ABSTRACT
Background
Optimizing genotype selection and understanding laying stage‐related changes in egg characteristics are critical for goose production due to the limited availability of hatching eggs and relatively low hatchability rates.
Objectives
This study evaluated the effects of genotype and laying stage on egg quality traits, chemical composition, hatchability and gosling characteristics in Linda, Mast and Toulouse breeder geese reared under standardized conditions.
Methods
Egg quality, composition and hatching performance were assessed at three laying stages in the three genotypes under identical management conditions.
Results
Genotype significantly influenced egg weight, selected egg quality traits, fatty acid composition and gosling weight, whereas overall hatchability did not differ markedly among genotypes. Toulouse geese generally produced heavier eggs and goslings, whereas Linda geese exhibited superior albumen quality traits. Laying stage significantly affected most egg quality and compositional parameters, with a general decline in egg nutrients, fertility, hatchability and gosling weight toward the end of the laying period. Fatty acid composition showed genotype‐ and stage‐related variations; however, these differences were relatively small.
Conclusion
The findings indicate that genotype has a limited effect on hatchability under same conditions, whereas egg weight and laying stage are critical determinants of gosling quality. In the Linda genotype, reduced egg weight, particularly at the late laying stage, was associated with lower gosling weight, highlighting the importance of stage‐specific incubation management in goose production.
Keywords: egg quality, goose genotype, hatching results, laying period
As the laying period advanced from the beginning to the end stage, dry matter, fat, protein and ash contents exhibited a progressive decline, with the highest nutrient concentrations recorded at the onset of lay.
1. Introduction
Hatching egg characteristics are critical importance, as embryonic growth and development begin within the egg (Onbaşılar et al. 2018a). Egg quality is generally described through external and internal traits. External characteristics, such as egg weight, shell thickness and shape index, have a direct influence on embryo development and hatching success (Grochowska et al. 2019). In addition, egg weight and internal quality determine the availability of nutrients for the embryo, providing essential resources required for successful emergence and subsequent transition to self‐sufficiency (Moran 2007). Internal quality traits are typically evaluated using parameters such as albumen index, yolk index, albumen pH, yolk pH and Haugh unit (Cüneydioğlu et al. 2022).
Among the internal components, yolk nutrients play a fundamental role, as they are absorbed and metabolized into lipoproteins, carbohydrates, amino acids and fatty acids, serving as the primary sources of energy and lipids throughout embryonic development (Ding et al. 2022). The lipid content of the egg not only influences embryonic oxygen supply but is also closely associated with oxidative processes (Nasri et al. 2020). Yolk lipids are particularly rich in fatty acids, the profile and quantity of which are shaped by both genetic and environmental factors (Kucharska‐Gaca et al. 2022). Previous studies have further demonstrated that the chemical composition of the yolk, especially the fatty acid profile, exerts a significant effect on hatchability and gosling development (Badzinski et al. 2002).
In geese, reproductive performance is fundamentally determined by the number of goslings produced per female across generations (Graczyk et al. 2018). Unlike other poultry species, geese exhibit a relatively short and distinctly seasonal reproductive cycle, lasting approximately 6 months (from late January to July). During this period, they produce fewer hatching eggs—around 40 in native breeds and approximately 70 in commercial breeds—whereas hatchability rates often remain below 80%. Moreover, high embryonic mortality further reduces the number of goslings produced per female (Łukaszewicz et al. 2019). Typically, geese are managed within a 4‐year reproductive cycle, with breeding activity strongly regulated by seasonal photoperiod changes (Graczyk et al. 2018).
Most existing research on egg quality, composition and hatching performance in geese has been limited to single genotypes, which restricts the ability to generalize findings across breeds. Mast geese are a native German genotype developed as a dual‐purpose bird suitable for both meat and egg production, characterized by moderate body weight and relatively high egg yield. Toulouse geese originate from France and have historically been selected primarily for large body size and meat production, resulting in high adult body weights but comparatively low egg production. In contrast, Linda geese originate from Russia and are widely used in commercial goose production due to their relatively higher reproductive performance combined with moderate body size. These differences in selection history and commercial use provide a strong rationale for their comparative evaluation in terms of egg quality, yolk composition and hatching performance under standardized rearing conditions. A comparative evaluation of goose genotypes under standardized conditions is particularly important for the goose industry, as it provides genotype‐specific information that can support breeder management, improve incubation outcomes and inform selection and conservation strategies, especially for low‐producing or locally adapted genotypes. In this context, the present study investigated egg quality traits, chemical composition, fatty acid profiles, hatchability and gosling characteristics at different laying stages in Linda, Mast and Toulouse breeder geese reared under identical management conditions. On the basis of these considerations, we hypothesized that (i) egg quality traits, yolk chemical composition and hatching performance would differ among genotypes, (ii) laying stage would significantly influence these parameters and (iii) genotype × laying stage interactions would occur, reflecting differential physiological responses across the laying period. Particular emphasis was placed on identifying traits most sensitive to genotype and laying stage and those with practical relevance for incubation outcomes.
2. Materials and Methods
2.1. Hatching Eggs and Experimental Design
In this study, the experimental material consisted of hatching eggs, which were obtained from breeder goose flocks of three genotypes: Linda, Mast and Toulouse. The eggs used in the experiment were collected from flocks comprising 375 Linda, 480 Mast and 510 Toulouse geese, each maintained at a female‐to‐male ratio of 2:1.
The breeder flocks were housed in environmentally controlled poultry houses, stocked at a density of 2 birds/m2, with straw used as bedding material. At the onset of laying, the geese were exposed to a photoperiod of 14 h light and 10 h darkness (14L:10D), which was increased by 30 min every 15 days. From 2 months after the onset of laying until the end of the production period, the photoperiod was maintained at 16L:8D. Breeders were fed layer feed 1 until peak egg production, after which layer feed 2 was provided. The chemical composition of the diets is presented in Table 1. Nutrient composition was analysed according to AOAC (2000), and metabolisable energy values were calculated using the equation of Carpenter and Clegg, as reported by Onbaşılar et al. (2025b). Feed and water were supplied ad libitum. Ambient temperature was maintained at 20–22°C, with relative humidity (RH) between 50% and 60% throughout the experimental period. Eggs were collected at 156 weeks of age (onset of laying), 166 weeks of age (peak production) and 170 weeks of age (end of laying) to represent biologically distinct laying stages. The peak egg production rates of Linda, Mast and Toulouse geese were 81%, 80% and 89%, respectively. At each laying stage, 112 eggs per genotype collected on the same day were selected based on average egg weight and suitability as hatching eggs.
