Larger and older fish individuals within a population tend to experience a larger mortality probability than smaller and younger individuals. This implies that fishing selects against life-history traits correlating with body size, such as growth capacity, reproductive investment and timing of maturation. It is currently unkown whether individuals vulnerable to fishing gear differ systematically from the average individual in terms of growth capacity and reproductive investment. Here, we present results that supports that angling does not constitute a stochastic process for targeting life-history traits in a marine sedentary fish populations. Individuals from a wild population of Serranus scriba were sampled using two different gears to obtain a random sample regarding life-history traits (beam trawl) and a hook-and-line-sample (angling). We fitted individual back-calculated size-at-age data to life-history models to obtain the parameters maximum size (Lmax) and reproduction investment (g). In line with expectations we found that individuals vulnerable to angling exhibited larger maximum sizes and lower values for reproductive investments, collectively indicating faster growing individuals in terms of somatic growth. Thus, our study suggests that systematic removal of vulnerable fish will exert selection pressures for increasing reproductive investment and smaller maximum sizes, which will penalize the average growth rate of individuals in the population.

1. Is angling a stochastic process for life‐history
traits? An empirical assessment for marine
coastal fisheries
Josep Alós1, Robert Arlinghaus2,3, Miquel Palmer1, Lucie Buttay1
and Alexandre Alonso‐Fernández4
1IMEDEA (CSIC‐UIB), Spain
2Leibniz‐Institute of Freshwater Ecology and Inland Fisheries , Berlin, Germany
3Inland Fisheries Management Laboratory, Humboldt‐University at Berlin, Germany
4IIM (CSIC), Spain

2. Overview
1. Fishing is almost never random. Typically, gear is designed to
remove some kinds of individuals, usually individuals that are
larger and, indirectly, older (e.g. mesh size of nets)
2. Fishing mortality is therefore size‐selective with respect both
to species and to phenotypic variation within species (Stokes
et al 1993; Jennings et al 1998)

3. Overview
3. Similarly, in recreational fisheries, vulnerability to capture can
be size‐related, but also depends on a fish’s decision to attack
and (or) ingest baited hooks (e.g. Cooke et al 2007).
4. In this context, individuals with lower cognitive abilities and
those with higher metabolism and growth capacity often take
more risks, rendering these fish more vulnerable to capture
(Reviewed in Uusi‐Heikkilä et al. 2008)

4. Overview
5. If some part of the phenotypic variation within species is due
to genetic differences between individuals, then fishing might
causes evolutionary change
6. Thus, behaviour‐driven vulnerability to fishing might
constitute an underappreciated mechanism for selection on
growth rate and (or) other life‐history traits (Uusi‐Heikkilä et
al. 2008)

5. Overview
7. The potential for evolution of behavioural and physiological
traits and its consequences for life history, yet largely
overlooked research area within the emerging context of
Fisheries‐induced evolution of FIE (Uusi‐Heikkilä et al. 2008)
8. Moreover, most of work are focused in freshwater
recreational fisheries and empirical evidences in marine wild
populations is still scarce

6. Objectives
The main objective of this study is to provide an
empirical prove to know if angling is a
selection process rather than a random
process for life‐history traits in marine coastal
fisheries
With the main task:
Estimating individual life‐history traits (reproduction investment,
infinite size, immature growth and maturation age/size) from
individuals randomly and angling sampled from a wild
population

7. The case study: marine coastal sedentary fish
Case study: Painted comber, Serranus scriba (Serranidae)
1. Simultaneous hermaphrodite (indeterminate spawners)
2. Maximum size (25 cm), short life‐span (maximum age 11 years), fast
growth and early age of maturation (~1st 2nd year)
3. Limited home range ( ~1 km2)
4. Low interest for commercial fisheries, but…
One of the most important targeted species for the recreational fishery
from Balearic Islands (Morales‐Nin et al 2005)

8. Materials and methods
Experimental site (EA):
1. 1 experimental area at Palma Bay (wild population)
2. Area of 1 km2 (~ mean of home range of S. scriba)

9. Materials and methods
Sampling methods (Experiment):
1. Random‐sample: based in beam trawl fishing (non‐selective
for life‐history traits a priori)
2. Angling‐sample: based in experiment angling session using
conventional recreational gears (Static fishing with natural
baits) ↔ High vulnerable fish
Vs.

