东海区小黄鱼繁殖模型优化选择及其管理应用研究

根据 1999-2008 年东海区渔业资源底拖网大面定点调查获取的小黄鱼(Larimichthys polyactis)渔业生物学资料,利用AIC(Akaike Information Criterion)与BIC(Bayesian Information Criterion),对小黄鱼的Ricker、Beverton-Holt和Cushing繁殖模型进行了拟合优度检验.针对选择的繁殖模型,经单因子相关分析和逐步回归分析,筛选对繁殖模型有重要影响的环境因子,经模型的拟合和检验,确定东海区小黄鱼的适用繁殖模型.结果表明:3种繁殖模型中,Ricker繁殖模型更适合模拟小黄鱼亲体与补充量关系,但吕泗渔场海域3-4月平均海水表温、7月海水表温和5月长江径流量、7-8月长江平均径流量以及当年夏季风速对模型中的补充量有着重要的影响;优化后的Ricker繁殖模型不仅可以提高东海区小黄鱼亲鱼量与补充量的拟合精度,而且可参考该模型修正当年度小黄鱼的资源管理目标,提高资源管理的科学性.     

黄海中南部小黄鱼当年幼鱼生长特征的研究

对2008年7～12月黄海中南部(120°12′～122°E,32°30′～35°N)小黄鱼当年幼鱼的生长特征进行了研究。结果表明,7～12月,小黄鱼当年幼鱼的体长月均增加13.22±5.65mm,体重月均增加4.34±0.65g,体长与体重的关系式为W=3.76×10-5L2.8303,幂指数b接近于3,生长接近匀速。通过研究耳石的长度、重量与体长、体重的关系,发现耳石的长度和重量与体长、体重关系密切,在3种回归方式(Liner,Power,Quadratic)中,乘幂函数的相关性最好。采用7种生长方程对小黄鱼当年幼鱼的生长进行拟合,从决定系数R2及残差平方和RSS的数值来看,VBGF(vonBertalanffy生长方程),LGF(Logistic生长方程),GGF(Gompertz生长方程)3种数学模型的拟合度较为接近,而Logarithmic,Inverse,Quadratic以及Cubic4种生长方程的拟合度较为接近且拟合效果明显好于VBGF,LGF,GGF。7种拟合模型中,Cubic生长方程对小黄鱼当年幼鱼生长的拟合度最高。     

虹鳟体质量和主要体尺性状异速生长的遗传分析

采用联合异速生长遗传模型,对中国水产科学研究院黑龙江水产研究所渤海冷水性鱼类试验站培育的F1杂交虹鳟(Oncorhynchus mykiss)和F2杂交虹鳟的体质量和体尺性状异速生长进行了遗传分析,旨在为虹鳟及其他水产动物异速生长的遗传参数估计和实现体质量与体型的同步选育奠定理论基础。首先采用逐步回归分析法选择体尺性状对体质量的异速生长显著的性状,建立最优表型联合静态、动态异速生长指数模型,随后构建了两个用于遗传分析多个静态、动态异速生长的随机回归模型。虹鳟多个体尺性状相对体质量的异速生长遗传分析结果表明,体长与体质量具有较大的异速生长指数,为1.633 8,且为正异速生长,而其他体尺性状相对体质量呈现负异速生长;遗传方差大小顺序和表型的偏异速生长指数一致,体宽和背鳍基长的异速生长的遗传相关最大,为-0.867 5,其次为体长和体高,为-0.619 4,最小的是-0.021 7,为体高和体宽。体长与体质量的动态异速生长的加性遗传方差估计值为0.292 9。结果表明,虹鳟体质量和主要体尺性状的异速生长是由遗传机制决定的,遗传方差及遗传相关的估计能够用来筛选优良亲本,这为进一步利用异速生长理论指导虹鳟及其他水产动物体质量与体型的同步选育提供了理论基础。     

