Coffee (Coffea L.) is a globally important crop, central to both agricultural economies and daily consumption patterns. Despite the importance, the recent decades revealed a concerning trend: coffee has become expensive and less consistently available, affecting both exporting countries and major importers. The United States exemplifies well this dynamic. As the world's largest consumer, the country relies heavily on stable international supply chains to support the internal market. Given this, ensuring the long-term resilience of the coffee supply chain has become a priority for the US market, requiring innovative strategies. This study presents an initial assessment of the viability of cultivating Coffea arabica in the southern United States, with a focus on local production in South Florida. Our hypothesis is that coffee could serve as an alternative specialty crop under subtropical conditions when supported by appropriate management practices. The contributions of this study are threefold. First, we evaluated the yield potential of four main C. arabica cultivars and provided a preliminary assessment of the yield and quality of coffee produced in Florida. Second, we developed a preliminary economic model to examine coffee as a potential alternative crop, integrating agronomic performance, production costs, and revenue projections. Third, we outline the current goals, limitations, and challenges associated with establishing coffee production in Florida, including climatic constraints, market segmentation, soil characteristics, and management considerations. Collectively, these findings offer the first insights into the opportunities and challenges of domestic coffee production, providing a framework for supporting coffee as a sustainable crop for subtropical regions of the United States.

Bacterial wilt, caused by Ralstonia spp., poses a major threat to blueberry (Vaccinium corymbosum) production due to its persistence and rapid spread through soil and infected stock, highlighting the need for genetic insights to guide breeding strategies. This study investigated the genetic basis of bacterial wilt resistance in blueberry using a genome-wide association study (GWAS) across two populations comprising 401 advanced selections from the University of Florida Blueberry Breeding and Genomics Program. A high-throughput screening assay was developed to evaluate southern highbush blueberry responses to bacterial wilt based on leaf wilting severity and stem necrosis. Capture sequencing identified 38,379 single-nucleotide polymorphisms. Moderate narrow-sense heritability estimates were observed for leaf severity (0.26) and stem necrosis (0.20), and GWAS identified five small-effect quantitative trait loci on chromosomes 1, 2, 5, and 11, each explaining 4.0%-7.4% of the phenotypic variance. Candidate gene analysis revealed putative pentatricopeptide repeat (PPR), serine/threonine protein kinase, and MYB-related proteins for leaf severity, and Mlo genes and polysaccharide biosynthesis genes for stem necrosis. Genomic selection (GS) analyses demonstrated potential for improving bacterial wilt resistance, with the GS de novo GWAS approach achieving the highest predictive ability by leveraging two key markers on chromosomes 1 and 11. These results elucidate the genetic architecture of bacterial wilt resistance in blueberries and provide resources for molecular breeding strategies to enhance resistance and ensure sustainable production.

Extensive selection for yield and disease resistance during tomato crop improvement has led to flavor loss in modern commercial tomatoes in comparison with heirloom varieties. To investigate the chemical and genetic architecture of tomato flavor through both domestication and improvement, we analyzed flavor-related chemicals across 558 globally collected accessions comprising wild relatives, semidomesticated and domesticated populations, including uncharacterized Latin American accessions. Key flavor volatiles exhibit major differences across accessions. A genome-wide association study was used to detect associations between genetic loci and flavor-related chemical contents, including sugars, acids, and volatiles. Multiple genetic loci linked to known genes encoding flavor metabolism enzymes, as well as many new loci, were identified. Among the newly identified loci, a gene encoding a previously uncharacterized lipase ( Sl-LIP100 ) was experimentally proven to have an important role in synthesis of lipid-derived flavor volatiles. This enzyme is responsible for synthesis of several important five- and six-carbon flavor volatiles. In sum, this study provides chemical and genetic insights into the evolution of tomato flavor during domestication and subsequent improvement, identifying the complexity of genetic control of fruit flavor chemicals as well as a number of new alleles that can be used to improve the contents of flavor-linked chemicals.

