**Author Contributions:** Conceptualization, J.D. and R.A.; Methodology, J.D. and K.M.; Validation, J.D., K.M. and R.A.; Formal analysis, J.D., K.M. and R.A.; Investigation, J.D.; Writing—original draft preparation, J.D., K.M. and R.A.; Supervision, R.A.; Funding acquisition, J.D. and R.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by "Parc Naturel Marins des Estuaires Picards et de la Mer d'Opale" (DECISION N◦2018-28 9 March 2018), and "European Maritime Fisheries Fund" and "Région Hauts de France" (PFEA621220CR0310022). This work also has been partially financially supported by the European Union (ERDF), the French Government, the Région Hauts-de-France and IFREMER, in the framework of the project CPER MARCO 2015–2020 (https://marco.univ-littoral. fr/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

**Institutional Review Board Statement:** The permission to collect fish in the estuaries and field site access was issued by the "Préfète de la region Normandie, préfète de la Seine Maritime, Direction interrégionale de la mer Manche Est-mer du Nord, Service Régulation des Activités et des Emplois Maritimes, Unité Réglementation des Ressources Marines (dram-npe@equipement.gouv.fr): Decision n◦196/2019". This study was conducted in accordance with European Commission recommendation 2007/526/EC, on revised guidelines for the accommodation and care of animals used for experimental and other scientific purposes.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The authors would like to thank K. Rabhi, M. Diop, M. Kazour and K. Boutin for their participation in the collection of the samples and A. Lheriau, R. Elleboode and A. Dussuel for their participation in the preparation of the otoliths. Finally, the authors are very grateful to the three anonymous referees and editor, who helped to greatly improve the initial manuscript with their positive and constructive comments.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

### *Communication* **Age and Growth of Quillback Rockfish (***Sebastes maliger***) at High Latitude**

**Camron J. Christoffersen, Dennis K. Shiozawa, Andrew D. Suchomel and Mark C. Belk \***

Department of Biology, Brigham Young University, Provo, UT 84602, USA<EMAIL_ADDRESS>(C.J.C<EMAIL_ADDRESS>(D.K.S<EMAIL_ADDRESS>(A.D.S.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +1-801-422-4154

**Abstract:** Data on age and growth of fishes is critical for effective management; however, growth rates documented in one location may not be representative of other locations, especially for species that occur across wide geographic ranges. *Sebastes maliger*, quillback rockfish, occur across a broad latitudinal range, but their growth patterns have been quantified only in the southern part of their range. To provide information for *S. maliger* in the more northern part of its range, we report age and growth patterns derived from otolith analysis from a population collected in southeast Alaskan waters. In southeast Alaska mean annual growth increments for years 1 and 2 range from 60–80 mm, and for ages 6–9 annual growth increments average about 20 mm. From age 10 on average the annual growth increment is about 5 mm. These data can be used in conjunction with harvest data to manage stocks of *S. maliger* in Alaskan waters.

**Keywords:** sagittal otolith; somatic growth; long-lived fishes

### **1. Introduction**

Information about species growth rates is crucial for effectively managing fish stocks [1]. Age-based growth models are used to assess most west coast groundfish stocks [2]. However, growth rates of fishes can vary among locations based on variation in water temperature, salinity, and food resource availability [3–6]. Water temperature is known to affect fish growth rates, but the relationship between temperature and growth can be positive, negative, or indeterminate among different species and locations [1,7–13]. Growth rates documented in one location may not be representative of other locations, especially for species that occur across wide geographic, and especially, latitudinal ranges [14].

*Sebastes maliger*, quillback rockfish, range from the Anacapa Passage (off the southern coast of California) to the Gulf of Alaska (Kenai Peninsula). They are a long-lived, benthicoriented species reaching a maximum age of 95 years and a maximum size of 61 cm (Figure 1). *Sebastes maliger* constitute an important segment of the commercial rockfish fishery and the recreational charter boat fishery [15]. However, published age and growth information for *S. maliger* is limited to one study conducted near Puget Sound, Washington, USA, in the southern part of the species' range [16]. No age and growth studies have been conducted in more northerly parts of *S. maliger* range, and we assume that temperature and other factors may influence growth of *S. maliger* at higher latitudes as it does in other species [9,10,12]. To provide information for *S. maliger* in the more northern part of its range, we report age and growth patterns from a population collected in southeast Alaska.

**Citation:** Christoffersen, C.J.; Shiozawa, D.K.; Suchomel, A.D.; Belk, M.C. Age and Growth of Quillback Rockfish (*Sebastes maliger*) at High Latitude. *Fishes* **2022**, *7*, 38. https://doi.org/10.3390/ fishes7010038

Academic Editors: Josipa Ferri and Filipe Martinho

Received: 22 November 2021 Accepted: 3 February 2022 Published: 5 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Figure 1.** Photograph of adult male *Sebastes maliger* captured in Frederick Sound, Alaska in June 2016 (photograph by Riley Nelson).

### **2. Materials and Methods**

We obtained 31 *S. maliger* (16 male and 15 female) via hook-and-line sampling in Frederick Sound near Admiralty Island, Alaska (57.1365◦ N, 134.1200◦ W) in mid-June 2016. We measured standard length and total length to the nearest mm and assigned a unique ID number to each fish. We determined sex and reproductive maturity by inspection of the gonads. We removed sagittal otoliths and stored them dry in small coin envelopes for further analysis.

We determined age at capture by counting presumptive annuli on sagittal otoliths. The term "presumptive annuli" was used because we did not conduct an age validation study; however, otolith annuli have been validated in several other species of rockfishes [8,17]. We prepared otoliths for counting using standard methods for long-lived fishes [18,19]. First, we created a transverse section by grinding down half of each otolith using 500, 800, and 1200 grit silicon-carbide grinding paper on a water-fed polishing wheel, then we burned the transverse section to provide greater contrast for identifying and counting presumptive annuli. We counted presumptive annuli under a dissecting microscope (Leica Wild M10 and Leica Wild M3C; Leica Microsystems Inc. Buffalo Grove, IL USA) at 6X magnification on a black background with reflected light. The burned surface of the otolith was covered with immersion oil to reduce refraction and reflection (Figure 2a). Presumptive annuli were counted independently by two observers. Of the 31 individual otoliths, 6 (19.3%) were estimated to be the same age, 20 (64.5%) were estimated at one year difference, and 5 (16.1%) were estimated at two years difference in age by the two observers (mean difference of counts between observers = 0.97, SE = 0.11). When counts differed between observers, we resolved disparities by mutual inspection.

**Figure 2.** (**a**) Transverse cross section of otolith from *S. maliger*. This view was used to count presumptive annuli for an age estimate (photo by Andrew Suchomel). (**b**) Photograph of convex surface of the otolith (sagitta) used to measure the distance from the core of presumptive annuli for ages 1–9 (red arrows) along the longest axis (red line; photograph by Camron Christoffersen). Otoliths pictured in a and b do not belong to the same individual.

To estimate the growth curve of *S. maliger* we combined two methods. First, we plotted size-at-capture versus estimated age to characterize the growth curve for ages 10+. The youngest fish in the sample was 10 years of age, so to estimate the growth curve for ages 1-9 we back-calculated individual size at age for the first nine years for all individuals in the sample from measurements of otolith presumptive annuli. Age estimates were derived from counts of presumptive annuli on the transverse section, whereas annual growth increments were measured on the convex surface of the otolith from photographs of the otolith taken under a Zeiss Axioskop microscope with a Nikon D810 camera. We measured the total radius of each otolith and the radius from the core to each of the first nine presumptive annuli along the longest axis on the convex surface of the otolith (Digimizer, https://www.digimizer.com/download.php). Measurements were scaled (in mm) by reference to a known-size object placed in each of the photographs (Figure 2b). We were not able to consistently resolve growth increments on the otolith from one individual, so the sample size for size at ages 1–9 was 30. Size of otoliths increased with size of fish [20], and the correlation between radius of otolith and total length was significant (t29 = 5.07, *p* = 0.00002, R-squared = 0.48). We back-calculated lengths at ages 1-9 using a modified Fraser-Lee formula [21]:

$$\mathbf{L}\_{\mathbf{x}} = \mathbf{L}\_{0} + ((\mathbf{L}\_{\mathbf{c}} - \mathbf{L}\_{0})(\mathbf{R}\_{\mathbf{x}} - \mathbf{R}\_{0}))/(\mathbf{R}\_{\mathbf{c}} - \mathbf{R}\_{0})\tag{1}$$

where: Lx = total length at age x; L0 = total length at birth (4.5 mm; [16]); Lc = total length at capture; Rx = radius of otolith at age x; Rc = radius of otolith at capture; R0 = radius of otolith at birth (estimated at 0.08 mm).

Lengths-at-age from each fish were averaged for ages 1–9 to produce the early part of the growth curve. We then combined this early trajectory with the best fit line (ordinary least squares regression) from the size-at-age captured plot to create the entire growth curve. To compare differences between sexes in size and age at capture we used a two-sample *t*-test. In addition, to facilitate comparison of the growth curve generated for *S. maliger* in southeastern Alaska waters, we fit the data to a von Bertalanffy growth equation [22].

### **3. Results**

In our sample, estimated ages ranged from 10 to 38, and total length at capture ranged from 316 to 474 mm. Growth rates of *S. maliger* were highest during their first two years of life and decreased as age progressed. Mean annual growth increments for ages 1 and 2 ranged from 60 to 80 mm, and declined to about 20 mm per year at ages 6–9. At ages greater than 10 years the average annual growth increment was about 5 mm (Figure 3). There was no difference between sexes in size at capture (*t*-test, *p* = 0.375), or age at capture (*t*-test, *p* = 0.1). Parameter values for this sample from the von Bertalanffy model are: k = 0.137, t0 = −0.78, and Lmax = 448 mm (Figure 3).

**Figure 3.** Two-part representation of growth curve for *S. maliger* captured in southeast Alaska USA, in 2016. The first nine years of growth are represented by mean total length (±1 standard error) for ages 1–9 (solid squares) obtained through back-calculations of growth increments of otolith annuli. Means are connected by a solid line representing the early growth trajectory. The second part of the growth curve represents growth from age 10+ as indicated by size at age captured. Females are represented by open circles, males are represented by closed circles, and the dotted line represents the best fit regression line. The von Bertalanffy growth function is indicated by the solid line that extends across all ages.

### **4. Discussion**

Growth of *S. maliger* in southeast Alaskan waters is similar to patterns and rates identified in the southern part of the species range [16]. Surprisingly, growth of *S. maliger* does not vary substantially from populations at lower latitudes even though the physical environment varies considerably across this range. This pattern of similar growth curves across wide geographic ranges is consistent with patterns of two other species of rockfish compared across latitude [23], where even populations from distant locations exhibit a high degree of phenotypic similarity in growth rates and patterns [24]. Heterogeneity in growth rates across latitudes is likely, in part, a response to temperature, but may also be due to differences in upwellings, productivity, population density and competition in the local environment [8,25].

Knowing age and size structure specifically for this location allows managers to assess causes and effects of growth rate variation more accurately [25]. This information also allows researchers to detect changes in growth rate or age structure in the *S. maliger* population caused by climate change or other factors as those changes occur [26,27]. Our age-growth curve for *S. maliger* provides a baseline for comparison of future studies. Thus, these data are a valuable addition to the management of an economically important species with a latitudinally large and ecologically variable range.

While 31 individuals might be considered a sub-optimal number, we suggest that our analysis for these data is simple. We estimated means for ages 1-9, and we used an ordinary least squares (OLS) regression of size at capture on estimated age to characterize growth at ages 10+. Sample sizes of 30 are more than adequate to estimate means (as evidenced by the narrow 95% confidence intervals), and to estimate a best fit line using OLS regression [28].

### **5. Conclusions**

We have not formally tested hypotheses. Our goal was to only estimate means and best-fit lines and to illustrate the relevance of such information. A formal testing of hypotheses requires the determination of precision generated by the sample size. But often one must weigh the cost of gathering data against the value of greater precision [28]. As such, we encourage researchers the world over to publish growth estimates derived from otolith analysis, even if sample sizes are modest (i.e., 30 or so, individuals). This is especially critical for poorly known species and for isolated locations. Such a collection of published growth data would be of great value for testing synthetic and long-standing hypotheses [24,29], as well as for management of populations [2,3].