浙江南部近海小黄鱼生长、死亡和单位补充量渔获量

浙江南部近海是东海种群小黄鱼(Larimichthys polyactis)的重要繁殖和育肥场所。根据2016年2月、5月、8月和11月采集的2023尾浙江南部近海小黄鱼全长、体长和体重等生物学信息,利用体长频率分布估算小黄鱼种群生长、死亡参数,并利用Beverton-Holt动态综合模型评估探讨单位补充量渔获量在不同自然死亡系数和渔具选择下随捕捞死亡系数的变化趋势。研究结果表明小黄鱼von Bertalanffy生长参数为渐近体长L_∞=22.58 cm,生长速率K=0.78/a,初始年龄t_a=-0.37 a;自然死亡系数M值为1.343,总死亡系数Z值为4.432,捕捞死亡系数F为3.089,开发率E为0.697,表明资源处于过度开发状态;小黄鱼的首次捕捞体长L_(50)=13.11cm,对应首次捕捞平均年龄t_c=0.743 a,小于临界年龄(0.886a)和体重生长的拐点年龄(0.979a),渔业主要捕捞对象为幼鱼和补充群体,无法保证资源的有效补充;根据B-H动态模型,当前的YPR值为15.279 g/ind,若降低捕捞强度到1.685,对应YPR_(max)为17.061 g/ind,与当前产量相比增幅11.66%;若提高开捕体长(13.11cm→16.0cm),YPR_(cur)会出现显著提高(15.279 g/ind→18.766 g/ind),增幅达22.82%,表明提高开捕体长要优于降低捕捞强度。当前东海小黄鱼群系处于小型低龄化和过度开发状态,建议将小黄鱼的开捕体长提高为15 cm,保证小黄鱼的产卵亲体量及资源的可持续发展。     

利用气候因子对Fox模型计算东海总经济鱼类CPUE的优化

气候因子是影响海洋鱼类资源的重要因素之一.本研究采用1951-1984年东海总渔获量和捕捞努力量数据,尝试将海表温度(SST)、冬夏季风、台风、长江径流4个气候因子引入Fox剩余产量模型中来对单位捕捞努力渔获量(CPUE)进行优化.在线性和指数回归模型中,运用AIC准则将气候因子对模型参数α进行逐步回归.结果均筛选出SST、夏季风2个主要气候因子作为模型的补充变量,经AIC准则判断,线性模型为最适模型.气候因子和参数α的线性回归模型回归系数R2为0.495,模型中各参数的P值都小于0.01,95%置信区间均不包含0.经气候因子优化后的模型对CPUE的拟合效果比优化前显著增强,说明SST、夏季风对东海渔业资源量的影响最为显著.     

环境因子对东南太平洋智利竹䇲鱼渔场时空分布异质性影响

基于2012—2018年4—8月我国东南太平洋智利竹?鱼(Trachurus murphyi)渔捞日志数据,应用地理权重回归模型(GWR)探究智利竹?鱼渔场资源分布与环境因子的空间异质性关系。结果表明,环境因子海面温度基于GWR模型回归的拟合优度为0.54,校正的拟合优度为0.34,赤池信息准则(Akaike Information Criterion,AIC)值为1022.08;叶绿素a浓度基于GWR模型回归的拟合优度为0.48,校正的拟合优度为0.36,AIC值为2321.95;海面温度异常值的拟合优度为0.74,校正的拟合优度为0.58,AIC值为2268.07;海面高度异常值的拟合优度为0.72,校正的拟合优度为0.59,AIC值为2201.93;作业水深的拟合优度为0.46,校正的拟合优度为0.42,AIC值为2675.07;海面温度异常对东南太平洋智利竹?鱼渔场时空分布影响最大。GWR模型便于发现资源分布的“热点”海域,可为我国智利竹筴鱼渔船生产提供科学依据。     

黄海双船变水层拖网网囊的网目选择性研究

为研究黄海双船变水层拖网网囊的网目选择性,2015年10月在黄海南部进行了网囊网目为40、54、60mm和70mm的套网拖网试验,采用Logistic选择模型和体周估算法对网囊网目选择性进行研究。结果表明,黄海双船变水层拖网渔获主要为蓝点马鲛、银鲳、带鱼、小黄鱼和鳀鱼;随着网囊网目尺寸的增大,逃逸率明显增加;极大似然法估算的模型参数,用AIC值检验拟合良好;估算出以变水层拖网捕捞达到可捕标准的部分经济鱼类相应的网囊网目尺寸,分别为小黄鱼71.0 mm、银鲳103.6mm、带鱼92.3mm、蓝圆鲹63.8mm。在黄海目前的渔业资源现状下,为保护小黄鱼、带鱼和鲐鲹等中小型经济鱼类的渔业资源,建议将双船变水层拖网的最小网囊网目尺寸定为60mm。     