Coffee is one of the world's most widely consumed beverages, derived mainly from Coffea arabica, known for its superior flavor, and Coffea canephora (Robusta/Conilon), valued for its resilience and higher caffeine content. Functional breeding aims to develop cultivars that combine productivity and stress tolerance with improved health-related traits and flavor quality. Because carbohydrate polymers account for more than 50% of green coffee bean dry weight and act as key precursors of aroma and bioactive compounds, we hypothesized that elucidating the genetic architecture underlying carbohydrate metabolites would create new opportunities for molecular breeding in coffee. To test this hypothesis, we integrated cell wall carbohydrate profiling with genomic analyses in a genetically diverse coffee collection encompassing both Coffea canephora and Coffea arabica. Genome-wide association studies identified 14 major genomic loci associated with variation in cell wall polysaccharide composition. Using cup quality as a functional trait endpoint, we further linked carbohydrate profiles with sensory evaluations and propose a framework for implementing marker-assisted selection to accelerate flavor improvement in coffee breeding programs.

Parthenocarpy is a desirable trait that enables fruit set in the absence of fertilization. While blueberries typically depend on pollination for optimal yield, certain genotypes can produce seedless fruits through facultative parthenocarpy, eliminating the need for pollination. However, the development of parthenocarpic cultivars has remained limited by the challenge of evaluating large breeding populations. Thus, establishing molecular breeding tools can greatly accelerate genetic gain for this trait. In the present study, we evaluated two blueberry breeding populations for parthenocarpic fruit set and performed genome-wide association studies (GWAS) to identify markers and candidate genes associated with parthenocarpy. We also compared the predictive ability (PA) of three molecular breeding approaches, including (i) genomic selection (GS); (ii) GS de novo GWAS (GSdnGWAS), which incorporates significant GWAS markers into the GS model as prior information; and (iii) in silico marker-assisted selection (MAS), where markers from GWAS were fitted as fixed effects with no additional marker information. GWAS analyses identified 55 marker-trait associations, revealing candidate genes related to phytohormones, cell cycle regulation, and seed development. Predictive analysis showed that GSdnGWAS consistently outperformed GS and MAS, with PAs ranging from 0.21 to 0.36 depending on the population of study and the specific markers utilized. MAS showed PAs comparable to GS in some cases, suggesting it could be a cost-effective alternative to genome-wide sequencing. Together, these findings demonstrate that molecular breeding techniques can be used to improve facultative parthenocarpy, offering new avenues to develop high-yielding blueberry varieties that are less reliant on pollination.

Blueberry (Vaccinium spp.) is among the most consumed soft fruits and an important source of health-promoting compounds. Among the key traits driving selection in breeding programs, yield is the most important. The standard way to measure yield is harvesting and weighing the total number of berries, a process that is laborious, expensive, prone to measurement errors, and not scalable to short production windows. To circumvent this, breeders rely on visual scores, an approach that offers scalability but includes subjectivity. In this study, we investigated the use of computer vision methods for fruit detection to guide breeding decisions. Our fundamental hypothesis is that integrating machine learning and molecular breeding could strengthen genetic analyses and support molecular breeding. To test it, a large blueberry breeding population was evaluated using different yield-related metrics, including fruit detection via computer vision, visual scores in different phenological stages, and total berry weight. Our contributions are threefold: (i) using computer vision, we better assessed yield potential, producing genetic parameters that improved residual control and leveraged genetic variation; (ii) we inferred the genetic basis of yield in blueberry and highlighted the importance of non-additive effects on phenotypic expression; and (iii) we showed that computer vision and visual scores combined in multivariate genomic prediction models resulted in better predictive abilities. Altogether, for the first time in the blueberry literature, we demonstrated how computer vision and molecular breeding can be integrated in the same framework to guide breeding decisions.

Anthracnose, caused by Colletotrichum gloeosporioides, poses a significant threat to blueberries, necessitating a deeper understanding of the genetic mechanisms underlying resistance to develop efficient breeding strategies. Here, we conducted a genome-wide association study on 355 advanced selections of southern highbush blueberry from the University of Florida Blueberry Breeding and Genomics Program. Visual scores and image analyses were used for assessing disease severity. The population was genotyped using Capture-Seq, detecting 38,379 single nucleotide polymorphisms. The study revealed a moderate narrow-sense heritability estimate (∼0.5) for anthracnose resistance in blueberries. Minor additive loci contributing to anthracnose resistance were identified on chromosomes 2, 3, 5, 6, 9, 10, and 12, using 2 different phenotyping approaches. Visual and image-based phenotyping captured complementary aspects of anthracnose resistance, identifying distinct, non-overlapping SNP associations. Candidate gene mining flanking significant associations unveiled key defense-related proteins, such as serine/threonine protein kinases, pentatricopeptide repeat-containing proteins, E3 ubiquitin ligases that have been well-known for their roles in plant defense signaling pathways. Our findings highlight the complex and quantitative resistance mechanism for anthracnose in blueberry, providing insights for breeding strategies and sustainable disease management.

