Has the displacement of capelin Mallotus villosus (Müller, 1776) feeding ground induced a phenotypic response?

doi:10.21203/rs.3.rs-5005160/v1

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Has the displacement of capelin Mallotus villosus (Müller, 1776) feeding ground induced a phenotypic response?

https://doi.org/10.21203/rs.3.rs-5005160/v1

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Capelin in the Iceland-East Greenland-Jan Mayen region has experienced a range shift over the last two decades potentially driven by climate change. The population now inhabits the east Greenland shelf during the late feeding season, instead of the north Iceland shelf as in the past. Spatial and temporal variation in phenotypic and life history traits such as body size, weight, length- and age-at-maturation, as well as body condition were used to comprehend the population response to environmental perturbations, using biological data spanning two decades. The findings showed that length-at-age, weight-at-age, body condition, and length-at-maturity increased over time, whereas age-at-maturity remained stable. A finer spatiotemporal modelling of length- and weight-at-age for each specific period, before and after the shift, showed density-dependent effects were most prominent for all ages where the size and condition of organisms have improved over the years likely because of reduced intra-specific competition. Temperature effects were more apparent for ages 1 and 2 where fish attain a smaller body size in warmer conditions, and a positive relationship was apparent with net primary productivity. By adjusting life-history traits to a new environment, the capelin population has exhibited a plastic response. A good understanding of the ecological processes that drive population response can prove useful for management in the future.

life history traits

length-at-maturity

body condition

Iceland-East Greenland-Jan Mayen capelin

spatial temporal modelling

Intra-specific variability in life history traits of organisms have long been used as a measure of population adaptive capability (Moran et al. 2016; MacLean and Beissinger 2017; Wieczynski et al. 2021; Gómez et al. 2023). An organism’s habitat plays a pivotal role in shaping these characteristics given that the transfer of heat and energy connects all organisms to their environment (Briscoe et al. 2023). Natural variations will occur in life history characteristics depending on how an organism responds to any natural and non-natural fluctuations in the environment (Shefferson 2014). As such, spatial and temporal variation in phenotypic and life history traits such as body size, weight, length- and age-at-maturation, and body condition can be used to comprehend the response of fish populations to environmental perturbations (Caselle et al. 2011; Casini et al. 2011; dos Santos Schmidt et al. 2020; Becker et al. 2020; Utne et al. 2021, 2022). Two classical ways in which marine organisms react to extrinsic stressors is by shifting distribution to another suitable habitat and/or adjusting their behavioral, phenotypic or physiological traits to adapt to new conditions (Seebacher et al. 2015; Rodriguez-Dominguez et al. 2022; Rubenstein et al. 2023). While there are plenty of studies targeting range shifts, the effects of local adaptation and potentially induced changes in the phenotype and life history traits of marine organisms is generally understudied (Seebacher et al. 2015; Peterson et al. 2019; Donelson et al. 2019). Recent studies have revealed that marine organisms are shifting their geographical distributions in response to climate change, with more pronounced effects observed at higher latitudes (Philippart et al. 2011; Pinsky et al. 2013; Rodriguez-Dominguez et al. 2022). Changes in oceanic conditions have been evident in the North Atlantic and sub-Arctic regions in recent times and are happening at a faster rate than in any other ocean basins (Olafsson et al. 2001; Qi et al. 2022; Rantanen et al. 2022). A weakening of the Atlantic subpolar gyre has led to a reduction in the inflow of freshwater into the North Atlantic region impacting the thermohaline circulation causing record high salinities (Hjálmar et al. 2005). Northward ocean heat transport has resulted in rising sea temperatures and a reduction in sea-ice extent in the Nordic seas with a significant increase in heat transport occurring around 2001 (Tsubouchi et al. 2021). A temperature increase in the northern shelf of Iceland was evident around this time with fluctuations in water mass circulation and mixing, leading to variations in current strength and heat transport (Jónsson and Valdimarsson 2012; Rantanen et al. 2022) with warming and salinification also evident in the Greenland Sea (Rudels et al. 2012; Lauvset et al. 2018). Range shifts towards the north of demersal fish in this region have been linked to ocean warming (Frainer et al. 2017; Campana et al. 2020; Post et al. 2021; Mason et al. 2021). Further, spatial distributional shifts of a key small pelagic fish, capelin (Mallotus villosus) have also been observed which coincides with the concurrent environmental changes (Carscadden et al. 2013).

One of the largest capelin stocks occupies the Iceland-East Greenland-Jan Mayen area (referred to as the Iceland-East Greenland-Jan Mayen (IEGJM) stock) and is of high ecological and economic importance. Capelin is a small highly migratory pelagic fish that covers a vast geographical region during its life cycle(Vilhjálmsson 2002) and inhabits the water column which makes them especially sensitive to variations in the physical environment (Bartolino et al. 2012; Lindegren et al. 2012; Andrews et al. 2020; Henriksen et al. 2021; Kamaruzzaman et al. 2021). Capelin grows up to 20 cm in length and has a short life span of 3–4 years making it more sensitive to fluctuations in the physical environment (Perry et al. 2005). During the spawning migration, capelin migrates clockwise along the shelf break from the north and east of Iceland in January to reach the spawning grounds located south and west of Iceland where spawning occurs in the warm waters around March (Vilhjálmsson 2002; Olafsdottir and Rose 2012; Singh et al. 2020). During the early 2000s, the stock distribution in autumn, which signifies the feeding period, started shifting from north of Iceland to east coast of Greenland (Carscadden et al. 2013, Fig. 1). Concurrently, the stock productivity also declined as evident from a lower spawning stock biomass and associated catch (ICES 2024).