10. Materials and methods
Biological sampling:
For each fish: Otolith extraction, total length (mm), age (years),
weight (g) and gonad extraction (batch fecundity and dry
weight)
120
y = 8E-06x3.0843
100 R2 = 0.9915
80
Weigth (g)
60
40
20
N=338
0
50 75 100 125 150 175 200
Total length (mm)

11. Materials and methods
Estimating life‐history traits (individual
growth and reproduction investment):
1. Estimating life‐history traits is almost
never easy at individual level (we need
to track the individual over time)
2. Ideally direct measures of the trait should be obtained
throughout their lifespan and only captivity and mark‐and‐
recapture programs (e.g. Smith et al 1997 or Zhang et al 2009)
allow it
3. However, the representativeness of captivity studies, and the
difficulties for mark‐and‐recapture programs (time scale and
effort, e.g. Palmer et al 2011) present different sources of bias
(altering biological traits)

12. Materials and methods
However, the back‐calculation of length‐at‐
age using growth marks in the otoliths,
can offer reliable methods to obtain
information on individual level over its
life‐span (Pilling et al 2008_CJFAS)
200
300
y = 57.7x - 20.872 180
250 r 2 = 0.8369 160
Total length (mm)
140
Total length (mm)
200
120
150
100
100 80
60
50
40
0 20
1.5 2 2.5 3 3.5 4 4.5 0
Otolith radius (mm) 0 2 4 6 8 10
Age (years)

13. Materials and methods
Estimating life‐history traits (Lester et al. 2004) fitting back‐
calculated data (4 main considerations)
1. The life time growth pattern (individual growth trajectory) is biphasic
characterized by a lineal growth in immature ages (all the energy is
invested in somatic growth)
2. Adult somatic growth is represented by a Von Bertalanffy (VB) growth
equation (the characteristic asymptotic shape arising primarily from the
allocation of energy to reproduction
5
3. Lester et al. 2004 model offered a
Fish size in otolith scale (mm)
biological interpretation of the VB
4
growth parameters (L∞, k and T0).
3
We can estimate the biological traits:
2
maximum immature growth (h),
1
reproduction investment (g), infinite size
(L∞) and size‐age of maturation (T) at 0
individual level 0 5 10 15
Age (years)

14. Materials and methods
4) Problem: species with short life‐span
Solution
Number of fish (%)
40
30
20
Fitting the longitudinal data in a
10 Bayesian context to include two
0
1 2 3 4 5 6 7 8 9 10
kinds of a priori information:
290 Age (years)
The estimation of the parameters
240 depends on the:1) Populations
190 mean, 2) Previous data published
and 3) Individual data
TL (mm)
140
90
Bayesian credibility intervals of the
posteriors distributions was used to
40
assess with the differences among
‐10 groups (Low and high “angling”
0 3 6 9 12 15 18
Age (years) vulnerable fish)

15. Materials and methods
Direct measures of reproduction investment:
1.Batch fecundity ~ “Quantity”
2.Mean dry weights of eggs ~ “Quality”
Frequentist statistics: GLMM
In all cases data were non‐independent
and hierarchically structured in fishing
trips which were considered as
random factor

16. Results
1. Sample size (fish size and age):
Fish size (mm) and age (years) frequency distributions was not
different among group‐samples (GLMM, p = 0.490 and GLMM,
p = 0.695 respectively)
1.0
Reproduction investment (g)
2. PCA
Maturation size
Immature growth
Fish size
• Independence of age and size
PCA Axis 2 (70.5%)
• Infinite size (L∞) and reproduc on
Age investment (g) negatively correlated
• High pre‐maturation somatic growth
Infinite size (h) associated with higher
maturation size
-1.0
N=337
-0.6 PCA Axis 1 (46.5%) 1.2

17. Results
3. Maximum fish size (Lmax):
The maximum size that the individual raise up to age ∞ (Lmax)
was different between vulnerability groups
240
220
High growth ability in high
vulnerable individuals
200
Lmax
(angling sample)
180
160
140
Low High

18. Results
4. Reproduction investment (g):
Strong evidence for the hypothesis that the indirect measure of
individual reproduction investment (g) differs between groups
Reproduction investement (g)
0.9
Low reproduction
investment in high
0.8
vulnerable individuals
(angling sample)
0.7
0.6
Low High

19. Results
5. Age of maturation (T) and immature growth rate (h):
Posterior distributions reveals no differences between
vulnerability groups for the age of maturation (T) and the
immature growth (h)
50
1.7
45
1.6
1.5
40
T
h
1.4
35
1.3
30
1.2
25
1.1
Low High Low High

20. Results
6. Summary: “averaged” individual trajectory per group
Angling are doing an artificial selection against grow faster
individuals with high grow capacity and less investment to
reproduction
4
Fishing selection
Fish size in otolith scale (mm)
3
2
1
Low
High
0
0 5 10 15 20
Age (years)

