# Assessing the freshwater distribution of yellow eel

Emilien Lasne, Pascal Laffaille

► To cite this version:

Emilien Lasne, Pascal Laffaille. Assessing the freshwater distribution of yellow eel. Knowledge and Management of Aquatic Ecosystems, 2009, 390-391 (390-91), 10.1051/kmae/2009004. hal-00744162

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# Assessing the freshwater distribution of yellow eel

É. Lasne(1), P. Laffaille(1,2)

## ABSTRACT

**Key-words:**
Anguilla anguilla, colonization, size classes, logistic model, population management

In the global context of the decline in wild species, modeling the distribution of populations is a crucial aspect of ecological management. This can be a major challenge, especially for species, such as the European eel, that have complex life cycles, exhibit cryptic behavior, or migrate over long distances. A review of the literature suggests that eel size data could be used to assess and analyze freshwater distribution of eel. We argue that analyses based on small yellow eels (≤ 300 mm) along the longitudinal course of rivers could provide a valuable tool for population monitoring. We propose a standardized catchment recruitment index and a colonization index based on the probability of occurrence (presence/absence data) using logistic models for different size classes. The model developed here provides a convenient guide for assessing yellow eel stages in freshwater areas, and should have concrete applications for management of the species.

## RÉSUMÉ

### Évaluation de la distribution des anguilles jaunes en eau douce

**Mots-clés :**
Anguilla anguilla, colonisation, classes de taille, modèle logistique, gestion des populations

Dans le contexte actuel de déclin des espèces, la modélisation de la distribution des populations est une étape importante pour la gestion de la biodiversité. Cependant, cette modélisation peut s'avérer difficile, particulièrement pour les espèces qui, comme l'anguille européenne, possèdent des cycles de vie complexes, ont un mode de vie cryptique ou effectuent de grandes migrations. Un examen de la littérature suggère que la taille des anguilles peut être utilisée comme critère pour évaluer et analyser la distribution des anguilles en eau douce. Nous suggérons que l'examen des patrons de distribution des petites anguilles (≤ 300 mm) le long des réseaux hydrographiques peut fournir des informations précieuses pour le suivi des populations. Nous proposons un indice standardisé de recrutement et de colonisation des bassins versants sur la

<footnote>(1) URU 420 Biodiversité et gestion des territoires, Service du Patrimoine Naturel, Université de Rennes 1 – Muséum National d'Histoire Naturelle, Campus Beaulieu, 35042 Rennes cedex, France</footnote>

<footnote>(2) UMR Écologie et Santé des Écosystèmes - INRA/Agrocampus Ouest, 65 rue de Saint-Brieuc - CS 84215, 35042 Rennes Cedex, France, emilien.lasne@agrocampus-ouest.fr</footnote>
base de l'analyse des probabilités d'occurrence de différentes classes de tailles grâce à des régressions logistiques. Le modèle développé ici fournit un cadre conceptuel et pratique pour l'évaluation des stocks d'anguilles jaunes en eau douce, et devrait trouver des applications concrètes pour la gestion de l'espèce.

# INTRODUCTION

Assessing and understanding the distribution of species is a central aspect of conservation ecology. This means that distribution models can be very helpful for conservation planning, and in the general context of biodiversity loss, the literature in this field has grown dramatically in recent years (Guisan and Zimmermann, 2000; Rushton et al., 2004; Guisan and Thuiller, 2005). In this context, one of the main objectives of models is to provide information about the current distribution and trends of a target population. Another objective is to identify the key factors that drive the patterns observed.

However, modeling species distribution poses numerous difficulties. These difficulties arise from constraints that are essentially related to methodology, and they depend to a large extent on the ecological traits of the target species (Rushton et al., 2004); in other words, on the complexity of the life cycle (number of stages and associated habitats), crypticity or rarity. For each context, one has to choose the most appropriate sampling technique and strategy (and the associated scale of investigation) to obtain an appropriate data set. In addition, powerful and reliable analytic methods are needed, and they must be standardized to allow comparisons within and between systems. Finally, even in countries where public opinion is attuned to environmental protection, financial, political and logistical resources for the management of natural spaces are limited (Aronson et al., 2006), and so costs must be kept low. Consequently, devising a methodology for the description and analysis of distribution or abundance patterns can be a real challenge. Besides, species with complex life cycles, extensive habitat requirements and variable density may be very sensitive to anthropogenic factors, and today are often under threat (for example, amphibians and fish in freshwater systems; Darwall and Vié, 2005), and so effective management measures are needed urgently.

The case of the European eel, *Anguilla anguilla*, provides a very good illustration of the problems of distribution modeling. This catadromous, semelparous and long-lived fish (Tesch, 2003) is severely threatened throughout its distribution area (Dekker, 2003a). Larvae (leptocephalii) drift across the Atlantic Ocean in the Gulf Stream to the continental shelf where they metamorphose into glass eels. The tide carries them to the coast, where they transform, first into elvers and then small yellow eels that colonize growth areas. Some of them settle in coastal water and estuaries, but others colonize freshwater (Daverat et al., 2005). This growth stage commonly lasts from 1 to more than 15 years (Rigaud et al., 2008). When they reach a threshold size, they metamorphose into the silver eels that migrate to the Sargasso Sea spawning area where they reproduce just once. Managers need to be able to monitor the freshwater population distribution and assess its current status (Feunteun, 2002; Baisez and Laffaille, 2005). However, such a complex life cycle makes this difficult.