Large-scale geographic shifts, such as those exhibited by capelin, can induce variations in life history traits because of exposure to new environmental conditions, changes in food supply and predation pressure (Cardinale et al. 2002). However, a knowledge gap exists in the long-term changes in the phenotypic and life history traits of capelin during the period of the distributional shift and decline in stock productivity. Therefore, biological data spanning two decades was used to investigate the spatial and temporal patterns in the life history traits of capelin in the Iceland-East Greenland-Jan Mayen area during two defined time periods designating the geographic shift in the stock. Community weighted traits were further related with selected abiotic and biotic parameters to investigate potential drivers of change.

Capelin biological data

Capelin was sampled during the annual Iceland-East Greenland-Jan Mayen autumn acoustic survey. Data from 2000 to 2021 was used for this study (Table S1). The autumn scientific surveys have been conducted to estimate the mature and juvenile component of the stock to assess an interim total allowable catch for capelin. The targeted area of the survey has been adapted over time in response to the observed shift in the late feeding distribution from the north of Iceland to the shelf areas of east-Greenland. Nowadays, the survey area extends along the east Greenland shelf break from 63°N to 75°30’N, over the Denmark Strait and along the shelf break north of the Westfjords peninsula and North Iceland, east to the 12°W meridian (Fig. 1). Since 2010, the survey has been conducted in September to avoid ice-cover along the east coast of Greenland as opposed to October and November in the past (Table S1).

During each survey, pelagic trawls were conducted in targeted schools throughout the survey area based on acoustic registrations. At each trawling station, a sample of 100 randomly sampled capelin were collected. For each fish, total length (TL; 1–5 mm; from the tip of the snout to the upper lobe of the pinched caudal fin) and total weight (W; 0.1 g) were recorded. The sex and maturity stage were classified based on established methods (Forberg 1983), and otoliths were extracted for age determination. Due to the small gonad size, it is not possible to distinguish macroscopically the sex of immature individuals. However, these data constitute a significant part of the dataset (51%) and were considered an essential component to study length-at-maturity (L₅₀). Therefore, we split the immature individuals based on the sex ratio estimated by each year. Additionally, any outlying observations were removed based on the length-weight relationship, and 4-year-old individuals were also omitted due to their low number. Data from 2002 was also excluded because of missing age information.

The analysis was restricted to when acoustic registration data from the surveys were available. The acoustic backscattering energy is measured in Nautical Area Scattering Coefficient (NASC) or sA (Maclennan et al. 2002). Based on an established target strength and length relationship for this capelin stock (Vilhjálmsson 1994) and length data from the samples, the backscattering energy was converted to population abundance (numbers of fish) within a defined spatial grid of 0.25° x 0.5° latitude and longitude by year creating a spatial temporal time series (Fig. 1). Since the schooling behavior of capelin can introduce bias in trawl sampling, individual trawl samples which may represent different quantities, were weighted with abundance in the given spatial grid and year to make them representative of the populations (Gjøsaeter 2000). First, annual proportions were generated by spatial grid, sex, age, maturity, length, and weights bins. Length and weight were binned into 0.5 cm and 0.5 g bins respectively. The abundance within each spatial and temporal grid was then proportionally allocated to make the sample representative of the true population. Morphological characteristics of capelin are known to differ by sex (Berg et al. 2021), therefore the patterns in life history parameters were examined by sex.

Temporal trends in life history parameters

Long-term changes in length-at-age, weight-at-age, body condition, length-at-maturity (L₅₀), age-at-maturity (A₅₀) and growth rate were investigated. A linear regression model was applied to study the long-term trend in length- and weight-at-age, and comparison among the ages were tested using analysis of covariance (ANCOVA).

A relative condition factor (K_n) was calculated by dividing the observed weight with expected weight (Le Cren 1951). The expected weight was calculated using the length-weight relationship (W = a × L^b), and the final length-weight formula (W = 4.7 × 10⁴ × L^3.85, p < 0.001, R² = 0.96) was derived using all data combined.

Maturity ogives, defined by the size and age at which 50% of the sampled fish were mature, were used to examine long term changes in maturation between sexes. These parameters measure the reproduction potential of a stock. The L₅₀ and A₅₀ were estimated for all year classes using a generalized linear model (GLM) with a binomial error distribution and a logit link (Magallanes 2020).

$$\:Y=\:\raisebox{1ex}{$1$}\!\left/\:\!\raisebox{-1ex}{$1+{exp}^{-\left(a+b\times\:L\right)}$}\right.$$

where Y is the percentage of mature individuals, a is intercept, b is slope and L is total length (cm).

In addition to plotting time trends, the data were split into two time periods 2000–2009 (Period 1) and 2010–2021 (Period 2) to summarize changes in the above life history characteristics. The years were split in such a manner to capture the shift in the spatial distribution of the stock from north of Iceland to east Greenland shelf (Fig. 1). Further, to assess whether any changes occurred in growth rate of capelin, the von Bertalanffy growth function (von Bertalanffy 1938) was used to compare the somatic growth between the two periods,

$$\:{L}_{a}={L}_{\infty\:}\:-\:\left({L}_{\infty\:}\:-\:{L}_{0}\right){e}^{-ka}$$

where L_a is the length-at-age, L₀ is the length-at-birth, k is the growth coefficient parameter and L_∞ is the asymptotic length. The von Bertalanffy growth model was fitted using the AquaticLifeHistory R package (Smart 2016). Capelin larvae length after hatching (L₀) was estimated to be 4 mm (Vilhjálmsson 1994).

