The application of otolith chemistry in fish life history assessment: review

Insignificant variations in otolith form have been working to determine whether fish have generally resided in different geographic regions from other fish of the same species, indicating potential isolation into distinct “stocks” that may require separate management (Bergenius et al., 2006). Additionally, otolith arrangement can provide understandings into ecological links of collected species by studying otoliths discovered in stomach contents and identifying the species they coined from (Baker & Sheaves, 2005). Otolith morphology can be categorized by numerous approaches, including linear measures (e.g length, breadth), internal otolith measurements (nucleus length, increment width), two-dimensional size measurements (area, perimeter), and outline form analysis. Fourier analysis (FA) has recently been utilized to predict otolith form, generating a series of cosine and sine curves from the coordinates of a traced outline (Bergenius et al., 2006).

It is important to think through several factors that may cause differences in otolith samples, including size, age, sex, and differences in year-class, before attributing variations in otolith shape to environmental influences or indicating distinct stocks. Analyzing otolith shape is beneficial in this aspect because it enables the quick assessment of numerous samples, especially with recent image processing methods. Employing image processing techniques is cost-effective, simplifies image reanalysis, allows for the creation of audit trails, and enhances speed and usability compared to traditional linear morphometric methods (Cadrin & Friedland, 1999).

Conventional methods of examining otolith structures traditionally view otoliths solely as timekeepers, offering only absolute age data and resulting in a singular value for each sampled specimen. This approach requires multiple specimens to provide longitudinal insights into a population. However, detailed observations of internal microstructures have introduced an additional dimension to this basic analytical approach. By conducting validation experiments that delineate the development of specific internal microstructures over time, it becomes feasible to correlate certain time intervals with particular life-history events or pinpoint specific regions within an otolith associated with documented life-history occurrences. For example, awareness of the timing of artificial marking or the documented capture date of a specimen enables researchers to trace back through the otolith to internal features that would have formed during specific events, such as the onset of maturation or periods of documented environmental disruption.

Identifying unique characteristics of otoliths associated with documented events can potentially enable these features to serve as proxies for similar events in otoliths from other individuals. This line of research presents numerous opportunities for delving into various facets of a fish’s otherwise concealed life history. For instance, growth patterns during early developmental stages can be inferred by measuring the widths of clearly defined otolith increments deposited in the fish’s early years (Elsdon et al., 2008). Otolith morphology can be characterized using various methods, including: 1. Linear measurements: such as length and width. 2. Internal otolith measurements: such as nucleus length and increment width. 3. Two-dimensional size measurements: such as area and perimeter. 4. Outline shape analysis: examining the overall shape of the otolith.

Furthermore, FA has been utilized to model otolith shape. This technique entails generating a sequence of cosine and sine curves derived from the coordinates of a traced outline, offering a precise mathematical portrayal of the otolith’s shape (Bergenius et al., 2006). Addressing potential sources of variation among otolith samples, such as differences in size, age, sex, and year-class, is crucial before attributing disparities in otolith morphology to environmental factors or as indicators of distinct stocks. Otolith shape analysis presents a valuable approach, as it permits the swift examination of numerous samples, particularly with modern image processing methods. Shape analysis techniques leveraging image processing are relatively cost-effective, simplify image reanalysis, enable the creation of audit trails, and provide enhancements in speed and user-friendliness compared to traditional linear morphometric techniques (Cadrin & Friedland, 1999). These advantages make otolith shape analysis a powerful tool for studying population dynamics and stock structure in fish populations.

