Every biologist needs a book to read in the life of its favoured organism. For ichthyologists, teleost scales have been telling stories over a century. Because of their external position, they are easy to remove. Among other applications, they give access to life history traits, such as age (at maturity or at migration) and growth (Figure 1). By delineating annuli (yearly rings deposited during winter) and measuring the associated interannuli spacing, one can estimate an individual’s growth trajectory and migratory status (Elliott and Chambers, 1996); however, measurements may vary across readers and scales, for a same fish.

Figure 1: Scale of a sea trout reflecting its life history traits.

Over time, researcher have come to the acceptance that multiple readings (numerous scales or numerous readers) are better to gain reliable information (Panfili et al., 2002). However, the number of scales or readers required to determine life history traits is not that easy to define because first, it depends on the fish species and second, the mention of the required methodology for one species is rarely explicit in the literature.  Over the past 30 years, the number of validation studies has increased (Campana, 2001), yet the number of required readings to capture biological variability is still unclear. When is it required to proceed to multiple readings? Which variable needs multiple readings (age, growth)? Should we read several scales, or have several readers, or both ?

Individual variation has become a foremost concern in biology, it is therefore paramount to come up with sampling designs able to minimize sampling effort while keeping information level steady: indeed, a reasonable shortcut to avoid redundancy and a waste of resources. Following that idea, Lucie Aulus and colleagues sampled scales of 60 fish originating from the Kerguelen Is. (one of our beloved destinations). For each fish, they determined total age at capture by counting annuli (TA) and measured the total radius (TR) on 4 different scales, using two different readers in a double blind manner (Figure 2).

They then decomposed data variance hierarchically in a nested and crossed manner, namely Fish–Reader–Scale to determine which levels account for the variance in growth and age (Figure 2). The reliability of both scale total radius and fish age was estimated by the r repeatability coefficient (Stoffel et al., 2017). This coefficient ranges from 0 to 1, a high value indicating that a similar result is more likely to be observed when repeating the observation (or measure) under consistent conditions.

Figure 3: Repeatability estimates (r) of (a) total radius (TR) and (b) total age (TA). Symbols and dashed lines indicate the median of the repeatability estimates (r) of Fish level, with uncertainty (i.e. 95% confidence intervals) indicated, obtained over 1000 bootstraps.

For scale total radius (TR), the repeatability was extremely high (97%, Figure. 3a), meaning that whatever the reader or the scale, the measure of total radius was very stable. Basically, it means that when sampled in a relatively well located area on the fish, total radius would be well estimated by using a single measure on a single scale by a single reader. On the contrary, for total age (TA) the repeatability was about 53% (Fig. 3b). Readers indeed sometimes disagreed in delineating annuli on a same scale, or different scales from a same fish provided different age readings consistent between readers.

So, two variables, but not the same repeatability, and yet, these two variables are often associated in various analyses, for instance, to build growth models. The present case study thus indicates that if age is a relative problem, total radius is not, and therefore total radius might not need to be sampled on every scale samples (avoid redundancy, right?). Second, by estimating repeatability, we also estimate the different sources of errors: these error estimates can be later reinjected into further models for total age, wherein we would read less scales per fish, allowing us to increase the number of fish studied for the same effort (to prevent wasting resource).

As soon as one envisions important amounts of scale analysis, such preliminary investigations to quantify errors should be a prerequisite: it can provide valuable insights for accurate modelling of individual variability. Such understanding of interindividual variability could have several applications in stock assessment and conservation. It could also save significant amount of resources for retrospective studies, for which scales collections are invaluable assets.

More details on our study here:

Aulus-Giacosa L., Aymes J.-C., Gaudin P., Vignon M. (2019) Hierarchical variance decomposition of fish scale growth and age to investigate the relative contributions of readers and scales. Marine and Freshwater Research , -.

Cited literature:

Campana, S.E., 2001. Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods. J. Fish Biol. 59, 197–242. https://doi.org/10.1111/j.1095-8649.2001.tb00127.x

Elliott, J., Chambers, S., 1996. A guide to the interpretation of sea trout scales., R & D Report. National Rivers Authority, Bristol (UK).

Panfili, J., De Pontual, H., Troadec, H., Wright, P.-J., 2002. Manuel de sclérochronologie des poissons, Editions Quae. ed. IFREMER : IRD, Plouzané, Paris ; France.