**Author Contributions:** Conceptualization, M.C.B., C.J.C., D.K.S.; methodology, M.C.B., C.J.C.; formal analysis, M.C.B., C.J.C.; investigation, M.C.B., C.J.C., D.K.S.; resources, M.C.B., D.K.S.; data curation, A.D.S.; writing—original draft preparation, M.C.B., C.J.C.; writing—review and editing, A.D.S., M.C.B., C.J.C., D.K.S.; visualization, A.D.S., M.C.B.; supervision, M.C.B., D.K.S.; project administration, M.C.B.; funding acquisition, M.C.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Collection of fish and the work performed on them was reviewed and supervised by the Brigham Young University Institutional Animal Care and Use Committee under BYU IACUC protocol 15-0602. We only used specimens caught by recreational fisherman that were already dead.

**Data Availability Statement:** The otoliths and data sets are accessioned in the fish range at the Life Science Museum at Brigham Young University. The data are available upon request.

**Acknowledgments:** We thank the Roger and Victoria Sant Foundation, Scott and Jody Jorgenson at Pybus Point Lodge, the College of Life Sciences and the Department of Biology at Brigham Young University for providing funding and support for this study. We also thank Mikaela Nielson, Yuka Yanagita, Michelle Nishiguchi, Aaron Esplin, Michael Sorenson, Peter Searle, Samantha Tilden, and Aaron Brooksby for their help with gathering data.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

### *Article* **Age, Growth, and Utility of Otolith Morphometrics as Predictors of Age in the European Barracuda,** *Sphyraena sphyraena* **(Sphyraenidae): Preliminary Data**

**Josipa Ferri \* and Andela Brzica ¯**

Department of Marine Studies, University of Split, 21000 Split, Croatia<EMAIL_ADDRESS>**\*** Correspondence<EMAIL_ADDRESS>Tel.: +385-21-558-195

**Abstract:** Age and growth of the European barracuda, *Sphyraena sphyraena*, were determined by examining sagittal otoliths belonging to fish sampled in the eastern Adriatic Sea, as the northernmost region of the Mediterranean. A total of 113 specimens (59 females, 53 males, and one individual of indeterminate sex), ranging from 23.4 to 42.5 cm in total length, were analyzed. The maximum observed age was 5 years for both females and males, and this barracuda population was dominated by 3 year old fish. Growth was described by the von Bertalanffy growth curve (L∞ = 55.58 cm, K = 0.12 year−1, t0 = −4.29 year, *R*2 = 0.580), and the growth performance index (Φ') was 2.57. Otolith length, width, and mass were measured, and the utility of these morphometrics as predictors of age in *S. sphyraena* was evaluated. The results showed that counting otolith annuli produced a better estimation of age than proposed linear models based on relationships between observed fish age and otolith morphometrics. In comparison with age and growth data available in the literature for *S. sphyraena*, our results from the Adriatic Sea provide more insights into the life-history traits of this species and can be used in the future effective management and conservation.

**Keywords:** age; growth; otoliths; morphometry; *Sphyraena sphyraena*

### **1. Introduction**

The family Sphyraenidae (barracudas), which is typically represented in shelf waters of tropical and temperate seas in many regions of the world, comprises only one genus, *Sphyraena*, and 28 species [1–3]. They are all active predatory and voracious fishes that can adapt to diverse ecological conditions [4,5]. Large adult barracudas are often found to be solitary, whereas juveniles and small adults are gregarious and aggregate to form schools [6]. In the Mediterranean, six Sphyraenidae species have been recorded so far: three Lessepsian (*S. chrysotaenia* Klunzinger, 1884; *S. flavicauda* Rüppell, 1838; *S. obtusata* Cuvier, 1829), two Atlanto-Mediterranean (*S. sphyraena* (Linnaeus, 1758); *S. viridensis* Cuvier, 1829), and one new species identified from the Gulf of Taranto (*S. intermedia* Pastore, 2009) [7,8].

The European barracuda, *S. sphyraena*, is a widespread species found in the eastern Atlantic (Bay of Biscay to Angola), including the Mediterranean and Black Sea, Canary Islands, and Azores [9]. It is a pelagic species living close to the surface [10], although individuals can be observed in a midwater, down to depths of 100 m [11]. In the Mediterranean, *S. sphyraena* is a specialized piscivore fish reaching 165 cm of the total length, while its size is usually between 30 and 50 cm [12]. This barracuda is a temperate species and generally shares habitats and resources with the thermophilic *S. viridensis* [5,12]. In comparison to other Sphyraenidae, only a few investigations have been carried out on the life-history traits of *S. sphyraena*. The growth [4,13], otolith shape analysis [8], reproductive biology [14], and feeding ecology [12] have been investigated in the southeastern Mediterranean. Otolith shape analysis has been also undertaken in the southwestern Mediterranean [7], and some patterns of reproductive biology have been reported in the northwestern part of the

**Citation:** Ferri, J.; Brzica, A. Age, Growth, and Utility of Otolith Morphometrics as Predictors of Age in the European Barracuda, *Sphyraena sphyraena* (Sphyraenidae): Preliminary Data. *Fishes* **2022**, *7*, 68. https://doi.org/10.3390/ fishes7020068

Academic Editors: Pedro Morais and Yongjun Tian

Received: 27 January 2022 Accepted: 15 March 2022 Published: 17 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

basin [5]. However, information regarding this barracuda species in the northernmost region of the Mediterranean (Adriatic Sea) has not yet been described in detail.

This study is the first attempt to investigate the age and growth of the eastern Adriatic population of the European barracuda; therefore, the aim is to provide better insight into the life-history of this species. Fish age was determined by counting annuli in sagittal otoliths; however, because this is one of the most time-consuming aging methods, we also evaluated the utility of otolith morphometrics (length, width, and mass) as an alternative.

### **2. Materials and Methods**

### *2.1. Sampling*

Samples of the European barracuda were obtained monthly from the local fish market between November 2020 and February 2021 and belonged to catches taken by purse and boat seiners in the central eastern Adriatic Sea during that period. Each fish was measured to the nearest 0.1 cm total length (TL) and weighed (W) to the nearest 0.1 g. Sex was determined by macroscopic analysis of the gonads, and sagittal otolith pairs were removed, cleaned, and stored dry in labeled Eppendorf tubes for later analyses. Sex ratio was tested for equality (female to male = 1:1) using the chi-square goodness-of-fit test at the 0.05 significance level. Length frequency distributions of females and males were compared using the Kolmogorov–Smirnov two-sample test.

### *2.2. Otolith Morphometrics*

Prior to polishing, otoliths were photographed using an Olympus DP-25 digital camera (Olympus Corporation, Hamburg, Germany) attached to an Olympus SZX10 stereo microscope. Otolith length (OL) and width (OW) were measured to the nearest 0.01 mm using Olympus CellˆA Imaging Software, version 2.6 (Build 1210). Otolith length was defined as the longest axis between anterior and posterior otolith edge, and width was defined as the distance from dorsal to ventral edge taken perpendicular to the length throughout the otolith focus. Otolith mass (OM) was weighed to the nearest 0.0001 g. Differences between left and right otoliths were tested by paired *t*-test, while ANCOVA was used to test for differences in otolith measures between females and males, considering fish length as a covariate. The statistical assumptions of these tests were tested using the Kolmogorov–Smirnov test of normality, and, when necessary, data were log-transformed. Relationships between fish total length and otolith morphometrics (length, width, and mass) were constructed using the linear model.

### *2.3. Age and Growth Estimation*

The medial face of a single, randomly selected otolith from each pair was polished using a wet abrasive paper and photographed under reflected light against a dark background, using a stereo microscope coupled with an Olympus DP-25 digital camera. Growth rings were visible as alternating opaque and translucent zones, and ages were assigned to fish specimens on the basis of their counts. Therefore, one opaque zone combined with one subsequent translucent zone was interpreted as one annulus, as already observed in *Sphyraena* species [2,15]. Each otolith was read by one reader, without auxiliary data on the fish size. A second reading was carried out 1 week later, and, when readings differed by one or more years, a third reading was made. We used images for the age estimation because their quality and ability to adjust contrast and brightness made it easier to interpret zoning patterns than direct observations under the stereo microscope.

The von Bertalanffy growth model was fitted to the estimated age–length dataset using a nonlinear least-square procedure of a Gauss–Newton algorithm: TL = L∞ (1 − exp(−K(t − t0))), where TL is the total length of fish at age t, L∞ is the estimated asymptotic length, K is a constant that determines the rate at which TL approaches L∞, and t0 is the hypothetical age at zero length. To compare the fish within the genus *Sphyraena*, the index Φ' was calculated as follows: Φ' = 2 log L∞ + log K, where Φ' is the growth performance of fish, L∞ is the asymptotic length derived from the von Bertalanffy growth model, and K is the growth

constant also derived from the von Bertalanffy growth model [16]. Once the age was estimated by counting annual growth rings, relationships between observed fish age and otolith morphometrics (length, width, and mass) were constructed using the linear model.

### **3. Results**

Of the 113 individuals of the European barracuda sampled, 59 (52.2%) were females, 53 (46.9%) were males, and one (0.9%) individual was of indeterminate sex. The sex ratio was not significantly skewed toward females or males (*p* = 0.57). The total length of females and males ranged from 28.5 to 42.5 cm and from 23.4 to 38.6 cm, respectively (Table 1). A higher proportion of males were smaller than females. Males dominated in the ≤31.0 cm length classes, whereas females were more abundant in the ≥36.0 cm length classes (Figure 1). The Kolmogorov–Smirnov two-sample test showed a significant difference between length frequency distributions of females and males (*p* < 0.01). The mean length of females (34.6 ± 3.75 cm) was significantly greater than that of males (32.0 ± 3.56 cm) (*t*-test, df = 1, *p* < 0.01).

**Table 1.** Range of total length and weight and mean total length and weight with standard deviation (SD) of the European barracuda, *Sphyraena sphyraena*, in the eastern Adriatic.

**Figure 1.** Length frequency distributions of the European barracuda, *Sphyraena sphyraena*, females and males in the eastern Adriatic.

Otoliths of *S. sphyraena* are spindle-shaped, with a short rostrum and poorly defined antirostrum. Their anterior region is peaked, while the posterior is oblique. No significant differences in morphometric measures (length, width, and mass) were found between left and right otoliths (paired *t*-test, *p* > 0.05 for all measures) and between females and males (ANCOVA, *p* > 0.05 for all measures); thus, data were pooled for both sexes, and mean values for each otolith pair were used in further analyses (Table 2). A linear model described relationships between fish total length and otolith length, width, and mass, with high coefficients of determination (≥0.805) (Table 3).

**Table 2.** Range of otolith measures (OL = otolith length, OW = otolith width, OM = otolith mass) and their mean values with standard deviation (SD) of the European barracuda, *Sphyraena sphyraena*, in the eastern Adriatic.

**Table 3.** Parameters of the linear regression between fish total length (TL) and otolith morphometrics (OL = otolith length, OW = otolith width, OM = otolith mass) of the European barracuda, *Sphyraena sphyraena*, in the eastern Adriatic (*a* = intercept value; *b* = regression slope; *R*2 = coefficient of determination).

Sagittal otoliths displayed alternating opaque and translucent zones under reflected light (Figure 2). The maximum age of the sampled European barracuda was 5 years for both females and males (Figure 3). Age distribution was similar for both sexes and was skewed to the right. The population was dominated by 3 year old fish, which represented almost 45% of the fish sampled. Fish younger than 3 years were poorly represented (we found only one 1 year old individual). Mean lengths (±standard deviation) of 1, 2, 3, 4, and 5 year old fish were 23.4 cm, 29.0 ± 0.41 cm, 32.4 ± 3.27 cm, 33.9 ± 3.29 cm, and 37.5 ± 3.15 cm, respectively. Females were larger than males at all ages. The von Bertalanffy growth curve was fitted to the age–length dataset estimated for all specimens (Figure 4). Estimated parameters were L∞ = 55.58 cm, K = 0.12 year−1, and t0 = −4.29 year (*R*2 = 0.580). The growth performance index (Φ') was 2.57 for the sampled fish. Relationships between observed fish age and otolith morphometrics are presented in Table 4. The linear model explained between 23.6% and 27.7% of the variation in age.

**Figure 2.** Sagittal otolith of the 5 year old European barracuda, *Sphyraena sphyraena*, in the eastern Adriatic.

**Figure 3.** Age distributions of the European barracuda, *Sphyraena sphyraena*, females and males in the eastern Adriatic.

**Figure 4.** Fitted von Bertalanffy growth curve of the European barracuda, *Sphyraena sphyraena*, in the eastern Adriatic.