In tetraploid F1 populations, traditional segregation distortion tests often inaccurately flag SNPs due to ignoring polyploid meiosis processes and genotype uncertainty. We develop tests that account for these factors. Genotype data from tetraploid F1 populations are often collected in breeding programs for mapping and genomic selection purposes. A common quality control procedure in these groups is to compare empirical genotype frequencies against those predicted by Mendelian segregation, where SNPs detected to have segregation distortion are discarded. However, current tests for segregation distortion are insufficient in that they do not account for double reduction and preferential pairing, two meiotic processes in polyploids that naturally change gamete frequencies, leading these tests to detect segregation distortion too often. Current tests also do not account for genotype uncertainty, again leading these tests to detect segregation distortion too often. Here, we incorporate double reduction, preferential pairing, and genotype uncertainty in likelihood ratio and Bayesian tests for segregation distortion. Our methods are implemented in a user-friendly R package, menbayes. We demonstrate the superiority of our methods to those currently used in the literature on both simulations and real data.

Coffee is one of the most widely consumed beverages globally, and Coffea canephora has become increasingly relevant for breeding programs due to its resilience to heat and other stresses. This species includes two commercially cultivated botanical varieties, Conilon and Robusta, which exhibit complementary traits and represent a valuable genetic resource. The germplasm collection maintained at the Brazilian institutions serve as an important source of C. canephora genetic diversity. In this study, we genotyped 387 C. canephora genotypes using over 42,000 SNP markers and combined this molecular information with field data to support breeding strategies focused on mate allocation and hybrid development. Population structure analyses revealed three major genetic clusters, largely aligned with the Conilon and Robusta botanical varieties. Predicted outcomes of selected crosses revealed high levels of heterozygosity and allelic complementarity in the offspring. Genetic parameter estimates showed that non-additive effects accounted for more than 40

Climate change poses a growing threat to global coffee production, particularly for Coffea arabica, the most widely cultivated species. Coffea canephora (Robusta), with greater tolerance to heat and environmental stress, represents a critical genetic resource for sustaining future supply. Despite its increasing importance, the species is still relatively understudied with respect to population structure and trait architecture—factors that are important for guiding breeding efforts. Here, we combine population genetic analyses with genomic prediction to inform the improvement of C. canephora using a representative breeding collection from West Africa. First, we characterized the genetic structure of the cultivated germplasm and confirmed the presence of three main genetic pools: Robusta, Conilon, and Guinean. Second, we quantified phenotypic variation and genetic parameters for 11 agronomic traits, demonstrating a significant contribution of non-additive effects—particularly for yield. Third, we evaluated the performance of genomic prediction models incorporating additive and dominance effects, and proposed their integration into a reciprocal recurrent selection scheme to exploit heterosis. Altogether, our findings highlight the utility of incorporating structured genetic diversity and non-additive effects into breeding strategies. The framework presented here provides a foundation for improving the predictive accuracy and long-term adaptability of C. canephora, with broader implications for genomic-assisted breeding under climate stress.

To induce sexual maturation in captivity, eels rely on hormonal treatments, but this process is costly and time-consuming. As an alternative, different types of conditioning, also referred as pre-treatment, have been assessed to ease hormonal treatment response. Recent studies have shown that migrating eels experience a wide range of temperatures, varying from 12 °C at night to as low as to 8 °C during the day. Therefore, this study evaluates the effects of low-temperature (10 °C) seawater pre-treatments of different durations (2 and 4 weeks) on male eel reproduction. The eye, gonadosomatic and hepatosomatic indexes from control (without thermic seawater pre-treatment) and pre-treated fish were measured. Blood and testis samples were also collected for sex steroid and histology analysis, respectively. Eels pre-treated for 2 weeks demonstrated increased progestin levels, comparing with the control group. Eels pre-treated for 4 weeks showed significantly higher gonadosomatic index and elevated androgens and estradiol levels in comparison with the remaining groups. In eels pre-treated for 2 and 4 weeks, there was an increase in the proportion of spermatogonia type B cells compared to undifferentiated spermatogonia type A, a differentiation process that was not observed in the control group. Cold seawater pre-treatment induced early sexual maturation, including steroid production, which consequently stimulated biometric changes and increased spermatogonia differentiation. Following the pre-treatments, eels started receiving standard hormonal treatment (with recombinant human chorionic gonadotropin at 20 °C). Pre-treated males started to spermiate earlier than the control group. In some treatment weeks, pre-treated individuals registered higher values of sperm density, motility, and kinetic parameters. Moreover, an economic evaluation was carried out relating the investment made in terms of hormone injections with the volume of high-quality sperm obtained from each experimental group. The low temperature pre-treatments demonstrated their economic effectiveness in terms of hormone treatment profitability, increasing the production of high-quality sperm in the European eel. Thus, this in vivo study suggests that cold seawater pre-treatment may increase sensitivity to the hormone applied during standard maturation treatment.

