Growth model of the reared sea urchin Paracentrotus ... - SciViews

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plot(edat0q[, "age"], edat0q[, "0.975"], ylim=c(0,65), xlab="Time elapsed from metamorphosis in days", ylab="Diameter increase in mm", main="Quantile regressions with SSfuzremOrig1") points(edat0q[, "age"], edat0q[, "0.5"], col=2) points(edat0q[, "age"], edat0q[, "0.025"], col=4) lines(edat0q[, 1], predict(edat0f.975, newdata=list(age=edat0q[, 1])), col=1) lines(edat0q[, 1], predict(edat0f.5, newdata=list(age=edat0q[, 1])), col=2) lines(edat0q[, 1], predict(edat0f.025, newdata=list(age=edat0q[, 1])), col=4) legend(1500, 20, c("quantile 0.975", "quantile 0.5 (median)", "quantile 0.025"), col=c(1,2,4), lty=1, pch=1) #Rem: for the graph of Fig.4 in the paper, it is: #windows(8, 6) #plot(edat0q[, "age"], edat0q[, "0.975"], ylim=c(0,65), xlab=expression(paste("Time ", italic("t'"), " in days")), ylab=expression(paste("Diameter increase ", italic("D'"), " in mm")), pch=2) #points(edat0q[, "age"], edat0q[, "0.5"], pch=1) #points(edat0q[, "age"], edat0q[, "0.025"], pch=6) #lines(edat0q[, 1], predict(edat0f.975, newdata=list(age=edat0q[, 1])), lty=2) #lines(edat0q[, 1], predict(edat0f.5, newdata=list(age=edat0q[, 1])), lty=1) #lines(edat0q[, 1], predict(edat0f.025, newdata=list(age=edat0q[, 1])), lty=4) #legend(1700, 18, c("quantile 0.975", "quantile 0.5 (median)", "quantile 0.025"), lty=c(2,1,4), pch=c(2,1,6)) Pause() #Rem: the following code takes very long to run, so it is deactivated #just remove comment marks to run it... #cat("\n ---- Calculating parameters estimation for quantiles every 5% step ----\n\n") #cat("... it takes some time, please, be patient...\n") #edat0f.pars
SimRes
Page 1 and 2:
U L B bio mar UNIVERSITE LIBRE DE B
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Acknowledgements ACKNOWLEDGEMENTS W
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Abstract ABSTRACT A rearing protoco
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Résumé RESUME Un protocole d'éle
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Table of contents TABLE OF CONTENTS
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ANNEXES............................
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List of figures LIST OF FIGURES Fig
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List of figures Figure 28. A. Histo
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List of tables LIST OF TABLES Table
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List of equations LIST OF EQUATIONS
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List of equations Equation 33. Para
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List of symbols LIST OF SYMBOLS Var
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List of symbols IW Immersed weight:
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Introduction 27
Page 31 and 32:
Foreword 30
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General introduction this study. Se
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General introduction intense fishin
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General introduction habitats: inte
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General introduction substrate to s
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General introduction mm (Spirlet et
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Growth models General introduction
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. The logistic function for asympto
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d. The von Bertalanffy curves Gener
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Y Y 1 Y• 0.8 0.6 0.4 0.2 General
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Y 1 Y• 0.8 0.6 Y•êH1+bL 0.4 0.
