Sticklebacks are common freshwater fish that often form small shoals in ponds, streams and rivers across the Northern Hemisphere, where they can encounter water ranging from clear to deeply murky. Turbidity, the cloudiness of water caused by suspended particles such as algae, mud and silt, can make it harder for aquatic animals to see food, other animals or potential threats. The release notes that turbidity is increasing in many rivers and coastal waters because of farming, mining, construction and other forms of pollution.

Researchers from Bristol’s School of Biological Sciences examined how shoals of three-spined sticklebacks responded as their water became cloudier and then observed their reactions when food suddenly appeared. In murkier conditions, individual fish generally took longer to move toward the food than fish in clear water. Individuals in larger groups, however, responded more quickly overall. The results indicate that both group size and the degree of coordination within a group influenced how effectively the fish responded when visibility was limited.

The relationship between coordination and response time differed across group sizes and levels of turbidity. In small groups in murky water, fish reached the food more quickly when they stayed close together and swam in the same direction. In larger groups in clear water, by contrast, tighter coordination slowed the group’s response. Before the food appeared, fish in murky water tended to swim closer together, align their movements more closely and respond more readily to the behaviour of nearby group members, particularly in smaller groups.

These observations show behavioural plasticity, meaning that the fish adjusted their social responses to changing environmental conditions rather than following one fixed pattern of grouping. The study’s central result is that increased cohesion, alignment and responsiveness to neighbours helped sticklebacks mitigate some of the information lost through turbid water. The compensation was not complete: the release reports that fish in murkier water still took longer to reach the unexpected food than those in clear water.

Corresponding author Christos Ioannou, Professor of Behavioural Ecology, said the results showed that the fish “were able to compensate for this challenge by changing their shoaling behaviour.” According to Ioannou, their shoals became more coordinated in poor conditions, with fish moving and aligning themselves together more closely, allowing the coordinated groups to find food faster than if the animals had not coordinated.

Co-author Sean Rands, Senior Lecturer, said the findings show that behavioural rules governing animal grouping can change in response to environmental challenges. He noted that shifts between hot, dry periods and heavy rains can produce sudden surges of muddy, highly turbid water in rivers and streams, creating conditions in which flexible social behaviour may matter. The study therefore documents how the tested sticklebacks modified shoaling and foraging responses under different water-clarity conditions, linking their collective behaviour to the challenge of finding food when visual information is restricted.

Subject of Research: How water turbidity changes three-spined stickleback shoaling and food-search behaviour

Article Title: Muddying the waters makes fish more sociable, study finds

Article References: Muddying the waters makes fish more sociable, study finds. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: three-spined sticklebacks, shoaling behaviour, water turbidity, collective behaviour, behavioural plasticity, animal coordination, foraging behaviour, aquatic pollution, group size, fish shoals, Muddying, waters

The Ayu is a small, salmon-like fish that reproduces in rivers. After hatching, its larvae drift downstream with river currents into coastal waters, where they spend the winter before returning upstream to mature for the following spawning season. Sandy shorelines and estuaries are therefore central to the species’ life cycle, functioning as transitional nurseries where young fish can feed, grow and survive before migration. These habitats are not uniform refuges. Temperature, salinity, turbidity, mangrove cover, access to river sources and distance from cities can all alter the conditions encountered by larvae and juveniles, while coastal development and aquaculture may compound the pressures.

To examine these relationships, the research team sampled locations in Ka Long, Ba Lat and Dinh. They collected 63 samples from Ka Long in Quang Ninh province between September and December 2021, 77 from Ba Lat in Nam Dinh province between March and April 2022, and 54 from Dinh in Ninh Thuan province between May and June 2022. At each location, the researchers measured water temperature, salinity, turbidity and chlorophyll-a. Larvae and juveniles were separated under a dissecting microscope and identified by external morphology. Catch per unit effort, or CPUE, was calculated from small-seine hauls designed to capture fish in shallow shoreline habitats.

