Body Proportional Hypothesis Research Articles

Models for length back-calculation in Caspian kutum, Rutilus kutum (Pisces: Cyprinidae) from the Caspian Sea

The Caspian Kutum, Rutilus Kutum (Kamensky 1901) specimens were sampled by purse seine in the northern Iranian coast of the Caspian Sea at four locations: Feridoonkenar Shahed, Mahmoudabad Khoram, Lariim Azadi fishing coop, and the Shiroud River in Ramsar city. “Back-calculation” is a retrospective method of estimating the characteristics of growth of fish in terms of length and rate of growth in the years preceding capture. Back-calculation of fish lengths at previous ages from scales or otoliths is a widely used approach to estimate both individual and population growth history. The back-calculated lengths of the Caspian kutum,Rutilus kutum (Kamensky 1901) were obtained using six different models, namely scale proportional hypothesis, body proportional hypothesis, Fraser Lee, nonlinear scale proportional hypothesis, nonlinear body proportional hypothesis, and the newest method, Morita Matsuishi model. The results showed that the preferred back-calculation models is Fraser Lee model for both males and females, while the nonlinear body proportional hypothesis is only for the females.

Growth of bluefin tuna (Thunnus thynnus) in the North-eastern Atlantic and Mediterranean based on back-calculation of dorsal fin spine annuli

The growth of bluefin tuna (Thunnus thynnus) in the North-eastern Atlantic and Mediterranean was analysed using the back-calculation of the dorsal fin spine annuli. Spines from a total of 2678 bluefin tuna, collected from 1984 and 1990–2010, were analyzed. The specimens ranged from 45 to 287cm straight fork length (SFL). Both linear and power regression functions showed a significant relationship between spine radius and SFL, but power function fitted better than linear (SFL=42.978R0.884, r2=0.98), as also was found in previous studies when a wide range of sizes was available. Two back-calculation equations were used to obtain the back-calculated SFL, Fraser-Lee and Body Proportional Hypothesis (BPH), showing similar results. The Von Bertalanffy growth parameters obtained with the selected BPH model were: SFL=349.5(1−e−0.086(t+0.814)). Back-calculated lengths and growth parameters showed similarities with those from previous literature.

Validating Back‐Calculation Models Using Population Data

AbstractOtolith back‐calculation, an estimation of size and growth at some previous age, is a valuable and widely used method of tracking the traits of surviving individuals. Although numerous back‐calculation models have been developed, the accuracy of model prediction is rarely assessed before using a model on wild populations. Using the Atlantic Silverside Menidia menidia, we tested the precision of four commonly used back‐calculation models (body proportional hypothesis, modified Fry, biological intercept, and time‐varying growth). We calculated the moments of the observed length frequency distribution (mean, variance, skewness, and kurtosis) and compared them with the moments of the back‐calculated length frequency distribution predicted by each model. We also compared how the most accurate back‐calculation model changed under various scenarios of size‐selective mortality and found that bias in back‐calculation could not be explained by size‐selective mortality. The differences between functional forms over a wide range of fish sizes were slight for the biological intercept and modified Fry models, whereas the time‐varying growth and body proportional hypothesis models differed considerably. We found that all models performed well for at least one measure of accuracy. However, the modified Fry model was the most accurate overall when back‐calculating to the observed population moments.

