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- Oxford University Press
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- © The Author(s) 2023. Published by Oxford University Press and the Society for Experimental Biology.
- eISSN
- 2051-1434
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- 10.1093/conphys/coac079
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Abstract
Volume 11 • 2023 10.1093/conphys/coac079 Research article Timing and frequency of high temperature events bend the onset of behavioural thermoregulation in Atlantic salmon (Salmo salar) 1,2,3, 2,4 1,2 5 1,2,4 Antóin M. O’Sullivan , Emily M. Corey , Elise N. Collet , Jani Helminen , R. Allen Curry , 1 1,2,4 Chris MacIntyre and Tommi Linnansaari FOREM, University of New Brunswick, Fredericton, Fredericton, New Brunswick, NB E3B 5A3, Canada Canadian Rivers Institute, University of New Brunswick, New Brunswick, NB E3B 5A3, Canada O’Sullivan Ecohydraulics Inc., Fredericton, New Brunswick, Canada Biology, University of New Brunswick, Fredericton, Canada Natural Resources Institute Finland, Helsinki, Uusimaa, 00790, Finland *Corresponding author: FOREM, University of New Brunswick, Fredericton, Fredericton, New Brunswick, NB E3B 5A3, Canada Email: aosulliv@unb.ca .......................................................................................................................................................... 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. Key words: underwater camera, thermal refuge, thermal hysteresis, thermal aggregation, salmonid, Atlantic salmon Editor: Dr. Steven Cooke Received 7 July 2022; Revised 25 November 2022; Editorial Decision 1 December 2022; Accepted 22 December 2022 Cite as: O’Sullivan AM, Corey EM, Collet EN, Helminen J, Curry RA, MacIntyre C, Linnansaari T (2023) Timing and frequency of high temperature events bend the onset of behavioural thermoregulation in Atlantic salmon (Salmo salar) . Conserv Physiol 11(1): coac079; doi:10.1093/conphys/coac079. .......................................................................................................................................................... .......................................................................................................................................................... © The Author(s) 2023. Published by Oxford University Press and the Society for Experimental Biology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/ by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Research article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... a range of temperatures (Ebersole et al., 2001; Sutton et al., Introduction 2007). In rainbow trout, for instance, no consistent temper- The influence of temperature on the functioning of biota is ature was found to induce thermal refuge use; rather, the pervasive. Perhaps nowhere is the fingerprint of temperature movements occurred over a range of temperatures between more apparent than in the metabolic rates of biota (see 18.0 and 25.7 C(Ebersole et al., 2001). These findings differ Brown et al., 2004; Clarke, 2006). The metabolic rate and from those observed for juvenile coho salmon (Oncorhynchus mass influences the sustenance of all life, from unicellular to kisutch) where thermal refuge use started between 22.0 and multicellular organisms. The dependence of metabolic rate on 25.0 C (see Brewitt and Danner, 2014). Similarly, Wilbur et al. temperature influences the global distribution of coldwater (2020) found juvenile Atlantic salmon used thermal refuges ◦ ◦ stenotherms, such as Atlantic salmon (Salmo salar)(Elliott at temperatures between 25 C and 27 C. This leaves the and Elliott, 2010; Morelli et al., 2020). Whilst salmonids question, what might explain this variability in aggregations generally occupy cool rivers, extreme heat events can lead onset temperatures? to ambient thermal regimes that exceed critical thermal tol- The phenomenon of hysteresis is inherent in biotic and erance thresholds (Elliot, 1991; Frechette et al., 2021). The abiotic processes, and therefore has found widespread use in effects of exposure to critical thermal regimes are metabol- the fields of physics, hydrology and kinesiology, to mention ically, physiologically and energetically costly (Lennox et but a few ((Jiles, 1994; Brassard et al., 2017; Wondzell and al., 2018; Little, Loughland, and Seebacher, 2020; Morash Ward, 2022). Most simply, hysteresis can be summated as et al., 2021). When temperatures exceed critical thresholds follows: the state of a system depends on what has happened in natural settings, salmonids seek out cool-water thermal to it in the past and what is happening to it in the present. refuges to offset physiological and energetic stresses induced With that, one can conceptualize that the effect of a past by the thermal conditions of the river (Corey et al., 2020; extreme temperature event on an ectotherm will influence the Ebersole, Liss, and Frissell, 2003; Huntsman, 1942; Keefer physiological condition of the organism in the present. This and Caudill, 2016). This thermoregulatory behaviour is ubiq- relationship can be also be used to conceptualize the effects uitous amongst salmonids from juveniles to adults and high- of the repeated thermal stress on the thermal thresholds that lights the importance of cool-water refuges for the survival of induce behavioural thermoregulation in salmonids, or in this salmonids (O’Sullivan et al., 2021a; Torgersen et al., 1999). study, juvenile Atlantic salmon. Conceptually, by increasing the number of bahavioural thermoregulation events over a In ecology, the term kinetics is used to describe metabolic window of time, the temperature that induces such behaviour rate as a function of temperature (Brown et al., 2004; Clarke, will decrease. Conversely, as the time since a behavioural ther- 2006). This relationship can be decanted into a simple expo- moregulation event increases the fish would recover, thereby nential model, and has implications for all of Earth’s biota, returning its thermal threshold to its upper limit. from ants (Shapley, 1924) and bovine (Parkhurst, 2010)to vegetation (Hollister, Webber, and Bay, 2005). Indeed, the role In this study, we hypothesize that the thermal thresh- of temperature on biological activities and the correspondent olds underpinning behavioural thermoregulation in juvenile exponential relationship with temperature has been known Atlantic salmon are not static, but are temporally dynamic for over