The model predicted mean CPUE values of 105.10 Ayu per haul in Ka Long and 52.86 per haul in Ba Lat. These are the modeled estimates reported in Table 4, not observed mean catches from field sampling. No Ayu were found at any of the 54 Dinh sites. Ka Long also displayed a broader predicted range in abundance, from 0.35 to 732.28 individuals per haul, compared with 0.60 to 218.50 at Ba Lat. The absence of Ayu at Dinh was associated with environmental conditions that differed markedly from those in the northern estuaries, including higher average turbidity and greater salinity.

The researchers integrated these field measurements with a Digital Elevation Model, geographic information on mangroves and urban areas, and sun-glint-corrected Sentinel-2 satellite imagery. They then used an Adaptive Neuro-Fuzzy Inference System, a framework that combines fuzzy logic with neural-network learning, to estimate Ayu CPUE. An ant colony optimization algorithm was added to search for the model’s most useful parameters and reduce the complexity of the environmental input. For each estuary, 70% of observations were used for training and 30% for validation, allowing the researchers to compare predictions with measured catches across both sampled and unsurveyed locations.

Ant colony optimization selected temperature, salinity and turbidity as the three strongest environmental correlates of Ayu presence. In Ka Long, their correlation coefficients with CPUE were 0.47, 0.54 and 0.40, respectively; at Ba Lat, the corresponding values were 0.49, 0.50 and 0.42. Mangrove proximity, distance from the river source and distance from urban centers also showed comparatively strong relationships. The model identified favorable conditions as temperatures of about 19–25°C, salinity below 15 parts per thousand and turbidity below 18 nephelometric turbidity units, with the highest CPUE category associated with water below 13 parts per thousand salinity and below 10 turbidity units.

These modeled relationships offer hypotheses about differences between the sampled estuaries; they do not by themselves establish the cause of the absence at Dinh. Dinh’s water reached maximum values of 25.27°C, 34.43 parts per thousand salinity and 43.90 turbidity units, with means of 20.09°C, 16.34 parts per thousand and 35.27 turbidity units. Although its mean temperature fell within the favorable range, mean salinity exceeded the preferred threshold and turbidity was substantially higher. The estuary also has a history of aquaculture development, including shrimp farming, as well as other human pressures. The findings therefore point to the combined effects of hydrography and human activity rather than a single environmental variable.

Performance metrics showed that the hybrid model could reproduce observed patterns with reasonable accuracy. At Ka Long, the coefficient of determination was 0.84 for training data and 0.79 for validation data, with a root mean square error of 7.00 individuals per haul and a mean absolute error of 3.10. At Ba Lat, the corresponding training and validation values were 0.81 and 0.75, while root mean square error was 5.54 and mean absolute error 2.41. Area under the receiver operating characteristic curve was 0.804 for Ka Long and 0.793 for Ba Lat. These results support the model as a practical tool for mapping Ayu nursery habitat, although its performance has not yet been established across a broad national network of estuaries.

The analysis indicates that predicted nursery suitability differs between the surveyed estuaries and within them. Ka Long appears to provide the strongest nursery conditions, and its maximum predicted CPUE was about 3.4 times that of Ba Lat, but even there the distribution was patchy. That concentration means losses in a limited number of productive sites could have disproportionate consequences for the population. Mapping environmental thresholds and predicted abundance can help conservation authorities prioritize estuarine sections, design field monitoring and identify changes associated with climate variability, urban expansion, aquaculture and habitat fragmentation. Further validation across other Vietnamese estuaries will be essential before the model can be treated as a broad management tool.

Editorial correction: The mean CPUE values of 105.10 and 52.86 are model predictions, not observed mean catches. The headline and introduction now reflect the three-estuary survey scope and the 2025 publication date. A total of 194 sites has been removed because the source’s stated total conflicts with its listed sample counts and requires clarification.