In this study, we present an approach to developing a logistic model for monitoring freshwater eel distribution, which is supported by examples such as those presented in Lasne and Laffaille (2008). We present the conservation and ecological background that led to the assumptions that underlie a standardized catchment recruitment index and a colonization index.

# CONSERVATION AND ECOLOGICAL BACKGROUND

## > DEFINING THE PROBLEM AND THE OBJECTIVES

The European eel has long been considered a stress-tolerant species (Tesch, 2003). However, today it is declining sharply throughout its distribution area (Moriarty and Dekker,
1997; Dekker, 2003b). Although we do not understand the exact causes of this decline, it is speculated that the main factors involved are probably changes in oceanographic conditions that are assumed to be linked to climate changes (Desaunay and Guérault, 1997; Knights, 2003; Bonhommeau *et al.*, 2008), habitat changes and the obstruction of migration by dams and other physical obstacles (Feunteun, 2002; Laffaille *et al.*, 2004, 2007), and the impact of parasites and chemicals (Robinet and Feunteun, 2002; Kirk, 2003). In this context, Russel and Potter (2003) suggested that the precautionary approach is the most appropriate way to consider European eel stock management. Indeed, as we have so little information about the marine phase and are unable to control oceanographic conditions, the best we can do is to optimize the production of spawners (in terms of biomass and quality) in continental areas. In this context, Feunteun (2002) and Baisez and Laffaille (2005) suggest that the best scale of investigation is that of the catchment area. As a first step and for management purposes, we need indicators to monitor the continental stages. Such indicators should make it possible: (1) to assess the current status and trends of the catchment population, (2) to assess the effects of changes in aquatic systems or the efficiency of management measures, and (3) to compare freshwater systems. Such indicators could be combined in a local eel report card. An eel report card seems to be the best way of measuring the impact of the management actions, and providing advice about how to maintain or improve the eel population, taking into account the mortality of the species on the catchment scale (Baisez and Laffaille, 2005).

Yellow eel stock assessments and surveys are common, but they have been based on different sampling methods (Naismith and Knights, 1990; Knights *et al.*, 1996; Jellyman and Graynoth, 2005; Laffaille *et al.*, 2005a). They provide evaluations in terms of the relative or absolute abundance, spatial distribution and size- or age-class distribution patterns (Lobon-Cervia *et al.*, 1995; Smogor *et al.*, 1995; Ibbotson *et al.*, 2002; Laffaille *et al.*, 2004; Laffaille *et al.*, 2005c; Lasne *et al.*, 2008). However, they often only report on a small geographical area. To date, it has not been possible to obtain a description of the state of the whole continental stock (Dekker, 2000). Moreover, local population attributes (e.g. mean size, age at maturity, sex ratio, growth rates, body condition, etc.) depend closely on the characteristics of the site (Vøllestad, 1992; Panfili and Ximénès, 1994; Dekker, 2000; Yalcin-Ozdilek *et al.*, 2006; Edeline, 2007; Lasne *et al.*, 2008; Lobon-Cervia and Iglesias, 2008; Rigaud *et al.*, 2008), which makes inter-system comparisons very complex. In addition, sampling efficiency depends to a large extent on the kinds of gear, sampling methods and techniques that are used, and these differ from one research team to another (see Naismith and Knights, 1990; Laffaille and Rigaud, 2008; Reid *et al.*, 2008). Simple distribution models for the assessment of continental yellow eel population trends on a large scale (i.e. at least the catchment scale) are needed to enable us to cope with these problems. Furthermore, large-scale models are particularly convenient for managers (Fleishman *et al.*, 2001), and Collares-Pereira and Cowx (2004) have shown that fish conservation and management actions are most efficient when they encompass the whole catchment area.

> **THE "SIZE-DEPENDENT INFORMATION" HYPOTHESIS**

Ecological shifts occur throughout the eel's lifetime (Rigaud *et al.*, 2008). As a consequence, the factors that influence eel distribution may also change during the lifetime of individuals. The distribution of individuals at different life stages is bound to depend on and reflect particular processes. Thus, we have to find out what determines the distribution of the various life stages – and in turn, what kind of information assessing them may yield.