a century (Brown et al., 2004—see Boltzmann, within a summer. To test our hypothesis, we developed and 1872; Arrhenius 1889). However, biological activities cannot deployed custom-made underwater camera systems in known increase exponentially in perpetuity; at some point, the organ- Atlantic salmon thermal refuges to observe the timing of ism must reduce its temperature, or die (Parkhurst, 2010; behavioural thermoregulation events in a natural system. We Corey, 2022); alas, behavioural thermoregulation. Myriad used these data to develop and test a suite of new models studies have given credence to these mechanistic understand- to predict the timing of behavioural thermoregulation based ings; for example, (Santos, Castañeda, and Rezende, 2011) on the theory of hysteresis; that is, timing of behavioural used the Gompertz equation (Gompertz, 1825) to examine thermoregulation (modelled state) is inherently dependent the heat tolerance in small fruit flies (Drosophila). However, on the “history” of previous thermoregulation events of the lacking in literature (at least to the authors knowledge) are exposed individuals, resulting in variable, rather than static, investigations establishing (a) the recovery of ectotherms threshold temperature. subjected to extreme temperatures in the wild, and (b) the effects repeated exposure to extreme temperatures may have on ectothermic organisms. Methods Contemporary research has revealed species-specific and Study area geographic variability in the water temperatures that induce behavioural thermoregulation in salmonids (Brewitt, Danner, This study was conducted in the Little Southwest Miramichi and Moore, 2017; Corey et al., 2020; Sutton, Deas, Tanaka, (LSW-M) river, a tributary of the Miramichi River, New Soto, and Corum, 2007). Whilst some studies have found Brunswick, Eastern Canada—Figure 1a. The LSW-M has a specific temperatures induce movements to thermal refuges topographic drainage area ∼ 1300 km and is climatically (Dugdale et al., 2016; Corey et al., 2020), others have found characterized by cold winters and warm summers (Caissie, .......................................................................................................................................................... 2 Conservation Physiology • Volume 11 2023 Research article .......................................................................................................................................................... Breau, Hayward, and Cameron, 2013; Linnansaari and Cun- Underwater cameras jak, 2010). In summer, maximum water temperature across Custom-made video cameras—2020 the Miramichi catchment displays a wide range of variability, ◦ ◦ with some tributaries measuring > 30 C and others ∼ 15.0 C A custom-made underwater camera system was developed (O’Sullivan et al., 2021b). The Miramichi region was once the by coupling a Raspberry Pi Zero W—microcomputer with a top producer of Atlantic salmon in North America; however, Raspberry Pi Camera Module V2–8 Megapixel, 1080p, (red- the population is in a state of steep decline since at least the green-blue) RGB spectra (RPI-CAM-V2—Figure 2a and b). 1970s (Samways, 2017). The system was programmed to turn on at the top of each hour between 05:00 and 21:00. During this time, the system Our study sites are two known thermal refuges on recorded a video for 30 seconds, after which it was com- the LSW-M. The upstream thermal refuge is Parks Brook manded to shut off, and all data was saved to a 16-GB micro (Figures 1a, b). Parks Brook has a topographic drainage SD card (Figure 2c). This command structure maximized the area ∼ 19 km , and is groundwater influenced (O’Sullivan, power bank charge (Portable Charger RAVPower 26800mAh Linnansaari, and Curry, 2019; O’Sullivan et al., 2021b— Power Bank 26800—Figure 2d). The camera system was Figure 1b). The second thermal refuge, Otter Brook, is housed inside a custom-made acrylonitrile butadiene styrene located ∼ 8.5 km downstream of Parks Brook, and has a (ABS) pipe, diameter = 40 mm, with covers on both ends. This topographic drainage area ∼ 11 km (Figure 1a). Otter Brook camera housing was placed, together with the power bank is more groundwater dominated than Parks Brook (Kurylyk and a desiccant (i.e. silica gel beads), into a larger ABS pipe et al., 2014; Morgan and O’Sullivan, 2022; O’Sullivan, (diameter = 100 mm), with a transparent acrylic sheeting lens Linnansaari, and Curry, 2019), and is relatively cooler with glued to the permanent lid of the ABS pipe; a threaded lid a substantially longer thermal plume (or thermal effect) than was attached to the opposite end thus providing an access Parks Brooks (Figure 1b and c). point (Figure 2e). All ABS joints were fused with ABS adhesive and additional waterproofing silicon was applied. The ABS Thermographs (Hobo UA-002-64 Pendant Temperature/- housing was mounted to a cinder block using a steel wire Light data logger—64 KB) housed in a white uPVC pipe were (Figure 2e). Two underwater cameras were deployed at the mounted to cinder blocks and subsequently stationed in the Parks Brook refuge between June 16 and August 31, 2020, two thermal refuges. Thermographs were placed in the main and orientated as illustrated in Figure 1b. When river tem- stem LSW-M, slightly upriver and adjacent to Parks Brook ◦ peratures were < 27 C until June Finally, the site was visited and Otter Brook thermal plumes, and within each thermal every 7 days to download data and to change the power bank. plume (Figure 1b and c). Temperatures were recorded every 30 minutes between 16 June and 31 August 2020 at the Parks Time-lapse still cameras—2021 Brook refuge and between 1 June and 31 August 2021 at the Otter Brook refuge. For the summer of 2021, we sought to increase the temporal resolution of our cameras, whilst also increasing the battery life of the system. To do this, we used Brinno TLC200 Pro Main river and refuge thermal regimes Time-Lapse Cameras © (Figure 2f). These cameras have an To establish if the thermal regimes of the main river and image resolution of 720p, and dependent upon temperature hydrogeologically distinct thermal refuges (Kurylyk, Bourque, and shooting interval, the four AA batteries can last up to and MacQuarrie, 2013; O’Sullivan et al., 2021b) differed we 42 days. The Brinno cameras were placed in a custom-made compared the regimes within and across years. As the maxi- housing identical to the 2020 design (Figure 2g). The cameras mum temperature is the most critical metric to