A multivariate model for the prediction of pregnancy following laparoscopic artificial insemination of sheep.

A multivariate model for the prediction of pregnancy following laparoscopic artificial insemination of sheep. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A multivariate model for the prediction of pregnancy following laparoscopic artificial insemination of sheep. E.A. Spanner, S.P. de Graaf, J.P. Rickard This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4821205/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The causes of variation in the success of laparoscopic artificial insemination (AI) in sheep are not well understood. As such, this study incorporated the contributions of multiple male and female factors relevant to the success of AI into a comprehensive prediction model for pregnancy success. Data from Merino ewes (N = 30 254) including age, uterine tone (1; pale/flaccid-5; turgid/pink), intra-abdominal fat (1; little to no fat present-5; high fat), time of insemination and sire used, were recorded during AI. A subset of semen per sire (N = 388) was thawed and assessed for volume, subjective motility, sperm concentration, and morphology. Sperm motility (CASA), viability and acrosome integrity (FITC-PNA/PI), membrane fluidity (M540/Yo-Pro), mitochondrial superoxide production (Mitosox Red/Sytox Green), lipid peroxidation (Bodipy C11), level of intracellular reactive oxygen species (H 2 DCFDA) and DNA fragmentation (Acridine Orange) were also assessed 0, 3 and 6h post-thaw. Logistic binomial regression revealed sperm concentration (P < 0.001), CASA parameters at 0h (PCA3; P = 0.03), viable acrosome intact sperm at 6h (P = 0.02), abnormal morphology (P < 0.001), uterine tone (P < 0.001) and intra-abdominal fat (P = 0.03) of ewes influenced likelihood of pregnancy. Results generated will help standardise the pre-screening and selection of semen and ewes prior to artificial breeding programs, reducing variation in the success of sheep AI. Biological sciences/Biotechnology/Animal biotechnology Biological sciences/Genetics/Animal breeding Sheep Sperm Morphology Concentration Acrosome Viability Uterine tone Intra-abdominal fat Motility Laparoscopic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The efficient and sustainable production of sheep requires consistent genetic improvement, most rapidly achieved through the application of assisted reproductive technologies, such as laparoscopic artificial insemination. It is generally accepted that 70% of ewes inseminated via laparoscopic AI should fall pregnant [ 1 ], yet variation in success is apparent between geographical regions, across breeding seasons, sires and even ejaculates of the same sire [ 2 – 5 ]. This uncertainty surrounding the reliability of AI outcomes has contributed to waning adoption and subsequent negative flow on effects to the rate of genetic and production gains to the national flock. Identifying specific female and male fertility factors, particularly in vitro semen characteristics which are linked to pregnancy success following AI, would enable producers and breeding companies to screen sires and frozen samples prior to breeding programs, eliminating samples likely to give sub-optimal results. This would help reduce variability in program success and give the industry greater confidence in the application of artificial insemination. The ability to predict the success of AI based on a sire's semen characteristics or ewes’ condition has long been sought. While several factors are known to influence fertility both during natural and artificial insemination [ 3 , 4 , 6 – 34 ], correlations to fertility outcomes in sheep have been largely contradictory and fail to consider multiple male and female factors in the same study, comparing within the individual ewe rather than flock average. Our previous work [ 35 ] analysed data collected on ewes during AI and showed uterine tone (considered a proxy for the physiological response of ewes to the oestrous synchronisation protocol) to be an important indicator of AI success. Ewes, which scored a uterine tone of 4 or 5 at the time of AI, recorded a 12.41% increase in pregnancy rate compared to ewes, which scored a uterine tone of 1 or 2 (P < 0.05). Similarly, ewes that scored a uterine tone of 3 recorded notably lower pregnancy rates compared to ewes with a uterine tone score of 4 or 5 (P < 0.05). This recent study also highlighted the large proportion of variation in pregnancy rate contributed by the site or location where AI was occurring and the sire used. It is therefore now imperative to assess the influence of ewe factors, like uterine tone, on fertility in conjunction with the in vitro characteristics of the semen used for AI in the same model. Today, with the advance of objective semen assessment techniques, there is a wide variety of semen parameters known to collectively define a ‘fertile’ spermatozoon [ 36 ]. Previous research on bulls [ 37 – 39 ], rams [ 12 , 40 , 41 ], stallions [ 42 , 43 ], and boars [ 44 ] have all identified a correlation between sperm motility and velocity parameters [ 41 , 45 – 47 ], as well as sperm morphology [ 48 – 50 ] with pregnancy success. Additional research has also looked at the concentration at which sperm is frozen prior to AI, as a proxy for insemination dose [ 7 , 8 ] impacting pregnancy success. However, the use of flow cytometry and intracellular fluorochromes now enables us to study alterations in sperm membrane phospholipids [ 51 , 52 ], cell viability, acrosome integrity [ 13 , 53 ], DNA fragmentation [ 54 ], and measures of excessive reactive oxygen species [ 40 , 55 ] and mitochondrial function [ 56 ]. While certain studies have adeptly assessed the influence of these specific semen characteristics on ram fertility following AI [ 13 , 15 , 53 , 57 , 58 ], limited standardisation in their application across studies has contributed to contradictions in their described effect on fertility. Notably, stemming from the multitude of tests available for a single trait, a lack of direct comparison between samples inseminated and analysed, as well as reduced sample sizes, have limited the likelihood of successful fertility prediction [ 36 ]. As such, the present study sought to determine the influence of female data collected during AI and in vitro semen assessment characteristics post-thaw on the likelihood or probability of pregnancy occurring following laparoscopic AI in sheep. Results will lead to a better understanding of which in vitro semen traits correlate to fertility and, therefore, the fertility potential of a particular frozen-thawed sample. The design of a model to predict AI would also facilitate the identification of accurate standards for the sheep artificial breeding community. When optimal semen is combined with a fertile, well-conditioned ewe, the likelihood of pregnancy should increase, reducing the variability of AI programs and reproductive failure. 2. Methods 2.1. Ethics and Animals The data used in this research was generously donated by artificial breeding companies and stud breeders during routine commercial AI operations. Animals are not directly involved in this study, as such, no additional ethical approval was required. The management of ewes and rams adhered to the standard industry practices and requirements for each site, and all methods complied with relevant guidelines and regulations. All methods are reported in accordance with the ARRIVE guidelines. 2.2. Breeding Season and Location of the Animals AI data was collected in Australia between November and April during 2020-21 (N = 9 817 ewes, 123 sires, 10 sites), 2021-22 (N = 8 253 ewes, 116 rams, 9 sites) and 2022-23 (N = 12 184 ewes, 149 rams, 11 sites). Sites were located in the Central and South Wheat Belt of WA, Central North of Victoria, Murray Land Yorke Peninsula of South Australia and the Central West, Tablelands and Northwest Slopes and plains of NSW. Animals were selected and managed for artificial breeding programs as per individual commercial stud preferences. 2.3. Experimental Design Merino ewes (N = 30 254, split across three breeding seasons and 30 commercial AI programs conducted on farms located in NSW, VIC, SA and WA) were synchronised for oestrus and assessed for uterine tone, intra-abdominal fat, age, PMSG dose and time of insemination post-CIDR removal as part of routine artificial insemination protocols, as per the previous study [ 35 ]. Following industry standards, ejaculates from Merino sires (N = 388) were collected and immediately laparoscopically inseminated (fresh; N = 29 ejaculates) or frozen (N = 359) as either pellets (N = 239) or straws (N = 120), thawed and then laparoscopically inseminated into ewes. Parameters including season, day, site, sire and type of semen used, uterine tone, and intra-abdominal fat were recorded during AI. Synchronised ewes (0.3g progesterone CIDR; Zoetis, Australia and eCG; Minitube, VIC, Australia) received approximately 0.2mL of semen per uterine horn. Each ewe underwent pregnancy scanning approximately 55 days post AI using standard industry practice. Approximately 2 pellets or 5 straws per batch per sire used for insemination were sent to The University of Sydney, for advanced in vitro semen assessment. 2.4. Assessment of Ewe Factors 2.4.1. Assessment of ewe age The colour of the ear tag located in the left ear of each ewe indicated the year of birth. Each colour represents a year of drop (Table 1 ). This was then subtracted from the current year (2020, 2021, 2022, or 2023) of AI to determine the age of each ewe at AI. Table 1 Year of drop coloured electric ear tag identification for sheep (modified from [ 105 ]). Year of Drop Colour Ear Tag 2017 White 2018 Orange 2019 Green 2020 Purple 2021 Yellow 2.4.2. Assessment of intra-abdominal fat score During laparoscopic AI, the internal fat covering the abdominal organs was visualised and subjectively assessed by the technicians performing the insemination, scoring the ewe between one (little to no fat present) to five (high abundance of fat present) (Supplementary File 1) as per the pervious study [ 35 ]. 