TABLE 1.
Chemical composition of the feeds.
| Layer feed 1 | Layer feed 2 | |
|---|---|---|
| Dry matter, % | 88.61 | 88.90 |
| Crude protein, % | 9.54 | 10.27 |
| Ether extract, % | 2.24 | 2.47 |
| Crude ash, % | 1.81 | 1.96 |
| Metabolisable energy, kcal/kg | 2914 | 2912 |
In this study, each individual egg was considered the experimental unit. Of the selected eggs, 12 were used for egg quality analyses and 100 for hatching performance evaluation. In total, 1008 eggs were examined throughout the experiment.
2.2. Determination of Egg Quality and Composition
In each laying stage, 12 eggs of each genotype were weighed. Shell breaking strength was assessed using an egg‐breaking tester with a static compression device (Dr.‐Ing. Georg Wazau Mess‐und Prüfsysteme GmbH, Berlin, Germany). The contents of the egg were carefully transferred onto a flat surface. Eggshell thickness was measured with a micrometre (Mitutoya, No. 1044N, 0.01–5 mm; Kawasaki, Japan) at three different points (upper end, lower end and middle). The length and width of the albumen and the diameter of the yolk were measured using a digital caliper. The height of the yolk and the albumen were measured with a tripod micrometre (Mitutoya, No. 2050‐08, 0.01–20 mm; Kawasaki, Japan). By using these values, yolk index [(yolk height/yolk diameter) × 100], albumen index [(albumen height/average of albumen length and albumen width) × 100] and Haugh units [100 × log(H + 7.57 − 1.7W 0.37), where H is albumen height and W is egg weight] were calculated (Onbaşılar et al. 2018b; Kılınç et al. 2023; Deniz et al. 2025). The ratios of the egg components were expressed as a percentage of the total egg weight. The pH values of the yolk and albumen for each sample were determined using a digital pH meter (model SG2‐ELK, Mettler Toledo, Barcelona, Spain, Demir et al. 2025). Egg quality analyses were carried out within 24 h of egg collection, and all measurements were performed by the same operator to minimize inter‐observer variability. In the albumen, dry matter, protein and ash contents were determined, whereas in the yolk, dry matter, protein, ash and lipid contents were analysed, following the procedures described by AOAC (2000) and Uğurlu et al. (2025).
The method of Bligh and Dyer (1959) was followed to determine the fatty acids in the yolk. The extracted fatty acids were saponified with 2% methanolic NaOH and then converted into fatty acid methyl esters using boron trifluoride in 35% methanol (Yaranoğlu and Yaranoğlu 2025). Afterward, n‐heptane and saturated NaCl were added to the fatty acid methyl esters. The organic phase remaining on top of the tubes was transferred to vials, and the fatty acids were determined using Shimadzu GCMS‐QP2020NX model GC–MS equipped with a Restek Capillary Column (100 m length, 0.25 mm i.d. × 0.20 mm film). The injector temperature was set to 240°C, whereas the MS detector interface temperature was set to 270°C and the ion source to 200°C. The split ratio was 1:100 and the total injection volume was 1 µL. The oven temperature was initially programmed to 100°C for 4 min and then increased to 240°C at a ramp rate of 5°C/min. The total chromatogram run time was 60 min. The peaks obtained from the chromatogram were identified using the MS library and verified with FAME Mix 37 (Supelco, Merck, USA) as an internal control to confirm the detected fatty acids. Helium was used as the carrier gas. Instrument parameters were kept constant throughout all analyses, and fatty acid identification was verified using a commercial FAME Mix 37 standard to ensure analytical consistency. Desired fatty acids (DFA), lipid nutritional value (NV), atherogenic indexes (AI) and thrombogenic index (TI) were calculated according to the equations described by Orkusz et al. (2021) and Onbaşılar et al. (2025a).
2.3. Incubation and Hatching Results
The remaining eggs from each breeder age group were incubated in the same setter, maintained at 37.7°C and 55%–60% RH, with automatic turning at 90° every hour. On Day 27 of incubation, eggs were transferred to the hatcher, which was set at 36.5°C and 75%–80% RH, and placed in individual boxes to enable the identification of each hatched gosling. On Day 31, healthy goslings were removed from the hatcher and weighed. Relative gosling weight was calculated as (gosling weight/egg weight) × 100. The number of unhatched eggs was recorded, and hatchability of set eggs ([number of hatched goslings/total number of eggs set] × 100) as well as hatchability of fertile eggs ([number of hatched goslings/number of fertile eggs set] × 100) were calculated (Varol Avcılar et al. 2024).
2.4. Statistical Analyses
Sample size considerations were based on the factorial design and Cohen's conventional effect size thresholds (f = 0.10 small, f = 0.25 medium and f = 0.40 large). For egg quality traits, generalized linear models (GLMs) were applied in a 3 × 3 factorial design (genotype × laying stage) with 100 eggs per cell (total N = 900). With α = 0.05, this sample size provides power well above 90% to detect medium effects (Cohen's f = 0.25) for both main effects and genotype × laying stage interactions. For fatty acid composition analyses, GLMs were applied in the same 3 × 3 factorial design with 12 eggs per genotype × laying stage (total n = 108). Under α = 0.05, this sample size provides adequate power to detect moderate‐to‐large effects (approximately f ≥ 0.35 for ∼90% power).
Statistical analyses were performed using SPSS Statistics version 23.0 (IBM Corp., Armonk, NY, USA) and R software (version 4.5.1; R Foundation for Statistical Computing, Vienna, Austria). GLMs were used to evaluate the effects of genotype, laying stage and their interaction (genotype × laying stage) on egg quality traits and fatty acid composition. For continuous response variables, GLMs assuming a normal distribution with an identity link function were applied. When significant main effects were detected, pairwise comparisons of estimated marginal means were conducted using Tukey's adjustment to account for multiple comparisons.
Hatching outcomes were analysed separately from egg quality traits. Differences in hatching proportions among genotypes within each laying stage were evaluated using the chi‐square test.