21. Results
7. Direct measures of reproduction investment (batch fecundity
and dry weight of eggs):
Beam trawl
Angling
0.020
10
9
log ( batch fecundity )
0.015
Egg Weight (mg)
8
7
6
0.010
5
Low
4
P < 0.01 Conf.Int. 95% P < 0.05
0.005
High
Conf.Int. 95%
3
50 100 150 200 250 Low High
Fish Length(mm)

22. Results
8. Relationship between Indirect measures and direct measures
of reproduction investment
There was a significant relationship among batch fecundity and
dry weights of eggs and the reproduction investment obtained
from the otoliths
0.020
4.5
log ( Batch Fecundity )
4.0
0.015
Egg Weight (mg)
3.5
0.010
3.0
0.005
2.5
P < 0.01 P < 0.05
0.000
2.0
0.6 0.7 0.8 0.9 1.0 0.6 0.7 0.8 0.9 1.0
g g

23. Discussion: general
Is angling a stochastic (random) process for life‐
history traits in marine wild populations?
The answer is no
Some individuals have Vulnerable fish
higher probability to be caught Non‐vulnerable fish

24. Discussion: methods
1. General results showed good performance of the Bayesian
framework to estimate individual life‐history traits Lmax , g, h
and T (Credibility intervals are relatively small and unbiased
for all the parameters)
2. Life‐history parameters were successfully estimated at
individual level
3. Estimations were independent of fish size and age
5
Fish size in otolith scale (mm)
4
3
2
1
0
0 5 10 15
Age (years)

25. Discussion: growth
Our empirical approach demonstrated how angling exercises an
artificial selection against faster grow individuals
This result is well known (e.g. Biro and Post 2008_PNAS), but our
case‐study is one of the first studies in marine wild populations
4
Fishing
Fish size in otolith scale (mm)
selection
3
2
1 Low
High
0
0 5 10 15 20
Biro & Post PNAS 2008 Age (years)

26. Discussion: growth
This fast grower individuals have higher grow ability with larger
maximum sizes
In terms of fish size (length‐at‐age) “be smaller” should be the
optimal strategy to increase survival in an mortality‐
environment dominated by angling
240
220
200
Lmax
180
160
140
Low High

27. Discussion: reproduction investment (indirect measures)
Fish sampled by angling have lower values of reproduction
investment
Angling exercises an artificial selection against the individuals that
invest less energy to reproduction (and invest more energy to
somatic growth)
In this scenario, increase
investment of energy to
reproduction rather than
somatic growth should be
the “optimal life‐history
strategy” in exploited
populations
Lester et al 2004 PRSLB 2008

28. Discussion: reproduction investment (direct measures)
Direct measures of reproduction investment (Quantity ~ batch
fecundity and Quality ~ dry weigth of eggs) agreed with
indirect estimations (g)
Direct and indirect measures are correlated <‐> good to get a
“averaged” measure of reproduction investment in indirect
spawners ( batch fecundity is too variable at individual level)
Shuter et al 2005 CJFAS

29. Discussion: age of maturation (T)
It is expected that the age of maturation and fishing mortality are
negatively correlated, and exploited population tended to
mature earlier
Thus fishing should drive selection against later maturation
individuals
There were no differences (but a tendency) among the age of
maturation among the two kind of sampling
1.7
1.6
1.5
Two reasons explain that result:
T
1.4
1.3
1) Early maturation per se (short life
1.2
1.1
span) <‐> mature at 1+ years
Low High
2) In early maturations species,
relationship among T and M is not so
clear Lester et al 2004 PRSLB 2008

30. Discussion: immature growth (h)
Values of growth prior maturation h, (mm year‐1) were highly
variable and posterior distribution were highly overlapped
Here, we can not sure if the negative results is consequence of
the method (early maturation of Serranus results in poor
information in early stages) or the true lack of differences
4
50
Fish size in otolith scale (mm)
45
3
40
2
h
35
30
1
Low
25
High
0
Low High
0 5 10 15 20
Age (years)

31. Conclusions and implications
Given the high heritability of this life‐history traits and the
intensity of size‐selective fish harvest of this species,
evolutionary responses in this sedentary fish population could
modify optimal strategies (driven evolutionary responses to
“be smaller”)
Fisheries‐induced evolution
Phenotype
Physiology
Genotype Behavior Vulnerability Selection
Life‐history

32. Danke schön
&
Thank you for your attention