# Evidence for long-term change in length, mass and migration phenology of anadromous spawners in French Atlantic salmon Salmo salar

Guillaume Bal, Lucie Montorio, Etienne Rivot, Etienne Prévost, Jean-Luc

Baglinière, Marie Nevoux

► To cite this version:

Guillaume Bal, Lucie Montorio, Etienne Rivot, Etienne Prévost, Jean-Luc Baglinière, et al.. Evidence for long-term change in length, mass and migration phenology of anadromous spawners in French Atlantic salmon Salmo salar. Journal of Fish Biology, 2017, 90 (6), pp.2375-2393. 10.1111/jfb.13314. hal-01561009

HAL Id: hal-01561009

https://institut-agro-rennes-angers.hal.science/hal-01561009

Submitted on 28 Mar 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
# Evidence for long-term change in length, mass and migration phenology of anadromous spawners in French Atlantic salmon Salmo salar

Guillaume Bal, Lucie Montorio, Etienne Rivot, Etienne Prévost, Jean-Luc Baglinière, Marie Nevoux

<figure><img src="image_1.png" /></figure>

## To cite this version:

Guillaume Bal, Lucie Montorio, Etienne Rivot, Etienne Prévost, Jean-Luc Baglinière, et al.. Evidence for long-term change in length, mass and migration phenology of anadromous spawners in French Atlantic salmon Salmo salar. Journal of Fish Biology, 2017, 90 (6), pp.2375-2393. 10.1111/jfb.13314. hal-01561009

HAL Id: hal-01561009

https://institut-agro-rennes-angers.hal.science/hal-01561009v1

Submitted on 28 Mar 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution - ShareAlike 4.0 International License
1 Title:

2 Evidence for long term change in length, weight and migration phenology of anadromous

3 spawners in French Atlantic salmon Salmo salar

4

5

6 G. Bal¹,², L. Montorio¹, E. Rivot¹, E. Prévost³,⁴, J.-L. Baglinière¹,⁴ and M. Nevoux¹,⁴

7 ¹ESE, Ecology and Ecosystem Health, Agrocampus Ouest, INRA, 35042 Rennes, France.

8 ²Marine Institute, Rinville, Oranmore, Co. Galway, Ireland.

9 ³ECOBIOP, INRA, Univ. Pau & Pays Adour, Aquapôle, Quartier Ibarron, 64310 Saint Pée sur Nivelle, France.

10

11 ⁴ONEMA, Pôle GEST'AQUA, 65 rue de Saint-Brieuc, F-35042 Rennes, France.

12

13 Author to whome correspondence should be addressed:

14 M Nevoux, phone: +33 223 485 235; email: marie.nevoux@inra.fr

15

16 Running headline:

17 Changing salmon size and phenology in France

18
1 Title:

2 Evidence for long term change in length, weight and migration phenology of anadromous

3 spawners in French Atlantic salmon Salmo salar

4

5

6 G. Bal¹,², L. Montorio¹, E. Rivot¹, E. Prévost³,⁴, J.-L. Baglinière¹,⁴ and M. Nevoux¹,⁴

7 ¹ESE, Ecology and Ecosystem Health, Agrocampus Ouest, INRA, 35042 Rennes, France.

8 ²Marine Institute, Rinville, Oranmore, Co. Galway, Ireland.

9 ³ECOBIOP, INRA, Univ. Pau & Pays Adour, Aquapôle, Quartier Ibarron, 64310 Saint Pée
10 sur Nivelle, France.

11 ⁴ONEMA, Pôle GEST'AQUA, 65 rue de Saint-Brieuc, F-35042 Rennes, France.

12

13 Author to whome correspondence should be addressed:

14 M Nevoux, phone: +33 223 485 235; email: marie.nevoux@inra.fr

15

16 Running headline:

17 Changing salmon size and phenology in France

18
19 Abstract

20 We provide new data on Atlantic salmon *Salmo salar* life history traits across France, using a
21 recreational angling database coming from fed by a mandatory catch declaration scheme over
22 a large temporal (1987-2013) and spatial (n = 34 rivers) scale including three regions (genetic
23 units). We reported new insights on changes in *S. salar* life history traits, with a decline in
24 individual length, weight and a delay in the migration phenology of adult *S. salar* returning to
25 French rivers. Similarities in time trends between regions may be attributable to common
26 changes in environmental conditions at sea. Results also revealed different rates of change
27 between early maturing fish (1SW fish) and late maturing fish (2SW fish), phenotypic traits
28 being less variable and less impacted by environmental variations in freshwater returning
29 2SW fish than in 1SW ones. This study highlights the necessity to account for the variability
30 in life history strategies when exploring mechanisms of phenotypic change in *S. salar*.

31 Detailed data on *S. salar* in France have long been missing from the literature. This large
32 scale study on French populations contributes to reduce the gap in knowledge by providing
33 further empirical evidence of a global pattern in *S. salar* life history shared by all populations
34 across the Atlantic Ocean, which is consistent with the widespread hypothesis of global
35 response of populations to a major ecological mechanism during the marine phase of the life
36 cycle.

37

38

39 Keywords

40 body size, environmental buffering, environmental variability, life history strategy, *Salmo*
41 *salar*

42
19 Abstract

20 We provide new data on Atlantic salmon *Salmo salar* life history traits across France, using a
21 recreational angling database coming from fed by a mandatory catch declaration scheme over
22 a large temporal (1987-2013) and spatial (n = 34 rivers) scale including three regions (genetic
23 units). We reported new insights on changes in *S. salar* life history traits, with a decline in
24 individual length, weight and a delay in the migration phenology of adult *S. salar* returning to
25 French rivers. Similarities in time trends between regions may be attributable to common
26 changes in environmental conditions at sea. Results also revealed different rates of change
27 between early maturing fish (1SW fish) and late maturing fish (2SW fish), phenotypic traits
28 being less variable and less impacted by environmental variations in freshwater returning
29 2SW fish than in 1SW ones. This study highlights the necessity to account for the variability
30 in life history strategies when exploring mechanisms of phenotypic change in *S. salar*.

31 Detailed data on *S. salar* in France have long been missing from the literature. This large
32 scale study on French populations contributes to reduce the gap in knowledge by providing
33 further empirical evidence of a global pattern in *S. salar* life history shared by all populations
34 across the Atlantic Ocean, which is consistent with the widespread hypothesis of global
35 response of populations to a major ecological mechanism during the marine phase of the life
36 cycle.

37

38

39 Keywords

40 body size, environmental buffering, environmental variability, life history strategy, *Salmo*
41 *salar*

42
43 Introduction

44

45

46 Global climate warming in the Atlantic Ocean is responsible for rising sea surface temperature since the 1970's (Levitus et al., 2000; Polyakov et al., 2009). There is also strong evidence that climate change is altering ecosystem functioning, which is driving a major shift, observable across multiple trophic levels, in the North Atlantic (Durant et al., 2004;

50 Beaugrand et al., 2008). Concomitantly, Atlantic salmon Salmo salar L. 1758 populations have suffered a tremendous decline in abundance across their distribution range (Jonsson & Jonsson, 2004; Chaput, 2012; ICES, 2016), together with marked changes in life history traits and population structure. In particular, a decline in the proportion of late maturing fish has been reported for many European and North American stocks (Baglinière et al., 2004; ICES, 2016; Jonsson et al. 2016; Otero et al. 2012). Those changes have been accompanied by declines in age-specific length and weight of fish (Aprahamian et al., 2008; Bacon et al., 2009; Jonsson et al. 2016), together with a delay in adult migration timing (Todd et al., 2012; Valiente et al., 2011). Such changes are expected to affect population growth rate, raising concerns about the viability of this species of great patrimonial and economical value.

60

61

62 Identifying the causal link between latent driving factors and those phenotypic changes is difficult and may consist in one of the greatest challenges for fisheries ecology, and for S. salar in particular (Crozier & Hutchings, 2014). Due to a complex life cycle shared between 65 freshwater and the marine environment, diadromous fishes are vulnerable to various factors of stress in a hierarchy of spatial scales (Armstrong et al., 1998; Mather et al., 1998; Lindburg & 66 Waldman, 2009). Evidence now accumulates for a major impact of changes in the marine 67
43 Introduction

44

45

46 Global climate warming in the Atlantic Ocean is responsible for rising sea surface temperature since the 1970's (Levitus et al., 2000; Polyakov et al., 2009). There is also strong evidence that climate change is altering ecosystem functioning, which is driving a major shift, observable across multiple trophic levels, in the North Atlantic (Durant et al., 2004;

50 Beaugrand et al., 2008). Concomitantly, Atlantic salmon Salmo salar L. 1758 populations have suffered a tremendous decline in abundance across their distribution range (Jonsson & Jonsson, 2004; Chaput, 2012; ICES, 2016), together with marked changes in life history traits and population structure. In particular, a decline in the proportion of late maturing fish has been reported for many European and North American stocks (Baglinière et al., 2004; ICES, 2016; Jonsson et al. 2016; Otero et al. 2012). Those changes have been accompanied by declines in age-specific length and weight of fish (Aprahamian et al., 2008; Bacon et al., 2009; Jonsson et al. 2016), together with a delay in adult migration timing (Todd et al., 2012; Valiente et al., 2011). Such changes are expected to affect population growth rate, raising concerns about the viability of this species of great patrimonial and economical value.

60

61

62 Identifying the causal link between latent driving factors and those phenotypic changes is difficult and may consist in one of the greatest challenges for fisheries ecology, and for S. salar in particular (Crozier & Hutchings, 2014). Due to a complex life cycle shared between 65 freshwater and the marine environment, diadromous fishes are vulnerable to various factors of stress in a hierarchy of spatial scales (Armstrong et al., 1998; Mather et al., 1998; Lindburg & 66 Waldman, 2009). Evidence now accumulates for a major impact of changes in the marine 67
68 environment encountered by *S. salar* (Jonsson & Jonsson, 2004; ICES, 2016; Jonsson *et al.*, 2016). Both direct and indirect effects of rising sea surface temperature have been proposed.
69
70 Temperature increase would impede post-smolt growth (Friedland *et al.*, 2000, 2005). In
71 addition, temperature increase would decrease the productivity of North Atlantic pelagic food
72 web, thus indirectly affecting feeding opportunities for salmon at sea (Friedland *et al.*, 2000;
73 Peyronnet *et al.*, 2007; McCarthy *et al.*, 2008; Jensen *et al.*, 2011). In particular, the
74 abundance and/or energy content of key pelagic crustaceans and small pelagic fish, on which
75 *S. salar* largely prey, may have declined (Beaugrand & Reid, 2012; Friedland *et al.*, 2013;
76 Mills *et al.*, 2013; Miller *et al.*, 2014). Recent stock rebuilding in some pelagic fish competing
77 for similar resource as *S. salar* may further amplify this process (Jonsson *et al.* 2016;
78 Mäntyniemi *et al.*, 2012).

79

80

81 Simultaneous temporal trends in growth, length, weight and/or phenology variations have
82 been observed in Norway (Jensen *et al.*, 2011), Ireland (Quinn *et al.*, 2006), and Scotland
83 (Summers, 1995; Todd *et al.*, 2008; Bacon *et al.*, 2011). Given that those populations breed in
84 very distant rivers, the existence of a large-scale mechanism taking at sea has been suggested.
85 To go further, large scale data for Southern populations is required to confirm the influence
86 and the extent of such global change. Yet, precise ecological and demographic mechanisms
87 that underline those changes are still not clear (Crozier and Hutchings, 2014). Despite elusive
88 causes, there is a need to better understand the factors regulating *S. salar* response to ongoing
89 environmental change, for management and policy purposes.

90

91
environment encountered by *S. salar* (Jonsson & Jonsson, 2004; ICES, 2016; Jonsson *et al.*, 2016). Both direct and indirect effects of rising sea surface temperature have been proposed. Temperature increase would impede post-smolt growth (Friedland *et al.*, 2000, 2005). In addition, temperature increase would decrease the productivity of North Atlantic pelagic food web, thus indirectly affecting feeding opportunities for salmon at sea (Friedland *et al.*, 2000; Peyronnet *et al.*, 2007; McCarthy *et al.*, 2008; Jensen *et al.*, 2011). In particular, the abundance and/or energy content of key pelagic crustaceans and small pelagic fish, on which *S. salar* largely prey, may have declined (Beaugrand & Reid, 2012; Friedland *et al.*, 2013; Mills *et al.*, 2013; Miller *et al.*, 2014). Recent stock rebuilding in some pelagic fish competing for similar resource as *S. salar* may further amplify this process (Jonsson *et al.* 2016; Mäntyniemi *et al.*, 2012).

79

80

Simultaneous temporal trends in growth, length, weight and/or phenology variations have been observed in Norway (Jensen *et al.*, 2011), Ireland (Quinn *et al.*, 2006), and Scotland (Summers, 1995; Todd *et al.*, 2008; Bacon *et al.*, 2011). Given that those populations breed in very distant rivers, the existence of a large-scale mechanism taking at sea has been suggested. To go further, large scale data for Southern populations is required to confirm the influence and the extent of such global change. Yet, precise ecological and demographic mechanisms that underline those changes are still not clear (Crozier and Hutchings, 2014). Despite elusive causes, there is a need to better understand the factors regulating *S. salar* response to ongoing environmental change, for management and policy purposes.

90

91
In *S. salar*, anadromous adults mature after one winter at sea (1SW) or more than one winter at sea (MSW, i.e. up to five years) (Hutchings & Jones, 1998; Aas *et al.*, 2011). The differences in their respective life history traits (e.g. body size, growth rate, age at maturity, fecundity or migration timing) reflect the strategy of resource allocation to growth, survival and reproduction (Cody, 1966; Williams, 1966; Schaffer, 1983; Stearns, 1976). Under this strategy, alternative tactics seem to be partially determined by sex and genes (Fleming, 1996; Barson et al., 2015). Females, whose breeding success is strongly related to body size (Fleming, 1996), are more abundant among MSW, while males tend to mature earlier i.e. most of them are 1SW (Hutchings & Jones, 1998; Aas *et al.*, 2011). Thus, any change in resource availability or more generally in environmental conditions at sea is likely to induce contrasted response in 1SW and MSW, because of difference in their resource allocation (Stearns, 1976; Gaillard et al., 1989; Reznick et al., 1996). In Southern Europe, growing areas at sea used by MSW are farther away than in 1SW, resulting in an even greater contrast in mortality during sea migration between tactics. For instance, somatic growth might be more impacted by poor environmental condition at sea in 1SW, as energy allocation might be primarily dedicated to maturation in this group. Also, differences in age and sex-ratio between sea-age classes may confer different ability in buffering external forcing in 1SW and MSW, as already observed in mammals for instance (Coulson *et al.*, 2001). Monitoring life history traits by sea age class is essential to better understand and predict species response to ongoing global changes and provide sound insights for the management of *S. salar* across its distribution range (Armstrong *et al.*, 1998; Mather *et al.*, 1998).