Multivariate differences in fatty acid composition were assessed using permutational multivariate analysis of variance (PERMANOVA) based on Bray–Curtis dissimilarity matrices, which are appropriate for proportional compositional data. PERMANOVA was performed using the adonis2 function of the vegan package in R, with 9999 permutations, a reduced model, and marginal sums of squares. The statistical model included genotype, laying stage and their interaction as fixed factors. To ensure that significant PERMANOVA results were not driven by heterogeneity of within‐group dispersion, homogeneity of multivariate dispersions was assessed using PERMDISP (betadisper function, vegan package). Differences in dispersion among groups were evaluated using permutation‐based ANOVA with 9999 permutations.
When overall or stage‐specific PERMANOVA results indicated significant or marginal genotype‐related differences, similarity percentage (SIMPER) analysis was conducted to identify the fatty acids contributing most to between‐group dissimilarities. SIMPER analyses were based on Bray–Curtis distances, and fatty acids were ranked according to their average contribution to dissimilarity. Variables were retained and reported until a cumulative contribution of approximately 75% was reached, providing a parsimonious and biologically interpretable summary of the main drivers of multivariate separation.
Multivariate patterns in fatty acid composition were visualized using principal coordinates analysis (PCoA) based on Bray–Curtis dissimilarities, consistent with the distance metric used in PERMANOVA. Ordination was performed on the matrix of individual fatty acids without prior transformation and used for descriptive purposes only. Samples were displayed in a two‐dimensional ordination space, with points coloured by genotype and shaped by laying stage. Ninety‐five per cent confidence ellipses were drawn to illustrate dispersion across laying stages.
Individual fatty acids were further analysed using GLMs with genotype, laying stage and their interaction included as fixed factors. Analyses were conducted on original (non‐transformed) data, as inspection of distributions revealed no severe skewness or zero inflation. To control for multiple testing across individual fatty acids, p values for each model term (genotype, laying stage and genotype × laying stage) were adjusted separately using the Benjamini–Hochberg false discovery rate (FDR) procedure. Effect sizes were quantified using partial eta squared (η 2 p). Statistical significance was defined as FDR‐adjusted q < 0.05.
In addition, summary lipid classes and nutritional indices (including saturated fatty acid [SFA], mono‐unsaturated fatty acid [MUFA], poly‐unsaturated fatty acid [PUFA], unsaturated fatty acid [UFA], PUFA/SFA, n‐3, n‐6, n‐6/n‐3, DFA, AI and TI), which are derived from individual fatty acids, were analysed using the same GLM framework as secondary outcomes to aid biological interpretation. Unless otherwise stated, statistical significance was evaluated at p < 0.05.
3. Results
The lowest egg weight was found in the Linda geese (p < 0.001, Table 2). The egg weight decreased from 157 g at the beginning of the lay to 131 g at the end of the lay (p < 0.001). Quality parameters of eggs obtained from different goose genotypes according to the laying stage are presented in Table 3. Genotype affected the examined quality parameters (p < 0.05) except for the thickness and percentage of egg shell and yolk pH. However, laying stage affected the all examined quality parameters (p < 0.05) except the shell percentage. Yolk composition was found similar in examined eggs obtained from different genotypes (Table 4). At the end of the laying stage, compared to the beginning, dry matter, ash, fat and protein of yolk decreased (p < 0.001). In the albumen of eggs obtained from Linda, Mast and Toulouse geese, the dry matter levels were determined to be 10.30%, 10.16% and 9.80%, respectively (p < 0.05, Table 5). At the end of the laying stage, compared to the beginning, the dry matter of albumen decreased (p < 0.001).
TABLE 2.
Egg weight of different goose genotypes according to the laying stage.
| Genotype | Laying stage | Egg weight (g) |
|---|---|---|
| Linda | Beginning | 119 |
| Peak | 116 | |
| End | 115 | |
| Mast | Beginning | 178 |
| Peak | 140 | |
| End | 138 | |
| Toulouse | Beginning | 173 |
| Peak | 144 | |
| End | 141 | |
| Linda | 117y | |
| Mast | 152x | |
| Toulouse | 153x | |
| Beginning | 157a | |
| Peak | 133b | |
| End | 131b | |
| SEM | 0.506 | |
| p | ||
| Genotype | *** | |
| Laying stage | *** | |
| Genotype × laying stage | *** | |
Note: Difference between values with different letters (x,y) on the same column is statistically significant for genotypes. Difference between values with different letters (a,b) on the same column is statistically significant for laying stage.
Abbreviation: SEM, standard error of mean.
p < 0.001.
TABLE 3.
Quality parameters of egg obtained from different goose genotypes according to the laying stage.
| Genotype | Laying stage | Egg breaking strength (kg/cm2) | Egg shell thickness (µm) | Egg shell percentage | Yolk percentage | Albumen percentage | Yolk pH | Albumen pH | Yolk index (%) | Albumen index (%) | Haugh unit |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Linda | Beginning | 6.74 | 496 | 12.94 | 32.53 | 54.69 | 5.60 | 7.87 | 29.69 | 10.42 | 80.26 |
| Peak | 6.38 | 532 | 13.55 | 32.53 | 53.90 | 6.24 | 8.26 | 28.94 | 10.79 | 82.12 | |
| End | 6.97 | 645 | 12.95 | 31.69 | 54.90 | 6.24 | 8.39 | 28.26 | 9.32 | 73.83 | |
| Mast | Beginning | 6.92 | 556 | 13.16 | 40.01 | 46.60 | 5.53 | 8.22 | 33.90 | 8.52 | 63.42 |
| Peak | 6.27 | 495 | 11.98 | 33.56 | 54.46 | 6.21 | 8.63 | 29.41 | 9.58 | 73.76 | |
| End | 6.72 | 682 | 13.40 | 35.81 | 50.79 | 6.19 | 8.59 | 28.94 | 8.11 | 65.46 | |
| Toulouse | Beginning | 6.97 | 547 | 12.97 | 37.06 | 49.97 | 5.45 | 7.98 | 35.11 | 8.13 | 62.23 |
| Peak | 7.02 | 535 | 12.82 | 35.76 | 51.32 | 6.27 | 8.51 | 28.14 | 7.48 | 61.29 | |
| End | 7.13 | 714 | 13.76 | 35.17 | 51.07 | 6.20 | 8.54 | 28.90 | 7.55 | 63.18 | |
| Linda | 6.69y | 558 | 13.15 | 32.24y | 54.50x | 6.03 | 8.18y | 28.96y | 10.18x | 78.74x | |
| Mast | 6.64y | 578 | 12.85 | 36.46x | 50.62y | 5.98 | 8.48x | 30.75x | 8.74y | 67.55y | |
| Toulouse | 7.04x | 599 | 13.19 | 36.00x | 50.78y | 5.97 | 8.03z | 30.72x | 7.72z | 62.23z | |
| Beginning | 6.87a | 533b | 13.03a | 36.53a | 50.42b | 5.53b | 8.03b | 32.90a | 9.02a | 68.63b | |
| Peak | 6.56b | 521b | 12.78b | 33.95b | 53.23a | 6.24a | 8.47a | 28.83b | 9.29a | 72.39a | |
| End | 6.94a | 680a | 13.37a | 34.22b | 52.25a | 6.21a | 8.51a | 28.70b | 8.33b | 67.49b | |
| SEM | 0.061 | 6.855 | 0.119 | 0.302 | 0.321 | 0.011 | 0.021 | 0.217 | 0.111 | 0.701 | |
| p | |||||||||||
| Genotype | * | — | — | *** | *** | — | *** | ** | *** | *** | |
| Laying stage | * | *** | — | ** | ** | *** | *** | *** | ** | * | |
| Genotype × laying stage | — | — | * | * | ** | — | — | *** | — | * | |
Note: Difference between values with different letters (x,y,z) on the same column is statistically significant for genotypes. Difference between values with different letters (a,b) on the same column is statistically significant for laying stage.
