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Thirty-five experimental fisheries reveal the mechanisms of selection

Sébastien Nusslé1, Andrew P. Hendry2, Roland A. Knapp1, Michael T. Bogan3, Anna M. Sturrock4, Stephanie M. Carlson1 •
University of California, Berkeley1, McGill University2, University of Arizona3, University of California, Davis4
26 May 2017-bioRxiv (Cold Spring Harbor Labs Journals)-pp 141259
TL;DR: Fisheries have been described as large-scale evolutionary experiments; yet such “experiments” tend to be poorly replicated and therefore lack the predictive power essential for designing appropriate management strategies to minimize the effects of fisheries-induced selection.
Abstract: Fisheries have been described as large-scale evolutionary experiments; yet such "experiments" tend to be poorly replicated and therefore lack the predictive power essential for designing appropriate management strategies to minimize the effects of fisheries-induced selection Large-scale removal of non-native trout from 35 montane lakes in California provided repeated experimental fisheries that allowed us to explore how environmental parameters affect the three potential contributors to overall selection: the fitness-trait correlation, trait variability, and fitness variability Our results demonstrate that fishing rapidly altered the size structure of harvested populations, and that the magnitude of change was primarily driven by the fitness-trait correlation (net selectivity) Fishing-induced selection was repeatable overall but was also influenced by environmental (lake size and quality) and demographic (size structure) parameters Decomposing fishing-induced selection into its key components can improve the management of stocks experiencing fishing-induced selection by identifying the drivers of selection and therefore the appropriate target for management

Summary (2 min read)

Jump to: [Introduction] – [Catch and length 18] – [Estimating selection differentials 15] – [Data and statistics 1] and [Authors contributions 5]

Introduction

  • Fisheries have been described as large-scale evolutionary experiments; yet such 2 “experiments” tend to be poorly replicated and therefore lack the predictive power 3 essential for designing appropriate management strategies to minimize the effects 4 of fisheries-induced selection.
  • The authors study helps explain why the outcomes of fishing-induced selection reported in 18 the literature are contradictory 4.
  • Disentangling these 13 factors and the different components of selection (fitness-trait correlation, trait 14 variability, fitness variability) is critical to producing flexible and relevant 15 management strategies, but does rely on repeated measurements of selection.
  • 16 To date, fish have been completely removed from over 60 locations (lakes and 17 rivers); and from these, the authors analyzed lake populations where at least 15 fish were 18 captured over at least three fishing events.

Catch and length 18

  • The authors assessed the selective effect of fishing by correlating fish length with catch 19 intensity.
  • The authors used the same type of model to 4 assess the effect of fishing intensity on the variability of length-at-capture, which 5 was measured after each fishing event, as the standard deviation of the length of all 6 the fish remaining in the lake.
  • For the sake of simplicity, the authors first assumed that fish did not grow during the study 13 period (typically 2-3 years), which would result in a conservative 14 estimate of selection.
  • The authors performed the same tests (linear mixed models) on the 7 random simulations, allowing direct comparison of the observed and simulated 8 data.
  • If any reproduction 21 occurred, it likely occurred during the first year following removal initiation and 22 likely produced few fish as most adults were removed quickly.

Estimating selection differentials 15

  • For each lake, the authors compared the mean length of surviving fish after each fishing 16 event (1092 events) to the mean length before the same fishing event, yielding a 17 selection differential for each event in each lake.
  • Using this covariance, the authors can disentangle the different components of 20 selection into the products of (1) the correlation between the trait and fitness, (2) 21 the variability (standard deviation) of fitness, and (3) the variability of the trait 1 under selection (see below) 10.
  • The standard deviation of the length in the 7 initial population.
  • To assess if catch intensity also affected the variability in selection differentials, the authors 17 binned catch intensity into 5% intervals and estimated, for each lake, the average 18 selection differential in each 5% interval.
  • This value represents the magnitude of length change when catch 8 increases and can be understood as fishing-induced selection.

Data and statistics 1

  • The data that support the findings of this study belong to the National Park Service 2 and the California Department of Fish and Wildlife, and can be accessed through 3 them upon reasonable request.
  • Significance 13 tests were computed according to Kuznetsova et al. 44 using the package lmerTest.
  • The importance in fishery management of leaving 5 the big ones.