**Reliability and accuracy of information according to size**

There may be a high degree of heterogeneity of traits between and within continental populations of eel. In particular, size and age relationships are very variable. Yellow eels range from 0+ to more than 15 years in age, and from just under 70 mm to more than 1300 mm (Rigaud *et al.*, 2008). Differences in individual size may be attributable to age
differences as much as to the differences in growth rates that depend mainly on local and/or
regional conditions, e.g. temperature and salinity of the growth habitat (Yalcin-Ozdilek et al.,
2006; Edeline, 2007) and sex (Vøllestad, 1992; Naismith and Knights, 1993; Aprahamian,
2000; Graynoth and Taylor, 2000; Melià et al., 2006). The more time individuals spend in
continental waters, the more variable these size-age relationships become (Panfili and
Ximénès, 1994; Poole and Reynolds, 1996, 1998; Graynoth and Taylor, 2004). As a
consequence, the size of small eels is a better predictor of age than that of large eels,
notably silver ones. For instance, we can assume with reasonable confidence that European
eels ≤300 mm in length range from 0+ to 4 years (Panfili and Ximénès, 1994; Poole and
Reynolds, 1996, 1998; Melià et al., 2006), whereas it is very difficult to age larger eels on the
basis of length alone (Mounaix, comm. pers.). In addition, the distribution of large eels results
from the combination of all the events the individual has undergone since they reached the
freshwater system. As a consequence, it is not possible to extract any precise information
from the resulting distribution patterns. Therefore, despite the fact that, as suggested by
Feunteun (2002), surveying escaping silver eels is “a major research scope in the coming
years” since it is “a relevant way to assess the efficiency of restoration programs because it
gives a measurement of the continental dynamics of populations”, we think that using
seaward migrating dynamics to evaluate the continental population and the efficiency of
restoration programs would in fact be neither precise nor parsimonious, because spawner
production results from numerous, complex events, some of which may have happened
many years earlier. In contrast, far fewer factors influence the distribution of small eels. It
would therefore seem to be more accurate and more reliable to base comparisons within or
between systems on small eels, because we have more information about them and their
recent history. Following this argument, it may be possible to distinguish other classes of
smaller individuals, ≤150 mm or 150–300 mm, for instance, and so on, to improve the quality
of the data. The smallest eels originate from the most recent recruitment events (0+ and 1+),
so focusing on these eels looks like an interesting approach. However, there may be a risk
attached to excessively reducing the range of classes, because this would enhance the
noise (e.g. measurement errors or artifacts), to the detriment of the real trends we want to
detect. In addition, the inland penetration of the smallest eels is also limited by the time
available to colonize upstream reaches, independently of other factors. Finally, there is a
trade-off between accuracy and parsimony in the description of yellow eel distribution.
Consequently, we suggest that using two size classes (≤300 and > 300 mm eels) offers a
good compromise, particularly as this distinction is biologically relevant.

Differences in the nature of information according to size

Migration behavior, habitat and feeding requirements all change during the continental life of
eels (Rigaud et al., 2008). More precisely, several studies suggest that around the 300-mm
size several changes occur (Michel and Oberdorff, 1995; Baisez, 2001; Feunteun et al., 2003;
Laffaille et al., 2003, 2004). Firstly, elvers and small yellow eels are mainly invertivorous and
live in shallow water, whereas larger eels are found in deeper water, where they feed on other
fish including small eels. This size-related habitat use on the meso- or microhabitat scale
should be taken into account when devising sampling procedures. For instance, the size
structure observed could be biased if all the different habitats are not investigated (Laffaille
et al., 2003), or if the gear used is not the same (Baisez, 2001). Despite some exceptions
(Lamson et al., 2006), large eels generally exhibit little migration behavior and tend to
become sedentary (“home-range dwellers”), though some remain “nomadic” (Baisez, 2001;
Ibbotson et al., 2002; Feunteun et al., 2003; Laffaille et al., 2005b). In UK rivers, Ibbotson
et al. (2002) showed that density-dependent mechanisms mainly apply to eels aged
0-4 years, whereas larger ones disperse randomly. The environmental and intrinsic factors
involved in migration behavior change when individuals grow up, and it is these pressures
that determine the distribution of different size classes along the course of freshwater rivers
(Feunteun et al., 2003). Thus, the distribution of different size classes on both large and small
scales reflects various mechanisms depending on eel size. Finally, 300 mm corresponds to
the size from which silvering may occur in males (Dekker *et al.*, 1998; Feunteun *et al.*, 2000; Laffaille *et al.*, 2006). Above this size, individuals may leave freshwater and head for the Sargasso Sea, and so the distribution patterns of > 300 mm eels that we observe may be biased, and the relationships between the factors we are tracking and eel distribution substantially distorted.

## PREDICTIONS

This size-dependent information hypothesis enables us to predict continental eel distribution patterns.

### ASSESSMENT OF FRESHWATER STOCK STATE AND TRENDS

Several authors suggest that density dependence leads to greater upstream migrations, especially in small eels (e.g. Moriarty, 1986; Ibbotson *et al.*, 2002; Feunteun *et al.*, 2003; Rigaud *et al.*, 2008). In other words, the more the downstream areas are already occupied, the more eels are forced to go upstream to settle in suitable unoccupied places. This has been shown especially in small eels (Briand *et al.*, 2005). As a consequence, spatio-temporal surveys of distribution patterns and identification of the “high-density area” (according to Feunteun *et al.*, 2003) of small eels within a catchment area should provide information about downstream occupancy fluctuations. These patterns result mainly from a combination of different parameters: freshwater recruitment (above the tidal limit; see Laffaille *et al.*, 2007), the stock already present (i.e. catchment occupancy according to a patchy fluid mosaic as proposed by Feunteun *et al.*, 2003), habitat accessibility (Lasne and Laffaille, 2008) and habitat suitability (Laffaille *et al.*, 2004). Generally speaking, monitoring small eel distribution patterns should provide information about the state of continental stocks. Moreover, since small eels are at the beginning of their continental life, spatio-temporal analysis of small eel distribution should provide indications about future trends in yellow eel stocks and future seaward-migrating silver eel production (Feunteun *et al.*, 2000; Allen *et al.*, 2006). For instance, an expanding upstream distribution (i.e. the distance inland colonized) of small eels indicates rejuvenation of the population, whereas a decreasing upstream distribution suggests aging and a future decline in silver eel production.