Age and growth of loggerhead sea turtles in southern Brazil

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 456:255-268 (2012) - DOI: https://doi.org/10.3354/meps09681 Age and growth of loggerhead sea turtles in southern Brazil Roberta Petitet1,2,4,*, Eduardo R. Secchi3, Larisa Avens5, Paul G. Kinas2 1Programa de Pós-graduação em Oceanografia Biológica, Instituto de Oceanografia, 2Laboratório de Estatística Ambiental, Instituto de Matemática, Estatística e Física (IMEF), and 3Laboratório de Tartarugas e Mamíferos Marinhos, Instituto de Oceanografia; Universidade Federal do Rio Grande (FURG), Avenida Itália km 8, CEP 96201-900, Rio Grande, Rio Grande do Sul, Brazil 4Centro de Recuperação de Animais Marinhos - CRAM-FURG, Rua Capitão Heitor Perdigão, no. 10, CEP 96200-580, Centro, Rio Grande, Rio Grande do Sul, Brazil 5NOAA Fisheries, Southeast Fisheries Science Center, Center for Coastal Fisheries and Habitat Research, 101 Pivers Island Road, Beaufort, North Carolina 28516, USA *Email: rpetitet@hotmail.com ABSTRACT: The juvenile and subadult stages of loggerhead sea turtles Caretta caretta are poorly studied in Brazil. We present age estimates and a growth model for loggerhead sea turtles in the South Atlantic Ocean obtained through skeletochronological analysis of humeri from both neritic and oceanic stage individuals. The age estimation was based on the number of growth marks in the humerus; however, for larger turtles early growth marks were resorbed and therefore it was necessary to use a correction factor to account for the lost growth marks. The estimated ages indicate that the oceanic stage lasts 8 to 19 yr (average: 11.5 yr). Schnute’s growth model was fit to age-at-length data, as its shape is versatile and it does not require data from all life stages (i.e. hatchlings through adults near asymptotic size). However, because the shape of Schnute’s curve was almost linear for the age window comprising our data, a linear regression ultimately provided a better fit to the data, yielding an age at maturation estimate of 32 yr. Somatic growth rates were calculated through incorporation of the Body Proportional Hypothesis into conversion of growth mark measurements to estimates of carapace length. Growth rates from neritic stage South Atlantic loggerheads were similar to those reported for neritic loggerhead sea turtles from the North Atlantic Ocean, but were lower than those of oceanic loggerheads from the South Atlantic Ocean. This finding suggests that local environmental conditions influence loggerhead sea turtles’ growth rates as well as the energy expenditure during migrations, energy allocation and genetic origin. KEY WORDS:Age · Growth · Skeletochronology · Bayesian inference · Schnute’s curve · Caretta caretta Full text in pdf format PreviousNextCite this article as: Petitet R, Secchi ER, Avens L, Kinas PG (2012) Age and growth of loggerhead sea turtles in southern Brazil. Mar Ecol Prog Ser 456:255-268. https://doi.org/10.3354/meps09681 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 456. Online publication date: June 07, 2012 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2012 Inter-Research.

Age and growth analysis of the chub,Squalius squalus(Bonaparte, 1837), in the Assino Creek (Umbria, Italy)

In this study, the population structure, growth parameters and condition of chub, Squalius squalus , from the Assino Creek, a tributary of the River Tiber, were investigated. A total of 1311 specimens were caught monthly from March 2008 to May 2009 by electrofishing. Total length (TL ± 0.1 cm) and weight (W ± 0.1 g) were recorded; age estimation was based on scalimetry and sex was determined by macroscopic observation of the gonads. Age composition ranged from 0+ to 11+ . Total length varied from a minimum of 4.1 cm to a maximum of 48.8 cm, for a weight of 0.5 g and 1233.0 g, respectively. Length-weight regression was log 10 W = −2.201 + 3.127log 10 TL for males and log 10 W = −2.273 + 3.190log 10 TL for females. Previous growth was determined with back-calculation from scale measurements using the non-linear Body Proportional Hypothesis. Theoretical length growth was estimated with von Bertalanffy’s model. Condition was evaluated by relative weight. In Italy there are few available data about the biology of Squalius squalus : the aim of this study was therefore to fill this lack of information by investigating some important aspects of the growth of the population in Assino Creek.

Accuracy and Precision of Estimates of Back‐Calculated Channel Catfish Lengths and Growth Increments Using Pectoral Spines and Otoliths

AbstractBack‐calculation of lengths at previous ages using measurements of growth increments on the bony structures of fish is a common practice, yet studies validating this procedure are few. Our objective was to determine the accuracy and precision of back‐calculated lengths and the last growth increment for channel catfish Ictalurus punctatus using pectoral spines and otoliths. In recent studies, the last growth increment has been related to the length of the fish at the start of the growing season to determine size‐specific growth. Growth increments on basal sections of pectoral spines were measured along three radii—posterior (SP), anterior (SA), and lateral (SL)—and otoliths were measured along the ventral radius by three independent readers. Back‐calculated lengths derived from eight models (biological intercept, body‐proportional hypothesis [BPH] linear, BPH nonlinear, Dahl–Lee, Fraser–Lee, Morita–Matsuishi, scale‐proportional hypothesis [SPH] linear, and SPH nonlinear) for each bony structure radius were compared with the actual growth histories of captive channel catfish to determine the best model and radius (for pectoral spines) for the two bony structures. The SPH linear model for SL provided the most accurate data for pectoral spines and was also appropriate for otoliths. Back‐calculated lengths and last growth increments were considerably more accurate and precise for SL than for otoliths. The SPH linear model provided an initial length–last growth increment relationship that was similar to the observed relationship for SL but not for otoliths. We also estimated last growth increments and the initial length–last growth increment relationship from the traditionally used Dahl–Lee model for SP and found that these estimates were similar to those derived from the SPH linear model for SL. We recommend using the SPH linear model for SL to estimate length‐at‐age and growth increment data. However, the Dahl–Lee model for SP also provides reasonable estimates of the last growth increment.