**Table 4.** Relationships between observed fish age and otolith morphometrics (OL = otolith length, OW = otolith width, OM = otolith mass) of the European barracuda, *Sphyraena sphyraena*, in the eastern Adriatic, described using the linear model (age = *a* (otolith morphometric) + *b*); *R*2 = coefficient of determination.

### **4. Discussion**

Despite its wide distribution, studies concerning the age of *S. sphyraena* are currently available only for the southeastern Mediterranean populations (Egyptian waters) [4,13]. The maximum age estimated in our study using whole otolith readings (5 years) was lower than the maximum age demonstrated by Allam et al. [13] (8 years) using the same method and a similar length range. However, only 4% of the specimens collected in Egyptian waters were older than 5 years [13]. On the other hand, our age readings were higher than those reported in another available study (3 years) [4]. The most striking difference in the life span of *S. sphyraena* seems to occur among populations from the same area. As far as

growth parameters are concerned, there was good agreement, and parameters estimated in our study were very similar to the only available growth data on this species for Egyptian waters: L∞ = 55.27 cm and K = 0.123 year−1 [13]. This similarity may be attributable to the dominance of individuals in the same age classes in both populations, in conjunction with the already mentioned small proportion of the oldest individuals (>5 years) in Egyptian waters. In addition, the growth increments for both populations were greatest in the first 2 years. All observed similarities were also reflected in almost the same values for Φ': 2.57 and 2.58 [13] for the northern and southeastern Mediterranean populations, respectively. The low estimate of t0 can be explained by the few fish ≤28.0 cm and a complete absence of the fish <23.0 cm in our samples. In fact, if smaller fish were sampled in both areas, t0 would have been higher and closer to 0. Consequently, the population age structure shows a lack of fish in the first two age classes, principally due to accessibility to the resources on the fish market. Although females and males showed differences in growth, as females were larger than males at all ages sampled, we acknowledge that our study is not conclusive about possible differences in growth between sexes. More data, particularly from smaller and younger fish, would improve estimates and ensure modeling for *S. sphyraena* females and males separately. However, although the sample size was relatively small for young individuals, the sample covered a wide length and age range; thus, the results of this study are still reliable and valuable. By producing such preliminary growth estimates for *S. sphyraena* in the studied area, we take an important step toward facilitating future studies and management strategies.

The index of growth performance is a useful tool for comparing the growth curves of different populations of the same species and of different species belonging to the same genus or family [17]. Calculated from published data of L∞ and K in Mediterranean *Sphyraena* species, the values of this index were the highest in *S. viridensis* (2.95 in Egyptian waters [13] and 3.33 in Algerian waters [15]) and the lowest in *S. chrysotaenia* (2.46 [13] and 2.62 [2] in Egyptian waters). In *S. flavicauda* from Egyptian waters, recorded values were 2.77 [13] and 2.82 [2]. These variations could be attributed to environmental conditions affecting distribution, population structure, and growth patterns [18]. The obtained Φ' value in this study was within the range of values for different *Sphyraena* species in the Mediterranean (2.46 < Φ' < 3.33). In general, growth studies providing the von Bertalanffy growth parameters for other *Sphyraena* species are scarce, and the main point of reference is represented by data provided for the southeastern Mediterranean populations.

Interpreting annual growth rings in otoliths, as in this study, is the most frequently used method for estimating individual fish age [19]. On the other hand, it has several disadvantages; it is subjective, laborious, time-consuming, and dependent on the readers' experience [20]. In that context, several studies have analyzed and demonstrated a close relationship between the size of the otoliths and fish age [20–25]. A linear model described all relationships between otolith measures and fish age, and the age of the investigated barracuda could be best predicted from the otolith length. However, we must emphasize that models did not provide precise estimates, and this could be explained by the overlap in lengths between fish of different age (especially in the age classes 3 and 4) and a lack of the fish in the first two age classes. Despite that, the observed otolith morphology of *S. sphyraena* generally corresponded to descriptions for this species from the eastern Atlantic and Mediterranean [7,8,26], and it can be used in the identification of species (e.g., in feeding studies or species separation within *Sphyraena* genus).

### **5. Conclusions**

This study provides the first analysis of European barracuda age and growth in the northernmost region of the Mediterranean. We showed that otoliths can be used for aging, and our estimates of growth seems reasonable for a species with a moderate life span. Counting otolith annuli produced better estimation of age than proposed linear models based on relationships between observed fish age and otolith morphometrics. However, these preliminary results should be used with caution, and further studies with larger

sample sizes collected throughout the year are required to refine the age and growth information for *S. sphyraena* in the Adriatic. Similar studies are, therefore, encouraged within the Mediterranean to achieve a more comprehensive understanding of the lifehistory traits of this temperate fish species.

**Author Contributions:** Conceptualization, J.F.; methodology, J.F.; software, J.F.; validation, J.F. and A.B.; formal analysis, J.F. and A.B.; investigation, J.F. and A.B.; resources, J.F. and A.B.; data curation, J.F.; writing—original draft preparation, J.F.; writing—review and editing, J.F.; visualization, J.F.; supervision, J.F. All authors read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Code of Ethics of the University Department of Marine Studies; we only used specimens obtained from the fish market that were already dead.

**Data Availability Statement:** Data from this study are available from the corresponding author upon request (J.F.: josipa.ferri@unist.hr).

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

### *Article* **Automatic Fish Age Determination across Different Otolith Image Labs Using Domain Adaptation**

**Alba Ordoñez 1,\*, Line Eikvil 1, Arnt-Børre Salberg 1, Alf Harbitz 2 and Bjarki Þór Elvarsson 3**

	- 2 Institute of Marine Research, 9294 Tromsø, Norway<EMAIL_ADDRESS>
**Abstract:** The age determination of fish is fundamental to marine resource management. This task is commonly done by analysis of otoliths performed manually by human experts. Otolith images from Greenland halibut acquired by the Institute of Marine Research (Norway) were recently used to train a convolutional neural network (CNN) for automatically predicting fish age, opening the way for requiring less human effort and availability of expertise by means of deep learning (DL). In this study, we demonstrate that applying a CNN model trained on images from one lab (in Norway) does not lead to a suitable performance when predicting fish ages from otolith images from another lab (in Iceland) for the same species. This is due to a problem known as *dataset shift*, where the *source data*, i.e., the dataset the model was trained on have different characteristics from the dataset at test stage, here denoted as *target data*. We further demonstrate that we can handle this problem by using domain adaptation, such that an existing model trained in the source domain is adapted to perform well in the target domain, without requiring extra annotation effort. We investigate four different approaches: (i) simple adaptation via image standardization, (ii) adversarial generative adaptation, (iii) adversarial discriminative adaptation and (iv) self-supervised adaptation. The results show that the performance varies substantially between the methods, with adversarial discriminative and self-supervised adaptations being the best approaches. Without using a domain adaptation approach, the root mean squared error (RMSE) and coefficient of variation (CV) on the Icelandic dataset are as high as 5.12 years and 28.6%, respectively, whereas by using the self-supervised domain adaptation, the RMSE and CV are reduced to 1.94 years and 11.1%. We conclude that careful consideration must be given before DL-based predictors are applied to perform large scale inference. Despite that, domain adaptation is a promising solution for handling problems of dataset shift across image labs.

**Keywords:** fish age determination; Greenland halibut; deep learning; dataset shift; domain adaptation

### **1. Introduction**

Otoliths are small calcium carbonate structures that form part of the balance organ in the inner ear of fish [1]. Due to their rich content of various information, they could be referred to as the "tachograph" of the fish. Fish otolith sciences have evolved in a range of research fields, from paleontology to stock discrimination and fish age determination. For example, the isotopic fingerprints of fish otoliths have proven to give insight into the water bodies which have been previously occupied by fish [2]. In this area, fish otoliths as old as 172 million years have been used [3]. Otoliths have also been used to distinguish different species. This has been, for instance, useful for studying the diet of sea mammals [4] or seabirds [5] by examining otoliths from feces samples. Other interesting studies have been carried out for long-lived fish in the Pacific [6], where radio-carbon data obtained from otoliths have provided valuable information on carbon flux in the oceans and have also been used to validate fish age.

When it comes to fish age determination, otolith images from captured fish are manually read, with in the order of a million images read every year [7]. Age-readers count

**Citation:** Ordoñez, A.; Eikvil, L.; Salberg, A.-B.; Harbitz, A.; Elvarsson, B.Þ. Automatic Fish Age Determination across Different Otolith Image Labs Using Domain Adaptation. *Fishes* **2022**, *7*, 71. https://doi.org/10.3390/fishes7020071

Academic Editor: Josipa Ferri

Received: 14 February 2022 Accepted: 17 March 2022 Published: 18 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

age zones analogous to counting age rings in a tree, typically using a microscope or highresolution images. However, this process is challenging and time-consuming [8]. During the last decades, automatic techniques have been proposed to automate this tedious activity (e.g., [9]). More recently, the use of deep learning (DL) has received more and more attention with promising results shown for Greenland halibut [10,11], snapper and hoki [12], red mullet [13] and Atlantic salmon [14].

However, the construction of DL systems can be challenged and hindered by several factors. The developed system trained at one otolith image lab where it works well, may not necessarily work well when applied to otolith images from another lab for the same species. In such cases, the system may have trouble generalizing to unseen data from another lab, due to data characteristics that are different from those at the training stage.

The discrepancy between data coming from different labs may have a range of explanations. From personal communication with reader experts the following was noted: different countries manage stocks that might have otoliths with varying readabilities due to different fish environments and catch seasons. Ages may also be read using thin sectioning of otoliths in some labs instead of reading the whole otolith (e.g., for Greenland halibut). The conservation and preparation of the otoliths might influence the age readability as well, one option being the storage of the otolith in an envelope and another being to freeze the otolith in water [15]. Camera quality, lighting conditions and other imaging setup conditions may also affect image characteristics, in addition to the magnifying glass equipment.

Acquiring new annotations such that it is possible to train the DL system with a more diverse dataset can also be challenging, both in terms of effort and availability of expertise. This process requires trained age-reading experts. Multiple methods are employed to minimize error, such as age-reading workshops where age determined by different readers from various labs are compared [16]. Reference otoliths are also used in training and are periodically reread by readers to ensure consistency [17]. Lack of highly trained age-reading experts can nevertheless lead to backlogs of otoliths that have not been annotated.

To address those challenges and increase the willingness to automate the age-reading using DL, it could be of interest to show that an already trained system could generalize to novel otolith images coming from other labs, while not requiring extra annotation effort.

The challenging situation where we observe different characteristics between the data the model was trained on and the data used at test stage is often referred to as dataset shift [18,19]. Training a classifier which performs well in this scenario can be addressed using domain adaptation (DA) [20], i.e., adapting models trained on data from a source domain to a different domain, known as the target domain. DA is typically carried out in a setting where the training data from the source domain is labelled, but little or no labels are available from the target domain. The case where we do not have labels at all in the target domain is commonly described as unsupervised domain adaptation (UDA). As we could more frequently face the scenario where we would like to test trained models on unlabeled otolith images, in this paper, we will consider solving a UDA task for automating fish age determination across different image labs, using DL.

There has been an effort made to develop deep neural networks that could handle UDA. In a recent review study, Zhao et al. [21] divided UDA approaches into four categories: (i) discrepancy-based, (ii) adversarial discriminative methods, (iii) adversarial generative methods and (iv) self-supervised methods. The approaches from the two adversarial categories were said to score best on performance and have been very popular. The discrepancy-based approaches were said to have lower performance and be less applicable to complex datasets, while the self-supervision-based methods represented a class of newer approaches that were shown to be robust and applicable to complex datasets.

Based on this, the aim of this work has been to investigate different UDA approaches from categories (ii)–(iv). We intended to see how they performed for the problem of generalizing a model trained for fish age prediction on otolith images from a lab in one country, to do prediction on images from a different lab in a different country, without new manual annotation.

The experiments were carried out based on otolith images from Greenland halibut acquired by the Institute of Marine Research (Norway) and the Marine and Freshwater Research Institute (Iceland). We adapted a trained network using otolith images and labels from the Norwegian lab (source domain) to otolith images coming from the Icelandic lab (target domain).

### **2. Materials and Methods**

### *2.1. Data*

The source and target datasets were collected by the Institute of Marine Research (Norway) and by the Marine and Freshwater Research Institute (Iceland), respectively.

The source dataset was a subset of the one described in Moen et al. [10], it consisted of 4109 images of paired right and left otoliths (collected between 2006 and 2017) having a resolution of 2596 × 1944 pixels. Each otolith pair was separated leading to 8218 single right and left otolith images, with labeled ages ranging from 1 to 26 years. Only ages read by two experienced readers from the same lab were used and only one reader for each otolith. Co-readings between the two readers revealed a negligible between-reader bias, independent of age.