Authors contributions 5

  • SN, APH, AMS, and SMC 6 discussed the analyses and wrote the manuscript.
  • RAK and MTB collected data and 7 provided feedback on the manuscript.
  • 8 Random fishing is expected to slightly reduce variability (right-hand panel, slope of 9 variability-catch relationship = -0.03 ± 0.04), but less than non-random fishing 10 where the slope of the variability-catch relationship is expected to be ten times 11 larger (0.32 mm ± 0.07 mm/%).
  • Dashed black lines represent 4 the average response using 5% and 10% growth corrections, and black dotted lines 5 represent the response with correction for reproduction after the initiation of the 6 experimental fisheries (see Methods).

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Figures (1)

Content maybe subject to copyright    Report

Thirty-five% experimental% fisheries% reveal! t he#1"
mechanisms)of)selection"2"
Sébastien"Nusslé
1*
,"Andrew"P."Hendry
2
,"Roland"A."Knapp
3
,"Michael"T."Bogan
1,4
,"Anna"3"
M."Sturrock
1
,"&"Stephanie"M."Carlson
1
"4"
"5"
snussle@gmail.com"6"
"
7"
1
Department" of" Environmental" Science," Policy" &" Management," University" of"8"
California,"Berkeley,"CA,"USA"9"
2
Redpath" Museum" &" Department" of" Biology," McGill" University," Montreal," Quebec,"10"
Canada"11"
3
Sierra" Nevada" Aquatic" Research" Laboratory," University" of" California," Mammoth"12"
Lakes,"CA,"USA"13"
4
Current" address:"School"of"Natural"Resources"and"the"Environment,"University"of"14"
Arizona,"Tucson,"AZ,"USA"15"
" !16"
certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was notthis version posted May 26, 2017. ; https://doi.org/10.1101/141259doi: bioRxiv preprint
Abstract!1"
Fisheries" have" been" describ ed" as" large-scale" evolutionary" experiments;" yet" su ch"2"
“experiments”"tend"to"be"poorly"replicated"and"therefore"lack"the"predictive"power"3"
essential"for" designing" appropriate" management"strategies" to" minimize" the"effects"4"
of" fisheries-induced" selection." Large-scale" removal" of" non-native" trout" from" 35"5"
montane"lakes"in" California"provided"repeated"experimental" fisheries"that"allowed"6"
us"to"explore"how"environmental"parameters"affect"the"three"potential"contributors"7"
to" overall" selection:" the" fitness-trait" correlation," trait" variability," and" fitness"8"
variability."Our"results"demonstrate"that"fishing"rapidly"altered"the"size"structure"of"9"
harvested" populations," and" that" the" magnitude" of" change" was" primarily" driven" by"10"
the" fitness-trait" correlation" (net" selectivity)." Fishing-induced" selection" was"11"
repeatable"overall"but"was"also"influenced"by"environmental"(lake"size"and"quality)"12"
and" demographic" (size" structure)" parameters." Decomposing" fishing-induced"13"
selection" into" its" key" components" can" improve" the" management" of" stocks"14"
experiencing" fishing-induced" selection" by" identifying" the" drivers" of" selection" and"15"
therefore"the"appropriate"target"for"management."16"
Text!17"
By" targeting" the" oldest," largest," and" most" fecund" individuals," size-selective" fishing"18"
can" reduce" recruitment" and" yield"
1,2
" and" induce" evolutionary" changes" that"19"
negatively" influence" stock" p rodu ctivity," resiliency," and" recovery"
3,4
." However," two"20"
major" issues" have" hindered" efforts" to" incorporate" fishing-induced" selection" into"21"
certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was notthis version posted May 26, 2017. ; https://doi.org/10.1101/141259doi: bioRxiv preprint
conservation"plans"and" management"strategies"
5-7
."First,"studies"of" fishing-induced"1"
selection" are" typically" not" well" replicated" within " a" sp ecies," and" therefore" lack" the"2"
potential"to"test"for"causal"drivers"through"comparative"analyses"
8
."Second,"selection"3"
can" be" shaped" by" three" key" components" (the" fitness-trait" correlation," trait"4"
variability,"and"fitness"variability)"
9,10
"that"have"not"been"considered"individually"in"5"
fisheries"science," and"yet" have" different"implications" for"management" (see"below)."6"
We" circumvented" these" previous" limitations" through" a" study" of" fishing-induced"7"
selection"in"35"independent"populations"of"fishes"(brook"trout,"Salvelinus*fontinalis;"8"
and"rainbow"trout,"Oncorhynchus*mykiss)."9"
The" study" populations" were" fished"to" extirpation" through" a" large-scale" non-native"10"
fish" removal" as" part" of" a" habitat" restoration" and" endangered" species" recovery"11"
program"in"high"elevation"lakes"of"California’s"Sierra"Nevada."Over"45,000"fish"were"12"
removed"by"means"of"gillnets"and"electrofishing,"with"length-at-capture"and"date-at-13"
capture"recorded" for" nearly" every"captured"fish"
11,12
."After" every" fishing" event," we"14"
estimated"the"selection"differential"on"fish"length"as"the"difference"in"the"population"15"
mean" trait" value" before" and" after" selection." This" differential" is" equal" to" the"16"
covariance"between"the"trait"and"relative"fitness"
9,10
,"which"can"be"partitioned"into"17"
