Paper: Submerged cage aquaculture of marine fish: a review of the biological challenges and opportunities

Surface-based cages are the dominant production technology for the marine finfish aquaculture industry. However, issues such as extreme weather events, poor environmental conditions, interactions with parasites, and conflicts with other coastal users are problematic for surface-based aquaculture. Submerged cages may reduce many of these problems and commercial interest in their use has increased. However, a broad synthesis of research into the effects of submerged culture on fish is lacking. Here, we review the current status of submerged fish farming worldwide, outline the biological challenges that fish with fundamentally different buoyancy control physiologies face in submerged culture, and discuss production benefits and problems that might arise from submerged fish farming. Our findings suggest that fish with closed swim bladders, and fish without swim bladders, may be well-suited to submerged culture. However, for fish with open swim bladders, such as salmonids, submergence is more complex as they require access to surface air to refill their swim bladders and maintain buoyancy. Growth and welfare of open swim bladder fish can be compromised by submergence for long periods due to complications with buoyancy regulation, but the recent addition of underwater air domes to submerged cages can alleviate this issue. Despite this advance, a greater understanding of how to couple advantageous environmental conditions with submerged culture to improve fish growth and welfare over the commercial production cycle is required if submerged cages are to become a viable alternative to surface-based cage aquaculture.

Relative research effort over time on submerged cage finfish aquaculture, measured by the number of journal articles published in each year). Dots represent studies presenting empirical evidence of the outcomes of submerged culture, with references provided for the first published evidence of submerged culture for each fish species.

Sievers M, Korsøen Ø, Warren-Myers F, Oppedal F, Macaulay G, Dempster T (in press). Submerged cage aquaculture of marine fish: a review of the biological challenges and opportunities. Reviews in Aquaculture.

LINK TO COME

Paper: Remote estimation of aquatic light environments using machine learning: A new management tool for submerged aquatic vegetation

Submerged aquatic vegetation (SAV; e.g. seagrasses, macroalgae), forms key habitats in shallow coastal systems that provide a plethora of ecosystem services, including coastal protection, climate mitigation and supporting fisheries production. Light limitation is a critical factor influencing the growth and survival of SAV, thus it is important to understand how much light SAV needs, and receives, to effectively assess the risk that light limitation poses. Light monitoring is commonly used to inform environmental decision making to minimise loss of SAV habitat, but the temporal and spatial extent of monitoring is often limited by cost and logistical difficulties. An ability to remotely estimate light across different locations can therefore improve the conservation and management of SAV habitats. Here we combine an extensive monitoring program with publicly available data and machine learning to develop a model that estimates the light reaching submerged seagrasses in a shallow subtropical embayment in southern Queensland, Australia. Our model accurately predicts the intensity of photosynthetically active radiation (PAR) reaching the canopy of SAV from entirely remotely available data. The best performing model predicted light intensity with >99% at the management relevant daily, and 14-day rolling average time resolutions. This model enables monitoring of light available to SAV without an ongoing need for in-water instruments, minimising cost and risk to personnel, and improving assessment speed. The technique can be applied to SAV management plans in shallow waters throughout the world, where suitable remote public data is available.

BLOG POST: https://catchmenttocoast.org/2021/04/29/is-the-seagrass-getting-enough-light-artificial-intelligence-can-tell-us/

Pearson RM, Collier CJ, Brown CJ, Rasheed MA, Bourner J, Turschwell MP, Sievers M, Connolly RM (2021). Remote estimation of aquatic light environments using machine learning: A new management tool for submerged aquatic vegetation. Science of the Total Environment, 782:146886.

https://www.sciencedirect.com/science/article/pii/S0048969721019562?casa_token=ecwa0RDhQbIAAAAA:WieF2IbV4mYcbquRa5T89abBfBbrp8CZ8p_lOCLl2IDLElBVPB8Bi3ZaTUvFlJtaAP3wHRgAWXc

Paper: Opportunities for improving recognition of coastal wetlands in global ecosystem assessment frameworks

• Global ecosystem assessments inform conservation funding priorities.

Seagrass, saltmarsh and mangroves are under-recognized in global assessments.

• Ecosystem assessments often overlook important functions, like fishery nurseries.

• Synthesis could fill gaps in data for global scale assessments.

• We recommend priorities for filling gaps in global assessments.

BLOG POST: https://globalwetlandsproject.org/filling-gaps-in-global-assessments-of-ecosystems-to-benefit-coastal-wetland-conservation/

Example of knowledge frontiers for linking coastal wetland ecosystem extent and condition indicators for analysis of fishery productivity fish stock assessments, given current global datasets and syntheses for saltmarsh, mangrove and seagrass habitats.