Abiotic and biotic drivers of change

To further explore whether any abiotic and biotic drivers contributed towards the variability in length- and weight-at-age, the following selected set of abiotic predictors were considered, sea surface temperature, sea surface salinity, and net primary production (NPP). Temperature and salinity were extracted from E.U. Copernicus Marine Environment Monitoring Service (CMEMS) Global Ocean Reanalysis and Simulations product (Jean-Michel et al. 2021). NPP was obtained from the CMEMS Global Ocean Biogeochemistry Hindcast (Perruche 2018). The environmental variables were averaged within 0.25° x 0.5° latitude and longitude by year and merged with the spatial temporal capelin data. Additionally, abundance in numbers of fish was compiled for each of the spatial grid cells by year as a biotic indicator of change to study density dependent effects.

A Random Forest (RF) model (Breiman 2001) was fitted using the R package randomForest (Liaw and Wiener 2002) on data spanning from 2000 to 2019. RF is a popular non-parametric machine learning technique applied to study the nonlinear response of organisms to changes in the environment (Beukhof et al. 2019b). It is appealing because it is independent of data distribution assumptions, can handle spatial autocorrelation, and is known for its high predictive power (Cutler et al. 2007). Independent models were constructed for length- and weight-at-age as response variables. Due to confounding effects in the data introduced by the shift in the geographical distribution of capelin which consequently influenced the timing of the survey and area coverage, the model formulation considered the response within each period separately by fitting two independent models for the time periods, resulting in four final models. Temperature and salinity were correlated (variance inflation factor > 5), and only temperature was retained as it is known to be a main driver of change for ectotherms (Zuo et al. 2012). The explanatory variables in the final models were abundance, temperature and NPP, with age as a factor to study capelin response at different life-stages. The goodness-of-fit of the models was measured using r squared. The variable importance was measured using the change in mean squared error (MSE) using the package ‘randomForestExplainer’ (Paluszynska et al. 2020). The partial response plots by age were analyzed to visualize the relationship between length- and weight-at-age and environmental variables.

All analysis was conducted using the R statistical software (R Core Team 2022).

Temporal trends in life history parameters

The linear regression models showed an overall increase in both length and weight-at-age for both sexes over the time series (p < 0.001). The increase in length was higher for males in comparison with females (Fig. 2a, b). The increase in weight was steepest for age 2 males (Fig. 2c). For females, age 2 and age 3 showed a similar increase in weight for the whole time series (Fig. 2c, d). The overall mean length across ages increased by 1.1 cm for both males and females between the two time periods (Table 1). The overall mean weight increase was 4.2 g for females and 4.8 g for males. The proportion of mature fish for both sexes also increased in Period 2 (Table 1) with a proportional increase of 0.18 for females and a 0.11 for males.

The relative condition of capelin increased in Period 2, when average values were in general above 1 for both sexes (1.023 for females and 1.033 for males) in comparison with Period 1 when the values were mostly below 1 (0.966 for females and 0.975 for males) (Table 1). The highest condition was recorded in the years 2012, 2016 and 2017 (Fig. 2d, e).

Although variable among years, the L₅₀ showed a significant increasing trend throughout the time series for both sexes (p < 0.001). The L₅₀ values ranged from 12.9 to 14.7 cm for females and from 13.1 to 15.2 cm for males, both sexes reaching its highest point in 2015 (Fig. 3a). A comparison between time periods showed an increase of 0.65 for females and 0.72 for males (Table 1). The A₅₀ showed a slight positive increase which was not significantly different over the time series and showed little increase between the time periods (Fig. 3b, Table 1).

Table 1

A summary of the estimated phenotypic and life history traits of capelin by sex and time period depicting before (Period 1) and after (Period 2) the shift in stock distribution.
Trait	Female		Male
Trait	Period 1	Period 2	Period 1	Period 2
Length range / Mean (cm) Age 1 Age 2 Age 3	7.5–16.0 / 10.4 10.5–17.5 / 14.4 13.5–18.5 / 15.9	7.5–16.0 / 11.4 9.5–19.0 / 15.0 11.0–19.0 / 16.1	7.5–16.5 / 10.6 10.0–19.0 / 15.1 14.5–18.5 / 16.7	7.5–17.5 / 11.4 9.5–19.5 / 15.7 11.0–19.5 / 16.8
Weight range / Mean (g) Age 1 Age 2 Age 3	1.0–18.5 / 4.1 4.0–28.5 / 13.3 10.0–32.5 / 18.9	1.0–26 / 6.0 3.0–40.5 / 16.5 5.0–48.0 / 21.7	1.0–22.0 / 4.4 3.5–42.0 / 16.4 12.5–41.0 / 24.2	1.0–31.5 / 6.1 3.0–44.0 / 20.5 5.0–49.0 / 27.0
Proportional composition	0.637	0.592	0.363	0.41
Proportion mature	0.205	0.388	0.274	0.383
Condition factor	0.966	1.023	0.975	1.033
L50	13.23	13.88	13.86	14.58
A50	1.47	1.65	1.56	1.70
Growth coefficient	0.920	1.124	0.836	0.999

The growth rate increased in Period 2 for both males and females with an estimated increase in growth coefficient from 0.92 to 1.124 for females and from 0.836 to 0.999 for males. Females tend to grow at a faster rate than males. (Fig. 4, Table 1).

Abiotic and biotic drivers of change

An evaluation of model performance revealed that the length-at-age models explained 68% of the variance in the data for Period 1 and 70% for Period 2. The variance explained in weight-at-age was slightly lower with 58% for Period 1 and 62% for Period 2. The differences in length and weight of fish at different ages account for a high percentage of the variability, as expected. The second most important variable was abundance for all four models. Temperature and NPP showed very slight differences in MSE increase with temperature being slightly higher for all models expect Period 1 length-at-age model. Thus, these two variables could be ranked equally important (Table 2).