drive the onset were programmed to take still photos every 10-minutes from of thermal aggregations, we compared the daily maximum 05:00 to 21:00, between May 31 and September 2 2021. Two thermal regimes. We compared (a) the main river and thermal camera units were deployed at the Otter Brook refuge, and refuge temperature within a summer, i.e. main river compared their orientation is illustrated in Figure 1c. with thermal refuge, (b) the main river summer temperatures between years, i.e. 2020 compared with 2021, and (c) the ther- Definition of aggregation observations mal refuges between years, i.e. 2020 compared with 2021. As the sample sizes differed between years, we performed a series The role of underwater cameras in both years was to collect of Mann–Whitney U tests. Further, in 2020 the Parks Brook date-time information on the timing of behavioural ther- thermal refuge temperature logger was highly influenced by moregulation aggregation events by juvenile Atlantic salmon. thermal mixing with the main river until 24 June, 2020, at For the purposes of this study the onset of a behavioural which point the logger was moved further into the plume. As thermoregulation event was defined as the presence of ≥10 these data were excluded from statistical comparisons, this Atlantic salmon parr (Corey et al., 2020; Dugdale et al., created an uneven and non-normally distributed sample set 2016; Figure 3). In some instances, aggregations can remain to compare the within years difference for the 2020 data set, in place for days (e.g. Corey, 2022). As the focus of this and further supporting the necessity to use non-parametric study was the onset temperature of thermal aggregations, we analyses. In all tests α = 0.05. defined the onset of a new aggregation as one where the prior .......................................................................................................................................................... 3 Research article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... Figure 1: A map showing the study sites (Parks Brook (2020 data) and Otter Brook (2021 data)) superimposed on a thermal infrared image (TIR) of the study river, the Little Southwest Miramichi, New Brunswick, Canada (LSW-M; Panel a). The thermal profile between Parks and Otter Brook is presented in (b) while (c) and (d) delineate the Park and Otter Brook thermal refuges, respectively; and include the camera orientation within refuges and plumes and location of main-stem temperature loggers. aggregation had dispersed. These events were easily separated area is brook trout (Figure 3). Whilst brook trout (both juve- from the baseline non-aggregation events due to the general nile and adult) were also commonly observed in our imaging, low density of juvenile Atlantic salmon in the Miramichi River their density in the studied area, and therefore frequency in (Chaput, Douglas, and Hayward, 2016) and their territorial our imaging, was very low. Furthermore, brook trout were nature during non-thermal events ((Linnansaari and Cunjak, generally easily identifiable due to the size differences (see 2010)(Figure 3). The high resolution of our underwater e.g. Figure 3d for an adult brook trout within an aggre- camera videos and images allowed confident identification of gation), or due to their white leading edge in their anal aggregating fishes to species (i.e. juvenile Atlantic salmon); fin, and the lack of easily identifiable “parr marks” typical the only other coldwater stenothermic salmonid in the studied for juvenile Atlantic salmon. Additionally, some blacknose .......................................................................................................................................................... 4 Conservation Physiology • Volume 11 2023 Research article .......................................................................................................................................................... Figure 2: A schematic detailing the components of the custom-made underwater camera for 2020 (the Raspberry Pi system) and 2021 (the Brinno system). The Raspberry Pi systems are video cameras that collected a 30 second video every hour from 05:00 to 21:00 each day of the studied. The Brinno system is a time-lapse camera that collected a photo every 10 minutes from 05:00 to 21:00 each day of the study. A full description of these systems is provided in the main text. dace (Rhinichthys atratulus) were observed in our imagery; behavioural thermoregulation in juvenile Atlantic salmon however, these were also easily identifiable by the markings. have found sigmoidal shaped responses to thermal stress (Breau et al., 2011; Corey, Linnansaari, Cunjak, and Currie, Corey (2022) found once an aggregation event has 2017; Corey, 2022). From these studies, we conceptualized occurred within the LSW-M, juvenile salmon display high that the sigmoidal curves represent half of a hysteresis loop. fidelity towards reaches with the thermal refuges. In such As such, we deduced that to model aggregation onset tem- instances, the juvenile salmon abandoned the reach they were peratures (hereafter AOT), two model components will be located in prior to the aggregation event, if the reach did not required. We included two mathematical components that contain a thermal refuge. This fidelity towards reaches with relate the time since physiologically challenging conditions the thermal refuges remained until the autumn, when fish have been observed (Time since Event [TsE] and their fre- returned to abandoned reaches. Coupling the similar thermal quency (Frequency of Events [FoE]) to model AOT. regimes between our study sites, and the findings on refuge fidelity and abandonment of territories without refuges (as For the TsE component, the conceptual relationship per Corey et al. [in review]), we make the assumption that between AOTs and physiological stress for juvenile Atlantic the majority of the fish we observed are consistently using the salmon is shown via a loading curve, where (i) details the refuges. inflection point of accumulating physiological stress (induced by temperature) (Figure 4a). As temperature increases, physiological stress accumulates exponentially (ii), as has Analytical models been empirically demonstrated in laboratory studies (Cindy Breau et al., 2011; Corey et al., 2017). However, the Previous empirical in situ investigations in Atlantic Canada exponential increase in physiological stress cannot continue and affiliated physiological experiments to better understand .......................................................................................................................................................... 