Flavor is a crucial aspect of the eating experience, reflecting evolving consumer preferences for fruits with enhanced quality. Modern fruit breeding programs prioritize improving quality traits aligned with consumer tastes. However, defining fruit-quality attributes that significantly impact consumer preference is a current challenge faced by the industry and breeders. This study proposes a data-driven approach to statistically model the relationship between fruit-quality parameters and consumers' overall liking. Our primary hypothesis suggests that the interplay between fruit-quality attributes and consumer preferences may reach a critical value, serving as new empirical benchmarks for fruit quality. Using extensive historical datasets accounting for sensory, biochemical, and genomic information described in blueberry, we first demonstrated that multivariate adaptive regression splines (MARS) could be used to identify specific values of fruit-quality traits that significantly affect consumer perception by using nonlinear spline regressions on estimating threshold points. We harnessed genomic information and carried out genomic selection (GS) for five fruit-quality traits evaluated on the original scale and after classified via the MARS approach. This study provides a pioneering consumer-centric and data-driven approach to defining fruit-quality standards and supporting molecular breeding that has broad applications to breeding programs from any species.

AbstractGenomic prediction is a modern approach that uses genome‐wide markers to predict the genetic merit of unphenotyped individuals. With the potential to reduce the breeding cycles and increase the selection accuracy, this tool has been designed to rank genotypes and maximize genetic gains. Despite this importance, its practical implementation in breeding programs requires critical allocation of resources for its application in a predictive framework. In this study, we integrated genetic and data‐driven methods to allocate resources for phenotyping and genotyping tailored to genomic prediction. To this end, we used a historical blueberry (Vaccinium corymbosun L.) breeding dataset containing more than 3000 individuals, genotyped using probe‐based target sequencing and phenotyped for three fruit quality traits over several years. Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data‐driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long‐term implication, we carried out a simulation study and emphasized that data‐driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. Altogether, we provided a data‐oriented decision‐making approach for breeders by demonstrating that critical breeding decisions associated with resource allocation for genomic prediction can be tackled through a combination of statistics and genetic methods.

AbstractThe integration of high‐throughput technologies such as near‐infrared spectroscopy (NIRS) for phenomic‐assisted selection in plant breeding has gained relevance in recent years. In blueberry, the use of phenomic selection could enable selection in the early stages, where thousands of seedlings are visually selected, and the use of genomic selection (GS) is cost‐prohibitive. In this study, we compared phenomic and GS in 372 genotypes, which were phenotyped for multiple fruit quality traits across 2 years. Our contribution is fourfold: (i) phenomic and GS methods have comparable predictive performances for multiple traits; (ii) leaves can achieve the highest genetic gains in the long term among NIRS of different biological tissues (leaf and fruit); (iii) BayesB, mixed models, and random forest resulted in the best predictive results across traits for optimizing phenomic prediction; and finally (iv) attention was drawn to the possibility of using phenomic prediction across environments. Altogether, for the first time in the blueberry literature, the utility of NIRS for phenomic‐assisted selection is demonstrated. While the primary focus is on blueberries, this approach can be evaluated in other fruit trees.