113

114

115 In this study we investigated the long term change in length, weight and migration phenology of French *S. salar* populations over the last three decades, using a recreational angling

116
In *S. salar*, anadromous adults mature after one winter at sea (1SW) or more than one winter at sea (MSW, i.e. up to five years) (Hutchings & Jones, 1998; Aas *et al.*, 2011). The differences in their respective life history traits (e.g. body size, growth rate, age at maturity, fecundity or migration timing) reflect the strategy of resource allocation to growth, survival and reproduction (Cody, 1966; Williams, 1966; Schaffer, 1983; Stearns, 1976). Under this strategy, alternative tactics seem to be partially determined by sex and genes (Fleming, 1996; Barson et al., 2015). Females, whose breeding success is strongly related to body size (Fleming, 1996), are more abundant among MSW, while males tend to mature earlier i.e. most of them are 1SW (Hutchings & Jones, 1998; Aas *et al.*, 2011). Thus, any change in resource availability or more generally in environmental conditions at sea is likely to induce contrasted response in 1SW and MSW, because of difference in their resource allocation (Stearns, 1976; Gaillard et al., 1989; Reznick et al., 1996). In Southern Europe, growing areas at sea used by MSW are farther away than in 1SW, resulting in an even greater contrast in mortality during sea migration between tactics. For instance, somatic growth might be more impacted by poor environmental condition at sea in 1SW, as energy allocation might be primarily dedicated to maturation in this group. Also, differences in age and sex-ratio between sea-age classes may confer different ability in buffering external forcing in 1SW and MSW, as already observed in mammals for instance (Coulson *et al.*, 2001). Monitoring life history traits by sea age class is essential to better understand and predict species response to ongoing global changes and provide sound insights for the management of *S. salar* across its distribution range (Armstrong *et al.*, 1998; Mather *et al.*, 1998).

113

114

115 In this study we investigated the long term change in length, weight and migration phenology of French *S. salar* populations over the last three decades, using a recreational angling

116
database coming from fed by a mandatory catch declaration scheme over a large spatial (n = 117 34 rivers) scale. Although the available literature accumulates evidence of temporal changes 118 in life history traits in a high number of S. salar populations, only few results concern 119 Southern Europe rivers (Valiente et al., 2011) and no results have been published for France 120 so far. Extending the geographical range for which evidence of changes are documented is a 121 prerequisite to test for the hypothesis of a common response of S. salar to environmental 122 conditions at sea, and then refine our understanding of the factors driving those reported 123 changes. We described how S. salar length, weight, body condition factor and annual 124 migration timing have changed over the period 1987-2013 in French rivers. We investigated 125 whether common patterns could be detected in geographically distant populations. We then 126 tested the hypothesis of a tactic-dependent response to environmental variation in S. salar. 127 Finally, we assessed the difference in individual response in 1SW and MSW, by analyzing the 128 relative rate of change in key life history traits between 1SW and MSW over the study period. 129 Overall, results brought new contributions to the understanding of the demographic and 130 evolutionary forces that control S. salar response to environmental change in the Southern 131 part of the species distribution range. 132

133

134

135

136 MATERIALS & METHODS

137

138 Recreational fishery data

139

140
database coming from fed by a mandatory catch declaration scheme over a large spatial (n = 117 34 rivers) scale. Although the available literature accumulates evidence of temporal changes 118 in life history traits in a high number of S. salar populations, only few results concern 119 Southern Europe rivers (Valiente et al., 2011) and no results have been published for France 120 so far. Extending the geographical range for which evidence of changes are documented is a 121 prerequisite to test for the hypothesis of a common response of S. salar to environmental 122 conditions at sea, and then refine our understanding of the factors driving those reported 123 changes. We described how S. salar length, weight, body condition factor and annual 124 migration timing have changed over the period 1987-2013 in French rivers. We investigated 125 whether common patterns could be detected in geographically distant populations. We then 126 tested the hypothesis of a tactic-dependent response to environmental variation in S. salar. 127 Finally, we assessed the difference in individual response in 1SW and MSW, by analyzing the 128 relative rate of change in key life history traits between 1SW and MSW over the study period. 129 Overall, results brought new contributions to the understanding of the demographic and 130 evolutionary forces that control S. salar response to environmental change in the Southern 131 part of the species distribution range. 132

133

134

135

136 MATERIALS & METHODS

137

138 Recreational fishery data

139

140
Analyses are based on the French recreational angling database fed by a mandatory catch declaration scheme over a large temporal (1987-2013) and spatial (n = 34 rivers) scale. In France, *S. salar* recreational rod and line fishery takes place in more than 40 rivers of the Atlantic and Channel coasts, which are characterized by uneven spatial distribution and river profiles. These rivers are mainly localized in three regions that represent more than 90% of French *S. salar* abundance: Normandy, Brittany and Aquitaine (Fig. 1). This regional stratification results from previous analyses of both the genetic structure of French *S. salar* populations and based on their demographic and morphometric characteristics (Prévost, 1987; Perrier *et al.*, 2011). In addition to recreational fishery, a small commercial fishery takes place in the Adour River, Aquitaine region (on average 10.5 % of the annual salmon catches). The fishing season extends from the beginning of March to the end of October for rod fishing and to the end of July for commercial fishing. Since 1987, *S. salar* fishery rely on a national compulsory catch declaration scheme which offers a national sampling survey of adult *S. salar* and feed one of the largest dataset for salmonid fishery in Europe. Catch data are collected by anglers and centralized by the National Center for the Interpretation of Salmonid Catches. For each fish caught, the recorded data are date (D, day of the year), river, size (L, total length in mm), weight (W, in g) and a sample of scales. All scales are analyzed following a standardized protocol (Baglinière *et al.*, 1985) to estimate both the time spent in freshwater during the juvenile phase (freshwater age) and the time spent at sea during the adult phase of the life cycle, i.e. (sea-age). Current French populations are mainly composed of one sea winter (1SW), two sea winters (2SW) and rarely three sea winters (3SW) salmon. However, because of the unbalanced fishing effort between sea-age classes (being historically more intense on large 2SW fish than on 1SW ones), the data do not allow us to draw any inference on the relative abundance of the two tactics and its variation over time.
Analyses are based on the French recreational angling database fed by a mandatory catch declaration scheme over a large temporal (1987-2013) and spatial (n = 34 rivers) scale. In France, *S. salar* recreational rod and line fishery takes place in more than 40 rivers of the Atlantic and Channel coasts, which are characterized by uneven spatial distribution and river profiles. These rivers are mainly localized in three regions that represent more than 90% of French *S. salar* abundance: Normandy, Brittany and Aquitaine (Fig. 1). This regional stratification results from previous analyses of both the genetic structure of French *S. salar* populations and based on their demographic and morphometric characteristics (Prévost, 1987; Perrier *et al.*, 2011). In addition to recreational fishery, a small commercial fishery takes place in the Adour River, Aquitaine region (on average 10.5 % of the annual salmon catches). The fishing season extends from the beginning of March to the end of October for rod fishing and to the end of July for commercial fishing. Since 1987, *S. salar* fishery rely on a national compulsory catch declaration scheme which offers a national sampling survey of adult *S. salar* and feed one of the largest dataset for salmonid fishery in Europe. Catch data are collected by anglers and centralized by the National Center for the Interpretation of Salmonid Catches. For each fish caught, the recorded data are date (D, day of the year), river, size (L, total length in mm), weight (W, in g) and a sample of scales. All scales are analyzed following a standardized protocol (Baglinière *et al.*, 1985) to estimate both the time spent in freshwater during the juvenile phase (freshwater age) and the time spent at sea during the adult phase of the life cycle, i.e. (sea-age). Current French populations are mainly composed of one sea winter (1SW), two sea winters (2SW) and rarely three sea winters (3SW) salmon. However, because of the unbalanced fishing effort between sea-age classes (being historically more intense on large 2SW fish than on 1SW ones), the data do not allow us to draw any inference on the relative abundance of the two tactics and its variation over time.
166
167 The ordinal date of capture (D) was considered as an index for the date of river entry. Indeed,
168 as already discussed in Bacon et al. (2009), the rod capture data mostly concerns newly
169 arrived fresh-run fish, often caught in lower parts of rivers just after their entry in freshwater.
170 Several studies have shown that the movements and capture rates of S. salar decline rapidly
171 after river entry (e.g., Thorley et al., 2005), suggesting that S. salar may be harder to catch by
172 rod after several days in freshwater.

173
174
175 Because salmonids are considered to have a standard body shape, we used the condition
176 coefficient of Fulton (K) as a proxy for S. salar body condition. It is calculated for each fish
177 as K= W/L³ (Bolger & Connolly, 1989; Nash et al., 2006).

178

179

180 Quality control and data processing

181

182

183 Fish spending three winters at sea became scarce in French populations while repeat spawners
184 are very rare (Baglinière & Porcher, 1994). Data from those fish were too few to allow
185 reliable statistical analysis. They were thus removed from the study. Unrealistic combination
186 of length and weight (i.e., K outside the range [0.4, 1.6]) coming from erroneous records,
187 were discarded. The date of capture was also used as a filter criterion on the dataset. In
188 France, S. salar migration is depending on sea age and hydrological conditions, with a MSW
189 early run and a 1SW summer run (Baglinière & Porcher, 1994). Some years, late records are
190 observed in the dataset, which correspond to a late upstream migration from the estuary as no
166
167 The ordinal date of capture (D) was considered as an index for the date of river entry. Indeed,
168 as already discussed in Bacon et al. (2009), the rod capture data mostly concerns newly
169 arrived fresh-run fish, often caught in lower parts of rivers just after their entry in freshwater.
170 Several studies have shown that the movements and capture rates of S. salar decline rapidly
171 after river entry (e.g., Thorley et al., 2005), suggesting that S. salar may be harder to catch by
172 rod after several days in freshwater.

173
174
175 Because salmonids are considered to have a standard body shape, we used the condition
176 coefficient of Fulton (K) as a proxy for S. salar body condition. It is calculated for each fish
177 as K= W/L³ (Bolger & Connolly, 1989; Nash et al., 2006).

178

179

180 Quality control and data processing

181

182

183 Fish spending three winters at sea became scarce in French populations while repeat spawners
184 are very rare (Baglinière & Porcher, 1994). Data from those fish were too few to allow
185 reliable statistical analysis. They were thus removed from the study. Unrealistic combination
186 of length and weight (i.e., K outside the range [0.4, 1.6]) coming from erroneous records,
187 were discarded. The date of capture was also used as a filter criterion on the dataset. In
188 France, S. salar migration is depending on sea age and hydrological conditions, with a MSW
189 early run and a 1SW summer run (Baglinière & Porcher, 1994). Some years, late records are
190 observed in the dataset, which correspond to a late upstream migration from the estuary as no
late run of "silver fish" are recorded on index rivers with fish trap facilities in France (e.g. Scorff River, E. Prévost, pers. com.). Also, the recorded length and weight of fish caught late in the season may be a biased proxy of the phenotypic traits of those fish at the time of their return. Indeed, fish caught late in the season often stayed several weeks in river waiting for favorable river discharge conditions to migrate upstream to spawning grounds. During this waiting period, fish may lose weight and the length of males may start to increase with the development of secondary sexual characters, such as the elongation of the upper jaw in the male salmon (Maisse & Baglinière, 1986; Baglinière & Porcher, 1994). Because sexual dimorphism is large enough to allow sex determination in salmon from September, we decided to only consider fish caught until the end of August for our analysis. Furthermore, fishery management could interfere with fish sampling, with late running fish being more likely to be under-represented in the dataset. Indeed, since 1994, *S. salar* recreational fishery in French rivers closes to preserve the targeted stock of spawners as soon as the allocated river-specific quota is reached (catch release is not implemented in France; Porcher & Prévost (1996)). Fishing closure often took place early in the season, leading to an uneven sampling of fish in fall.

In total, our analysis included 27,709 individual records (11,466 1SW fish and 16,243 2SW fish) collected over the period 1987-2013 on 34 rivers. Sample size per river was too small, thus records from single rivers were pooled into three study regions (Normandy, Brittany, and Aquitaine) to reduce sampling noise (Table I, Fig. 1). Despite an unbalanced number of river per regions and contrasted river profiles, this regionalization of the catch data reflects the spatial distribution of *S. salar* abundance in France. Populations from the Loire River and from Northern France, which represent less than 5% of the records, were excluded from the
late run of "silver fish" are recorded on index rivers with fish trap facilities in France (e.g. Scorff River, E. Prévost, pers. com.). Also, the recorded length and weight of fish caught late in the season may be a biased proxy of the phenotypic traits of those fish at the time of their return. Indeed, fish caught late in the season often stayed several weeks in river waiting for favorable river discharge conditions to migrate upstream to spawning grounds. During this waiting period, fish may lose weight and the length of males may start to increase with the development of secondary sexual characters, such as the elongation of the upper jaw in the male salmon (Maisse & Baglinière, 1986; Baglinière & Porcher, 1994). Because sexual dimorphism is large enough to allow sex determination in salmon from September, we decided to only consider fish caught until the end of August for our analysis. Furthermore, fishery management could interfere with fish sampling, with late running fish being more likely to be under-represented in the dataset. Indeed, since 1994, *S. salar* recreational fishery in French rivers closes to preserve the targeted stock of spawners as soon as the allocated river-specific quota is reached (catch release is not implemented in France; Porcher & Prévost (1996)). Fishing closure often took place early in the season, leading to an uneven sampling of fish in fall.

In total, our analysis included 27,709 individual records (11,466 1SW fish and 16,243 2SW fish) collected over the period 1987-2013 on 34 rivers. Sample size per river was too small, thus records from single rivers were pooled into three study regions (Normandy, Brittany, and Aquitaine) to reduce sampling noise (Table I, Fig. 1). Despite an unbalanced number of river per regions and contrasted river profiles, this regionalization of the catch data reflects the spatial distribution of *S. salar* abundance in France. Populations from the Loire River and from Northern France, which represent less than 5% of the records, were excluded from the
216 dataset for three main reasons: i) salmon fishing in the Loire River has been forbidden since
217 1994; ii) salmon catches in Northern France are low; iii) stocking practice takes place on all
218 these rivers, which may mask ecological signals (Le Cam et al., 2015).

219

220

221 Statistical analyses

222

223 Characterizing temporal trends and the impact of covariates

224

225

226 Temporal trends in D, L, W and K (see Supporting Information A1 and A2) were analyzed
227 using Generalized Additive Models (GAM) as implemented in the R package mgcv, V. 1.8-6
228 (Wood, 2011) to capture the non-linearity in the data. Independent models were built for each
229 of the four response variables, here denoted Z for genericity. The effects of the sea-age class
230 (1SW or 2SW), and regions (Normandy, Brittany, Aquitaine) were introduced as categorical
231 effects, and the Year was considered as a continuous variable in the non-parametric smooth
232 trend. The most complex model considered was:

233 $Z_{Y,A,R,i} \sim A \times R + s(Y, A \times R) + \varepsilon_i,$

234 where Z_{Y,A,R,i} is the variable D, L, W or K for any fish i of sea-age A, caught at year Y in
235 region R, ‘A×R’ is a sea-age by region interaction, s(Y, A×R) is a sea-age by region specific
236 smooth time trend, and ε_i are independent and identically distributed residuals ~ N(0,σ²) for
237 any individual fish i. To test for the existence of trends specific to sea-age and region, simpler
238 nested models without time trend, or with identical time trend for each region and/or sea-age
239 were considered. Competing models were compared using the Akaike Information Criterion
240 (AIC; Burnham & Anderson, 2002). The reduction of the model deviance as the models get

237

238

239

240
216 dataset for three main reasons: i) salmon fishing in the Loire River has been forbidden since
217 1994; ii) salmon catches in Northern France are low; iii) stocking practice takes place on all
218 these rivers, which may mask ecological signals (Le Cam et al., 2015).