### ASSESSMENT OF CHANGES IN RIVER LONGITUDINAL CONNECTIVITY

Changes in the longitudinal (i.e. downstream-upstream) connectivity of rivers are one of the main causes of the decline in eels in local freshwaters (see, for example, Domingos *et al.*, 2006). Feunteun (2002) suggested that the opening of migration pathways was one of the most important management tools, mainly by constructing fishways (Legault, 1994; Knights and White, 1998; Laffaille *et al.*, 2005c) or dam management (Legault, 1990; Laffaille *et al.*, 2007). Scientists and managers need to assess the results of modifications of river network connectivity, and small eels should be good bioindicators of connectivity and permit the detection of dysfunctions (Feunteun *et al.*, 1998; Lasne and Laffaille, 2008). For instance, systems which have no small eels in reaches where they would be expected to be present may suffer from low longitudinal connectivity, and could therefore be targeted for management measures (e.g. the installation of ladders, dam removal or dam management). Improvements in the connectivity of river networks can be expected to lead to a rapid recolonization by small eels (see, for example, the case of the River Vilaine, France, in Feunteun *et al.*, 2003 and Briand *et al.*, 2005).

### INTRA- AND INTER-SYSTEM COMPARISONS OF FRESHWATER SYSTEMS

It is useful to compare different catchment areas in order to assess global trends (e.g. on the scale of the whole European eel population) as well as local ones. Indeed, local abundance
and distribution along rivers depend firstly on the number of glass eels that reach the tidal limit, but also secondly on local features, e.g. hydrology, size, attractiveness and longitudinal connectivity of the catchment areas, which determine catchment colonization. Similar trends observed across different systems or regions (improvement, reduction or stability of eel distribution) should provide information about the state of the global eel population. On a smaller scale, the comparison of patterns in neighboring catchment areas (which therefore a priori have a similar eel input) should provide information about local characteristics, such as longitudinal connectivity or habitat quality (Lasne and Laffaille, 2008).

# MODELING EEL DISTRIBUTION IN RIVERS

## > ASSESSING DISTRIBUTION PATTERNS WITH LOGISTIC MODELS

Various kinds of data can be used to assess eel distribution. Some studies use the absolute or relative density (e.g. Lobon-Cervia et al., 1995; Ibbotson et al., 2002; Laffaille et al., 2003; Imbert et al., 2008). The problem with this type of data is that the methods available do not allow reliable standardization of the data due to factors such as differing sampling efficiency in shallow versus deep habitats (Naismith and Knights, 1990; Jellyman and Graynoth, 2005). It has also been shown that sampling efficiency is lower at high population densities (Laffaille et al., 2005a). In addition, it is not usually possible to implement any given protocol (i.e. using the same sampling gear and the same sampling method) over a large spatial scale. In a recent paper (Lasne and Laffaille, 2008), we showed that logistic models are a very convenient way to assess yellow eel distribution patterns along rivers. Firstly, such models rely on presence-absence data, which are easier to obtain than abundance data (Manel et al., 2001; Royle et al., 2005), especially for eel populations in open and/or large water systems (Naismith and Knights, 1990; Jellyman and Graynoth, 2005). Faced with the widespread problem related to the difficulty of obtaining reliable and usable data sets, recent advances in conservation biology have suggested that presence-absence data are particularly suitable for the assessment of species distribution in a conservation context (MacKenzie, 2005; Vojta, 2005). Despite the simplicity of such input data, they can provide reliable information for analyzing species distribution, especially in a conservation context (Joseph et al., 2006), and above all the distributions of fish assemblages (Ibarra et al., 2005; Lasne et al., 2007a, 2007b), certain fish species (Oberdorff et al., 2001; Pont et al., 2005), migratory fishes (Eikaas and MacIntosh, 2006) and eel (Broad et al., 2001; Lasne and Laffaille, 2008). In fish, electrofishing methods are among the most common ways of sampling fish. Despite its limitations under some conditions (e.g. in deep habitats), electrofishing is recognized as being an efficient method of sampling various size classes of eel in freshwaters (Laffaille and Rigaud, 2008). Of course, fish detection is also influenced by the sampling procedure, e.g. the surface area prospected, the duration of sampling, number of passes, etc. (Laffaille and Rigaud, 2008), but variables of this type could be integrated into the model when sampling protocols across sites are contrasted (Pont et al., 2006, 2007).

Logistic regressions based on presence-absence data can be used to predict the occurrence probability as a function of independent variables (Peeters and Gardeniers, 1998; Broad et al., 2001). Broad et al. (2001), who studied eel spatial distribution, constructed the following model:

$$ \text{Occurrence probability} = \frac{e^{\beta_0 + \beta_1 \times X1 + \dots + \beta_n \times Xn}}{1 - e^{\beta_0 + \beta_1 \times X1 + \dots + \beta_n \times Xn}} $$

where β are regression coefficients, with β0 as the intercept, and X1 to Xn are the n independent variables we want to test, i.e. the distance to the tidal limit (km), time (freshwater population trend in a catchment area) and space (comparisons between freshwater catchment areas). The best models are selected according to Burnham and Anderson (2002) using the Akaike Information Criterion (AIC = 2 × log-likelihood + 2n, where n is the number of estimated parameters).
<figure><img src="image_2.png" /><figcaption>**Figure 1**</figcaption></figure>

Occurrence probability patterns of small eels (≤300 mm) along three virtual catchment areas. Several metrics can be used to depict catchment patterns, and make inter- or intrasystem comparisons (see text for details). Note that catchment areas 1 and 3 are of equal length, whereas catchment area 2 is smaller.