Subject of Research: Environmental controls and nursery distribution of Ayu larvae in three Vietnamese estuaries

Article Title: Assessment of the nursery environment and distribution of Ayu, Plecoglossus altivelis ( Actinopterygii , Osmeriformes , Plecoglossidae ), in Vietnam

Article References: Chu, H. X., Le, T. Q., Nguyen, N. M., Tong, A. H. T., Bui, H. Q., Hoang, H., Nguyen, H. P., Nguyen, H. D., & Do, A. N. T. (2025). Assessment of the nursery environment and distribution of Ayu, Plecoglossus altivelis (Actinopterygii, Osmeriformes, Plecoglossidae), in Vietnam. Acta Ichthyologica et Piscatoria, 55, 19-29. https://doi.org/10.3897/aiep.55.140634

Image Credits: AI Generated

DOI: 10.3897/aiep.55.140634

Keywords: Ayu fish, Plecoglossus altivelis, nursery habitat, Vietnam estuaries, machine learning, water salinity, water turbidity, river temperature, conservation, remote sensing, Assessment, nursery

The Spermonde Archipelago, which stretches off the coast of South Sulawesi near Makassar, contains roughly 120 coral islands and sits within one of the planet’s great centers of marine biodiversity. The reefs of this region are part of the Coral Triangle, an area renowned for its extraordinary species richness. Yet the archipelago’s reefs are under mounting stress from pollution, destructive fishing practices, coastal development, and a range of other human activities. The scale of the decline is sobering: live coral cover in the region has fallen from 45 percent to 28 percent over the past two decades. That downward trajectory underscores why effective, repeatable monitoring is not a luxury but a necessity. Without accurate information about what is happening on the reef, managers cannot target interventions, evaluate their success, or adapt to changing conditions. The new study set out to address a deceptively simple question that had never before been systematically answered for this region: which monitoring approach works best under the dynamic and often challenging conditions of the Spermonde Archipelago?

To answer that question, the researchers evaluated three widely used methods for assessing reef condition. The first, Line Intercept Transects, or LIT, is a classic technique in which divers record the type of substrate found at intervals along a measured line laid across the reef. It yields precise, quantitative data about what covers the seafloor, but it demands trained divers, careful underwater work, and specialized equipment. The second method, Underwater Photo Transects, or UPT, involves photographing the seafloor along transect lines and then estimating substrate coverage from those images, either by manual annotation or software analysis. UPT reduces the time divers spend underwater and creates a permanent visual record, but it still depends on diving expertise and clear water for usable images. The third approach, drone surveys, captures broad aerial views of the reef from above the water surface. Drones can cover large areas quickly and safely, without the need for experienced divers, but their ability to distinguish fine substrate categories is limited, particularly when the water itself is murky.

The study sites were chosen to represent a gradient of water-quality conditions across the archipelago. Lae-Lae Island, located close to the mainland and the city of Makassar, experiences highly turbid water clouded by sediment and influenced by urban runoff. Kodingareng Lompo Island, farther offshore, has moderate water clarity. Lanyukang Island, the most distant of the three, enjoys relatively clear water. By applying the same monitoring toolkit across these contrasting environments, the team could assess not only how the reefs differed but also how the reliability of each method changed with water conditions. The results were instructive. Both LIT and UPT showed better reef conditions in the clearer waters of Kodingareng Lompo and Lanyukang, where live hard coral remained relatively abundant. In contrast, the turbid waters surrounding Lae-Lae revealed a reef dominated by rock, coral rubble, and dead coral covered with algae, a signature of degradation. Drone mapping delivered a valuable broader perspective of reef structure, but in the highly turbid waters of Lae-Lae it could not reliably distinguish between different substrate types, a limitation that matters when the goal is to quantify coral cover.

These findings point to a practical conclusion: the three methods are complementary rather than interchangeable. LIT and UPT provide the detailed, substrate-level information needed to track coral health and mortality, but they require trained divers and specialized gear, which raises costs and limits how often surveys can be repeated. Drone mapping, by contrast, can survey large reef areas efficiently and without diving expertise, making it attractive for routine, wide-scale surveillance, but it sacrifices the fine detail that underwater methods capture. For a country with thousands of islands and limited monitoring resources, the implication is that a tiered strategy, using drones for broad coverage and underwater surveys for detailed assessment at key sites, offers the most realistic path to sustained reef monitoring. Such a strategy aligns directly with United Nations Sustainable Development Goal 14, Life Below Water, which calls for the conservation and sustainable use of marine ecosystems, and it supports SDG 13, Climate Action, by strengthening the capacity to detect and respond to climate-driven pressures on reefs.