Abbreviation: SEM, standard error of mean.
—p > 0.05.
p < 0.05.
p < 0.01.
p < 0.001.
TABLE 4.
Composition of yolk obtained from different goose genotypes according to the laying stage.
| Genotype | Laying stage | Dry matter (%) | Ash (%) | Fat (%) | Protein (%) |
|---|---|---|---|---|---|
| Linda | Beginning | 55.92 | 1.84 | 30.93 | 23.16 |
| Peak | 54.56 | 1.76 | 31.84 | 20.96 | |
| End | 51.86 | 1.69 | 30.20 | 19.97 | |
| Mast | Beginning | 56.00 | 1.78 | 31.69 | 22.53 |
| Peak | 54.37 | 1.88 | 31.23 | 21.26 | |
| End | 51.51 | 1.55 | 30.37 | 19.59 | |
| Toulouse | Beginning | 56.22 | 1.81 | 31.38 | 23.03 |
| Peak | 53.84 | 1.80 | 31.23 | 21.81 | |
| End | 51.33 | 1.58 | 30.06 | 19.68 | |
| Linda | 54.11 | 1.76 | 30.99 | 21.36 | |
| Mast | 53.96 | 1.74 | 31.10 | 21.13 | |
| Toulouse | 53.80 | 1.73 | 30.89 | 21.17 | |
| Beginning | 56.05a | 1.81a | 31.33a | 22.91a | |
| Peak | 54.25b | 1.81a | 31.43a | 21.01b | |
| End | 51.57c | 1.61b | 30.21b | 19.75c | |
| SEM | 0.065 | 0.009 | 0.096 | 0.126 | |
| p | |||||
| Genotype | — | — | — | — | |
| Laying stage | *** | *** | *** | *** | |
| Genotype × laying stage | — | *** | — | — | |
Note: Difference between values with different letters (a,b,c) on the same column is statistically significant for laying stages.
Abbreviation: SEM, standard error of mean.
—p > 0.05.
p < 0.001.
TABLE 5.
Composition of albumen obtained from different goose genotypes according to the laying stage.
| Genotype | Laying stage | Dry matter (%) | Ash (%) | Protein (%) |
|---|---|---|---|---|
| Linda | Beginning | 10.66 | 0.65 | 10.01 |
| Peak | 10.66 | 0.73 | 9.93 | |
| End | 9.57 | 0.69 | 8.88 | |
| Mast | Beginning | 10.71 | 0.65 | 10.07 |
| Peak | 10.42 | 0.74 | 9.68 | |
| End | 9.33 | 0.59 | 8.75 | |
| Toulouse | Beginning | 10.68 | 0.68 | 10.01 |
| Peak | 9.63 | 0.70 | 8.93 | |
| End | 9.07 | 0.55 | 8.53 | |
| Linda | 10.30x | 0.69 | 9.61 | |
| Mast | 10.16xy | 0.66 | 9.50 | |
| Toulouse | 9.80y | 0.64 | 9.15 | |
| Beginning | 10.68a | 0.66b | 10.03a | |
| Peak | 10.24b | 0.72a | 9.51b | |
| End | 9.33c | 0.61c | 8.72c | |
| SEM | 0.083 | 0.006 | 0.082 | |
| p | ||||
| Genotype | * | — | — | |
| Laying stage | *** | *** | *** | |
| Genotype × laying stage | — | *** | — | |
Note: Difference between values with different letters (x,y) on the same column is statistically significant for genotypes. Difference between values with different letters (a,b,c) on the same column is statistically significant for laying stage.
Abbreviation: SEM, standard error of mean.
p > 0.05.
p < 0.05.
p < 0.001.