5 Research article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... Figure 3: An example of underwater camera photos captured with each system. Panels (a) and (b) are images from the Raspberry Pi camera system in the Parks Brook refuge when it is void of fish during non-stressful temperatures, and when juvenile Atlantic salmon are using the refuge during thermally stressful conditions, respectively. Images from the Brinno TLC200 Pro Time-Lapse Cameras © show the Otter Brook refuge during 2021 void of fish during non-stressful temperatures (c), and when juvenile Atlantic salmon, and other species (Brook trout and black nose dace), using the refuge during thermally stressful conditions (d). in perpetuity; the fish have a critical thermal maxima threshold leads to shorter pathway(s) to AOT, in comparison dictated by their physiological constraints (Corey et al., 2017; to a fully reset baseline condition. Mathematically, the TsE Morash et al., 2021). We conceptualize that when a point of component of the AOT model takes the form: physiological stress saturation is reached, a thermal refuge will be sought by juvenile salmon in nature (iii—Figure 4a; TsE = a × log λ + b (1) i.e. AOT). Once a thermal refuge is found, the temperature begins to reduce, consequently alleviating physiological stress. As the loading curve is exponential, we conceptualize the where TsE is the aggregation onset temperature, a, and b are unloading curve will be the inverse, or mathematically, the empirically derived coefficients, and λ is the time since an unloading curve will follow a logarithmic function. These event. In this study, we measured λ in days. The units for a curves intercept and thus close the system, representing a ◦ and b are in temperature ( CorF). loop (Figure 4a). A simplified illustration of the TsE model component is Under our conceptual model, the subsequent points of presented in Figure 4c. The AOT as a function of time since AOT are driven by prior physiological stress history, and event is shown as a logarithmic curve, and is positively related are thereby represented by the concept of thermal hysteresis to onset aggregation temperature. (Figure 4b). With that, point (i) illustrates a hypothetical inflection point where physiological stress begins to accu- In addition to the TsE component (time required for full mulate and leads to the initial aggregation defined at point metabolic recovery), we conceptualized that the frequency (a). In this instance, the fish’s physiological stress threshold of aggregation events (FoE) can reduce the fish’s thermal is lower as it has not had sufficient time for full metabolic threshold. We postulate that this will also take the form of recovery; it will therefore have a lower thermal aggregation a hysteresis loop; however, the FoE loop’s loading/unloading threshold (point b; Figure 4b). As time since the aggregation is the inverse of the TsE loop (Figure 4a, d). We conceptualize event increases, the AOT thresholds increases—points c, d, that the unloading curve for the FoE model component will e, respectively (Figure 4b), until a full metabolic recovery is be an exponent function (Figure 4d). Similar to the TsE achieved, and the hysteresis loop is reset. In the hysteresis component, point (i) is the inflection point at which the loop, whilst the inflection points—points ii, iii, iv, and v are physiological stress is initiated and increases as a function of considered to be static—the fish’s lower physiological stress temperature (Figure 4d). This increase follows a logarithmic .......................................................................................................................................................... 6 Conservation Physiology • Volume 11 2023 Research article .......................................................................................................................................................... Figure 4: The development of our conceptual hysteresis-based models. Panels a-c details the development of the time since event (TsE) model, and Panels d-f details the development of the frequency of events (FoE) model. In panels (a and d) the fish is conceptualized to begin to accumulate physiological stress at point (i), with stress increasing along the loading curve (ii) until a point of stress saturation is reached (iii).At this point, the fish will seek thermal refuge and defines the aggregation onset temperature (AOT). Once in the refuge, the stress reduces along the loading (iv). A full and detailed description of these models is provided in the main text. Panels (b and e) illustrate how the TsE and FoE models vary through time as a function of time since an event and event frequency, respectively. Finally, panels (c) and (f) illustrate each model as single line. trend (ii) until a physiological stress saturation point (i.e. Similar to the TsE model, the AOT points are fluid for AOT) is reached (iii) (Figure 4d). At this point, thermal refuge the FoE model, and the process is governed by a hysteresis is sought, and the unloading curve follows an exponential loop where increasing frequency of events reduces the AOT trend (iv—Figure 4d), thereby completing a loop. (Figure 4e). Increasing the event frequency by one event only .......................................................................................................................................................... 