The partial response curves from the RF models showed consistent non-linear relationships between both length- and weight-at-age and environmental variables (Fig. 5). A negative relationship with abundance is evident for all ages for both length- and weight-at-age indicating a density-dependent effect (Fig. 5a, d, g, j). The negative relationship with temperature is more pronounced at age 1 and age 2 for all models except for fish weight in Period 2. At age 3, the temperature effects do not appear to be significant for both fish length and weight (Fig. 5b, e, h, k). A positive relationship is seen with NPP for both length and weight in Period 1 for all ages (Fig. 5c, i). On the other hand, in Period 2, the effect of NPP does not appear significant for length and weight at age 1. For older ages however a certain preference for NPP is evident where a negative relationship is observed with NPP > 4.

Table 2

Model performance and variable importance for the Random Forest models
Model	Length-at-age Period 1	Length-at-age Period 2	Weight-at-age Period 1	Weight-at-age Period 2
MSE increase
Age	3.22	3.18	25.51	40.96
Abundance	0.88	0.72	7.09	9.10
Temperature	0.81	0.44	5.32	7.31
NPP	0.82	0.39	5.20	7.27
Goodness-of-fit (r²)	0.68	0.70	0.58	0.62

This study demonstrates that the life history traits of the Iceland-East Greenland-Jan Mayen capelin have changed over the last two decades, coinciding with a shift in their physical habitat during the late feeding season. While the possible causes for the habitat shift are not investigated in the present study, species distribution models have shown that the displacement of capelin in the region was correlated with a combination of physical environmental factors including temperature, salinity, NPP, and currents, which explain the spatial distributional shifts (Singh et al. 2024). The model predictions also suggested that suitable conditions do not exist for capelin at its former feeding grounds (Singh et al. 2024). Capelin was therefore shown to be sensitive to environmental changes but the consequences on the population’s life history traits were not investigated. The objectives of the present study were therefore to investigate potential changes in the life history traits of the IEGJM capelin population consecutive to its displacement along the east Greenland shelf during the late feeding season. First, we observed an overall increase in length- and weight-at-age over the years for both sexes. Notably, the body condition has also improved, implying an increase in the overall fitness of individuals during the study period (Fig. S1). Variations in length-at-age can influence both maturity and fecundity (Zimmermann et al. 2018). Two reproductive traits commonly studied for fish populations includes L₅₀ and A₅₀ which define the length and age at which 50% of the individuals are expected to be mature enough to spawn (Mainguy et al. 2024). Alongside, the increase in length- and weight-at-age of capelin an increase in L₅₀ was also evident suggesting that reproductive capability is achieved at larger body size in recent time. However, larger body size does not necessarily correspond to older age as the A₅₀ has remained stable. In the new habitat along the east Greenland shelf the population is exposed to partly colder and fresher conditions than before (Online Resource 1 Fig. S2b). In addition, the mature component of the stock inhabits the northern east Greenland shelf, and the immature component is mainly located in the southern part (Fig. 1). According to the ‘temperature size rule’ for ectotherms (Atkinson 1994), individual bodies tend to reach smaller optimal sizes in warmer environments, and therefore smaller size at maturity. Ectotherms generally display reduced body growth in these environments due to lower dissolved oxygen availabilities which exert constraints on the metabolism and affect consumption rate (Daufresne et al. 2009; Berggren et al. 2022). This could partly explain the observed lower L₅₀ and growth rate in Period 1, which corresponds to a warmer period, since capelin is a cold-water pelagic species.

When conducting a finer spatiotemporal analysis of each specific period, before and after the shift, a relationship of length- and weight-at-age to ecological processes, including population size, food availability and climate became apparent. Since the early 2000s, the biomass of the capelin stock has generally been smaller than before, leading to lower total allowable catch and yield (ICES 2024; Online Resource 1 Fig. S2a). Although this study did not investigate the reduction in biomass, the increase in length and weight, in relation to the declining biomass demonstrates a density-dependent effect. This implies that the size and condition of organisms have improved over the years likely because of reduced intra-specific competition (Arranz et al. 2016). The concept of density-dependence is prevalent in ecology, where higher population densities may lead to competition for resources such as food, thus affecting the biological response of individuals, which can become evident through slower growth rates (Zimmermann et al. 2018). Density-dependent effect was observed in the IEGJM capelin across all ages during the two periods analyzed (Period 1 and Period 2). Hixon and Johnson (2009) suggest that “Direct density dependence occurs when the population growth rate varies as a causative inverse function of population size or density.” This causal relationship is reflective in our analysis, where the life history traits of capelin increase when abundance decreases, and vice versa. Concurrently, the juvenile index of capelin has fluctuated with high indices measured for the years 2020 and 2021 followed by a reduction in 2022 and 2023 (ICES 2024), which might suggest that the increased L50 and length-at-age, as a response to the recent events, has induced a higher reproductive potential in some recent years. However, disentangling the forces at play, e.g. the effects of climate change, including habitat shift in the capelin distribution, from the fluctuations in population biomass, on life history traits of a species remains a challenge. Although density dependent effects carry more weight in our analysis, a combination of these with climate driven factors leading to changes in temperature and food availability likely induced the observed changes in the IEGJM capelin life-history traits.