the" product" of" t hree" components:" the" correl at ion" b et w een" the" trait" (body" lengt h)"18"
and"fitness"(captured"="0,"not-captured"="1),"the"variability"(standard"deviation)"of"19"
fitness,"a nd"the"variability"(standard"deviation)"of"the"trait."We"then"related"among-20"
population" variation" in" these" components" of" selection" to" among-population"21"
variation"in"lake"physical"characteristics"(lake"surface,"maximum"depth,"elevation),"22"
demographic" (fish" length," population" size," density)," fishing" gear" (gillnet,"23"
certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was notthis version posted May 26, 2017. ; https://doi.org/10.1101/141259doi: bioRxiv preprint
electrofishing)," and" fishing" intensit y" (proportion" of" the" population" captured,"1"
number"of"fishing"events,"and"time"until"extirpation)"(see"Extended"Table"1).""2"
Repeatable yet variable fishery selection 3"
In" most" populations," the" largest" fish" were" quickly" and" consistently" removed" by"4"
fishing," which" dramatically" altered" the" population" size" structure." Both" mean" fish"5"
length"and"its"variability"decreased"with"increasing"cumulative"catch"(Figure"1)."On"6"
average," fish" length" decreased" by" -1.03" mm" ±" 0.13" mm" (SD)" for" each" additional"7"
percent"of"the"population"removed" (i.e.,"“mm/%”;"linear" mixed"regression:"t
33.2
"="-8"
7.86,"p" <"0.001)."The" average"shift" in"mean" length"from"the" start"to" the"end" of"the"9"
fish" removal" period" was" ~100" mm" (45%" of" initial" mean" body" length)." Fish" length"10"
variability" (here" measured" as" SD" –" similar" results" were" obtained" for" CV)" also"11"
decreased"with"increasing"cumulative"catch:"0.32"mm" ±"0.07"mm/%" (linear"mixed"12"
regression:" t
25.8
" =" -4.60," p" <" 0.001)." These" estimates" are" conservative" because" the"13"
mean" duration" of" fish" removal" efforts" was" 2.5" years," during" which" time" survivors"14"
would" continue" to" grow ." With" a" moderate" growth" correction" (5%" per" year," see"15"
Methods)," the" estimates" were" -1.11" ±" 0.13" mm/%" for" mean" length" (linear" mixed"16"
regression:"t
33.4
"="-8.68,"p"<"0.001)"and"-0.35"±"0.06"mm/%"for"variability"in"l ength"17"
(linear"mixed"regression:"t
25.3
"="-5.4,"p"<"0.001)."With"a"larger"–"but"still"plausible"–"18"
growth"correction"(10%"per"year),"the"estimates"were"1.18"±"0.13"mm/%"for"mean"19"
length"(linear"mixed"regression:"t
33.2
"="-9.37,"p"<"0.001)"and"-0.37"±"0.06"mm/%"for"20"
variability" in" l engt h" (linear" mixed" regression:" t
25.1
" =" -6.06," p" <" 0.001)." These"21"
decreases" could" not" be" explained" by" random" harvesting" because" permutation"22"
certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was notthis version posted May 26, 2017. ; https://doi.org/10.1101/141259doi: bioRxiv preprint
simulations,"in"which"mortality"was"random,"i.e.,"independent"of"fish"length,"yielded"1"
non-significant"slope"coefficients"for"mean"length"(Figure" 2a,"one-sample"t-test:"t
99
"="2"
-1.173,"p"="0.25)"and"much"lower"slope"coefficients"for"length"variability"(Figure"2b,"3"
one-sample"t-test:"t
99
"="-9.54,"p"<"0.01)."4"
The" negative" association" between" mean" length" and" cumulative" catch" yielded"5"
increasingly" negative" selection" differentials" (i.e.," acting" against" large" fish)" with"6"
increasing" cumulative" catch" (Figure" 3)." Most" differentials" were" negative" (918" of"7"
1092),"with"the"few" positive"estimates"tending"to"occur"when" precision"was" low"(i.e.,"8"
when"few"individuals"remained"in"the"population;"Figure"3)."The"magnitude"of"the"9"
selection" differential" increased" with" increasing" cumulative" catch:" -0.31" ±" 0.14"10"
mm/%" (mixed-model" linear" regression:" t
30.2
" =" -2.20," p" <" 0.05)." As" above," these"11"
effects"were"even"stronger"when"length"was"corrected"for"growth"during"the"study"12"
period:"-0.35"±"0.13"mm/%"with"a"5%"correction"(linear"mixed"regression:"t
30.2
"="-13"
2.6," p" <" 0.05);" and" -0.39" ±" 0.12" mm/%" with" a" 10%" correction" (linear" mixed"14"
regression:"t
30.4
"="-3.17,"p"<"0.01)."Important ly,"even"moderate"fishing"intensity"(30-15"
40%"of"the"population"captured)"led"to"selection"differentials"greater"than"10"mm."16"
That"is,"fish"surviving"to"that"level"of"fishing"were,"on"average,"10-20"mm"(6-13%)"17"
smaller"than"those"in"the"original"population"(Figure"3)."These"estimates"are"similar"18"
to" other" freshwater" fish" populations" subject" to" low" or" moderate" fishing" intensity,"19"
where"selection"differential"estimates"on"individual"growth"tend"to"be"5-17%"
13,14
."20"
Among-population" variability" in" selection" differentials" increased" dramatically" at"21"
harvest"levels"above"80%"(Figure"3)."Indeed,"modeling"the"distribution"of"selection"22"
certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was notthis version posted May 26, 2017. ; https://doi.org/10.1101/141259doi: bioRxiv preprint
Citations
More filters
Journal Article•DOI•
Human influences on the strength of phenotypic selection.