Figure 1

Patrons d'occurrence des anguilles ≤ 300 mm le long de trois bassins versants virtuels. Différentes métriques peuvent être utilisées pour décrire l'occupation des bassins versants (détails dans le texte).
N.B. : Les bassins versants 1 et 3 sont de longueur semblable alors que le 2 est plus court.

> ANALYSIS OF LOGISTIC DISTRIBUTION PATTERNS

The general pattern is for the occurrence probability of eels to decrease upstream (Lasne and Laffaille, 2008; Figure 1). However, different patterns may occur in small systems (Laffaille et al., 2003). Several metrics can be used to depict patterns and make inter- or intrasystem comparisons. First, the distance at which the occurrence probability is 0.5 (or, e.g., 0.2 in catchment areas where population densities are low) could be used as an index of catchment colonization resulting in both freshwater recruitment and longitudinal connectivity of rivers. In the example presented in Figure 1, upstream colonization is highest in catchment area 2, and lowest in catchment area 3. Secondly, the occurrence probability in downstream reaches (OP) can be used as an index of freshwater recruitment. On the basis of this assumption, recruitment is highest in catchment area 1 and lowest in catchment area 3. However, the OP in catchment area 2 is lower than that in catchment area 1, whereas conversely, D2 > D1. In such a situation, it is likely that downstream habitat suitability and/or accessibility is lower in catchment area 2 than in catchment area 1.

Such a metric could also be used to assess temporal trends. In Figure 1, catchment areas 1 and 3 could actually correspond to patterns observed for the same system at two different times. Thus, changes in the shape of the curves, and more specifically in the two metrics, could be used to quantify the changes. In the Loire catchment basin, this model revealed size-related distribution patterns, and also showed the consequences of the individual characteristics of the catchment area (e.g. in terms of longitudinal connectivity) for these patterns.

CONCLUSION

Despite the complexity of the life history of the European eel, it is possible to produce simple
models that are appropriate for management purposes. Exhaustive – and therefore
complex – models, such as those based on eel abundance and which integrate a whole set
of factors, are also desirable, but so far, it seems to be unrealistic to attempt to develop them
on a very large scale, since neither environmental nor fish abundance data are available
everywhere or standardized. One of our objectives here was to stimulate the analysis of
presence-absence data, and the pooling of data sets from various European countries. Such
"pan-European" work using various data sources has been used successfully to develop a
fish-based index (Pont et al., 2006, 2007). A European eel model is clearly needed, and we
think that it is now a realistic goal. First attempts on the scale of the French territory are
currently in process (unpublished data) and they are very promising.

In addition, it seems to be time to develop and use standardized sampling methods (see Laffaille et al., 2005a; Reid et al., 2008). To guide researchers in such developments, it must be kept in mind that there is a trade-off between the quality/accuracy of data and their volume. Other things being equal, sampling for absolute density data requires a huge investment (in terms of time and/or manpower) and the sampling area is necessarily limited. Conversely, sampling a lot of sites using a short electrofishing session in favorite eel habitats to collect presence data makes it possible to reallocate sampling efforts to other sampling sites. The former approach is probably more suitable for small-scale studies (for instance, to study small-scale patterns), whereas the latter seems to be more appropriate for larger-scale studies.

REFERENCES

Allen M., Rosell R. and Evans D., 2006. Predicting catches for the lough Neagh (Northern Ireland) eel fishery based on stock inputs, effort and environmental variables. *Fish. Manag. Ecol.*, **13**, 251–260.

Aprahamian M.W., 2000. The growth rate of eel in tributaries of the lower River Severn, England, and its relationship with stock size. *J. Fish Biol.*, **56**, 223–227.

Aronson J., Clewell A.F., Blignaut J.N. and Milton S.J., 2006. Ecological restoration: A new frontier for nature conservation and economics. *J. Nat. Conserv.*, **14**, 135–139.

Baisez A., 2001. Optimisation des suivis des indices d'abondances et des structures de taille de l'anguille européenne (*Anguilla anguilla*, L.) dans un marais endigué de la côte atlantique : relations espèce-habitat, Ph.D. Dissertation, University of Toulouse 3, France.

Baisez A. and Laffaille P., 2005. Un outil d'aide à la gestion de l'anguille : le tableau de bord anguille du bassin Loire. *Bull. Fr. Pêche Piscic.*, 378-379, 115-130.

Bonhommeau S., Chassot E. and Rivot E., 2008. Fluctuations in European eel (Anguilla anguilla) recruitment resulting from environmental changes in the Sargasso Sea. *Fish. Oceanogr.*, **17**, 32–44.

Briand C., Fatin D., Fontenelle G. and Feunteun E., 2005. Effect of re-opening of a migratory pathway for eel (A. anguilla) at a watershed scale. *Bull. Fr. Pêche Piscic.*, 378-379, 67-86.

