TY - JOUR
T1 - Using observed residual error structure yields the best estimates of individual growth parameters
AU - Curiel-Bernal, Marcelo V.
AU - Aragón-Noriega, E. Alberto
AU - Cisneros-Mata, Miguel
AU - Sánchez-Velasco, Laura
AU - Jiménez-Rosenberg, S. Patricia A.
AU - Parés-Sierra, Alejandro
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/9
Y1 - 2021/9
N2 - Obtaining the best possible estimates of individual growth parameters is essential in studies of physiology, fisheries management, and conservation of natural resources since growth is a key component of population dynamics. In the present work, we use data of an endangered fish species to demonstrate the importance of selecting the right data error structure when fitting growth models in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of California increasingly studied in recent times due to a perceived threat of extinction. Previous works estimated individual growth using the von Bertalanffy model assuming a constant variance of length-at-age. Here, we reanalyze the same data under five different variance assumptions to fit the von Bertalanffy and Gompertz models. We found consistent significant differences between the constant and nonconstant error structure scenarios and provide an example of the consequences using the growth performance index φ′ to show how using the wrong error structure can produce growth parameter values that can lead to biased conclusions. Based on these results, for totoaba and other related species, we recommend using the observed error structure to obtain the individual growth parameters.
AB - Obtaining the best possible estimates of individual growth parameters is essential in studies of physiology, fisheries management, and conservation of natural resources since growth is a key component of population dynamics. In the present work, we use data of an endangered fish species to demonstrate the importance of selecting the right data error structure when fitting growth models in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of California increasingly studied in recent times due to a perceived threat of extinction. Previous works estimated individual growth using the von Bertalanffy model assuming a constant variance of length-at-age. Here, we reanalyze the same data under five different variance assumptions to fit the von Bertalanffy and Gompertz models. We found consistent significant differences between the constant and nonconstant error structure scenarios and provide an example of the consequences using the growth performance index φ′ to show how using the wrong error structure can produce growth parameter values that can lead to biased conclusions. Based on these results, for totoaba and other related species, we recommend using the observed error structure to obtain the individual growth parameters.
KW - Error structure
KW - Growth performance
KW - Multimodel inference
KW - Totoaba
UR - http://www.scopus.com/inward/record.url?scp=85114862458&partnerID=8YFLogxK
U2 - 10.3390/fishes6030035
DO - 10.3390/fishes6030035
M3 - Artículo
AN - SCOPUS:85114862458
SN - 2410-3888
VL - 6
JO - Fishes
JF - Fishes
IS - 3
M1 - 35
ER -