The target dataset was composed of 3501 right otolith images, that were obtained from images of paired right and left otoliths having a resolution of 2048 × 1536 pixels. The otoliths were collected between 2015 and 2020 from the Icelandic autumn ground fish survey [22] and labeled ages ranged from 1 to 20 years. The ages were determined by a single reader.

Examples of image variation across the Norwegian and the Icelandic lab are shown in Figure 1, where one can observe some differences in terms of background, lighting conditions and magnification. The age distribution estimated by readers for the Icelandic dataset was such that the age label space was inside the range of the label space estimated by the Norwegian lab (Figure 2).

**Figure 1.** Examples of otolith images (resized to 224 × 224 pixels) corresponding to different ages (4–16 years) predicted by human readers. (**a**) Images acquired and annotated by the Norwegian lab; (**b**) Images acquired and annotated by the Icelandic lab.

**Figure 2.** Age frequency distribution of predicted ages by human readers in the Norwegian dataset (gray) and the Icelandic dataset (blue).

### *2.2. UDA for Age Classification*

We considered three different UDA approaches (Figure 3) to adapt an existing age classification system for otolith images to a new target domain, such that we could still have a suitable classification performance. For that, we exploited images from a source domain *DS* and a target domain *DT* as well as labels only from *DS*. Common for the three approaches was a weight sharing constraint between a feature extractor for the *DS* images and the *DT* images, allowing us to learn a domain-invariant feature space. Another common module was the classifier learned from the *DS* data. Since the output of the feature extractor was supposed to be domain-invariant after being optimized during the training process, we expected that using that module together with the classifier could produce meaningful age predictions on images from *DT*.

### 2.2.1. Adversarial Generative Adaptation

The first method we used was a generative adversarial adaptation (Figure 3a), based on the coupled generative adversarial networks (CoGAN) proposed by Liu and Tuzel [23]. It consists of a pair of GANs and the idea of using more than one GAN was compelling to us by its originality. Moreover, the method showed promising results on UDA tasks when it was introduced [23].

Our CoGAN had generators that synthesized images by taking as input a 100-dimensional noise vector. We used the same networks as proposed in the implementation taken from [24].

Feature extractors were used to output feature representations for the discriminators. The architecture of the feature extractors was based on the commonly used convolutional neural network (CNN) ResNet [25] and took images with the default input size of 224 × 224 pixels. Those networks were initialized using the weights from the source-CNN (excluding the last classification layer), i.e., the CNN model trained to classify otolith ages using images and labels from *DS*. The discriminator models were simply estimating the probabilities that the generated images were real or synthesized for each of the domains.

Following [23], we tied the weights of the first few layers of the generators as well as the weights from the feature extractors. This weight sharing allowed us to learn a domaininvariant feature space, without requiring the existence of any pairs of corresponding images in the two domains. For classifying otolith ages, the classifier was added to the feature extractor and corresponded to the last layer of the ResNet model.

We trained the whole architecture by jointly solving the CoGAN learning problem (CoGAN related loss [23]), involving images from *DS* and *DT* and the age classification problem in *DS* (cross-entropy classification loss).

**Figure 3.** Illustration of the different UDA architectures utilized for automatic age determination of otoliths across Norwegian (source) and Icelandic (target) labs. (**a**) Adversarial generative adaptation (CoGAN); (**b**) Adversarial discriminative adaptation (CDAN); (**c**) Self-supervised adaptation (SimCLR).

### 2.2.2. Adversarial Discriminative Adaptation

The second method was an adversarial discriminative adaptation (Figure 3b) and differed from the previous approach in that it did not require generators. The implementation followed the state-of-the-art method conditional adversarial domain adaptation (CDAN) proposed by Long et al. [26]. As in the previous approach, the feature extractor was initialized with the weights of the source-CNN model (taking input images of size 224 × 224) and therefore shared the same architecture (excluding the last classification layer). The classifier predictor corresponded to the last layer of the ResNet model. The discriminator was a multilayer perceptron with two hidden layers having ReLU as a non-linear activation function. It received an input *x* defined as a joint variable of the extracted feature representations and the classifier predictions. This network architecture ( *x* → 1024 → 1024 → 2) was used to classify whether the input was coming from *DS* or *DT*. The size of the hidden layers was chosen to match the default implementation of CDAN available from [27].

This architecture was trained to solve a minimax optimization problem. The term to be minimized included the cross-entropy classification loss (using data and labels from *DS*), combined with the adversarial loss derived from the discriminator which tried to match the distribution of the source and target domains involving images from *DS* and *DT* (we followed [26] and fixed the combination parameter to 1). This last term relating to the discriminator was the one that was also maximized in the minimax optimization problem (see [26] for further details).

### 2.2.3. Self-Supervised Adaptation

The last method we chose to test for UDA used self-supervision (Figure 3c) and differed from the previous approach in that the discriminator component was replaced by a pretext network. In self-supervised learning, a supervised task is created out of the unlabeled original data. Typically, the original images are transformed and the pretext network needs to learn how to predict certain aspects related to the transformations. Predicting image rotations [28], solving a jigsaw puzzle [29], retrieving colors from grayscale images [30] are examples of pretext tasks. The idea of including the pretext network was to be able to learn again a domain invariant feature representation using unlabeled otolith images from the source and target domains.

We based our implementation on the generic method proposed by Xu et al. [31] and available from [32]. As carried out in [31], we initially experimented with image rotation prediction as a pretext task. However, the pretext network was rapidly finding the solution during training and the learned feature representations from the otolith images did not help for DA. We decided instead to select a more challenging pretext task, using the simple framework for contrastive learning of visual representation (SimCLR) proposed by Chen et al. [33]. This method was chosen for its simplicity of implementation and its good performance that achieved significant advances in state-of-the-art self-supervised learning. In SimCLR, the pretext network is trained to recognize positive samples, i.e., different augmented views of the same image and distinguish them from the negatives, i.e., augmented views of other images from the dataset. We followed the recommendations from [33] and used random cropping, resizing, color distortions and Gaussian blur as data augmentations. The pretext network was a multilayer perceptron with one hidden layer having a ReLU as non-linearity. The network received as input extracted features *x* from the ResNet model feature extractor and outputted a 26-dimensional feature representation ( *x* → 512 → 512 → 26). The size of the hidden layer and the output were chosen to match the default implementation of SimCLR available from [34]. As for the adversarial approaches, the feature extractor was initialized with the weights of the source-CNN model.

The architecture of the self-supervised adaptation was trained to learn jointly the pretext task (using unlabeled images from *DS* and *DT*) and the age classification task, where the classifier predictor received images and labels from *DS*. The losses relating to each of the tasks were added and minimized during training. The pretext task loss was the InfoNCE loss [35], which is a popular choice of loss function for contrastive learning aiming to pull feature representations that are close and push away representations that are different. As pointed out by [33], learning such a task benefits from having large batch sizes such that a large number of negative samples is obtained for comparison with every single positive sample. To be able to use contrastive learning to our advantage for DA, the feature extractor took as input images of size 96 × 96 pixels (instead of 224 × 224), ensuring a sufficiently large batch size.

### 2.2.4. Implementation Details

We implemented the above methods using the PyTorch framework on a single GTX 1080 Ti GPU with 11 GB memory. For a fair comparison, we used the same ResNet architecture for the feature extractors. Given our hardware resources, we chose a ResNet18 to be able to choose a large batch size for the self-supervised adaptation. For the layers that were not part of the feature extractors (initialized using the Source-CNN), the initialization was done using random values scaled according to the method proposed in [36]. For each of the models, we ran five trials where different random number generators were used for the initialization. This helped in building a certain confidence in the performance of the different algorithms when comparing them, by checking whether or not the results were obtained by chance (consistency of the model).

For all the approaches, we followed Moen et al. [10] and used a constant learning rate of 0.0004 and the Adam optimizer [37]. For adversarial DA approaches, we checked the default parameters from the CoGAN implementation taken from [38] and chose a batch size of 64. As self-supervised learning benefits from large batch sizes [33], we chose a batch size of 512 (according to our computing resources). For all the methods, a number of 200 epochs was selected.

### *2.3. Other Considered Classifiers*

To better understand how the three UDA approaches performed, we considered comparing them with other classifiers, all defined by a ResNet18 architecture that was trained either on image data acquired from the Norwegian lab or the Icelandic lab.

First, we considered ResNet18 networks trained on otolith images acquired and annotated by the Norwegian lab. The models classified ages into one of the 26 categories (from 1 to 26 years as defined by the labels from the source dataset) and the classification was performed on two versions of resized images: 224 × 224 and 96 × 96. We directly deployed these models on otolith images from the Icelandic lab, without any adaptation or finetuning. These experiments were referred to as the *lower performance bound*, as we expected them not to perform that well on the target data given the observed image variation across the Norwegian and the Icelandic lab (Figure 1). We also applied these models on the Norwegian source data and we denoted the experiments as the *Norwegian bound*.

Next, we considered ResNet18 networks trained on the Icelandic data, where during training, we used the age labels provided by Iceland. This provided us with a *higher performance bound*, as we expected to have better results than not using labels from Iceland at all. These models classified ages into one of the 20 categories (from 1 to 20 years as defined by the labels from the target dataset).

Finally, we considered a model where we performed a simpler domain adaptation via a standardization preprocessing step. This was a semi-automatic approach derived by practitioners with the aim of reducing the variability in background and resolution between labs. The approach was based on a semi-automatic thresholding to remove the background. Then, images were resized so that the vertical extension of the otolith covered 90% of the vertical extension of the image. After standardizing the otolith images, we trained the ResNet18 models on the Norwegian dataset (images of size 224 × 224 and 96 × 96) and deployed them on the preprocessed images from Iceland. This provided us with another domain adaptation approach denoted *standardization*. It was simpler than the three other considered UDA methods (Figure 3) that required both images from the Norwegian and the Icelandic labs when training. Examples of standardized images are shown in Figure 4.

For all the above models, a small validation set was used to choose the batch size (128) and to control when to terminate the training process, setting a maximum number of epochs of 150, as done in Moen et al. [10]. For the experiments denoted as lower performance bound, Norwegian bound and the standardization approach, the validation set was extracted from the source dataset, whereas for the higher performance bound we used a validation set extracted from the target data. We chose the same learning rate/optimizer as for the UDA approaches and we ran five trials for each of the models. For each trial, different random number generators were used for the initialization of the networks that also followed the method from [36].

A summary of the different experiments carried out in this study, with their corresponding characteristics is reported in Table 1.

**Figure 4.** Examples of otolith images where the standardization preprocessing step was applied. (**a**) Standardized version of images from Figure 1a acquired and annotated by the Norwegian lab; (**b**) Standardized version of images from Figure 1b acquired and annotated by the Icelandic lab.

**Table 1.** Summary of the different experiments with their associated characteristics.

### *2.4. Performance Measurement*

For assessing the performance of the different classifiers, we considered as carried out in [11] the root mean squared error (RMSE) between age prediction and read age, as well as the mean coefficient of variation (CV) of independent estimators (human and DL system) calculated for each given otolith. For both metrics, the lower the values the better.

### **3. Results**

The results from our experiments are summarized in Tables 2 and 3 and also illustrated in Figures 5–7. Rows 1 and 2 of Table 2 report the performance of age prediction by experimenting with the Norwegian bound, where models were trained and tested on otolith images acquired and annotated by the Norwegian lab. The classification was performed on the two versions of resized images: 224 × 224 and 96 × 96. Reasonably good RMSE/CV results were obtained considering five different trials (with relatively low variation over these splits ≤ 0.10 for the RMSE) and were comparable to the earlier study of Moen et al. [10] (RMSE = 1.65 years. and CV = 9%), although they tested on a smaller dataset. Resizing the images to different resolutions had a very limited effect on the performance.

**Table 2.** Summary of the performances achieved on the experiments that are not considered as domain adaptation, i.e., Norwegian bound and lower/higher performance bounds. For each experiment, the ResNet18 model was trained 5 times (using 5 different random number generators) and we reported the averaged RMSE/CV together with standard deviation over the 5 trials (quantity after ± sign).

**Table 3.** Summary of the performances achieved on the experiments that are considered as domain adaptation, i.e., simple standardization approach and the UDA methods. For each experiment, the model was trained 5 times (using 5 different random number generators) and we reported the averaged RMSE/CV and standard deviation over the 5 trials.