Age and growth of Atlantic bluefin tuna,Thunnus thynnus(Osteichthyes: Thunnidae), in the Mediterranean Sea

Summary The objective of the study was to describe the biometry of Mediterranean bluefin tuna, Thunnus thynnus, the biology of which is not yet well understood. A total of 504 specimens was collected from 1998 to 2005 in the central part of the Mediterranean basin. They were sexed and measured; fork lengths (FL) ranged from 51.0 to 255.0 cm while body weights (W) ranged from 2.6 to 247.0 kg. The first spiniform ray (spine) of the first dorsal fin was removed and cross-sectioned near the condyle base in order to count annuli for age estimation. The regression coefficient (b) of the female FL–W relationship was significantly higher than that of the male, and both sexes displayed a negatively allometric growth (b < 3); male regression equation: ln W = )2.942 + 2.730 ln FL; female regression equation: ln W = )3.660 + 2.878 ln FL. Based on counts of the translucent zones in the sections of the first ray of the first dorsal fin, estimated ages ranged from 1 to 15 years for males and 1 to 14 years for females. The correlation between the spine ray (R) and FL fit the allometric model best; the R–FL regression equations of the two sexes did not differ significantly and the overall equation was: ln FL = 3.721 + 0.851 ln R. Due to the R–FL allometric correlation, estimates of fork lengths at previous ages, FLi, were back-calculated with a body proportional hypothesis. Von Bertalanffy growth equations were derived from both observed and back-calculated FLs-at-age, which did not differ significantly. Moreover, no significant difference was found between the growth equations of the two sexes; the overall equation was FLt = 373.08 [1)e )0.07(t + 1.76) ]. Weight-at-age values were derived from the von Bertalanffy predicted FLs-atage by the FL–W correlation equations for males and females. The paper represents the first comprehensive study on the biometry, including age and growth, of bluefin tuna captured in the Mediterranean Sea.

The back-calculation of size and growth from otoliths: Validation and comparison of models at an individual level

Multiple internal and external tagging experiments tested the applicability of five back-calculation models (Biological Intercept, Modified-Fry, Body Proportional Hypothesis, Time-Varying Growth, and an Age-Effects model) as predictors of individual growth trajectories of two marine cleaning gobies, Elacatinus evelynae and E. prochilos, that were raised in aquaria under conditions that resulted in variable growth. Mixed-effect model analyses of longitudinal records of otoliths and somatic growth collected at the individual level revealed that E. evelynae and E. prochilos produced daily increments on their otoliths for up to two months post-settlement and that the Modified-Fry model provided the most accurate size-at-age estimates despite the presence of age, growth and time-varying growth effects in the dataset. Very large errors in predicted size were produced by the Age-Effects model. The four other back-calculation models all provided slightly biased estimates of back-calculated size-at-age, with the Modified-Fry model providing the least biased estimates. Regardless of bias, both experimental and theoretical evidence indicated that back-calculated size was a better proxy of fish length than otoliths radius. Relationships between fish length and otoliths radius were allometric at the level of individuals, which explained why the Modified-Fry model performed better. However, this allometry was undetectable at the population level. This study represents the first attempt to validate modern back-calculation models using longitudinal data collected and analysed at the individual level. Our results suggest the use of the Modified-Fry model as a conservative approach in routine back-calculations of fish size at age from otoliths.

Larval and juvenile Australian smelt Retropinna semoni somatic and otolith growth parameters – implications for growth interpretation of wild fish

Abstract – Relationships between fish length, otolith size, age and weight were assessed for a population of wild Australian smelt (Retropinna semoni) larvae and juveniles captured over a 4‐year period to aid further interpretation of growth and condition during the early life history of the species. Nonlinear smoothed generalized additive models best described the fish–otolith size relationship during the larval and juvenile period, indicating that the proportionality between fish length and otolith size varies in relation to size. It is proposed that back‐calculated predictions of fish size at a previous age or otolith size, accounting for individual variation is possible by assuming a body proportional hypothesis. Growth rate of larval and juvenile Australian smelt was best described using the Gompertz model that indicated a steady decline in growth rate after around 30 days of age. The allometric growth of larval and juvenile Australian smelt established from the length/weight relationship can subsequently be used to assess the condition of fish within this population using a relative condition or relative weight condition index. The results of the study have provided significant information to enable more precise growth reconstruction and condition assessment for the species in Australian lowland rivers.