Otolith morphometry is preferred over the traditional method of morphometric and meristic characters of the fish body because otoliths are usually not affected by the short-term changes in fish condition or preservation of samples (Campana & Casselman 1993; Neves et al., 2023). Geometric outline and landmark methods are commonly used to study otolith shape variations between or among fish groups by removing size-dependent variation using an allometric approach (Stransky, 2014). Otolith shape variation analysis by geometric outline method involves capturing otolith outline and deriving Cartesian coordinates (x, y) using image analysis tools like scanning electron microscopy, light microscopy, TPSDig, MorphoJ and ImageJ (Stransky, 2014). Several methods are being used for fitting outlines; however, the FA is most commonly employed (Özpiçak et al., 2024; Stransky, 2014) but has limited applicability for otoliths which show significant and complex curvatures. The issues with FA can be alleviated by using Elliptical Fourier analysis (EFA) which decomposes the complex curves of otoliths by generating the sum of harmonically related ellipses (Crampton, 1995; Stransky, 2014; Tracey et al., 2006;). The Wavelet transform can be used as a substitute to the usually applied Fourier transform. Moreover, the Wavelet transform can resolve the problem of poor estimation of sharp edges of otoliths associated with Fourier transform (Libungan & Pálsson, 2015). The software products for FA of 2D outlines are HANGLE, HMATCH, and HCURVE (Crampton & Haines, 1996) while for EFA, EFAwin (Isaev MA & Denisova, 1995), SHAPE package (Iwata & Ukai, 2002; Sadighzadeh et al., 2012), and ShapeR and Momocs packages in the R environment (Brophy et al., 2016; Denechaud et al., 2020; Libungan & Pálsson, 2015) are commonly used. The Shape R package is more common as it has been specifically designed for otolith shape study while Momocs on the other hand is used for general image and shape analysis.

The analysis of fish otolith composition is becoming increasingly important for fisheries researchers. The calcium carbonate making up the otolith mainly comes from the surrounding water. As the otolith grows, new calcium carbonate crystals form. Just like any crystal structure, there are spaces in the lattice during crystal formation, allowing trace elements from the water to attach to the otolith. Examining the trace elemental composition or isotopic signatures of these trace elements within a fish otolith offers clues about the water environments where the fish previously lived (Patterson et al., 1993). The utilization of fish otoliths dating back as far as 172 million years has simplified the examination of the habitats where the fish flourished. Furthermore, robotic micromilling devices have been utilized to retrieve extremely detailed records of the fish’s life history, including diet, temperature variations throughout its life, and its birthplace. One of the most extensively researched trace and isotopic signatures is strontium, thanks to its similarity in charge and ionic radius to calcium (Patterson et al., 1993).

However, researchers can delve into more specific signatures by analyzing multiple trace elements within an otolith. A commonly employed technique for this purpose is laser ablation inductively coupled plasma mass spectrometry, which can measure various trace elements simultaneously. Alternatively, a secondary ion mass spectrometer can be utilized, offering higher chemical resolution but capable of measuring only one trace element at a time. Finfish (class Osteichthyes) possess three pairs of otoliths: sagittae (singular sagitta), lapilli (singular lapillus), and asterisci (singular asteriscus). The sagittae, being the largest, are positioned just behind the eyes and are approximately level with them vertically. The lapilli and asterisci (the smallest of the three pairs) are located within the semicircular canals. Typically, the sagittae and lapilli are made of aragonite, although vaterite abnormalities can occur (Reimer et al., 2016), while the asterisci are typically composed of vaterite. The shapes and relative sizes of the otoliths vary among fish species. Generally, fish from highly structured habitats such as reefs or rocky bottoms (e.g., snappers, groupers, many drums, and croakers) exhibit larger otoliths compared to fish that spend most of their time swimming rapidly in straight lines in the open ocean (e.g., tuna, mackerel, dolphinfish).

Flying fish have notably large otoliths, possibly due to their need for balance while leaping out of the water to “fly” through the air. Often, distinct morphological features of an isolated otolith can help identify the fish species. Throughout their lives, fish otoliths accumulate layers of calcium carbonate and a gelatinous matrix. The rate of accumulation varies with the fish’s growth, typically slowing during winter and accelerating during summer, forming rings reminiscent of tree rings. By counting these rings, the fish’s age in years can be determined. Typically, the sagitta, being the smallest of the three pairs of otoliths, is used for age and growth studies. Moreover, in many species, the accumulation of calcium carbonate and gelatinous matrix alternates on a daily basis, allowing for the determination of fish age in days (Boss, 1999). This additional data, often acquired under a microscope, yields valuable insights for early life history studies. Measuring the thickness of individual rings has been employed in certain species to estimate fish growth, as it’s been assumed that fish growth correlates directly with otolith growth. However, some studies have challenged this assumption, showing that there isn’t always a direct relationship between body growth and otolith growth. In instances of reduced or no body growth, otoliths may continue to accumulate, suggesting that the direct correlation might be with metabolism rather than growth itself (Nazir & Khan, 2021).

