Catch Data Can Unravel Elasmobranch Aggregation Dynamics and Group Behaviours

Abstract

Elasmobranchs (i.e., sharks, skates, rays), known for their cognitive abilities and complex behaviours, often form aggregations that are thought to be crucial for their survival and evolutionary success. However, understanding the drivers behind these aggregations remains challenging due to the dynamism of the marine environment and the difficulty of observing these species directly. Here, we aim to address these challenges by introducing a methodological framework for analysing catch data to infer aggregation behaviour. Within this framework, we outline key metrics to explore, such as the number and density of individuals captured, phenotypic traits, drivers of co-occurrence, individual identification, and kin structure. We then demonstrate how to use this framework in a case study of juvenile blacktip reef sharks (Carcharhinus melanopterus) in Moorea, French Polynesia, to determine its real-world application and identify potential limitations. Our results reveal that juvenile blacktip reef sharks around Moorea tend to aggregate during early life stages and that these aggregations appear non-social, indicative of environmental rather than social drivers. We also find that, while catch data can provide valuable insights into elasmobranch aggregations, they must be complemented with targeted research methods to maximise the available data advised within our framework. As findings from our case study demonstrate, this framework has the capacity to broaden our knowledge of elasmobranch aggregations and social behaviours, underscoring the importance of dedicated efforts in research and conservation to manage these vulnerable species effectively.

Keywords: animal behaviour; fisheries; interactions; marine conservation; sociality.

FIGURE 1

Juvenile blacktip reef shark catch data. (a) Number of sharks captured per deployment in Moorea between 2013 and 2023. (b) Time differences between individual shark captures. Data were only included for deployments in which more than one shark was captured.

FIGURE 2

Sex ratio in juvenile blacktip reef sharks. Relationship between sex ratio and (a) number of sharks per deployment for all deployments in which at least two sharks were captured, and (b) number of sharks simultaneously captured. Sex ratio ranges from 0 (all females) to 1 (all males), and colours indicate multi‐sex (orange) or same sex (green) co‐occurring individuals.

FIGURE 3

Partial response plots from a GAM built using data at the deployment scale (a–c) and for simultaneous capture instances (d). Only statistically significant relationships are depicted. At the deployment scale, these include (a) sex ratio and number of sharks captured per deployment, and variation in sex ratio (b) by year and (c) throughout the sampling season among deployments. For simultaneous captures, (d) month was a significant predictor. Monthly data are shown for all months that sampling occurred and for which sufficient data were available (see text for details).

FIGURE 4

Number and size range of juvenile blacktip reef sharks. Relationship between variance in fork length (the difference between maximum and minimum sizes, in mm) and the number of juvenile blacktip reef sharks for (a) all deployments in which at least two sharks were captured and (b) instances of simultaneous capture.

FIGURE 5

Partial response plots from a GAM built using data at the deployment scale. Only statistically significant relationships are depicted. These include (a) between body size variance (calculated as the difference in maximum and minimum length of sharks captured, in mm) and number of sharks captured per deployment, and (b) size variance throughout the sampling season among deployments. Monthly data are only shown for months that sampling occurred.