219

220

221 Statistical analyses

222

223 Characterizing temporal trends and the impact of covariates

224

225

226 Temporal trends in D, L, W and K (see Supporting Information A1 and A2) were analyzed
227 using Generalized Additive Models (GAM) as implemented in the R package mgcv, V. 1.8-6
228 (Wood, 2011) to capture the non-linearity in the data. Independent models were built for each
229 of the four response variables, here denoted Z for genericity. The effects of the sea-age class
230 (1SW or 2SW), and regions (Normandy, Brittany, Aquitaine) were introduced as categorical
231 effects, and the Year was considered as a continuous variable in the non-parametric smooth
232 trend. The most complex model considered was:

233 $Z_{Y,A,R,i} \sim A \times R + s(Y, A \times R) + \varepsilon_i,$

234 where Z_{Y,A,R,i} is the variable D, L, W or K for any fish i of sea-age A, caught at year Y in
235 region R, ‘A×R’ is a sea-age by region interaction, s(Y, A×R) is a sea-age by region specific
236 smooth time trend, and ε_i are independent and identically distributed residuals ~ N(0,σ²) for
237 any individual fish i. To test for the existence of trends specific to sea-age and region, simpler
238 nested models without time trend, or with identical time trend for each region and/or sea-age
239 were considered. Competing models were compared using the Akaike Information Criterion
240 (AIC; Burnham & Anderson, 2002). The reduction of the model deviance as the models get

237

238

239

240
241 more parameters (i.e. increase in complexity) was also used to comment on the relative
242 importance of the covariates in explaining the data variability. No data transformation was
243 needed as the residuals from the models did not reveal any departure from the assumptions of
244 normality and homogeneity of variance.

245

246

247 Common fluctuations among regions

248

249

250 Time trends in each life history trait were then examined to test for common temporal
251 fluctuations among regions (for any sea-age strategy). To ensure that two regions would not
252 appear to be correlated simply because both showed comparable long-term trends, any long
253 term trend (low frequency) in the data was first removed by fitting a spline a priori forced to
254 be very smoothed by fixing the degree of freedom to 5 in the GAM procedure. Correlations
255 across years between any two regions were then analysed on the de-trended time series by
256 calculating the Pearson correlation coefficient. Following the methodology developed in
257 Pyper and Peterman (1998), the significance of the correlation was tested using modified
258 Student t-tests with a reduced degree of freedom to correct for any remaining autocorrelation
259 in the de-trended time series.

260

261

262 Comparing rates of change between strategies

263

264
241 more parameters (i.e. increase in complexity) was also used to comment on the relative
242 importance of the covariates in explaining the data variability. No data transformation was
243 needed as the residuals from the models did not reveal any departure from the assumptions of
244 normality and homogeneity of variance.

245

246

247 Common fluctuations among regions

248

249

250 Time trends in each life history trait were then examined to test for common temporal
251 fluctuations among regions (for any sea-age strategy). To ensure that two regions would not
252 appear to be correlated simply because both showed comparable long-term trends, any long
253 term trend (low frequency) in the data was first removed by fitting a spline a priori forced to
254 be very smoothed by fixing the degree of freedom to 5 in the GAM procedure. Correlations
255 across years between any two regions were then analysed on the de-trended time series by
256 calculating the Pearson correlation coefficient. Following the methodology developed in
257 Pyper and Peterman (1998), the significance of the correlation was tested using modified
258 Student t-tests with a reduced degree of freedom to correct for any remaining autocorrelation
259 in the de-trended time series.

260

261

262 Comparing rates of change between strategies

263

264
We investigated the relative rate of change in life history traits (L, W and K) over the study period to test for potential difference in the intensity of the response between strategies (1SW or 2SW). The average rate of change (in % per year) of each life history trait was directly estimated as the slope of a linear model over time, based on standardized data. The effects of the sea-age and region on the rate of change were introduced as categorical effects, and the year was considered as a continuous variable. Nested models without time trend or with time trends identical for each region and/or sea-age (additive models) were also considered. The best models were selected using the Akaike Information Criterion (AIC; Burnham & Anderson, 2002). Then for each region, the difference in the rate of change (i.e. slope) in the 1SW and 2SW strategies were tested using the Wald test using the 'glht' function of the 'multcomp' R package that automatically corrects for multiple comparison.
We investigated the relative rate of change in life history traits (L, W and K) over the study period to test for potential difference in the intensity of the response between strategies (1SW or 2SW). The average rate of change (in % per year) of each life history trait was directly estimated as the slope of a linear model over time, based on standardized data. The effects of the sea-age and region on the rate of change were introduced as categorical effects, and the year was considered as a continuous variable. Nested models without time trend or with time trends identical for each region and/or sea-age (additive models) were also considered. The best models were selected using the Akaike Information Criterion (AIC; Burnham & Anderson, 2002). Then for each region, the difference in the rate of change (i.e. slope) in the 1SW and 2SW strategies were tested using the Wald test using the 'glht' function of the 'multcomp' R package that automatically corrects for multiple comparison.
277 RESULTS

278

279 Differences in mean life history traits between regions and sea-ages

280

281

282 For each trait (L, W, K and D), the data was best explained by a GAM including both an average term and a smoothed temporal trend specific to each combination of region and sea-age (Table II). The average interaction term (region by sea-age) explained most of the observed variability in the data, revealing high differences in the average level of life history traits across sea-age classes and regions. The strongest difference occurred between the North West (Normandy and Brittany) and the South West (Aquitaine) regions. Fish from Aquitaine were noticeably larger and heavier than fish from Normandy and Brittany for both 1SW and 2SW fish. 1SW fish from Normandy were also heavier than in Brittany, but no clear difference was detected in 2SW fish between the two regions. 2SW fish from Aquitaine were caught later than fish from Normandy and Brittany, the average difference in the date of river entry between the three regions being much weaker for 1SW. Comparatively, the smoothed time trends explained a much lower part of the deviance, even when considering an interaction with sea-age and/or region (Table II).

295

296

297 Time trends in life history traits and common fluctuations between regions

298

299

300 Overall, the nonlinear smoothed temporal trends on the 1987-2013 period revealed a general delay in the date of river entry and a decrease in fish weight and length (Figure 2). However,

301
277 RESULTS

278

279 Differences in mean life history traits between regions and sea-ages

280

281

282 For each trait (L, W, K and D), the data was best explained by a GAM including both an average term and a smoothed temporal trend specific to each combination of region and sea-age (Table II). The average interaction term (region by sea-age) explained most of the observed variability in the data, revealing high differences in the average level of life history traits across sea-age classes and regions. The strongest difference occurred between the North West (Normandy and Brittany) and the South West (Aquitaine) regions. Fish from Aquitaine were noticeably larger and heavier than fish from Normandy and Brittany for both 1SW and 2SW fish. 1SW fish from Normandy were also heavier than in Brittany, but no clear difference was detected in 2SW fish between the two regions. 2SW fish from Aquitaine were caught later than fish from Normandy and Brittany, the average difference in the date of river entry between the three regions being much weaker for 1SW. Comparatively, the smoothed time trends explained a much lower part of the deviance, even when considering an interaction with sea-age and/or region (Table II).

295

296

297 Time trends in life history traits and common fluctuations between regions

298

299

300 Overall, the nonlinear smoothed temporal trends on the 1987-2013 period revealed a general delay in the date of river entry and a decrease in fish weight and length (Figure 2). However,

301
time trend were specific for each region and sea-age. In particular, the coefficient of condition of fish was more contrasted between sea ages than the other life history traits. K exhibited a clear downward trend in 1SW fish but remained rather stable in 2SW fish. Changes in the date of capture also exhibited differences between regions and sea age. 1SW fish from Normandy and Brittany showed the most important delays of return migration, with a lag of up to 40 days throughout the study period. Meanwhile, the observed delays for 2SW fish and 1SW fish in Aquitaine were only 20 days. Besides, smoothed time trends also suggested synchronous fluctuations across regions with a 7-8 years period, with high similarity between Brittany and Normandy (Figure 2). Variations in size and weight suggested a common temporal pattern across regions, characterized by repeated declines in both phenotypic traits around years 1990, 1998 and 2007.

The analysis of correlations between de-trended time series further support the hypothesis of a synchronous change in L, W, K or D across regions (Table III). Indeed, for each trait, most of the correlations between regions were positive and significant, even after accounting for autocorrelation. The strongest correlations between regions were found for L and W. The strongest correlations were also found between Brittany and Normandy, which are geographically close. Correlations between more distant regions (i.e. Brittany and Aquitaine, Normandy and Aquitaine) were generally weaker. Also, with the exception of D, between-region correlations seemed stronger in 2SW fish than in 1SW fish.

In general, the date of river entry showed opposite trends to length and weight, a later date of river entry being associated with a decline in the length and weight of fish. Interestingly, those
time trend were specific for each region and sea-age. In particular, the coefficient of condition of fish was more contrasted between sea ages than the other life history traits. K exhibited a clear downward trend in 1SW fish but remained rather stable in 2SW fish. Changes in the date of capture also exhibited differences between regions and sea age. 1SW fish from Normandy and Brittany showed the most important delays of return migration, with a lag of up to 40 days throughout the study period. Meanwhile, the observed delays for 2SW fish and 1SW fish in Aquitaine were only 20 days. Besides, smoothed time trends also suggested synchronous fluctuations across regions with a 7-8 years period, with high similarity between Brittany and Normandy (Figure 2). Variations in size and weight suggested a common temporal pattern across regions, characterized by repeated declines in both phenotypic traits around years 1990, 1998 and 2007.

The analysis of correlations between de-trended time series further support the hypothesis of a synchronous change in L, W, K or D across regions (Table III). Indeed, for each trait, most of the correlations between regions were positive and significant, even after accounting for autocorrelation. The strongest correlations between regions were found for L and W. The strongest correlations were also found between Brittany and Normandy, which are geographically close. Correlations between more distant regions (i.e. Brittany and Aquitaine, Normandy and Aquitaine) were generally weaker. Also, with the exception of D, between-region correlations seemed stronger in 2SW fish than in 1SW fish.

In general, the date of river entry showed opposite trends to length and weight, a later date of river entry being associated with a decline in the length and weight of fish. Interestingly, those
opposite smoothed time trends were also associated with negative correlations at a shorter
time scale, with 7-8 years periodicity in the date of river entry being in antiphase with those of
length and weight (Figure 2).

Rate of change over the study period (1987-2013)

Consistently with the analyses of non-linear trends, average rates of change in traits were almost all negative (Figure 3) and depended upon both region and sea-age (based on AIC and explained deviance, not shown). Overall, pairwise comparison tests of the difference between rate of change in traits (per sea-age and per region) revealed a stronger decline in W, L and K for 1SW fish than for 2SW fish. The largest rates of change were found for weight, with an average rate of -1.00% per year for 1SW fish and -0.32% per year for 2SW in Brittany. The body condition of 1SW fish declined by 0.20 % (Aquitaine) to 0.51 % (Normandy) per year while the rate of change in K was close to zero for 2SW and could even be positive in some regions. The relative rate of change in L was not different in 1SW and 2SW (p-value > 0.05), fish having lost on average 0.10% (Brittany) to 0.20% (Aquitaine) of their initial body length per year between 1987 and 2013.

DISCUSSION
opposite smoothed time trends were also associated with negative correlations at a shorter
time scale, with 7-8 years periodicity in the date of river entry being in antiphase with those of
length and weight (Figure 2).

330
331
332 Rate of change over the study period (1987-2013)

333
334
335 Consistently with the analyses of non-linear trends, average rates of change in traits were
336 almost all negative (Figure 3) and depended upon both region and sea-age (based on AIC and
337 explained deviance, not shown). Overall, pairwise comparison tests of the difference between
338 rate of change in traits (per sea-age and per region) revealed a stronger decline in W, L and K
339 for 1SW fish than for 2SW fish. The largest rates of change were found for weight, with an
340 average rate of -1.00% per year for 1SW fish and -0.32% per year for 2SW in Brittany. The
341 body condition of 1SW fish declined by 0.20 % (Aquitaine) to 0.51 % (Normandy) per year
342 while the rate of change in K was close to zero for 2SW and could even be positive in some
343 regions. The relative rate of change in L was not different in 1SW and 2SW (p-value > 0.05),
344 fish having lost on average 0.10% (Brittany) to 0.20% (Aquitaine) of their initial body length
345 per year between 1987 and 2013.

346
347
348 DISCUSSION
349

350
351

Our analysis provides new insights on temporal changes in *S. salar* life history traits across

352

France over the last three decades, and contributes to reduce the gap in knowledge by

353

providing further empirical evidence of a long suspected change in life history of *S. salar*

354

throughout its entire distribution area. Using a large dataset from rod and line and commercial

355

catches, we describe a decline in individual length, weight, together with a delay in the

356

migration phenology of adult *S. salar* returning to French rivers. Our analysis highlighted

357

similarities in time trends between regions, but the rate of change in life history traits clearly

358

depends upon the tactic at sea.

359

360

361

Strength and limitation of the data

362

363

364

Time trends in *S. salar* life history traits were analyzed using catch data from recreational and

365

commercial salmonid fishery. This unique dataset provides a large scale observatory of wild

366

French *S. salar* populations. Commercial fisheries of *S. salar* are almost all closed since the

367

early 1990's (ICES, 2016), and scientific data are only available for a few rivers and generally

368

cover only short periods of time. By contrast, recreational hook and line fishery are operating

369

in almost all rivers across the species distribution range. Thus, when available catch data

370

provides to date the broadest spatio-temporal sampling of *S. salar* populations across several

371

countries (Bacon *et al.*, 2009; Valiente *et al.*, 2011; Fjørtoft *et al.*, 2014).

372

373

374

The advantages offered by the large spatial and temporal coverage of this fishery dataset are

375

balanced by the difficulty to control the sampling effort. Recreational fishery data might
351

Our analysis provides new insights on temporal changes in *S. salar* life history traits across

352

France over the last three decades, and contributes to reduce the gap in knowledge by

353

providing further empirical evidence of a long suspected change in life history of *S. salar*

354

throughout its entire distribution area. Using a large dataset from rod and line and commercial

355

catches, we describe a decline in individual length, weight, together with a delay in the

356

migration phenology of adult *S. salar* returning to French rivers. Our analysis highlighted

357

similarities in time trends between regions, but the rate of change in life history traits clearly

358

depends upon the tactic at sea.

359

360

361

Strength and limitation of the data

362

363

364

Time trends in *S. salar* life history traits were analyzed using catch data from recreational and

365

commercial salmonid fishery. This unique dataset provides a large scale observatory of wild

366

French *S. salar* populations. Commercial fisheries of *S. salar* are almost all closed since the

367

early 1990's (ICES, 2016), and scientific data are only available for a few rivers and generally

368

cover only short periods of time. By contrast, recreational hook and line fishery are operating

369

in almost all rivers across the species distribution range. Thus, when available catch data

370

provides to date the broadest spatio-temporal sampling of *S. salar* populations across several

371

countries (Bacon *et al.*, 2009; Valiente *et al.*, 2011; Fjørtoft *et al.*, 2014).

