associated fish assemblages is provided, highlighting the need to consider P. oceanica meadows’ seascape context in conservation management and experimental design for fish community structure.

Improved accuracy for automated counting of a fish in baited underwater videos for stock assessment

The ongoing need to sustainably manage fishery resources necessitates fishery-independent monitoring of the status of fish stocks. Camera systems, particularly baited remote underwater video stations (BRUVS), are a widely-used and repeatable method for monitoring relative abundance, required for building stock assessment models. The potential for BRUVS-based monitoring is restricted, however, by the substantial costs of manual data extraction from videos. Computer vision, in particular deep learning models, are increasingly being used to automatically detect and count fish at low abundances in videos. One of the advantages of BRUVS is that bait attractants help to reliably detect species in relatively short deployments (e.g. 1 hr). The high abundances of fish attracted to BRUVS, however, make computer vision more difficult, because fish often occlude other fish. We build upon existing deep learning methods for identifying and counting a target fisheries species across a wide range of fish abundances. Using BRUVS imagery targeting a recovering fishery species, Australian snapper (Chrysophrys auratus), we tested combinations of three further mathematical steps likely to generate accurate, efficient automation: 1) varying confidence thresholds (CTs), 2) on/off use of sequential non-maximum suppression (Seq-NMS), and 3) statistical correction equations. Output from the deep learning model was accurate at very low abundances of snapper; at higher abundances, however, the model over-predicted counts by as much as 50%. The procedure providing the most accurate counts across all fish abundances, with counts either correct or within 1 to 2 of manual counts (R2 = 93.4%), used Seq-NMS, a 55% confidence threshold, and a cubic polynomial corrective equation. The optimised modelling provides an automated procedure offering an effective and efficient method for accurately identifying and counting snapper in BRUV footage. Further testing is required to ensure that automated counts of snapper remain accurate in the survey region over time, and to determine the applicability to other regions within the distributional range of this species. For monitoring stocks of fishery species more generally, the specific equations will differ but the procedure demonstrated here would help to increase the usefulness of BRUVS, while decreasing costs.

CORAL REEF CONDITION IN RELATION TO CORAL REEF FISH ABUNDANCES BEFORE MASS BLEACHING EVENT IN SIMEULUE ISLANDS, ACEH

Bordered with the Indian Ocean, Simeulue Islands is one of the outermost islands in Indonesia located in the west part of Aceh Province. Simeulue waters are productive areas due to the unpolluted condition yet and great of biomass. Three regions were particularly observed, those are Simeuluecut, Ganting, and Labuhan Bajau. In those areas, the existing marine tourism activities might influence the coral reef ecosystem studied. This study aimed to evaluate the condition of coral and coral reef fish in those three particular regions before mass bleaching event in 2016 triggered by ENSO. Point Intercept Transect (PIT) method was employed to record the percentage cover of coral, species diversity, and coral reef fish. Ganting waters was a moderate ecosystem area whith the percentage coverage was up to 45.62%. However, in Simeuluecut and Labuhan Bajau waters, the coral reef communities were excellent with coral percentage coverage reached 83.12% and 81.25 %, respectively. The highest abundance genera of coral reef fish was observed in Simueluecut waters. This condition was changed oppositely in 2016 when mass bleaching threatened Simeulue waters due to temperature anomaly triggered by ENSO phenomenon. The temperature increases almost 3oC for 6 months that undoubtedly induced bleaching that about 50% of coral colonies were dramatically declined in coral coverage and coral recruitment.

Marine fish traits follow fast-slow continuum across oceans

A fundamental challenge in ecology is to understand why species are found where they are and predict where they are likely to occur in the future. Trait-based approaches may provide such understanding, because it is the traits and adaptations of species that determine which environments they can inhabit. It is therefore important to identify key traits that determine species distributions and investigate how these traits relate to the environment. Based on scientific bottom-trawl surveys of marine fish abundances and traits of >1,200 species, we investigate trait-environment relationships and project the trait composition of marine fish communities across the continental shelf seas of the Northern hemisphere. We show that traits related to growth, maturation and lifespan respond most strongly to the environment. This is reflected by a pronounced “fast-slow continuum” of fish life-histories, revealing that traits vary with temperature at large spatial scales, but also with depth and seasonality at more local scales. Our findings provide insight into the structure of marine fish communities and suggest that global warming will favour an expansion of fast-living species. Knowledge of the global and local drivers of trait distributions can thus be used to predict future responses of fish communities to environmental change.

Environmental DNA for the enumeration and management of Pacific salmon

Pacific salmon are a keystone resource in Alaska, generating annual revenues of well over ∼US$500 million/yr. Due to their anadromous life history, adult spawners distribute amongst thousands of streams, posing a huge management challenge. Currently, spawners are enumerated at just a few streams because of reliance on human counters and, rarely, sonar. The ability to detect organisms by shed tissue (environmental DNA, eDNA) promises a more efficient counting method. However, although eDNA correlates generally with local fish abundances, we do not know if eDNA can accurately enumerate salmon. Here we show that daily, and near-daily, flow-corrected eDNA rate closely tracks daily numbers of returning sockeye and coho spawners and outmigrating sockeye smolts. eDNA thus promises accurate and efficient enumeration, but to deliver the most robust numbers will need higher-resolution stream-flow data, at-least-daily sampling, and a focus on species with simple life histories, since shedding rate varies amongst jacks, juveniles, and adults.

Challenges of transferring models of fish abundance between coral reefs

Reliable abundance estimates for species are fundamental in ecology, fisheries, and conservation. Consequently, predictive models able to provide reliable estimates for un- or poorly-surveyed locations would prove a valuable tool for management. Based on commonly used environmental and physical predictors, we developed predictive models of total fish abundance and of abundance by fish family for ten representative taxonomic families for the Great Barrier Reef (GBR) using multiple temporal scenarios. We then tested if models developed for the GBR (reference system) could predict fish abundances at Ningaloo Reef (NR; target system), i.e., if these GBR models could be successfully transferred to NR. Models of abundance by fish family resulted in improved performance (e.g., 44.1% <R2 < 50.6% for Acanthuridae) compared to total fish abundance (9% <R2 < 18.6%). However, in contrast with previous transferability obtained for similar models for fish species richness from the GBR to NR, transferability for these fish abundance models was poor. When compared with observations of fish abundance collected in NR, our transferability results had low validation scores (R2 < 6%,p > 0.05). High spatio-temporal variability of patterns in fish abundance at the family and population levels in both reef systems likely affected the transferability of these models. Inclusion of additional predictors with potential direct effects on abundance, such as local fishing effort or topographic complexity, may improve transferability of fish abundance models. However, observations of these local-scale predictors are often not available, and might thereby hinder studies on model transferability and its usefulness for conservation planning and management.