Analysis of juvenile tuna movements as correlated random walk

Kadota, Minoru; Torisawa, Shinsuke; Takagi, Tsutomu; Komeyama, Kazuyoshi

doi:10.1007/s12562-011-0415-y

Cited by 4 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, organisms like Daphnia pulex and Temora longicornis have been observed performing multifractal random walks, behaviors linked to mating, foraging, and predator evasion strategies [24,25]. Moreover, studies have illustrated that movements within fish populations can be depicted through biased random walks, incorporating both directional movement and exploratory behavior [26]. These findings underscore the complexity of fish movements, rooted in the principles of random walks.…”

Section: Fish Shoal Behavior Modelmentioning

confidence: 99%

Quantitative Assessment and Analysis of Fish Behavior in Closed Systems Using Information Entropy

Kadota,

Torisawa,

Takagi

2024

Fishes

Self Cite

View full text Add to dashboard Cite

This study introduces a method for quantitatively assessing the complexity and predictability of fish behavior in closed systems through the application of information entropy, offering a novel lens through which to understand how fish adapt to environmental changes. Utilizing simulations rooted in a random walk model for fish movement, we delve into entropy fluctuations under varying environmental conditions, including responses to feeding and external stimuli. Our findings underscore the utility of information entropy in capturing the intricacies of fish behavior, particularly highlighting the synchrony in collective actions and adaptations to environmental shifts. This research not only broadens our comprehension of fish behavior but also paves the way for its application in fields like aquaculture and resource management. Through our analysis, we discovered that smaller grid sizes in simulations capture detailed local fluctuations, while larger grids elucidate general trends, pinpointing a 2.5 grid as optimal for our study. Moreover, changes in swimming speeds and behavioral adaptations during feeding were quantitatively analyzed, with results illustrating significant behavior modifications. Additionally, employing a Gaussian mixture model helped to clarify the nuanced changes in fish behavior in response to altered light conditions, demonstrating the layered complexity of fish responses to environmental stimuli. This investigation confirms the efficacy of information entropy as a robust metric for evaluating fish shoal behavior, offering a fresh methodology for ecological and environmental studies, with promising implications for sustainable management practices.

show abstract

Section: Fish Shoal Behavior Modelmentioning

confidence: 99%

Quantitative Assessment and Analysis of Fish Behavior in Closed Systems Using Information Entropy

Kadota,

Torisawa,

Takagi

2024

Fishes

Self Cite

View full text Add to dashboard Cite

show abstract

“…For this purpose, we simulated the behavior of fish during feeding within our model. It is well-documented that certain species, such as tuna, exhibit phototaxis, actively avoiding light by diving deeper, a behavior that has significant implications for their feeding patterns [27].Typically, in a fish pen, when feed is introduced, fish converge around the area where the food is distributed, begin feeding, and subsequently resume random swimming within the pen. In our simulation, this behavior is replicated at a specific moment (time step 50), where all fish are relocated to the vicinity of the water surface's center, signifying the act of feeding.…”

Section: (Analysis-3) Behavioral Changes During Feeding and Entropymentioning

confidence: 99%

Quantitative Assessment and Analysis of Fish Behavior in Closed Systems Using Information Entropy

Kadota,

Torisawa,

Takagi

2024

Preprint

View full text Add to dashboard Cite

This study introduces a method for quantitatively assessing the complexity and predictability of fish behavior in closed systems through the application of information entropy, offering a novel lens to understand how fish adapt to environmental changes. Utilizing simulations rooted in a random walk model for fish movement, we delve into entropy fluctuations under varying environmental conditions, including responses to feeding and external stimuli. Our findings underscore the utility of information entropy in capturing the intricacies of fish behavior, particularly highlighting the synchrony in collective actions and adaptations to environmental shifts. This research not only broadens our comprehension of fish behavior but also paves the way for its application in fields like aquaculture and resource management. Through our analysis, we discovered that smaller grid sizes in simulations capture detailed local fluctuations, while larger grids elucidate general trends, pinpointing a 2.5m grid as optimal for our study. Moreover, changes in swimming speeds and behavioral adaptations during feeding were quantitatively analyzed, with results illustrating significant behavior modifications. Additionally, employing a Gaussian Mixture Model helped to clarify the nuanced changes in fish behavior in response to altered light conditions, demonstrating the layered complexity of fish responses to environmental stimuli. This investigation confirms the efficacy of information entropy as a robust metric for evaluating fish shoal behavior, offering a fresh methodology for ecological and environmental studies, with promising implications for sustainable management practices.

show abstract

“…The Correlated Random Walk (CRW) model has been adopted in broad literature to explain individual behavior of stochastic movement for animals, fishes, insects, etc. [34][35][36][37][38]. In the meantime, this random walk model can hardly be utilized to replicate the group behavior of animal herds since this model cannot establish a link between individual animal movement direction and the overall herd location, which is essential for maintaining the integrity of animal herds.…”

Section: Animal Movement Modelingmentioning

confidence: 99%

Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory

Kabir

Lee

2021

Applied Sciences

View full text Add to dashboard Cite

This paper addresses a wildlife monitoring problem using a team of unmanned aerial vehicles (UAVs) with the optimal transport theory. The state-of-the-art technology using UAVs has been an increasingly popular tool to monitor wildlife compared to the traditional methods such as satellite imagery-based sensing or GPS trackers. However, there still exist unsolved problems as to how the UAVs need to cover a spacious domain to detect animals as many as possible. In this paper, we propose the optimal transport-based wildlife monitoring strategy for a multi-UAV system, to prioritize monitoring areas while incorporating complementary information such as GPS trackers and satellite-based sensing. Through the proposed scheme, the UAVs can explore the large-size domain effectively and collaboratively with a given priority. The time-varying nature of wildlife due to their movements is modeled as a stochastic process, which is included in the proposed work to reflect the spatio-temporal evolution of their position estimation. In this way, the proposed monitoring plan can lead to wildlife monitoring with a high detection rate. Various simulation results including statistical data are provided to validate the proposed work. In all different simulations, it is shown that the proposed scheme significantly outperforms other UAV-based wildlife monitoring strategies in terms of the target detection rate up to 3.6 times.

show abstract

Analysis of juvenile tuna movements as correlated random walk

Cited by 4 publications

References 16 publications

Quantitative Assessment and Analysis of Fish Behavior in Closed Systems Using Information Entropy

Quantitative Assessment and Analysis of Fish Behavior in Closed Systems Using Information Entropy

Quantitative Assessment and Analysis of Fish Behavior in Closed Systems Using Information Entropy

Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory

Contact Info

Product

Resources

About