2.4.3. Assessment of uterine tone score At the same time as the intra-abdominal fat assessment, the tone of the uterus was scored as a subjective observation by the technicians and recorded as a value between one (pale, flaccid uterus) to five (bright pink turgid uterus) (Supplementary File 2) as per the previous study [ 35 ]. 2.4.4. Assessment of AI time post-CIDR removal As per the previous study [ 35 ], during oestrous synchronisation, each ewe was assigned a CIDR pull group to ensure ewes were inseminated within the optimal time frame. At the time of insemination in the cradle, the eID tag of each ewe was scanned using a Tru-Test XRS2 ( Tru-Test Datamars, Australia ) giving a time stamp for data collection in the cradle. To determine the time of AI post-CIDR removal, this was subtracted from the end time of the CIDR pull group each ewe was allocated. This was standardised across each CIDR pull group across all programs. Data was presented as hh:mm:ss post CIDR pull. 2.5. Advanced in vitro Assessment of Semen Characteristics Post-thaw A subset of the semen used from each sire was stored and thawed within the same breeding season and the AI program. Pellets (n = 2) were thawed in a glass thawing tube for 2 minutes in a 37ºC water bath with agitation, while straws (n = 4 straws) were thawed for 30 seconds in a 37⁰C water bath with agitation. The total volume per sample was recorded by suspending the sample in a pipette prior to being diluted 1:0.5 with PBS + 0.3% BSA (Phosphate Buffed Saline + 0.3% Bovine Serum Albumin; pH 7.4, osmolarity 297). This was then held at 37ºC over a 6h incubation period. Following an initial assessment of sperm concentration (described below; 3.5.1), an aliquot of each sample was taken at 0, 3 and 6h post-thaw and further diluted to 50×10 6 spm/mL with PBS + 0.3% BSA. 2.5.1. Assessment of sperm concentration, subjective motility, and percentage of abnormal morphology post-thaw The concentration of each frozen sample was determined using a NucleoCounter SP-100 (ChemoMetec) immediately post-thaw. 50 µl of semen was diluted with S100 reagent (ChemoMetec) and analysed according to the manufacturer's instructions. Subjective motility was first assessed after the initial 1:0.5 dilution with PBS + 0.3% BSA as well as following dilution of the sample to 50×10 6 spm/mL at each time point. The percentage of motile spermatozoa was subjectively assessed using a phase-contrast microscope (x100) described by Evans and Maxwell (1987). Samples (6 µL) were placed on slides and enclosed using a 22 x 22 mm coverslip warmed to 37⁰C. Values were obtained to the nearest 5% by examining five fields of each sample (kept on a heated slide and coverslip at 37⁰C). After thawing, 10uL of semen was fixed with 190uL (1 + 10 dilution) in 3% NaCl. Within a 24h timeframe, the percentage of abnormal spermatozoa was subjectively assessed using a phase-contrast microscope (x400). Capturing a minimum of 200 cells, the results were converted into the percentage of the sample containing spermatozoa with abnormal morphology. Morphological defects include head defects (detached heads, acrosomes reacted, amorphic heads), damaged midpieces (proximal droplets, bent midpieces) and tail abnormalities (distal reflex, coiled tails, broken tails). As such, this was a measure of the proportion of abnormal spermatozoa. 2.5.2. Assessment of sperm motility and kinetic analyses using a computer assisted sperm analysis (CASA) Sperm motility was measured using the computer-assisted sperm analysis ( HT CASA IVOS II (Animal Breeder) Version 1.13.7; Hamilton-Thorne, USA ) using the appropriate settings for ram spermatozoa (this includes amongst others; head size 10–42 µm 2 , progressive motility thresholds of straightness 80% and average path velocity 75 µm/s). Samples were further diluted to a concentration of 25×10 6 spm/mL with PBS + 0.3% BSA before 6 µL was placed on slides warmed to 37⁰C (Cell Vu; Millennium Sciences, Mulgrave, Victoria, Australia) and enclosed with a 22 x 22 mm coverslip. For each sample, eight fields of video recordings were recorded, capturing a minimum of 200 cells (frame rate 60 Hz). Motility and kinematic parameters were subsequently calculated including total and progressive motility, ALH, BCF, LIN, STR, VAP, VCL, VSL, and WOB. 2.5.3. Flow cytometric analysis Samples were assessed for a range of membrane and metabolic indicators at a final concentration of 10×10 6 spm/mL following staining with various fluorochromes. Using the CytoFLEX ( CytoFLEX and CytExport 2.0 Software Beckman Coulter; USA ), three lasers were employed; 50 mW 488nm, 50 nW 638 nm and 80 mW 405 nm. All samples were stained with the DNA probe Hoechst 33342 (final concentration of 1µg/mL), which has a fluorescence detection filter of 450/45 BP, to gate any possible debris in the sample. Sperm cells were isolated from total events based on 488nm forward and side scatter profiles. For each of the below variables, 10,000 sperm cells were analysed, and a minimum of 1000 Hoechst 33342 positive events were required to obtain valid results. 2.5.3.1. Assessment of sperm acrosome integrity and viability Sample preparation for sperm viability and acrosome integrity was performed as previously described [ 59 ], by staining a combination of Propidium Iodine (PI, final concentration 6µM) and Fluorescein isothiocyanate peanut agglutinin (FITC-PNA, final concentration 0.4 µg/mL) for 10 minutes at 37⁰C. PI and FITC-PNA fluorescence detection was on 690/50, and 525/40 nm bandpass (BP) filters, respectively. Cells were considered viable with intact acrosomes if cells were both PI and FITC-PNA negative. 2.5.3.2. Assessment of sperm membrane lipid fluidity Changes in lipid fluidity within the membrane of viable spermatozoa were assessed using a staining combination of both merocyanine 540 (M540, final concentration 0.83 µM) and Yo-Pro (final concentration 25nM) for 10 minutes at 37⁰C. The fluorescence of M540 and Yo-Pro was detected on a band-pass filter of 585/42 nm and 525/40 nm, respectively. A sperm population was considered viable if it was recorded as Yo-Pro negative. The median value for M540 fluorescence of this viable population was used to determine the relative membrane lipid fluidity. Results with a greater mean value corresponded to greater lipid destruction on the membrane, thus, greater membrane fluidity. 2.5.3.3. Assessment of mitochondrial superoxide production Mitochondrial superoxide production was assessed using a dual stain combination of Mitosox Red (final concentration 2.5 µM) and Sytox green (final concentration 30 Nm) for 20 minutes at 37⁰C. Mitosox Red fluorescence was detected at 585/42 and Sytox Green fluorescence on 525/40 BP filters. A sperm population of Sytox Green negative, “live”, was used to determine the median Mitsox Red fluorescence value relative to the amount of mitochondrial superoxide production. A positive control was created by combining each sample with 5 µM hydrogen peroxide to stimulate mitochondrial superoxide production. The positive control was used to assess the effectiveness of the stain and determine the appropriate gating of stained populations. 2.5.3.4. Assessment of lipid peroxidation Lipid peroxidation of the sperm membrane was assessed using Bodipy C11 (581/591). Samples were aliquoted and stained with the Bodipy C11 probe (final concentration 10 µM) at 37⁰C for the entirety of the 6h assessment. At each time point, the samples had a timed incubation for 30 minutes. Once staining was complete, samples were centrifuged for 10 minutes at 800 g. After removing the supernatant, each pellet was resuspended in a PBS + 0.3% BSA buffer and counterstained with PI (final concentration 6 µM) for 10 minutes at 37⁰C before running on the CytoFlex. The detection of lipid peroxidation was measured by both 585/42 and 525/40 bandpass filters. The live population was first gated and used to determine the percentage of cells with positive Bodipy C11 fluorescence. This indicated the relative change in lipid peroxidation of a given sample. A positive control was made up by combining aliquots of all samples and incubated with 5 µM hydrogen peroxide to induce greater lipid peroxidation. The positive control was used to assess the effectiveness of the stain and determine the appropriate gating of stained populations. 2.5.3.5. Assessment of intracellular reactive oxygen species (ROS) To determine the relative amount of oxygen species (ROS) within a cell, a staining combination of dichlorodihydrofluorescein diacetate acetyl ester (H 2 DCFDA final concentration 5 µM) and PI (final concentration 6 µM) was used. Samples were aliquoted and stained with the H 2 DCFDA at 37⁰C for the entirety of the 6h assessment. At each time point, the samples had a timed incubation of 1h before being centrifuged for 10 minutes at 800 g. After removing the supernatant, pellets are resuspended in a PBS + 0.3% BSA buffer and counterstained with PI (final concentration 6 µM) for 10 minutes at 37⁰C before running on the CytoFlex. The H 2 DCFDA fluorescence was determined on the 525/40 bandpass filter. The H2DCFDA fluorescences in live cells (PI negative) was used to measure intracellular ROS production. A positive control was made up by combining aliquots of all samples and incubated with 5 µM hydrogen peroxide to induce greater ROS. The positive control was used to assess the effectiveness of the stain and determine the appropriate gating of stained populations. 