[...]

Vincent Fugère1, Andrew P. Hendry1•
McGill University1
02 Oct 2018-Proceedings of the National Academy of Sciences of the United States of America
TL;DR: It is suggested that only some human disturbances might have large immediate evolutionary impacts in nature, and how human disturbances can sometimes weaken rather than strengthen selection is discussed, and why measuring the total effect of disturbances on selection is exceedingly difficult.
Abstract: Human activities are driving rapid phenotypic change in many species, with harvesting considered to be a particularly potent evolutionary force We hypothesized that faster evolutionary change in human-disturbed populations could be caused by a strengthening of phenotypic selection, for example, if human disturbances trigger maladaptation and/or increase the opportunity for selection We tested this hypothesis by synthesizing 1,366 phenotypic selection coefficients from 37 species exposed to various anthropogenic disturbances, including harvest We used a paired design that only included studies measuring selection on the same traits in both human-disturbed and control (not obviously human-disturbed “natural”) populations Surprisingly, this meta-analysis did not reveal stronger selection in human-disturbed environments; in fact, we even found some evidence that human disturbances might slightly reduce selection strength The only clear exceptions were two fisheries showing very strong harvest selection On closer inspection, we discovered that many disturbances weakened selection by increasing absolute fitness and by decreasing the opportunity for selection—thus explaining what initially seemed a counterintuitive result We discuss how human disturbances can sometimes weaken rather than strengthen selection, and why measuring the total effect of disturbances on selection is exceedingly difficult Despite these challenges, documenting human influences on selection can reveal disturbances with particularly strong effects (eg, fishing), and thus better inform the management of populations exposed to these disturbances

50 citations

Cites result from "Thirty-five experimental fisheries ..."

  • ...48), as do recent results from experimental fisheries (49)....

    [...]

Journal Article•DOI•
Implications of size-selective fisheries on sexual selection.

