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Review

Review of Age Estimation Techniques and Growth Models for Shelled Organisms in Marine Animal Forests

by
Ömerhan Dürrani
1,
Çağdaş Can Cengiz
2,
Halyna Gabrielczak
3,4,
Esra Özcan
5,
Madona Varshanidze
6,
Genuario Belmonte
7,8 and
Kadir Seyhan
1,*
1
Sürmene Faculty of Marine Science, Karadeniz Technical University, 61530 Trabzon, Türkiye
2
Mediterranean Fisheries Research Production and Training Institute, 07001 Antalya, Türkiye
3
Department of Water Quality, Institute of Marine Biology of National Academy of Sciences of Ukraine, 65076 Odesa, Ukraine
4
Department of Ecology and Vertabrate Zoology, University of Lodz, 90-136 Lodz, Poland
5
Department of Fisheries and Aquaculture, Faculty of Agriculture, University of Ankara, 06110 Ankara, Türkiye
6
Fisheries, Aquaculture and Water Biodiversity Department, National Environmental Agency, 0112 Tbilisi, Georgia
7
Department of Biological and Environmental Sciences and Technologies–DiSTeBA, University of Salento, Via Prov.le Lecce-Monteroni, 73100 Lecce, Italy
8
CoNISMa–Consorzio Nazionale Interuniversitario per le Scienze del Mare, Piazzale Flaminio 9, 00196 Rome, Italy
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(9), 1693; https://doi.org/10.3390/jmse13091693
Submission received: 19 June 2025 / Revised: 14 August 2025 / Accepted: 22 August 2025 / Published: 2 September 2025
(This article belongs to the Section Marine Biology)
Browse Figures
Versions Notes

Abstract

Marine shelled organisms exhibit diverse growth strategies shaped by species-specific traits and environmental conditions that critically influence their ecological roles, particularly within Marine Animal Forests (MAF), which are structurally complex habitats and biodiversity-rich habitats. This review compiles and compares empirical growth data for 16 bivalve and gastropod species across seven families, classified as full MAF contributors (Pinna nobilis, Flexopecten glaber, Pecten maximus, and Placopecten magellanicus), partial MAF contributors (Cerastoderma edule, C. glaucum, Chamelea gallina, Ruditapes philippinarum, Mercenaria mercenaria, Panopea generosa, Anadara kagoshimensis, A. inaequivalvis, and Tegillarca granosa), and ecologically relevant non-MAF species (Buccinum undatum, Hexaplex trunculus, and Rapana venosa). Age estimation methods included direct techniques, such as shell growth ring and opercular annulus analysis, alongside indirect approaches, such as length-frequency analysis, stable isotope profiling, and mark–recapture studies. Growth trajectories were modelled using von Bertalanffy growth function (VBGF) parameters to estimate the shell size from ages 1 to 4. Based on these estimates, species were categorised into slow, moderate, fast, and exceptional growth groups. These classifications were further explored through hierarchical clustering that grouped species according to their VBGF-derived growth values, revealing consistent and contrasting life history strategies. This comparative analysis should enhance the understanding of molluscan growth dynamics and support the conservation and management of MAF-associated ecosystems by informing restoration planning, guiding species selection, and contributing to evidence-based policy development.

1. Introduction

Marine Animal Forests (MAFs) represent some of the most dynamic and biodiverse marine ecosystems. They are characterised by the dense aggregation of organisms that form, maintain, or inhabit complex three-dimensional habitats [1,2]. Shelled organisms, including bivalves and gastropods, are both structural and functional components of these ecosystems [3,4]. Species such as Anadara inaequivalvis, Cerastoderma edule, Ruditapes philippinarum, and Pinna nobilis enhance ecosystem stability by improving water quality and nutrient cycling [5,6]. Conversely, predatory species, such as Rapana venosa and Hexaplex trunculus, regulate prey populations, contributing to trophic balance [7,8]. These species are ecologically significant and serve as biological indicators of environmental conditions through their growth- and age-related traits [9,10,11].
Understanding the growth dynamics of shelled organisms is crucial for understanding their ecological roles and ensuring MAF ecosystem sustainability [12,13]. The ability of these organisms to respond to environmental changes and anthropogenic pressures is influenced by growth rates, age structures, and life history traits [12,14]. However, comprehensive insights into these aspects require robust methodologies for age determination and growth modelling [15,16]. Techniques such as shell increment analysis, isotopic composition studies, advanced imaging, and shape-based approaches like elliptic Fourier analysis have proven instrumental in assessing age, growth, and site-specific morphological variation across diverse biological systems [17,18,19]. Coupled with growth models, such as the von Bertalanffy equation, these approaches provide critical data on species-specific growth trajectories and their adaptability to environmental gradients [20,21,22].
This review aimed to summarise and compare the current state of knowledge on age determination methods and growth modelling for key marine shelled organisms, including those fully, partially and not associated with MAFs. Particular emphasis was placed on the comparison of growth rates across species, highlighting interspecies variability and its ecological implications.