7 Research article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... moderately reduces the AOT to (b). As the event frequency observation were used to quantify the temperatures that increases, the thermal aggregation points decrease—points c, induced the onset of behavioural thermoregulation. All of the d, e, respectively (Figure 4e). This contraction characterizes a models were developed in MATLAB © using the curve fitting decrease in the fish’s thermal threshold. Mathematically, this application. Model performance was based on the coefficient FoE model component takes the form: of determination (R ), sum of squared errors (SSE) and root mean square error (RMSE). The above metrics and Akaike dω information criterion (AIC) were also used for the sensitivity FoE = ce (2) test to establish the best fit T equation (5). where c and d are empirically derived coefficients, and ω is the reduction rate. All units are in temperature ( C or F). The c parameter denotes the temperature of the first aggregation Results threshold, or events after full recovery has been achieved and has units of temperature ( C or F). The ω parameter takes the Thermal regimes and aggregation form: observations ω = (3) The thermal regime of the LSW-M during summer of 2020 was characterized by an average, maximum and minimum where σ is the number of events over a period (T). σ and T water temperature of 21.8, 29.7, 12.0 C, respectively, with are empirically derived from the underwater camera observa- a S.D. of 3.3 C(Table 1; Figure 5a). The thermal plume of tions. Finally, equation (3) is substituted in to equation (2), Parks Brook in 2020 had an average, maximum and minimum giving: temperature of 18.8, 26.6, 11.6 C, respectively, with a S.D. of FoE = ce (4) 2.8 C(Table 1). During summer of 2021 the LSW-M average ◦ ◦ water temperature was 1 C cooler (20.8 C) than in 2020, ◦ ◦ whilst the maximum was 1.8 C warmer (31.5 C), and the We used a sensitivity test to establish the best fit T across ◦ ◦ ◦ minimum was 0.7 C cooler (11.3 C), with a S.D. of 3.5 C the aggregation onset observations. This was completed using (Table 1). The thermal plume of Otter Brook during 2021 a moving window, where the window was defined by the fre- had an average, maximum and minimum temperature of 17.8, quency of unique aggregation events within T values ranging ◦ ◦ 25.1, 10.7 C, respectively, with a S.D. of 2.3 C. A complete from 7 to 14 days (Mesa, Weiland, and Wanger, 2002). time-series of daily maximum water temperatures in each year is shown in Figures 5a and b. Both thermal refuges had A simplified illustration of the FoE model component is maximum daily temperatures that were significantly cooler presented in Figure 4f. The onset aggregation temperature as than their corresponding main river temperatures in each a function of event frequency is shown as an exponential studied year (Table 2). curve, and this is negatively related to the onset aggregation temperature. During 2020, seven unique thermal aggregations were observed during the period of camera operation (Figure 5c). Both model components are necessary to predict the tem- th The first aggregation in 2020 occurred June 19 (Figure 5c). perature at which juvenile Atlantic salmon aggregate in ther- Beginning on 27 July 2020, a camera malfunction occurred mal refuges. To account for the inherent interactions between (see Figure 5c). This malfunction led to the cameras turning the time since a previous aggregation (TsE) and aggregation on and off randomly, and upon inspection of the camera frequency (FoE), we developed an integrate model. Mathe- components and source code, no cause was found. We did matically, this final model takes the form: observe aggregations during this time (27 July to 14 August 2020); however, the gaps in the data prevented the use of these T = a × log λ + b + ce (5) integrated observations as there was uncertainty around the timing of aggregation onset. The average AOT during the operational where the model parameters are detailed in equations (1), (2), period of the cameras for 2020 was 26.5 C, with a maximum, ◦ ◦ ◦ ◦ (3), and (4) above. Similarly, all units are in temperature ( C minimum, and S.D. of 27.1 C, 26.1 C and 0.4 C, respectively or F). (Table 1). In each year, n—1 data points were used to develop and During 2021, the issues that occurred during 2020 were test the models. This was required as the first aggregation remedied by using the high temporal resolution Brinno © provides a baseline from which to calculate time since an camera system. Twelve unique thermal aggregations occurred aggregation and the frequency of aggregations for the next in 2021, with the earliest occurring on 8 June, and the highest sequential aggregation. The 2021 data set was used to develop frequency of events occurring during August (Figure 5c). The the model coefficients, and the 2020 data set was used to average AOT during the operational period for 2021 was independently test the models. In each year, the main stem 26.2 C, with a maximum, minimum and S.D. of 27.0, 24.2 temperature loggers in tandem with the underwater camera and 0.6 C, respectively (Table 1). .......................................................................................................................................................... 8 Conservation Physiology • Volume 11 2023 Research article .......................................................................................................................................................... Table 1: A summary of main river (T ), thermal refuge (T ) and thermal aggregation onset temperatures (T ) for 2020 and 2021 r pl on Statistic 2020 2021 ◦ ◦ ◦ ◦ ◦ ◦ T ( C)(n = 3648) T ( C)(n = 3264) T ( C) (n =7) T ( C) (n = 4416) T ( C) (n = 4332) T ( C) (n = 12) r pl on r pl on Mean 21.8 18.8 26.5 20.8 17.8 26.2 Maximum 29.7 26.6 27.1 31.5 25.1 27.0 Minimum 12.0 11.6 26.1 11.3 10.7 24.2 SD 3.3 2.8 0.4 3.5 2.3 0.8 The 2020 and 2021 data is from Parks Brook and Otter Brook, respectively. Table 2: Statistical analyses results comparing main river and thermal refuge thermal regimes across sites and years, where T is the main river temperature and P is the thermal plume temperature Comparison U U Expected Variance p-value (standardized) value (U) (two-tailed) 2020 main river and thermal refuge max. 928 −6.7 2622.0 63787.6 <0.0001 Daily temperatures n = 76; n =69 (Tr) (Pl) 2021 main river and thermal refuge max. 7075 8.1 4186.0 128348.9 <0.0001 Daily temperatures n = 92; n =91 (Tr) (Pl) 2020 and 2021 main river max. Daily 2881.5 −2.0 3496.0 98446.6 0.05 temperatures n = 76; n =92 (2020) (2021) 2020 and 2021 thermal refuge max. Daily 4261.5 3.9 3139.5 84219.0 <0.001 temperatures n = 69; n =91 (2020) (2021) The bolded values denote those that are statistically significant. Analytical model results Discussion A total of n = 11 unique aggregation onsets from the 2021 Underwater cameras for behavioural data were used to calibrate the TsE model, and the model coef- thermoregulation studies ficients are provided in Table 3. The resulting model had an 2 ◦ ◦ Adj. R = 0.49, a SSE = 2.8 C, and a RMSE = 0.61 C(Table 3; In freshwater ecology/biology, tagging biota has provided Figure 6a and b). Testing the TsE model against 2020 data multitudinous insights into movements, drivers of move- 2 ◦ ◦ produced an R = 0.38; SSE = 1.18 C and a RMSE = 0.54 C ments, life history strategies and habitat use, to mention