Stoffel, M.A., Nakagawa, S., Schielzeth, H., 2017. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644. https://doi.org/10.1111/2041-210X.12797

Semelparous animals breed once, then die. But what does “once” mean? Some species comply with the so-called big bang reproduction, such as the well named Ephemera (mayflies), which lay one clutch of eggs and die within the same night. However, many species are considered semelparous while breeding several times within a single breeding season. Semélê herself mated several times with Zeus before being thunderstruck, admittedly after (in fact before) giving birth for the first time. Allis shad is such a semelparous species. It is also a capital breeder with determinate fecundity, which means that these fish start their one-month long spawning season with finite stocks of energy and eggs. They face an optimization challenge: matching egg and energy exhaustion. It would not be adaptive for them to either squander their energy and die with unlaid eggs, or survive long after having laid their last eggs. This challenge exists for every living organism, but it is probably more meaningful to species like Allis shad, which face a particularly steep rate of energy and egg exhaustion until death.

With this in mind, we documented the schedule of spawning acts and energy consumption of a few Allis shad in the field. For this, we caught them at the Uxondoa dam on the Nivelle River (Basque Country), at the end of their upstream migration, and tagged them with accelerometers that logged data (3D acceleration + temperature + pressure) until the fish’s death. Four kinds of information were obtained from these data (Fig. 1).

Fig. 1a

Fig. 1b

Fig. 1c

Fig. 1d

Figure 1. (a) shad spawning and (b) the corresponding 3D acceleration (xyz: red, green, blue) and pressure (pink) signal. (c) Tail beats and the corrsponding wave on the z-axis. (d) Tag verticality as an indicator of fish’s roundness.

First, as the spawning act consists in the fish pair spinning for a few seconds in approximately five one-meter diameter circles while thrashing the water surface with their tail, the corresponding pattern of acceleration and hydrostatic pressure was detected to assess the number and timing of spawning acts. Second, average tail beat frequency and temperature were computed for every minute and transformed in energy expenditure, using a model built for American shad^1,2. Third, the gravitational component of acceleration was used to regularly compute the angle between the tag and the vertical, an indicator of fish’s roundness. Fourth, the exact timing of death was detected as both a null dynamic acceleration indicating immobility, and a shift in gravitational acceleration indicating the fish rolled on its flank. Dead fish were retrieved, in order to collect the accelerometers and their data, and weigh the fish and their remaining oocytes.

Figure 2. The schedule of spawning and energy consumption. (a) Timing of shad spawning acts in the season – each colour is a different individual. (b) Cumulated energy consumed, estimated from temperature and tail beat frequency. (c) Change in tag verticality.

On average, a shad female performed 16 spawning acts distributed in six nights each separated by four nights without spawning (Fig. 2). The timing of spawning seemed to be influenced by both the physical and social environment, since the probability of spawning during one night increased with temperature, and spawning acts within a night were temporally aggregated both intra- and inter-individually. The metabolic model fed with temperature and tail beat frequency predicted a very steep energy consumption: on average 0.19kJ.min^-1, summing to 7193kJ for 26 days of spawning activity, more than American shad during their 230km and seven-week long upstream migration in the Connecticut River². Accordingly, shad thinned rapidly, especially during nights, and lost up to 53% of their initial weight. They died on average four days after their last spawning act, retaining 80g of ovaries, while the initial weight must have been around 200g.

So, shad females seem to rapidly expend their energy while spawning, and die with a significant amount of remaining eggs. Yet, shad in the Nivelle only have to ascend 13km to reach spawning grounds. How would they manage their spawning energy after a long upstream migration in a dammed river, with warming water? This management of egg and energy stock might be crucial for population conservation³. Methodologically, this study is a further step towards the monitoring of spawning activity and related energy expenditure in the field, and the field is where we (at least some of us) like to be!

Read the full story on BioRXiv: https://doi.org/10.1101/436295

Cited literature:

Density-dependence is a fundamental principle in ecology: it states that the growth, the survival, the fitness of individuals is directly related to local density. This is so because trophic resources are limited, a point stated by Malthus in 1798 that inspired Darwin’s theory of natural selection. Malthus had indeed predicted that demographic parameters should change with density. One interesting consequence of density dependence is that it tends to promote homeostatic dynamics: when density is low, survival is increased so to reach quickly an equilibrium point; once reached, the population size will not increase greatly simply because survival decreases due to high density. In a nutshell, this is the concept of population “resilience”.

Fishes, and especially salmonids, are no exception to this natural law. When resources per capita change, then individual fitness changes accordingly. Of course, if resources, or access to resources, are controlled by environmental variation, then environmental variation controls density dependent mechanisms in salmonid populations. There is a wealth of papers describing this density dependence in natural or experimental environments.