Otoliths, in contrast to scales, maintain their structure even during periods of reduced energy intake, rendering them an invaluable tool for determining the age of fish. Although fish continue to grow throughout their lives, the growth rate typically slows down in mature individuals. Consequently, the rings formed in otoliths during later life stages tend to be closer together. Moreover, a small proportion of otoliths in certain species may exhibit deformities as they age. Studies on the age and growth of fish are essential for understanding various phenomena including the timing and extent of spawning, recruitment patterns, habitat preferences, duration of larval and juvenile stages, and the overall age distribution within populations. This understanding is crucial for formulating effective fisheries management strategies (Lackmann et al., 2019).

Thus far, researchers have identified 50 different elements within otoliths, yet only seven of these elements—Li, Mg, Mn, Cu, Zn, Sr, and Ba—are commonly utilized to deduce past fish locations. This preference stems from the diverse environmental conditions and the fact that the concentrations of these elements in otoliths often surpass detection thresholds. In seawater, ions of hard acids like Li⁺, Mg²⁺, Ca²⁺, Sr²⁺, and Ba²⁺ form robust bonds with hydroxyl groups radicals, primarily existing as hydrated free ions. Typically, these ions exhibit longer residence times compared to the ocean’s mixing duration, leading to fluctuations in their concentrations based on salinity variations. Conversely, soft and intermediate acid ions such as Cu⁺⁺, Zn⁺⁺, and, to a lesser degree, Mn⁺⁺, bind to softer bases like Cl⁻ and CO₃⁻ in seawater. These bonds are relatively weak, rendering soft elements reactive and easily soluble in sea water (Henderson, 1984; Nazir & Khan, 2021).

The precise relative bioavailability of various elements for uptake in marine fishes remains poorly understood. Nevertheless, it is probable that these elements undergo fractionation during their transport across the gut, gills, and skin. In marine species, osmoregulation primarily takes place across gut membranes, thereby ensuring relatively stable concentrations of blood Ca, Na, K, Mg, and Cl. This occurs consistently across both marine and freshwater fishes, despite notable disparities in ambient concentrations of these elements (Campana, 1999). Following their entry into the circulatory system, the transport, availability, and eventual destiny of elements are contingent upon their chemical properties, metabolic necessities, and the constitution of the blood fluid. Elements that serve as integral components of functional enzymes or structural tissues are inclined to have extended mean residence times within the body, owing to the continual recycling of enzymes and tissues. Conversely, elements lacking significant structural or physiological roles may undergo rapid elimination, resulting in short residence times. Nevertheless, exceptions exist, notably with quintessentially toxic metals like Cd, Pb, and Hg, which exhibit prolonged residence times due to comparatively inefficient excretion mechanisms (Williams, 1971).

Consequently, the sequence of elemental alterations along an otolith might indicate variances in turnover durations and the quantity of body reservoirs for those specific elements. Elements characterized by brief turnover durations, like Mn and Ba, could be more suitable for scrutinizing intricate temporal variations compared to elements potentially entailing longer turnover durations, such as Sr and Pb. Within blood plasma, soft acid cations like Cu⁺⁺ and Zn⁺⁺, and conceivably Mn⁺⁺, are actively bound to histidine, methionine, and cysteine residues of plasma proteins. These elements are employed in metabolic processes or are actively conveyed to the liver for subsequent excretion. According to Williams (1971), less than 10% of the total blood Cu is presumed to exist in plasma as the free ion. Conversely, around 50% of the total Ca and Sr are approximated to be present in plasma as the free ion.

Consequently, there is a high likelihood of fractionation between hard and soft acid ions within the blood plasma. Magnesium (Mg) may display somewhat distinct behavior in comparison to other Group II elements due to its small ionic radius, except when hydrated, at which point it enlarges significantly. Additionally, Mg has been associated with stabilizing amorphous mineral phases during otolith biomineralization and, owing to its participation in various biological processes, is rigorously regulated within the body (Weiner, 2008). The ever-changing nature of metal ion speciation in blood plasma suggests that the makeup of blood, including its proteins and their ratios, could impact the ratio of free ions. Consequently, this may affect their capability to traverse the barrier between plasma and endolymph. Variations in the types and amounts of blood plasma proteins are expected to have a greater impact on the ions classified as soft acids, given their stronger connection with these proteins. Nevertheless, there’s a notable decrease in major elements in the endolymph, leaving primarily potassium (Kalish, 1991) indicates active discrimination against most ion types by the saccular epithelium (Payan et al., 2004).