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YY 3 2.5 2 1.5 1 0.5 General introd
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Table 1. Models used to fit sea urc
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General introduction model 1 for Ec
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General introduction method used. I
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General introduction Experiments in
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Aim of the thesis 62
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PART I: SET UP OF AN EXPERIMENTAL R
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Land-based closed-cycle echinicultu
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Aquaculture of echinoderms, includi
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6b. exploitation KCl water renewal
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earing cycle into seven stages is e
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(Grosjean et al, 1996, see Part III
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output. The quality of gametes is o
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average value of 19.5 days, a minim
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individuals were still alive 3 mont
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e. Discussion show similar GI and M
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The success of the present method l
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Fenaux (1990) for the same species
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encountered with food is its stabil
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to Christine Spirlet (ref. ERB 4001
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PART II Measurement for size in the
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Part II: Measurement for size in th
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Few studies have compared and discu
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d. Results and discussion The ambit
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Reproducibility of measurements The
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e. Conclusions fresh weight! Cautio
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principal axis 3 1 0.8 0.6 0.4 0.2
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a relative homogeneous growth of al
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110
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Part III: Experimental studies of t
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assumption of normality can be test
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occurred. From this moment on, and
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d. Results Size distribution among
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Nber of ind. 120 100 80 60 40 20 0
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Table 9. Statistics on the six batc
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Table 10. Size distribution of Fe e
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purpuratus; Dafni & Tobol, 1987: Tr
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Nbr. of ind. Nbr. of ind. A. Size d
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Nbr. of ind. Nbr. of ind. Nbr. d'in
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Part III: Experimental studies of t
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134
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Part IV: A growth model with intras
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. Introduction fuzzy-remanent funct
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considered factor on the curve's sh
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Diameter D 20 in mm Diameter D in m
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d. Results Theoretical consideratio
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some two-dimensional processes like
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implements this method ('nlrq' pack
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value just after metamorphosis (D0,
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quantifies maximum speed growth, k2
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Table 12. Results of quantile regre
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This way, one obtains a 4-parameter
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individuals related to the presence
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Finally, ∆D∞ should follow a no
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60 Diameter increase D' in mm 40 20
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e. Discussion Fig. 34B shows three
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Constraining parameters as we did i
Page 169 and 170: It does not consider respiration as
Page 171 and 172: this species. For other species, wh
Page 173 and 174: ecapture, McDonald & Pitcher, 1979;
Page 175 and 176: ∆D∞ D0 +∆D∞ − D0 +∆D∞
Page 177 and 178: evaluated on basis of biological kn
Page 179 and 180: ain conceptualizes complex objects.
Page 181 and 182: 180
Page 183 and 184: General conclusions individuals, wh
Page 185 and 186: Root models: y’ = ay m -by n (exp
Page 187 and 188: General conclusions - The diauxic g
Page 189 and 190: General conclusions model not on a
Page 191 and 192: References Baker, T.T., R. Lafferty
Page 193 and 194: References Dafni, J. & R. Tobol, 19
Page 195 and 196: References Ebert, T.A., 1999. Plant
Page 197 and 198: References intermedius on a rocky s
Page 199 and 200: References Guillou, M. & Ch. Michel
Page 201 and 202: References Kobayashi, S. & J. Taki,
Page 203 and 204: References McDonald, P.D. & T.J. Pi
Page 205 and 206: References Pouvreau, S., C. Bacher
Page 207 and 208: References Sellem, F., H. Langar &
Page 209 and 210: References Stumm, W. & J.J. Morgan,
Page 211 and 212: References Webster, S.K. & A.C. Gie
Page 213 and 214: 212
Page 215 and 216: Annexes 214
Page 217 and 218: # If no graphic device currently op
Page 219: lines(edatq[, 1], predict(edat.025,
Page 223 and 224: Annexes Code of functions required
Page 225 and 226: plot(cumsum(dat[,columns[1]])/sum(d
Page 227 and 228: for the CI!!! Annexes SEMean
Page 229 and 230: } Annexes # - y, a vector of classe
Page 231 and 232: plot(x, y, type="l", col=col, lty=l
Page 233 and 234: } Annexes } results[i, 1:5]
Page 235 and 236: } .value Annexes dimnames(.grad)
Page 237 and 238: #{ # # This is adapted from SSasymp
Page 239 and 240: Exp.ival
Page 241 and 242: SSallo
Page 243 and 244: .expr4
Page 245 and 246: .expr9
Page 247 and 248: . The 'nlrq' package for nonlinear
Page 249 and 250: nlrq.control package:nlrq R Documen
Page 251 and 252: gradSetArgs[[2]]), call("[", gradSe
Page 253 and 254: model.step
Page 259 and 260: Annexes metamorphosis. Acquisition
Page 261 and 262: Annexes ABSTRACT: The general allom
Page 263 and 264: Annexes libitum for 8, 16, 24, 32,
Page 265 and 266: Annexes EXECUTIVE SUMMARY: The aim
Page 267 and 268: Annexes KEYWORDS: Somatic growth, d
Page 269 and 270: Annexes amount of waste produced at
Page 271 and 272:
Annexes ABSTRACT: Multimodal distri
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