Multivariate differences in fatty acid composition were assessed using PERMANOVA based on Bray–Curtis dissimilarity. The overall genotype × laying stage interaction was marginally significant (R 2 = 0.065, p = 0.079), indicating stage‐dependent trends in genotype‐related variation (Table 6). Tests of multivariate dispersion (PERMDISP) indicated no significant differences in within‐group dispersion across genotypes (F = 0.07, p = 0.94) or laying stages (F = 0.42, p = 0.67), confirming that PERMANOVA results were not driven by heterogeneity of dispersion. Stage‐specific analyses revealed that genotype‐related differences were most pronounced during the peak laying stage, where genotype explained approximately 19.4% of the total variance in fatty acid profiles (R 2 = 0.194, p = 0.0058). In contrast, genotype effects were not significant during the beginning stage (R 2 = 0.102, p = 0.163) and were marginal during the end of laying stage (R 2 = 0.140, p = 0.055). On the basis of these findings, subsequent SIMPER analysis was focused on the peak laying stage to identify fatty acids driving the observed genotype differences, whereas results for the end of laying stage were considered exploratory. SIMPER analysis was conducted to identify the fatty acids contributing most to genotype‐related differences in fatty acid composition during the peak laying stage. The results indicated that C18:1n‐9, C16:0, C18:0 and C18:2n‐6 were the main contributors to the observed dissimilarity, together accounting for approximately 77% of the total Bray–Curtis dissimilarity. These findings suggest that both saturated (C16:0, C18:0) and unsaturated fatty acids (C18:1n‐9, C18:2n‐6) jointly drive genotype‐dependent variation in fatty acid profiles during peak laying stage (Table 7). During the late laying stage, SIMPER analysis was performed as an exploratory approach due to the marginal genotype effect observed in PERMANOVA (p = 0.055). The fatty acids contributing to genotype‐related variation during the late laying stage largely overlapped with those identified at peak production, suggesting a consistent influence of major saturated and unsaturated fatty acids across laying stages (Table S1). PCoA revealed clear multivariate structuring of fatty acid profiles according to laying stage, with partial separation also observed among genotypes (Figure 1). Samples from different laying stages showed distinct clustering patterns, as highlighted by the 95% confidence ellipses, indicating systematic stage‐related shifts in fatty acid composition. Genotype‐related differences were present but showed partial overlap across stages. These ordination patterns are consistent with the PERMANOVA results, which identified significant main effects of laying stage and genotype, whereas the genotype × laying stage interaction showed weaker or trend‐level effects. After Benjamini–Hochberg FDR correction, laying stage had a significant effect on several fatty acids, particularly C18:2n6, C23:0, C18:3n6 and C17:0 (q < 0.05), with moderate to large effect sizes (partial η 2 = 0.12–0.25). Genotype also significantly influenced fatty acid composition, with the strongest effects observed for C20:4n6, C17:1, C18:0 and C15:0. Although no genotype × laying stage interaction remained significant after FDR correction, several fatty acids (C17:1, C20:1 and C20:3n6) showed consistent trend‐level interactions (q ≈ 0.05–0.06), accompanied by moderate effect sizes, suggesting genotype‐specific temporal patterns (Table 8). In addition to individual fatty acids, summary lipid classes and nutritional indices (SFA, MUFA, PUFA, UFA, PUFA/SFA, n‐3, n‐6, n‐6/n‐3, DFA, AI and TI) were analysed using GLM (Table 9). These variables represent biologically meaningful aggregates derived from individual fatty acids and were therefore evaluated as complementary outcomes rather than as independent multivariate structures. Several indices showed significant main effects of laying stage and/or genotype, generally mirroring the patterns observed for individual fatty acids. No additional multivariate analyses were performed for these variables, as they are mathematically dependent on the individual fatty acid composition already assessed using PCA and PERMANOVA.
TABLE 6.
Permutational multivariate analysis of variance (PERMANOVA) results (Bray–Curtis distance, 9.999 permutations) showing the effects of genotype, laying stage and their interaction on fatty acid profiles, together with stage‐specific genotype effects.
| Analysis | Effect | df | R 2 | F | p |
|---|---|---|---|---|---|
| Overall model | Genotype × laying stage | 4 | 0.065 | 1.552 | 0.079 |
| Residual | 77 | 0.808 | |||
| Laying stage (beginning) | Genotype | 2 | 0.102 | 1.48 | 0.163 |
| Laying stage (peak) | Genotype | 2 | 0.194 | 3.25 | 0.006 |
| Laying stage (end) | Genotype | 2 | 0.140 | 1.96 | 0.055 |
| Dispersion test | Laying stage | 2 | — | 0.42 | 0.666 |
| Genotype | 2 | — | 0.07 | 0.937 |
Note: PERMANOVA was performed using adonis2 with marginal effects. Homogeneity of multivariate dispersions was assessed using betadisper and permutation tests (9.999 permutations). Significant effects are shown in bold.
TABLE 7.
Similarity percentage (SIMPER) analysis showing the fatty acids contributing most to genotype‐related differences in fatty acid profiles during the peak laying stage.
| Fatty acid | Average contribution | SD | Ratio | Contribution (%) | Cumulative (%) |
|---|---|---|---|---|---|
| C18:1n‐9 | 0.01046 | 0.00779 | 1.34 | 21.75 | 21.75 |
| C16:0 | 0.00962 | 0.00652 | 1.48 | 20.00 | 41.75 |
| C18:0 | 0.00890 | 0.00577 | 1.54 | 18.50 | 60.25 |
| C18:2n‐6 | 0.00789 | 0.00581 | 1.36 | 16.41 | 76.66 |
Note: Only fatty acids cumulatively explaining approximately 75% (76.7%) of the total dissimilarity are shown to highlight the main contributors and avoid overinterpretation of minor components.
FIGURE 1.
Principal coordinates analysis (PCoA) of fatty acid profiles based on Bray–Curtis dissimilarities [Points represent individual egg samples and are coloured by genotype and shaped by laying stage. Ellipses indicate 95% confidence regions for laying stages]. Genotype: 1: Linda, 2: Mast, 3: Toulouse. Laying stage: 1: Beginning, 2: Peak, 3: End.
TABLE 8.
Fatty acids showing significant or trend‐level effects of laying stage, genotype and genotype × laying stage interaction in two‐way ANOVA models after Benjamini–Hochberg false discovery rate (FDR) correction.
| FA | p | q(FDR) | Partial η 2 |
|---|---|---|---|
| Laying stage | |||
| c182n6 | <0.001 | <0.001 | 0.252 |
| c23 | 0.002 | 0.018 | 0.152 |
| c183n6 | 0.003 | 0.018 | 0.141 |
| c17 | 0.007 | 0.032 | 0.120 |
| Genotype | |||
| c204n6 | <0.001 | 0.000 | 0.220 |
| c171 | 0.001 | 0.009 | 0.168 |
| c18 | 0.002 | 0.012 | 0.147 |
| c15 | 0.003 | 0.014 | 0.138 |
| c201 | 0.019 | 0.068 | 0.098 |
| Genotype × laying stage | |||
| c203n6 | 0.003 | 0.054 | 0.185 |
| c171 | 0.006 | 0.054 | 0.167 |
| c201 | 0.010 | 0.060 | 0.156 |
| c17 | 0.037 | 0.167 | 0.123 |
Note: p values represent unadjusted probabilities from two‐way ANOVA models (genotype × laying stage). q values correspond to Benjamini–Hochberg false discovery rate–adjusted p values, applied separately for each model term. Partial eta squared (η 2 p) indicates effect size. Fatty acids with q < 0.05 were considered statistically significant, whereas values of 0.05 ≤ q < 0.10 were interpreted as trend‐level effects.