According to the hard and soft acid-base theory, soft metals tend to exhibit strong binding affinities with proteins, which suggests that they are likely to be incorporated into the otolith while bound to the organic matrix. This notion is supported by observed spatial variations in otolith copper (Cu), as documented by Milton et al. (2000), where an estimated 70–100% of Cu and 40–60% of otolith zinc (Zn) are linked with the protein matrix. The integration of these metals into the otolith mineral phase can occur through various mechanisms: (1) substitution for Ca, influenced by factors such as ion charge, radius, and crystal elasticity, (2) incorporation within dislocation sites on the crystal surface during growth, potentially influenced by kinetic factors like precipitation rate, or (3) potentially through inclusion in amorphous precursor mineral phases, as suggested by Weiner (2008).

The process of element fractionation during incorporation into the otolith becomes even more complex due to the additional influence of temperature. There is ongoing debate regarding whether these temperature effects are direct or indirect, with numerous studies suggesting that they may be linked to physiological mechanisms such as growth rate, which in turn are influenced by ambient temperature. Walther et al. (2010) discuss potential mechanisms driving “growth rate effects” on otolith microchemistry, dividing them into two main categories: kinetic and physiological. Kinetic theories propose that accelerated growth at higher temperatures results in increased entrapment of trace element impurities into the developing crystal, facilitated by the faster growth and higher prevalence of crystal defects (Gaetani & Cohen, 2006).

Another kinetic hypothesis posits that increased calcification rates lead to a higher influx of Ca⁺⁺ ions into the endolymph, thereby diluting other elements in the precipitating fluid. However, while studies have demonstrated that crystal growth rate impacts elemental partitioning in abiotic CaCO₃ formation, biogenic CaCO₃ undergoes formation via a transient amorphous phase associated with the organic matrix (Weiner, 2008). Consequently, direct extrapolation of patterns from inorganic crystal theory may be deceptive. Moreover, the physiological hypothesis revolves around the synchronization of somatic growth rates with protein synthesis, wherein significant alterations in the protein composition of biological fluids influence the availability of ions for absorption into the otolith (Kalish, 1991). This suggests that fluctuations in somatic growth rates may indirectly affect the integration of elements into the otolith by modifying the availability of ions for absorption.

Several studies have reported that the otolith chemistry is influenced by environmental factors (e.g., water chemistry, temperature, salinity, and their interactive effects) and physiological processes (e.g., growth, metabolism, and reproductive stage) (Elsdon & Gillanders, 2003; Gaetani & Cohen, 2006; Mazloumi et al., 2017; Stanley et al. 2015; Sturrock et al. 2014; Walsh & Gillanders, 2018; Walther et al., 2010). Therefore, the knowledge of how exogenous and endogenous factors affect otolith chemistry is essential to reconstruct the life history events and the migration pattern of fish species (Elsdon et al., 2008; Reis-Santos et al., 2013). The predominant source of the elements incorporated into the otoliths is the ambient water but for some elements such as Zn the main source is diet thereby the relative contribution may vary for different elements (Doubleday et al., 2013; Walther & Thorrold, 2006). The ambient water chemistry is regulated by several factors such as underlying geology, anthropogenic influences, precipitation, agricultural runoff, mixing of different water bodies, etc. (Elsdon et al., 2008). Therefore, the chemical composition may vary between water bodies or even within a water body at temporal and spatial scales (Elsdon et al., 2008; Kerr et al., 2007). Moreover, otoliths permanently record spatial and temporal variability in water chemistry (Campana et al., 2000; Dorval et al., 2005; Mateo et al., 2010; Miller, 2007).