a (Table 3; Figure 6b). few (Andrews et al., 2020; Curry, Bernatchez, Whoriskey, and Audet, 2010). However, some research suggests long-term A suite of sensitivity models were used for the FoE model risks associated with tagging, such as tissue infections (e.g. and a 14-day window was selected best time window to Adams et al., 1997). Such risks are particularly problematic examine the role of event frequency as it had the lowest when studying at risk species, such as the declining Miramichi AICc = −20.7 value (Table 4). Using the 14-day window, the 2 ◦ Atlantic salmon population. Our goal was to develop a resulting FoE model had an Adj. R = 0.89, a SSE = 0.64 C, ◦ method that is passive (thus, non-invasive), low-cost and and a RMSE = 0.29 C(Table 3; Figure 6a and b). Testing can operate independent of an external power source, i.e. the FoE model component against 2020 data produced an 2 ◦ ◦ remote regions. The underwater camera systems method met R = 0.69; SSE = 0.37 C and a RMSE = 0.30 C. all these criteria: (a) the optical sensor facilitated observations The integrated model, which accounts for both TsE and that were passive, i.e. we did not touch or disturb any fishes. 2 ◦ FoE components, produced an Adj. R = 0.90, a SSE = 0.74 C, We observed hundreds of Atlantic salmon parr, along with other species, such as brook trout (Salvelinus fontinalis) and and a RMSE = 0.23 C(Table 3; Figure 6e). Testing the white suckers (Catostomus commersonii); (b) The Camera integrated model against 2020 data produced an R = 0.82; ◦ ◦ SSE = 0.37 C and a RMSE = 0.22 C(Table 3). The model systems are low-cost ∼ $160–200, and are easy to construct residuals and relationships between each model parameter and operate; and (c) the cameras were dependent on battery and thermal thresholds are shown in Figure 6f. packs remove the need for an external power source. It is .......................................................................................................................................................... 9 Research article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... Table 3: Summaries for all models; the coefficient symbols relate to equations ( 1), (4)and (5) 2 2 Model Coefficients Training Adj. R Training Training Test R Test SSE Test R SSE RMSE RMSE Time since a = 0.497 0.61 0.57 2.80 0.56 0.38 1.18 0.54 Event (TsE) b = 25.5 Frequency of c = 26.85 0.90 0.89 0.74 0.29 0.69 0.37 0.30 Events (FoE) d= −0.17 Integrated a = 0.15 0.91 0.90 0.64 0.23 0.82 0.37 0.22 model b= −1038 c = 1065 d= −0.003 The model coefficients are derived from the 2021 data set, and the independent test data correspond to the 2020 data set Table 4: The results of the range of moving windows used to select the best model fit for the thermal reduction model equations (2) to ( 4)—see text Frequency sensitivity models (n = 11) Window Model Coefficients R SSE RMSE AIC AIC w Evidence c c i components ratio 14 c 26.85 0.90 0.74 0.29 −20.7 0 0.77 1.00 d −0.17 13 c 26.85 0.85 1.09 0.35 −16.4 4.31 0.09 8.62 d −0.16 12 c 26.85 0.85 1.09 0.35 −16.4 4.31 0.09 8.62 d −0.15 11 c 26.83 0.77 1.62 0.43 −12.0 8.66 0.01 76.11 d −0.14 10 c 26.82 0.70 2.17 0.49 −8.8 11.88 0.00 379.08 d −0.14 9 c 26.84 0.71 2.09 0.23 −9.3 11.42 0.00 301.14 d −0.13 8 c 26.91 0.76 1.74 0.44 −11.3 9.41 0.01 110.53 d −0.14 7 c 26.94 0.81 1.33 0.38 −14.3 6.46 0.03 25.26 d −0.15 clear that this method is a highly efficient tool in the field in remote locations, where no power sources exist, such as of aquatic ecology, and has myriad applications. A major arctic areas (Huusko et al., 2007). Additionally, battery life advantage of our underwater camera method compared with could be extended by setting the cameras to collect data less traditional tagging studies is it does not rely on a previously frequently, thereby reducing the need for repeated site visits. sampled subpopulation, but can assess the responses of any While limitations are inherent due to the passive nature of individual in the population responding to the stressor. A the optical sensor applied in this study, future studies could second major advantage is the utility of our systems for work integrate infrared sensors (IR) and IR—light-emitting diode .......................................................................................................................................................... 10 Conservation Physiology • Volume 11 2023 Research article .......................................................................................................................................................... (Hsp) 70 levels increased significantly at 27 C in the same river. However, for the overall data set, we observed ∼ 2.8 C variance in these thermal aggregation onset thresholds. What explains this variability in thermal aggregation onset temper- atures in this and many other previous studies? We believe that the variability documented in AOTs is a matter of fish’s thermal history—thermal hysteresis—wherein the onset temperature to the first aggregation of the sea- son (or to a subsequent thermal event but only after full metabolic recovery from any previous event) is a some- what rigid population-specific threshold (see above). How- ever, there is a marked reduction in the aggregation onset temperatures during subsequent thermal events. We propose this is likely caused by the latency in physiological stress metabolites (i.e. physiological thermal “baggage”) in fish’s bodies; resulting in the necessity to aggregate in lower tem- peratures—candidly termed thermal hysteria. This thermal hysteria is not always the same in absolute numbers, however, it appears to be governed by a process that can be accurately modeled using time since previous thermally taxing events and their frequency as variables. It is evident from our model results that the time since the onset of a previous aggregation event (TsE) plays a role in the variance of onset temperatures for thermal aggregations. Relatively few studies have examined the performance and recovery of salmonids after acute heat stress events and those that have are confined to the laboratory (see Gallant et al., 2017; Lewis et al., 2010; Lund and Tufts, 2003). Our results suggest that juvenile Atlantic salmon thermal thresholds do not return to pre-event thresholds until ∼ 12 to 18 days after Figure 5: The main stem maximum water temperatures observed acute heat stress events in the LSW-M. Further, it is apparent on the LSW-M during the summer of 2020 (grey line) and 2021 (black that