TABLE 9.
Total fatty acids and nutritional quality indices of the lipids (% of total fatty acids) in yolk of different goose genotypes according to the laying stage.
| Genotype | Laying stage | ΣSFA | ΣMUFA | ΣPUFA | ΣUFA | ΣUFA/ΣSFA | ΣPUFA/ΣSFA | DFA | NV | AI | TI |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Linda | Beginning | 41.26 | 51.52 | 7.27 | 58.78 | 1.44 | 0.18 | 68.63 | 2.07 | 0.52 | 1.25 |
| Peak | 39.67 | 50.93 | 9.45 | 60.74 | 1.53 | 0.24 | 69.50 | 2.04 | 0.50 | 1.19 | |
| End | 39.89 | 51.01 | 9.13 | 60.15 | 1.51 | 0.23 | 69.16 | 1.96 | 0.50 | 1.20 | |
| Mast | Beginning | 41.30 | 50.72 | 8.04 | 58.76 | 1.43 | 0.20 | 68.51 | 2.01 | 0.51 | 1.25 |
| Peak | 40.76 | 50.43 | 8.56 | 59.29 | 1.46 | 0.22 | 68.24 | 1.89 | 0.53 | 1.25 | |
| End | 42.09 | 50.30 | 7.67 | 57.97 | 1.39 | 0.19 | 67.81 | 1.88 | 0.55 | 1.31 | |
| Toulouse | Beginning | 41.36 | 51.56 | 7.12 | 58.68 | 1.42 | 0.17 | 69.39 | 2.07 | 0.52 | 1.29 |
| Peak | 40.20 | 50.16 | 9.71 | 59.87 | 1.49 | 0.24 | 70.69 | 2.16 | 0.47 | 1.20 | |
| End | 41.01 | 49.73 | 9.32 | 59.05 | 1.45 | 0.23 | 69.57 | 2.02 | 0.52 | 1.28 | |
| Linda | 40.27 | 51.15 | 8.62 | 59.77 | 1.49 | 0.22 | 69.10x | 2.02 | 0.51 | 1.21 | |
| Mast | 41.38 | 50.48 | 8.19 | 58.67 | 1.42 | 0.20 | 68.19y | 1.93 | 0.53 | 1.27 | |
| Toulouse | 40.86 | 50.49 | 8.71 | 59.20 | 1.45 | 0.22 | 69.88x | 2.08 | 0.50 | 1.26 | |
| Beginning | 41.31 | 51.27 | 7.48b | 58.74 | 1.43 | 0.18b | 68.84 | 2.05 | 0.52 | 1.26 | |
| Peak | 40.21 | 50.51 | 9.34a | 59.84 | 1.49 | 0.23a | 69.48 | 2.03 | 0.50 | 1.21 | |
| End | 41.00 | 50.35 | 8.71a | 59.06 | 1.45 | 0.21a | 68.84 | 1.95 | 0.52 | 1.26 | |
| SEM | 0.228 | 0.220 | 0.154 | 0.227 | 0.014 | 0.004 | 0.243 | 0.027 | 0.006 | 0.011 | |
| p | |||||||||||
| Genotype | — | — | — | — | — | — | * | — | — | — | |
| Laying stage | — | — | *** | — | — | *** | — | — | — | — | |
| Genotype × laying stage | — | — | — | — | — | — | — | — | — | — | |
Note: Difference between values with different letters (x,y) on the same column is statistically significant for genotypes. Difference between values with different letters (a,b) on the same column is statistically significant for laying stage.
Abbreviations: AI, atherogenic index; DFA, desired fatty acids; NV, nutritive value; SEM, standard error of mean; TI, thrombogenic index; ΣMUFA, mono‐unsaturated fatty acids; ΣPUFA, poly‐unsaturated fatty acids; ΣSFA, saturated fatty acids; ΣUFA, total unsaturated fatty acids.
p > 0.05.
p < 0.05.
p < 0.001.
When the genotype and laying stage groups were compared, there was no statistical difference (p > 0.05) in terms of hatching results (Table 10). Gosling weight and gosling percentage were found lowest in the Linda genotypes (p < 0.001, Table 11). The gosling weight decreased from 92.11 g at the beginning of lay to 72.17 g at the end (p < 0.001) and the gosling percentage decreased from 59.82% at the beginning of lay to 56.10% at the end (p < 0.05).
TABLE 10.
Hatching results of different goose genotypes according to the laying stage.
| Genotype | Laying stage | Fertility rate | Hatchability of fertile eggs | Hatchability of set eggs | Total embryonic mortality |
|---|---|---|---|---|---|
| Linda | Beginning | 90 | 89 | 80 | 11 |
| Mast | Beginning | 88 | 93 | 82 | 7 |
| Toulouse | Beginning | 88 | 92 | 81 | 8 |
| χ 2 | 0.27 | 1.12 | 0.13 | 1.12 | |
| p | — | — | — | — | |
| Linda | Peak | 95 | 91 | 86 | 10 |
| Mast | Peak | 92 | 95 | 87 | 5 |
| Toulouse | Peak | 94 | 94 | 88 | 6 |
| χ 2 | 0.79 | 1.27 | 1.18 | 1.27 | |
| p | — | — | — | — | |
| Linda | End | 31 | 77 | 24 | 23 |
| Mast | End | 27 | 56 | 15 | 44 |
| Toulouse | End | 29 | 72 | 21 | 28 |
| χ 2 | 0.39 | 3.47 | 2.63 | 3.47 | |
| p | — | — | — | — |
—p > 0.05.
TABLE 11.