The integration of microchemical composition analysis has introduced a new dimension to otolith studies. While the fundamental carbonate structure of otoliths consists of aragonite, trace elements can be assimilated into the aragonite lattice during formation, akin to any crystalline material. Examining the elemental or isotopic composition of an otolith often correlates with the levels of such elements in the habitats inhabited by individual fish. This correlation yields valuable insights into the environmental conditions encountered by the fish throughout its life history. Simultaneous measurement of various trace elements enhances the ability to differentiate specific elemental signatures associated with different water bodies (Xu et al., 2023). Chemical analysis of an otolith, through discrete sampling of microchemical trace elements across a section, can unveil seasonal or inter-annual patterns in otolith chemistry, indicating developmental migrations through diverse waters. Moreover, advancements in employing this archival information can enhance the age validation of wild-caught fish. A notable application is the utilization of radioactive isotopes stemming from historical nuclear weapons testing, incorporated onto otoliths as markers of known past events, enabling subsequent increment formation to be calibrated.

Despite the physiological differences between marine, euryhaline, and freshwater fishes, particularly in osmoregulation, laboratory studies indicate little inherent difference in otolith element incorporation mechanisms (Walther & Thorrold, 2006). This suggests that otolith chemistry can be a reliable tool for identifying marine species. While seawater Sr:Ca ratios exhibit little variation, ontogenetic effects on otolith Sr:Ca ratios are often observed in species spending part of their lifecycle in saltwater, especially during significant metabolic events like metamorphosis (Walther et al., 2010). Despite this, otolith Sr:Ca ratios in marine fishes show considerable interspecific differences, generally increasing with age (Kalish, 1989; Walther et al., 2010), and intra-annual fluctuations often surpass those seen in diadromous species. While various factors like temperature, age, growth rates, stress, and gonad maturation have been implicated, no single causal factor has been identified for these patterns.

It’s intriguing that in adult marine fishes like blue grenadier, Australian salmon, and bearded rock cod, factors such as growth rate and reproductive investment seem to exert a stronger influence on otolith Sr:Ca ratios than ambient temperature. However, the lack of systematic experimental works examining the relationship between temperature and otolith Sr:Ca across a full reproductive cycle leaves some questions unanswered. The correlations observed between otolith Sr:Ca ratios and physiological parameters such as gonadosomatic index and plasma protein concentrations in species like P. barbatus suggest that variations in protein composition during gonad development could affect the availability and uptake of Sr into the otolith. Similarly, in red drum and Atlantic salmon, fluctuations in otolith Sr:Ca ratios were linked to spawning activity and gonad maturation, respectively. These physiological influences on otolith element ratios could complicate interpretations of movement patterns and stock discrimination. However, they also offer potential for understanding reproductive biology, such as onset of sexual maturity and reproductive investment (Clarke & Friedland, 2004).

The resilience of fish otoliths to digestion renders them invaluable tools for dietary research, as they can be found intact in the digestive tracts and feces of seabirds and piscivorous marine mammals. By identifying otoliths recovered from these sources, researchers can reconstruct the prey composition of marine mammal and seabird diets. Additionally, segregating retrieved otoliths into right and left allows for the estimation of the minimum number of prey individuals ingested for a given fish species. Otolith size can also be utilized to retrospectively calculate prey size and biomass, thereby aiding in the estimation of marine mammal prey consumption and potential impacts on fish stocks (Arim & Naya, 2003).

Certainly, relying solely on otoliths for estimating cetacean or pinniped diets presents challenges due to potential erosion in the digestive tract, which can skew measurements of prey number and biomass. Fragile otoliths are particularly susceptible to underestimation in the diet. To address these biases, otolith correction factors have been devised through captive feeding experiments. These experiments involve feeding seals fish of known size, enabling the quantification of otolith erosion for different prey taxa (Grellier & Hammond, 2005). Complementing otolith analysis with other informative skeletal elements like fish vertebrae, jaw bones, teeth, and distinctive bones enhances prey identification and quantification, especially for species with delicate otoliths, such as Atlantic mackerel and Atlantic herring (Browne et al., 2002).

Gathering age-related information is frequently done to understand the development and mortality patterns among individuals in a harvested population. This aids in devising sustainable harvesting approaches through assessments of stock or evaluations of management strategies. Normally, age data help in understanding how populations change over time within a fishery area or jurisdiction under specific management protocols. When collecting samples for otolith extraction, it’s vital to ensure a diverse representation across the fishing area to adequately capture the population’s makeup. Although there may be variations in population dynamics within the fishery area, they are often not explicitly taken into account, and instead, average growth and mortality patterns are uniformly applied throughout the region (Kritzer et al., 2001). Certainly! Describing a population entails gathering a specimen sample that mirrors the distribution of individuals across different size, age, or growth categories observed in the natural population. This representative sample guarantees an accurate depiction of all facets of the wild population. Attaining such accuracy involves sampling individuals in alignment with their prevalence in the wild population. Employing random sampling techniques or gear with an equal likelihood of capturing any individual, regardless of its specific traits, ensures the acquisition of a representative sample (Andrew & Mapstone, 1987).