2. Materials and Methods

2.1. Literature Search Strategy

A targeted literature review was conducted to identify peer-reviewed studies focusing on age determination techniques, growth modelling approaches, and MAF-associated interspecific growth comparisons of marine shelled organisms. Google Scholar was used to conduct the search to ensure comprehensive coverage of scientific literature [23].
The search strategy was designed to capture a broad spectrum of methodologies and taxa. Boolean operators were used to combine the following thematic keyword groups:
  • Age determination: “age reading,” “growth increments,” “shell analysis,” “sclerochronology,” “isotope analysis,” “microstructure,” and “increment formation”
  • Growth modelling: “growth rate,” “growth modelling,” “von Bertalanffy,” “length-at-age,” “size-frequency analysis,” “growth curves”
  • Organism and ecosystem context: “bivalves,” “gastropods,” “shelled organisms,” “Marine animal forests,” “ecosystem engineers”
The search was restricted to studies that provided empirical data on growth or age estimation for species known to contribute to MAF structure and function. Both full and partial MAF contributors and ecologically relevant non-MAF species were included for comparative purposes.

2.2. Data Extraction and Classification

From the selected studies, data were systematically extracted and categorised into three primary domains:
  • Age determination methods: including direct techniques (e.g., shell increment analysis and operculum examination), indirect methods (e.g., length-frequency analysis), and advanced approaches (e.g., stable isotope analysis, mark–recapture, and laboratory-based validation).
  • Growth models: with emphasis on the use of the von Bertalanffy growth function (VBGF) and other length-at-age modelling frameworks. Parameters, such as asymptotic length (L∞), growth coefficient (K), and theoretical age at zero length (t0), were recorded where available.
  • Species-specific growth rates: including raw and modelled shell length data across age classes, with intra- and interspecific variability considered.
Each study was further classified by species, taxonomic family, geographic region, and methodological approach.

2.3. Statistical Analysis

Quantitative data on shell length at age were synthesised to evaluate growth patterns across species. Shell lengths at specific age classes (1, 1.5, 2, 2.5, 3, 3.5, and 4 years) were estimated for each species using VBGF parameters reported in the literature. These estimates enabled standardised comparisons of growth trajectories across species, regardless of the original study design or sampling approach.
To assess growth performance, the cumulative size gain was calculated as the difference between the estimated shell length at 4 years of age and that at 1 year of age (i.e., size at 4 years minus size at 1 year). This metric reflects net growth over a three-year interval, was selected to minimise early-life variability, and thus serves as a practical proxy for growth performance. The theoretical age at zero length (t0) in the VBGF may vary across studies and species, and in some cases, it may exceed age 1 (e.g., Panopea generosa in Cruz-Vásquez et al. [24], with t0 = 2.26). However, our method relies on predicted lengths at fixed age points derived from published VBGF parameters, allowing consistent interspecies comparisons. Confidence intervals were also calculated to reflect variability in growth estimates across studies.
Species-specific growth trajectories were visualised using locally estimated scatterplot smoothing (LOESS), a non-parametric regression technique that fits simple models to localised subsets of the data to reveal underlying trends without assuming a global functional form [25]. Shell size estimates were extracted for each species, reshaped into a long format, and plotted to display individual study estimates (grey lines), a smoothed LOESS trend (black line), and the associated 95% confidence interval (red band). Axis limits were adjusted to accommodate species-specific size ranges, ensuring consistent visual interpretation across taxa.
Hierarchical clustering analysis was also performed using average cumulative shell size gain values for each species. Euclidean distance was used to measure species dissimilarity based on these growth metrics, and complete linkage was applied to form clusters by maximising the distance between the most dissimilar members within each group. This method was chosen to ensure clear separation between growth categories and to reflect biologically meaningful differences in growth trajectories. The resulting dendrogram grouped species into clusters representing slow, moderate, fast, and exceptional growth strategies.
All statistical analyses and visualisations were conducted in R (version 4.5.0) [26] using RStudio (version 2025.5.0.496) [27] as the integrated development environment, with packages including stats [26], ggplot2 [28], and dendextend [29].