全唇裂腹鱼生长模型的筛选

2015年在西藏墨脱县布裙湖采捕到252尾全唇裂腹鱼Schizothorax integrilabiatus样本,通过耳石磨片观察分析其年龄组成,采用特殊Von Bertalanffy(VBGF)、Logistic、Gompertz和幂指数四个生长方程分别模拟全唇裂腹鱼的生长,利用最大似然法估计各模型的参数。结果表明:采集的样本共分为7个年龄组(即1~7龄),以2龄组数量最多。各模型的AIC(赤池信息量准则)和BIC(贝叶斯信息准则)值检验模型的拟合效果显示,VBGF生长方程最适合模拟全唇裂腹鱼的生长,其次为Gompertz、幂指数生长方程,而Logistic生长方程拟合效果最差。VBGF生长方程为:Lt=28.36×[1-e~(-0.14·(ti+0.57))]。由模型间AIC差值可知:Gompertz和Von Bertalanffy生长方程之间的模拟效果差别不太大,都能较好模拟全唇裂腹鱼的体长生长。     

应用STAR模型研究海州湾小黄鱼春季资源量的时空分布

根据2011年、2013—2016年春季在海州湾进行的渔业资源调查数据,应用结构化加性回归（Structured Additive Regression , STAR）框架,结合delta方法,根据对空间数据的不同处理方式构建了5种物种分布模型,并通过AIC(akaike information criterion)和交叉验证比较了模型应对两种数据类型的拟合效果和预测性能。结果表明,加入空间因子后的模型残差空间自相关性显著降低,且positive模型和delta模型在加入空间因子后模型的拟合效果的提升较binomial模型更明显。空间加性模型（Geoadditive Models）的AIC值最低;变系数模型（Varying Coef?cient Models）的决定系数和AUC最高,模型拟合效果最佳。预测性能上,空间加性模型准确度最高。在最优模型的基础上,本研究根据FVCOM模拟环境数据,利用delta空间加性模型预测了海州湾小黄鱼春季资源的空间分布,结果表明小黄鱼资源分布主要集中于南部和西部近岸地区,随着水深的增加而逐渐减少,且年间变动明显。本研究旨在为海州湾小黄鱼渔业资源的开发和保护提供科学依据。     

黄海南部和东海北部夏季小黄鱼幼鱼的空间分布研究

小黄鱼(Larimichthys polyactis)为我国近海生态系统中的重要经济种类，幼鱼补充群体的资源丰度和空间分布很大程度会影响小黄鱼总体种群动态，而幼鱼分布易受到环境因素影响而呈现一定的空间格局。为了解小黄鱼幼鱼的空间分布特征，基于2019年8月黄海南部和东海北部(30°30′N～35°00′N、120°00′E～127°00′E)小黄鱼幼鱼与环境调查数据，运用3种广义可加模型(generalized additive model, GAM):Tweedie-GAM、Delta Gamma-GAM和Delta Lognormal-GAM探究资源丰度与相关环境因子之间的关系。结果表明，从3种模型的拟合效果和预测能力看，Delta Gamma-GAM最优(均方根误差RMSE值为12 014.43,Pearson相关系数r值为0.461,Spearman秩相关系数ρ值为0.699)。小黄鱼的分布在空间上具有高度的集群性，集中分布范围在32°N～34°N、122°E～124°E的海域，并以此为中心向周围呈递减扩散分布。各环境因子中，小黄鱼幼鱼分布只与水深有显著性关联，表现为负相关的线...     

Additive genetic and other sources of variation in growth traits of juvenile black bream Acanthopagrus butcheri

We used a factorial mating design to estimate the contribution of additive genetic, non‐additive genetic and maternal effects to variation in growth traits of black bream Acanthopagrus butcheri (Munro) at 75, 130 and 180 days of age in the hatchery. Maternal genetic and environmental effects were greatest at 75 days of age, accounting for 9.1% of total phenotypic variance in wet weight, 11.4% of variance in standard length and 8.8% of variance in total length. At later ages maternal effects were much reduced, explaining 0.8–3.7% of phenotypic variance in growth traits. Additive genetic effects were greatest at 130 days of age, when they accounted for 17.4% of total phenotypic variance in wet weight, 21.4% of variance in standard length and 18.7% of variance in total length. Additive genetic effects were negligible (<1%) at 75 days of age and 4.8–5.5% of total phenotypic variance in growth traits at 180 days of age. Non‐additive genetic effects (which also included common environmental effects because of families being raised in the same tank) explained 5.8–7.3% of total phenotypic variance in growth traits at 75 days of age, but were much smaller at later ages. Variable stocking densities among tanks up to 75 days significantly affected all growth trait measurements below 180 days of age.     