Rows 3 and 4 of Table 2 report the results on the lower performance bound experiments, showing that without any DA involved, the performance on the Icelandic data was considerably lower than for the Norwegian data. In addition, a larger variation in performance was observed over the different trials (≥0.5 for the RMSE). The last two rows of Table 2 correspond to the higher performance bound. They demonstrated the potential for using a DL system to predict ages on the target data from the Icelandic lab, where good levels of RMSE/CV together with low variations over the five trials (≤0.04 for the RMSE) were achieved when training on these data. These observations were supported by the age-bias plots of Figure 5 displaying the age predictions (median over the five trials) of the lower and higher performance bounds (using images of size 224 × 224) against the human annotated age for the Icelandic data. We could observe that the lower performance bound (Figure 5a) completely overestimated the ages. The higher performance bound results were satisfactory (Figure 5b), although we noticed an underestimation for the older age groups, similar to that which was previously observed for the Norwegian data [10,11].

**Figure 5.** Model age predictions vs. human annotated age obtained on the Icelandic data for the lower and higher performance experiments (using images of size 224 × 224). For each model, the age predictions correspond to the calculated median from the predicted ages obtained over the 5 different trials. The scatters have an area proportional to the probability density of data. (**a**) Age predictions for the lower performance bound; (**b**) age predictions for the higher performance bound.

**Figure 6.** Visualization of the spread of the performance results obtained on the Icelandic data for the methods that are considered as domain adaptation. Each dot corresponds to one of the 5 trials. The red dashed lines delimiting the gray bars correspond to the higher (left) and lower (right) averaged performance bounds obtained with designated resolution (depending on the experiment). (**a**) Performance measured in terms of RMSE (years); (**b**) performance measured in terms of CV (%).

**Figure 7.** Age predictions of different adaptation models vs. human annotated age obtained from the Icelandic data. For each model, the age predictions correspond to the calculated median from the predicted ages obtained over 5 different trials. The scatters have an area proportional to the probability density of data. (**a**) Age predictions for the simple standardization approach (image resolution 96 × 96 pixels); (**b**) age predictions for the adversarial generative adaptation; (**c**) age predictions for the adversarial discriminative adaptation; (**d**) age predictions for the self-supervised adaptation.

In Table 3, we report the performance results obtained for the DA task, i.e., involving models trained on otolith images and labels from the Norwegian lab adapted to images from the Icelandic lab. For the different methods, we also visualize in Figure 6 the spread of the results for each of the five trials, represented as black dots. To be able to visualize the gap between domain adaptation methods and the higher and lower performance bounds that were tested on Icelandic images, each experiment was represented inside a gray bar. This was delimited to the left by the averaged RMSE/CV from the higher performance bound and to the right by the averaged results of the lower performance bound. In addition to this, a closer inspection of the age predictions for the different methods is presented in the age-bias plots of Figure 7.

From both Table 3 and Figure 6, we observed that the simple standardization approach improved on the lower performance bound and the results were comparable to those from the more complex adversarial generative approach. Similar patterns of age predictions were noticed when comparing the two approaches (Figure 7a,b). However, we found that the variation in performance was much lower for the standardization approach. For this case, we also noted that the performance for the low-resolution images was slightly better than for the high-resolution images.

The method based on the adversarial discriminative approach showed better performances than the generative approach both in terms of average RMSE/CV and associated variations. The best results were nonetheless obtained with self-supervised adaptation. It

achieved the lowest average RMSE/CV and got closest to the higher performance bound, while the variations were on a level with that of the adversarial discriminative approach (Figure 6). When comparing the predicted ages from the two approaches (Figure 7c,d), less underestimation was observed with the self-supervised adaptation. This, despite the fact we used a resolution of only 96 × 96 pixels compared to the 224 × 224 used by adversarial approaches.

### **4. Discussion**

Since the emergence of DL, several studies have been demonstrated to perform well on automatically predicting fish age from otolith images [10–14]. An important topic of this work was to assess whether a suitable performance from a DL system could be maintained on data from the same species but acquired in a different lab.

Using otolith images from Greenland halibut, we showed evidence in this paper that a DL system trained on data from a lab in Norway had difficulties, at test stage (Table 2, Figure 5a), for generalizing to novel otolith images from another lab in Iceland (lower performance bound model). The reason was a dataset shift, i.e., the images across the labs had different characteristics, although with a simple visual inspection (Figure 1) one could not have expected the bad performance obtained at test stage. By directly training a new system with data and labels from Iceland (higher performance bound model), we obtained a suitable classification performance (Table 2, Figure 5b). However, this required asking for manual annotations, which was not the optimal solution as the process had a cost in terms of effort and expertise.

For both lower and higher performance bounds, reducing the input image size from 224 × 224 to 96 × 96 pixels did not affect the performance. This finding was consistent with a previous study [11] showing that the internal structure of the otolith was not an important attribute for DL systems to determine accurately most of the ages. Thus, using lower-resolution images in those models was not problematic.

To address the performance challenges due to dataset shift, we investigated three different strategies of UDA to adapt the existing classifier trained on the Norwegian data to the Icelandic data, without requiring extra labeling effort from Iceland when training. The performance of the adversarial generative approach (CoGAN, Figure 7b) varied quite substantially compared to the adversarial discriminative approach (CDAN, Figure 7c) and the self-supervised approach (SimCLR, Figure 7d). The results from CoGAN were the worst and exhibited a higher variation across different runs of the model (Table 3, Figure 6), indicating the instability of this GAN-based approach. This contrasted with the review study of Zhao et al. [21] where generative approaches scored well in terms of performance. A possible explanation could be that in this study, the method was challenged when trying to generate images resembling the ones from the source and target domains, where perhaps high focus was dedicated to compensating for background differences observed across labs. More recent adversarial generative methods do not use the concept of training two GANs anymore and trying a more recent state-of-the art approach could be considered in future work. The other adversarial approach, CDAN, came closer to the higher performance bound and little variation across the different runs was observed (Table 3, Figure 6). SimCLR also provided stable results and led to the best performance on the target domain compared to the other two methods. In Zhao et al. [21], self-supervised approaches did not have the best performance score, but in our case it seemed that the pretext task of recognizing positive samples from negative samples was sufficiently good to make the model learn invariant representations across source and target domains. As suggested in [39], with the chosen pretext task, the attention was probably focused on lowand mid-level network representations that might have captured brightness and contrast characteristics. Those characteristics could be found with low-resolution images, that is why operating on images of size 96x96 pixels was not a problem.

Applying a standardization preprocessing to the images from the Norwegian lab, training a model with these data and deploying it on preprocessed data from the Icelandic

lab performed worse than the CDAN and the SimCLR approaches (Table 3, Figure 7a). Moreover, slightly higher variations across the runs were observed (Figure 6). This reflected that finding a solution solely based on data preprocessing was not enough to handle the dataset shift. There was a need for more elaborate adjustments in the deep neural network architecture and UDA using self-supervised learning (SimCLR) seemed the most promising alternative.

Finding an approach for automatic age determination across labs without requiring additional human expertise when training could have implications in two major respects. First, since acquiring otolith image data is less cost demanding than the human interpretation, the present study could raise the possibility of carrying out age determination of backlogs of otoliths that have not yet been annotated in other labs. Second, having a tool for adapting age-reading from one lab to another could help reduce the observed between-lab differences. This may streamline comparisons conducted in age-reading workshops such as [15,16]. In this context, it could also be interesting to combine age readings from different methods and estimate the relative merits of each. By using the approach proposed in [40], one could combine estimated ages while accounting for the biases and imprecisions of the different used methods. In this case, the results would be properly weighted before using them for stock assessment population models. Those could possibly be improved by combining the predictions of independent estimators, which is the case of manual predictions, based on analysis of otolith age zones and DL predictions, which are triggered by other aspects [11].

The results presented in this paper must be seen nonetheless in light of some limitations. We considered adapting a classifier that was trained on images from the right and left otoliths to images only belonging to the right otolith. The reason was that in the earlier phase of the study, we decided to use the same paired data as in Moen et al. [10] (including right and left otoliths) but when we prepared the training data for Iceland, we limited ourselves to the right-separated pair that we resized to 224 × 224 pixels and dismissed the rest of the image. This decision was not reviewed at the later stage of the results evaluation, but we plan in the future to analyze what would happen when including the left otolith images as well. Furthermore, the correspondence with sex and length could also be examined. The age distribution for Greenland halibut females and males is known to differ, with females tending to live longer and with a growth that exceeds that of males [41]. Incorporating information about the sex and length in the DL network as proposed in [42] may also be helpful.

To compare the performance of the different DL approaches, we used the CV, the RMSE and the age-bias plots. However, in the context of stock assessment, these quantities might be too limiting measures. Analyzing age distributions and examining the final stock assessment results would be important tasks for the future. It might happen when comparing methods (including different manual age readings) that the stock assessment results appear virtually the same, despite possible large differences in CV, RMSE, age-bias plots or vice-versa.

Finally, we considered adapting a classifier from a source to a target domain where the target age categories were included in the source label space (Figure 2). In the proposed UDA methods, we tried to match the whole source and target domains as if they fully shared the label space, which was not the case. This could have led to a suboptimal transferability of source examples, where in our study some age categories could have been over-represented (e.g., ages 10–12 as seen in Figure 7b–d). Looking for solutions to handle this, as proposed for instance in [43,44], will be an important task in the future. Another realistic scenario to be examined would be when the target label space has age categories that are not present in the source label space (e.g., transferring the age classifier trained on Icelandic data to Norwegian data). It could then happen that the techniques investigated in this paper would not perform that well. In that case, alternative solutions as proposed in [45] should be taken into consideration.

### **5. Conclusions**

In this work, the dataset shift across otolith images acquired from labs in Norway and Iceland was handled using domain adaptation strategies. We were able to adjust a DL model to provide satisfactory predictions on the Icelandic data. However, the performance depended strongly on the selected strategy. We observed that the CDAN and SimCLR approaches resulted in better performances, compared to the CoGAN or the simple adaptation method via standardization.

Even though common practice consists of validating a DL model on a holdout dataset during training, this step is not sufficient to guarantee that the model will have a welldefined behavior for unseen data. Hence, before DL-based predictors are considered to perform large scale inference on otolith images, a proper handling of dataset shift across different image labs is needed. The insights from this study pointed out that domain adaptation was a promising direction to consider, although analyzing model performance based on information shared between the source and target label spaces deserves further exploration. The hope is that such findings will contribute to the further development of DL techniques that could aid in reducing effort and availability of expertise in the otolith age-reading process.

**Author Contributions:** Conceptualization, A.O., L.E. and A.-B.S.; data curation, A.H. and B.Þ.E.; formal analysis, A.O.; funding acquisition, A.-B.S. and L.E.; investigation, A.O.; methodology, A.O., L.E. and A.-B.S.; project administration, A.-B.S.; resources, A.-B.S.; supervision, A.-B.S. and L.E; validation, A.H. and B.Þ.E.; writing—original draft, A.O., L.E., A.-B.S., A.H. and B.Þ.E.; writing review and editing, A.O. and A.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Research Council of Norway as part of the COGMAR project, grant number 270966.

**Institutional Review Board Statement:** Ethical review and approval were waived for this study as the otolith images were obtained from dead individuals that were not endangered or protected.

**Data Availability Statement:** The Norwegian otolith data presented in this study are openly available through the Norwegian Marine Data Center under the DOI https://doi.org/10.21335/NMDC19496 33559. The Icelandic otolith data can be made available upon request to the Marine and Freshwater Research Institute (Iceland).

**Acknowledgments:** We thank the Researach Council of Norway for funding, but also Auður Súsanna Bjarnadóttir and Sigurlína Gunnarsdóttir from the Marine and Freshwater Research Institute for their involvement in preparing the otolith dataset from Iceland (photography and age-reading) that was used in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

### *Article* **DeepOtolith v1.0: An Open-Source AI Platform for Automating Fish Age Reading from Otolith or Scale Images**

**Dimitris V. Politikos 1,\*, Nikolaos Sykiniotis 2, Georgios Petasis 3, Pavlos Dedousis 1, Alba Ordoñez 4, Rune Vabø 5, Aikaterini Anastasopoulou 1, Endre Moen 5, Chryssi Mytilineou 1, Arnt-Børre Salberg 4, Archontia Chatzispyrou 1 and Ketil Malde 5,6**

**Abstract:** Every year, marine scientists around the world read thousands of otolith or scale images to determine the age structure of commercial fish stocks. This knowledge is important for fisheries and conservation management. However, the age-reading procedure is time-consuming and costly to perform due to the specialized expertise and labor needed to identify annual growth zones in otoliths. Effective automated systems are needed to increase throughput and reduce cost. DeepOtolith is an open-source artificial intelligence (AI) platform that addresses this issue by providing a web system with a simple interface that automatically estimates fish age by combining otolith images with convolutional neural networks (CNNs), a class of deep neural networks that has been a dominant method in computer vision tasks. Users can upload otolith image data for selective fish species, and the platform returns age estimates. The estimates of multiple images can be exported to conduct conclusions or further age-related research. DeepOtolith currently contains classifiers/regressors for three fish species; however, more species will be included as related work on ageing will be tested and published soon. Herein, the architecture and functionality of the platform are presented. Current limitations and future directions are also discussed. Overall, DeepOtolith should be considered as the first step towards building a community of marine ecologists, machine learning experts, and stakeholders that will collaborate to support the conservation of fishery resources.