the recovery process initially occurs at an exponential line) is shown in (a). The corresponding temperature of the thermal rate and then plateaus towards the upper thermal thresh- plumes is shown in (b), where the grey dashed line relates to Parks old. A study on juvenile Chinook salmon (Oncorhynchus Brook and the black dashed line is Otter Brook. Between June 17 and tshawytscha) established that acute thermal stress induced a 24 in 2020, the Parks Brook temperature logger was influenced by 25-fold increase in liver Hsp 70, compared with a control thermal mixing from the main stem and was subsequently moved further into the plume; the aeff cted time period is shown in red. The group, and the metabolite presence lasted 2 weeks (Mesa et onset temperature of thermal aggregations is shown in (c) where the al., 2002). Whilst it is not possible to ascertain the physio- grey and black dots reflect 2020 and 2021 observations, respectively. logical drivers from our data, it may be that our TsE model Of note is the blue polygon which delimits a time-period during 2020 component is mapping these physiological processes via flux when the cameras malfunctioned, rendering the omission of data in thermal thresholds. It is also evident that the TsE model’s from this period—see text. performance decreased when tested against 2020 data. How- ever, the 2020 data is from the Parks Brook thermal refuge, (LED) lamps (Hermann, Chladek, and Stepputtis, 2020). A which is warmer than the Otter Brook thermal refuge. Further unit that combines optical and IR sensors, and LED lights the thermal regime of the main river also differed between would allow 24/7 collection of data, thus give complete years. We propose two possible reasons for the reduction in temporal coverage. our model’s performance: (a) fish that use the cooler Otter Brook refuge spend less time in thermally stressful conditions and therefore recovery occurs quicker, and this would most Thermal hysteresis likely change the model coefficients between years, and (2) the Our cameras revealed a relatively narrow thermal threshold main river thermal regime differs between years; this would for the first behavioural thermoregulation aggregation in Lit- likely change both metabolic and physiological processes, and ◦ ◦ tle Southwest Miramichi that was ∼ 26.7 C to 27.1 C. This the stress accumulation of the juvenile salmon. aligns to observations by Corey et al., (2020) whom observed A common concern in studies that have looked for changes aggregation onset at ∼ 27 C, and Lund, Caissie, Cunjak, in performance with acute thermal stress is that they typically Vijayan, and Tufts, (2002) whom observed heat shock protein .......................................................................................................................................................... 11 Research article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... Figure 6: Panels (a) and (b) are graphical illustration of the time since event model (TsE) relative to 2020 (grey dots) and 2021 (black dots) observations, and the correspondent model performances. Similarly, panels (c) and (d) detail the frequency of events model (FoE) relative to 2020 and 2021. The integrated model results are shown in (e) and (f) highlights the model residuals. only focus on a single acute heat shock event (e.g. Tunnah, this frequency increase can reduce thermal aggregation onset Currie, and MacCormack, 2017; Gallant et al., 2017). Mesa thresholds by ∼ 2.8 C in an 11-day window. By integrating et al. (2002) brought this concern to light, and suggested that the conceptual mechanisms of both the TsE and FoE models, multiple, cumulative stressor situations are far more likely for the integrated model’s predictive capacity increased, and its the fish in the wild. Our camera observations and modeled error decreased. These results illustrate the complexity under- outcomes offer credence to these concerns. The FoE model lying behavioural thermoregulation in Atlantic salmon—a component had a relatively high predictive capacity for both complexity that most likely extends to other salmonids. the training and test data sets. This supports the notion of As the climate warms, the frequency of extreme heat events an accumulated effect, where an increase in the frequency is predicted to increase (Brodeur et al., 2015). This fre- of aggregations leads to a concomitant decrease in thermal quency increase has the capacity to decrease thermal thresh- aggregation onset thresholds. Moreover, at its upper limit .......................................................................................................................................................... 12 Conservation Physiology • Volume 11 2023 Research article .......................................................................................................................................................... olds for behavioural thermoregulation in salmonids, ulti- Study limitations mately increasing the number of days required to offset an We acknowledge there are inherent limitations in our study. aggregation event/behavioural thermoregulation migration. It is possible that despite our best efforts, the UWCs did not With that, we encourage other researchers to test our hys- capture all the fish that are aggregating within the thermal teresis model in other rivers and on other species, both refuge due to the their placement and orientation. This could salmonid and non-salmonid. Whilst there are myriad factors be remedied by placing more UWCs in the refuge to ensure that confound the development of a universal theory of that as much of the refuge is photographed as possible; thus, aggregation thresholds for all ectotherms, we conceptualize limiting the likelihood of missing data points. The decision to an emergent mechanism may exist, similar to other thermal define an aggregation as the presence of ≥10 parr is arbitrary, ecology theories (see Brown et al., 2004). but is based on previous studies and knowledge of parr den- sity in this section of river (e.g. Breau, Cunjak, and Bremset, 2007; Dugdale et al., 2016; Corey et al., 2020). In the LSW- Management and conservation implications M, surveys conducted by DFO (2022) suggest the density of −2 juvenile salmon is stable with > 35 fish 100 m ; however, The current paradigm when conducting research and in rivers with lower densities the definition of what classifies implementing management strategies is to use species-specific an aggregation would