RESUMO
The role of temperature on biological activities and the correspondent exponential relationship with temperature has been known for over a century. However, lacking to date is knowledge relating to (a) the recovery of ectotherms subjected to extreme temperatures in the wild, and (b) the effects repeated extreme temperatures have on the temperatures that induce behavioural thermoregulation (aggregations). We examined these questions by testing the hypothesis that thermal thresholds which initiate aggregations in juvenile Atlantic salmon (AS) (Salmo salar) are not static, but are temporally dynamic across a summer and follow a hysteresis loop. To test our hypothesis, we deployed custom-made underwater camera (UWC) systems in known AS thermal refuges to observe the timing of aggregation events in a natural system and used these data to develop and test models that predict the temperatures that induce thermal aggregations. Consistent with our hypothesis our UWC observations revealed a range of aggregation onset temperatures (AOT) ranging from 24.2°C to 27.1°C, thus confirming our hypothesis that AOTs are dynamic across summer. Our models suggest it take ~ 11 days of non-thermally taxing temperatures for the AOT to rebound in the study river. Conversely, we found that as the frequency of events increased, the AOT declined, from 27.1°C to 24.2°C. Integrating both model components led to more robust model performance. Further, when these models were tested against an independent data set from the same river, the results remained robust. Our findings illustrate the complexity underlying behavioural thermoregulation in AS-a complexity that most likely extends to other salmonids. The frequency of extreme heat events is predicted to increase, and this has the capacity to decrease AOT thresholds in AS, ultimately reducing their resilience to extreme temperature events.
RESUMO
Imaging sonars are used around the world for fish population monitoring. The accuracy of the length measurements has been reported in multiple studies for relatively short (<15 m) ranges and high image resolution. However, imaging sonars are often used at longer ranges (i.e., >15 m) where the images produced from sonar returns become less detailed. The accuracy of the length measurements from the Adaptive Resolution Imaging Sonar (ARIS) was tested by releasing n = 69 known-sized adult Atlantic salmon (Salmo salar) directly into the sonar field at ranges between 15 and 29 m, and measuring their echoes manually by four users and semi-automatically using a computer workflow in Echoview software. Overall, the length measurements were very variable: compared to true (fork) lengths, the mean of differences varied between -9.9 cm and 7.8 cm in the human-generated datasets, and between -42.8 cm and -20 cm in the computer-generated dataset. In addition, the length measurements in different datasets were only in poor or moderate agreement with each other (intraclass correlation <0.61). Contrary to our expectations, the distance from the transducer or the subjectively assessed echo quality did not have an effect on the measurement accuracy in most of the datasets and when it did, the effect was not systematic between the datasets. Therefore, a size class and length prediction model was implemented in a Bayesian framework to group salmon into two size categories: One-Sea-Winter (<63 cm) and Multi-Sea-Winter (≥63 cm) groups. The model correctly predicted the size category in 83% of the fish in the computer-generated dataset and ranged from 68% to 74% in the human-generated datasets. We conclude that fish length measurements derived from long-range imaging sonar data should be used with caution, but post-processing can improve the usefulness of the data for specific purposes, such as adult Atlantic salmon population monitoring.