2.5.3.6. Assessment of DNA integrity At 0 and 6h post thaw, 40 µL of each sample (50×10 6 spm/mL) was aliquoted for DNA assessment. Each sample was washed by resuspending the sample in 2mL of PBS + 0.3% BSA and centrifuging for 10 minutes at 800 g. The supernatant was removed before resuspension and repeat centrifugation After the final spin, the supernatant was removed, and pellets (final concentration approx 2×10 6 sperm) were snap-frozen in liquid nitrogen for 30 seconds before being stored at -80⁰C until assessment. DNA fragmentation was measured using flow cytometry on a ( Cytek Aroura 3L; Sydney Flow Cytometry ) after staining with Acridine Orange (AO), as described by Evenson and Jost (2000), with some minor changes. In summary,, snap frozensamples were diluted to a concentration of 2×10 6 spm/mL with a TNE buffer (0.15 M NaCl, 0.01 M Tris HCl, 1mM disodium EDTA pH 7.4). A 100uL aliquot of the sample was taken and diluted with 200uL of Acid Detergent Solution (0.08 NHCl, 0.15 M NaCl, 0.1% Triton X 100 pH 1.2), which was gently mixed by swirling in hand for 30 seconds. Once mixed, samples were stained with 600uL of Acridine Orange (final concentration 6 µg/mL) for 3 minutes before being assessed using flow cytometry. Green (B2) and Red (V11) fluorescence were detected using the 528/21 and 644/27 band pass filters, respectively. The flow rate was set to slow, and a minimum of 1000 cells were recorded per sample. DNA fragmentation was determined by the relative amount of single-stranded DNA (ssDNA) in proportion to the total amount of spermatozoa (dsDNA + ssDNA) and indicated by the amount of red fluorescence regarding the total amount of fluorescence. 2.6. Measure of Fertility Depending on the AI program and site, the pregnancy status per ewe was determined approximately 55 days post-insemination. Ewes were fasted 24hrs prior to scanning. A real-time cutaneous ultrasound (Oviscan 6 with a 3.5 MHz probe) was used to scan each ewe to determine the presence of fetuses and their number. As per the previous study [ 35 ], pregnancy from AI was recorded as either 1 (pregnant) or 0 (empty), while the number of fetuses observed was recorded as the exact number. 2.7. Statistical Analysis Ewe ID was matched between AI and pregnancy datasets while sire ID was matched between AI and in vitro semen analysis datasets to create one Masterfile. All data was therefore compared within the individual ewe. Data was then cleaned to remove ewes without pregnancy data. All statistical analyses were performed on R Studio (Version 2023.09.1 + 494). In accordance with the previous study [ 35 ], the overall pregnancy data was assessed to determine average pregnancy and reproductive rates. AI success was determined by calculating the number of ewes pregnant over the total number of ewes inseminated. The reproductive rate was determined by calculating the number of offspring (fetal number) over the total number of ewes inseminated. Descriptive statistics were performed to evaluate the number of ewes inseminated and the percentage pregnant for each categorical factor level recorded, as well as site, sire ID and breeding season. All values included mean ± standard error of the mean (SEM) and were de-identified for anonymity. At 0, 3 and 6h post-thaw, the relationships between in vitro semen traits were assessed by Spearman rank correlation. As the CASA measurements were highly correlated, measures of total motility, progressive motility, ALH, BCF, LIN, STR, VAP, VCL, VSL, and WOB were combined using a Principal Component Analysis (PCA, Table 2 ) to reduce multiple testing bias and collinearity. The first 3 principal components were significant (eigenvalues > 1), and together accounted for 92% of the variation in the data (Table 2 ). PCA1, accounted for 54.92% of the variation and was interpreted as a composite measure of sperm velocity, with positive loadings (< ±0.30) from WOB, VSL, VAP, STR, LIN and BCF (Table 2 ). PCA2, accounting for 24.73% of the variation, had a strong positive loading of VCL, VAP and ALH (Table 2 ), and again is interpreted as a measure of sperm velocity. PCA3 contributed 12.90% of the variation and had a strong negative loading from Total Motility and Progressive Motility (-0.70 and − 0.53, respectively), interpreted as a measure of sperm motility. The significant components were subsequently used in the regression analyses. Table 2 Eigenvalues and variances explained by the first 3 PCAs at 0h post-thaw, along with the loading of each measurement within the PCA for CASA motility and velocity traits. PC1 PC2 PC3 Eigenvalue 5.49 2.47 1.29 Variance (%) 54.92 24.73 12.90 Total Motility 0.21 0.21 -0.70 Progressive Motility 0.29 0.23 -0.53 ALH -0.29 0.45 0.01 BCF 0.32 0.05 0.05 LIN 0.39 -0.21 0.09 STR 0.38 -0.19 0.11 VAP 0.32 0.37 0.26 VCL 0.03 0.60 0.26 VSL 0.36 0.28 0.26 WOB 0.39 -0.20 0.08 ALH, amplitude of lateral head displacement; BCF, beat-cross frequency; LIN, linearity; STR, straightness; VAP, average path velocity; VCL, curvilinear velocity; VSL, straight-line velocity; WOB, wobble; PCA, principal component. A logistical binomial regression analysis was used to examine the influence of female and male fertility traits on the probability of pregnancy post-AI. Additionally, an intraclass correlation coefficient (ICC) analysis was conducted to explain the proportion of variance attributed to individual factors within the random model, encompassing Site, Sire ID including those that were Frozen-thawed, and Thaw Day. A preliminary univariate analysis was then run to determine the impact of each individual factor on pregnancy achievement. The logistical binominal regression model was refined by the backward selection process, eliminating non-significant fixed effect variables and interactions (P > 0.05). The final multivariable model included only significant factors and interactions (P < 0.05). For all variables within the model, an odds ratio was performed to determine the likelihood of change in pregnancy following a single unit change in the factor whilst keeping the model consistent. This included the odds ratio percentage change and 95% CL. For any fixed categorical effects, an Emmeans pairwise comparison was performed to assess the significant difference between groups within the variable. Groups were considered significantly different to each other if the comparison returned a p-value of < 0.05. All values are reported with mean ±SEM. 3. Results 3.1. Overall Descriptive Statistics of the Dataset Data was collected from 30 254 ewes and 388 rams from 30 sites. Table 3 shows the total number of ewes and sires included in the dataset and the resultant fertility following laparoscopic artificial insemination across the 3 breeding seasons. Table 4 displays the mean ± SEM and range for each fertility factor recorded. For each in vitro semen parameter, it tracks the change of each trait 0, 3, and 6h post-thaw. Additionally, Supplementary File 3 describes the percentage of ewes' pregnancy for each level within the categorical factors recorded in the data set. 3.2. Contribution of Variation Caused by Random Terms The ICC analysis was performed on the random model to assess the proportion of total variance contributed by random terms. The proportion of variation in pregnancy success attributed to Site, Sire frozen-thawed, and Thaw Day was 53.57%, 22.02%, and 24.42%, respectively. The level of significance was not assessed on these variations. 3.2.1. The proportion of variation in pregnancy success contributed by site From the multivariable model, the site where data was collected contributed 53.57% of the variation detected between the random terms. With 30 sites across the 3 breeding seasons, the pregnancy rate ranged from 49.89–89.02% (Fig. 1 ). 3.2.2. The proportion of variation in pregnancy success contributed by sire From the multivariable model, 24.42% of the variation detected between the random terms was contributed by the sires with frozen-thawed semen used for AI. Across the 388 sires, the pregnancy rate ranged from 15.88–96.67%, averaging 68.54% (Fig. 2 ). 3.3. Factors within the model found to influence pregnancy following laparoscopic AI of sheep Despite 33 factors returning significant p values when considered in a univariate logistic model (data not shown), only 7 remained significant when included in the same binomial logistic regression model. Sperm freezing concentration (×10 6 spm/mL), the percent of morphologically abnormal spermatozoa , the proportion of viable spermatozoa with intact acrosomes at 6h post-thaw, CASA PCA3 at 0h post-thaw, uterine tone and intra-abdominal Fat of ewes, were found to significantly (P < 0.001, P < 0.001, P = 0.021, P = 0.033, P 0.05). 3.3.1. The impact of the number of sperm frozen on the probability of successful pregnancy The average freezing concentration for a pellet and straw was 722×10 6 ± 15.27 spm/mL, and 270.75×10 6 ± 16.47 spm/mL, respectively. Freezing concentration ranged from 81.29 to 2283.75×10 6 spm/mL and averaged 569.75 ± 2.06 spm/mL. Notably, there was no significant interaction observed between freezing concentration and package type within the model (p > 0.05). Therefore, freezing concentration was considered across package types. Following an odds ratio calculation, it was determined that an additional 100×10 6 spm/mL frozen in either a pellet or straw corresponded to a 5.09% increase in pregnancy probability (OR = 1.05, 95% CI: 1.05 to 1.05, Fig. 3 ). 3.3.2 The impact of abnormal sperm morphology on the probability of successful pregnancy following laparoscopic AI of sheep The average number of morphologically abnormal spermatozoa per frozen-thawed sires sample was 15.46 ± 0.06%, ranging from 2.5 to 70% abnormal spermatozoa. The odds of a 1% increase in abnormal spermatozoa corresponded to a 1.07% decrease (OR = 0.99, 95% CL: 0.98 to 1.00, Fig. 4 ) in the probability of a ewe being pregnant. 