Indeed, attaining a sampling approach completely devoid of selectivity is challenging and often not practical. Nonetheless, compensating for biases resulting from gear selectivity and ensuring representative samples are crucial endeavors. Properly distributing sampling efforts across the region of interest is vital for obtaining unbiased estimates of parameters. While it’s impractical to sample every subset of a domain, spreading sampling activities across the domain’s spectrum aids in capturing various factors that influence age estimates or derived parameters. Allocating sampling efforts randomly, stratifying them based on environmental conditions, or adhering to systematic patterns like transects or a grid assists in ensuring comprehensive coverage (Andrew & Mapstone, 1987).

Conclusively, achieving a sampling approach entirely free from selectivity is indeed difficult and often not feasible. However, recompensing for biases arising from gear selectivity and ensuring representative samples are essential tasks. Effectively distributing sampling efforts across the area of interest is crucial for obtaining unbiased estimates of parameters. While it is impracticable to sample every subset of a domain, spreading sampling activities across the domain’s range helps in capturing various factors influencing age estimates or derived parameters. Assigning sampling efforts randomly, stratifying them based on environmental conditions, or following systematic patterns like transects or grids aids in ensuring thorough coverage (Buckland et al., 2015).

Sampling the largest individuals available is often the most effective method for estimating longevity, as larger individuals are more likely to be older. However, in tropical fish species showing “table-top” growth, where individuals reach their maximum size relatively early in life, sampling numerous large individuals may be necessary to accurately estimate maximum age or long life. Estimating the age of transitional life history events, as opposed to terminal ones, presents greater challenges as it necessitates prior understanding of the species’ biology to pinpoint the appropriate target, such as size range, for sampling (Green et al., 2009).

Initially sampling a few individuals across a broad range of characteristics and then concentrating intensive sampling on the approximate sizes at which crucial ontogenetic events occur can be a moderately efficient strategy (Houde et al., 2022). This approach enables researchers to analyze data from the initial samples to determine the sizes at which significant events, like maturity or sex change, take place. While deriving such metrics from a comprehensive representative sample is preferable, the targeted strategy may be more feasible in scenarios with limited resources. For instance, it can suffice for establishing straightforward regulations such as minimum size limits or gear regulations to ensure individuals have adequate time to reproduce before being harvested (Green et al., 2009; Houde et al., 2022).

Sampling fishery catches can yield valuable insights into the characteristics of the harvested population, but it’s crucial to acknowledge that fishing activities themselves introduce biases into the sample (World Bank, 2012). Fishers often utilize selective gear targeting specific sizes or species, and they may operate in convenient or accessible areas, resulting in a non-random sample of the population. Consequently, even samples collected from catches in an unbiased manner may inherit biases from the fishing process. To mitigate biases associated with sampling fishery catches, several measures can be implemented. For example, it is crucial to avoid selectively sampling specimens based on market value, as this could introduce bias related to specific attributes of low-value fish. Additionally, ensuring that samples are collected from a diverse range of fishers across different ports or areas of the fishing grounds helps to more accurately capture variations in the population (Green et al., 2009).

Randomly sampling specimens from the catch of multiple fishers across different locations or from a diverse range of fishers working on the fishing grounds is an effective approach to avoid biases. This method ensures that the sample accurately reflects the characteristics of the harvested population. Employing a sequence of random numbers to select specimens from the catch provides a straightforward way to implement random sampling and minimize bias. Indeed, tailoring the sampling strategy to leverage any prior sorting of the catch is crucial for ensuring representative sampling (Lohr, 2021). If the catch has been sorted by size or location of capture, the sampling approach should align with this sorting to preserve representativeness. This ensures that the collected sample accurately mirrors the composition of the harvested population across various size classes or fishing areas. By distributing the sampling effort among the sorted size classes or fishing areas, comprehensive coverage of the fishing grounds can be attained, thereby enhancing the reliability of the collected data (Carpentieri, 2019).