Statistical modeling of baleen and body length at age in bowhead whales (Balaena mysticetus)

2012 ◽  
Vol 90 (8) ◽  
pp. 915-931 ◽  
Author(s):  
S.C. Lubetkin ◽  
J.E. Zeh ◽  
J.C. George
Keyword(s):  
Body Length ◽  
Growth Model ◽  
Growth Models ◽  
Information Criterion ◽  
Single Stage ◽  
Von Bertalanffy ◽  
Balaena Mysticetus ◽  
Bowhead Whales ◽  
Multi Stage ◽  
Age Estimates

We used baleen lengths and age estimates from 175 whales and body lengths and age estimates from 205 whales to test which of several single- and multi-stage growth models best characterized age-specific baleen and body lengths for bowhead whales ( Balaena mysticetus L., 1758) with the goal of determining which would be best for predicting whale age based on baleen or body length. Previous age estimates were compiled from several techniques, each of which is valid over a relatively limited set of physical characteristics. The best fitting single-stage growth model was a variation of the von Bertalanffy growth model for both baleen and body length data. Based on Bayesian information criterion, the two- and three-stage versions of the von Bertalanffy model fit the data better than did the single-stage models for both baleen and body length. The best baleen length models can be used to estimate expected ages for bowhead whales with up to 300–325 cm baleen, depending on sex, which correspond to age estimates approaching 60 years. The best body length models can be used to estimate expected ages for male bowhead whales up to 14 m, and female bowheads up to 15.5 m or ages up to approximately 40 years.

scholarly journals Demographics of Etmopterus lucifer (Lucifer dogfish)

2021 ◽  
Author(s):  
◽  
Annie Rose Galland
Keyword(s):  
New Zealand ◽  
Growth Model ◽  
Growth Models ◽  
Total Mortality ◽  
Information Criterion ◽  
Commercial Fishing ◽  
Precautionary Approach ◽  
Late Maturity ◽  
Trawl Fisheries ◽  
Age Estimates

<p>This study provides the first comprehensive description of the demographics of lucifer dogfish (Etmopterus lucifer) from the Chatham Rise, New Zealand during January 2012. Lucifer dogfish is a non-QMS species commonly taken as bycatch in New Zealand deepwater trawl fisheries, where it has low commercial value and is usually discarded. Sexual maturity of females was determined by assessing the condition of the ovary and uterus, and the width of the uterus and oviducal gland. Male maturity was assessed by determining clasper and testes condition, inner clasper length, testes length, and testes weight. A sample of lucifer dogfish was aged by counting growth bands on the internal section of the dorsal fin spine (n = 97), assuming annual deposition of bands. Intra- and inter-reader bias in age estimates was estimated, but count precision was high within (CV = 12.71 %) and between reader age estimates (11.98 %). A number of growth models were fitted to the length-at-age data, including the traditional and modified Von Bertalanffy growth formula (VBGF) and four cases of the Schnute growth model. Selection of the best growth model was based on the Akaike Information Criterion (AIC). The fourth case of the Schnute growth model best represented growth. Lucifer dogfish had an estimated age and length at maturity of 10.4 years and 34.0 cm respectively for males, and 13.0 years and 41.0 cm for females. The oldest observed fish were 17 and 14 years for males and females respectively. The total mortality estimates were in the range of 0.14 to 0.35 yr ⁻¹. Lucifer dogfish fed primarily upon mesopelagic fishes, with Hector’s lanternfish (Lampanyctodes hectoris) identified as being the most common prey. Lucifer dogfish had late maturity relative to its longevity. Although sampling of the population was likely to be incomplete, and biases in age estimates may have occurred, these observed life history characteristics indicate that productivity will be low, and as a consequence, the precautionary approach should be applied, as the potential impact of commercial fishing on this species is high.</p>

scholarly journals Demographics of Etmopterus lucifer (Lucifer dogfish)

2021 ◽  
Author(s):  
◽  
Annie Rose Galland
Keyword(s):  
New Zealand ◽  
Growth Model ◽  
Growth Models ◽  
Total Mortality ◽  
Information Criterion ◽  
Commercial Fishing ◽  
Precautionary Approach ◽  
Late Maturity ◽  
Trawl Fisheries ◽  
Age Estimates