[...]

Silva Uusi-Heikkilä1•
University of Jyväskylä1
29 Apr 2020-Evolutionary Applications
TL;DR: How reduced average body size and size variability in exploited populations might affect mate choice and mate competition is discussed and the effects of sex‐biased fisheries on mating systems are considered.
Abstract: Fisheries often combine high mortality with intensive size selectivity and can, thus, be expected to reduce body size and size variability in exploited populations. In many fish species, body size is a sexually selected trait and plays an important role in mate choice and mate competition. Large individuals are often preferred as mates due to the high fecundity and resources they can provide to developing offspring. Large fish are also successful in competition for mates. Fisheries-induced reductions in size and size variability can potentially disrupt mating systems and lower average reproductive success by decreasing opportunities for sexual selection. By reducing population sizes, fisheries can also lead to an increased level of inbreeding. Some fish species avoid reproducing with kin, and a high level of relatedness in a population can further disrupt mating systems. Reduced body size and size variability can force fish to change their mate preferences or reduce their choosiness. If mate preference is genetically determined, the adaptive response to fisheries-induced changes in size and size variability might not occur rapidly. However, much evidence exists for plastic adjustments of mate choice, suggesting that fish might respond flexibly to changes in their social environment. Here, I first discuss how reduced average body size and size variability in exploited populations might affect mate choice and mate competition. I then consider the effects of sex-biased fisheries on mating systems. Finally, I contemplate the possible effects of inbreeding on mate choice and reproductive success and discuss how mate choice might evolve in exploited populations. Currently, little is known about the mating systems of nonmodel species and about the interplay between size-selective fisheries and sexual selection. Future studies should focus on how reduced size and size variability and increased inbreeding affect fish mating systems, how persistent these effects are, and how this might in turn affect population demography.

8 citations

References
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R: A language and environment for statistical computing.

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R Core Team
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TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.

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Douglas M. Bates, Martin Mächler, Benjamin M. Bolker, Steven C. Walker
07 Oct 2015-Journal of Statistical Software
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TL;DR: The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects, and implementing the Satterthwaite's method for approximating degrees of freedom for the t and F tests.
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Journal Article•DOI•
The measurement of selection on correlated characters

[...]

Russell Lande1, Stevan J. Arnold1•
University of Chicago1
01 Nov 1983-Evolution
TL;DR: Measures of directional and stabilizing selection on each of a set of phenotypically correlated characters are derived, retrospective, based on observed changes in the multivariate distribution of characters within a generation, not on the evolutionary response to selection.
Abstract: Natural selection acts on phenotypes, regardless of their genetic basis, and produces immediate phenotypic effects within a generation that can be measured without recourse to principles of heredity or evolution. In contrast, evolutionary response to selection, the genetic change that occurs from one generation to the next, does depend on genetic variation. Animal and plant breeders routinely distinguish phenotypic selection from evolutionary response to selection (Mayo, 1980; Falconer, 1981). Upon making this critical distinction, emphasized by Haldane (1954), precise methods can be formulated for the measurement of phenotypic natural selection. Correlations between characters seriously complicate the measurement of phenotypic selection, because selection on a particular trait produces not only a direct effect on the distribution of that trait in a population, but also produces indirect effects on the distribution of correlated characters. The problem of character correlations has been largely ignored in current methods for measuring natural selection on quantitative traits. Selection has usually been treated as if it acted only on single characters (e.g., Haldane, 1954; Van Valen, 1965a; O'Donald, 1968, 1970; reviewed by Johnson, 1976 Ch. 7). This is obviously a tremendous oversimplification, since natural selection acts on many characters simultaneously and phenotypic correlations between traits are ubiquitous. In an important but neglected paper, Pearson (1903) showed that multivariate statistics could be used to disentangle the direct and indirect effects of selection to determine which traits in a correlated ensemble are the focus of direct selection. Here we extend and generalize Pearson's major results. The purpose of this paper is to derive measures of directional and stabilizing (or disruptive) selection on each of a set of phenotypically correlated characters. The analysis is retrospective, based on observed changes in the multivariate distribution of characters within a generation, not on the evolutionary response to selection. Nevertheless, the measures we propose have a close connection with equations for evolutionary change. Many other commonly used measures of the intensity of selection (such as selective mortality, change in mean fitness, variance in fitness, or estimates of particular forms of fitness functions) have little predictive value in relation to evolutionary change in quantitative traits. To demonstrate the utility of our approach, we analyze selection on four morphological characters in a population of pentatomid bugs during a brief period of high mortality. We also summarize a multivariate selection analysis on nine morphological characters of house sparrows caught in a severe winter storm, using the classic data of Bumpus (1899). Direct observations and measurements of natural selection serve to clarify one of the major factors of evolution. Critiques of the "adaptationist program" (Lewontin, 1978; Gould and Lewontin, 1979) stress that adaptation and selection are often invoked without strong supporting evidence. We suggest quantitative measurements of selection as the best alternative to the fabrication of adaptive scenarios. Our optimism that measurement can replace rhetorical claims for adaptation and selection is founded in the growing success of field workers in their efforts to measure major components of fitness in natural populations (e.g., Thornhill, 1976; Howard, 1979; Downhower and Brown, 1980; Boag and Grant, 1981; Clutton-Brock et