Brown CJ, Adame MF, Buelow CA, Frassl MA, Lee SY, Mackey B, McClure EC, Pearson RM, Rajkaran A, Rayner TS, Sievers M (2021). Opportunities for improving recognition of coastal wetlands in global ecosystem assessment frameworks. Ecological Indicators, 126:107694.

https://www.sciencedirect.com/science/article/pii/S1470160X21003599

Paper: An overview of ecological traps in marine ecosystems

Humans are altering marine ecosystems at unprecedented rates, and these changes can result in animals selecting poor-quality habitats if the cues they use become misleading. Such “ecological traps” increase extinction risk, reduce ecosystem resilience, and are a consequence of human-induced rapid environmental change. Although there is growing evidence for traps impacting terrestrial species, the phenomenon has so far received little attention from marine scientists. To explore why so few studies have attempted to identify traps in the ocean, we conducted a literature review of the major drivers of marine environmental change to determine how their impacts on habitat choice and species fitness are being assessed. From this we summarize the current evidence for marine traps, present case studies to show why the phenomenon is potentially common in the ocean, highlight ways to advance awareness and understanding of traps, and demonstrate how this information can help improve management of marine environments.

BLOG POST: https://catchmenttocoast.org/2021/05/04/when-good-animals-like-bad-habitats-ecological-traps-in-the-marine-environment/

Swearer SE, Morris RL, Barrett LT, Sievers M, Dempster T, & Hale R (2021). An overview of ecological traps in marine ecosystems. Frontiers in Ecology and the Environment.

https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/fee.2322

Paper: Evaluating multiple stressor research in coastal wetlands: A systematic review

• Combinations of anthropogenic stressors are degrading coastal wetlands globally.

• Multiple stressor studies focus on seagrass, and effects of nutrients and temperature.

• Relevant stressor pairs, such as sedimentation and contaminants, are unstudied.

• Experiments are conducted in controlled conditions with reduced ecological realism.

• Varying stressor intensity and timing to reflect natural systems will enhance realism.

BLOG POST: https://catchmenttocoast.org/2021/01/29/multiple-stressors-in-coastal-wetlands-shifting-our-focus-to-real-world-scenarios/

Ostrowski A, Connolly RM, Sievers M (2020). Evaluating multiple stressor research in coastal wetlands: A systematic review. Marine Environmental Research, 105239.

https://www.sciencedirect.com/science/article/abs/pii/S0141113620310060#undfig1

Paper: Indian Sundarbans mangrove forest considered endangered under Red List of Ecosystems, but there is cause for optimism

Accurately evaluating ecosystem status is vital for effective conservation. The Red List of Ecosystems (RLE) from the International Union for the Conservation of Nature (IUCN) is the global standard for assessing the risk of ecosystem collapse. Such tools are particularly needed for large, dynamic ecosystem complexes, such as the Indian Sundarbans mangrove forest. This ecosystem supports unique biodiversity and the livelihoods of millions, but like many mangrove forests around the world is facing substantial pressure from a range of human activities. Holistic, standardised and quantitative environment risk assessment frameworks are essential here, because previous assessments have either been qualitative in nature, or have generally considered single threats in isolation. We review these threats and utilise the RLE framework to quantitatively assess the risk of ecosystem collapse. Historical clearing and diminishing fish populations drove a status of Endangered (range: Vulnerable to Endangered), and ongoing threats including climate change and reduced freshwater supply may further impact this ecosystem. However, considering recent change, the outlook is more optimistic. Mangrove extent has stabilised, and analysis of mangrove condition highlights that only a small proportion of the forest is degraded. Using the RLE provides an authoritative avenue for further protection and recognition of the issues facing this UNESCO World Heritage Site. We also identify knowledge and data gaps in the Sundarbans that are likely common to coastal systems globally. By articulating these and presenting opportunities and recommendations, we aim to further the conservation goals of the IUCN and the implementation of its new assessment framework.

BLOG POST: https://catchmenttocoast.org/2020/10/09/cautious-optimism-for-the-mighty-indian-sundarbans-mangrove-forest/

Sievers M, Chowdhury MR, Adame MF, Bhadury P, Bharagava R, Buelow C, Friess DA, Ghosh A, Hayes MA, McClure EC, Pearson RM, Turschwell MP, Worthington TA and Connolly RM (2020). Indian Sundarbans mangrove forest considered endangered under Red List of Ecosystems, but there is cause for optimism. Biological Conservation, 251, 108751.

https://www.sciencedirect.com/science/article/pii/S0006320720308090?casa_token=5yZzAavuZXYAAAAA:eF-7kU-NE77ad8xLrt_XcZE8FblHUm5p0GSm8bRwpCic7Jql0Idh0dbsWoCPx0_UDRgfvrzaK0Y

Paper: Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats

Environmental monitoring guides conservation and is particularly important for aquatic habitats which are heavily impacted by human activities. Under-water cameras and uncrewed devices monitor aquatic wildlife, but manual processing of footage is a significant bottleneck to rapid data processing and dissemination of results. Deep learning has emerged as a solution, but its ability to accurately detect animals across habitat types and locations is largely untested for coastal environments. Here, we produce five deep learning models using an object detection framework to detect an ecologically important fish, luderick (Girella tricuspidata). We trained two models on footage from single habitats (seagrass or reef) and three on footage from both habitats. All models were subjected to tests from both habitat types. Models performed well on test data from the same habitat type (object detection measure: mAP50: 91.7 and 86.9% performance for seagrass and reef, respectively) but poorly on test sets from a different habitat type (73.3 and 58.4%, respectively). The model trained on a com-bination of both habitats produced the highest object detection results for both tests (an average of 92.4 and 87.8%, respectively).