372

373

374

The advantages offered by the large spatial and temporal coverage of this fishery dataset are

375

balanced by the difficulty to control the sampling effort. Recreational fishery data might
376 indeed suffer from non-random sampling caused by fishery management rules and intrinsic
377 variability in fishing effort (Bacon et al., 2009). Although catch data virtually come from
378 newly arrived fresh-run fish only (Thorley et al., 2005; Bacon et al., 2009), the date of capture
379 may still provide a biased estimate of the date of river entry, and this bias may differ between
380 regions. For instance, fish from Aquitaine generally undertake a longer journey in freshwater
381 before being caught (longer rivers) than fish from Brittany and Normandy (smaller and
382 shorter rivers). Hence, the late migration timing reported for Aquitaine (2SW) may be partly
383 explained by a specific spatial setting compared to the other regions rather than by a latter
384 return to coastal waters. This late migration can also reflect a distinct flow regime in early
385 spring, that is driven by melting snow on the Adour - Gaves rivers (where most catches come
386 from). By contrast, we assume that our results on the relative variability and the rate of
387 change in this trait within a given region are robust.

388

389

390 By removing catches made in the end of fishing season, our analysis might miss key
391 ecological signal. In particular, we would not be able to detect any potential compensatory
392 mechanism that would occur between early and late migrating fish. For instance one can
393 hypothesize that a decline in body size among studied fish might be balanced by an increasing
394 number of large fish in a potential late run. Although we cannot rule out such a process, the
395 late captures represented only a small proportion of fish in France, which may thus have a
396 limited impact on our results. Migration monitoring using a fish trap on the Scorff River
397 shows that less than 10% of all 1SW and 2SW fish were trapped in fall, with no evidence of
398 growth compensation (E. Prévost, pers. com.).

399
376 indeed suffer from non-random sampling caused by fishery management rules and intrinsic
377 variability in fishing effort (Bacon et al., 2009). Although catch data virtually come from
378 newly arrived fresh-run fish only (Thorley et al., 2005; Bacon et al., 2009), the date of capture
379 may still provide a biased estimate of the date of river entry, and this bias may differ between
380 regions. For instance, fish from Aquitaine generally undertake a longer journey in freshwater
381 before being caught (longer rivers) than fish from Brittany and Normandy (smaller and
382 shorter rivers). Hence, the late migration timing reported for Aquitaine (2SW) may be partly
383 explained by a specific spatial setting compared to the other regions rather than by a latter
384 return to coastal waters. This late migration can also reflect a distinct flow regime in early
385 spring, that is driven by melting snow on the Adour - Gaves rivers (where most catches come
386 from). By contrast, we assume that our results on the relative variability and the rate of
387 change in this trait within a given region are robust.
388

389
390 By removing catches made in the end of fishing season, our analysis might miss key
391 ecological signal. In particular, we would not be able to detect any potential compensatory
392 mechanism that would occur between early and late migrating fish. For instance one can
393 hypothesize that a decline in body size among studied fish might be balanced by an increasing
394 number of large fish in a potential late run. Although we cannot rule out such a process, the
395 late captures represented only a small proportion of fish in France, which may thus have a
396 limited impact on our results. Migration monitoring using a fish trap on the Scorff River
397 shows that less than 10% of all 1SW and 2SW fish were trapped in fall, with no evidence of
398 growth compensation (E. Prévost, pers. com.).
399
Lastly, although raw data were available at the scale of single rivers, samples were pooled at the regional scale to dampen the effect of uncontrolled sampling variability and local heterogeneity. Indeed, the low signal-to-noise ratio due to low sample size or uncontrolled variation of the fishing effort in space and time for single rivers (e.g., due to alleged report of the fishing effort from one river to the other within the fishing season) would impede the detection of time trends. Aggregating data on several rivers of the same region helped disentangling the temporal variation attributable to large scale environmental change, from local factors.

408

409

Evidence for change in phenotypic traits

411

412

In France, differences in average phenotypic traits and migration date were detected between regions. Interestingly, those patterns have already been described in previous analyses on older data (Prévost, 1987), thus highlighting the stability of the regional contrast over several decades. As discussed previously, the date of river entry may be overestimated in Aquitaine due to uneven sampling design across regions. Genetic drivers of the migration timing may also be considered (Hansen & Jonsson 1991). But this effect is unlikely to explain weight and length differences. Such phenotypic differences are likely to reflect distinct genetic pools (Perrier at al., 2011).

421

422

Overall, this study highlighted important change in phenotypic traits in French *S. salar*
populations over the period 1987-2013. In all regions, we detected a significant decline in fish

424
Lastly, although raw data were available at the scale of single rivers, samples were pooled at the regional scale to dampen the effect of uncontrolled sampling variability and local heterogeneity. Indeed, the low signal-to-noise ratio due to low sample size or uncontrolled variation of the fishing effort in space and time for single rivers (e.g., due to alleged report of the fishing effort from one river to the other within the fishing season) would impede the detection of time trends. Aggregating data on several rivers of the same region helped disentangling the temporal variation attributable to large scale environmental change, from local factors.

408

409

Evidence for change in phenotypic traits

411

412

In France, differences in average phenotypic traits and migration date were detected between regions. Interestingly, those patterns have already been described in previous analyses on older data (Prévost, 1987), thus highlighting the stability of the regional contrast over several decades. As discussed previously, the date of river entry may be overestimated in Aquitaine due to uneven sampling design across regions. Genetic drivers of the migration timing may also be considered (Hansen & Jonsson 1991). But this effect is unlikely to explain weight and length differences. Such phenotypic differences are likely to reflect distinct genetic pools (Perrier at al., 2011).

421

422

Overall, this study highlighted important change in phenotypic traits in French *S. salar*
populations over the period 1987-2013. In all regions, we detected a significant decline in fish
length and weight, as well as a delay in the date of river entry. Results are consistent with
patterns already reported in several other rivers in Northern Europe. Similar declines in S.
*salar* length (about 1 cm per decade), weight (200-400 g per decade) or 1SW body condition
have been observed in Scotland (Bacon *et al.*, 2009; Todd *et al.*, 2011), Ireland (Quinn *et al.*,
2006), Norway (Jonsson *et al.*, 2016) or North America (Friedland *et al.*, 2005). The observed
delay in migration timing (about 5-10 days per decade) is also congruent with previous
findings in Scotland (Youngson *et al.*, 2002; Todd *et al.*, 2012), England (Aprahamian *et al.*,
2008) or Spain (Valiente *et al.*, 2011).

433

434

Moreover, our correlation analysis highlighted common inter-annual fluctuations in
phenotypic traits across all three French regions. Interestingly, we detected more similarities
in the temporal variability of those traits between neighboring French Northern regions
(Brittany and Normandy) than between these two Northern regions and the more distant
Aquitaine. These results support the hypothesis of a segregation of populations by distance
(Jensen *et al.*, 2011; Juanes *et al.*, 2004; Perrier *et al.*, 2011), which may be driven by spatial
or temporal segregation in post-smolt migration route and feeding zone at sea (Bacon *et al.*,
2009; Jensen *et al.*, 2011) or to a difference in river profiles (Juanes *et al.*, 2004; Valiente *et
al.*, 2011). French S. *salar* populations face environmental conditions that differ in many ways
from the conditions encountered by Northern Europe populations (e.g. longer migration
routes, higher freshwater temperature, high anthropic pressure ...). However, the impact of a
large scale phenomenon in the marine environment, initially described in Northern Europe,
seemed to prevail in France as well. Therefore, by providing empirical evidence for Southern
populations, our study nicely complements existing literature to better describe the “big
picture”.
length and weight, as well as a delay in the date of river entry. Results are consistent with
patterns already reported in several other rivers in Northern Europe. Similar declines in S.
salar length (about 1 cm per decade), weight (200-400 g per decade) or 1SW body condition
have been observed in Scotland (Bacon et al., 2009; Todd et al., 2011), Ireland (Quinn et al.,
2006), Norway (Jonsson et al., 2016) or North America (Friedland et al., 2005). The observed
delay in migration timing (about 5-10 days per decade) is also congruent with previous
findings in Scotland (Youngson et al., 2002; Todd et al., 2012), England (Aprahamian et al.,
2008) or Spain (Valiente et al., 2011).

Moreover, our correlation analysis highlighted common inter-annual fluctuations in
phenotypic traits across all three French regions. Interestingly, we detected more similarities
in the temporal variability of those traits between neighboring French Northern regions
(Brittany and Normandy) than between these two Northern regions and the more distant
Aquitaine. These results support the hypothesis of a segregation of populations by distance
(Jensen et al., 2011; Juanes et al., 2004; Perrier et al., 2011), which may be driven by spatial
or temporal segregation in post-smolt migration route and feeding zone at sea (Bacon et al.,
2009; Jensen et al., 2011) or to a difference in river profiles (Juanes et al., 2004; Valiente et
al., 2011). French S. salar populations face environmental conditions that differ in many ways
from the conditions encountered by Northern Europe populations (e.g. longer migration
routes, higher freshwater temperature, high anthropic pressure ...). However, the impact of a
large scale phenomenon in the marine environment, initially described in Northern Europe,
seemed to prevail in France as well. Therefore, by providing empirical evidence for Southern
populations, our study nicely complements existing literature to better describe the "big
picture".
450

451

452 A response to marine environmental change

453

454

455 Taken together, results suggest a globally coherent response across French populations and beyond, in Northern Europe. They strongly support the hypothesis of a common environmental mechanism affecting *S. salar* population throughout its distribution area.

456
457
458 Temporal similarities across regional patterns, e.g. time series varying in phase or antiphase,
459 strongly suggest a response to common environmental drivers jointly impacting the marine
460 phase of the life cycle of different populations spawning in distant rivers. Several studies
461 suggested that a major decline in growth condition would be responsible for the decrease in S.
462 salar length and weight (Friedland et al., 2000; Peyronnet et al., 2007; McCarthy et al., 2008;
463 Jensen et al., 2011), while within river migration timing increased. As a mechanism, it has
464 been suggested that bottom-up control on food resources during the early marine phase of
465 post-smolts may have negatively impacted both growth and survival (Friedland et al., 2009;
466 Jensen et al., 2011; Beaugrand & Reid, 2012). Triggered by ocean warming, a major trophic
467 shift in the North Atlantic Ocean has been documented in the 1990s, with reported changes in
468 zooplankton communities up to seabird population dynamics (Durant et al., 2004; Beaugrand
469 et al., 2008). As a result, decrease in prey abondance and energetic quality may have altered
470 S. salar growth potential at sea (Otero et al., 2012, Mills et al., 2013). Jonsson et al. (2016)
471 further noted that direct effect of temperature are unlikely to explain observed changes but
472 argued in favor of an impact of stock rebuilding in the Herring Clupea harengus L. 1758, or
473 in other Clupeides species, which may compete for resources with salmon smolt, as observed
474 in the Baltic Sea.
450

451

452

A response to marine environmental change

453

454

455

Taken together, results suggest a globally coherent response across French populations and beyond, in Northern Europe. They strongly support the hypothesis of a common environmental mechanism affecting *S. salar* population throughout its distribution area.

456

457

458

Temporal similarities across regional patterns, e.g. time series varying in phase or antiphase, strongly suggest a response to common environmental drivers jointly impacting the marine phase of the life cycle of different populations spawning in distant rivers. Several studies

459

460

461

suggested that a major decline in growth condition would be responsible for the decrease in *S. salar* length and weight (Friedland *et al.*, 2000; Peyronnet *et al.*, 2007; McCarthy *et al.*, 2008;

462

463

Jensen *et al.*, 2011), while within river migration timing increased. As a mechanism, it has been suggested that bottom-up control on food resources during the early marine phase of

464

465

post-smolts may have negatively impacted both growth and survival (Friedland *et al.*, 2009; Jensen *et al.*, 2011; Beaugrand & Reid, 2012). Triggered by ocean warming, a major trophic

466

467

shift in the North Atlantic Ocean has been documented in the 1990s, with reported changes in zooplankton communities up to seabird population dynamics (Durant *et al.*, 2004; Beaugrand

468

469

*et al.*, 2008). As a result, decrease in prey abundance and energetic quality may have altered *S. salar* growth potential at sea (Otero *et al.*, 2012, Mills *et al.*, 2013). Jonsson *et al.* (2016)

470

471

further noted that direct effect of temperature are unlikely to explain observed changes but argued in favor of an impact of stock rebuilding in the Herring *Clupea harengus* L. 1758, or

472

473

in other Clupeides species, which may compete for resources with salmon smolt, as observed in the Baltic Sea.

474
It has been demonstrated that the mortality of fish at sea is size dependent (Lorenzen, 1996; Gislason et al., 2010). In particular, S. salar marine mortality is believed to be mostly caused by predation and will then be size- and growth-dependent (Friedland et al., 1993; Friedland et al., 2000), i.e. larger and more robust fish have a higher probability to escape predation. As a consequence, decreasing growth rate at sea may also be responsible for a decline in marine survival, as also suggested by synchronous patterns across Northern Europe reported by several authors (Friedland et al., 1993; Friedland et al., 2000; Peyronnet et al., 2007; Chaput 2012). However, our catch data do not inform about survival in French populations.

Nevertheless, S. salar monitoring using a fish trap on the Scorff river showed substantial change in marine survival, the average return rate decreasing from 10,3 % (1997-2003) in the 2000's to 4,8 % in recent years (2009-2013) (ICES, 2016).

The temporal coherence (i.e. direct anti-phase) in declining length/weight and delaying run migration observed in our populations appeared compatible with such a degradation of feeding conditions at sea. Within a given sea-age class, the spawning run lasts several weeks, and its seasonal timing is expected to be driven by the tradeoff between reproduction and survival. The reproductive benefit associated with extra feeding opportunities at sea would be balanced against the risk of mortality during an extra time at sea (Clark, 1994). As a consequence, decreasing growth rate may call for an extra time at sea, aiming at maximizing growth potential, as observed in some Scottish rivers (Todd et al., 2012). However, if resource conditions turn out to be so degraded, the decline in growth rate would be too large to be compensated by a mere delay in the date of river entry. Such a mechanism may generate
It has been demonstrated that the mortality of fish at sea is size dependent (Lorenzen, 1996; Gislason et al., 2010). In particular, S. salar marine mortality is believed to be mostly caused by predation and will then be size- and growth-dependent (Friedland et al., 1993; Friedland et al., 2000), i.e. larger and more robust fish have a higher probability to escape predation. As a consequence, decreasing growth rate at sea may also be responsible for a decline in marine survival, as also suggested by synchronous patterns across Northern Europe reported by several authors (Friedland et al., 1993; Friedland et al., 2000; Peyronnet et al., 2007; Chaput 2012). However, our catch data do not inform about survival in French populations.

Nevertheless, S. salar monitoring using a fish trap on the Scorff river showed substantial change in marine survival, the average return rate decreasing from 10,3 % (1997-2003) in the 2000's to 4,8 % in recent years (2009-2013) (ICES, 2016).