Estimation of genetic parameters from pedigreed populations: lessons from analysis of alevin weight in Coho salmon (Oncorhynchus kisutch)

The purpose of the study was to assess the impact of various model structures on REML estimates of variance components using data on alevin weight from two replicate populations from the Genetic Improvement Program for Coho salmon (Chile). Data consisted of 130 d alevin weight from a dams-nested-within-sires mating design over two consecutive generations. Relationship information included direct and collateral relatives but parental individuals lacked records. The construction of a range of animal models considered random effects of direct additive genetic, maternal additive genetic and full-sib family effects as well as the covariance of direct and maternal genetic effects. Fixed effects of year (generation) and spawn date of dams within year were considered and also evaluated. The relative effectiveness of various models in describing the data set were assessed using likelihood ratio tests. The results demonstrated the importance of the correct interpretation of effects in the data set, particularly those effects that can influence the resemblance between relatives. The data structure, as well as the animal model applied, markedly influenced the magnitude of variance component estimates. Models based on year as the only fixed effect did not describe the data nearly as effectively as models containing both year and spawn data of dams within year. Simple models based on animal as the only random effect gave upward biased estimates of additive genetic variance. The most appropriate model for the data set was one based on both year and spawn date as fixed effects, and animal and full-sib family as random effects. The results from models combining maternal genetic and full-sib family effects to exploit the full covariance structure of the data showed that there was confounding between these variance component estimates. The most consistent interpretation of this result was that common environmental effects and non-additive genetic effects were more important sources of variability than maternal genetic effects. The study also demonstrated high variability in parameter estimates for replicate populations.     

Alternative error distribution models for standardization of catch rates of non-target species from a pelagic longline fishery: billfish species in the Venezuelan tuna longline fishery

Alternative error distributions were evaluated for calculating indices of relative abundance for non-target species using catch and effort data from commercial fisheries. A general procedure is presented for testing the underlying assumptions of different error distributions. Catch rates, from an observer program, of billfish caught mainly as bycatch in a pelagic tuna longline fishery in the Western Central Atlantic were standardized. Although catches of billfishes are not common in pelagic tuna longline fisheries, these fisheries are one of the main sources of fishing mortality for these stocks in the central Atlantic due to the magnitude and spatial extent of longline fishing effort. Billfish CPUE data are highly skewed with a large proportion of zero observations. Delta distribution models can accommodate this type of data, and involve modeling the probability of a non-zero observation and the catch rate given that the catch is non-zero separately. Three different Delta models were compared against other error distributions, including the lognormal, log-gamma, and Poisson. Diagnostic checks and deviance table analyses were performed to identify the best error distribution and the set of factors and interactions that most adequately explained the observed variability. The results indicated that the Delta-lognormal model (a binomial error distribution for the probability of a non-zero catch and lognormal error for the positive catch rates) complied best with the underlying characteristics of the data set. Analyses of catch rates for blue marlin, white marlin and sailfish confirmed the spatio-temporal nature of their distribution in the central Atlantic and Caribbean Sea. Also, the analyses indicated that catch rates of billfish differed among fishing vessel types; larger vessels had a higher probability of catching blue marlin, the more oceanic-oriented species, and lower probabilities of catching the more coastal-oriented species white marlin and sailfish. Standardized catch rates indicated in general a lower relative abundance for blue and white marlin in the most recent years, although estimated confidence intervals overlap through the years especially for white marlin.     