3. Results

3.1. Synthesis of Species and Methodological Scope

This review summarises findings from an extensive collection of studies focusing on the growth dynamics of 16 key marine shelled species. These species represent seven families across the Bivalvia and Gastropoda classes: Arcidae, Cardiidae, Hiatellidae, Pectinidae, Veneridae, Buccinidae, and Muricidae. Figure 1 illustrates the geographical distribution of these studies.
Our analysis encompasses species integral to MAF formation, such as the noble pen shell (P. nobilis), the smooth scallop (Flexopecten glaber), the great scallop (Pecten maximus), and the Atlantic Sea scallop (Placopecten magellanicus). We also include species that partially contribute to MAF ecosystems, such as the common cockle (C. edule), the lagoon cockle (Cerastoderma glaucum), the striped venus (Chamelea gallina), the Manila clam (R. philippinarum), the hard clam (Mercenaria mercenaria), the geoduck (P. generosa), and the blood clams (Anadara kagoshimensis, A. inaequivalvis, and Tegillarca granosa). Non-MAF predatory gastropods, including the common whelk (Buccinum undatum), the banded dye-murex (Hexaplex trunculus), and the veined rapa whelk (R. venosa), were also examined to provide a broader ecological context. Table 1 presents a summary of these species, including their habitats, feeding biology, and representative images.
The reviewed literature, comprising 42 studies, yielded a substantial dataset with numerous records for each species. Among the bivalves, P. nobilis was the most extensively documented, followed by P. maximus, C. edule, R. philippinarum, and P. generosa. In contrast, species such as T. granosa, A. inaequivalvis, A. kagoshimensis, and F. glaber were less frequently the focus of research. B. undatum was the most prominently studied gastropod species, with R. venosa and H. trunculus also receiving significant attention.
The methodologies for age determination varied considerably across the literature surveyed (Figure 1). A clear majority of studies favoured direct methods, primarily through the analysis of growth rings on the shells of both bivalves and some gastropods. For certain gastropod species, such as B. undatum and R. venosa, operculum analysis served as a reliable alternative for age estimation. Indirect approaches, such as length-frequency analysis, were also employed, although less commonly. A smaller subset of studies used alternative techniques, including stable isotope analysis, mark–recapture experiments, and laboratory-based growth trials, to ascertain the age and growth patterns of the rats.

3.2. Comparison of Growth Strategies Across Families

To investigate the diverse growth strategies among these species, we utilised the VBGF parameters derived from the literature (Table 2). By estimating shell lengths at sequential ages, we calculated the increase in cumulative shell size between the first and fourth years of life. This metric, along with its 95% confidence intervals, allowed for a standardised comparison of growth performance. The species were classified into four distinct growth categories based on this analysis, revealing a spectrum of life history strategies (Figure 2):
  • Slow Growers: A. inaequivalvis, C. glaucum, C. gallina, C. edule, and H. trunculus
  • Moderate Growers: T. granosa, M. mercenaria, R. philippinarum, and F. glaber
  • Fast Growers: P. magellanicus, P. maximus, A. kagoshimensis, R. venosa, P. generosa, and B. undatum
  • Exceptional Grower: P. nobilis
Figure 2 visually depicts the mean cumulative size gain and associated confidence intervals for each species, providing a clear depiction of these interspecific growth disparities.

3.2.1. Arcidae

Divergent growth patterns were evident within the Arcidae. A. kagoshimensis demonstrated rapid and sustained growth, achieving a considerable size by year four. In contrast, A. inaequivalvis exhibited more modest growth. Both species exhibited an initial burst of growth within the first 1.5 years. T. granosa exhibited a moderate growth rate with high individual variability, suggesting a plastic response to environmental conditions (Figure 3).

3.2.2. Buccinidae

B. undatum exhibited substantial intraspecific variability. Although the average growth was fast, shell sizes varied widely at each age class. The most significant increase occurred between years 1 and 2.5, after which growth decelerated (Figure 3).

3.2.3. Cardiidae

The two cockle species displayed distinct growth dynamics. C. edule had a pronounced early growth phase, reaching a large portion of its adult size within the first few years. C. glaucum followed a slower, more uniform growth trajectory (Figure 3).

3.2.4. Hiatellidae

P. generosa exhibited substantial and continuous growth, which is consistent with its long-lived life history. The shell size significantly increased each year, reaching large dimensions by year four (Figure 3).

3.2.5. Muricidae

The predatory gastropods showed contrasting strategies. H. trunculus grew slowly and steadily, with gradual increases in shell size. Conversely, R. venosa exhibited moderate to fast growth and high variability across populations (Figure 3).

3.2.6. Pectinidae

P. maximus and P. magellanicus were characterised by rapid early growth, reaching substantial sizes by year 4. F. glaber showed moderate, stable growth. P. nobilis stood out with exceptional growth and high variability, reaching sizes far beyond those of other species (Figure 3).

3.2.7. Veneridae

Species in this family generally exhibited moderate growth. R. philippinarum, M. mercenaria, and C. gallina all showed strong early growth, particularly between years 1 and 2.5, followed by a tapering growth rate (Figure 3).