**Keywords:** fish otoliths; deep learning; CNN; age determination; web tool

### **1. Introduction**

Every year, the ages of thousands of fish are determined from otoliths or scales in the framework of national data collection fishery programs, supporting several objectives, such as the estimation of parameters for the demographic and population dynamics of fish stocks and the fitting of stock assessment models and length-at-age growth curves [1,2]. Age information is manually extracted by expert readers who count daily or annual growth zones in otoliths using a microscope or high-resolution images [2,3]. However, this is a labor-intensive, time-consuming, and costly process to perform [4]. This limits the number of fish that can be age-analyzed, and monitoring programs must—to a larger extent—rely on growth-at-age models to determine the age composition of fish populations [1]. This

**Citation:** Politikos, D.V.; Sykiniotis, N.; Petasis, G.; Dedousis, P.; Ordoñez, A.; Vabø, R.; Anastasopoulou, A.; Moen, E.; Mytilineou, C.; Salberg, A.-B.; et al. DeepOtolith v1.0: An Open-Source AI Platform for Automating Fish Age Reading from Otolith or Scale Images. *Fishes* **2022**, *7*, 121. https://doi.org/10.3390/ fishes7030121

Academic Editor: Josipa Ferri

Received: 29 April 2022 Accepted: 27 May 2022 Published: 29 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

underlines the need for automated tools that could be practically used by otolith scientists to facilitate a more streamlined analysis in their laboratories.

Methods to automatically read otoliths for fish age extraction have been proposed using diverse data, such as images, fish biological features (e.g., fish length, catch data, sex) and geometrical features (shape and the opaque and translucent zonation patterns) [4–6]. Although these methods have shown good predictability, they require additional biological and geometric information beyond otoliths and a complex preprocessing stage to extract certain features from otolith images (e.g., measurement of translucent and opaque rings) before being able to determine fish age. This tends to limit their applicability as automated solutions for fish ageing.

Deep learning is a subfield of artificial intelligence (AI) that has revolutionized automation in a wide range of real-world applications related to images, text, audio, and videos [7,8]. Convolutional neural networks (CNNs) are a dominant class of deep neural networks for processing images that are being widely used, among others, for image classification (note: Image classification is the task of identifying the class that an image represents, e.g., predicting gender from a face image. In our case, prediction of age as a class category from an otolith image is a classification task.) [9] and image regression (note: Image regression is the task of predicting a continuous variable from an image, e.g., predicting house price from a house image. In our case, prediction of age as a numeric value from an otolith image is a regression task.) [10,11]. CNNs receive images as input, while the whole learning process is carried out in the network; they learn sequentially from simple shapes (lines, edges, etc.) in the first layers, to more detailed patterns in the next layers, and finally, classes of objects or numeric features in the final layers. This is achieved through a stack of alternately arranged convolutional, pooling, and activation layers, followed by a fully connected layer that performs the image classification or regression task (Figure A1, Appendix A). The key advantage of CNNs lies in their efficiency in capturing the spatial interaction between adjacent pixels in an image and, hence, extracting meaningful features that are able to correctly resolve the computer vision task [12]. For more details about CNNs, the reader is referred to the recent review paper [9].

In recent years, CNNs have received increased attention for automating fish age estimation from otolith images, as shown for Greenland halibut (*Reinhardtius hippoglossoides*) [13,14], snapper (*Pagrus auratus*) and hoki (*Macruronus novaezelandiae*) [15], Atlantic salmon (*Salmo salar*) [16], and red mullet (*Mullus barbatus*) [17]. These studies provided evidence that deep learning could offer an automated methodology for the analysis of otolith images, although with varying levels of accuracy, and provide cost-efficient and effective support towards the sustainability and management of fishery resources.

Despite the aforementioned AI advancements in fishery science, otolith researchers may not have the computing expertise or budget to take advantage of AI tools. In addition, even when AI algorithms are employed, without a community, scientists may face obstacles in implementing their case studies, collaborating with each other, and, on the whole, contributing to the field. DeepOtolith (http://otoliths.ath.hcmr.gr/, accessed on 1 March 2022, Figure 1) brings together AI researchers, fish scientists, and software developers to bridge the gap between state-of-the-art computing techniques and otolith research, providing a simple web interface that automatically estimates fish age by combining otolith images with deep learning. The user can export age estimates from multiple images to conduct further age-related research. At present, the platform contains models for three fish species (Table 1, Figure 2). Additional species, however, can be incorporated as related works will be published in the future. This image analysis platform is a Python application (https://www.python.org, accessed in 2001) that uses Python Flask (https: //flask.palletsprojects.com/en/2.1.x/, accessed in 2010) and ReactJS [18] to operate as a webserver at the front-end. The source code for DeepOtolith, as well as sample otolith/scale images for experimentation, are available at: https://github.com/dimpolitik/Deep-Otolith (accessed on 1 March 2022). Below, the platform and its structure are introduced; the three

otolith case studies that are currently available on the platform are reviewed; and finally, the functionality of the platform is demonstrated.

**Figure 1.** Landing page of the DeepOtolith platform (http://otoliths.ath.hcmr.gr/, accessed on 1 March 2022), which applies deep learning techniques to predict fish age from otolith images.

**Table 1.** Fish species that are currently available on the DeepOtolith platform.

**Figure 2.** Fish species that are currently processed through the DeepOtolith platform: (**a**) Greenland halibut (*Reinhardtius hippoglossoides*) otolith, (**b**) Atlantic salmon (*Salmo salar*) scale, and (**c**) red mullet (*Mullus barbatus*) otolith.

### **2. Materials and Methods**

*2.1. Platform Architecture*

The architecture of DeepOtolith is shown in Figure 3. The tool consists of two main components: the front-end, visible to the end user, and the back-end, where all processing takes place. On the front-end, the user can select one of the three currently available fish species and then consecutively upload otolith images (note: Images can be uploaded as separate files, not as a directory or a zip file. Images should be in .png or .jpg format.) of the selected species. The uploaded images are transferred to the back-end, where they are preprocessed according to the requirements of each respective species model; for example, images may be resized and loaded to the resolution expected by the corresponding trained CNN model. After preprocessing has been completed, the weights of the CNN for the selected fish species are loaded (Figure 3); the trained networks have been stored offline, and no new training is required. Then, the loaded model is applied to the preprocessed images to make the predictions, which are returned to the front-end. For each user-provided image, the probabilities of each predicted fish age group are displayed in a bar-plot. The age group with the highest probability defines the resulting fish age prediction. Beyond visualization, the user can download age predictions as a CSV file. Each row in the CSV file contains: the image name, the probability in % for each age group, and the resulting age prediction based on the age group with the highest probability. When the two highest probabilities in age prediction are below 10%, then the message "->Difference of two largest probabilities <10%: Additional validation by experts is recommended" is returned as a message. We note that a limit rate of 30 images per minute was imposed to prevent upload errors due to latency (enforced by the access control layer, as shown in Figure 3).

**Figure 3.** The architecture of the DeepOtolith platform.

### *2.2. Case Studies*

### 2.2.1. Greenland Halibut (*Reinhardtius hippoglossoides*)

The automatic age determination of Greenland halibut otoliths was based on the work of [14], who focused on explaining the decisions of deep neural networks used for fish age prediction. The considered dataset was a subset of the one described in Moen et al. (2018), which consisted of pre-existing otolith images from the Institute of Marine Research (IMR, Bergen, Norway) collected between 2006 and 2017. For the acquisition of the images, the whole paired right and left otoliths were first collected and put into plastic trays for transportation, where salt or water was added to keep them moist until they could be frozen in the lab. This was done for preservation reasons prior to image capture. After the thawing and cleaning processes, the paired otoliths (or single otoliths if the corresponding pair was damaged or lost) were immersed in water and placed under a stereomicroscope on a white background with transmitted light such that the digital images could be taken. Those were finally imported into Photoshop and calibrated to a 10 mm scale. The resolution of the images was 2596 × 1944 pixels.

In the deep learning training process, ref. [14] adopted the VGG19 CNN architecture [19]. They used 8218 training sample images of right and left otoliths. The original images, were resized to fit a 224 × 224 square, as expected in the VGG19 model. The model was trained to classify ages into one of 26 categories (from 1 to 26 years), and age labels were provided by human experts based on current recommendations in the field. Using a test set of right otoliths composed of 165 samples, the trained model attained a root mean square error (RMSE) of 1.69 years between age prediction and age reading by experts. The predicted error was comparable to the earlier study of Moen et al. (2018) (RMSE = 1.65 years), which used a regression network based on the Inception v3 architecture [20] to automate the Greenland halibut ageing.

### 2.2.2. Atlantic Salmon (*Salmo salar*)

Vabø et al. [16] utilized an implementation of the EfficientNetB4 CNN network [21], with an input image resolution of 380 × 380, to automate the age estimation of Atlantic salmon (*Salmo salar*) scales. EfficientNetB4 was trained using transfer learning, with pretrained weights from ImageNet [22]. The dataset used consisted of a total of 9056 highresolution images of salmon scales sampled by the Institute of Marine Research in Bergen (IMR), Norway (from 2015 to 2018) and Rådgivende Biologer (from 2016 to 2017) in rivers along the coast of Norway. Salmon scale photos were taken using a Nikon SMZ25 stereomicroscope with a Nikon Digital Sight DS-Fi2 camera using an SHR Plan Apo 1× objective. The images were captured with a resolution of 2560 × 1920 pixels on a light gray background and postprocessed using NIS Elements D software. The images were annotated both for sea and river ages by expert readers. Two independent networks were trained for separately predicting river and sea age. each task. Age prediction was treated as a regression problem, returning a decimal number that was rounded to the nearest integer age and compared with the ground truth. From the total dataset, 8286 images were annotated with sea age, and 6238 were annotated with river age. Sea ages ranged from 1 to 9 years, with 2 years being the most frequent age (50.6%), followed by age 1 and age 3. River ages ranged from 1 to 6 years, with age 3 seen most frequently (56.5%), followed by age 2.

The prediction of sea age obtained an accuracy of 86.99%, while the predictive accuracy of river age was 63.2%. The study also included a test of the network's performance in comparison with six human readers on an additional dataset of 150 scales. This revealed that the ground truth estimates of river age by expert readers exhibited higher variance and lower levels of agreement compared to sea age, and this may indicate why this task appeared more difficult for the CNN to attain high accuracy [16]. Additionally, the CNN overpredicted the age of 1 year, whereas predictions were best for 2 and 3 years sea age and 3 years river age. This can be partially attributed to the imbalanced distribution of ages in the salmon imagery. Specifically, 90.2% of images had a river age of 2 or 3 years, when only 6% were 4-year-olds and 3% were 1-year-olds.

### 2.2.3. Greek Red Mullet (*Mullus barbatus*)

The automatic age estimation of the Greek red mullet (*Mullus barbatus*) was based on [17]. The dataset included 5027 otolith images, provided by the Hellenic Centre for Marine Research (HCMR) database, along with the age readings and fish length (body size in mm) of each individual fish. For the acquisition of the images, the whole otolith was used without any treatment; it was placed in a petri dish with the inner face looking upwards and immersed in water. The petri dish was placed under a stereoscope on a black background. Reflected cold LED light 50 W was used, provided by two photonic goosenecks, and adjusted to illuminate the whole surface of the otolith. Digital images were taken under a magnification of 16× with a resolution 768 × 576 pixels. Since different lighting conditions, zoom levels, or backgrounds of the tested images may impact the performance of the CNNs, the webpage users should consider, as possible, the above protocols to attain a more reliable age estimation. The age of red mullet in the dataset ranged from 0 to 11 years old. Due to the low number of specimens aged >5 years old (~6%), these were merged into the 5+ age group.