Like the recent modelling analyses that connected capelin distribution to its physical environment (Singh et al., 2024), we also found a relationship between temperature, NPP, and life history traits variation over the study period. A negative relationship with temperature for age 1 and age 2 capelin with respect to length and weight alludes to varying effects of temperature at different life stages (Lindmark et al. 2022). For capelin, the temperature effects are more significant for the younger ages and the growing stages where fish may attain a smaller body size at warmer temperatures (Zuo et al. 2012). For older capelin (age 3) temperature does not seem to influence body size. The growth rate seems to slow down by age 3 (Fig. 4) and more energy is likely invested in gonad development for spawning that occurs following winter. Thermal tolerance of marine organisms can change as they mature (MacKenzie et al. 2012; Lindmark et al. 2022) leading to varying responses by age. In a similar manner, primary production is known to affect fish size and biomass (Norman et al. 2022). NPP was used as a proxy for food availability. Higher NPP was observed along east Greenland shelf in comparison with north Iceland shelf (Online Resource 1 Fig. S2c). This is because the region mainly consists of Atlantic origin water, which has a relatively high nutrient concentration due to more efficient renewal in the surface layer through eddy diffusion (Håvik et al. 2019). A positive relationship between NPP and length- and weight-at-age in Period 1 indicates that body size is larger with higher productivity. In Period 2, along the east Greenland shelf, lower productivity appeared ideal for older individuals. This could be a confounding effect of habitat because older individuals mainly inhabit the northern part of the east Greenland shelf which has lower NPP levels. The levels of NPP nonetheless can support specific prey types such as krill, which mature capelin prefer to feed on (Gislason and Silva 2012).

The ability of a population to alter its physiological, behavioral, or morphological traits in response to changes in environmental conditions in a compensatory manner reflects phenotypic plasticity (Hooker et al. 2023). This adaptive capability allows individual fish and populations to modify body size, growth rate, and size-at-maturation to ensure optimal growth and a better survival of the species. Phenotypic plasticity is a crucial factor in the resilience of a fish population to changes under climate, habitat conditions and exploitation pressures. Capelin is an opportunistic feeder with a short lifespan, and these characteristics enable them to rapidly react to environmental variations (Beukhof et al. 2019b). The ability of capelin to adjust to a new environment by shifting its phenotypic mean and phenotypic variability (Hooker et al. 2023) in key traits such as length-, weight-at-age and body condition (Online Resource 1 Fig. S1) reflects a plastic response indicating resilience.

Reliably predicting how organisms will respond to environmental perturbations and climate change is a challenging task; nonetheless, it is crucial for effectively managing marine populations in the future. Life history traits inherently determine the growth, reproduction and survival of a population (Beukhof et al. 2019a; Lindegren et al. 2020; Caballero-Huertas et al. 2022). Hence, the overall productivity of a stock can be affected by variations in these traits which can consequently dictate the fisheries yield leading to socio-economic implications (MacLean and Beissinger 2017; Lojo et al. 2022). Therefore, studying life history traits of fish populations has been a subject of long-standing interest. The ability of an organism to withstand environmental variability can be constraints-dependent leading to non-uniform response across species and habitats (Briscoe et al. 2023; Beaudry-Sylvestre et al. 2024; Lawlor et al. 2024), hence site- and species-specific studies are essential. Local adaptation to a novel climate niche, combined with plasticity in traits, can help organisms maintain stable performance across diverse environmental conditions (Morgan et al. 2024). By investigating changes in life history traits, the adaptability of capelin to biotic and abiotic habitat changes can be better understood which will be valuable for conservation and management in the future for this stock.

This study concludes that the displacement of IEGJM capelin habitat during its late feeding period in autumn has induced a phenotypic response. An increase in key life history traits including length- and weight-at-age, body condition, length and age at 50% maturity, and growth rate have been observed. During the last two decades the capelin population has experienced high fishing pressure, spatial distributional shifts during the late feeding season, and high fluctuations in the spawning and juvenile index. Disentangling the effects of these perturbations on the life history characteristics of the population can be challenging. Indeed, our study tends to indicate that the observed changes in capelin life-history traits can be related to the combined effect of density-dependent processes, and changes in the physical environment including temperature and net primary productivity. Combined with the habitat prediction models recently carried out (Singh et al. 2024), the present analyses show that the capelin population has exhibited a plastic response by adapting to a new environment. The IEGJM capelin inhabits a very specific ecoregion which is at the forefront of climate change. Understanding the ecological processes that drive population responses can prove useful for management in the future.

Competing Interests

Christophe Pampoulie is a guest editor of the Special Issue “Capelin in a changing environment” and the peer-review process for this article was independently handled by another guest editor or a member of the journal editorial board.

Author Contribution

W.S.: Conceptualization, data analysis, writing - original draft, writing - reviewing and editing. S.B.: Conceptualization, data curation, data analysis, writing – reviewing and editing. C.P: Validation, writing – reviewing and editing.

Acknowledgement

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 869383 (ECOTIP). This study has been conducted using E.U. Copernicus Marine Service Information https://doi.org/10.48670/moi-00019, https://doi.org/10.48670/moi-00021. The authors would like to acknowledge Kristinn Gudnason for compiling the ocean model output. We would also like to thank the stock assessors of the Iceland-East Greenland-Jan Mayen capelin, Birkir Bardarson and Sigurður Þ. Jónsson, for compiling the acoustic data that was used for this study.

Data Availability

Data can be made available upon request.

