Monitoring shrimp behavior in relation to feed provision, location and time of day in an experimental aquaculture pond

A primary challenge in penaeid shrimp farming is suboptimal feed utilization, leading to inefficiencies in production. Better insights into shrimp feeding behavior may provide pathways to address these inefficiencies. While previous studies have highlighted the influence of feed dispersal and time of day on penaeid shrimp behavior in controlled settings, comprehensive analyses of their behavior in production ponds remain limited. The aim of this study was to understand shrimp group feeding behavior in ponds, and how this relates to feed provision, location within the pond, and time of day. Three consecutive trials were performed in an experimental aquaculture pond (28 m 2 ) in Zhuhai, China, stocked with juvenile whiteleg shrimp Litopenaeus vannamei (7.1 ± 0.4 g, mean ± S.E.) at 20 ind.m −2 . Each trial comprised 7 days of acclimation followed by 6 days of observation. Three underwater cameras were placed on the bottom of the pond at different locations, with feed provided at different pond locations six times a day between 07:00 and 19:00. Shrimp within the field of view for each camera were tracked using a combination of automated (YOLACT, You Only Look At CoefficienTs) and manual techniques, allowing for calculation of key behavioral metrics associated with individual and group behavior. Shrimp gathered in large numbers around the feeding area shortly after feed provision (i.e. 1 min after) and a gradient in shrimp density was found 10 min after feed provision from high densities inside the feeding area (89.8 ± 5.5 ind.m −2 ) to low densities on the opposite edge of the pond (11.9 ± 2.3 ind.m −2 ). Shrimp were more evenly distributed across the pond at night compared to daytime. Although no schooling behavior was observed, shrimp movements were on average twice as fast across the whole pond shortly after feed provision compared to before feeding. Movements inside the feeding area were less polarized (i.e. reduced alignment in the animals’ heading direction relative to that of the group) after feed provision. This study provides a first insight into shrimp feeding behavior in aquaculture ponds, which could help with inefficiencies in production of this species. • Time and feeding effects on shrimp behavior were filmed in an aquaculture pond. • Shrimp were more disorganized in the feeding area after feeding than before. • Shrimp moved faster across the whole pond after feed provision. • More shrimp were observed near the center of the pond during the day than at night.

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Publication Details

Journal
Applied Animal Behaviour Science
Published
2025-04-17
DOI
https://doi.org/10.1016/j.applanim.2025.106636
Citations
2
Primary Topic
Water Quality Monitoring Technologies
Type
article
Field-Weighted Citation Impact
1.63
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article

Monitoring shrimp behavior in relation to feed provision, location and time of day in an experimental aquaculture pond

Jean‐Benoît Darodes de Tailly, Matthew A.G. Owen, Jonas Keitel, Katherine A. Sloman et al.
2 citations
Applied Animal Behaviour Science
Water Quality Monitoring Technologies
1.63
article

Monitoring shrimp behavior in relation to feed provision, location and time of day in an experimental aquaculture pond

Jean‐Benoît Darodes de Tailly, Matthew A.G. Owen, Jonas Keitel, Katherine A. Sloman, José M. Alcaraz Calero, Mhairi E. Alexander, Ignacio Martinez Alpiste
article en
2 citations

Abstract

A primary challenge in penaeid shrimp farming is suboptimal feed utilization, leading to inefficiencies in production. Better insights into shrimp feeding behavior may provide pathways to address these inefficiencies. While previous studies have highlighted the influence of feed dispersal and time of day on penaeid shrimp behavior in controlled settings, comprehensive analyses of their behavior in production ponds remain limited. The aim of this study was to understand shrimp group feeding behavior in ponds, and how this relates to feed provision, location within the pond, and time of day. Three consecutive trials were performed in an experimental aquaculture pond (28 m 2 ) in Zhuhai, China, stocked with juvenile whiteleg shrimp Litopenaeus vannamei (7.1 ± 0.4 g, mean ± S.E.) at 20 ind.m −2 . Each trial comprised 7 days of acclimation followed by 6 days of observation. Three underwater cameras were placed on the bottom of the pond at different locations, with feed provided at different pond locations six times a day between 07:00 and 19:00. Shrimp within the field of view for each camera were tracked using a combination of automated (YOLACT, You Only Look At CoefficienTs) and manual techniques, allowing for calculation of key behavioral metrics associated with individual and group behavior. Shrimp gathered in large numbers around the feeding area shortly after feed provision (i.e. 1 min after) and a gradient in shrimp density was found 10 min after feed provision from high densities inside the feeding area (89.8 ± 5.5 ind.m −2 ) to low densities on the opposite edge of the pond (11.9 ± 2.3 ind.m −2 ). Shrimp were more evenly distributed across the pond at night compared to daytime. Although no schooling behavior was observed, shrimp movements were on average twice as fast across the whole pond shortly after feed provision compared to before feeding. Movements inside the feeding area were less polarized (i.e. reduced alignment in the animals’ heading direction relative to that of the group) after feed provision. This study provides a first insight into shrimp feeding behavior in aquaculture ponds, which could help with inefficiencies in production of this species. • Time and feeding effects on shrimp behavior were filmed in an aquaculture pond. • Shrimp were more disorganized in the feeding area after feeding than before. • Shrimp moved faster across the whole pond after feed provision. • More shrimp were observed near the center of the pond during the day than at night.

Applied Animal Behaviour ScienceVol. 287
NHS Lanarkshire (GB), Skretting (Norway) (NO), University of the West of Scotland (GB)
Openalex Percentile: Top 18%
Water Quality Monitoring Technologies
1.63
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