need to revised. Another limitation of thermal thresholds for salmonids, with these thresholds our method is underscored by the difficultly of enumerating assumedly static through time. Isaak et al., (2015) used a individuals. Counting the number of fish in a refuge was size threshold to delineate thermal habitats of bull trout possible in some instances, but in others the density is simply (Salvelinus confluentus) and cutthroat trout (Oncorhynchus too high, and making it impossible to discern fish that have clarkii), whilst O’Sullivan et al. (2021b) used homogeneous already been enumerated from ones that have not. Finally, thermal threshold criteria for Atlantic salmon and brook the seemingly sample size (n = 12) in this study is a source of trout based on age class. In eastern Canada, Fisheries and uncertainty. Whilst the sample size is small, it must be viewed Oceans apply a homogeneous thermal threshold to protect in relation to the phenomenon it represents. These fish have Atlantic salmon from angling during periods of heat stress high thermal tolerances, with the first aggregation occurring (DFO, 2012). Our results reveal these true physiological at 27 C (also see Corey, 2022). It is not uncommon for thresholds are not static, rather they are dynamic and the main-stem of the LSW-M to reach temperatures between vary with exposure and time, and support a growing ◦ ◦ 25 C and 30 C during the summer (Morgan and O’Sullivan, repertoire of research that highlights the deficiencies in 2022), the mean temperatures during this study period were using binary thresholds (Martin et al., 2020; Fitzgerald ◦ ◦ 21.8 C and 20.8 C during 2020 and 2021, respectively. As and Martin, 2022). Our results have broad implications such, the number of days throughout a summer when river for our understanding of how salmonids are affected by temperatures reach the threshold to initiate behavioural ther- extreme heat events, and how to design ecologically relevant moregulation in juveniles is relatively low. Even so, our mod- management plans. One such suggestion is the development els were transferable between years, thereby offering credence of real-time, river specific, thermal stress indices (TSI). For to the conceptual model and the underlying mechanism. instance, our model could be coupled with real-time in-stream temperature data to provide a TSI. The TSI would vary as a function of temperature, thereby accounting for thermal stress Data availability statement threshold variance. Fisheries managers could the use this TSI tool to apply warm water closures that are ecologically The underlying data for the analyses performed in this study meaningful. Such a tool would also be useful for scientists. is included as an.xlsl file in the supplementary information. Currently, electrofishing in New Brunswicks’ salmon rivers ceases when water temperatures > 23 C (DFO, 2013). This Authors’ contribution management protocol is designed to limit fish stress; however, our results indicate in the lower LSW-M, aggregation AM.O’S: conceptualized and designed the study, built camera thresholds can be as low as 24.2 C. Breau et al., (2011) systems, conducted field work, developed analytical mod- found the amount of 2+ juvenile salmon displaying stress els and wrote the first draft; E.M.C.: reviewed and edited; with increasing water temperature increased in a sigmoidal E.N.C.: reviewed and edited; J.H.: built camera systems, fashion (see Figure 2 in Breau et al., 2011). The inflection reviewed and edited; R.A.C.: reviewed and edited; C. M.: built point showing an increase in stress response occurred camera systems, reviewed and edited; T.L.: conceptualized ◦ ◦ between 20–22 C and plateaued ∼ > 26 C. Considering and designed the study, reviewed and edited. our results reveal these thermal thresholds can reduce, we suggest a dynamic water warm electrofishing protocol; one that reflects the variance in river thermal regimes and the Acknowledgements concomitant time dependent response in juvenile salmon. This suggestion addresses the concerns posed by Corey et al., First, the authors would like to thank Lord Pisces. We (2017). would also like to thank the Atlantic Salmon Conservation .......................................................................................................................................................... 13 Research article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... Foundation, New Brunswick Innovation Foundation, and Caissie D, Breau C, Hayward J, Cameron P (2013) Water tempera- New Brunswick Wildlife Trust Fund, for funding. The first ture characteristics of the Miramichi and Restigouche River. Moncton, authors Postdoctoral Fellowship is funded through the NB, Retrieved from https://dfo-mpo.gc.ca/csas-sccs/publications/ McCain Foundation Postdoctoral Fellowship in Innovation. resdocs-docrech/2012/2012_165-eng.html E. Collet is funded by Mitacs, the Atlantic Salmon Conserva- Chaput G, Douglas SG, Hayward J (2016) Biological Characteristics tion Foundation, and the Miramichi Salmon Association. The and Population Dynamics of Atlantic Salmon (Salmo salar) from the authors thank three anonymous reviewers for their excellent Miramichi River, New Brunswick, Canada. Moncton, NB, Canadian Sci- suggestions that made this a stronger paper. The first author ence Advisory Secretariat (CSAS) Research Document would also like to thank E. Torenvliet for thought provoking conversations on the management implications of our results. Clarke, A. (2006) Temperature and the metabolic the- Finally, the authors would like to thank M. Richard, A. Pugh, ory of ecology. Functional Ecology 20: 405–412. and A. Morgan for help collecting these data and fun days in https://doi.org/10.1111/j.1365-2435.2006.01109.x the field, and S. Currie and M. Keefer for earlier discussions Corey E (2022) The biological significance of thermal refuges to juvenile relating to this work. One love. Atlantic salmon (Salmo salar) in a changing climate. University of New Brunswick, Fredericton, New Brunswick, Canada. Corey E, Linnansaari T, Cunjak RA, Currie S (2017) Physiological effects References of environmentally relevant, multi-day thermal stress on wild juve- nile Atlantic salmon (Salmo salar). Conservation Phys Ther 5: cox014. 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Journal
Conservation Physiology – Oxford University Press
Published: Jan 18, 2023
Keywords: underwater camera; thermal refuge; thermal hysteresis; thermal aggregation; salmonid; Atlantic salmon