Andrews S, Leroux SJ, Fortin MJ (2020) Modelling the spatial-temporal distributions and associated determining factors of a keystone pelagic fish. ICES J Mar Sci 77:2776–2789. https://doi.org/10.1093/icesjms/fsaa148
Arranz I, Mehner T, Benejam L, et al (2016) Density-dependent effects as key drivers of intraspecific size structure of six abundant fish species in lakes across Europe. Can J Fish Aquat Sci 73:519–534. https://doi.org/10.1139/cjfas-2014-0508
Atkinson D (1994) Temperature and organism size: A biological law for ectotherms? Adv Ecol Res 25:1–58
Bartolino V, Ciannelli L, Spencer P, et al (2012) Scale-dependent detection of the effects of harvesting a marine fish population. Mar Ecol Prog Ser 444:251–261. https://doi.org/10.3354/meps09434
Beaudry-Sylvestre M, Benoît HP, Hutchings JA (2024) Coherent long-term body-size responses across all Northwest Atlantic herring populations to warming and environmental change despite contrasting harvest and ecological factors. Glob Chang Biol 30(3):e17187. https://doi.org/10.1111/gcb.17187
Becker JR, Cieri MD, Libby DA, et al (2020) Temporal variability in size and growth of Atlantic herring in the Gulf of Maine. J Fish Biol 97:953–963. https://doi.org/10.1111/jfb.14430
Berg F, Shirajee S, Folkvord A, et al (2021) Early life growth is affecting timing of spawning in the semelparous Barents Sea capelin (Mallotus villosus). Prog Oceanogr 196: https://doi.org/10.1016/j.pocean.2021.102614
Berggren T, Bergström U, Sundblad G, Östman Ö (2022) Warmer water increases early body growth of northern pike (Esox lucius), but mortality has larger impact on decreasing body sizes. Can J Fish Aquat Sci 79(5):771–781. https://doi.org/10.1139/cjfas-2020-0386
Beukhof E, Dencker TS, Pecuchet L, Lindegren M (2019a) Spatio-temporal variation in marine fish traits reveals community-wide responses to environmental change. Mar Ecol Prog Ser 610:205–222. https://doi.org/10.3354/meps12826
Beukhof E, Frelat R, Pecuchet L, et al (2019b) Marine fish traits follow fast-slow continuum across oceans. Sci Rep 9:17878. https://doi.org/10.1038/s41598-019-53998-2
Breiman L (2001) Random Forests. Mach Learn 45:5-32
Briscoe NJ, Morris SD, Mathewson PD, et al (2023) Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology. Glob Chang Biol 29:1451–1470
Caballero-Huertas M, Vargas-Yánez M, Frigola-Tepe X, et al (2022) Unravelling the drivers of variability in body condition and reproduction of the European sardine along the Atlantic-Mediterranean transition. Mar Environ Res 179: https://doi.org/10.1016/j.marenvres.2022.105697
Campana SE, Stefánsdóttir RB, Jakobsdóttir K, Sólmundsson J (2020) Shifting fish distributions in warming sub-Arctic oceans. Sci Rep 10: https://doi.org/10.1038/s41598-020-73444-y
Cardinale M, Casini M, Arrhenius F (2002) The influence of biotic and abiotic factors on the growth of sprat (Sprattus sprattus) in the Baltic Sea. Aquat Living Resour 15(5):273-281
Carscadden JE, Gjøsæter H, Vilhjálmsson H (2013) A comparison of recent changes in distribution of capelin (Mallotus villosus) in the Barents Sea, around Iceland and in the Northwest Atlantic. Prog Oceanogr 114:64–83. https://doi.org/10.1016/j.pocean.2013.05.005
Caselle JE, Hamilton SL, Schroeder DM, et al (2011) Geographic variation in density, demography, and life history traits of a harvested, sex-changing, temperate reef fish. Can J Fish Aquat Sci 68:288–303. https://doi.org/10.1139/F10-140
Casini M, Kornilovs G, Cardinale M, et al (2011) Spatial and temporal density dependence regulates the condition of central Baltic Sea clupeids: Compelling evidence using an extensive international acoustic survey. Popul Ecol 53:511–523. https://doi.org/10.1007/s10144-011-0269-2
Cutler DR, Edwards TC, Beard KH, et al (2007) Random Forests for classification in ecology. Ecol 88(11):2783-2792
Daufresne M, Lengfellner K, Sommer U (2009) Global warming benefits the small in aquatic ecosystems. Proc Natl Acad Sci U S A. 2009 Aug 4;106(31):12788-93. doi: 10.1073/pnas.0902080106. Epub 2009 Jul 20. PMID: 19620720; PMCID: PMC2722360.
Donelson JM, Sunday JM, Figueira WF, et al (2019) Understanding interactions between plasticity, adaptation and range shifts in response to marine environmental change. Philos Trans R Soc B Biol Sci 374(1768)
dos Santos Schmidt TC, Devine JA, Slotte A, et al (2020) Environmental stressors may cause unpredicted, notably lagged life-history responses in adults of the planktivorous Atlantic herring. Prog Oceanogr 181: https://doi.org/10.1016/j.pocean.2019.102257
Forberg KG (1983) Maturity classification and growth of capelin, Mullotus villosus villosus (M), oocytes. J Fish Biol 22(4):485-496
Frainer A, Primicerio R, Kortsch S, et al (2017) Climate-driven changes in functional biogeography of Arctic marine fish communities. Proc Natl Acad Sci U S A 114:12202–12207. https://doi.org/10.1073/pnas.1706080114
Gislason A, Silva T (2012) Abundance, composition, and development of zooplankton in the Subarctic Iceland Sea in 2006, 2007, and 2008. ICES J Mar Sci 69:1263–1276. https://doi.org/10.1093/icesjms/fss070
Gjøsaeter H (2000) Studies on the Barents Sea Capelin (Mallotus villosus Müller), with emphasis on growth. Dissertation. Institute of Fisheries Biology, University of Bergen, Norway