3.3.3 The impact of sperm viability and acrosome integrity on the probability of successful pregnancy following laparoscopic AI of sheep At 6h post-thaw, the average percentage of acrosome intact and viable spermatozoa for frozen-thawed sires was 11.74% ± 0.04%, ranging from 0 to 34.64%. An analysis of the data revealed that the odds of a 1% increase in the number of viable sperm with intact acrosomes corresponded to a 1.01% increase (OR = 1.01, 95% CI: 0.34 to 2.56) in the probability of ewes being pregnancy (Fig. 5 ). 3.3.4 The impact of CASA motility and velocity traits (CASA PCA3) on the probability of successful pregnancy following laparoscopic AI of sheep As seen in Supplementary File 4, there is a clear and strong correlation between CASA traits at 0, 3 and 6h post-thaw. Following the formation of the PCA variable (Table 2 ), Although PCA1 and PCA2 explained a significant amount of variation, only PCA3 remained significant in the final model (P = 0.033). PCA3 had a strong negative loading from Total Motility and Progressive Motility (-0.70 and − 0.53, respectively), which is interpreted as a measure of sperm motility. At 0h post-thaw, the average total motility for frozen-thawed sires was 40.77% ± 0.12%, ranging from 5.8 to 89.5%. The average CASA progressive motility for frozen-thawed sires was 30.21 ± 0.10%, ranging from 2.3 to 79.8%. The odds ratio analysis of CASA PCA3 indicated an inverse association with pregnancy outcomes, with the odds of pregnancy decreasing by 0.37% for every standard deviation away from the average for motility (OR = 1.00, 95% Cl: 1.00 to 0.99, Fig. 6 ). This infers that reduced sperm motility characteristics, as represented by higher CASA PCA3 scores, are associated with reduced odds of pregnancy. 3.3.5. The impact of uterine tone on the probability of successful pregnancy Within the model, an increase in uterine tone had a positive effect on the probability of pregnancy rate. The average uterine tone score was 3.22 ± 0.0005, ranging from 1 to 5. As seen in Supplementary File 3, a uterine tone score of 1 + 2 (57.88%, N = 2780 ewes) scored a significantly lower AI success rate than a uterine tone score of 3 (65.20%, N = 12387 ewes) and 4 + 5 (70.29%, N = 7086 ewes) (P < 0.0001, < 0.001, respectively). Additionally, ewes with a uterine tone score of 3 recorded significantly lower probability of pregnancy than ewes which scored a uterine tone score of 4 + 5 (P = 0.0091). The odds ratio of achieving a successful pregnancy was 5.55% higher for ewes that scored a uterine tone score of “3” compared to a uterine tone score of “1 + 2” (OR = 1.06, 95% Cl: 0.59 to 1.88, Fig. 7 ). Similarly, the odds of AI pregnancy were 7.21% higher for the category "4 + 5" compared to "1 + 2" (OR = 1.07, 95% Cl: 0.44 to 2.60, Fig. 7 ). 3.3.6 The impact of intra-abdominal fat on the probability of successful pregnancy following laparoscopic AI of sheep The model also calculated that an increase in intra-abdominal fat had a positive effect on the probability of pregnancy rate. The average intra-abdominal fat score was 3.06 ± 0.005, ranging from 1 to 5. As seen in Supplementary File 3, an intra-abdominal fat score of 1 + 2 (59.36%, N = 3895 ewes) scored a significantly lower AI success rate than an intra-abdominal fat score of 4 + 5 (71.94%, N = 4748 ewes, P = 0.038,). There was no significant difference in pregnancy rate between ewes which scored an intra-abdominal fat score of 3 (65.60%, N = 13385 ewes) and 1 + 2 or 3 to 4 + 5 (P > 0.05). The odds ratio of achieving a successful pregnancy was 5.37% higher for ewes, with an intra-abdominal fat score of “3” compared to an intra-abdominal fat score of “1 + 2” (OR = 1.05, 95% Cl: 0.89 to 1.25, Fig. 8 ). Similarly, the odds of successful pregnancy were 6.81% higher for the intra-abdominal fat category "4 + 5" compared to "1 + 2" (OR = 1.07, 95% Cl: 0.78 to 1.47, Fig. 8 ). 4. Discussion This study investigated the impact of female factors recorded during AI and male in vitro semen traits assessed post-thaw on the probability of pregnancy occurring in sheep. This study has considered male and female fertility traits simultaneously, comparing the pregnancy success of the individual ewe rather than across flock averages. The resultant fertility model indicated that an increase in the number of sperm frozen per pellet or straw, the percentage of viable spermatozoa with intact acrosomes and greater sperm motility will result in a positive linear increase in the probability of achieving pregnancy. Additionally, insemination of ewes with a uterine tone and intra-abdominal fat score of 4 or 5 are more likely to result in a successful pregnancy than ewes, which score 1 or 2. Conversely, a higher proportion of abnormal spermatozoa in a sample is associated with reduced chances of pregnancy. Notably, there is an ongoing contribution of variation by the site or location where the AI was performed, the sire used for AI, and the thaw date. As such, the influence of each fertility factor within the model is further modified depending on these random terms. Nevertheless, the identification of predictive in vitro semen traits can now be used to pre-screen and select sires or frozen semen samples prior to use in a breeding program. This will help to improve the success rates of artificial breeding programs, offering producers an effective tool to increase genetic and production gains in a challenging industry. Further studies will help determine the accuracy and precision of the model to optimise thresholds as semen standards for the artificial breeding sheep industry. The impact of the number of sperm frozen on the probability of successful pregnancy The concentration at which sperm is frozen has the potential to impact cryosurvival post-thaw as well as subsequent insemination dose. In the current study, the number of sperm frozen in either a pellet or straw was found to influence the likelihood of successful pregnancy following laparoscopic AI. For every additional 100×10 6 spm/mL frozen, the probability of achieving pregnancy increased by 5.09% (Fig. 3 ). However, there was no interaction with package type. While there is currently no agreed-upon standard for the industry, it is generally assumed that a pellet should be frozen between 600–800×10 6 spm/mL and used to inseminate approximately 3 ewes. On the other hand, a straw should be around 200–300×10 6 spm/mL and equate to only 1 dose per ewe [ 60 ]. Pleasingly, in the current study, the average concentration of pellets and straws was 722×10 6 ± 15.27 and 270.75×10 6 ± 16.47 spm/mL, respectively, suggesting the data collected accurately reflected current protocols used throughout the sheep artificial breeding industry in Australia. The authors interpret the above result as the number of spermatozoa frozen as a proxy for the insemination dose. For laparoscopic AI, it is recommended that each ewe should receive approximately 25×10 6 motile sperm or 12.5×10 6 motile sperm/horn [ 61 ]. Assessing the sperm concentration of a pellet or straw post-thaw ensures a more reliable insemination dose, equal to or more than 25 million motile sperm. The recommended laparoscopic insemination dose in sheep has remained largely unchanged since the mid-80s, when it first emerged as a reproductive tool for sheep [ 62 ]. Previous studies have demonstrated that an increase in motile sperm dose from 0.5 to 50×10 6 spm resulted in lambing rates of 27–62% [ 61 ]. Which then later, a sperm dose of 20×10 6 spm/mL achieved a rate of 76.8% [ 32 ]. In contrast, other studies have also found no difference in conception rates when doses were reduced from 52.2×10 6 to 13×10 6 spm/mL [ 31 , 63 ]. The lack of significant difference in these studies may be related to the number of ewes used per treatment structure, which limits statistical power, or the compounding effects of sperm type (fresh, liquid stored, or frozen) or diluents used, ultimately making it difficult to compare results across studies. It's important to emphasise that while higher numbers of sperm per pellet or straw frozen theoretically offer increased insemination doses, it's important to find the balance between optimal freezing conditions to ensure sperm survival and effective insemination doses. This concept has been studied abundantly in previous literature [ 7 , 64 – 66 ] across livestock species. Studies [ 7 , 66 ] found that freezing at concentrations above 600×10 6 spm/mL reduced sperm viability, acrosome integrity and motility. Attributed to the excessive build-up of free radicals, it’s proposed to cause changes in the sperm: cryoprotective agents [ 67 ]. The higher the amount of cryoprotectant per sperm cell, the higher the percentage of microdomains (unfrozen water channels), leading to better quality post-thaw [ 65 ]. Alternate studies [ 66 ] reported lambing rates of 57.1% when sperm was frozen at 800×10 6 spm/mL and inseminated at 160×10 6 spermatozoa. This was compared to 81.2% when sperm was frozen at 200×10 6 spm/mL and inseminated at 40×10 6 spermatozoa. Similar results were recorded by standardising doses to 25 million sperm [ 7 ]. Sperm frozen at 200 and 400×10 6 spm/ml recorded a higher lambing rate of 57.5% compared to sperm frozen at 800×10 6 spm/mL, which returned a lambing rate of 45.5%. In any event, it is clear that the concentration at which sperm is frozen directly impacts the insemination dose, which can then further alter pregnancy results. Semen must be frozen at an appropriate concentration to not only mitigate the impacts of freeze-thaw damage on the sperm cell but also optimise the number of sperm per ewe per insemination dose. With an increase in the accuracy and number of technologies currently available on the market that can objectively measure sperm concentration, it should be easier to ensure samples