Selecting parents and crosses is a critical step for a successful breeding program. The ability to design crosses with high means that will maintain genetic variation in the population is the goal for long-term applications. Herein, we describe a new computational package for mate allocation in a breeding program. SimpleMating is a flexible and open-source R package originally designed to predict and optimize breeding crosses in crops with different reproductive systems and breeding designs. Divided into modules, SimpleMating first estimates the cross performance (criterion), such as mid-parental value, cross total genetic value, and/or usefulness of a set of crosses. The second module implements an optimization algorithm to maximize a target criterion while minimizing next-generation inbreeding. The software is flexible, enabling users to specify the desired number of crosses, set maximum and minimum crosses per parent, and define the maximum allowable parent relationship for creating crosses. As an outcome, SimpleMating generates a mating plan from the target parental population using single or multi-trait criteria. For example, we implemented and tested SimpleMating in a simulated maize breeding program obtained through stochastic simulations. The crosses designed via SimpleMating showed a large genetic mean over time (up to 22% more genetic gain than conventional genomic selection programs, with lesser loss of genetic diversity over time), supporting the use of this tool, as well as the use of data-driven decisions in breeding programs.

Off-season flowering in daylength-sensitive plants is an intriguing characteristic that represents an opportunity to expand crop's production timing and regions, and potentially speed up breeding. Southern Highbush Blueberry (SHB) is classified as a short-day plant, with a blooming period in the northern hemisphere ranging from late winter through spring. In Florida, some blueberry genotypes have consistently bloomed during the fall season, suggesting that flower bud initiation occurred during the summer. Such a characteristic indicates a facultative response to daylength or day-neutrality capacity, leading to the off-season flowering observed. To understand the genetic basis of off-season flowering in SHB, we performed a genome-wide association study (GWAS), followed by transcriptomic analysis of contrasting genotypes. Among the 536 genotypes evaluated, 14 % bloomed during the fall, of which seven presented consistently high scores for off-season flowering capacity. The GWAS revealed 12 single nucleotide polymorphisms (SNPs) significantly associated with the off-season flowering trait, which together accounted for 17 % of the phenotypic variance. A gene homologous to an RNA helicase and reported to modulate florescence formation and flowering time in other plant species, was identified within a genomic region associated with the off-season flowering. This gene was up-regulated in a fall-flowering blueberry genotype. Additionally, we detected down-regulation of flowering repressors and up-regulation of MADS-box transcription factors known to be involved in the fate of floral meristems. Our findings provide insights into the genetic and molecular basis of this complex trait in blueberry. These results can be useful for marker-assisted selection and further functional gene validation to accelerate the development of blueberry varieties suitable for off-season production.

The global production and consumption of blueberry (Vaccinium spp.), a specialty crop known for its abundant bioactive and antioxidant compounds, has more than doubled over the last decade. To hold this momentum, plant breeders have begun to use quantitative genetics and molecular breeding to guide their decisions and select new cultivars that are improved for fruit quality. In this study, we leveraged our inferences on the genetic basis of fruit texture and chemical components by surveying large breeding populations from northern highbush blueberries (NHBs) and southern highbush blueberries (SHBs), the two dominant cultivated blueberries. After evaluating 1065 NHB genotypes planted at the Oregon State University, and 992 SHB genotypes maintained at the University of Florida for 17 texture-related traits, evaluated over multiple years, our contributions consist of the following: (i) we drew attention to differences between NHB and SHB materials and showed that both blueberry types can be differentiated using texture traits; (ii) we computed genetic parameters and shed light on the genetic architecture of important texture attributes, indicating that most traits had a complex nature with low to moderate heritability; (iii) using molecular breeding, we emphasized that prediction could be performed across populations; and finally (iv) the genomic association analyses pinpointed some genomic regions harboring potential candidate genes for texture that could be used for further validation studies. Altogether, the methods and approaches used here can guide future breeding efforts focused on maximizing texture improvements in blueberries.

An approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1–3 and 1–5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and categorical phenotypes found that investing in the evaluation of 600–1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using visual scores traits to explore genetic information in breeding programs and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping.