3.3. Hierarchical Clustering of Growth Strategies

A hierarchical clustering analysis was conducted based on average cumulative shell size gain to further elucidate relationships in growth dynamics. The resulting dendrogram visually confirms the four distinct growth strategy clusters: Slow, Moderate, Fast, and Exceptional (Figure 4). The primary division in the dendrogram isolates P. nobilis, underscoring its unique high growth rate. The remaining species form two main clades. The first category includes both slow and moderate growers. Slow growers (H. trunculus, C. glaucum, C. gallina, C. edule, and A. inaequivalvis) are clearly separated from moderate growers (F. glaber, R. philippinarum, M. mercenaria, and T. granosa). The second major clade consists exclusively of fast growers. One sub-cluster groups the gastropods B. undatum and R. venosa with the bivalves A. kagoshimensis and P. generosa. The other subcluster includes the scallops P. maximus and P. magellanicus, indicating a shared rapid growth trajectory.

4. Discussion

This review summarises the substantial variability in growth dynamics among 16 marine shelled mollusc species, distributed across seven families within the classes Bivalvia and Gastropoda. These differences reflect a continuum of life history strategies shaped by evolutionary adaptations, ecological roles, and environmental conditions [76,77,78,79]. The consistent application of the VBGF across studies—regardless of whether age estimation was direct (e.g., shell rings, opercula) or indirect (e.g., length-frequency analysis)—enabled standardised comparisons and robust classification of growth strategies [80,81].
Hierarchical clustering of cumulative shell growth revealed four distinct categories: slow, moderate, fast, and exceptional growers (Figure 2 and Figure 4). Slow-growing species, such as C. edule, C. gallina, and H. trunculus, exhibit gradual, uniform growth, often associated with long lifespans and resilience in stable or low-productivity environments [82,83,84]. These species typically contribute to habitat stability and are more vulnerable to overexploitation due to slower recovery rates [85,86]. In contrast, fast-growing species—including R. venosa, B. undatum, P. generosa, and A. kagoshimensis—demonstrated rapid early growth, particularly within the first 2.5 years. This strategy is likely to enhance survival in dynamic or disturbed habitats by maximising early fecundity and resource acquisition [87,88,89,90]. Notably, P. nobilis emerged as an exceptional grower, showing remarkable shell size gains and high variability, with estimated sizes at age four ranging from 221 to 750 mm (Figure 3). As the largest bivalve in the Mediterranean—reaching up to 1200 mm, although typically ranging from 200 to 400 mm—it can live up to 27 years [52,91], underscoring its ecological importance as a keystone species in Mediterranean assemblages.
The observed decline in growth rates after 2.5 years across most species reflects energy trade-offs associated with maturation, reproduction, and shell maintenance [20,92]. These patterns align with ecological theory on fast–slow life history trade-offs, where fast growers prioritise early reproduction at the cost of longevity, whereas slow growers invest in long-term survival and structural contributions to ecosystems [93,94]. Understanding these growth dynamics is essential for conservation and aquaculture. During juvenile stages, fast-growing species may benefit from protective measures, such as seasonal closures or nursery habitat preservation [86,95]. Conversely, long-lived, slow-growing species may require long-term conservation strategies, including habitat restoration, stricter harvest regulations, and climate-resilient management approaches [85,86].
The ecological roles of these species are closely related to their growth strategies. Fast growers significantly contribute to biomass turnover and fisheries productivity, supporting the blue economy [96,97]. In contrast, slow-growing bivalves act as ecosystem engineers in MAFs, enhancing habitat complexity, biodiversity, and water quality [3,98,99]. This functional diversity enhances the resilience of the ecosystem: long-lived engineers maintain structural integrity, while opportunistic species facilitate recovery after disturbance [100]. Future research should integrate physiological processes, such as energy assimilation, metabolic costs, and phenotypic plasticity, into growth models, while also considering empirical factors that influence digestion and nutrient utilisation [7,63,101,102,103,104]. Incorporating these factors, along with genomic tools and dynamic energy budget models, will improve species responses to environmental change and inform climate-smart aquaculture and conservation strategies [105,106].
Notably, a recent study provided the first direct ageing data for R. venosa in the Turkish Black Sea using statoliths—a method not previously applied in the region [22]. As this study was published after the completion of our analysis, it is not incorporated into the current dataset. However, it represents a significant advancement in gastropod age validation and offers valuable insights for future growth modelling and regional stock assessments.