Multimodel approaches in shark and ray growth studies: strengths,weaknesses and the future

Multimodel frameworks are common in contemporary elasmobranch growth literature. These techniques offer a proposed improvement over individual growth functions by incorporating additional candidate models with alternative characteristics. Sigmoid functions (e.g. Gompertz and logistic) are a popular alternative to the commonly used von Bertalanffy growth function (VBGF) as they are hypothesized to better suit certain taxa based on body shape (such as batoids) or reproductive mode (such as egg‐layers). However, this hypothesis has never been tested. This study examined 74 elasmobranch multimodel growth studies by comparing the growth curves of their respective candidate models. Hypotheses regarding model performances were rejected as the VBGF was equally likely to fit best for all taxa and reproductive modes. Subsequently, no individual model was suited to be used a priori. Differences between candidate model fits were greatest at age zero with Gompertz and logistic functions providing estimates that were 15% and 23% larger on average than the VBGF, respectively. However, length‐at‐age estimates of the different models became negligible at older ages. Differences between candidate models were mostly small (≤5%), and the multimodel framework only marginally affected length‐at‐age estimates. However, there were cases where some candidate models provided inappropriate fits that contrasted considerably to the best fitting model. In some of these instances, a single‐model framework could have yielded biologically unrealistic growth estimates. Therefore, no study could pre‐empt whether or not it required a multimodel framework. A framework was subsequently recommended to maximize the accuracy of model fits for elasmobranch length‐at‐age estimates using multimodel approaches.     

不同模型估计牙鲆家系间生长性状遗传参数的比较分析

利用中国水产科学研究院北戴河中心实验站2006~2009年间牙鲆选育家系240日龄生长性状的测定记录,对4种不同动物模型估计的生长性状遗传参数进行了比较分析。不同模型分别包括了加性遗传效应、母体遗传效应和全同胞效应。利用MTDFREML程序采用非求导约束极大似然法(DFREML)估计各模型中的方差组分。用似然比检验对不同模型的差异进行检验。结果显示,对于牙鲆240日龄体重、体长和体高,母体遗传效应和全同胞效应都有显著的影响,应用模型Ⅳ进行分析,体重、体长和体高的遗传力分别为0.30、0.32和0.39。体重与体长、体高的正向遗传相关分别为0.93和0.95;体长与体高的正向遗传相关为0.90。     

Beyond ‘lognormal versus gamma’: discrimination among error distributions for generalized linear models

The process of model selection includes making an assumption about the distribution of ‘errors’ about the mean response. Generalized linear models (GLMs) offer considerable flexibility in this regard. However, graphical methods for identifying potential error distributions can fail to discriminate among sets of candidate error distributions. I examine an information-theoretic approach to this issue, which ranks candidate models (error distributions) using Akaike's information criterion (AIC). I evaluate the effectiveness of this technique using Monte Carlo simulation by generating pseudorandom data from five skewed distributions: lognormal, gamma, Weibull, log-logistic, and inverse Gaussian. I then fit each data set under all five distributional assumptions, and examine how well AIC identifies the distribution that generated the data. On the basis of the simulations, I suggest that AIC is effective at identifying the data-generating distribution, given moderate to large sample sizes. I then fit four candidate models to data drawn from a mixture of four distributions with common expectations and coefficients of variation (CVs). AIC did not show strong support for a particular candidate model given small samples of ‘mixed’ data, although larger samples selected the gamma distribution for CVs of 0.5 and 1.0, and the Weibull distribution for CVs of 1.5 and 2.0. Finally, I apply this technique in a GLM setting to several fisheries-independent and -dependent data sets to select the error distribution that is best supported by the data. Twenty-one out of 24 fisheries data sets examined showed strong support for one of the five candidate error distributions and the remaining moderate support for two.     

Estimation of genetic parameters for growth traits of Japanese flounder <Emphasis Type="Italic">Paralichthys olivaceus</Emphasis> using an animal model

To estimate genetic parameters of growth traits in Japanese flounder Paralichthys olivaceus, full-sib and half-sib families were produced in three consecutive years at the Beidaihe Central Experiment Station in China. Each year 8–28 families were produced. The body weight, body length and body depth at 180, 240, and 360 days of age were measured for 5,224 individuals. Four animal models were used to examine the phenotypic variation of growth traits and were compared using the likelihood ratio test. The results showed that estimates for additive genetic effect heritabilities varied greatly depending on the model, trait and age. The maternal effect had a significant impact on phenotypic variation only for body depth at 180 days of age, which explained 49% of the phenotypic variance. The ratio of full-sib effect to phenotypic variation ranged from 0.09 to 0.22. Growth traits all exhibited low heritability (0.13–0.39), indicating that there is the potential for family selection breeding for these traits in Japanese flounder. Using the full model with the fixed, full-sib family, additive and maternal genetic effects, genetic correlations among the three traits for fish of the same age were estimated to be more than 0.80. Generally, the genetic correlations gradually increased as age increased.     