are frozen at accurate concentrations, regardless of package type. Further studies must now take the spread of data collected in the current study and establish thresholds that could be used as standards in the industry. When considered with other factors in the model, this would help to standardise the dose of semen used for artificial insemination and increase the chances of pregnancy success. The impact of the percentage of abnormal spermatozoa on the probability of successful pregnancy The assessment of sperm morphology is common practice during routine basic semen assessment for a number of species, including stallions [ 43 , 68 ], bulls [ 69 ] and boars [ 70 ], yet its correlation with the fertility of frozen-thawed ram spermatozoa has been contradictory [ 11 , 13 , 71 ]. In the current study, results reported that for every 1% increase in the percentage of abnormal spermatozoa within a frozen sample, a 1.07% decrease in the probability of a ewe falling pregnant would be observed (Fig. 4 ). In our study, sperm morphology was classified as either abnormal or normal with this approach aligning with the methods and results reported by previous ram studies [ 52 ]. In this study, a significant difference in the percentage of abnormal ram spermatozoa was reported between groups that exhibited high and low fertility (4.46 ± 0.30% compared to 13.46 ± 1.37%, respectively). Our current study builds upon these results by directly comparing the morphology of samples inseminated per ewe rather than the fertility average of a group of individuals. As reviewed [ 36 ], the morphology of an individual spermatozoon is an important indicator of its fertilising potential and has been proven in a number of species including; deer [ 72 ], bull [ 69 , 73 ] and stallions [ 43 , 68 ] and rams [ 10 , 11 ]. In some species, studies have attempted to further classify abnormalities into different morphological structures, such as head, acrosome, midpiece and tail, to further link types of abnormalities with causes of infertility [ 74 ]. In stallions [ 43 , 68 ], observed aberrations of the head, acrosome, and midpiece, including detached heads, coiled and bent tails, and premature germ cells however, only percent of normal sperm was found to have a significant impact on the percentage of pregnant mares per cycle. This could suggest that better methods of standardised assessment are needed, which consider the protocol and equipment used as well as the assessor's experience before more detailed classifications of morphological abnormalities can be used to predict the fertility of samples. At least, in the cattle industry, standards to measure bull sperm morphology are frequently used to assess and grade the quality of bull samples [ 50 ], To date, nothing of this detail exists for the sheep industry. Thus, the results of the current study are a positive step forward for the industry, providing a comprehensive data set that accurately reflects a negative relationship between increasing morphology abnormalities and sheep fertility following AI. Even more so, the results provide evidence that even basic morphology assessment is a key parameter that should be considered when assessing the quality of ram samples during fertility assessment. The influence of the percentage of viable, acrosome intact spermatozoa on the probability of successful pregnancy The current model revealed that for every 1% increase in the number of viable sperm with intact acrosomes at 6h post-thaw, a 1.01% increase in the probability of pregnancy occurred (Fig. 5 ). A viable, acrosome intact spermatozoon is one that maintains a constant, functioning plasma membrane around the entire cell and contains the presence of acrosomal enzymes along the cellular membrane of the sperm head prior. Combined, it is essential for the normal functioning of the cell, implying that sperm are capable of transitioning to the site of fertilisation, fusing with the zona pellucida, undergoing the acrosome reaction [ 38 , 75 ] and achieving successful fertilisation [ 38 ]. The relationship between sperm viability and fertility following insemination has been extensively proven across multiple species, recording a correlation ‘r’ score of r = 0.32, 0.64, 0.64, 0.05, 0.68 and 0.28 in bulls [ 37 , 38 , 69 ], dairy bulls [ 76 ], stallions [ 77 ] and boars [ 78 ], respectively. Briefly, these studies agreed with that presented in the current study where, the greater the proportion of viable sperm and acrosomal integrity, the greater the probability of pregnancy occurring. Of the literature above, only one paper [ 37 ] successfully measured the viability of bull sperm at 0 and 4h post-thaw. However, as the current study measured viability at 6h, this could more closely imitate the environment sperm are exposed to following deposition within the female tract. In general, sperm are inseminated just prior to a ewe ovulating; therefore, they are required to survive for up to 6-12h before interacting with an oocyte. At least for laparoscopic AI, measuring the level of viability or live: dead with intact acrosomes at 6h would ensure the population of sperm deposited in the uterus was capable of achieving fertilisation after incubation at 37ºC (artificially post-thaw in a water bath or in vivo within the reproductive tract). Opposing this trend, [ 13 ] measured no importance or significance of ram sperm viability to pregnancy rates. Yet importantly, in contrast to the current study, this paper compared sperm viability to previously recorded fertility following cervical AI with a very low number of samples used [ 13 ]. It also raises the question of whether the predictive power of sperm traits can be used interchangeably across insemination methods. Given the path sperm take to achieve fertilisation after deposition in the cervix differs greatly from being deposited directly into the uterine horns, the reliance on specific sperm traits would be altered. This suggests a similar study should be conducted for other reproductive technologies to ensure all methods of AI and physiological responses to sperm transport are accounted for when predicting the fertility of ram sperm. Nonetheless, these results have the potential to establish industry standards which would be crucial for enhancing reproductive outcomes. The impact of motility and velocity traits assessed using CASA (PCA3) on the probability of successful pregnancy The use of a principal component analysis (PCA) for CASA variables underscores the positive impact of sperm motility and kinetic traits on pregnancy likelihood while considering the extreme correlation between factors. Of all the PCAs considered within the current dataset, PCA3 exhibited the greatest influence on pregnancy, primarily driven by total and progressive motility (-0.70 and − 0.53, respectively). Analysis of the odds ratios revealed a 0.37% increase in the odds of pregnancy occurring for every negative standard deviation away from the average (Fig. 6 ). This indicates that as total and progressive motility values increase, the PCA3 loadings or value decreases. Thus, as PCA3 values decline, the likelihood of pregnancy occurring increases. In other words, the model makes biological sense given a sample with high total and progressive motility is also likely to exhibit efficient metabolism of substrates and be more capable of achieving fertilisation [ 79 , 80 ] so the probability of achieving pregnancy increases. Given the importance of sperm motility to sperm function, extensive research in several species has focused on the relationship between motility as determined by CASA and fertility. Studies across livestock species, including bulls [ 38 , 69 , 81 ], stallions [ 43 ], deer [ 72 ], rats [ 82 ], human [ 83 ], salmon [ 84 ] and rams [ 4 ], have all shown similar results to the current study where, as total sperm motility and average path velocity increase, the likelihood of pregnancy also increases [ 38 , 43 , 72 , 81 ]. Previous research [ 4 ] has demonstrated the relationship between motility assessed by CASA and fertility of cryopreserved ram sperm. They saw a direct relationship between an increase in the average-path velocity (VAP), the curvilinear velocity (VCL) and the head beat-cross frequency (BCF), correlated with the percentage of ewes pregnant following AI (R 2 = 0.678, 0.745, 0.852, respectively). This is not always the case in all literature, and some studies [ 13 , 85 ], have reported a lack of correlation between kinematic parameters and fertility following laparoscopic AI in sheep. The contradictory results in previous ram studies are unsurprising, given the different protocols and diluents used for assessment, making it difficult to compare across studies. As such, the need for standardisation across the industry is vital. The Impact of Uterine Tone on the Probability of Successful Pregnancy The present study revealed a clear linear relationship between the uterine tone of ewes at the time of AI and the resultant pregnancy. Pregnancy rates increased from groups 1 + 2 (58.13%), 3 (65.30%) and 4 + 5 (71.04%; p < 0.05, Fig. 7 ). Changes in uterine tone have been previously studied in mares [ 86 – 88 ] and dairy cows [ 89 ], and more recently in ewes [ 35 ]. The current fertility model presented above confirmed the results of our previous study, highlighting the importance of uterine tone even when semen factors are also considered in the same model. The probability of pregnancy occurring in the current study was 7.21% higher for ewes with a uterine score of “4 + 5” compared to those ewes observed to score a uterine tone of “1 + 2". The relationship between uterine tone, sperm quality and fertility is hypothesised to be related to the response of the ewe to the oestrous synchronisation [ 27 ] and coinciding deposition of semen