4,990 citations

Journal Article•DOI•
When can marine reserves improve fisheries management

[...]

Ray Hilborn1, Kevin Stokes2, Jean Jacques Maguire, Tony Smith3, Louis W. Botsford4, Marc Mangel5, Jose Maria Orensanz6, Ana M. Parma6, Jake Rice, Johann D. Bell7, Kevern L. Cochrane, Serge M. Garcia, Stephen J. Hall8, G. P. Kirkwood9, Keith Sainsbury3, Gunnar Stefansson10, Carl J. Walters11 •
University of Washington1, Wellington Management Company2, Hobart Corporation3, University of California, Davis4, University of California, Santa Cruz5, National Scientific and Technical Research Council6, WorldFish7, Australian Institute of Marine Science8, Imperial College London9, University of Iceland10, University of British Columbia11
01 Jan 2004-Ocean & Coastal Management
TL;DR: Marine reserves are a promising tool for fisheries management and conservation of biodiversity, but they are not a panacea for fishery management problems as discussed by the authors, and their successful use requires a case-by-case understanding of the spatial structure of impacted fisheries, ecosystems and human communities.

665 citations

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Frequently Asked Questions (11)
Q1. How many fish were assigned a fitness value after each fishing event?

after each fishing event, fish that had been captured were 7 assigned a fitness value of zero, and one if not captured. 

Since there is only one value per 2 catch interval, the authors used linear models to assess how the variability in selection 3 differentials was affected by catch intensity (Figure 3b). 

The authors then 22assessed the relationship between catch intensity and length-at-capture by means of 1 linear mixed-models; these models included length-at-capture as response variable, 2 catch intensity and species as a fixed effects, and lake ID as a random effect, allowing 3 the slope and intercept to vary among lakes. 

If any reproduction 21 occurred, it likely occurred during the first year following removal initiation and 22likely produced few fish as most adults were removed quickly. 

To do so, the authors applied two growth scenarios: a slow growth correction 20 assuming 5% length increase per year and a fast growth correction assuming 10% 21 length increase per year. 

Random fishing 6 is not expected to alter the size structure of the population (left-hand panel, slope of 7 the length-catch relationship is 0.08 ± 1.00, not significantly different from zero). 

2To test if random sampling could explain the observed selective effects of fishing - 3 estimated as the slopes of the length-catch intensity relationships (Figure 1a), and 4 variability-catch intensity relationships (Figure 1b) - the authors performed 100 simulations 5 where mortality occurred randomly; i.e., the authors used random permutations of the time-6 at-capture of each fish. 

overall selection 14 was the response variable, the three components of selection were predictor 15 variables (Figure 4), and the logarithm of population size was the weighting 16 parameter. 

14When possible, fish were prevented from reaching spawning areas in the associated 15 streams by makeshift dams and/or gillnets to block access to inlet and outlet 16 streams. 

16To assess if catch intensity also affected the variability in selection differentials, the authors 17 binned catch intensity into 5% intervals and estimated, for each lake, the average 18 selection differential in each 5% interval. 

To estimate the 1 potential effect of reproduction after the initiation of the fisheries, the authors performed a 2 sensitivity analysis by repeating the analysis after removing fish smaller than 50 3 mm that were caught after the first year of survey, i.e., 334 fish potentially born after 4 removal initiation.