Broad T.L., Townsend C.R., Arbuckle C.J. and Jellyman D.J., 2001. Microhabitat use by longfinned eels in New Zealand streams with contrasting riparian vegetation. *J. Fish Biol.*, **59**, 1385–1400.

Burnham K.P. and Anderson D.R., 2002. Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn., New York, Springer-Verlag, 488 p.

Collares-Pereira M.J. and Cowx I.G., 2004. The role of catchment scale environmental management in freshwater fish conservation. *Fish. Manag. Ecol.*, **11**, 302–312

Darwall W.R.T. and Vié J.C., 2005. Identifying important sites for conservation of freshwater biodiversity: extending the species-based approach. *Fish. Manag. Ecol.*, **12**, 287–293

Daverat F., Tomas J., Lahaye M., Palmer M. and Elie P., 2005. Tracking continental habitat shifts of eels using otolith Sr/Ca ratios: validation and application to the coastal, estuarine and riverine eels of the Gironde-Garonne-Dordogne watershed. *Mar. Freshw. Res.*, **56**, 619–627.

Dekker W., 2000. The fractal geometry of the European eel stock. *ICES J. Mar. Sci.*, **57**, 109–121.

Dekker W., 2003a. Did lack of spawners cause the collapse of the European eel, *Anguilla anguilla? Fish. Manag. Ecol.*, **10**, 365–376.

Dekker W., 2003b. Status of the European eel stock and fisheries. In: Aida K., Tsukamoto K. and Yamauchi K. (eds.), Eel Biology, Tokyo, Springer-Verlag, 237–254.

Dekker W., Van Os B. and Van Willigen J., 1998. Minimal and maximal size of eel. *Bull. Fr. Pêche Piscic.*, 349, 195–197.
Desaunay Y. and Guerault D., 1997. Seasonal and long-term changes in biometrics of eel larvae: a
possible relationship between recruitment variation and north Atlantic ecosystem productivity.
*J. Fish Biol.*, **51**, 317–339.

Domingos I., Costa J.L. and Costa M.J., 2006. Factors determining length distribution and abundance of the European eel, *Anguilla anguilla*, in the River Mondego (Portugal). *Fresh. Biol.*, **51**, 2265-2281.

Edeline E., 2007. Adaptive phenotypic plasticity of eel diadromy. *Mar. Ecol.-Prog. Ser.*, **341**, 229-232.

Eikaas H.S. and MacIntosh A.R., 2006. Habitat loss through disruption of constrained dispersal networks. *Ecol. Appl.*, **16**, 987-998.

Feunteun E., 2002. Management and restoration of European eel population (*Anguilla anguilla*): an impossible bargain. *Ecol. Engin.*, **18**, 575-591.

Feunteun E., Acou A., Guillouet J., Laffaille P. and Legault A., 1998. Spatial distribution of an eel population (A. anguilla) in a small coastal catchment of Northern Brittany (France). Consequences of hydraulic works. *Bull. Fr. Pêche. Piscic.*, **349**, 129-139.

Feunteun E., Acou A., Laffaille P. and Legault A., 2000. European eel (A. anguilla): prediction of spawner escapment from continental population parameters. *Can. J. Fish. Aquatic Sci.*, **57**, 1627-1635.

Feunteun E., Laffaille P., Robinet T., Briand C., Baisez A., Olivier J.-M. and Acou A., 2003. A review of upstream migration and movements in inland waters by Anguillid eels: towards a general theory. *In*: Aida K., Tsukamoto K. and Yamauchi K. (eds.), Eel biology, Tokyo Springer-Verlag, 191-213.

Fleishman E., Mac Nally R., Fay J.P. and Murphy D.D., 2001. Modelling and predicting species occurrence using broad-scale environmental variables: an example with butterflies of the Great Basin. *Cons. Biol.*, **15**, 1674-1685.

Graynoth E. and Taylor M.J., 2000. Influence of different rations and water temperatures on the growth rates of shortfinned eels and longfinned eels. *J. Fish Biol.*, **57**, 681-699.

Graynoth E. and Taylor M., 2004. Growth of juvenile eels (*Anguilla spp.*) in lowland streams in New Zealand. *Fish. Res.*, **66**, 95-106.

Guisan A. and Thuiller W., 2005. Predicting species distribution: offering more than simple habitat models. *Ecol. Lett.*, **8**, 993-1009.

Guisan A. and Zimmermann N.E., 2000. Predictive habitat distribution models in ecology. *Ecol. Model.*, **135**, 147-186.

Ibarra A.A., Park Y.S., Brosse S., Reyjol Y., Lim P. and Lek S., 2005. Nested patterns of spatial diversity revealed for fish assemblages in a west European river. *Ecol. Fresh. Fish*, **14**, 233-242.

Ibbotson A., Smith J., Scarlett P. and Aprahamian M., 2002. Colonization of freshwater habitats by the European eel (*Anguilla anguilla*). *Fresh. Biol.*, **47**, 1696-1706.

Imbert H., de Lavergne S., Gayou F., Rigaud C. and Lambert P., 2008. Evaluation of relative distance as new descriptor of yellow European eel spatial distribution. *Ecol. Fresh. Fish*, **17**, 520-527.