5. Conclusions

Marine shelled organisms exhibit various growth strategies that reflect their ecological roles and evolutionary adaptations. This review highlights the importance of growth variability in shaping population dynamics and ecosystem functions. The consistent use of the VBGF across studies enabled meaningful comparisons and revealed distinct growth patterns across families. Rapid early growth in species such as R. venosa, B. undatum, and T. granosa suggests adaptive responses to predation and competition, whereas slower-growing species such as H. trunculus and P. nobilis contribute to long-term habitat stability. These findings underscore the need for tailored conservation strategies to account for species-specific growth dynamics.
When integrated with growth models, direct and indirect age estimation methods offer powerful tools for sustainable marine resource management. Future research should focus on refining these methodologies, incorporating environmental variables, and leveraging emerging technologies, such as remote sensing and molecular tools. Such efforts are critical for conserving MAF and ensuring marine ecosystems’ resilience despite accelerating anthropogenic change.
Nonetheless, the heterogeneity and geographic unevenness of the available literature constrained this review, which may affect the robustness of interspecific comparisons. Additionally, the targeted search strategy may have excluded some relevant or unpublished studies, potentially leading to the underrepresentation of certain taxa or regions. Despite these limitations, this review should serve as a valuable synthesis of growth dynamics in marine shelled organisms and provide a foundation for species-specific, adaptive conservation and management strategies.

Author Contributions

Conceptualisation, K.S. and Ö.D.; methodology, Ö.D. and K.S.; data curation, Ö.D., K.S., Ç.C.C., H.G., E.Ö., M.V. and G.B.; writing—original draft preparation, Ö.D. and K.S.; validation, Ç.C.C., H.G., E.Ö., M.V. and G.B.; supervision, K.S. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the European Cooperation in Science and Technology (COST) under COST Action CA20102, Marine Animal Forests of the World (MAF-WORLD). Additional support was provided by the Italian Ministry of University and Research’s project funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4–Call for Tender No. 3138 of 16 December 2021, rectified by Decree No. 3175 of 18 December 2021, and co-funded by the European Union–NextGeneration EU. Award Number: Project Code CN_00000033, Concession Decree No. 1034 of 17 June 2022, adopted by the Italian Ministry of University and Research, CUP D33C22000960007. Project Title: National Biodiversity Future Centre–NBFC. This additional support relates specifically to Genuario Belmonte.

Data Availability Statement

All data presented in this study are available through the references listed in Table 2.

Acknowledgments

This work is based on the MAF-WORLD Training Workshop, “Changes in the Marine Biota (MAF) of the Black Sea,” held at Karadeniz Technical University, Trabzon, Türkiye, from 18 to 21 October 2023. The workshop was supported by COST Action CA20102, Marine Animal Forests of the World (MAF-WORLD).

Conflicts of Interest

The authors have no conflicts of interest to declare.