BLOG POST: https://catchmenttocoast.org/2020/11/18/deep-learning-for-ecological-monitoring-performance-in-novel-habitats-and-benefits-of-varied-training-data/

Ditria E, Sievers M, Lopez-Marcano S, Jinks EJ, Connolly RM (2020). Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats. Environmental Monitoring and Assessment, 192, 1-8

https://link.springer.com/article/10.1007/s10661-020-08653-z

Paper: Artificial intelligence meets citizen science to supercharge ecological monitoring

Citizen science and artificial intelligence (AI) are often used in isolation for ecological monitoring, but their integration likely has emergent benefits for management and scientific inquiry. We explore the complementarity of citizen science and AI for ecological monitoring, highlighting key opportunities and challenges. We show that strategic integration of citizen science and AI can improve outcomes for conservation activities. For example, coupling the public engagement benefits of citizen science with the advanced analytical capabilities of AI can increase multi-stakeholder accord on issues of public and scientific interest. Furthermore, both techniques speed up data collection and processing compared with conventional scientific techniques, suggesting that their integration can fast-track monitoring and conservation actions. We present key project attributes that will assist project managers in prioritizing the resources needed to implement citizen science, AI, or preferably both.

BLOG POST: https://catchmenttocoast.org/2020/11/09/integrating-artificial-intelligence-and-citizen-science-can-supercharge-ecological-monitoring/

The Opportunities (Top) and Challenges (Bottom) of Citizen Science (Left) and Artificial Intelligence (Right) for Ecological Monitoring, Including Integration Opportunities (Top Center Overlap) and Challenges Common to Both

McClure EC, Sievers M, Brown CJ, Buelow C, Ditria EM, Hayes MA, Pearson RM, Tulloch VJD, Unsworth RKF and Connolly RM (2020). Artificial intelligence meets citizen science to supercharge ecological monitoring. Patterns.

https://www.sciencedirect.com/science/article/pii/S2666389920301434#fig1

Paper: Impacts of management practices on blue carbon stocks and greenhouse gas fluxes in coastal ecosystems – A meta-analysis

Global recognition of climate change and its predicted consequences has created the need for practical management
strategies for increasing the ability of natural ecosystems to capture and store atmospheric carbon. Mangrove forests,
saltmarshes and seagrass meadows, referred to as blue carbon ecosystems (BCEs), are hotspots of atmospheric CO2 storage due to their capacity to sequester carbon at a far higher rate than terrestrial forests. Despite increased effort to understand the mechanisms underpinning blue carbon fluxes, there has been little synthesis of how management activities influence carbon stocks and greenhouse gas (GHG) fluxes in BCEs. Here, we present a global meta-analysis of 111 studies that measured how carbon stocks and GHG fluxes in BCEs respond to various coastal management strategies. Research effort has focused mainly on restoration approaches, which resulted in significant increases in blue carbon after 4 years compared to degraded sites, and the potential to reach parity with natural sites after 7–17 years. Lesser studied management alternatives, such as sediment manipulation and altered hydrology, showed only in-creases in biomass and weaker responses for soil carbon stocks and sequestration. The response of GHG emissions to management was complex, with managed sites emitting less than natural reference sites but emitting more compared to degraded sites. Individual GHGs also differed in their responses to management. To date, blue carbon management studies are underrepresented in the southern hemisphere and are usually limited in duration (61% of studies ❤ years duration). Our meta-analysis describes the current state of blue carbon management from the available data and highlights recommendations for prioritizing conservation management, extending monitoring time frames of BCE carbon stocks, improving our understanding of GHG fluxes in open coastal systems and redistributing management and research effort into understudied, high-risk areas.

O’Connor JJ, Fest BJ, Sievers M, and Swearer SE. (2020). Impacts of management practices on blue carbon stocks and greenhouse gas fluxes in coastal ecosystems – A meta-analysis. Global Change Biology, 26, 1354-1366.

https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.14946?casa_token=7U9HLUhWMUMAAAAA%3AI2nUQh5d2MKx3VORBQhDi_qdpof5G-Xpxhtkgv4hXCJ87trNiQXNhZRxWQVDXm3SW69IhwRO2CCDmwbg