Gosling weight and gosling percentage of different goose genotypes according to the laying stage.
| Genotype | Laying stage | Gosling weight (g) | Gosling percentage |
|---|---|---|---|
| Linda | Beginning | 76.01 | 64.28 |
| Peak | 75.64 | 65.42 | |
| End | 70.67 | 62.56 | |
| Mast | Beginning | 101.93 | 57.87 |
| Peak | 75.09 | 54.52 | |
| End | 70.82 | 51.94 | |
| Toulouse | Beginning | 98.40 | 57.32 |
| Peak | 79.41 | 56.19 | |
| End | 75.01 | 53.80 | |
| Linda | 74.11y | 64.09x | |
| Mast | 82.62x | 54.77y | |
| Toulouse | 84.27x | 55.77y | |
| Beginning | 92.11a | 59.82a | |
| Peak | 76.71b | 58.71ab | |
| End | 72.17c | 56.10b | |
| SEM | 0.644 | 0.549 | |
| p | |||
| Genotype | *** | *** | |
| Laying stage | *** | * | |
| Genotype × laying stage | *** | — | |
Note: Difference between values with different letters (x,y) on the same column is statistically significant for genotypes. Difference between values with different letters (a,b,c) on the same column is statistically significant for laying stages.
Abbreviation: SEM, standard error of mean.
p > 0.05.
p < 0.05.
p < 0.001.
4. Discussion
In geese, where all eggs are used for hatching, egg weight is of greater importance compared to other poultry species, as it integrates both reproductive efficiency and early post‐hatch viability. A significant positive correlation was described between egg weight and hatching weight (Onbaşılar et al., 2014). As egg weight increases, gosling weight also increases, which is generally associated with improved early growth performance, positively affecting the gosling's performance during the fattening period. In all three laying stages, Linda goose eggs were found to have lower weights compared to the eggs from the Mast and Toulouse genotypes. Rather than indicating a direct causal mechanism, this difference may be associated with genotype‐related variation in reproductive capacity, as the rate of oocyte growth and albumen secretion in the oviduct is vital. Reduced egg weight can negatively impact fattening performance, suggesting potential production‐level implications, indicating that lighter eggs from Linda geese may have practical consequences for gosling growth efficiency. Additionally, according to Salamon (2020), small goose eggs (<140 g in 1‐year‐old geese and <150 g in 2‐year‐old or older geese) are considered unsuitable for artificial incubation due to their low hatchability. Consistent with this threshold, our findings showed that egg weights in Mast and Toulouse geese exceeded 150 g only at the beginning of the laying stage, highlighting that egg size constraints may increasingly limit incubation success toward the end of the laying period rather than across genotypes alone.
In chickens, egg weight increases as the laying stage progresses (Şekeroğlu et al. 2024; Tainika et al. 2024). However, in geese, the opposite pattern has been observed. In all three genotypes, egg weight decreased as the laying stage progressed. This decrease was very slight in Linda geese, leading to an interaction between genotype and laying stage. This interaction likely reflects baseline differences in egg weight rather than a fundamentally distinct temporal response, as the initial egg weight in Linda geese was much lower compared to the other goose breeds. Mroz and Lepek (2003) conducted a study with Polish geese and observed a significant decline in egg weight from early to late laying, consistent with our findings. Similarly, Liu et al. (2021) noted that age‐related changes in ovulation and uterine secretions may influence egg weight across laying seasons, supporting the view that temporal changes are a general physiological phenomenon rather than genotype‐specific anomalies.
High egg‐breaking strength is particularly important in geese due to the limited number of eggs produced annually, as any mechanical loss directly reduces reproductive output. Reduced eggshell thickness compromises egg durability and increases susceptibility to bacterial penetration, thereby negatively affecting embryo viability (Taşdemir et al. 2021; Coulibaly et al. 2024). Eggshell thickness depends on calcium absorption and its transfer to the shell (Vieco‐Galvez et al. 2021), as well as the efficiency of the shell gland calcification process in the goose oviduct (Reshag and Khalaf 2021). On the basis of these findings, eggs obtained from the Toulouse genotype are expected to be more resistant to external conditions; however, this structural advantage should be interpreted as a supportive trait rather than a direct determinant of hatchability.
Yolk characteristics also varied among genotypes. The higher yolk ratio in the Toulouse and Mast genotypes may contribute to enhanced nutrient availability for the developing embryo. Conversely, the lower yolk percentage in eggs from Linda geese was accompanied by a reduced yolk index. These differences reflect variation in egg component allocation rather than direct evidence of altered embryonic metabolism. Albumen quality showed distinctive genotype‐related differences as well. The higher albumen pH observed in Mast geese may be associated with a relatively weaker vitelline membrane, which regulates the diffusion of substances between yolk and albumen. A weakened vitelline membrane increases permeability and facilitates migration of yolk components into the albumen (Dang et al. 2023), although direct measurements of membrane integrity were not performed in the present study.
The Haugh unit, a widely used indicator of albumen quality (Samli et al. 2005), declines with decreasing albumen protein quality (Akter et al. 2014). Despite superior shell and yolk traits in Toulouse geese, lower albumen index and Haugh unit values were observed, indicating that egg quality traits do not necessarily improve synchronously and should be interpreted collectively rather than individually.
Toward the end of the laying stage, all quality characteristics except shell breaking strength and thickness deteriorated. This suggests that hatching eggs obtained late in the laying period may be suboptimal for embryo development. The genotype‐dependent variation observed in albumen index and Haugh unit over time indicates an interaction between genotype and laying stage. Salamon (2020) similarly reported a decline in goose egg quality as the laying season progressed, supporting the concept that temporal deterioration may outweigh genotype effects in late production stages.
Analysis of egg nutrient composition showed that genotype did not significantly affect yolk protein and fat content or albumen protein content. These values were consistent with previously reported ranges for different goose genotypes (Lu et al. 2025). Therefore, the lack of significant genotype‐related differences in egg protein and fat levels is more likely attributable to biological uniformity under controlled feeding conditions rather than a true absence of physiological relevance. Embryo development relies on nutrient availability from yolk and albumen compartments (Onbaşılar et al. 2018a; Yadgary and Uni 2012). Albumen proteins play a critical role in embryonic tissue synthesis (Willems et al. 2014). As the laying stage progressed, decreases in protein content in both yolk and albumen were observed, which may limit embryonic growth potential rather than directly causing reduced hatchability. Mazanowski et al. (2005) similarly reported reductions in yolk protein content toward the end of the laying season.