artificially with subsequent ovulation in the ewe [ 90 ]. An increase in uterine tone corresponds to a surge in oestrogen levels, causing epithelial uterine gland cells and rough endoplasmic reticulum to expand [ 91 ] as well as an increase in blood flow to the area [ 35 ]. This, in turn, results in an increase in hypertrophy and contractions, aiding sperm transport from the cervix to the oviduct [ 92 , 93 ]. It also implies ovulation is imminent, signposting optimal insemination time and increased chances of fertilisation [ 35 ] should sperm quality be appropriate. To prove this theory, it would be of interest to conduct further studies to track hormone profile changes, uterine tone alterations and follicular development across the ewe oestrous cycle via laparoscope to pinpoint the exact time between a uterine tone score 4 or 5 and ovulation. Overlaid with the ability of different sperm types to survive incubation, this would enable us to accurately predict the optimum time of insemination in relation to uterine tone, increasing pregnancy success after AI. For now, the results above suggest that uterine tone could be a useful tool for screening ewes prior to insemination. A technician could observe the tone of ewes subjectively (once standardised) and exclude those ewes with a uterine tone lower than 3.5. A more stringent assessment of uterine tone at the time of AI could, therefore, reduce the variability in pregnancy and improve the overall AI success rate. The Impact of Intra-abdominal Fat on the Probability of Successful Pregnancy Similar trends were also observed in the intra-abdominal fat level of ewes undergoing AI. The study revealed a clear linear relationship between intra-abdominal fat and AI success in sheep, showing an increase in pregnancy rate from groups 1 + 2 (59.77%), 3 (65.70%) and 4 + 5 (72.56%, Fig. 8 ). From these findings, the probability of a ewe with an intra-abdominal fat score of 4 or 5 was 6.81% higher than ewes’, which scored an intra-abdominal fat score of “1 + 2”. When considered with the other factors in the model, this would increase the success rate of laparoscopic AI in sheep. The inclusion of intra-abdominal fat in a fertility model is a novel parameter yet to be fully explored in livestock species. Previous research has focused heavily on the use of a ewes’ body condition score (BCS) as the gold standard for pre-breeding soundness or fertility assessments [ 36 ] given it identifies a ewes’ nutritional status, health, and reproductive potential. Several studies in sheep have been able to successfully standardise BCS scores and link to fertility success [ 94 – 96 ], yet limited studies have linked internal fat levels with fertility [ 97 ]. A small proportion of papers have demonstrated a significant correlation between BCS, internal fat deposits [ 98 ] and ultrasound subcutaneous fat depth within the abdominal region of the ewe [ 95 ]. Studies have recommended maintaining a BCS 3–3.5 will help optimise reproductive performance (conception rate, little size, weaning rate and oestrus cycles to conception) and flock profitability [ 99 ]. The current study saw an increase in the likelihood of pregnancy when ewes had an intra-abdominal fat score of 4 or 5. This suggests that while BCS and internal fat score are likely correlated to some degree, the scale used to assess internal fat score means at the upper limits, intra-abdominal fat score should still be considered individually and likely acts as a more accurate predictor for pregnancy following AI. To provide a recommendation or standard for the industry, the current results suggest that intra-abdominal fat levels should be assessed at the time of AI, with those below 3 likely excluded from insemination. When matched with the correct sperm type of adequate quality, this will maximise AI success. Implications for Industry and Future Research Objectives This study is the first to successfully combine both male and female fertility traits into a single logistic regression model to predict the likelihood of pregnancy occurring in a ewe following laparoscopic AI. While this fertility model is a critical first step for industry, the impact of the environment on the success of laparoscopic AI programs must still be considered. Extensive research has investigated the role of the environment, such as heat stress, humidity, cold snaps and rainfall, on sheep fertility [ 100 – 104 ], yet its explicit role during AI has yet to be fully elucidated when male and female factors are considered in the same study. This is likely due to the difficulty involved in accurately and reliably investigating this research question around Australia. Environmental studies involving temperature-controlled rooms are expensive and logistically challenging when considering the current study needed to examine fertility records of over 30,000 sheep to reach significance. Whatever the solution, environmental conditions such as those accounted for in the random model used above (site and day) must be examined in future studies to understand all factors known to influence sheep fertility. To establish thresholds for each variable in our predictive paradigm, the model must first be validated against a collection of ‘unseen data’. Future studies will aim to collect data in the same way as described above and enter it into the model to not only calculate measures of discrimination and calibration but also determine whether there is an overall cumulative effect of these predictors on the probability of pregnancy occurring in a ewe. From here, the spread of data for each variable will be used to set accurate, concise, and viable semen standards to improve the likelihood of pregnancy occurring. Ultimately though, the results presented in the current study now offer the ability to pre-screen rams, their semen samples and ewes prior to use in an artificial breeding program. This will help remove sub-fertile individuals, increasing the chances of pregnancy and the cost-benefits associated with reproductive technologies. With industry consultation, these results could help improve the efficiency of artificial breeding in sheep globally. 5. Conclusion Efficient use of superior genetics is paramount to maximise production benefits. This study has finally revealed the coveted link between in vitro semen parameters, ewe traits, and pregnancy following laparoscopic AI, increasing the potential of artificial breeding programs to reduce breeding inefficiencies and improve sheep reproductive potential. The results of this multivariate logistic regression model can now be used to explain the variation detected in pregnancy success following laparoscopic AI. An increase in freezing concentration (and thus sperm per insemination dose), percentage of viable, acrosome intact spermatozoa and motility kinematics will result in a positive linear increase in the probability of pregnancy occurring in a ewe. In addition, ewes with a uterine tone and intra-abdominal fat score of 4 or 5 are more likely to result in pregnancy than ewes with a score of 1 or 2. Finally, a decrease in the percentage of spermatozoa in a sample with morphological abnormalities will increase pregnancy probability. However, it is still important to consider the overarching variation contributed by the environment, site or location where AI occurred, as well as the sire used, as these factors will further modify the influence of the predictive factors mentioned above. Nevertheless, this fertility model is an important first step in revolutionising the artificial breeding sheep industry, providing a comprehensive dataset to mine trends and establish thresholds to troubleshoot in vitro sperm processing and artificial insemination protocols. Following validation and if used effectively, these results will be able to predict the likelihood of pregnancy occurring following laparoscopic AI, restoring industry confidence and increasing the adoption within the sheep industry. Declarations Acknowledgements: The authors would like to acknowledge the ongoing support, time and donation of data from Merino stud breeders and artificial breeding companies around Australia including Livestock Breeding Services (NSW), Central West Genetics (NSW), Westbreed (WA), Genstock Australia (NSW), Genstock (WA) and Brecon Breeders (SA). They also acknowledge Dr Michelle Humphries, Livestock Breeding Services for her assistance in obtaining imaging for the standardisation of uterine tone and intra-abdominal fat scores. Staff and students within the Animal Reproduction Group, The University of Sydney are also thanked for their dedication, time and assistance towards data collection and advanced semen assessment. Sydney Informatics Hub (SIH), University of Sydney are also acknowledged for their guidance and support on the statistical analysis of data contained within this project. The Australian Wool Innovation (AWI) invests in research, development, innovation and marketing activities along the global supply chain for Australian wool. AWI is grateful for its funding, which is primarily provided by Australian wool-growers through a wool levy and by the Australian Government which provides a matching contribution for eligible R&D activities. Author Contributions: This is the original work of the authors. E.A. Spanner: Conceptualisation, Data collection, Statistical analysis, Writing – original draft, Writing – review and editing. S.P. de Graaf: Conceptualisation, Writing – review and editing. J.P. Rickard: Supervision , Conceptualisation, Data collection, Project administration, Writing – review and editing. Data availability Statement: The dataset generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Financial Disclosure Statement: Dr J.P. Rickard and Miss E.A. Spanner were supported by funding from the McCaughey Memorial Institute. This work was supported by the Australian Wool Innovation [ON-00837] and NSW Merino Breeders’ Association Trust. Competing Interests Statement: The authors declare no competing interests. References K. G. 