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Figure 1. Distribution, species, and age estimation methods in marine shelled organisms.
Figure 1. Distribution, species, and age estimation methods in marine shelled organisms.
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Figure 2. Shell size gain was estimated using the von Bertalanffy growth function parameters from Table 2. Error bars represent 95% confidence intervals (CIs). Based on cumulative size gain over three years, species are grouped into four growth categories—Slow (8–13 mm), Moderate (16–21 mm), Fast (25–66 mm), and Exceptional (>150). These categories are used only for visualisation purposes and do not imply strict biological thresholds.
Figure 2. Shell size gain was estimated using the von Bertalanffy growth function parameters from Table 2. Error bars represent 95% confidence intervals (CIs). Based on cumulative size gain over three years, species are grouped into four growth categories—Slow (8–13 mm), Moderate (16–21 mm), Fast (25–66 mm), and Exceptional (>150). These categories are used only for visualisation purposes and do not imply strict biological thresholds.
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Figure 3. Shell size estimates across various ages for 16 mollusc species, derived from von Bertalanffy growth function parameters detailed in Table 2. Grey lines represent individual study estimates, the black line shows the LOESS trend, and the red band indicates the 95% confidence interval.
Figure 3. Shell size estimates across various ages for 16 mollusc species, derived from von Bertalanffy growth function parameters detailed in Table 2. Grey lines represent individual study estimates, the black line shows the LOESS trend, and the red band indicates the 95% confidence interval.
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Figure 4. Hierarchical clustering of 16 marine shelled species based on estimated shell length growth data at age intervals of 1, 1.5, 2, 2.5, 3, 3.5, and 4 years, calculated using species-specific von Bertalanffy growth function parameters from Table 2. The y-axis represents Euclidean distance, indicating dissimilarity in growth trajectories among species.
Figure 4. Hierarchical clustering of 16 marine shelled species based on estimated shell length growth data at age intervals of 1, 1.5, 2, 2.5, 3, 3.5, and 4 years, calculated using species-specific von Bertalanffy growth function parameters from Table 2. The y-axis represents Euclidean distance, indicating dissimilarity in growth trajectories among species.
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Table 1. Summary of species characteristics, including habitat and feeding biology (from SeaLifeBase [30]), and shell images accessed via World Register of Marine Species (WoRMS) [31], hosted externally by Kapeller [32] and Trausel and Slieker [33].
Table 1. Summary of species characteristics, including habitat and feeding biology (from SeaLifeBase [30]), and shell images accessed via World Register of Marine Species (WoRMS) [31], hosted externally by Kapeller [32] and Trausel and Slieker [33].
Images accessed via WoRMS Editorial Board [31] From SeaLifeBase [30]
HabitatsFeeding Biology
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Anadara inaequivalvis [32]		Coastal areas with sandy or muddy substrates, often in brackish waters.Filter feeder that consumes plankton and organic particles.
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Anadara kagoshimensis [32]		Shallow marine environments with sandy or muddy bottoms.Filter feeder feeding on suspended organic matter and plankton.
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Tegillarca granosa [33] (actual size: 43 mm)		Intertidal and subtidal zones with muddy substrates often found in estuaries.Filter feeder, primarily consuming phytoplankton.
Jmse 13 01693 i004
Cerastoderma edule [32]		Sandy or muddy substrates in intertidal zones, common in estuaries and lagoons.Filter feeder feeding on phytoplankton and organic detritus.
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Cerastoderma glaucum [33]		Brackish and marine environments, often in lagoons and estuaries.Filter feeder that consumes plankton and organic matter.
Jmse 13 01693 i006
Panopea generosa [33]		Deep sandy or muddy substrates in subtidal zones.Filter feeder placed on plankton and suspended organic material.
Jmse 13 01693 i007
Flexopecten glaber [33]		Sandy or muddy substrates in shallow subtidal zones.Filter feeder, primarily consuming phytoplankton.
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Pecten maximus [32]		Sandy gravel or coarse substrates in subtidal zones.Filter feeder feeding on phytoplankton and organic particles.
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Placopecten magellanicus [33]		Deep, sandy, or gravel substrates in cold waters.Filter feeder that consumes phytoplankton and organic detritus.
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Pinna nobilis [33]		Seagrass meadows in shallow, sheltered marine environments.Filter feeder placed on plankton and suspended organic material.
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Chamelea gallina [32]		Sandy or muddy substrates in shallow coastal waters.Filter feeder that consumes phytoplankton and organic particles.
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Mercenaria mercenaria [32]		Sandy or muddy substrates in estuaries and lagoons.Filter feeder feeding on phytoplankton and organic detritus.
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Ruditapes philippinarum [32]		Sandy or muddy substrates in the intertidal and shallow subtidal zones.Filter feeder that consumes phytoplankton and organic matter.
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Buccinum undatum [33]		Cold, deep marine waters with sandy or muddy substrates.Scavenger and predator feeding on carrion and small invertebrates.
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Hexaplex trunculus [32]		Rocky or sandy substrates in shallow coastal waters.Carnivorous, preying on bivalves and other molluscs.
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Rapana venosa [32]		Sandy or muddy substrates in estuaries and shallow coastal waters.Carnivorous, feeding on bivalves and other molluscs.
Table 2. Overview of age determination methods, and von Bertalanffy growth function (VBGF) parameters. The growth coefficient (K) is expressed in year−1, and the theoretical age at zero length (t0) is given in years.
Table 2. Overview of age determination methods, and von Bertalanffy growth function (VBGF) parameters. The growth coefficient (K) is expressed in year−1, and the theoretical age at zero length (t0) is given in years.
SpeciesAge DeterminationVBGFStudy AreaReferences
AgeingMethodL∞ (mm)kt0
Anadara inaequivalvisOtherLength-frequency29.651.190.30Sufa Lagoon, TürkiyeAcarli et al. [34]
Anadara kagoshimensisIndirectLength-frequency121.780.25−0.33Western Black SeaDağtekin [35]
Tegillarca granosaDirectShell growth ring35.400.37−0.14Balik Pulau, MalaysiaMirzaei et al. [36]