The yolk represents the primary lipid and energy source for embryonic development (Kucharska‐Gaca et al. 2022). The results indicate that laying stage is the primary determinant of egg fatty acid composition, with genotype‐related differences becoming most apparent during peak production. Multivariate analyses showed clear separation of fatty acid profiles across laying stages, whereas genotype effects were stage‐dependent and strongest at peak lay. The absence of significant differences in multivariate dispersion confirms that these patterns reflect true compositional shifts rather than differences in variability. SIMPER analysis revealed that genotype‐related differences at peak lay were driven by a small number of major fatty acids, particularly C16:0, C18:0, C18:1n‐9 and C18:2n‐6, which together explained most of the observed dissimilarity. These fatty acids represent key structural and metabolic lipid components, suggesting that genotype influences core lipid metabolism rather than minor constituents. Univariate GLM analyses supported the multivariate findings, showing significant main effects of laying stage and genotype for several fatty acids after FDR correction, with moderate to large effect sizes. Although genotype × laying stage interactions did not remain significant after correction, trend‐level effects were observed for selected fatty acids, consistent with the marginal interaction detected in PERMANOVA. Analyses of lipid classes and nutritional indices largely mirrored the patterns observed for individual fatty acids, reinforcing the biological relevance of the compositional changes. Overall, these findings demonstrate that egg fatty acid composition is shaped predominantly by physiological stage, with genotype‐specific differences emerging under peak production conditions. Analysis of summary lipid classes and ratios further supports the dominant role of laying stage in shaping egg lipid quality. The PUFA/SFA ratio, total PUFA and UFA/SFA were significantly higher during the peak laying stage compared with the beginning of lay, indicating a shift toward a more unsaturated and nutritionally favourable lipid profile at maximal production. In contrast, genotype and genotype × laying stage interaction effects were not significant for these aggregated indices, suggesting that genetic differences observed at the level of individual fatty acids are attenuated when fatty acids are considered functional groups. The lack of genotype effects for summary indices indicates that, whereas genotype influences the relative contribution of specific fatty acids, the overall balance between saturated and unsaturated fractions remains largely conserved across genotypes. This finding aligns with the multivariate and univariate analyses, where genotype‐related differences were driven primarily by a limited number of major fatty acids rather than broad shifts in lipid classes. Together, these results highlight laying stage as the principal determinant of egg lipid nutritional quality, with genotype exerting more subtle, compositional effects that do not substantially alter global lipid indices. The laying stage exerted only minor effects on fatty acid composition. However, lower ΣPUFA and ΣPUFA/ΣSFA ratios at the beginning of the laying stage may be considered unfavourable, as PUFAs are essential for neural and visual development of embryos (Santos‐Silva et al. 2002). Nevertheless, no direct association between PUFA levels and hatchability was demonstrated in the present study. It is considered desirable for AI and TI values to remain below 1 (Boz et al. 2019). In eggs from all the examined genotypes, the AI value was below 0.56, whereas the TI value ranged between 1.19 and 1.31. Razmaitė et al. (2014) reported that the AI value in egg yolks from 3‐year‐old geese was 0.42, and the TI value was 0.85. The NV index further indicates that the fat content of goose eggs is healthy (Boz et al. 2021). In the present study, NV index ranged from 1.88 to 2.16 across all examined genotypes.
Fertility and hatchability are key economic traits and fundamental elements of reproductive success. Although several fatty acid differences reached statistical significance, their biological relevance should be interpreted cautiously, as these variations were relatively small and not directly linked to hatchability outcomes. The hatch results of all three genotypes were similar in each of the three laying stages. However, in the last laying stage, there was a significant decrease in the number of fertile eggs and hatching results in all three genotypes. It was observed that the negative changes in the internal quality and composition of eggs obtained at the end of the laying stage were reflected in the hatch results. Although hatchability rates were comparable among genotypes, the commercial relevance of Toulouse eggs lies in their greater egg weight, superior shell strength and higher gosling weight. These traits are directly associated with improved post‐hatch growth potential and reduced handling losses, which are economically important in commercial goose production systems. Hatchability and gosling weight were considered primary outcome variables, whereas egg quality traits, chemical composition and fatty acid profiles were evaluated as supporting parameters to aid biological interpretation. Previous studies have also reported age‐related differences in fertility. Eroglu and Erişir (2022) demonstrated fertility rates of 83.51%, 85.92% and 86.66% among breeders of different ages (1, 2 and 3 years old, respectively). Similarly, Biesiada‐Drzazga et al. (2016) reported fertility rates of 79.4%, 82.5% and 82.5% in breeders of 1, 2 and 3 years old, respectively. Seasonal effects on fertility and hatchability have also been documented. Boz et al. (2019) reported fertility rates in February, March, April and May as 76.33%, 76.34%, 65.69% and 69.80%, respectively. Hatchability of fertile eggs during these months was 77.32%, 74.25%, 61.23% and 60.87%, whereas hatchability of set eggs was 59.32%, 57.0%, 40.24% and 44.48%. Variations in rearing conditions and breeder age may account for discrepancies in study results.
In the present study, it was determined that in the Linda genotype, along with low egg weight, gosling weight was also low. However, the gosling percentage was found to be higher in this genotype. Similarly, parallel to the progression of the laying stage, both gosling weight and percentage decreased.
5. Conclusion
Overall, genotype effects on hatchability were limited under the conditions of this study. Genotype‐related differences were mainly observed in egg weight, gosling weight, selected egg quality traits and fatty acid composition, indicating that genotype selection remains relevant for traits linked to post‐hatch performance rather than hatchability itself. In the present study, lower egg weights observed in Linda genotype eggs during the late laying stage were associated with reduced gosling weight at hatch, highlighting egg weight as a practical criterion for commercial incubation decisions. Furthermore, the laying stage significantly influenced egg characteristics, suggesting that incubation management, including egg storage conditions, should be adjusted according to both laying stage and genotype. Given the limited annual availability of goose eggs, strategies to mitigate the negative effects observed toward the end of the laying period are warranted.
Author Contributions
Study conception, design, material preparation, data collection, data analysis and writing of the manuscript were performed by Sabiha Gülanar Aslan, Esin Ebru Onbaşılar and Sakine Yalçın.
Funding
This study was supported by Ankara University Scientific Research Projects (TDK‐2023‐2806).
Ethics Statement
All research reported in this research has been conducted in an ethical and responsible manner and is in full compliance with all relevant codes of experimentation and legislation. All animal experiments were approved by the Animal Care and Use Committee of Ankara University (2023‐1).
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Supporting Table 1: SIMPER results for the end of laying stage.
Acknowledgements
This study was summarized from the PhD thesis of the first author.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting Table 1: SIMPER results for the end of laying stage.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.