Spatiotemporal variability in squid catch rates and their environmental associations in coastal waters of Langkawi and Kedah, Malaysia
This study analyzes spatiotemporal variability in squid catch rates in Langkawi and Kedah, Malaysia, revealing that while chlorophyll-a is the strongest environmental correlate, catch rates are predominantly driven by complex interactions between time, location, and environmental conditions, with temporal effects significantly confounding simple environmental relationships.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
The ocean is not a static backdrop; it is a living, breathing system where the abundance of marine life shifts with the tides, the seasons, and the invisible chemistry of the water. For fisheries managers, understanding these shifts is a matter of food security and economic stability, yet predicting where fish or squid will be found remains one of the most challenging tasks in marine science. A key metric used to gauge this is the catch per unit effort, a simple ratio that measures how much seafood is caught for every hour a boat spends fishing. While this number often serves as a proxy for how many animals are in the water, it is rarely a direct count. Instead, it is a complex signal influenced by the behavior of the animals, the skill of the fishers, the type of gear used, and the changing environment. When researchers try to link catch rates to environmental factors like water temperature or nutrient levels, they must untangle these threads carefully, because a pattern that looks strong when looking at all the data at once might simply be an illusion created by the timing of the surveys.
In the coastal waters off Langkawi and Kedah in northwestern Malaysia, a team of researchers set out to decode these patterns for squid, a short-lived and fast-growing creature that is vital to local fisheries. Over the course of a year, from November 2024 to November 2025, they conducted 65 fishing trips using a small, local vessel equipped with lights to attract the squid at night. The team recorded exactly how much squid was caught and how long the fishing took, converting these raw numbers into a standardized rate of catch per hour. Simultaneously, they measured the conditions of the water at each spot, noting the depth, temperature, salinity, acidity, and the concentration of chlorophyll-a, a pigment that indicates the presence of microscopic plant life at the base of the food web. With these data in hand, the scientists used advanced statistical tools to see if they could find a consistent rule that explained why squid were abundant in some places and times but scarce in others.
The results revealed a story of striking variability. The amount of squid caught per hour fluctuated wildly, ranging from a modest 0.15 kilograms to a massive 8 kilograms in a single hour, with an average of about 1.5 kilograms. This wide swing suggested that the squid were not evenly distributed but were instead clustered in specific areas at specific times. When the researchers looked for a single environmental factor that could explain these swings, one stood out: chlorophyll-a. In a broad look at all the data combined, higher levels of this green pigment were strongly linked to higher squid catches. It seemed logical to assume that more plant life meant more food for the squid, which in turn meant more squid for the fishers. However, the story became more nuanced when the researchers examined the data more closely, separating the observations by the specific date they were collected.
Once the team accounted for the differences between the sampling dates, the strong link between chlorophyll-a and squid catches weakened significantly. This finding suggested that the initial strong connection was largely driven by the fact that both the squid catches and the chlorophyll levels changed together over the course of the year, rather than because the squid were responding to the local food supply at every single spot. In other words, the squid were more abundant during certain months when the water was also richer in nutrients, but within a single night, the amount of food in the immediate water did not reliably predict where the squid would be. The researchers found that the timing of the survey was just as important as the water conditions themselves.
To make sense of these complex interactions, the team built a sophisticated computer model that could handle non-linear relationships, meaning it could detect patterns that were not simple straight lines. This model, which combined the environmental data with the location and the time of year, was able to explain nearly 70 percent of the variation in the catch rates. It showed that the squid's presence was the result of a mix of factors: the time of year, the specific location, and the environmental conditions, all working together. The model remained stable even when the single highest catch was removed from the data, confirming that the pattern was real and not just an artifact of one unusually good night. In contrast, when the researchers tried a different type of predictive tool, a machine learning algorithm known as Random Forest, it failed to predict the catches accurately when tested on new data. This failure highlighted that while complex patterns exist, they are difficult to forecast without a deep understanding of the underlying temporal structure.
The study also looked at the map of the fishing grounds to see if there were permanent "hotspots" where squid always gathered. While one area in the south-central part of the study zone did show a very high catch rate, and a few other spots had moderate success, the researchers were careful not to label these as fixed, permanent zones. Because the sampling locations changed from day to day, and because the catch rates varied so much over time, a high catch on one night did not guarantee the same result on the next. The absence of a consistent spatial pattern in the remaining data suggested that the squid were moving through the area rather than staying put in a single location.
Ultimately, the research paints a picture of a fishery that is highly dynamic and responsive to the rhythms of the ocean. The squid in these waters are not simply waiting in a fixed spot for the fishers to find them; their availability is a shifting target influenced by the season, the location, and the broader state of the marine environment. While chlorophyll-a emerged as the strongest environmental signal, its influence is deeply intertwined with the passage of time, making it a poor standalone predictor for where to fish on any given night. The findings suggest that managing this fishery requires a flexible approach that recognizes these rapid changes. Rather than relying on a single environmental rule or a static map of productive waters, effective management will need to monitor these patterns continuously, adapting to the seasonal and spatial shifts that define the life of these short-lived creatures. The study provides a solid foundation for future monitoring, emphasizing that to understand the squid, one must look at the whole picture of time, place, and environment, rather than searching for a single cause.
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