What elevates the study beyond a technical comparison of methods is its second pillar: the systematic incorporation of community perspectives. The researchers conducted focus group discussions with 33 residents across the three islands, exploring how local people perceive reef degradation and what conservation measures they believe would work in their own contexts. The results were revealing in both encouraging and sobering ways. On the encouraging side, communities were clearly aware that their reefs are in decline. More than half of the respondents considered reef conditions to be either deteriorating or severely deteriorating, a perception that matched the ecological data. On the sobering side, their understanding of the causes of decline was more limited. Only 26.5 percent of participants were able to correctly identify different stages of coral health when shown images, suggesting a gap between lived awareness of decline and the ecological literacy needed to interpret it precisely.

Despite that gap, the discussions demonstrated that residents recognize the specific environmental problems affecting their islands and can propose solutions tailored to local conditions. On Lae-Lae, where turbid and waste-affected water was the dominant concern, participants emphasized waste management as the priority. On Kodingareng Lompo, residents proposed combining patrols and enforcement with coral transplantation, an approach that pairs protection with active restoration. On Lanyukang, where water clarity remains relatively high and the reef is in better condition, participants placed greater emphasis on controlling destructive fishing, aiming to prevent the degradation that has already occurred elsewhere. This pattern is significant for conservation planners: communities are not blank slates waiting for external prescriptions. They hold contextual knowledge about their environment and can articulate locally appropriate responses, but they need support to translate that knowledge into effective action.

The study also quantified a critical barrier to community-driven conservation: readiness. Only 29.4 percent of respondents reported a high level of readiness to participate in reef conservation and monitoring, and half said they had never taken part in any conservation activity organized by outside groups. These numbers reveal an unmet demand for engagement infrastructure. The researchers argue that the way to close this gap is through simple monitoring tools that non-specialists can use, practical training that builds ecological and technical skills, and financial support that makes participation sustainable for people who depend on the sea for their livelihoods. They further recommend embedding community-based efforts within integrated coastal zone management, marine protection, and climate adaptation programs at the provincial and national levels, so that local action is reinforced rather than isolated. As Professor Jompa explained, the team aimed to provide an evidence-based foundation for conservation policies while enhancing the adaptive capacity of local communities and stakeholders in responding to evolving ecological challenges in the Spermonde Archipelago.

The broader lesson from the Spermonde Archipelago extends well beyond Indonesia. Around the world, coral reef conservation has often been designed top-down, with scientists and governments setting priorities and communities cast as implementers or bystanders. This study demonstrates a different model, one in which scientific monitoring defines the ecological baseline and the most reliable tools for tracking change, while community knowledge identifies local pressures, shapes acceptable solutions, and provides the sustained presence that no external project can match. Empowering communities with the knowledge, tools, and resources to participate in monitoring and management could produce conservation strategies better suited to the diverse conditions of Indonesia’s reef ecosystems, which vary enormously in water quality, exposure, and human use. As Professor Jompa noted, successful mitigation requires partnerships involving communities, government support, NGO training, and academic assistance. With live coral cover already down to 28 percent and climate pressures intensifying, the window for such partnerships is narrowing, but the Spermonde study shows that the ingredients for effective, locally grounded reef stewardship are already present, waiting to be combined.

Subject of Research: Comparative coral reef monitoring methods and community-based conservation in Indonesia's Spermonde Archipelago

Article Title: How combining science and community knowledge could shape coral reef conservation in Indonesia

Article References: How combining science and community knowledge could shape coral reef conservation in Indonesia. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: coral reefs, Indonesia, Spermonde Archipelago, marine conservation, community-based monitoring, Line Intercept Transects, Underwater Photo Transects, drone surveys, water turbidity, SDG 14, climate resilience, Hasanuddin University