<p>This study provides the first comprehensive description of the demographics of lucifer dogfish (Etmopterus lucifer) from the Chatham Rise, New Zealand during January 2012. Lucifer dogfish is a non-QMS species commonly taken as bycatch in New Zealand deepwater trawl fisheries, where it has low commercial value and is usually discarded. Sexual maturity of females was determined by assessing the condition of the ovary and uterus, and the width of the uterus and oviducal gland. Male maturity was assessed by determining clasper and testes condition, inner clasper length, testes length, and testes weight. A sample of lucifer dogfish was aged by counting growth bands on the internal section of the dorsal fin spine (n = 97), assuming annual deposition of bands. Intra- and inter-reader bias in age estimates was estimated, but count precision was high within (CV = 12.71 %) and between reader age estimates (11.98 %). A number of growth models were fitted to the length-at-age data, including the traditional and modified Von Bertalanffy growth formula (VBGF) and four cases of the Schnute growth model. Selection of the best growth model was based on the Akaike Information Criterion (AIC). The fourth case of the Schnute growth model best represented growth. Lucifer dogfish had an estimated age and length at maturity of 10.4 years and 34.0 cm respectively for males, and 13.0 years and 41.0 cm for females. The oldest observed fish were 17 and 14 years for males and females respectively. The total mortality estimates were in the range of 0.14 to 0.35 yr ⁻¹. Lucifer dogfish fed primarily upon mesopelagic fishes, with Hector’s lanternfish (Lampanyctodes hectoris) identified as being the most common prey. Lucifer dogfish had late maturity relative to its longevity. Although sampling of the population was likely to be incomplete, and biases in age estimates may have occurred, these observed life history characteristics indicate that productivity will be low, and as a consequence, the precautionary approach should be applied, as the potential impact of commercial fishing on this species is high.</p>

scholarly journals Comparison of growth models to describe growth from birth to 6 years in a Beninese cohort of children with repeated measurements

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e035785
Author(s):  
Shukrullah Ahmadi ◽  
Florence Bodeau-Livinec ◽  
Roméo Zoumenou ◽  
André Garcia ◽  
David Courtin ◽  
...  
Keyword(s):  
Growth Model ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Information Criterion ◽  
Height Growth ◽  
Sub Saharan Africa ◽  
Individual Growth ◽  
Secondary Outcome ◽  
Growth Trajectories ◽  
Best Fitting

ObjectiveTo select a growth model that best describes individual growth trajectories of children and to present some growth characteristics of this population.SettingsParticipants were selected from a prospective cohort conducted in three health centres (Allada, Sekou and Attogon) in a semirural region of Benin, sub-Saharan Africa.ParticipantsChildren aged 0 to 6 years were recruited in a cohort study with at least two valid height and weight measurements included (n=961).Primary and secondary outcome measuresThis study compared the goodness-of-fit of three structural growth models (Jenss-Bayley, Reed and a newly adapted version of the Gompertz growth model) on longitudinal weight and height growth data of boys and girls. The goodness-of-fit of the models was assessed using residual distribution over age and compared with the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-fitting model allowed estimating mean weight and height growth trajectories, individual growth and growth velocities. Underweight, stunting and wasting were also estimated at age 6 years.ResultsThe three models were able to fit well both weight and height data. The Jenss-Bayley model presented the best fit for weight and height, both in boys and girls. Mean height growth trajectories were identical in shape and direction for boys and girls while the mean weight growth curve of girls fell slightly below the curve of boys after neonatal life. Finally, 35%, 27.7% and 8% of boys; and 34%, 38.4% and 4% of girls were estimated to be underweight, wasted and stunted at age 6 years, respectively.ConclusionThe growth parameters of the best-fitting Jenss-Bayley model can be used to describe growth trajectories and study their determinants.

Age estimation for young bowhead whales (Balaena mysticetus) using annual baleen growth increments

2008 ◽  
Vol 86 (6) ◽  
pp. 525-538 ◽  
Author(s):  
S. C. Lubetkin ◽  
J. E. Zeh ◽  
C. Rosa ◽  
J. C. George
Keyword(s):  
Growth Parameters ◽  
Age Determination ◽  
Population Level ◽  
Exponential Model ◽  
Growth Increment ◽  
Balaena Mysticetus ◽  
Nonlinear Mixed Effects Model ◽  
Bowhead Whales ◽  
Growth Increments ◽  
Age Estimates

We compiled age estimates and baleen plate δ13C data from 86 bowhead whales ( Balaena mysticetus L., 1758). We used previous whale age estimates based on aspartic acid racemization (AAR) and corpora counts to extend the use of δ13C data for age determination from cycle counting to a modified exponential model using annual baleen growth increments. Our approach used the growth increment data from individual whales in a nonlinear mixed effects model to assess both population-level and whale-specific growth parameters. Although age estimates from baleen-based models become less precise as the whales age, and baleen growth and length near steady state, the growth increment model shows promise in estimating ages of bowhead whales 10–13.5 m long with baleen lengths <250 cm, where other techniques are less precise or the data are scarce. Ages estimated using the growth increment data from such whales ranged from 6.4 to 19.8 years.