Decreasing uncertainty in catch rate analyses using Delta-AdaBoost: An alternative approach in catch and bycatch analyses with high percentage of zeros

The gillnet data of walleye (Sander vitreus), yellow perch (Perca flavescens), and white perch (Morone americana), collected by a fishery-independent survey (Lake Eire Partnership Index Fishing Survey, PIS) from 1989 to 2008, contained 75–83% of zero observations. AdaBoost algorithm was applied to the model analyses with such fishery data for each species. The 3- and 5-fold cross-validations were conducted to evaluate the performance of each candidate model. The performance of the delta model consisting of one generalized additive model and one AdaBoost model (Delta-AdaBoost) was compared with five candidate models. The five candidate models included: the delta model comprising two generalized linear models (Delta-GLM), the delta model comprising two generalized linear models with polynomial terms up to degree 3 (Delta-GLM-Poly), the delta model comprising two generalized additive models (Delta-GAM), the generalized linear model with Tweedie distribution (GLM-Tweedie), and the generalized additive model with Tweedie distribution (GAM-Tweedie). To predict the presence/absence of fish species, the performance of AdaBoost model was compared in terms of error rate with conventional generalized linear and additive models assuming a binomial distribution. Results from 3- and 5-fold cross-validation indicated that Delta-AdaBoost model yielded the smallest training error (0.431–0.433 for walleye, 0.528–0.519 for yellow perch and 0.251 for white perch) and test error (0.435–0.436 for walleye, 0.524 for yellow perch and 0.254–0.255 for white perch) on average, followed by Delta-GLM-Poly model for yellow perch and white perch, and Delta-GAM model for walleye. In the prediction of the presence/absence of fish species, AdaBoost model had the lowest error rate, compared with generalized linear and additive models. We suggested AdaBoost algorithm to be an alternative to deal with the high percentage of zero observations in the catch and bycatch analyses in fisheries studies.     

Discriminating alternative stock–recruitment models and evaluating uncertainty in model structure

Fish stock–recruitment (S–R) assessment is one of the most essential keystones for fisheries management. Yet the analysis involves a variety of uncertainties. Amidst these difficulties, uncertainty in model structure is perhaps the most problematical to investigate because no rigorous statistical techniques can be used to explore the fundamental biological processes in S–R relationships. In this paper, I used computer simulations to investigate: (1) the differences between the estimated parameters of alternative S–R models as a function of stock characteristics: population growth rate, data range, fishing mortality, and process noise; and (2) the probability of selecting a correct model using information criteria. Two popular S–R functions, the Ricker and the Beverton–Holt models, were used as examples. Time series data were generated from a known S–R model and fitted by alternative models. The results show that when the two models fit the data similarly well, significant differences in parameters existed between the alternative models. The Ricker model tended to underestimate the population growth rate (initial slope) and the carrying capacity parameter, whereas the Beverton–Holt model overestimated these parameters. The management quantities (e.g., optimal virgin stock size) produced by one model were more conservative (i.e., larger optimal stock size or lower optimal harvest rate) under some conditions but became less conservative under other conditions. The differences between the alternative models were functions of the population growth rate, long-term fishing mortality, and data range of the stock size. The correct and incorrect models were statistically indistinguishable. For typical fishery data the probability of selecting the correct model based on information criteria was approximately 0.70 for the Ricker model and 0.61 for the Beverton–Holt model.     

Difficulties associated with modeling growth in the Atlantic angel shark (Squatina dumeril)

Several methods were used in an attempt to develop an age and growth model for the Atlantic angel shark (Squatina dumeril). Band counts from vertebral sections, which were fit to the traditional von Bertalanffy growth equation, the Gompertz growth equation, and the two-parameter von Bertalanffy growth equation, did not produce realistic parameter estimates. Additionally, a length-based Bayesian model was applied to fishery-independent length–frequency data, and a full Bayesian model was fitted to length-at-age data to estimate parameters for von Bertalanffy growth equation. Both the length-based and full Bayesian models failed to converge; the length–frequency data showed high bimodality unrelated to season, year, or other factors, and band counts were not predictable by length. Vertebral band counts were not valid for ageing Atlantic angel sharks, and length-based methods, which require normally distributed length–frequencies, were not appropriate for this data set. This study represents the first attempt at modeling age and growth for this species and provides research guidelines for future research initiatives.