Over the past decade, genomic selection (GS) has gained significant traction as a valuable tool for predicting the phenotypic performance in plant breeding populations and for expediting the development of new cultivars. Diverse statistical models and approaches have been developed to facilitate the integration of GS into plant breeding practices, with a growing emphasis on strategies that enhance accurate and resource-efficient prediction. Since its inception in 2010, the sweet sorghum [Sorghum bicolor (L.) Moench] breeding program at Centre Haitien d'Innovation en Biotechnologies et pour une Agriculture Soutenable has taken the lead in endeavors to cultivate and introduce varieties that exhibit resilience against both abiotic and biotic stresses. Among these challenges, drought stress holds particular prominence, given the reliance of growers on unpredictable rainfall patterns for successful sorghum production. The central objective of this study was to assess the predictive ability of genomic prediction models across varying environmental conditions in Haiti, employing two statistical methods. Our assessment encompassed 12 distinct sorghum traits, with genomic predictions conducted both within and across irrigated and water-stress treatments, executed at different planting dates. Overall, the two methods showed similar results. Prediction accuracy was notably higher for within-environment scenarios (ranging from 0.30 to 0.71) as opposed to across-environment scenarios (ranging from 0.08 to 0.68). Furthermore, there was considerable variation in the prediction accuracy for all traits, with "total soluble solids" displaying the highest mean value (0.71), while "total stem number" exhibited the lowest (0.38). The attained genomic prediction accuracies in this study offer encouraging insights for the integration of GS strategies in small-scale breeding programs, particularly those aimed at enhancing drought tolerance. The phenotypic distribution of the 12 phenotypic sorghum traits was close to symmetric. Prediction accuracy varied significantly among all traits, with total soluble solids having the highest mean value, while maturity time and grain yield exhibited the lowest. Prediction accuracy was higher within environments than across environments. Bayes B and Bayes ridge regression performed similarly for all 12 traits. The prediction accuracy for grain yield across environments involving the water stress 2 condition is low. Genomic heritability is higher in favorable environmental conditions than unfavorable conditions. Total soluble solids exhibited high genomic heritability across all water regime levels.

We aimed to evaluate the efficiency of hard-gelatin and hard-hydroxypropyl methylcellulose (HPMC) capsules as biodegradable alternative containers to plastic straws in European eel (Anguilla anguilla), gilthead seabream (Sparus aurata) and European sea bass (Dicentrarchus labrax) sperm cryopreservation. Sperm samples from each European eel (n = 12) were diluted 1:8:1 (sperm: extender P1+5 % egg yolk: methanol). Gilthead seabream (n = 12) samples were individually diluted in a cryoprotectant solution of 5 % Me2SO + NaCl 1 % plus BSA (10 mg mL-1) at a ratio of 1:6 (sperm: cryoprotectant solution). European sea bass (n = 10) sperm from each male was diluted in non-activating medium (NAM) at a ratio of 1:5.7 (sperm: NAM), and 5 % of Me2SO was added. The diluted European eel and sea bass sperm aliquots (0.5 mL) were individually filled in plastic straws (0.5 mL), hard-gelatin, and HPMC capsules (0.68 mL). Gilthead seabream diluted sperm (0.25 mL) were filled in plastic straws (0.25 mL) and identical capsules described. All samples were frozen in liquid nitrogen vapor and stored in a liquid nitrogen tank. Sperm kinetic parameters were evaluated by CASA-Mot software. Sperm membrane integrity was performed using a Live and Dead KIT and an epifluorescence microscope. To quantify DNA damage, the alkaline comet assay was performed and TailDNA (TD-%) and Olive Tail Moment (OTM) were evaluated by CaspLab software. Sperm cryopreservation of the three Mediterranean species in straws, gelatin, or HPMC capsules reduced the kinetic parameters and cell membrane integrity. Generally, the post-thawing samples cryopreserved in straws and capsules did not differ for the kinetic parameters and cell membrane integrity, except for European sea bass sperm, where the samples stored in gelatin capsules showed higher velocities (VCL 100; VSL - 76; VAP - 90 mu m s- 1) than the sperm stored in HPMC capsules (VCL - 87; VSL - 59; VAP - 73 mu m s-1). The cryopreservation process did not damage the sperm DNA of European eel and European sea bass, regardless of the containers used. On the other hand, gilthead seabream sperm cryopreserved in gelatin (TD - 9.8 %; OTM 9.7) and HPMC (TD - 11.1 %; OTM - 11.2) capsules showed higher DNA damage than fresh samples (TD - 3.6 %; OTM - 2.7) and the sperm stored in straws (TD - 4.4 %; OTM - 5.2). The hard-gelatin and HPMC biodegradable capsules can be used as an alternative to straws for European eel, gilthead seabream, and European sea bass sperm cryopreservation.