Age and growth of the sharpsnout seabream Diplodus puntazzo (Cetti, 1777) inhabiting the Canarian archipelago, estimated by reading otoliths and by backcalculation

The yearly nature in growth increment formation in the otoliths of Diplodus puntazzo aged 1–10 years from the Canary Islands was validated. Each translucent zone in the otoliths represented an annulus with a year's growth represented by an opaque and its adjacent hyaline zone. Lengths at age were described by the specialised von Bertalanffy growth model ( L ∞ = 541 mm, k = 0.182 year −1, and t 0 = −2.531 years), the Schnute growth model ( y 1 = 171 mm, y 2 = 509 mm, a = 0.022, b = 2.59) and the seasonalised von Bertalanffy growth model ( L ∞ = 546 mm, k = 0.176 year −1, t 0 = −2.21 years, C = 0.712, t s = 0.798). Individuals grow fast during the first year of life, attaining approximately 45% of their maximum length. The body proportional hypothesis and the power length–radius relationship used to backcalculate the growth trajectories showed that the growth backcalculation model could be used to provide reasonable estimates of growth in this species. A power relationship ( a = 36.19 and v = 1.705) was estimated between the total length and the otolith radius. Backcalculated lengths were similar to those predicted by the growth models. Growth parameters estimated from the backcalculated lengths at age were: L ∞ = 508 mm, k = 0.197 year −1, t 0 = −1.731 years. Data on length and age used to estimate the parameters of the von Bertalanffy growth model showed that males and females have similar growth rates.

Growth trends of Queensland east coast Spanish mackerel (Scomberomorus commerson) from otolith back-calculations

The growth of narrow-barred Spanish mackerel (Scomberomorus commerson) was examined over a broad spatial scale encompassing the east coast of Queensland, Australia. Our aim was to use back-calculation to identify potential biases in growth estimation, and to provide a more recent estimate of growth for Spanish mackerel on the east coast of Queensland. Growth trends were determined using the body proportional hypothesis (BPH) back-calculation model and compared between back-calculated and observed length-at-age data. The back-calculated von Bertalanffy growth function parameters estimated for males and females, respectively, were L∞ = 1047 mm, K = 0.75, t0 = –0.18 and L∞ = 1248 mm, K = 0.51 and t0 = –0.39. Significant differences in growth of Spanish mackerel sampled among different regions along the east coast were observed. A comparison of back-calculated and observed length-at-age data revealed significant biases in the observed length-at-age data for younger aged fish. Our findings contrast with previous estimates of growth for Spanish mackerel from the east coast of Queensland and highlight the use of back-calculation for the first few annuli as a means of avoiding biases in growth estimation arising from gear selectivity associated with fishery-dependent samples of fast-growing fish.

A new model of growth back-calculation incorporating age effect based on otoliths

We developed a simple back-calculation model that includes the age effect in which otolith size increases continuously during no-growth periods. To evaluate the validity of our proposed model, we back-calculated the past fish lengths and growth rates using the new model and seven traditional back-calculation models (scale-proportional hypothesis (SPH), body-proportional hypothesis (BPH), Fraser–Lee, biological intercept, nonlinear SPH, nonlinear BPH, and modified Fry) using the otoliths of individually tag-recaptured white-spotted char (Salvelinus leucomaenis). The estimated fish lengths corresponded well to observed fish lengths for simple traditional (SPH, BPH, and Fraser–Lee) and the new models. However, the back-calculated growth rates did not correspond to observed growth rates except for the new model. All previous models had considerable bias; growth rates of slow-growing fish were overestimated. As our model incorporating the age effect did not show such bias, this bias would be attributed to the age effect. Our proposed back-calculation model incorporating the age effect should be useful to estimate past growth rates at the individual level.

Validation of back‐calculation formulae for cod otoliths

This paper tries to determine the best back‐calculation formula (BCF) for cod otoliths. The cod were tagged individually and measured regularly throughout a 3‐year period. Six different BCFs were compared: (1) Fraser–Lee with an intercept estimated from a linear regression of fish length (L) on otolith length (S), (2) Fraser—Lee with an intercept estimated from a symmetrical regression line of L on S; (3) the ‘body proportional hypothesis’ (BPH) for linear relationship; (4) the ‘scale proportional hypothesis’ (SPH) for linear relationship; (5) the BPH for non‐linear relationship; and (6) the SPH for non‐linear relationship. The ‘body proportional hypothesis’ seems to be more correct than the ‘scale proportional hypothesis’, and the non‐linear BCF seems to give the best results for cod otoliths.

Validation of back-calculation formulae for cod otoliths

This paper tries to determine the best back-calculation formula (BCF) for cod otoliths. The cod were tagged individually and measured regularly throughout a 3-year period. Six different BCFs were compared: (1) Fraser–Lee with an intercept estimated from a linear regression of fish length (L) on otolith length (S), (2) Fraser—Lee with an intercept estimated from a symmetrical regression line of L on S; (3) the ‘body proportional hypothesis’ (BPH) for linear relationship; (4) the ‘scale proportional hypothesis’ (SPH) for linear relationship; (5) the BPH for non-linear relationship; and (6) the SPH for non-linear relationship. The ‘body proportional hypothesis’ seems to be more correct than the ‘scale proportional hypothesis’, and the non-linear BCF seems to give the best results for cod otoliths.