Ranking among one of the most potent environmental change, rainfall variation shapes many aspects of salmon life history. It controls for trophic resources by affecting the availability of preys, but it also determines local density for salmon themselves, by changing water discharge in rivers. Climate change reshuffling our knowledge of rainfall patterns, it becomes paramount to investigate how this parameter can affect the resilience of salmon populations.

Our lab set up an experiment in a semi-natural channel, where we introduced wild Atlantic salmon juveniles from known parents. In this channel, we created several replicates for a simple design combining two density levels (2.5 and 5 fish.m²) and two water discharge levels (Low Flow =70 m³.h^-1 and High Flow = 110 m³.h^-1, see Figure 2). 4 replicates were created for each condition, totalizing 960 juveniles originating from 7 families. We monitored individual growth and survival in each experimental condition especially during the first summer. The data indicate that at High Flow, survival and growth are strongly controlled by density: this was the expected mechanism at work, which fosters population resilience. But at Low Flow, this density dependent effect nearly disappeared, on both survival and growth. Environmental change, through river flow dynamics in summer, would impact negatively one of the fundamental mechanisms that govern the persistence and stability of salmon populations.

Although this pattern itself is already interesting, because it teaches us that the dynamics of our resources may be less resilient than it used to be, it also shadows a number of possible explanations that are probably not mutually exclusive. You can discover more about this experiment: family effects, standard metabolism, and expression of nutritional metabolism related genes, it is all here.

References:

Bardonnet A., Lepais O., 2015. Interactions and effects of density, environment and parental origin on Y-O-Y Atlantic salmon survival, growth and early maturation. IV International symposium on « Advances in the population ecology of stream salmonids”, May 25-29, Girona, Spain.

Bardonnet A., Lepais O., Bolliet V., Panserat S., Salvado J.-C., Prévost, E., 2017. Impact of low flow on young-of-year Atlantic salmon: density-dependent and density-independent factors interact to decrease population resilience. 50th Anniversary Symposium of the Fisheries Society of the British Isles, 3-7 July, Exeter, UK.

Our lab has long been involved in monitoring the colonization of sub-Antarctic Kerguelen Islands by introduced salmonids: decades of data and samples, which we try to mine and perpetuate. There is a wealth of questions that can be investigated using such extraordinary framework. A very common question is « how will these fish fare in an unknown environment ? ». In more scientific terms, are they ready to survive there, or do they need to adapt, can they adapt, and how will they do it ?

Our long term monitoring indicates that all species did not fare evenly (Lecomte et al. 2013). Some of them disappeared, others persisted, and one became invasive: the nefarious brown trout (Labonne et al. 2013). The partial migration strategy of this species seems to have fitted perfectly in the sub-Antarctic environment of Kerguelen, blotted with fjords, lakes and lagoons. The very first natural generation produced sea trout – although their genitors originated from tenths of generations reared and isolated in fish farms in Europe. During the first generations, our lab maintained a tight monitoring of the dynamics in the one of the two first populations. We benefited from this work, data and samples, and explored at what speed these fish were growing at sea, depending on their age of departure from freshwater, on their sex and on their birth date (cohort effect).

Our results are somewhat surprising (Jarry et al. 2018): these fish possibly never fared better than in this far corner of the world, at least regarding their life at sea. We found growth rates among the fastest we know about, for both sexes (see figure below). We also found that their reproductive investment was rather high, and did not differ between males and females. In other terms, their fitness seems nearly stellar, and growth did not seem strongly limited by reproductive investment, or vice versa. Although we did not have yet estimated the survival rates of these sea trout, we suspect it has been extremely high during the first steps of colonization (Jarry et al. 1998). We even have found very old individuals among sea trout (Labonne et al. 2013). Of course, not everything is bright for our fish, and some stages of their early life in freshwater might be especially taxing since no other fish lived in these freshwater before these introductions, and brown trout is a known fish predator. We recently found for instance that juveniles tended to adapt their feeding behaviour to carbohydrates consumption, which may provoke negative consequences through physiological disorders (Marandel et al. 2017).

Yet they keep on colonizing, and currently try to settle in very eutrophic rivers. How do they choose their next eldorado ? Well, we thought you might want to know, so we are now deploying a monitoring protocol on the colonization front, thanks to our colleagues from OTN, University of Dalhousie , and NTNU, which will allow the acoustic tracking of sea trout, right on the spot where new virgin rivers are available. More details here.

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All this work is founded and supported by the French Polar Institute.