The Inception v3 CNN model [20] was trained using transfer learning with otolith images of resolution 400 × 400 as input, considering fish age estimation as a multi-class classification task with six age groups (Age-0, Age-1, Age-2, Age-3, Age-4, Age-5+). The potential benefit of multitask learning was also explored to improve the network's predictability, with the auxiliary task being the prediction of fish size. The enhanced neural network simultaneously received as input the otolith images and predicted fish age and length. The results showed that, without multitask learning, the ages of the red mullet were predicted correctly by 64.4%, performing better in the younger Age-0 and Age-1 classes (F1 score > 0.8) and moderately in the older age classes (F1 score between 0.50–0.54). Multitask learning increased the correct age prediction to 69.2%, with an additional 28.2% being within 1 year of error; this also proved a better approach to estimate older age groups, increasing accuracy between 3–23%. Additionally, the multitask network achieved a root mean square error (RMSE) of 0.56 years between predicted and human-based age predictions. The moderate accuracy in predicting older age groups can be attributed to the objective difficulty in distinguishing the growth zones in the otoliths of older fish, as well as to the low number of older-fish otoliths in the dataset. This was verified in age-reading workshops [23,24], where age estimations of older fish showed high variability amongst reader experts.

### **3. Results**

To demonstrate the functionality of the platform, 30 images from each fish species were used, along with age estimates from human readers. For instance, the red mullet species was selected, and the images were uploaded on the platform (note: When the file size of the images is large (>2 MB), it is suggested to upload them consecutively through the "Add Files" button (Figure 4)). The platform estimates the fish age for each image separately (Figure 4). Then, age predictions can be extracted into a CSV file using the "Export to CSV" button (default name: "export.csv"; this name can be changed to a different filename) (Figure 4). The content form of the CSV file after being loaded into Excel can be found in the Supplementary Materials (Suppl\_red\_mullet.xls); the "Refresh page" button allows the user to restart age prediction after completing the limit of 30 images per minute or testing a new species.

Outside of the platform, the predicted age frequency of red mullet was compared with human age estimates (Figure 5a). Although the dataset is small enough to extract general conclusions, we noticed that model predictions tend to underestimate the age-5 class, leading to higher occurrences of ages 3 and 4. This can be partially explained by the fact that during the training of the CNN model for the red mullet, the age-5 class also included ages 5 to 9, due to their small representation in the dataset.

The corresponding results of human against AI age estimates and the exported CSV file for Greenland halibut can be found in Figure 5b and Supplementary Materials (Suppl\_Greenland\_halibut.xls); for Atlantic salmon (river age), the results can be found in Figure 5c and Supplementary Materials (Suppl\_Atlantic\_salmon\_rive\_age.xls); and for Atlantic salmon (river age), the results can be found in Figure 5d and Supplementary Materials (Suppl\_Atlantic\_salmon\_sea\_age.xls).

**Figure 4.** Snapshot of otolith images for the red mullet species, along with predicted probabilities for each age group. The highest probability implies the predicted age for the specific image. The user can upload at once or consecutively 30 images per minute through the "Add Files" button and extract age predictions in a CSV file (default name: "export.csv") using the "Export to CSV" button. The "Refresh page" button allows the user to restart the age prediction process after completing the limit of 30 images per minute or testing a new species.

**Figure 5.** Age frequency distributions of human estimates against AI predictions for (**a**) red mullet (*Mullus barbatus*), (**b**) Greenland halibut (*Reinhardtius hippoglossoides*), (**c**) Atlantic salmon (*Salmo salar*; river age), and (**d**) Atlantic salmon (*Salmo salar*; sea age). For each species, we used a dataset of 30 images.

### **4. Discussion**

Fish ageing information is vital for extracting knowledge about the biological traits (e.g., mortality rates, when fish mature, recruitment success) and status of fish stocks [25]. Public access to cutting-edge AI solutions is the novel feature that DeepOtolith brings to the otolith community. The goal of this system was to decrease the amount of research time and effort needed to perform fish age estimation. The availability of the models can also stimulate further research, as it allows comparison with the included models/papers. The platform directly benefits otolith scientists with a non-computer background who seek to process their datasets for the selective species currently available on the platform. In addition, the platform may serve as a reference point from which deep learning experts and marine ecologists can communicate their suggestions to improve the existing models or their interest in developing their own model and uploading it to DeepOtolith.

In fact, the actual age of a fish is on continuous scale. However, expert readers count annual rings in otoliths or scales to determine fish age, hence, providing human estimates as integers/classes/groups. In most fish assessments, age is also considered as age group, not as a continuous variable. Accordingly, CNN models predict fish age as (i) a discrete integer/class/group, if the prediction is treated as a classification problem (Greenland halibut and red mullet case studies) or (ii) as a continuous value, if prediction is treated as a regression task (Atlantic salmon case study).

It should be noted that DeepOtolith, as with any platform built to save time and money, empowers the user with automated methods but does not entirely replace conventional methods of research. The reported differences in age accuracy and error estimates among the case studies may be explained by the different life spans of the studied species (Table 1), the size of the datasets, and the adopted CNNs. Moreover, in all case studies, the performance of trained CNNs was more moderate for older fish than for younger fish. These remarks imply that users should pay attention to the way they use the platform; case studies are species-specific and should not be used for other species; and the uncertainty in age prediction, especially for older fish, should be considered.

In general, implementing a CNN algorithm in otolith imagery comes with several challenges. First, CNNs do not have an inherent level of accuracy because their accuracy is highly dependent on the data provided. Specifically, the otolith of a given species has its own distinct morphometric gestures (shape, surface area, diameter, anatomy), while each fish species has its own lifespan and life history, resulting in different ways that otolith growth zones are formed as the fish gets older [25]. Second, otolith datasets are often imbalanced, with fewer images for older fish since these are less captured in overexploited stocks. Third, there is an increased difficulty, even for experienced human readers, in distinguishing the annual ring at older ages due to high uncertainty in assigning a growth zone as an age year [24]. Fourth, the age readability of the same fish species may be influenced when it is captured from different regions or analyzed by different labs. This can be attributed to several factors, such as different fish environments and catch seasons, different protocols for the conservation and preparation of the otoliths, and other imaging setup conditions (camera quality, lighting conditions, zoom levels) [26]. All these complexities tend to cause considerable difficulty in the training process and performance of CNNs, and, overall, make the development of a single generic CNN for the age determination of multiple species seem, for the moment, unattainable.

For each case study, a different imaging setup of the otoliths or scales was adopted. Since different lighting conditions, zoom levels, or backgrounds of the tested images may impact the performance of the CNNs, the webpage users should consider, as possible, the above protocols to attain a more reliable age estimation.

The present work can be expanded in several directions. First, suitable adaptations to the network architectures and training procedures of the current fish species on the platform, combined with the collection of more images, will potentially improve the performance of CNNs. Second, DeepOtolith currently supports three species, so the integration of additional species into the platform is a primary future step. Third, technical improvements

in the infrastructure of the platform will allow the direct uploading of large (order of thousands) image datasets. Fourth, additional tools in the platform, such as automating the estimation of von Bertalanffy growth curves [27], performing an uncertainty analysis to quantify potential biases in age predictions, and comparing human- and model-based estimates on the platform, are worthy issues for future consideration. Finally, in future work, the platform could be further extended to enable the identification of fish species and families by otolith images, a topic that has been addressed in the past with Fourier transform and discriminant analysis methods [28], and otolith shape analysis [29].

It is also worth noting that the fish age-reading process is subject to the experience and the relative bias of a reader on different aspects, such as the identification of the first annual ring, the axis of the otolith used for the measurements, and the date of fish birth (ICES, 2012). This often results in significant differences in age estimates among readers. To overcome this issue, results among readers or among readings of the same reader are compared to understand observed variations (ICES, 2012). Besides different readings by the same or various readers, common interpretations of an image (i.e., the most frequent age interpretation) are considered decisive for the potential age group. Similarly, in the platform, the trained CNNs were configured to provide either a single age estimation for each image corresponding to the highest probability, along with ages of lower probability (Greenland halibut and red mullet case studies), or a single continuous value as an age prediction (Atlantic salmon case study).

AI has a tremendous toolkit that could potentially be used in otolith research. For instance, unsupervised methods [30] have been proposed to automatically group images into clusters without the need for manual annotation. As CNNs require thousands of labelled images to be fully trained before being able to generalize their learning to unseen scenarios, unsupervised learning could eliminate the time needed for annotating thousands of otolith images with human-based age estimates. With the exception of CNNs, other deep learning-based methods (e.g., adversarial generative adaptation, adversarial discriminative adaptation, self-supervised adaptation) have been used to transfer the knowledge gained from predicting fish ages from otolith images from one lab to the same species in another lab without requiring extra labelling effort [26].

The development of web-based systems in marine science to support automatic systems should be considered a valid goal. In the past, the FAbOSA project (https: //www.imagescience.de/old\_pages/fabosa/start.htm, accessed in 2003) aimed to automate fish age estimation using otolith shape analysis, and the web-based environment AFORO (http://aforo.cmima.csic.es/upload\_img\_en.jsp, accessed on 13 September 2005) was designed to process otolith images for fish species identification by combining morphometric features of otoliths and signal analysis [29]. Recently, ref. [31] released Flukebook (https://www.flukebook.org/, accessed on 27 December 2021), an open-source AI platform for cetacean photo identification and detection. Finally, other platforms powered by AI for fish catch detection and optimizing farmed fish production (http://www.ai.fish/, accessed on 1 November 2019; https://xpertsea.com/valuable-insights#xpercount, accessed in 2021) can also be found on the web. On the whole, the present work should be viewed as a first step towards this direction.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/fishes7030121/s1.

**Author Contributions:** Conceptualization, D.V.P. and N.S.; data curation, A.A., A.C., C.M., A.O., E.M. and K.M.; formal analysis, D.V.P., N.S., G.P. and P.D.; methodology, D.V.P., G.P., R.V., A.O., A.-B.S., E.M. and K.M.; writing—original draft, D.V.P., A.O. and E.M.; writing—review and editing, D.V.P., G.P., R.V., A.O., A.-B.S., A.A., A.C., C.M., A.O., E.M., K.M. and P.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was partly funded by the Research Council of Norway as part of the COG-MAR project, grant number 270966.

**Institutional Review Board Statement:** Ethical review and approval were waived for this study as the otolith images were obtained from dead individuals that were not endangered or protected. We also declare that the uploaded images were neither stored nor sent to third parties.

**Data Availability Statement:** The data applied to build the model for Greenland halibut are openly available through the Norwegian Marine Data Center under the DOI https://doi.org/10.21335 /NMDC1949633559 (accessed on 1 March 2022). Sample otolith/scale images for experimenting with DeepOtolith are available at: https://github.com/dimpolitik/Deep-Otolith (accessed on 1 March 2022).

**Acknowledgments:** We thank HCMR-NOC for the technical support to release the website, and the Research Council of Norway for funding part of this study. We express our gratitude to the two reviewers for taking the time to review the present work and their useful comments. The authors have declared that no competing interests exist.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Figure A1.** Schematic diagram of a convolutional neural network architecture, composed by a stack of alternately arranged convolutional, pooling, and activation layers, followed by fully connected

### **References**

layers and a softmax function.

**Appendix A**

### *Article* **The Lifetime Migratory History of Anadromous Brook Trout (***Salvelinus fontinalis***): Insights and Risks from Pesticide-Induced Fish Kills**

**Scott D. Roloson 1,2,\*, Kyle M. Knysh 2, Sean J. Landsman 3, Travis L. James 4, Brendan J. Hicks 5 and Michael R. van den Heuvel 2**

**Abstract:** Brook trout populations in Prince Edward Island, Canada, have experienced over 50 pesticide-related fish kills since the 1960s. Life history evaluation of large sea-run brook trout recovered following two fish kill events was compared with a reference river using strontium:calcium otolith microchemistry. This study examined the dual hypotheses that anadromous brook trout are more likely to arise from sea-run mothers, and that freshwater entry timing makes them vulnerable to pesticide-induced fish kills. A total 89% of the fish exhibited an anadromous life history, and 77% of these were offspring of anadromous mothers, suggesting that anadromy is dominant in progeny of sea-run mothers. This study adds to our understanding of the maternal inheritance of anadromy in sea-run brook trout populations. Additionally, freshwater entry precedes the majority of fish kill events, illustrating that the overlap between migration and pesticide runoff contributes to the cumulative population risks to sea-run brook trout.