The temporal coherence (i.e. direct anti-phase) in declining length/weight and delaying run migration observed in our populations appeared compatible with such a degradation of feeding conditions at sea. Within a given sea-age class, the spawning run lasts several weeks, and its seasonal timing is expected to be driven by the tradeoff between reproduction and survival. The reproductive benefit associated with extra feeding opportunities at sea would be balanced against the risk of mortality during an extra time at sea (Clark, 1994). As a consequence, decreasing growth rate may call for an extra time at sea, aiming at maximizing growth potential, as observed in some Scottish rivers (Todd et al., 2012). However, if resource conditions turn out to be so degraded, the decline in growth rate would be too large to be compensated by a mere delay in the date of river entry. Such a mechanism may generate
opposite patterns of temporal variability in migration timing and length or weight (using detrended data), as observed in French populations. Interestingly, Valiente *et al.* (2011) also reported negative correlations between medium term fluctuations of length/ weight and the date of river entry.

504

505

Different responses between 1SW and 2SW

507

508

Our results stressed that the relative change in weight and condition of returning *S. salar* was weaker and less variable in 2SW than in 1SW. Similar differences between 1SW and 2SW seemed to be present in Scottish (Bacon *et al.*, 2009) and Norwegian populations (Jonsson & Jonsson, 2004) as well. Thus, there is a need to better account for differences in *S. salar* anadromous tactics to better understand the demographic and ecological mechanisms underlining population changes.

515

516

1SW and 2SW *S. salar* have first similar migration routes at the beginning of their journey at sea and then separated migration routes where they may encounter different growth condition at sea. One cannot exclude that change in the feeding condition experienced by 1SW has been more stringent than in 2SW fish, thus explaining a weaker decline in 2SW weight. A proportionally lower decrease in the length of 2SW fish may also point at some compensatory mechanism. For instance, 2SW may move toward more favorable feeding grounds which would then support a higher growth rate over the second year at sea (Auer *et al.*, 2010; Hogan & Friedland, 2010). However, this hypothesis appears poorly supported by recent studies that
opposite patterns of temporal variability in migration timing and length or weight (using detrended data), as observed in French populations. Interestingly, Valiente *et al.* (2011) also reported negative correlations between medium term fluctuations of length/ weight and the date of river entry.

504

505

Different responses between 1SW and 2SW

507

508

Our results stressed that the relative change in weight and condition of returning *S. salar* was weaker and less variable in 2SW than in 1SW. Similar differences between 1SW and 2SW seemed to be present in Scottish (Bacon *et al.*, 2009) and Norwegian populations (Jonsson & Jonsson, 2004) as well. Thus, there is a need to better account for differences in *S. salar* anadromous tactics to better understand the demographic and ecological mechanisms underlining population changes.

515

516

1SW and 2SW *S. salar* have first similar migration routes at the beginning of their journey at sea and then separated migration routes where they may encounter different growth condition at sea. One cannot exclude that change in the feeding condition experienced by 1SW has been more stringent than in 2SW fish, thus explaining a weaker decline in 2SW weight. A proportionally lower decrease in the length of 2SW fish may also point at some compensatory mechanism. For instance, 2SW may move toward more favorable feeding grounds which would then support a higher growth rate over the second year at sea (Auer *et al.*, 2010; Hogan & Friedland, 2010). However, this hypothesis appears poorly supported by recent studies that
analyzed growth patterns on fish scales for Northern populations, showing that growth increment in 2SW during the second year at sea was not significantly higher than growth rate over their first year at sea (Hogan & Friedland, 2010; Jensen et al., 2011; Jonsson et al., 2016).

529

530

In *S. salar*, the duration of the marine phase of the life cycle may reflect different strategy of resource allocation between key fitness components (e.g. survival, growth, reproduction).

Intrinsic difference in allocation rule may generate contrasted response to a given change in resource availability between 1SW and 2SW fish. Females are largely dominant among 2SW fish (Baglinière & Porcher, 1994; Fleming, 1996; Barson et al. 2015), thus suggesting that difference in allocation rule between 1SW and 2SW could emerges from difference in allocation rules between males and females. Noteworthy, males and females have specific physiological and behavioral constraints over the reproduction period; they are also characterized by distinct tactics at sea. In *S. salar*, female's fitness is more strongly driven by body size than male's one because sexual maturation process requires greater energetic needs in females than in males (i.e. to maximize fecundity). The tight dependence between sex and sea age classes might explain the difference in individual's response to changes in environmental conditions between sea ages classes. As a result, 1SW fish (preferentially males) might have favored activities related to survival at the expense of mass gain, and 2SW fish (preferentially females) might have favored a large body size as a way to maximize fecundity in surviving individuals, as suggested by our data.

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548
analyzed growth patterns on fish scales for Northern populations, showing that growth increment in 2SW during the second year at sea was not significantly higher than growth rate over their first year at sea (Hogan & Friedland, 2010; Jensen et al., 2011; Jonsson et al., 2016).

529

530

In *S. salar*, the duration of the marine phase of the life cycle may reflect different strategy of resource allocation between key fitness components (e.g. survival, growth, reproduction).

Intrinsic difference in allocation rule may generate contrasted response to a given change in resource availability between 1SW and 2SW fish. Females are largely dominant among 2SW fish (Baglinière & Porcher, 1994; Fleming, 1996; Barson et al. 2015), thus suggesting that difference in allocation rule between 1SW and 2SW could emerges from difference in allocation rules between males and females. Noteworthy, males and females have specific physiological and behavioral constraints over the reproduction period; they are also characterized by distinct tactics at sea. In *S. salar*, female's fitness is more strongly driven by body size than male's one because sexual maturation process requires greater energetic needs in females than in males (i.e. to maximize fecundity). The tight dependence between sex and sea age classes might explain the difference in individual's response to changes in environmental conditions between sea ages classes. As a result, 1SW fish (preferentially males) might have favored activities related to survival at the expense of mass gain, and 2SW fish (preferentially females) might have favored a large body size as a way to maximize fecundity in surviving individuals, as suggested by our data.

547

548
In more details, the rate of change was not uniform for all life history traits in 1SW and 2SW.
Actually, we highlighted a similar response in 1SW and 2SW across regions for body length.
In *S. salar*, growth and body size are considered as key drivers of marine survival and female
fecundity (Thorpe *et al.*, 1984; Fleming, 1996; Jonsson *et al.*, 1996). But a large body size is
also a key feature of reproduction success in anadromous males, as it confers an advantage in
terms of intra-sexual competition for mate (Fleming, 1996). Therefore fish length appears as
an important fitness component (Marschall *et al.*, 1998) in both males and females, i.e. in both
1SW and 2SW fish. Not surprisingly, length happened to be the most preserved life history
trait in our analysis: low variability and weak decline. Similarly Todd at al. (2008) reported a
disproportional decline in body weight compared with length in Scottish 1SW fish (22% vs.
4% declines, respectively). This pattern seems consistent with the life history theory, which
states that phenotypic traits the more strongly connected to fitness are expected to be the more
preserved - or canalized - against environmental stochasticity (Stearns & Kawecki, 1994;
Pfister, 1998; Caswell, 2001; Gaillard & Yoccoz, 2003). At the population level, the relative
conservatism in body length, compared to other traits, can be perceived as a positive signal for
population viability.

565

566

567 Conclusion

568

569

This large scale study on French populations contributes to reduce the gap in knowledge by
providing further empirical evidence of a long suspected change in life history of *S. salar*
throughout its North East distribution area. Results also revealed different rates of change
between 1SW and 2SW fish, thus pointing out the importance of considering the complexity

573
In more details, the rate of change was not uniform for all life history traits in 1SW and 2SW.
Actually, we highlighted a similar response in 1SW and 2SW across regions for body length.
In *S. salar*, growth and body size are considered as key drivers of marine survival and female
fecundity (Thorpe *et al.*, 1984; Fleming, 1996; Jonsson *et al.*, 1996). But a large body size is
also a key feature of reproduction success in anadromous males, as it confers an advantage in
terms of intra-sexual competition for mate (Fleming, 1996). Therefore fish length appears as
an important fitness component (Marschall *et al.*, 1998) in both males and females, i.e. in both
1SW and 2SW fish. Not surprisingly, length happened to be the most preserved life history
trait in our analysis: low variability and weak decline. Similarly Todd at al. (2008) reported a
disproportional decline in body weight compared with length in Scottish 1SW fish (22% vs.
4% declines, respectively). This pattern seems consistent with the life history theory, which
states that phenotypic traits the more strongly connected to fitness are expected to be the more
preserved - or canalized - against environmental stochasticity (Stearns & Kawecki, 1994;
Pfister, 1998; Caswell, 2001; Gaillard & Yoccoz, 2003). At the population level, the relative
conservatism in body length, compared to other traits, can be perceived as a positive signal for
population viability.

565

566

567 Conclusion

568

569

This large scale study on French populations contributes to reduce the gap in knowledge by
providing further empirical evidence of a long suspected change in life history of *S. salar*
throughout its North East distribution area. Results also revealed different rates of change
between 1SW and 2SW fish, thus pointing out the importance of considering the complexity

573
574 of life history strategies when exploring mechanisms of changes in *S. salar* phenotypic traits.

575 We hope that this long due analysis for France would encourage other authors to make their
576 dataset available to the scientific community, which is a prerequisite to improve our
577 understanding of major cross-border ecological mechanisms.

578

579

580 Acknowledgments

581

582

583 The authors are grateful to the National Center for the Interpretation of Salmonid Catches for
584 running the national catch declaration scheme on Atlantic salmon in France, with the
585 technical and financial support of INRA 1036 U3E (R&D unit Onema-INRA « Gest’Aqua »).
586 Lucie Montorio was funded by the ‘Region Bretagne’ and INRA.
574 of life history strategies when exploring mechanisms of changes in *S. salar* phenotypic traits.

575 We hope that this long due analysis for France would encourage other authors to make their
576 dataset available to the scientific community, which is a prerequisite to improve our
577 understanding of major cross-border ecological mechanisms.

578

579

580 Acknowledgments

581

582

583 The authors are grateful to the National Center for the Interpretation of Salmonid Catches for
584 running the national catch declaration scheme on Atlantic salmon in France, with the
585 technical and financial support of INRA 1036 U3E (R&D unit Onema-INRA « Gest’Aqua »).
586 Lucie Montorio was funded by the ‘Region Bretagne’ and INRA.
587 REFERENCES

588

589

590 Aas, Ø., Klemetsen, A., Einum, S. and Skurdal, J. (2011). *Atlantic salmon ecology*. Oxford, UK; Wiley-Blackwell.doi:10.1002/9781444327755.fmatter

592 Aprahamian, M. W., Davidson, I. C. and Cove, R. J. (2008). Life history changes in Atlantic salmon from the River Dee, Wales. *Hydrobiologia*, 602; 61–78.doi:10.1007/s10750-008-

594 9286-3

595 Armstrong, J. D., Grant, J. W., Forsgren, H. L., Fausch, K. D., DeGraaf, R. M., Fleming, I.

596 A., Prowse, T. D. and Schlosser, I. J. (1998). The application of science to the management of Atlantic salmon (*Salmo salar*): integration across scales. *Canadian Journal of Fisheries and Aquatic Sciences*, 55; 303–311.doi:10.1139/d98-014

599 Auer, S. K., Arendt, J. D., Chandramouli, R. and Reznick, D. N. (2010). Juvenile compensatory growth has negative consequences for reproduction in Trinidadian guppies (*Poecilia reticulata*). *Ecology Letters*, 13; 998–1007.doi:10.1111/j.1461-0248.2010.01491.x

602 Bacon, P. J., Palmer, S. C. F., MacLean, J. C., Smith, G. W., Whyte, B. D. M., Gurney, W. S. C. and Youngson, A. F. (2009). Empirical analyses of the length, weight, and condition of adult Atlantic salmon on return to the Scottish coast between 1963 and 2006. *ICES Journal of Marine Science*; doi: 10.1093/icesjms/fsp096

606 Bacon, P. J., Gurney, W. S., McKenzie, E., Whyte, B., Campbell, R., Laughton, R., Smith, G. and MacLean, J. (2011). Objective determination of the sea age of Atlantic salmon from the sizes and dates of capture of individual fish. *ICES Journal of Marine Science; Journal du Conseil*, 68; 130–143.doi:10.1093/icesjms/fsq142

610 Baglinière, J. L., Bomassi, P., Bousquet, B., Chancerel, F., De Pontual, H., Dumas, J.,

611 Euzenat, G., Fontenelle, G., Fournel, F., Gayou, F. and others (1985). La determination de
587 REFERENCES

588

589

590 Aas, Ø., Klemetsen, A., Einum, S. and Skurdal, J. (2011). *Atlantic salmon ecology*. Oxford, UK; Wiley-Blackwell.doi:10.1002/9781444327755.fmatter

592 Aprahamian, M. W., Davidson, I. C. and Cove, R. J. (2008). Life history changes in Atlantic salmon from the River Dee, Wales. *Hydrobiologia*, 602; 61–78.doi:10.1007/s10750-008-

594 9286-3

595 Armstrong, J. D., Grant, J. W., Forsgren, H. L., Fausch, K. D., DeGraaf, R. M., Fleming, I.

596 A., Prowse, T. D. and Schlosser, I. J. (1998). The application of science to the management of Atlantic salmon (*Salmo salar*): integration across scales. *Canadian Journal of Fisheries and Aquatic Sciences*, 55; 303–311.doi:10.1139/d98-014

599 Auer, S. K., Arendt, J. D., Chandramouli, R. and Reznick, D. N. (2010). Juvenile compensatory growth has negative consequences for reproduction in Trinidadian guppies (*Poecilia reticulata*). *Ecology Letters*, 13; 998–1007.doi:10.1111/j.1461-0248.2010.01491.x

602 Bacon, P. J., Palmer, S. C. F., MacLean, J. C., Smith, G. W., Whyte, B. D. M., Gurney, W. S. C. and Youngson, A. F. (2009). Empirical analyses of the length, weight, and condition of adult Atlantic salmon on return to the Scottish coast between 1963 and 2006. *ICES Journal of Marine Science*; doi: 10.1093/icesjms/fsp096

606 Bacon, P. J., Gurney, W. S., McKenzie, E., Whyte, B., Campbell, R., Laughton, R., Smith, G. and MacLean, J. (2011). Objective determination of the sea age of Atlantic salmon from the sizes and dates of capture of individual fish. *ICES Journal of Marine Science; Journal du Conseil*, 68; 130–143.doi:10.1093/icesjms/fsq142

610 Baglinière, J. L., Bomassi, P., Bousquet, B., Chancerel, F., De Pontual, H., Dumas, J.,

611 Euzenat, G., Fontenelle, G., Fournel, F., Gayou, F. and others (1985). La determination de
l'âge par scalimétrie chez le saumon atlantique (*Salmo salar*) dans son aire de répartition méridionale; utilisation pratique et difficultés de la méthode. *Bulletin Français de la Pêche et de la Pisciculture*; 69-105.

Baglinière, J. L. and Porcher, J. P. (1994). Caractéristiques des stocks de reproducteurs et comportement lors de la migration génésique. *Le saumon Atlantique*; 2-905434.

Baglinière, J. L., Denais, L., Rivot, E., Porcher, J. P., Prévost, E., Marchand, F. and Vauclin, V. (2004). Length and age structure modifications of the Atlantic salmon (*Salmo salar*) populations of Brittany and Lower Normandy from 1972 to 2002. *UMR INRA-ENSA, Ecobiologie et Qualité des Hydrosystèmes Continentaux, Conseil supérieur de la pêche, UESP, INRA, Rennes*.