Jellyman D.J. and Graynoth E., 2005. The use of fyke nets as a quantitative capture technique for freshwater eels (*Anguilla spp.*) in rivers. *Fish. Manag. Ecol.*, **12**, 237-247.

Joseph L.N., Field S.A., Wilcox C. and Possingham HP., 2006. Presence-absence versus abundance data for monitoring threatened species. *Cons. Biol.*, **20**, 1679-1687.

Kirk R.S., 2003. The impact of *Anguillicola crassus* on European eels. *Fish. Manag. Ecol.*, **10**, 385-394.

Knights B., 2003. A review of the possible impacts of long-term oceanic and climate changes and fishing mortality on recruitment of anguillid eels of the Northern Hemisphere. *Sc. Tot. Env.*, **310**, 237-244.

Knights B. and White E., 1998. Enhancing immigration and recruitment of eels: the use of passes and associated trapping system. *Fish. Manag. Ecol.*, **5**, 459-471.

Knights B., White E. and Naismith I.A., 1996. Stock assessment of European eel *Anguilla anguilla* L. *In*: Cowx I.G. (ed.), Stock assessment in inland fisheries, Fishing News Books, 431-447.

Laffaille P. and Rigaud C., 2008. Indicateurs de colonisation et de sédentarisation. *In*: Adam G., Feunteun E., Prouzet P. and Rigaud C. (eds.), L'anguille européenne, Indicateurs d'abondance et de colonisation, QUAE, Paris, 230-275.

Laffaille P., Feunteun E., Baisez A., Robinet T., Acou A., Legault A. and Lek S., 2003. Spatial organisation of European eel (*Anguilla anguilla* L.) in a small catchment. *Ecol. Fresh Fish.*, **12**, 254-264.

Laffaille P., Baisez A., Rigaud C. and Feunteun E., 2004. Habitat preferences of different European eel size classes in a reclaimed marsh: a contribution to species and ecosystem conservation. *Wetlands*, **24**, 642-651.

Laffaille P., Briand C., Fatin D., Lafage D. and Lasne E., 2005a. Point sampling the abundance of European eel (*Anguilla anguilla*) in freshwater areas. *Archiv. Hyd.*, **162**, 91-98.
Laffaille P., Acou A. and Guillouet J., 2005b. The yellow European eel (*Anguilla anguilla* L.) may adopt a sedentary lifestyle in inland freshwaters. *Ecol. Fresh. Fish*, **14**, 191–196.

Laffaille P., Acou A., Guillouet J. and Legault A., 2005c. Temporal changes in European eel, *Anguilla anguilla*, stocks in a small catchment after installation of fish passes. *Fish. Manag. Ecol.*, **12**, 123–129.

Laffaille P., Acou A., Guillouët J., Mounaix B. and Legault A., 2006. Patterns of silver eel (*Anguilla anguilla* L.) sex ratio in a catchment. *Ecol. Fresh. Fish*, **15**, 583–588.

Laffaille P., Caraguel J.M. and Legault A., 2007. Temporal patterns in the upstream migration of European eels (*Anguilla anguilla*) at the Couesnon estuarine dam. *Estuar. Coast. Shelf Sci.*, **73**, 81–90.

Lamson H.M., Shiao J.C., Iizuka Y., Tzeng W.N. and Cairns D.K., 2006. Movement patterns of American eels (*Anguilla rostrata*) between salt- and freshwater in a coastal watershed, based on otolith microchemistry. *Mar. Biol.*, **149**, 1567–1576.

Lasne E. and Laffaille P., 2008. Analysis of distribution patterns of yellow European eels in the Loire catchment using logistic models based on presence-absence of different size-classes. *Ecol. Fresh Fish.*, **17**, 30–37.

Lasne E., Lek S. and Laffaille P., 2007a. Patterns in fish assemblages in the Loire floodplain: the role of hydrological connectivity and implications for conservation. *Biol. Cons.*, **139**, 258–268.

Lasne E., Bergerot B., Lek S. and Laffaille P., 2007b. Fish zonation and indicator species for the evaluation of the ecological status of rivers: example of the Loire basin (France). *River Res. Appl.*, **23**, 877–890.

Lasne E., Acou A., Vila-Gispert A. and Laffaille P., 2008. European eel distribution and body condition in a river floodplain: effect of longitudinal and lateral connectivity. *Ecol. Fresh. Fish.*, **17**, 4, 567–576.

Legault A., 1990. Gestion des barrages estuariens et migration des anguilles. *Int. Rev. Ges. Hydrol.*, **75**, 819–825.

Legault A., 1994. Étude préliminaire du recrutement fluvial de l'anguille. *Bull. Fr. Pêche. Piscic.*, 335, 33–41.

Lobon-Cervia J. and Iglesias T., 2008. Long-term numerical changes and regulation in a river stock of European eel *Anguilla anguilla*. *Fresh. Biol.*, **53**, 1832–1844.

Lobon-Cervia J., Utrilla C.G. and Rincon P.A., 1995. Variations in the population dynamics of the European eel *Anguilla anguilla* (L.) along the course of a Cantabrian river. *Ecol. Fresh. Fish*, **4**, 17–27.

MacKenzie D.I., 2005. What are the issues with presence-absence data for wildlife managers? *J. Wild. Manag.*, **69**, 849–860.