IndirectLength-frequency73.400.58−0.49Kakinada Bay, IndiaNarasimham [37]
Cerastoderma eduleDirectShell growth ring34.360.640.00Bay of Saint-Brieuc, FrancePonsero et al. [38]
DirectShell growth ring28.270.038.99Mundaka Estuary, SpainJelesias and Navarro [39]
DirectShell growth ring40.700.740.30Mira Channel, PortugalMaia et al. [40]
DirectShell growth ring35.800.64−0.95NE Atlantic Coasts (Ireland, Wales, and France)Mahony et al. [41]
DirectShell growth ring45.040.22−3.24Ireland
DirectShell growth ring42.740.29−2.34Ireland
DirectShell growth ring36.150.34−2.25Wales
DirectShell growth ring43.240.40−0.63Ireland
DirectShell growth ring40.740.47−0.40Ireland
DirectShell growth ring34.290.34−2.07France
DirectShell growth ring31.201.43−0.05Merja Zerga, Morocco; French Atlantic coastGam et al. [42]
DirectShell growth ring38.401.30−0.08Arcachon Bay, France; Moroccan Atlantic Coast
Cerastoderma glaucumDirectShell growth ring38.280.21−1.27Lake Timsah, EgyptMohammad et al. [43]
DirectShell growth ring37.860.22−0.96Lake Timsah, Egypt
DirectShell growth ring46.450.15−2.17S. Sinai Coast, Egypt
DirectShell growth ring32.170.36−0.38S. Sinai Coast, Egypt
Panopea generosaDirectShell growth ring134.000.19−4.40Gulf of California, MexicoCalderon-Aguilera et al. [44]
DirectShell growth ring163.400.19−2.99San Felipe, MexicoAragón-Noriega et al. [45]
157.500.11−1.2Puerto Peñasco, Mexico
DirectShell growth ring122.160.502.26Guaymas-Empalme Bay, MexicoCruz-Vásquez et al. [24]
DirectShell growth ring122.590.26−1.78Gulf of California, MexicoHidalgo-De-La-Toba et al. [46]
DirectShell growth ring135.000.20−0.07Gulf of California, MexicoHidalgo-de-la-Toba et al. [47]
Flexopecten glaberOtherMark/recapture54.000.24−1.30Bulgarian Black SeaTodorova et al. [48]
Pecten maximusDirectShell growth ring109.700.670.50Ría de Vigo, SpainChauvaud et al. [49]
DirectShell growth ring155.900.200.36Austevoll, Norway
DirectShell growth ring101.100.680.47Île de Ré, France
DirectShell growth ring103.600.830.56The Bay of Brest, France
DirectShell growth ring108.400.870.58Bay of Seine, France
DirectShell growth ring108.400.610.48Plymouth, UK
DirectShell growth ring143.600.260.41Holyhead, UK
DirectShell growth ring137.000.250.40Scarborough, UK
DirectShell growth ring146.900.230.19Campbeltown, UK
DirectShell growth ring127.200.280.42Bessaker, Norway
DirectShell growth ring133.500.230.54Brønnøysund, Norway
DirectShell growth ring144.500.240.56Træna, Norway
Placopecten magellanicusOtherNursery/farming176.500.190.55Sunnyside, NL, Canada (10 m)MacDonald and Thompson [50]
OtherNursery/farming158.400.160.10Sunnyside, NL, Canada (31 m)
OtherNursery/farming166.900.210.51St. Andrews, NB, Canada (10 m)
OtherNursery/farming166.000.210.53St. Andrews, NB, Canada (31 m)
OtherNursery/farming155.900.220.32New Jersey Coast, USA (31 m)
Pinna nobilisDirectShell growth ring439.000.21−0.57Es Freus, SpainGarcía-March et al. [51]
DirectShell growth ring624.000.19−0.05W. Mediterranean Sea, Spain
DirectShell growth ring587.000.19−0.40Tabarca Island, Spain
DirectShell growth ring654.000.15−0.95Port-Cros, France
DirectShell growth ring399.000.290.24La Olla, Spain
DirectShell growth ring582.000.37−0.06Mar Menor, Spain
DirectShell growth ring456.000.21−0.88Moraira, Spain
DirectShell growth ring607.000.240.12El Racó, Spain
DirectShell growth ring569.003.90−0.04Dénia, Spain
DirectShell growth ring560.004.50−0.04Île des Embiez, France
DirectShell growth ring655.001.90−1.78Balearic Islands, Spain
DirectShell growth ring750.002.60−0.03Alfacs Bay, Spain
DirectShell growth ring456.000.21−0.88Moraira, SpainGarcía-March et al. [51]
DirectShell growth ring60.700.240.12El Racó, Spain
DirectShell growth ring569.000.24−0.04Dénia, Spain
DirectShell growth ring560.000.300.28Île des Embiez, France
DirectShell growth ring655.000.13−1.78Balearic Islands, Spain
DirectShell growth ring750.000.18−0.03Alfacs Bay, Spain
DirectShell growth ring631.000.17−0.67Site SO, W. Mediterranean *
DirectShell growth ring430.000.23−0.47Site EO, W. Mediterranean *
DirectShell growth ring565.000.30−0.05Site LG, W. Mediterranean *
DirectShell growth ring560.000.14−3.04Moraira Bay and Illa Grossa, SpainGarcia-March et al. [52]
DirectShell growth ring573.000.160.02Moraira Bay and Illa Grossa, Spain
DirectShell growth ring452.700.14−3.80Cabrera Island, SpainNebot Colomer et al. [53]
DirectShell growth ring452.700.14−0.80Cabrera Island, Spain
DirectShell growth ring459.400.15−3.44Foradada Islet, Spain
DirectShell growth ring459.400.15−0.44Foradada Islet, Spain
OtherNursery/farming290.601.160.18Vila Joiosa, SpainHernandis et al. [54]
Chamelea gallinaDirectShell growth ring37.550.71−0.01Portugal Coast and the Mediterranean SeaGaspar et al. [55]
DirectShell growth ring43.900.26−0.84Ancona, ItalyBargione et al. [56]
DirectShell growth ring27.250.61−0.14Russian Black SeaBoltacheva and Mazlumyan [57]
DirectShell growth ring36.110.79−0.45Huelva Coast, SpainDelgado et al. [58]
DirectShell growth ring26.600.22−1.21Black Sea (closed area)Dalgiç et al. [59]
DirectShell growth ring28.880.21−1.29Black Sea (non-dredged)
DirectShell growth ring26.000.16−1.96Black Sea (dredged)
Mercenaria mercenariaDirectShell growth ring84.500.11−1.59Narragansett Bay, RI, USA
DirectShell growth ring80.130.15−0.54Southampton Water, UK
Ruditapes philippinarumDirectShell growth ring65.200.34−0.93Tagus Estuary, PortugalMoura et al. [60]
DirectShell growth ring51.010.17−1.07Goheung Coast, South KoreaYoon et al. [61]
IndirectLength-frequency43.320.70−0.27Poole Harbour, UKHumphreys et al. [62]
DirectShell growth ring60.000.20−0.15Jiaozhou Bay, ChinaFan et al. [63]
DirectShell growth ring67.140.33−0.91Bandırma Bay, TürkiyeÇolakoğlu and Palaz [64]
DirectShell growth ring36.900.74−0.24Mutsu Bay, JapanSugiura and Kikuya [65]
DirectShell growth ring68.340.22−0.42Gimje Coast, South KoreaChung et al. [66]
DirectShell growth ring42.830.46−0.59Jeonbuk, S. KoreaPark and Kim [67]
Buccinum undatumIndirectLength frequency101.100.39−3.19Shetland, UKShelmerdine et al. [68]
IndirectLength-frequency102.040.40−3.20Shetland, UK
IndirectLength-frequency104.870.30−2.17E. Shetland, UK
IndirectLength-frequency99.020.39−2.66E. Shetland, UK
IndirectLength-frequency185.670.03−3.65W. Shetland, UK
IndirectLength-frequency157.520.09−0.32W. Shetland, UK
OtherStable isotopes66.050.390.96S. England Coast, UK
OtherStable isotopes112.490.120.60Normano-Breton Gulf, FranceSantarelli and Gros [69]
DirectOperculum115.550.08−1.77Courtown, IrelandFahy et al. [70]
DirectOperculum121.720.130.28Howth/Kish Bank, Ireland
DirectOperculum121.660.11−0.36S. Irish Sea
Hexaplex trunculusOtherLaboratory38.270.09−0.31Bizerte Lagoon, TunisiaLahbib et al. [71]
OtherMark/recapture---Ria Formosa Lagoon, PortugalVasconcelos et al. [72]
Rapana venosaIndirectLength-frequency102.900.09−1.25Trabzon, TürkiyeKasapoğlu [73]
IndirectLength-frequency112.350.31−0.49Samsun, TürkiyeSağlam et al. [74]
DirectOperculum199.650.10−2.48Yellow Sea, KoreaChoi and Ryu [75]
* SO: Sheltered open sea, EO: Exposed open sea, and LG: Lagoons.
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MDPI and ACS Style