Barson, N.J., Aykanat, T., Hindar, K., Baranski, M., Bolstad, G.H., Fiske, P., Jacq, C., Jensen, A.J., Johnston, S.E., Karlsson, S., Kent, M., Moen, T., Niemelä, E., Nome, T., Næsje, T.F., Orell, P., Romakkaniemi, A., Sægrov, H., Urdal, K., Erkinaro, J., Lien, S. & Primmer, C.R. (2015) Sex-dependent dominance at a single locus maintains variation in age at maturity in salmon. *Nature*, 528, 405-408.

Beaugrand, G. and Reid, P. C. (2012). Relationships between North Atlantic salmon, plankton, and hydroclimatic change in the Northeast Atlantic. *ICES Journal of Marine Science; Journal du Conseil*, 69; 1549-1562.doi:10.1093/icesjms/fss153

Beaugrand, G., Edwards, M., Brander, K., Luczak, C. and Ibanez, F. (2008). Causes and projections of abrupt climate-driven ecosystem shifts in the North Atlantic. *Ecology letters*, 11; 1157-1168.doi:10.1111/j.1461-0248.2008.01218.x

Bolger, T. and Connolly, P. L. (1989). The selection of suitable indices for the measurement and analysis of fish condition. *Journal of Fish Biology*, 34; 171-182.doi: 10.1111/j.1095-8649.1989.tb03300.x
612 l'âge par scalimétrie chez le saumon atlantique (*Salmo salar*) dans son aire de répartition
613 méridionale; utilisation pratique et difficultés de la méthode. *Bulletin Français de la Pêche et*
614 *de la Pisciculture*; 69-105.

615 Baglinière, J. L. and Porcher, J. P. (1994). Caractéristiques des stocks de reproducteurs et
616 comportement lors de la migration génésique. *Le saumon Atlantique*; 2-905434.

617 Baglinière, J. L., Denais, L., Rivot, E., Porcher, J. P., Prévost, E., Marchand, F. and Vauclin,
618 V. (2004). Length and age structure modifications of the Atlantic salmon (*Salmo salar*)
619 populations of Brittany and Lower Normandy from 1972 to 2002. *UMR INRA-ENSA,
620 Ecobiologie et Qualité des Hydrosystèmes Continentaux, Conseil supérieur de la pêche,
621 UESP, INRA, Rennes.*

622 Barson, N.J., Aykanat, T., Hindar, K., Baranski, M., Bolstad, G.H., Fiske, P., Jacq, C., Jensen,
623 A.J., Johnston, S.E., Karlsson, S., Kent, M., Moen, T., Niemelä, E., Nome, T., Næsje, T.F.,
624 Orell, P., Romakkaniemi, A., Sægrov, H., Urdal, K., Erkinaro, J., Lien, S. & Primmer, C.R.
625 (2015) Sex-dependent dominance at a single locus maintains variation in age at maturity in
626 salmon. *Nature*, 528, 405-408.

627 Beaugrand, G. and Reid, P. C. (2012). Relationships between North Atlantic salmon,
628 plankton, and hydroclimatic change in the Northeast Atlantic. *ICES Journal of Marine
629 Science; Journal du Conseil*, 69; 1549-1562.doi:10.1093/icesjms/fss153

630 Beaugrand, G., Edwards, M., Brander, K., Luczak, C. and Ibanez, F. (2008). Causes and
631 projections of abrupt climate-driven ecosystem shifts in the North Atlantic. *Ecology letters*,
632 11; 1157-1168.doi:10.1111/j.1461-0248.2008.01218.x

633 Bolger, T. and Connolly, P. L. (1989). The selection of suitable indices for the measurement
634 and analysis of fish condition. *Journal of Fish Biology*, 34; 171-182.doi: 10.1111/j.1095-
635 8649.1989.tb03300.x
636 Burnham, K. P. and Anderson, D. R. (2002). Model selection and multimodel inference; a
practical information-theoretic approach. Springer Science & Business Media.

638 Caswell, H. (2001). *Matrix population models*. John Wiley & Sons, Ltd.

639 Chaput, G. (2012). Overview of the status of Atlantic salmon (*Salmo salar*) in the North
640 Atlantic and trends in marine mortality. *ICES Journal of Marine Science; Journal du Conseil*,
641 69; 1538–1548.doi:10.1093/icesjms/fss013

642 Clark, C. W. (1994). Antipredator behavior and the asset-protection principle. *Behavioral
643 Ecology*, 5; 159–170.doi:10.1093/beheco/5.2.159

644 Cody, M. L. (1966). A general theory of clutch size. *Evolution*; 174–184.doi:
645 10.2307/2406571

646 Coulson, T., Catchpole, E. A., Albon, S. D., Morgan, B. J., Pemberton, J. M., Clutton-Brock,
647 T. H., Crawley, M. J. and Grenfell, B. T. (2001). Age, sex, density, winter weather, and
648 population crashes in Soay sheep. *Science*, 292; 1528–1531.doi:

649 10.1126/science.292.5521.1528

650 Crozier, L. G. and Hutchings, J. A. (2014). Plastic and evolutionary responses to climate
651 change in fish. *Evolutionary applications*, 7; 68–87.doi:10.1111/eva.12135

652 Durant, J. M., Anker-Nilssen, T., Hjermann, D. Ø. and Stenseth, N. C. (2004). Regime shifts
653 in the breeding of an Atlantic puffin population. *Ecology Letters*, 7; 388–394.doi:
654 10.1111/j.1461-0248.2004.00588.

655 Fjørtoft, H. B., Borgstrøm, R. and Skaala, Ø. (2014). Differential changes in growth patterns
656 of anadromous brown trout and Atlantic salmon from the River Etneelva over a 25-year
657 period. *Marine Biology Research*, 10; 301–307.doi:10.1080/17451000.2013.810753

658 Fleming, I. A. (1996). Reproductive strategies of Atlantic salmon; ecology and evolution.
659 *Reviews in Fish Biology and Fisheries*, 6; 379–416.doi:10.1007/BF00164323
636 Burnham, K. P. and Anderson, D. R. (2002). Model selection and multimodel inference; a
practical information-theoretic approach. Springer Science & Business Media.

638 Caswell, H. (2001). *Matrix population models*. John Wiley & Sons, Ltd.

639 Chaput, G. (2012). Overview of the status of Atlantic salmon (*Salmo salar*) in the North
640 Atlantic and trends in marine mortality. *ICES Journal of Marine Science; Journal du Conseil*,
641 69; 1538–1548.doi:10.1093/icesjms/fss013

642 Clark, C. W. (1994). Antipredator behavior and the asset-protection principle. *Behavioral
643 Ecology*, 5; 159–170.doi:10.1093/beheco/5.2.159

644 Cody, M. L. (1966). A general theory of clutch size. *Evolution*; 174–184.doi:
645 10.2307/2406571

646 Coulson, T., Catchpole, E. A., Albon, S. D., Morgan, B. J., Pemberton, J. M., Clutton-Brock,
647 T. H., Crawley, M. J. and Grenfell, B. T. (2001). Age, sex, density, winter weather, and
648 population crashes in Soay sheep. *Science*, 292; 1528–1531.doi:

649 10.1126/science.292.5521.1528

650 Crozier, L. G. and Hutchings, J. A. (2014). Plastic and evolutionary responses to climate
651 change in fish. *Evolutionary applications*, 7; 68–87.doi:10.1111/eva.12135

652 Durant, J. M., Anker-Nilssen, T., Hjermann, D. Ø. and Stenseth, N. C. (2004). Regime shifts
653 in the breeding of an Atlantic puffin population. *Ecology Letters*, 7; 388–394.doi:
654 10.1111/j.1461-0248.2004.00588.

655 Fjørtoft, H. B., Borgstrøm, R. and Skaala, Ø. (2014). Differential changes in growth patterns
656 of anadromous brown trout and Atlantic salmon from the River Etneelva over a 25-year
657 period. *Marine Biology Research*, 10; 301–307.doi: 10.1080/17451000.2013.810753

658 Fleming, I. A. (1996). Reproductive strategies of Atlantic salmon; ecology and evolution.
659 *Reviews in Fish Biology and Fisheries*, 6; 379–416.doi:10.1007/BF00164323
660 Friedland, K. D., Reddin, D. G. and Kocik, J. F. (1993). Marine survival of North American and European Atlantic salmon; effects of growth and environment. *ICES Journal of Marine Science; Journal du Conseil*, 50; 481–492.doi:10.1006/jmsc.1993.1051

663 Friedland, K. D., Hansen, L. P., Dunkley, D. A. and MacLean, J. C. (2000). Linkage between ocean climate, post-smolt growth, and survival of Atlantic salmon (*Salmo salar* L.) in the North Sea area. *ICES Journal of Marine Science; Journal du Conseil*, 57; 419–429.doi:10.1006/jmsc.1999.0639

667 Friedland, K. D., Chaput, G. and MacLean, J. C. (2005). The emerging role of climate in post-smolt growth of Atlantic salmon. *ICES Journal of Marine Science; Journal du Conseil*, 62; 1338–1349.doi:10.1016/j.icesjms.2005.04.013

670 Friedland, K. D., MacLean, J. C., Hansen, L. P., Peyronnet, A. J., Karlsson, L., Reddin, D. G., Maoiléidigh, N. Ó. and McCarthy, J. L. (2009). The recruitment of Atlantic salmon in Europe. *ICES Journal of Marine Science; Journal du Conseil*, 66; 289–304.doi:

673 10.1093/icesjms/fsn210

674 Friedland, K. D., Kane, J., Hare, J. A., Lough, R. G., Fratantoni, P. S., Fogarty, M. J. and Gaillard, J.-M., Pontier, D., Allaine, D., Lebreton, J. D., Trouvilliez, J. and Clobert, J. (1989). An analysis of demographic tactics in birds and mammals. *Oikos*, 59–76.doi: 10.2307/3566088

678 Gaillard, J.-M. and Yoccoz, N. G. (2003). Temporal variation in survival of mammals; a case of environmental canalization? *Ecology*, 84; 3294–3306.doi:10.1890/02-0409

680 Gislason, H., Daan, N., Rice, J. C. and Pope, J. G. (2010). Size, growth, temperature and the natural mortality of marine fish. *Fish and Fisheries*, 11; 149–158.doi:10.1111/j.1467-6822.2010.00076.x
681 Gueguen, J. C. and Prouzet, P. (1994). Le saumon atlantique; biologie et gestion de la ressource. Editions Quae.
660 Friedland, K. D., Reddin, D. G. and Kocik, J. F. (1993). Marine survival of North American and European Atlantic salmon; effects of growth and environment. *ICES Journal of Marine Science; Journal du Conseil*, 50; 481–492.doi:10.1006/jmsc.1993.1051

663 Friedland, K. D., Hansen, L. P., Dunkley, D. A. and MacLean, J. C. (2000). Linkage between ocean climate, post-smolt growth, and survival of Atlantic salmon (*Salmo salar* L.) in the North Sea area. *ICES Journal of Marine Science; Journal du Conseil*, 57; 419–429.doi:10.1006/jmsc.1999.0639

667 Friedland, K. D., Chaput, G. and MacLean, J. C. (2005). The emerging role of climate in post-smolt growth of Atlantic salmon. *ICES Journal of Marine Science; Journal du Conseil*, 62; 1338–1349.doi:10.1016/j.icesjms.2005.04.013

670 Friedland, K. D., MacLean, J. C., Hansen, L. P., Peyronnet, A. J., Karlsson, L., Reddin, D. G., Maoiléidigh, N. Ó. and McCarthy, J. L. (2009). The recruitment of Atlantic salmon in Europe. *ICES Journal of Marine Science; Journal du Conseil*, 66; 289–304.doi:

673 10.1093/icesjms/fsn210

674 Friedland, K. D., Kane, J., Hare, J. A., Lough, R. G., Fratantoni, P. S., Fogarty, M. J. and Gaillard, J.-M., Pontier, D., Allaine, D., Lebreton, J. D., Trouvilliez, J. and Clobert, J. (1989). An analysis of demographic tactics in birds and mammals. *Oikos*, 59–76.doi: 10.2307/3566088

678 Gaillard, J.-M. and Yoccoz, N. G. (2003). Temporal variation in survival of mammals; a case of environmental canalization? *Ecology*, 84; 3294–3306.doi:10.1890/02-0409

680 Gislason, H., Daan, N., Rice, J. C. and Pope, J. G. (2010). Size, growth, temperature and the natural mortality of marine fish. *Fish and Fisheries*, 11; 149–158.doi:10.1111/j.1467-6822.2010.00076.x
681 Gueguen, J. C. and Prouzet, P. (1994). Le saumon atlantique; biologie et gestion de la ressource. Editions Quae.
684 Hansen, L. P. and Jonsson, B. (1991). Evidence of a genetic component in the seasonal return
685 pattern of Atlantic salmon, *Salmo salar* L. *Journal of Fish Biology*, 38; 251–258.doi:
686 10.1111/j.1095-8649.1991.tb03111.x

687 Hogan, F. and Friedland, K. D. (2010). Retrospective growth analysis of Atlantic salmon
688 *Salmo salar* and implications for abundance trends. *Journal of Fish Biology*, 76; 2502–
689 2520.doi:10.1111/j.1095-8649.2010.02650.x

690 Hutchings, J. A. and Jones, M. E. (1998). Life history variation and growth rate thresholds for
691 maturity in Atlantic salmon, *Salmo salar*. *Canadian Journal of Fisheries and Aquatic
692 Sciences*, 55; 22–47.doi:10.1139/d98-004

693 ICES, 2016. Report of the Working Group on North Atlantic Salmon (WGNAS), 30 March–8
694 April 2016, Copenhagen, Denmark. ICES CM 2016/ACOM:10. 323 pp.

695 Jensen, A. J. J., Fiske, P., Hansen, L. P. P., Johnsen, B. O. O., Mork, K. A. A. and Næsje, T.
696 F. F. (2011). Synchrony in marine growth among Atlantic salmon (*Salmo salar*) populations.

697 *Canadian Journal of Fisheries and Aquatic Sciences*, 68; 444–457.doi: 10.1139/F10-156

698 Jonsson, N., Jonsson, B. and Fleming, I. A. (1996). Does early growth cause a phenotypically
699 plastic response in egg production of Atlantic salmon? *Functional Ecology*; 89–96.

700 doi:10.2307/2390266

701 Jonsson, N. and Jonsson, B. (2004). Size and age of maturity of Atlantic salmon correlate
702 with the North Atlantic Oscillation Index (NAOI). *Journal of Fish Biology*, 64; 241–

703 247.doi:10.1111/j.1095-8649.2004.00269.x

704 Jonsson B., Jonsson, N and Albretsen (2016). Environmental change influences the life
705 history of salmon *Salmo salar* in the North Atlantic. *Journal of fish biology* 88, 618–637.