Manel S., Williams H.C. and Ormerod S.J., 2001. Evaluating presence-absence models in ecology: the need to account for prevalence. *J. Appl. Ecol.*, **38**, 921–931.

Melià P., Bevacqua D., Crivelli A.J., De Leo G.A., Panfili J. and Gatto M., 2006. Age and growth of *Anguilla anguilla* in the Camargue lagoons. *J. Fish Biol.*, **68**, 876–890.

Michel P. and Oberdorff T., 1995. Feeding habits of fourteen European freshwater fish species. *Cybium*, **19**, 5–46.

Moriarty C., 1986. Riverine migration of young eels *Anguilla anguilla* (L.). *Fish. Res.*, **4**, 43–58.

Moriarty C. and Dekker W., 1997. Management of European eel fisheries. *Fish. Bull.*, **15**, 1–110.

Naismith I.A. and Knights B., 1990. Studies of sampling methods and of techniques for estimating populations of eels, *Anguilla anguilla* L. *Aquac. Fish. Manag.*, **21**, 357–367.

Naismith I.A. and Knights B., 1993. The distribution, density and growth of the European eel (*A. anguilla*) in the freshwater catchment of the River Thames. *J. Fish Biol.*, **42**, 217–226.

Oberdorff T., Pont D., Hugueny B. and Chessel D., 2001. A probabilistic model characterizing fish assemblages of French rivers: a framework for environnemental assessment. *Fresh. Biol.*, **46**, 399–416.

Panfili J. and Ximénès M.C., 1994. Évaluation de l'âge et croissance de l'anguille européenne (*A. anguilla*) en milieu continental. Méthodologie, validation, application en Méditerranée, comparaison en Europe. *Bull. Fr. Pêche Piscic.*, 335, 43–66.

Peeters E. and Gardeniers J.J.P., 1998. Logistic regression as a tool for defining habitat requirements of two common gammarids. *Fresh. Biol.*, **39**, 605–615.
Pont D., Hugueny B. and Oberdorff T., 2005. Modelling habitat requirement of European fishes: do species have similar responses to local and regional environmental constraints? *J. Fish. Aquatic Sci.*, **62**, 163–175.

Pont D., Hugueny B., Beier U., Goffaux D., Melcher A., Noble R., Rogers C., Roset N. and Schmutz S., 2006. Assessing river biotic condition at a continental scale: a European approach using functional metrics and fish assemblages. *J. Applied Ecol.*, **43**, 70–80.

Pont D., Hugueny B and Rogers C., 2007. Development of a fish-based index for the assessment of river health in Europe: the European Fish Index. *Fish. Manag. Ecol.*, **14**, 427–439.

Poole W.R. and Reynolds J.D., 1996. Age and growth of yellow eel, *Anguilla anguilla* (L.), determined by two different methods. *Ecol. Fresh. Fish*, **5**, 86–95.

Poole R.W. and Reynolds J.D., 1998. Variability in growth rate in European eel *Anguilla anguilla* (L.) in a western Irish catchment. *Biol. Environ.*, **98**, 141–145.

Reid S.M., Yunker G. and Jones N.E., 2008. Evaluation of single-pass backpack electric fishing for stream fish community monitoring. *Fish. Manag. Ecol.*, **16**, 1–9.

Rigaud C., Laffaille P., Prouzet P., Feunteun E., Diaz E., Castellano J. and De Casamajor M.-N., 2008. Des compléments sur la biologie l'anguille européenne. *In*: Adam G., Feunteun E., Prouzet P. and Rigaud C. (eds.), L'anguille européenne, Indicateurs d'abondance et de colonisation, QUAE, Paris, 43–86.

Robinet T. and Feunteun E., 2002. Sublethal effects of exposure to chemical compounds. A cause for decline in Atlantic eels? *Ecotox.*, **11**, 265–277.

Royle J.A., Nichols J.D. and Kery M., 2005. Modelling occurrence and abundance of species when detection is imperfect. *Oikos*, **110**, 353–359.

Rushton S.P., Ormerod S.J. and Kerby G., 2004. New paradigms for modelling species distributions? *J. Appl. Ecol.*, **41**, 193–200.

Russell I.C. and Potter E.C.E., 2003. Implications of the precautionary approach for the management of the European eel, *Anguilla anguilla*. *Fish. Manag. Ecol.*, **10**, 395–401.

Smogor R.A., Angermeier P.L. and Gaylord C.K., 1995. Distribution and abundance of American Eels in Virginia Streams: Tests of null models across spatial scales. *Trans. Am. Fish. Soc.*, **124**, 789–803.

Tesch F.W., 2003. The eel, Thorpe J.E. (ed.), Blackwell Science, Oxford (UK), 416 p.

Vojta C.D., 2005. Old dog, new tricks: innovations with presence-absence information. *J. Wild. Manag.*, **69**, 845–848.

Vøllestad L.A., 1992. Geographic variation in age and length at metamorphosis of maturing European eel: environmental effects and phenotypic plasticity. *J. Animal Ecol.*, **61**, 41–48.

Yalcin-Ozdilek S., Gumus A. and Dekker W., 2006. Growth of European eel in a Turkish river at the south eastern limit of its distribution. *Electronic J. Icthyol.*, **2**, 55–64.