Dürrani, Ö.; Cengiz, Ç.C.; Gabrielczak, H.; Özcan, E.; Varshanidze, M.; Belmonte, G.; Seyhan, K. Review of Age Estimation Techniques and Growth Models for Shelled Organisms in Marine Animal Forests. J. Mar. Sci. Eng. 2025, 13, 1693. https://doi.org/10.3390/jmse13091693

AMA Style

Dürrani Ö, Cengiz ÇC, Gabrielczak H, Özcan E, Varshanidze M, Belmonte G, Seyhan K. Review of Age Estimation Techniques and Growth Models for Shelled Organisms in Marine Animal Forests. Journal of Marine Science and Engineering. 2025; 13(9):1693. https://doi.org/10.3390/jmse13091693

Chicago/Turabian Style

Dürrani, Ömerhan, Çağdaş Can Cengiz, Halyna Gabrielczak, Esra Özcan, Madona Varshanidze, Genuario Belmonte, and Kadir Seyhan. 2025. "Review of Age Estimation Techniques and Growth Models for Shelled Organisms in Marine Animal Forests" Journal of Marine Science and Engineering 13, no. 9: 1693. https://doi.org/10.3390/jmse13091693

APA Style

Dürrani, Ö., Cengiz, Ç. C., Gabrielczak, H., Özcan, E., Varshanidze, M., Belmonte, G., & Seyhan, K. (2025). Review of Age Estimation Techniques and Growth Models for Shelled Organisms in Marine Animal Forests. Journal of Marine Science and Engineering, 13(9), 1693. https://doi.org/10.3390/jmse13091693

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