Introduction
Despite their remarkable ability to adapt to all kinds of environments,
the European eel, Anguilla anguilla, population has been in
decline, at least since the 1960s (ICES, 2019; Dekker & Beaulaton,
2016). Recruitment to freshwater habitats decreased by more than 90% in
the early 1980s and since 2008 the European eel has been listed as
Critically Endangered on the International Union for Conservation of
Nature (IUCN) red list (Jacoby & Gollock, 2014). Causes of the decline
are related to habitat loss, overfishing, climate change, pollution,
parasites and diseases (Aschonitis et al., 2017; Drouineau et al.,
2018).
The European eel is semelparous and panmictic (Als et al., 2011). It
spawns in the Sargasso Sea, but is distributed across Europe, from
northern Norway to northern Africa and far into the Mediterranean
(Schmidt & Regan, 1923; Dekker, 2003a). Larvae drifting with the Gulf
Stream metamorphose into glass eels when they reach the continental
shelf. These glass eels colonize coastal and freshwater habitats where
they spend their growth phase until they start to mature into silver
eels which will migrate back to the Sargasso Sea for spawning (Bertin,
1956; Tesch, 2003).
The status of the stock is primarily assessed through time series of
recruiting glass eels (or elvers) to freshwater at different monitoring
stations across Europe (ICES, 2000). A severe reduction in glass eel
recruitment, more marked in the northern part of the distribution area,
became apparent in the early 1980s (Moriarty, 1986,1990; Dekker, 2003b,
2004; ICES, 2016; Bornarel et al., 2017). However, signs of decrease in
the standing stock (yellow stage) date from the 1960s (Aalto et al.,
2016; Dekker, 2003c).
At some of the monitoring stations, like in the river Imsa, Norway, both
upstream ascending elvers and downstream migrating silver eels are
trapped and counted (Sandlund et al., 2017). The Imsa reference
time-series was started in the 1970s, and the age distribution and the
population dynamics in this catchment were especially studied in the
1980s and 1990s, yielding fundamental knowledge on the ecology of
European eels (Vøllestad et al., 1986; Vøllestad & Jonsson, 1986, 1988;
Vøllestad, Jonsson, Hvidsten, & Næsje, 1994). Following the awareness
of the European eel population crash, countries across Europe developed
management plans in accordance with a European Union Regulation (EC
1100/2007). Although a non-EU country, Norway followed in 2010, with a
ban on eel fishing and the Imsa time-series became even more relevant to
monitor the local sub-stock (Poole et al. 2018).
Knowledge on age structure is essential to assess the status of a
population. Determination of age in fish is challenging, especially in
long-lived species such as the European eel. For this species, otoliths
(or earstones) are prepared so that annual rings (annuli), marking
periods of fast and slow growth, become visible and can be counted to
give an age estimate (Moriarty, 1973, 1983, Svedäng, Wickström,
Reizenstein, Holmgren, & Florenius, 1998). Four main methods have been
used for preparing otoliths: 1) Grinding and polishing, 2) slicing, 3)
burning and cracking, and 4) clearing of whole otoliths in ethanol
(in toto method). It has been debated what is the better method
(Vøllestad & Næsje, 1988; ICES, 2009). Different methods yield
different estimates (Moriarty & Steinmetz 1979). The suitability of
each method depends on the age and growth of individuals (Vøllestad,
Lecomte-Finiger, & Steinmetz, 1988). For example, burning and cracking
is more suitable for slow-growing eels, because of the shape of their
otolith and the numerous but short growth increments (Vøllestad &
Næsje, 1988). The “in toto” method (clearing whole otoliths) is fast
and inexpensive, but best suited for young eels (Vøllestad and Næsje,
1988). During recent years, a manual regarding the best practice for
ageing eels has been developed (ICES, 2009, 2011). However, methods are
still debated due to a general lack of validation and of different
growth patterns in such a widespread species.
Once annuli are revealed, interpretation remains challenging. For
example, traumatic events, such as high temperatures in the summer,
diseases, or stress can cause supernumerary check or “false” checks
(Domingos, Costa, & Costa, 2006; Graynoth, 1999; ICES, 2009; Tzeng, Wu,
& Wickström, 1994; Svedäng et al., 1998). In addition, observations can
vary between readers, over time and between laboratories.
Age estimates have been validated in some cases using chemical marking
of otoliths (Chrisnall & Kalish 1993; Dekker, 1986; Oliveira, 1996;
Svedäng et al., 1998), external color marking (Chisnall & Kalish 1993;
Poole & Reynolds 1996) or indirect methods using individual
mark-recapture techniques (Poole & Reynolds 1996; Beentjes & Jellyman,
2015), or by introducing eels in a pristine waterbody (Vøllestad &
Næsje 1988; Wickström, Westin, Clevestam, 1996; ICES, 2009). However,
validations for the European eel have been done mostly on young
individuals with a maximum age of 14 years and unfortunately, there are
still too few examples of validation of ageing methods in eels.
In the earlier years (1980-90s), the age of silver eels from the river
Imsa was determined using the in toto method (IT), and it was
later suspected that these ages were underestimated. Since the otoliths
had been maintained, it was decided to reanalyze them using the
consensus method: grinding and polishing method (GP) and to compare both
age estimates. Additionally, new otolith samples were collected in the
2010s and treated in the same way (GP method) to investigate possible
changes in their age distribution of silver eels since the decline of
the population (Poole et al., 2018). Finally, using this new dataset,
the possible relationship between the number of ascending recruits and
the number of descending silver eels was examined to reanalyze previous
models (Vøllestad & Jonsson 1988) established for the Imsa eel stock
during the period 1975-1987.