Biological and statistical interpretation of size-at-age, mixed-effects models of growth

被引:16
作者
Vincenzi, Simone
Jesensek, Dusan [1 ]
Crivelli, Alain J. [2 ]
机构
[1] Tolmin Angling Assoc, Most Na Soci, Tolmin, Slovenia
[2] Stn Biol Tour Valat, F-13200 Arles, France
来源
ROYAL SOCIETY OPEN SCIENCE | 2020年 / 7卷 / 04期
关键词
growth; von Bertalanffy; Gompertz; mixed-effects models; LIFE-HISTORY; INDIVIDUAL GROWTH; AUTOMATIC DIFFERENTIATION; EVOLUTIONARY DEMOGRAPHY; POPULATION-DYNAMICS; VITAL-RATES; CONSEQUENCES; INFERENCE; GOMPERTZ; VARIABILITY;
D O I
10.1098/rsos.192146
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The differences in life-history traits and processes between organisms living in the same or different populations contribute to their ecological and evolutionary dynamics. We developed mixed-effect model formulations of the popular size-at-age von Bertalanffy and Gompertz growth functions to estimate individual and group variation in body growth, using as a model system four freshwater fish populations, where tagged individuals were sampled for more than 10 years. We used the software Template Model Builder to estimate the parameters of the mixed-effect growth models. Tests on data that were not used to estimate model parameters showed good predictions of individual growth trajectories using the mixed-effects models and starting from one single observation of body size early in life; the best models had R-2 > 0.80 over more than 500 predictions. Estimates of asymptotic size from the Gompertz and von Bertalanffy models were not significantly correlated, but their predictions of size-at-age of individuals were strongly correlated (r > 0.99), which suggests that choosing between the best models of the two growth functions would have negligible effects on the predictions of size-at-age of individuals. Model results pointed to size ranks that are largely maintained throughout the lifetime of individuals in all populations.
引用
收藏
页数:15
相关论文
共 80 条
  • [1] Comparison of three nonlinear and spline regression models for describing chicken growth curves
    Aggrey, SE
    [J]. POULTRY SCIENCE, 2002, 81 (12) : 1782 - 1788
  • [2] NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION
    AKAIKE, H
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1974, AC19 (06) : 716 - 723
  • [3] ALM GUNNAR, 1959, REPT INST FRESHWATER RES DROTTNINGHOLM, V40, P5
  • [4] [Anonymous], 2006, DATA ANAL USING REGR, DOI DOI 10.1017/CBO9780511790942
  • [5] [Anonymous], 2013, THESIS COLUMBIA U NE
  • [6] The estimation of potential yield and stock status using life-history parameters
    Beddington, JR
    Kirkwood, GP
    [J]. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2005, 360 (1453) : 163 - 170
  • [7] Bell A., 2019, QUAL QUANT, V53, P1051, DOI [10.1007/S11135-018-0802-X/FIGURES/2, DOI 10.1007/S11135-018-0802-X/FIGURES/2, 10.1007/s11135-018-0802-x, DOI 10.1007/S11135-018-0802-X]
  • [8] BAYESIAN-APPROACH FOR A NON-LINEAR GROWTH-MODEL
    BERKEY, CS
    [J]. BIOMETRICS, 1982, 38 (04) : 953 - 961
  • [9] Predictive Inference and Scientific Reproducibility
    Billheimer, Dean
    [J]. AMERICAN STATISTICIAN, 2019, 73 : 291 - 295
  • [10] Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS
    Bolker, Benjamin M.
    Gardner, Beth
    Maunder, Mark
    Berg, Casper W.
    Brooks, Mollie
    Comita, Liza
    Crone, Elizabeth
    Cubaynes, Sarah
    Davies, Trevor
    de Valpine, Perry
    Ford, Jessica
    Gimenez, Olivier
    Kery, Marc
    Kim, Eun Jung
    Lennert-Cody, Cleridy
    Magnusson, Arni
    Martell, Steve
    Nash, John
    Nielsen, Anders
    Regetz, Jim
    Skaug, Hans
    Zipkin, Elise
    [J]. METHODS IN ECOLOGY AND EVOLUTION, 2013, 4 (06): : 501 - 512