Back

Tracking spatial patterns and nocturnal arousal in an undisturbed natural population of the pulse-type weakly electric fish Gymnotus omarorum

Migliaro, A.; Pedraja, F.; Mucha, S.; Benda, J.; Silva, A.

2024-07-02 animal behavior and cognition
10.1101/2024.06.29.600875 bioRxiv
Show abstract

Assessing animals locomotor and activity-rest patterns in natural populations is challenging. It requires individual identification and behavioral tracking in sometimes complex and inaccessible environments. Weakly electric fish are advantageous models for remote monitoring due to their continuous emission of electric signals (EODs). Gymnotus omarorum is a South American freshwater pulse-type weakly electric fish. Previous manual recordings of restrained individuals in the wild showed a spatial distribution compatible with territoriality and a nocturnal increase in EOD rate interpreted as arousal. This interdisciplinary study presents the development of low-cost amplifiers for remote EOD recordings and the refinement of tracking algorithms that provide individual recognition of Gymnotus omarorum in the wild. We describe natural daily spacing patterns of undisturbed individuals that are compatible with territoriality, although heterogeneous across sampling sites, and confirm that all resident fish showed a robust nocturnal increase of EOD rate likely associated with daily variations of water temperature. HIGHLIGHTSO_LISuccessful remote individual tracking of wild pulse type weakly electric fish C_LIO_LIG. omarorum spacing patterns are compatible with known nocturnality and territoriality C_LIO_LIResidents keep their diurnal resting sites and move within small areas during the night C_LIO_LIThe robust nocturnal electric arousal of residents is linked to water temperature peak C_LI

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.