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Abecia et al. , "Temperature and rainfall are related to fertility rate after spring artificial insemination in small ruminants," Int J Biometeorol, vol. 60, no. 10, pp. 1603–1609, 2016/10/01 2016, doi: 10.1007/s00484-016-1150-y . D. Đuričić, M. Benić, I. Ž. Žaja, H. Valpotić, and M. Samardžija, "Influence of season, rainfall and air temperature on the reproductive efficiency in Romanov sheep in Croatia," Int J Biometeorol, vol. 63, no. 6, pp. 817–824, 2019/06/01 2019, doi: 10.1007/s00484-019-01696-z . Meat and Livestock Australia. "Electronic Identification (EID) for the sheep industry in Australia." MLA. (accessed. Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFilesEASPANNER.pdf Tables.docx Cite Share Download PDF Status: Published Journal Publication published 11 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 Sep, 2024 Reviews received at journal 20 Sep, 2024 Reviewers agreed at journal 16 Sep, 2024 Reviews received at journal 11 Sep, 2024 Reviewers agreed at journal 04 Sep, 2024 Reviewers invited by journal 02 Sep, 2024 Editor assigned by journal 02 Sep, 2024 Editor invited by journal 12 Aug, 2024 Submission checks completed at journal 12 Aug, 2024 First submitted to journal 29 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4821205","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":350843154,"identity":"273ba909-5c6f-40f3-8b4c-25f298655567","order_by":0,"name":"E.A. Spanner","email":"data:image/png;base64,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","orcid":"","institution":"The University of Sydney","correspondingAuthor":true,"prefix":"","firstName":"E.A.","middleName":"","lastName":"Spanner","suffix":""},{"id":350843157,"identity":"3fa1fa8a-3877-4559-a737-9affa3531e60","order_by":1,"name":"S.P. de Graaf","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"S.P.","middleName":"","lastName":"de Graaf","suffix":""},{"id":350843159,"identity":"41f79e96-3985-41e4-bf5d-cdd33c5805ce","order_by":2,"name":"J.P. Rickard","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"J.P.","middleName":"","lastName":"Rickard","suffix":""}],"badges":[],"createdAt":"2024-07-29 10:29:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4821205/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4821205/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-79253-x","type":"published","date":"2024-11-11T15:57:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64180348,"identity":"56cdf4f1-3c1e-47c0-840c-d11caad2406e","added_by":"auto","created_at":"2024-09-09 14:37:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6410,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in pregnancy rates for each site (deidentified, N=30) recorded during the 2021, 2022 and 2023 breeding seasons. Calculated as the proportion of ewes pregnant compared to the total number of ewes inseminated per site.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4821205/v1/1223870b850b40cfb823d39b.png"},{"id":64180355,"identity":"ac43c585-3bbb-413c-b88f-b195720c4391","added_by":"auto","created_at":"2024-09-09 14:37:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99502,"visible":true,"origin":"","legend":"\u003cp\u003ePregnancy rate for each sire (deidentified) throughout the three breeding seasons, n=388. Calculated as the proportion of ewes pregnant compared to the total number of ewes inseminated per sire. The number of ewes inseminated per sire ranged from 7 to 361, with an average of 69.74 ± 2.15 ewes.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4821205/v1/8212b1daf8663d82c444c26a.png"},{"id":64180351,"identity":"03e4dd7b-2eba-4db8-ab29-a770e738e847","added_by":"auto","created_at":"2024-09-09 14:37:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28506,"visible":true,"origin":"","legend":"\u003cp\u003eThe Odds Ratio plot shows the relationship between an increase in freezing concentration per pellet or straw on the predicted probability of a ewe falling pregnant if she was laparoscopically inseminated with that sample. The predicted probability was generated from the model in RStudio, with 95% Confidence Interval (shaded blue area). Black markers along the x-axis indicate the spread of raw data per individual sire within the model. The blue line indicates the odds ratio of the sample at the given freezing concentration.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4821205/v1/f56bc037e9931d60fd57437a.png"},{"id":64180349,"identity":"14070d44-1916-4fb1-87df-3b116f677392","added_by":"auto","created_at":"2024-09-09 14:37:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25079,"visible":true,"origin":"","legend":"\u003cp\u003eThe Odds Ratio plot shows the relationship between an increase in the number of abnormal spermatozoa on the predicted probability of an ewe falling pregnant if she was laparoscopically inseminated with that sample. The predicted probability was generated from the model in RStudio, with 95% Confidence Interval (shaded blue area). Black markers along the x-axis indicate the spread of raw data per individual sire within the model. The blue line indicates the odds ratio of the sample at the given abnormal morphology percent.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4821205/v1/b3a8b1c5e949ee003176c769.png"},{"id":64180353,"identity":"1b86532d-41e0-4897-aa17-b218e876c45d","added_by":"auto","created_at":"2024-09-09 14:37:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":27306,"visible":true,"origin":"","legend":"\u003cp\u003eThe Odds Ratio plot showing the effect of the percentage of Acrosome Intact and Viable spermatozoa on the predicted probability of an ewe falling pregnant if she was laparoscopically inseminated with that sample. The predicted probability was generated from the model in RStudio, with 95% Confidence Interval (shaded blue area). Black markers along the x-axis indicate the spread of raw data per individual sire within the model. The blue line indicates the odds ratio of the sample at the given percentage of viable spermatozoa with intact acrosomes.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4821205/v1/136dc8096064fa9154d73af5.png"},{"id":64180358,"identity":"c9b823ca-6fa0-4545-a722-2a11ca504a92","added_by":"auto","created_at":"2024-09-09 14:37:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5214,"visible":true,"origin":"","legend":"\u003cp\u003eThe Odds Ratio plot shows the relationship between the CASA PC3 at 0h post-thaw on the predicted probability of an ewe falling pregnant if she was laparoscopically inseminated with that sample. The PC3 is centred around the mean value of 0, and each individual point is represented by its deviation (distance) from this mean, measured in standard deviations. The predicted probability was generated from the model in RStudio, with 95% Confidence Interval (shaded blue area). Black markers along the x-axis indicate the spread of each PC3 point calculated by the PCA. The blue line indicates the odds ratio of the sample at the given abnormal morphology percent.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4821205/v1/782301ead62cb98c5e398e73.png"},{"id":64180357,"identity":"3c62c455-edcc-46c8-ba16-ac0fe6683fba","added_by":"auto","created_at":"2024-09-09 14:37:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":24036,"visible":true,"origin":"","legend":"\u003cp\u003eThe Odds Ratio plot shows the relationship between the uterine tone groups on the predicted probability of an ewe falling pregnant if she was laparoscopically inseminated. The predicted probability was generated from the model in RStudio, with 95% Confidence Interval (pink error bars). The blue dots indicate the odds ratio of the ewe with the given uterine tone score.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4821205/v1/cd35ff863ae3633a1e77b427.png"},{"id":64180356,"identity":"c219ee2a-8ba6-41a1-a6f1-90017e5bfc21","added_by":"auto","created_at":"2024-09-09 14:37:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5153,"visible":true,"origin":"","legend":"\u003cp\u003eThe Odds Ratio plot shows the relationship between the intra-abdominal fat groups on the predicted probability of an ewe falling pregnant if she was laparoscopically inseminated. The predicted probability was generated from the model in RStudio, with 95% Confidence Interval (pink error bars). The blue dots indicate the odds ratio of the ewe with the given intra-abdominal fat score.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4821205/v1/3975113b71fedf897c671794.png"},{"id":69285369,"identity":"7031dbe4-fcd1-4505-bd4a-e4bc69fd8a23","added_by":"auto","created_at":"2024-11-18 19:25:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1580542,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4821205/v1/3e570b2d-c31d-4282-ac00-f0577a27c770.pdf"},{"id":64180352,"identity":"c7bc76d2-0f43-41af-8f7e-a6d5b58da0a6","added_by":"auto","created_at":"2024-09-09 14:37:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":312574,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFilesEASPANNER.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4821205/v1/dc7ca8bda605f3d3e3ac761e.pdf"},{"id":64180970,"identity":"00cc8aae-3298-4320-8bd5-8126131fc0ca","added_by":"auto","created_at":"2024-09-09 14:45:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24412,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4821205/v1/beca4e1772e31c77a74b2c93.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A multivariate model for the prediction of pregnancy following laparoscopic artificial insemination of sheep.