706 doi:10.1111/jfb.12854
684 Hansen, L. P. and Jonsson, B. (1991). Evidence of a genetic component in the seasonal return
685 pattern of Atlantic salmon, *Salmo salar* L. *Journal of Fish Biology*, 38; 251–258.doi:
686 10.1111/j.1095-8649.1991.tb03111.x

687 Hogan, F. and Friedland, K. D. (2010). Retrospective growth analysis of Atlantic salmon
688 *Salmo salar* and implications for abundance trends. *Journal of Fish Biology*, 76; 2502–
689 2520.doi:10.1111/j.1095-8649.2010.02650.x

690 Hutchings, J. A. and Jones, M. E. (1998). Life history variation and growth rate thresholds for
691 maturity in Atlantic salmon, *Salmo salar*. *Canadian Journal of Fisheries and Aquatic
692 Sciences*, 55; 22–47.doi:10.1139/d98-004

693 ICES, 2016. Report of the Working Group on North Atlantic Salmon (WGNAS), 30 March–8
694 April 2016, Copenhagen, Denmark. ICES CM 2016/ACOM:10. 323 pp.

695 Jensen, A. J. J., Fiske, P., Hansen, L. P. P., Johnsen, B. O. O., Mork, K. A. A. and Næsje, T.

696 F. F. (2011). Synchrony in marine growth among Atlantic salmon (*Salmo salar*) populations.

697 *Canadian Journal of Fisheries and Aquatic Sciences*, 68; 444–457.doi: 10.1139/F10-156

698 Jonsson, N., Jonsson, B. and Fleming, I. A. (1996). Does early growth cause a phenotypically
699 plastic response in egg production of Atlantic salmon? *Functional Ecology*; 89–96.

700 doi:10.2307/2390266

701 Jonsson, N. and Jonsson, B. (2004). Size and age of maturity of Atlantic salmon correlate
702 with the North Atlantic Oscillation Index (NAOI). *Journal of Fish Biology*, 64; 241–

703 247.doi:10.1111/j.1095-8649.2004.00269.x

704 Jonsson B., Jonsson, N and Albretsen (2016). Environmental change influences the life
705 history of salmon *Salmo salar* in the North Atlantic. *Journal of fish biology* 88, 618–637.

706 doi:10.1111/jfb.12854
707 Juanes, F., Gephard, S. and Beland, K. F. (2004). Long-term changes in migration timing of
708 adult Atlantic salmon (*Salmo salar*) at the southern edge of the species distribution. *Canadian
709 Journal of Fisheries and Aquatic Sciences*, 61; 2392-2400.doi: 10.1139/f04-207

710 Le Cam, S., Perrier, C., Besnard, A.-L., Bernatchez, L. and Evanno, G. (2015). Genetic and
711 phenotypic changes in an Atlantic salmon population supplemented with non-local
712 individuals; a longitudinal study over 21 years. *Proceedings of the Royal Society of London B;
713 Biological Sciences*, 282; 20142765.doi:10.1098/rspb.2014.2765

714 Levitus, S., Antonov, J.I., Boyer, T.P. & Stephens, C. (2000) Warming of the world ocean.
715 *Science*, 287, 2225-2229.

716 Limburg, K. E. and Waldman, J. R. (2009). Dramatic declines in North Atlantic diadromous
717 fishes. *BioScience*, 59; 955-965. doi: 10.1525/bio.2009.59.11.7Lorenzen, K. (1996). The
718 relationship between body weight and natural mortality in juvenile and adult fish: a
719 comparison of natural ecosystems and aquaculture. *Journal of fish biology*, 49; 627-
720 642.doi:10.1111/j.1095-8649.1996.tb00060.x

721 Maisse, G. & Baglinière, J-L., 1986. Le sexage morphologique du Saumon atlantique (Salmo
722 salar). *Bulletin Français de la Pêche et de la Pisciculture*, 300, 13-18.

723 Marschall, E. A., Quinn, T. P., Roff, D. A., Hutchings, J. A., Metcalfe, N. B., Bakke, T. A.,
724 Saunders, R. L. and Poff, N. L. (1998). A framework for understanding Atlantic salmon
725 (*Salmo salar*) life history. *Canadian Journal of Fisheries and Aquatic Sciences*, 55; 48-
726 58.doi:10.1139/d98-007

727 Mather, M. E., Parrish, D. L., Folt, C. L. and DeGraaf, R. M. (1998). Integrating across
728 scales; effectively applying science for the successful conservation of Atlantic salmon (*Salmo
729 salar*). *Canadian Journal of Fisheries and Aquatic Sciences*, 55; 1-8.doi: 10.1139/d98-000
707 Juanes, F., Gephard, S. and Beland, K. F. (2004). Long-term changes in migration timing of
708 adult Atlantic salmon (*Salmo salar*) at the southern edge of the species distribution. *Canadian
709 Journal of Fisheries and Aquatic Sciences*, 61; 2392-2400.doi: 10.1139/f04-207

710 Le Cam, S., Perrier, C., Besnard, A.-L., Bernatchez, L. and Evanno, G. (2015). Genetic and
711 phenotypic changes in an Atlantic salmon population supplemented with non-local
712 individuals; a longitudinal study over 21 years. *Proceedings of the Royal Society of London B;
713 Biological Sciences*, 282; 20142765.doi:10.1098/rspb.2014.2765

714 Levitus, S., Antonov, J.I., Boyer, T.P. & Stephens, C. (2000) Warming of the world ocean.
715 *Science*, 287, 2225-2229.

716 Limburg, K. E. and Waldman, J. R. (2009). Dramatic declines in North Atlantic diadromous
717 fishes. *BioScience*, 59; 955-965. doi: 10.1525/bio.2009.59.11.7Lorenzen, K. (1996). The
718 relationship between body weight and natural mortality in juvenile and adult fish: a
719 comparison of natural ecosystems and aquaculture. *Journal of fish biology*, 49; 627-
720 642.doi:10.1111/j.1095-8649.1996.tb00060.x

721 Maisse, G. & Baglinière, J-L., 1986. Le sexage morphologique du Saumon atlantique (Salmo
722 salar). *Bulletin Français de la Pêche et de la Pisciculture*, 300, 13-18.

723 Marschall, E. A., Quinn, T. P., Roff, D. A., Hutchings, J. A., Metcalfe, N. B., Bakke, T. A.,
724 Saunders, R. L. and Poff, N. L. (1998). A framework for understanding Atlantic salmon
725 (*Salmo salar*) life history. *Canadian Journal of Fisheries and Aquatic Sciences*, 55; 48-
726 58.doi:10.1139/d98-007

727 Mather, M. E., Parrish, D. L., Folt, C. L. and DeGraaf, R. M. (1998). Integrating across
728 scales; effectively applying science for the successful conservation of Atlantic salmon (*Salmo
729 salar*). *Canadian Journal of Fisheries and Aquatic Sciences*, 55; 1-8.doi: 10.1139/d98-000
730 McCarthy, J. L., Friedland, K. D. and Hansen, L. P. (2008). Monthly indices of the post-smolt growth of Atlantic salmon from the Drammen River, Norway. *Journal of Fish Biology*, 72; 1572–1588.doi:10.1111/j.1095-8649.2008.01820.x

733 Miller, K. M., Teffer, A., Tucker, S., Li, S., Schulze, A. D., Trudel, M., Juanes, F., Tabata, A., Kaukinen, K. H., Ginther, N. G. and others (2014). Infectious disease, shifting climates, and opportunistic predators; cumulative factors potentially impacting wild salmon declines. *Evolutionary applications*, 7; 812–855.doi:10.1111/eva.12164

737 Mills, K. E., Pershing, A. J., Sheehan, T. F. and Mountain, D. (2013). Climate and ecosystem linkages explain widespread declines in North American Atlantic salmon populations. *Global Change Biology*, 19; 3046–3061.doi:10.1111/gcb.12298

740 Nash, R. D., Valencia, A. H. and Geffen, A. J. (2006). The origin of Fulton's condition factor—setting the record straight. *Fisheries*, 31; 236–238.

742 Otero, J., Jensen, A.J., L'Abée-Lund, J.H., Stenseth, N.C., Storvik, Geir O. & Vøllestad, L.A. (2012). Contemporary ocean warming and freshwater conditions are related to later sea age at maturity in Atlantic salmon spawning in Norwegian rivers. *Ecology and Evolution*, 2; 2192–2203.

746 Perrier, C., Guyomard, R., Baglinière, J.-L. and Evanno, G. (2011). Determinants of hierarchical genetic structure in Atlantic salmon populations; environmental factors vs. anthropogenic influences. *Molecular Ecology*, 20; 4231–4245.doi:10.1111/j.1365-294X.2011.05266.x

750 Peyronnet, A., Friedland, K. D., Maoiléidigh, N. Ó., Manning, M. and Poole, W. R. (2007).

751 Links between patterns of marine growth and survival of Atlantic salmon *Salmo salar*, L. *Journal of Fish Biology*, 71; 684–700.doi:10.1111/j.1095-8649.2007.01538.x
730 McCarthy, J. L., Friedland, K. D. and Hansen, L. P. (2008). Monthly indices of the post-smolt growth of Atlantic salmon from the Drammen River, Norway. *Journal of Fish Biology*, 72; 1572–1588.doi:10.1111/j.1095-8649.2008.01820.x

733 Miller, K. M., Teffer, A., Tucker, S., Li, S., Schulze, A. D., Trudel, M., Juanes, F., Tabata, A., Kaukinen, K. H., Ginther, N. G. and others (2014). Infectious disease, shifting climates, and opportunistic predators; cumulative factors potentially impacting wild salmon declines. *Evolutionary applications*, 7; 812–855.doi:10.1111/eva.12164

737 Mills, K. E., Pershing, A. J., Sheehan, T. F. and Mountain, D. (2013). Climate and ecosystem linkages explain widespread declines in North American Atlantic salmon populations. *Global Change Biology*, 19; 3046–3061.doi:10.1111/gcb.12298

740 Nash, R. D., Valencia, A. H. and Geffen, A. J. (2006). The origin of Fulton's condition factor—setting the record straight. *Fisheries*, 31; 236–238.

742 Otero, J., Jensen, A.J., L'Abée-Lund, J.H., Stenseth, N.C., Storvik, Geir O. & Vøllestad, L.A. (2012). Contemporary ocean warming and freshwater conditions are related to later sea age at maturity in Atlantic salmon spawning in Norwegian rivers. *Ecology and Evolution*, 2; 2192–2203.

746 Perrier, C., Guyomard, R., Baglinière, J.-L. and Evanno, G. (2011). Determinants of hierarchical genetic structure in Atlantic salmon populations; environmental factors vs. anthropogenic influences. *Molecular Ecology*, 20; 4231–4245.doi:10.1111/j.1365-294X.2011.05266.x

750 Peyronnet, A., Friedland, K. D., Maoiléidigh, N. Ó., Manning, M. and Poole, W. R. (2007).

751 Links between patterns of marine growth and survival of Atlantic salmon *Salmo salar*, L. *Journal of Fish Biology*, 71; 684–700.doi:10.1111/j.1095-8649.2007.01538.x
753 Pfister, C. A. (1998). Patterns of variance in stage-structured populations; evolutionary
754 predictions and ecological implications. Proceedings of the National Academy of Sciences,
755 95; 213–218.doi:10.1073/pnas.95.1.213

756 Polyakov, IV, Alexeev, VA, Bhatt, US, Polyakova EI and Zhang X (2009). North Atlantic
757 warming: patterns of long-term trend and multidecadal variability. Clim Dyn 34:439–457.
758 doi:10.1007/s00382-008-0522-3

759 Porcher, J. P. and Prévost, E. (1996). Pêche du saumon dans les cours d'eau du Massif
760 Amoricain. Fixation du nombre total de captures autroisé (TAC) par bassin. Notice
761 explicative et propositions pour l'année.

762 Prévost, E. (1987). Recherches sur le saumon atlantique (*Salmo salar* L.) en France. Thèse de
763 Docteur-Ingénieur, Sci. Agron. Halieutique, Ec. Nat. Sup. Agron. Rennes, 1 vol.

764 Prouzet, P., Le Bail, P. Y. and Heydorff, M. (1984). Sex ratio and potential fecundity of
765 Atlantic salmon (*Salmo salar* L.) caught by anglers on the Elorn River (Northern Brittany,
766 France) during 1979 and 1980. *Aquaculture Research*, 15; 123–130.doi: 10.1111/j.1365-
767 2109.1984.tb00843.x

768 Pyper, B. J. and Peterman, R. M. (1998). Comparison of methods to account for
769 autocorrelation in correlation analyses of fish data. *Canadian Journal of Fisheries and
770 Aquatic Sciences*, 55; 2127–2140.doi:10.1139/f98-104

771 Quinn, T. P., McGinnity, P. and Cross, T. F. (2006). Long-term declines in body size and
772 shifts in run timing of Atlantic salmon in Ireland. *Journal of Fish Biology*, 68; 1713–
773 1730.doi:10.1111/j.0022-1112.2006.01017.x

774 R Core Team (2015). *R: A Language and Environment for Statistical Computing*. Vienna,
775 Austria; R Foundation for Statistical Computing. https://www.R-project.org.
753 Pfister, C. A. (1998). Patterns of variance in stage-structured populations; evolutionary
754 predictions and ecological implications. *Proceedings of the National Academy of Sciences,*
755 95; 213–218.doi:10.1073/pnas.95.1.213

756 Polyakov, IV, Alexeev, VA, Bhatt, US, Polyakova EI and Zhang X (2009). North Atlantic
757 warming: patterns of long-term trend and multidecadal variability. *Clim Dyn* 34:439–457.
758 doi:10.1007/s00382-008-0522-3

759 Porcher, J. P. and Prévost, E. (1996). Pêche du saumon dans les cours d'eau du Massif
760 Amoricain. Fixation du nombre total de captures autroisé (TAC) par bassin. Notice
761 explicative et propositions pour l'année.

762 Prévost, E. (1987). Recherches sur le saumon atlantique (*Salmo salar* L.) en France. Thèse de
763 Docteur-Ingénieur, Sci. Agron. Halieutique, Ec. Nat. Sup. Agron. Rennes, 1 vol.

764 Prouzet, P., Le Bail, P. Y. and Heydorff, M. (1984). Sex ratio and potential fecundity of
765 Atlantic salmon (*Salmo salar* L.) caught by anglers on the Elorn River (Northern Brittany,
766 France) during 1979 and 1980. *Aquaculture Research, 15*; 123–130.doi: 10.1111/j.1365-7672109.1984.tb00843.x

768 Pyper, B. J. and Peterman, R. M. (1998). Comparison of methods to account for
769 autocorrelation in correlation analyses of fish data. *Canadian Journal of Fisheries and
770 Aquatic Sciences, 55*; 2127–2140.doi:10.1139/f98-104

771 Quinn, T. P., McGinnity, P. and Cross, T. F. (2006). Long-term declines in body size and
772 shifts in run timing of Atlantic salmon in Ireland. *Journal of Fish Biology, 68*; 1713–773 1730.doi:10.1111/j.0022-1112.2006.01017.x

774 R Core Team (2015). *R: A Language and Environment for Statistical Computing*. Vienna,
775 Austria; R Foundation for Statistical Computing. https://www.R-project.org.
776 Reznick, D. N., Rodd, F. H. and Cardenas, M. (1996). Life-history evolution in guppies

777 (Poecilia reticulata; Poeciliidae). IV. Parallelism in life-history phenotypes. American

778 *Naturalist*; 319–338.doi:10.1086/285854

779 Schaffer, W. M. (1983). The application of optimal control theory to the general life history

780 problem. *American Naturalist*; 418–431.