Gómez JM, González-Megías A, Armas C, et al (2023) The role of phenotypic plasticity in shaping ecological networks. Ecol Lett 26:S47–S61. https://doi.org/10.1111/ele.14192
Håvik L, Almansi M, Våge K, Haine TWN (2019) Atlantic-origin overﬂow water in the east Greenland current. J Phys Oceanogr 49:2255–2269. https://doi.org/10.1175/JPO-D-18-0216.1
Henriksen O, Rindorf A, Brooks ME, et al (2021) Temperature and body size affect recruitment and survival of sandeel across the North Sea. ICES J Mar Sci 78:1409–1420. https://doi.org/10.1093/icesjms/fsa
Hixon MA, Johnson DW (2009) Density Dependence and Independence. Encyclopedia of Life Sciences. In eLS, (Ed.). https://doi.org/10.1002/9780470015902.a0021219
Hátún, H, Britt, A, Drange H, et al (2005) Influence of the Atlantic subpolar gyre on the thermohaline circulation. Science (1979) 309:1841–1844
Hooker OE, Adams CE, Chavarie L (2023) Arctic charr phenotypic responses to abrupt generational scale temperature change: an insight into how cold-water fish could respond to extreme climatic events. Environ Biol Fishes 106:909–922. https://doi.org/10.1007/s10641-022-01363-0
ICES (2024) Northwestern Working Group (NWWG). ICES Scientific Reports 6(39)
Jean-Michel L, Eric G, Romain BB, et al (2021) The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis. Front Earth Sci (Lausanne) 9: https://doi.org/10.3389/feart.2021.698876
Jónsson S, Valdimarsson H (2012) Hydrography and circulation over the southern part of the Kolbeinsey Ridge. ICES Journal of Marine Science 69:1255–1262. https://doi.org/10.1093/icesjms/fss101
Kamaruzzaman YN, Mustapha MA, Ghaffar MA (2021) Determination of Fishing Grounds Distribution of the Indian Mackerel in Malaysia’s Exclusive Economic Zone Off South China Sea Using Boosted Regression Trees Model. Thalassas 37:147–161. https://doi.org/10.1007/s41208-020-00282-0
Lauvset SK, Brakstad A, Våge K, et al (2018) Continued warming, salinification and oxygenation of the Greenland Sea gyre. Tellus A: Dyn Meteorol Oceanogr 70:1–9. https://doi.org/10.1080/16000870.2018.1476434
Lawlor JA, Comte L, Grenouillet G, et al (2024) Mechanisms, detection and impacts of species redistributions under climate change. Nat Rev Earth Environ 5:351–368
Le Cren (1951) The Length-Weight Relationship and Seasonal Cycle in Gonad Weight and Condition in the Perch (Perca fluviatilis). J Anim Ecol 20:201. https://doi.org/10.2307/1540
Liaw A, Wiener M (2002) Classification and Regression by randomForest. R News 2:18–22
Lindegren M, Dakos V, Gröger JP, et al (2012) Early detection of ecosystem regime shifts: A multiple method evaluation for management application. PLoS One 7: https://doi.org/10.1371/journal.pone.0038410
Lindegren M, Rindorf A, Norin T, et al (2020) Climate- And density-dependent regulation of fish growth throughout ontogeny: North Sea sprat as a case study. ICES J Mar Sci 77:3138–3152. https://doi.org/10.1093/icesjms/fsaa218
Lindmark M, Ohlberger J, Gårdmark A (2022) Optimum growth temperature declines with body size within fish species. Glob Chang Biol 28:2259–2271. https://doi.org/10.1111/gcb.16067
Lojo D, Cousido-Rocha M, Cerviño S, et al (2022) Assessing changes in size at maturity for the European hake (Merluccius merluccius) in Atlantic Iberian waters. Sci Mar 86: https://doi.org/10.3989/scimar.05287.046
MacKenzie BR, Meier HEM, Lindegren M, et al (2012) Impact of climate change on fish population dynamics in the baltic sea: A dynamical downscaling investigation. Ambio 41:626–636. https://doi.org/10.1007/s13280-012-0325-y
MacLean SA, Beissinger SR (2017) Species’ traits as predictors of range shifts under contemporary climate change: A review and meta-analysis. Glob Chang Biol 23:4094–4105. https://doi.org/10.1111/gcb.13736
Maclennan DN, Fernandes PG, Dalen J (2002) A consistent approach to definitions and symbols in fisheries acoustics. ICES J Mar Sci 59:365–369. https://doi.org/10.1006/jmsc.2001.1158
Magallanes JT (2020) sizeMat: Estimate Size at Sexual Maturity. R package version 112
Mainguy J, Bélanger M, Ouellet-Cauchon G, de Andrade Moral R (2024) Monitoring reproduction in fish: Assessing the adequacy of ogives and the predicted uncertainty of their L50 estimates for more reliable biological inferences. Fish Res 269: https://doi.org/10.1016/j.fishres.2023.106863
Mason JG, Woods PJ, Thorlacius M, et al (2021) Projecting climate-driven shifts in demersal fish thermal habitat in Iceland’s waters. ICES J Mar Sci 78:3793–3804. https://doi.org/10.1093/icesjms/fsab230
Moran E V., Hartig F, Bell DM (2016) Intraspecific trait variation across scales: Implications for understanding global change responses. Glob Chang Biol 22:137–150
Morgan R, Andreassen AH, Åsheim ER, et al (2024) Reduced physiological plasticity in a fish adapted to stable temperatures. PNAS 119(22). https://doi.org/10.1073/pnas
Norman S, Nilsson KA, Klaus M, et al (2022) Effects of Habitat-Specific Primary Production on Fish Size, Biomass, and Production in Northern Oligotrophic Lakes. Ecosyst 25: https://doi.org/10.1007/s10021-021-0073
Olafsdottir AH, Rose GA (2012) Influences of temperature, bathymetry and fronts on spawning migration routes of Icelandic capelin (Mallotus villosus). Fish Oceanogr 21:182–198. https://doi.org/10.1111/j.1365-2419.2012.00618.x
Olafsson J, Olafsdottir SR, Benoit-Cattin A, et al (2001) Rate of Iceland Sea acidification from time series measurements. Biogeosciences 6(11):2661-2668