A new way to estimate the age of bowhead whales (Balaena mysticetus) using ovarian corpora counts

2011 ◽  
Vol 89 (9) ◽  
pp. 840-852 ◽  
Author(s):  
J.C. George ◽  
E. Follmann ◽  
J. Zeh ◽  
M. Sousa ◽  
R. Tarpley ◽  
...  
Keyword(s):  
Corpus Luteum ◽  
Pregnancy Rate ◽  
Sexual Maturity ◽  
Standard Errors ◽  
Reproductive Parameters ◽  
Balaena Mysticetus ◽  
Bowhead Whales ◽  
Aspartic Acid Racemization ◽  
Sexually Mature ◽  
Age Estimates

We used lengths and reproductive data for bowhead whales ( Balaena mysticetus L., 1758) harvested by Alaskan Eskimos to estimate female reproductive parameters and age. Data from 117 females determined that 75 were sexually mature and 42 were immature. Estimated length at sexual maturity was 13.35 m. Counts of ovarian corpora were obtained from 50 mature females. Corpora and baleen data were used with aspartic acid racemization (AAR) data to obtain estimated age at sexual maturity (ASM) at ≈26 years. The number of corpora counted in both ovaries (or estimated when only one ovary was counted) was used with ASM and estimated ovulation rate (OR) to obtain corpora age estimates ranging from 26 to 149 years. A stone harpoon tip recovered from whale 92B2 was consistent with her corpora age of 133 years. The correlation between corpora and AAR age estimates was 0.77. Estimated standard errors of corpora ages tended to be somewhat higher than those for comparable AAR ages. A sample of potentially mature females examined for maturity and presence of a corpus luteum and (or) fetus provided an OR value of 0.332·year–1 and an estimated pregnancy rate of 0.326·year–1, implying intervals between ovulations and pregnancies of 3.0 and 3.1 years.

scholarly journals Selecting the best growth model for elasmobranches

2015 ◽  
Author(s):  
Kwang-Ming Liu ◽  
Chiao-Bin Wu ◽  
Shoou-Jeng Joung ◽  
Wen-Pei Tsai
Keyword(s):  
Growth Model ◽  
Growth Models ◽  
Stock Assessment ◽  
Gompertz Model ◽  
Information Criterion ◽  
Age And Growth ◽  
Deep Waters ◽  
Extended Longevity ◽  
Best Fit ◽  
Two Parameter

Age and growth information is essential for accurate stock assessment of fish, and growth model selection may influence the result of stock assessment. Previous descriptions of the age and growth of elasmobranches relied mainly on the von Bertalanffy growth model (VBGM). However, it has been noted that sharks, skates and rays exhibit significant variety in size, shape, and life-history traits. Given this variation, the VBGM may not necessarily provide the best fit for all elasmobranches. This study attempts to improve the accuracy of age estimates by testing four growth models—the VBGM, two-parameter VBGM, Robertson (Logistic) and Gompertz models—to fit observed and simulated length-at-age data for 37 species of elasmobranches. The best growth model was selected based on corrected Akaike’s Information Criterion (AICc), the AICc difference, and the AICc weight. The VBGM and two-parameter VBGM provide the best fit for species with slow growth and extended longevity (L∞ > 100 cm TL, 0.05 < k < 0.15 yr-1), such as pelagic sharks. For fast-growing small sharks (L∞ < 100 cm TL, kr or kg > 0.2 yr-1) in deep waters and for small-sized demersal skates/rays, the Robertson and the Gompertz models provide the best fit. The best growth models for small sharks in shallow waters are the two-parameter VBGM and the Robertson model, while all the species best fit by the Gompertz model are skates and rays.

scholarly journals Age estimation and the best growth model selection of the tentacled blenny Parablennius tentacularis (Brünnich, 1768) in the southeastern Black Sea

2021 ◽  
Vol 38 (2) ◽  
pp. 229-236
Author(s):  
Ayşe Van ◽  
Aysun Gümüş ◽  
Melek Özpiçak ◽  
Serdar Süer
Keyword(s):  
Growth Model ◽  
Black Sea ◽  
Growth Parameters ◽  
Growth Models ◽  
Information Criterion ◽  
Current Data ◽  
Logistic Growth ◽  
Data Set ◽  
Frequency Distributions ◽  
Selection Of

By the study's coverage, 522 individuals of tentacled blenny (Parablennius tentacularis (Brünnich, 1768)), were caught with the bottom trawl operations (commercial fisheries and scientific field surveys) between May 2010 and March 2012 from the southeastern Black Sea. The size distribution range of the sample varied between 4.8-10.8 cm. The difference between sex length (K-S test, Z=3.729, P=0.000) and weight frequency distributions (K-S test, Z=3.605, P=0.000) was found to be statistically significant. The length-weight relationship models were defined as isometric with W = 0.009L3.034 in male individuals and positive allometric with W = 0.006L3.226 in female individuals. Otolith and vertebra samples were compared for the selection of the most accurate hard structure that can be used to determine the age. Otolith was chosen as the most suitable hard structure. The current data set was used to predict the best growth model. For this purpose, the growth parameters were estimated with the widely used von Bertalanffy, Gompertz and Logistic growth functions. Akaike's Information Criterion (AIC), Lmak./L∞ ratio, and R2 criteria were used to select the most accurate growth models established through these functions. Model averaged parameters were calculated with multi-model inference (MMI): L'∞ = 15.091 cm, S.E. (L'∞) = 3.966, K'= 0.232 year-1, S.E. (K') = 0.122.