781 Stearns, S. C. (1976). Life-history tactics: a review of the ideas. Quarterly review of biology;

782 3–47.doi:10.1086/409052

783 Stearns, S. C. and Kawecki, T. J. (1994). Fitness sensitivity and the canalization of life-history

784 traits. *Evolution*; 1438–1450.doi:10.2307/2410238

785 Summers, D. w. (1995) Long-term changes in the sea-age at maturity and seasonal time of

786 return of salmon, *Salmo salar* L., to Scottish rivers. *Fisheries Management and Ecology, 2,*

787 147–156.

788 Thorpe, J. E., Miles, M. S. and Keay, D. S. (1984). Developmental rate, fecundity and egg

789 size in Atlantic salmon, *Salmo salar* L. *Aquaculture*, 43; 289–305.doi:10.1016/0044-

790 8486(84)90030-9

791 Todd, C. D., Hughes, S. L., Marshall, C., MacLean, J. C., Lonergan, M. E. and Biuw, E.

792 (2008). Detrimental effects of recent ocean surface warming on growth condition of Atlantic

793 salmon. *Global change biology*, 14; 958–970.doi:10.1111/j.1365-2486.2007.01522.x

794 Todd, C. D., Friedland, K. D., MacLean, J. C., Hazon, N. and Jensen, A. J. (2011). In; Getting

795 into hot water? Atlantic salmon responses to climate change in freshwater and marine

796 *environments*. John Wiley & Sons, Inc.; Hoboken, NJ, USA. pp.409–443.

797 Todd, C. D., Friedland, K. D., MacLean, J. C., Whyte, B. D., Russell, I. C., Lonergan, M. E.

798 and Morrissey, M. B. (2012). Phenological and phenotypic changes in Atlantic salmon

799 populations in response to a changing climate. *ICES journal of marine science*, 69; 1686–

800 1698.doi:10.1093/icesjms/fss151
776 Reznick, D. N., Rodd, F. H. and Cardenas, M. (1996). Life-history evolution in guppies

777 (Poecilia reticulata; Poeciliidae). IV. Parallelism in life-history phenotypes. American

778 *Naturalist*; 319–338.doi:10.1086/285854

779 Schaffer, W. M. (1983). The application of optimal control theory to the general life history

780 problem. *American Naturalist*; 418–431.

781 Stearns, S. C. (1976). Life-history tactics: a review of the ideas. Quarterly review of biology;

782 3–47.doi:10.1086/409052

783 Stearns, S. C. and Kawecki, T. J. (1994). Fitness sensitivity and the canalization of life-history

784 traits. *Evolution*; 1438–1450.doi:10.2307/2410238

785 Summers, D. w. (1995) Long-term changes in the sea-age at maturity and seasonal time of

786 return of salmon, *Salmo salar* L., to Scottish rivers. *Fisheries Management and Ecology, 2,*

787 147–156.

788 Thorpe, J. E., Miles, M. S. and Keay, D. S. (1984). Developmental rate, fecundity and egg

789 size in Atlantic salmon, *Salmo salar* L. *Aquaculture*, 43; 289–305.doi:10.1016/0044-

790 8486(84)90030-9

791 Todd, C. D., Hughes, S. L., Marshall, C., MacLean, J. C., Lonergan, M. E. and Biuw, E.

792 (2008). Detrimental effects of recent ocean surface warming on growth condition of Atlantic

793 salmon. *Global change biology*, 14; 958–970.doi:10.1111/j.1365-2486.2007.01522.x

794 Todd, C. D., Friedland, K. D., MacLean, J. C., Hazon, N. and Jensen, A. J. (2011). In; Getting

795 into hot water? Atlantic salmon responses to climate change in freshwater and marine

796 *environments*. John Wiley & Sons, Inc.; Hoboken, NJ, USA. pp.409–443.

797 Todd, C. D., Friedland, K. D., MacLean, J. C., Whyte, B. D., Russell, I. C., Lonergan, M. E.

798 and Morrissey, M. B. (2012). Phenological and phenotypic changes in Atlantic salmon

799 populations in response to a changing climate. *ICES journal of marine science*, 69; 1686–

800 1698.doi:10.1093/icesjms/fss151
801 Valiente, A. G., Juanes, F. and Garcia-Vazquez, E. (2011). Increasing regional temperatures

802 associated with delays in Atlantic salmon sea-run timing at the southern edge of the European

803 distribution. *Transactions of the American Fisheries Society*, 140; 367–373.

804 doi:10.1080/00028487.2011.557018

805 Williams, G. C. (1966). Natural selection, the costs of reproduction, and a refinement of

806 Lack's principle. *American naturalist*; 687–690.

807 Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood

808 estimation of semiparametric generalized linear models. *Journal of the Royal Statistical*

809 Society; Series B (Statistical Methodology), 73; 3–36.doi:10.1111/j.1467-9868.2010.00749.x

810 Youngson, A. F., MacLean, J. C. and Fryer, R. J. (2002). Rod catch trends for early-running

811 MSW salmon in Scottish rivers (1952-1997): divergence among stock components. *ICES*

812 *Journal of Marine Science*, 59; 836–849.doi:10.1006/jmsc.2002.119

813

814
801 Valiente, A. G., Juanes, F. and Garcia-Vazquez, E. (2011). Increasing regional temperatures

802 associated with delays in Atlantic salmon sea-run timing at the southern edge of the European

803 distribution. *Transactions of the American Fisheries Society*, 140; 367–373.

804 doi:10.1080/00028487.2011.557018

805 Williams, G. C. (1966). Natural selection, the costs of reproduction, and a refinement of

806 Lack's principle. *American naturalist*; 687–690.

807 Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood

808 estimation of semiparametric generalized linear models. *Journal of the Royal Statistical*

809 Society; Series B (Statistical Methodology), 73; 3–36.doi:10.1111/j.1467-9868.2010.00749.x

810 Youngson, A. F., MacLean, J. C. and Fryer, R. J. (2002). Rod catch trends for early-running

811 MSW salmon in Scottish rivers (1952-1997): divergence among stock components. *ICES*

812 *Journal of Marine Science*, 59; 836–849.doi:10.1006/jmsc.2002.119

813

814
815 Tables

816 Table I. Sample size: number of 1SW and 2SW *S. salar* per region considered in the study.

<table><thead><tr><th>Age</th><th>Normandy</th><th>Brittany</th><th>Aquitaine</th></tr></thead><tbody><tr><td>1SW</td><td>3716</td><td>12053</td><td>959</td></tr><tr><td>2SW</td><td>3110</td><td>9577</td><td>3870</td></tr></tbody></table>

817

818
815 Tables

816 Table I. Sample size: number of 1SW and 2SW *S. salar* per region considered in the study.

<table><thead><tr><th>Age</th><th>Normandy</th><th>Brittany</th><th>Aquitaine</th></tr></thead><tbody><tr><td>1SW</td><td>3716</td><td>12053</td><td>959</td></tr><tr><td>2SW</td><td>3110</td><td>9577</td><td>3870</td></tr></tbody></table>

817

818
Table II. Modelling date of river entry, length, weight and condition in *S. salar* caught in France as a function of sea age (A), region (R) or year (Y). Deviance explained, F-test p-values and delta AIC are presented for each model. s() indicated GAM smoothing.

<table><thead><tr><th rowspan="2">Covariates</th><th colspan="2">Date of river entry</th><th colspan="2">Length</th><th colspan="2">Weight</th><th colspan="2">Condition</th></tr><tr><th>Dev exp</th><th>ΔAIC</th><th>Dev exp</th><th>ΔAIC</th><th>Dev exp</th><th>ΔAIC</th><th>Dev exp</th><th>ΔAIC</th></tr></thead><tbody><tr><td>A×R</td><td>61.26</td><td>-91970</td><td>71.94</td><td>-153634</td><td>68.68</td><td>-91117</td><td>3.34</td><td>-11314</td></tr><tr><td>A×R+s(Y)</td><td>63.7</td><td>-1790</td><td>72.84</td><td>-889</td><td>70.17</td><td>-1335</td><td>5.24</td><td>-533</td></tr><tr><td>A×R + s(Y,by=A)</td><td>64.47</td><td>-572</td><td>72.95</td><td>-95</td><td>70.29</td><td>-96</td><td>6.26</td><td>-283</td></tr><tr><td>A×R+s(Y,by=A×R)</td><td>64.95</td><td>-313</td><td>73.17</td><td>-161</td><td>70.55</td><td>-177</td><td>6.65</td><td>-61</td></tr></tbody></table>
Table II. Modelling date of river entry, length, weight and condition in *S. salar* caught in France as a function of sea age (A), region (R) or year (Y). Deviance explained, F-test p-values and delta AIC are presented for each model. s() indicated GAM smoothing.

<table><thead><tr><th rowspan="2">Covariates</th><th colspan="2">Date of river entry</th><th colspan="2">Length</th><th colspan="2">Weight</th><th colspan="2">Condition</th></tr><tr><th>Dev exp</th><th>ΔAIC</th><th>Dev exp</th><th>ΔAIC</th><th>Dev exp</th><th>ΔAIC</th><th>Dev exp</th><th>ΔAIC</th></tr></thead><tbody><tr><td>A×R</td><td>61.26</td><td>-91970</td><td>71.94</td><td>-153634</td><td>68.68</td><td>-91117</td><td>3.34</td><td>-11314</td></tr><tr><td>A×R+s(Y)</td><td>63.7</td><td>-1790</td><td>72.84</td><td>-889</td><td>70.17</td><td>-1335</td><td>5.24</td><td>-533</td></tr><tr><td>A×R + s(Y,by=A)</td><td>64.47</td><td>-572</td><td>72.95</td><td>-95</td><td>70.29</td><td>-96</td><td>6.26</td><td>-283</td></tr><tr><td>A×R+s(Y,by=A×R)</td><td>64.95</td><td>-313</td><td>73.17</td><td>-161</td><td>70.55</td><td>-177</td><td>6.65</td><td>-61</td></tr></tbody></table>
Table III. Cross-correlations in date of river entry, length, weight and condition between regions for each sea-age class. The significance of the tests is presented as follow: "****", "**", "*" and "." correspond to p-values inferior or equal to 0.001, 0.01, 0.05 and 0.1, respectively. The p-values are corrected to account for the autocorrelation in the times series.

<table><thead><tr><th></th><th>Normandy /<br>Brittany</th><th>Normandy /<br>Aquitaine</th><th>Brittany /<br>Aquitaine</th></tr></thead><tbody><tr><td>1SW Date</td><td>0.46*</td><td>0.38*</td><td>0.01</td></tr><tr><td>1SW Length</td><td>0.35.</td><td>0.56**</td><td>0.57**</td></tr><tr><td>1SW Weight</td><td>0.46*</td><td>0.44*</td><td>0.68***</td></tr><tr><td>1SW Condition</td><td>0.47*</td><td>0.34.</td><td>0.45*</td></tr><tr><td>2SW Date</td><td>0.32.</td><td>0.01</td><td>-0.02</td></tr><tr><td>2SW Length</td><td>0.75***</td><td>0.54**</td><td>0.52**</td></tr><tr><td>2SW Weight</td><td>0.86***</td><td>0.57**</td><td>0.42*</td></tr><tr><td>2SW Condition</td><td>0.66***</td><td>0.46*</td><td>0.25</td></tr></tbody></table>
Table III. Cross-correlations in date of river entry, length, weight and condition between regions for each sea-age class. The significance of the tests is presented as follow: "****", "**", "*" and "." correspond to p-values inferior or equal to 0.001, 0.01, 0.05 and 0.1, respectively. The p-values are corrected to account for the autocorrelation in the times series.

<table><thead><tr><th></th><th>Normandy /<br>Brittany</th><th>Normandy /<br>Aquitaine</th><th>Brittany /<br>Aquitaine</th></tr></thead><tbody><tr><td>1SW Date</td><td>0.46*</td><td>0.38*</td><td>0.01</td></tr><tr><td>1SW Length</td><td>0.35.</td><td>0.56**</td><td>0.57**</td></tr><tr><td>1SW Weight</td><td>0.46*</td><td>0.44*</td><td>0.68***</td></tr><tr><td>1SW Condition</td><td>0.47*</td><td>0.34.</td><td>0.45*</td></tr><tr><td>2SW Date</td><td>0.32.</td><td>0.01</td><td>-0.02</td></tr><tr><td>2SW Length</td><td>0.75***</td><td>0.54**</td><td>0.52**</td></tr><tr><td>2SW Weight</td><td>0.86***</td><td>0.57**</td><td>0.42*</td></tr><tr><td>2SW Condition</td><td>0.66***</td><td>0.46*</td><td>0.25</td></tr></tbody></table>
832 Figure captions

833 Figure 1. Description of the three regions considered in the analysis: Normandy, Brittany and
834 Aquitaine. In each region, the name of the rivers that provided S. *salar* captures are presented.

835
836 Figure 2. Change in the date of river entry, length, weight and condition in 1SW and 2SW S. *salar* from Normandy, Brittany and Aquitaine.

838
839 Figure 3. Linear rate of change in the date of river entry (D), length (L), weight (W) and
840 condition (K) in 1SW and 2SW S. *salar* from Normandy (N), Brittany (B) and Aquitaine (A).

841 The difference in the rates of change between 1SW and 2SW was assessed with a Wald
842 test. The significance of the test is presented as follow: "***", "**", "*" and "." correspond to
843 p-values inferior or equal to 0.001, 0.01, 0.05 and 0.1, respectively.

844
845
832 Figure captions

833 Figure 1. Description of the three regions considered in the analysis: Normandy, Brittany and
834 Aquitaine. In each region, the name of the rivers that provided S. *salar* captures are presented.

835

836 Figure 2. Change in the date of river entry, length, weight and condition in 1SW and 2SW S. *salar* from Normandy, Brittany and Aquitaine.

838

839 Figure 3. Linear rate of change in the date of river entry (D), length (L), weight (W) and
840 condition (K) in 1SW and 2SW S. *salar* from Normandy (N), Brittany (B) and Aquitaine (A).

841 The difference in the rates of change between 1SW and 2SW was assessed with a Wald
842 test. The significance of the test is presented as follow: "***", "**", "*" and "." correspond to
843 p-values inferior or equal to 0.001, 0.01, 0.05 and 0.1, respectively.

844

845
<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>
853 Figure 3

<figure><img src="image_5.png" /></figure>

854

855
853 Figure 3

<figure><img src="image_6.png" /></figure>

854

855
856 Supporting information

857 Figure captions

858

859 Figure A1. Boxplots of raw data for the date of river entry, the length, the weight and the condition (K) of 1SW fish caught by the French hook and line recreational salmon fishery (spring and summer catches).

862

863 Figure A2. Boxplots of raw data for the date of river entry, the length, the weight and the condition (K) of 2SW fish caught by the French hook and line recreational salmon fishery (spring and summer catches).

866
856 Supporting information

857 Figure captions

858

859 Figure A1. Boxplots of raw data for the date of river entry, the length, the weight and the condition (K) of 1SW fish caught by the French hook and line recreational salmon fishery (spring and summer catches).

862

863 Figure A2. Boxplots of raw data for the date of river entry, the length, the weight and the condition (K) of 2SW fish caught by the French hook and line recreational salmon fishery (spring and summer catches).

866
<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /></figure>
<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /></figure>