Paluszynska A, Biecek P, Jiang Y (2020) randomForestExplainer: Explaining and Visualizing Random Forests in Terms of Variable Importance. R package version 0101
Perruche C (2018) Product User Manual for the Global Ocean Biogeochemistry Hindcast GLOBAL_REANALYSIS_BIO_001_029. Version 1.
Perry AL, Low PJ, Ellis JR, Reynolds JD (2005) Ecology: Climate change and distribution shifts in marine fishes. Science (1979) 308:1912–1915. https://doi.org/10.1126/science.1111322
Peterson ML, Doak DF, Morris WF (2019) Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Glob Chang Biol 25:775–793
Philippart CJM, Anadón R, Danovaro R, et al (2011) Impacts of climate change on European marine ecosystems: Observations, expectations and indicators. J Exp Mar Biol Ecol 400:52–69
Pinsky ML, Worm B, Fogarty MJ, et al (2013) Marine taxa track local climate velocities. Science (1979) 341:1239–1242. https://doi.org/10.1126/science.1239352
Post S, Werner KM, Núñez-Riboni I, et al (2021) Subpolar gyre and temperature drive boreal fish abundance in Greenland waters. Fish Fish 22:161–174. https://doi.org/10.1111/faf.12512
Qi D, Ouyang Z, Chen L, et al (2022) Climate change drives rapid decadal acidification in the Arctic Ocean from 1994 to 2020. Science 377(6614):1544-1550
R Core Team (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria
Rantanen M, Karpechko AY, Lipponen A, et al (2022) The Arctic has warmed nearly four times faster than the globe since 1979. Commun Earth Environ 3: https://doi.org/10.1038/s43247-022-00498-3
Rodriguez-Dominguez A, Connell SD, Coni EOC, et al (2022) Phenotypic responses in fish behaviour narrow as climate ramps up. Clim Change 171: https://doi.org/10.1007/s10584-022-03341-y
Rubenstein MA, Weiskopf SR, Bertrand R, et al (2023) Climate change and the global redistribution of biodiversity: substantial variation in empirical support for expected range shifts. Environ Evid 12(1):1-21
Rudels B, Korhonen M, Budus G, et al (2012) The East Greenland Current and its impacts on the Nordic Seas: Observed trends in the past decade. ICES J Mar Sci 69:841–851
Seebacher F, White CR, Franklin CE (2015) Physiological plasticity increases resilience of ectothermic animals to climate change. Nat Clim Chang 5:61–66. https://doi.org/10.1038/nclimate2457
Shefferson RP (2014) Why are life histories so variable? Nature Education Knowledge 1(12):1
Singh W, Bárðarson B, Jónsson S, et al (2020) When logbooks show the path: Analyzing the route and timing of capelin (Mallotus villosus) migration over a quarter century using catch data. Fish Res 230: https://doi.org/10.1016/j.fishres.2020.105653
Singh W, Gudnason K, Montanyès M, Lindegren M (2024) Climate driven response of the Iceland-East Greenland-Jan Mayen capelin distribution. Fish Oceanogr. doi:10.1002/FOG.12702
Smart, J. J., Chin, A. , Tobin, A. J. and Simpfendorfer, C. A. (2016) Multimodel approaches in shark and ray growth studies: strengths, weaknesses and the future. Fish Fish 17: 955-971. doi:10.1111/faf.12154
Tsubouchi T, Våge K, Hansen B, et al (2021) Increased ocean heat transport into the Nordic Seas and Arctic Ocean over the period 1993–2016. Nat Clim Chang 11:21–26. https://doi.org/10.1038/s41558-020-00941-3
Utne KR, Pauli BD, Haugland M, et al (2021) Poor feeding opportunities and reduced condition factor for salmon post-smolts in the Northeast Atlantic Ocean. ICES J Mar Sci 78:2844–2857. https://doi.org/10.1093/icesjms/fsab163
Utne KR, Skagseth Ø, Wennevik V, et al (2022) Impacts of a Changing Ecosystem on the Feeding and Feeding Conditions for Atlantic Salmon During the First Months at Sea. Front Mar Sci 9: https://doi.org/10.3389/fmars.2022.824614
Vilhjálmsson H (1994) The Icelandic Capelin Stock. Capelin (Mallotus villosus Müller) in the Iceland- Greenland-Jan Mayen area. Rit Fiskideilda 13:281pp
Vilhjálmsson H (2002) Capelin biology and ecology: Capelin (Mallotus villosus) in the Iceland-East Greenland-Jan Mayen ecosystem. ICES J Mar Sci 59(5):870–883
von Bertalanffy L (1938) A quantitative theory of organic growth (inquiries on growth laws. II). Hum Biol 10:181–213
Wieczynski DJ, Singla P, Doan A, et al (2021) Linking species traits and demography to explain complex temperature responses across levels of organization. PNAS 118:1–10. https://doi.org/10.1073/pnas.2104863118/-/DCSupplemental
Zimmermann F, Ricard D, Heino M (2018) Density regulation in Northeast Atlantic fish populations: Density dependence is stronger in recruitment than in somatic growth. J Anim Ecol 87:672–681. https://doi.org/10.1111/1365-2656.12800
Zuo W, Moses ME, West GB, et al (2012) A general model for effects of temperature on ectotherm ontogenetic growth and development. Proc R Soc B Biol Sci 279:1840–1846. https://doi.org/10.1098/rspb.2011.2000

Competing interest reported. Christophe Pampoulie is a guest editor of the Special Issue “Capelin in a changing environment” and the peer-review process for this article was independently handled by another guest editor or a member of the journal editorial board.

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Has the displacement of capelin Mallotus villosus (Müller, 1776) feeding ground induced a phenotypic response?

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