Age and growth estimates of the jumbo flying squid (Dosidicus gigas) off Peru

2019 ◽  
Vol 32 ◽  
pp. 7
Author(s):  
Carlos Goicochea-Vigo ◽  
Enrique Morales-Bojórquez ◽  
Viridiana Y. Zepeda-Benitez ◽  
José Ángel Hidalgo-de-la-Toba ◽  
Hugo Aguirre-Villaseñor ◽  
...  
Keyword(s):  
Growth Model ◽  
Growth Pattern ◽  
Growth Models ◽  
Information Criterion ◽  
Growth Patterns ◽  
Age And Growth ◽  
Dosidicus Gigas ◽  
Asymptotic Growth ◽  
Schnute Model ◽  
Gompertz Growth Model

Mantle length (ML) and age data were analyzed to describe the growth patterns of the flying jumbo squid, Dosidicus gigas, in Peruvian waters. Six non-asymptotic growth models and four asymptotic growth models were fitted. Length-at-age data for males and females were analysed separately to assess the growth pattern. Multi-model inference and Akaike's information criterion were used to identify the best fitting model. For females, the best candidate growth model was the Schnute model with L∞ = 106.96 cm ML (CI 101.23–110.27 cm ML, P < 0.05), age at growth inflection 244.71 days (CI 232.82–284.86 days, P < 0.05), and length at growth inflection 57.26 cm ML (CI 55.42–58.51 cm ML, P < 0.05). The growth pattern in males was best described by a Gompertz growth model with L∞ = 127.58 cm ML (CI 115.27–131.80 cm ML, P < 0.05), t0 = 21.8 (CI 20.06–22.41, P < 0.05), and k = 0.007 (CI 0.006–0.007, P < 0.05). These results contrast with the growth model previously reported for D. gigas in the region, where the growth pattern was identified as non-asymptotic.

scholarly journals The influence of sample distribution on growth model output for a highly-exploited marine fish, the Gulf Corvina (Cynoscion othonopterus)

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5582 ◽  
Author(s):  
Derek G. Bolser ◽  
Arnaud Grüss ◽  
Mark A. Lopez ◽  
Erin M. Reed ◽  
Ismael Mascareñas-Osorio ◽  
...  
Keyword(s):  
Growth Model ◽  
Marine Fish ◽  
Growth Models ◽  
Growth Patterns ◽  
Von Bertalanffy ◽  
Model Output ◽  
Age Classes ◽  
Equal Distribution ◽  
Sample Distribution ◽  
Richards Model

Estimating the growth of fishes is critical to understanding their life history and conducting fisheries assessments. It is imperative to sufficiently sample each size and age class of fishes to construct models that accurately reflect biological growth patterns, but this may be a challenging endeavor for highly-exploited species in which older fish are rare. Here, we use the Gulf Corvina (Cynoscion othonopterus), a vulnerable marine fish that has been persistently overfished for two decades, as a model species to compare the performance of several growth models. We fit the von Bertalanffy, Gompertz, logistic, Schnute, and Schnute–Richards growth models to length-at-age data by nonlinear least squares regression and used simple indicators to reveal biased data and ensure our results were biologically feasible. We then explored the consequences of selecting a biased growth model with a per-recruit model that estimated female spawning-stock-biomass-per-recruit and yield-per-recruit. Based on statistics alone, we found that the Schnute–Richards model described our data best. However, it was evident that our data were biased by a bimodal distribution of samples and underrepresentation of large, old individuals, and we found the Schnute–Richards model output to be biologically implausible. By simulating an equal distribution of samples across all age classes, we found that sample distribution distinctly influenced model output for all growth models tested. Consequently, we determined that the growth pattern of the Gulf Corvina was best described by the von Bertalanffy growth model, which was the most robust to biased data, comparable across studies, and statistically comparable to the Schnute–Richards model. Growth model selection had important consequences for assessment, as the per-recruit model employing the Schnute–Richards model fit to raw data predicted the stock to be in a much healthier state than per-recruit models employing other growth models. Our results serve as a reminder of the importance of complete sampling of all size and age classes when possible and transparent identification of biased data when complete sampling is not possible.

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