**Keywords:** brook trout; fish kill; anadromy; otolith microchemistry

### **1. Introduction**

The health of many aquatic ecosystems has been compromised by the cumulative impacts of human activities. Biodiversity in freshwater is declining faster than in terrestrial or marine environments [1]. While estuaries, which link fresh and marine waters, experience reduced biodiversity in systems affected by human development [2]. Diadromous fish species move between fresh and marine waters and factors that compromise the timing or restrict the spatial extent of movements pose a threat to migration [3–5]. Due to the multiple and cumulative threats to the habitat of diadromous fishes, many populations have been subject to pronounced declines and many species have experienced population extirpations within their historic range [6,7]. As human activities, including climate change, continue to infringe upon these environments, coherent conservation strategies are needed to protect diadromous species and the ecosystems they occupy [8,9].

Brook trout (*Salvelinus fontinalis*) are native to eastern North America and coastal populations are known to adopt a mixture of resident and sea-run life histories (i.e., facultative anadromy) [3,10]. Due to the spatial and temporal complexity of the habitat of anadromous brook trout, populations have been subject to declines across much of their native range,

**Citation:** Roloson, S.D.; Knysh, K.M.; Landsman, S.J.; James, T.L.; Hicks, B.J.; van den Heuvel, M.R. The Lifetime Migratory History of Anadromous Brook Trout (*Salvelinus fontinalis*): Insights and Risks from Pesticide-Induced Fish Kills. *Fishes* **2022**, *7*, 109. https://doi.org/ 10.3390/fishes7030109

Academic Editor: Josipa Ferri

Received: 12 April 2022 Accepted: 10 May 2022 Published: 11 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

particularly south of the Bay of Fundy [11,12]. The sea-run life history strategy is thought to have an adaptive basis as anadromous mothers produce more eggs with enhanced fitness-related traits such as a shorter developmental time, faster juvenile growth rates, and higher survival [13]. Moreover, the progeny of sea-run females are the dominant proportion of young-of-year fish in areas where anadromous and resident spawners overlap [14]. Linking the contribution of anadromous parents to subsequent generations has primarily been explored in young-of-year offspring via stable-isotope analysis [14,15]. However, the marine isotopic signatures from anadromous mothers begin to change when juveniles commence exogenous feeding, making it difficult to detect the contribution of anadromous parents beyond early life stages [14,15]. Genetic differentiation has been used as a means of evaluating anadromous contributions [13] and sympatric sea-run and resident brook trout have been found to be genetically divergent, which might occur if sea-run progeny are themselves more likely to exhibit this phenotype [16]. In one brook trout study, Theriault et al. [17] used genetic methods to infer the heritability of the anadromous tactic as sea-run parentage was positively correlated with ultimate size at age 1. However, there are still knowledge gaps on the overall contribution of anadromous parents to subsequent generations and the perpetuation of the migratory life history.

Prince Edward Island (PEI), in eastern Canada, is well known for its anadromous brook trout populations, but the province also has a history of pesticide-related fish kills, where brook trout are the dominant species affected [18]. Since the 1960s, there have been at least 50 fish kills related to pesticide runoff. One study examining changes in the fish community after a fish kill found that brook trout suffered higher mortality than non-native rainbow trout (*Oncorhynchus mykiss*) and that the effects were strongest on young-of-year fish, but all sizes were affected [19]. Atlantic salmon (*Salmo salar*) were formerly widespread in the province, but overharvest and habitat deterioration have reduced their populations, making them less common in pesticide-related fish kills [18]. Overall, there is a lack of understanding of how these events influence the distribution, abundance and population demographics in local species assemblages.

Otoliths, or ear stones, incorporate certain environmental constituents in proportion to ambient concentration, and chemical analysis can be used to reconstruct chronological life history patterns [20]. In salmonid fishes, and some other species, elevated strontium concentrations are associated with marine occupation and prior to spawning, maternal signatures can be passed to eggs, and by examining otolith primordia of offspring, the maternal life history of offspring can be elucidated [21,22]. To date, opportunities to study the anadromous life history of large, sexually mature brook trout through otolith chemistry have been rare because otolith sampling is lethal and sacrificing large numbers of sexually mature fish would be ethically questionable given the significant reproductive contribution of these individuals to subsequent generations [13,23].

The overall aim of this study was to explore why brook trout are predisposed to the risk of pesticide-related fish kills and to use otolith microchemistry to explore the demographics of anadromous life history in coastal brook trout populations. Firstly, we examined how the timing of freshwater entry of anadromous brook trout relates to the timing of pesticide fish kills by analyzing 11 years of counting-fence data from an anadromous population on Montague River. It was predicted that freshwater entry would precede the occurrence of pesticide runoff events. Additionally, otolith microchemistry was used to establish the anadromous life history patterns of brook trout collected following fish kills in comparison to the signatures of samples taken from the Montague River counting fence. The prediction was that anadromous individuals would be more likely to arise from anadromous mothers, and that anadromous individuals would conduct repeated migrations once the anadromous life history strategy was adopted.

### **2. Materials and Methods**

### *2.1. Study Sites*

PEI has an area of 5560 km2 and is characterized by many short watersheds fed predominantly by groundwater. Temperatures in most rivers are suitable for salmonids, remaining below 18 ◦C throughout the year [24–26]. Following Holocene sea level rise, many estuaries are considered to be drowned river valleys, and these relatively large and productive estuaries are highly conducive for anadromous fishes [27,28]. Agricultural land use influences fish populations in the province as approximately 50% of the province is used for agricultural production [29,30]. As a consequence of the highly agricultural land-use sedimentation, nitrogen loading from fertilizers and pesticide runoff events are common phenomena in PEI waterways [31–33]. The present study took place in three PEI watersheds, Mill River (50.1 km2), Montague River (65.4 km2), and Trout River (51.9 km2) (Figure 1).

**Figure 1.** Map showing the location of Prince Edward Island, Canada, and the three study watersheds.

### *2.2. Fish Community*

The freshwater fish community on PEI is generally dominated by salmonids [29]. Brook trout are ubiquitous and the primary sportfish in the province, especially anadromous brook trout which have strong cultural and socioeconomic value [29]. Current landuse patterns, and historic activities play a dominant role in structuring current salmonid communities in PEI watersheds [26]. Atlantic salmon were widespread prior to human colonization but are now lost from over 60% of the watersheds the species once occupied and now populations are stronger in forested watersheds with abundant cobble substrate [34,35]. Non-native rainbow trout are present in approximately 30 rivers across the province. Rainbow trout tend to thrive in higher sloped reaches and in watersheds with higher agricultural land use, while brook trout juveniles tend to be dominant in headwater reaches [25]. Other common species in PEI, which have been recovered following pesticide fish kills include American eel (*Anguilla rostrata*), white perch (*Morone americana*) and several stickleback species (Gasterosteus spp.) [18]. Other diadromous species that are

seasonally present in PEI waterways during spawning migrations including rainbow smelt (*Osmerus mordax*), blueback herring (*Alosa aestivalis*), and alewife (*Alosa pseudoharengus*) [18].

### *2.3. Timing of Brook Trout Movements and Fish Kills*

Given the paucity of data on run timing for anadromous brook trout, a historic dataset from a fish trap at Knox's Dam, Montague River was evaluated to establish the timing of freshwater return of anadromous brook trout. The fish trap was located in a technical concrete pool-and-weir-style fishway located at the head of tide. Fish moving upstream through the trap were considered to be moving from saltwater into freshwater. Historic data from the period 1982–1993 were provided by D. Cairns (Department of Fisheries and Oceans) and the counting fence was operated by the authors from 2011 to 2013. Fish were enumerated and individually measured (fork length, mm) and released upstream of the barrier. In total, 11 years of brook trout movement and abundance were evaluated including the total number of brook trout moving upstream each year, including 1982–1985, 1989–1990, 1991, 1993, and 2011–2013. Entry date was determined by finding the day of the year (Julian day) at which 50% of brook trout tend to be entering the river had arrived. Large female brook trout tend to be among the first to enter freshwater (3; S. Roloson, see results); therefore, the 50% cutoff includes the majority of the large breeding females. This date was used to compare the dates of fish kill events, in order to determine potential overlap between the timing of fish entry into freshwater and the incidence of pesticide runoff.

Data on fish kill timing were accessed from public records available online [18]. Of the 57 fish kills with information available from 1962 to 2019, 55 of these events list a specific date and two others list "summer" without a specific date. The Julian day of the fish kills with a specific date was used to enable comparisons of the 'day of the year' between subsequent years and with brook trout movement timing.

### *2.4. Fish Sampling and Otolith Microchemistry*

Brook trout were opportunistically collected from two rivers following pesticide runoff events. The first fish kill sampled was on Trout River, which experienced a pesticide runoff event on 5 July 2012, and a subsample of 15 trout from 21.4 to 36.9 cm were collected on 6 July 2012 for otolith sampling. The Mill River fish kill occurred on 28 July 2013. Specimens were collected by local watershed officials who visually observed deceased trout on streambanks and on the streambed. Specimens were transported back to the lab for measuring fork length (mm) and weight (g) and extraction of saggital otoliths. Following removal, otoliths were rinsed with deionized water, left to dry for 24 h and stored in acid-washed 50 mL glass vials.

The third brook trout population was sampled from Montague River, where a counting fence captured fish as they ascended into freshwater from the estuary, so this river served as a reference as these fish were known to exhibit an anadromous life history. A subset of 31 fish from 15.6–38.1 cm were sampled for otolith microchemistry between 6 June and 29 June 2012. While fish at Montague River were sampled randomly during freshwater entry, samples at fish kills were opportunistically collected, and smaller fish could have been underrepresented due to the difficulty in finding and recovering smaller individuals.

Otoliths were processed at the Mass Spectroscopy Suite University of the Waikato in Hamilton, New Zealand. Prior to sampling, otoliths were stored in acid-washed glass vials and handled with non-metallic utensils to avoid trace metal contamination. Otoliths were mounted sulcus side up and sequentially polished with 1000, 2000, and 4000 grain wetted silicone carbide waterproof sandpaper until primordial nuclei were exposed. Decontamination procedures are outlined in Roloson et al. 2019 [36]. Once polished, otoliths were mounted on a single glass slide in batches [15–20] to allow for more efficient laser operation. Otoliths were analyzed with a Perkin Elmer Elan SCIEX DRCII laser ablation inductively coupled mass spectrometer (LAICP–MS) with a New Wave Research Nd:YAG

213 nm wavelength laser. To account for instrument drift, National Institute of Standards and Technology (NIST) 612 standard reference material was ablated prior, during and following sample ablation and for 1 min of every 15 min of laser operation time. The otolith core was identified using a transmitted light microscope and life history transects were oriented from the otolith primordia to the outer edge. The laser was set to a 20 μm beam diameter width at a frequency of 20 Hz and a travelling speed of 10 m·s–1. Brook trout otoliths were analyzed with the same protocols and run during the same sample runs as anadromous rainbow trout from PEI [36].

### *2.5. Statistical Analysis*

Across all years, brook trout fresh water entry timing was summarized as the mean percent of total run entering freshwater each day. The mean daily proportion of entry was fitted using a Gaussian function: daily entry = *a*·exp(−(Julian Day−*b*) 2/(2·*c*2), where *a* represents the maximum daily entry proportion, *b* the day on which the maximum occurred, and *c* the variance around the day. Fish kill date was represented as a cumulative proportion of fish kills for each Julian day. The median fish kill day was determined by fitting a two parameter logistic regression to the data of the form: cumulative proportion of fish kills = 100/(1 + 10(log10(Median Julian Day−Julian Day)·(Hillslope)), where hillslope is the shape parameter and median Julian day the day when half of the fish kills occurred. All summary statistics and curve fits were performed in Statistica v. 13.5.

Assessment of life history signatures was based upon previously established methods [37–39]. To assess natal core signatures, we took the mean otolith core Sr:Ca values from the first 25 sequential data points located in first 200 μm of the transect [37,40]. Rather than using a threshold to designate freshwater or marine core signatures, we used the univariate *K*-means clustering in the "factoextra" package in R, which uses an algorithm to separate core signatures into two groups based on differences in the data [37,40]. Differences in anadromous migratory history are much more explicit upon visual examination of line scans as it is common for migratory individuals to have Sr:Ca peaks reaching 4–7 mmol/mol. Therefore, to designate migratory life history, we followed Austin et al [38], who studied the closely related bull trout (*Salvelinus confluentus*). In that study, individuals with a Sr:Ca ratio that remained below 1 mmol/mol Sr:Ca were considered freshwater residents, and those with a ratio over 1 mmol/mol Sr:Ca were considered anadromous.

### **3. Results**

### *3.1. Timing of Brook Trout Movement into Fresh Water*