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe efficient and sustainable production of sheep requires consistent genetic improvement, most rapidly achieved through the application of assisted reproductive technologies, such as laparoscopic artificial insemination. It is generally accepted that 70% of ewes inseminated via laparoscopic AI should fall pregnant [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], yet variation in success is apparent between geographical regions, across breeding seasons, sires and even ejaculates of the same sire [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This uncertainty surrounding the reliability of AI outcomes has contributed to waning adoption and subsequent negative flow on effects to the rate of genetic and production gains to the national flock. Identifying specific female and male fertility factors, particularly \u003cem\u003ein vitro\u003c/em\u003e semen characteristics which are linked to pregnancy success following AI, would enable producers and breeding companies to screen sires and frozen samples prior to breeding programs, eliminating samples likely to give sub-optimal results. This would help reduce variability in program success and give the industry greater confidence in the application of artificial insemination.\u003c/p\u003e \u003cp\u003eThe ability to predict the success of AI based on a sire's semen characteristics or ewes\u0026rsquo; condition has long been sought. While several factors are known to influence fertility both during natural and artificial insemination [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], correlations to fertility outcomes in sheep have been largely contradictory and fail to consider multiple male \u003cem\u003eand\u003c/em\u003e female factors in the same study, comparing within the individual ewe rather than flock average. Our previous work [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] analysed data collected on ewes during AI and showed uterine tone (considered a proxy for the physiological response of ewes to the oestrous synchronisation protocol) to be an important indicator of AI success. Ewes, which scored a uterine tone of 4 or 5 at the time of AI, recorded a 12.41% increase in pregnancy rate compared to ewes, which scored a uterine tone of 1 or 2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, ewes that scored a uterine tone of 3 recorded notably lower pregnancy rates compared to ewes with a uterine tone score of 4 or 5 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This recent study also highlighted the large proportion of variation in pregnancy rate contributed by the site or location where AI was occurring and the sire used. It is therefore now imperative to assess the influence of ewe factors, like uterine tone, on fertility in conjunction with the \u003cem\u003ein vitro\u003c/em\u003e characteristics of the semen used for AI in the same model.\u003c/p\u003e \u003cp\u003eToday, with the advance of objective semen assessment techniques, there is a wide variety of semen parameters known to collectively define a \u0026lsquo;fertile\u0026rsquo; spermatozoon [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Previous research on bulls [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], rams [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], stallions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and boars [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] have all identified a correlation between sperm motility and velocity parameters [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], as well as sperm morphology [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] with pregnancy success. Additional research has also looked at the concentration at which sperm is frozen prior to AI, as a proxy for insemination dose [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] impacting pregnancy success. However, the use of flow cytometry and intracellular fluorochromes now enables us to study alterations in sperm membrane phospholipids [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], cell viability, acrosome integrity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], DNA fragmentation [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and measures of excessive reactive oxygen species [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and mitochondrial function [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. While certain studies have adeptly assessed the influence of these specific semen characteristics on ram fertility following AI [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], limited standardisation in their application across studies has contributed to contradictions in their described effect on fertility. Notably, stemming from the multitude of tests available for a single trait, a lack of direct comparison between samples inseminated and analysed, as well as reduced sample sizes, have limited the likelihood of successful fertility prediction [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs such, the present study sought to determine the influence of female data collected during AI and \u003cem\u003ein vitro\u003c/em\u003e semen assessment characteristics post-thaw on the likelihood or probability of pregnancy occurring following laparoscopic AI in sheep. Results will lead to a better understanding of which \u003cem\u003ein vitro\u003c/em\u003e semen traits correlate to fertility and, therefore, the fertility potential of a particular frozen-thawed sample. The design of a model to predict AI would also facilitate the identification of accurate standards for the sheep artificial breeding community. When optimal semen is combined with a fertile, well-conditioned ewe, the likelihood of pregnancy should increase, reducing the variability of AI programs and reproductive failure.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Ethics and Animals\u003c/h2\u003e \u003cp\u003eThe data used in this research was generously donated by artificial breeding companies and stud breeders during routine commercial AI operations. Animals are not directly involved in this study, as such, no additional ethical approval was required.\u003c/p\u003e \u003cp\u003eThe management of ewes and rams adhered to the standard industry practices and requirements for each site, and all methods complied with relevant guidelines and regulations. All methods are reported in accordance with the ARRIVE guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Breeding Season and Location of the Animals\u003c/h2\u003e \u003cp\u003eAI data was collected in Australia between November and April during 2020-21 (N\u0026thinsp;=\u0026thinsp;9 817 ewes, 123 sires, 10 sites), 2021-22 (N\u0026thinsp;=\u0026thinsp;8 253 ewes, 116 rams, 9 sites) and 2022-23 (N\u0026thinsp;=\u0026thinsp;12 184 ewes, 149 rams, 11 sites). Sites were located in the Central and South Wheat Belt of WA, Central North of Victoria, Murray Land Yorke Peninsula of South Australia and the Central West, Tablelands and Northwest Slopes and plains of NSW. Animals were selected and managed for artificial breeding programs as per individual commercial stud preferences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Experimental Design\u003c/h2\u003e \u003cp\u003eMerino ewes (N\u0026thinsp;=\u0026thinsp;30 254, split across three breeding seasons and 30 commercial AI programs conducted on farms located in NSW, VIC, SA and WA) were synchronised for oestrus and assessed for uterine tone, intra-abdominal fat, age, PMSG dose and time of insemination post-CIDR removal as part of routine artificial insemination protocols, as per the previous study [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Following industry standards, ejaculates from Merino sires (N\u0026thinsp;=\u0026thinsp;388) were collected and immediately laparoscopically inseminated (fresh; N\u0026thinsp;=\u0026thinsp;29 ejaculates) or frozen (N\u0026thinsp;=\u0026thinsp;359) as either pellets (N\u0026thinsp;=\u0026thinsp;239) or straws (N\u0026thinsp;=\u0026thinsp;120), thawed and then laparoscopically inseminated into ewes. Parameters including season, day, site, sire and type of semen used, uterine tone, and intra-abdominal fat were recorded during AI. Synchronised ewes (0.3g progesterone CIDR; Zoetis, Australia and eCG; Minitube, VIC, Australia) received approximately 0.2mL of semen per uterine horn. Each ewe underwent pregnancy scanning approximately 55 days post AI using standard industry practice. Approximately 2 pellets or 5 straws per batch per sire used for insemination were sent to The University of Sydney, for advanced \u003cem\u003ein vitro\u003c/em\u003e semen assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Assessment of Ewe Factors\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Assessment of ewe age\u003c/h2\u003e \u003cp\u003eThe colour of the ear tag located in the left ear of each ewe indicated the year of birth. Each colour represents a year of drop (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This was then subtracted from the current year (2020, 2021, 2022, or 2023) of AI to determine the age of each ewe at AI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eYear of drop coloured electric ear tag identification for sheep\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003e(modified from [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of Drop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColour Ear Tag\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurple\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Assessment of intra-abdominal fat score\u003c/h2\u003e \u003cp\u003eDuring laparoscopic AI, the internal fat covering the abdominal organs was visualised and subjectively assessed by the technicians performing the insemination, scoring the ewe between one (little to no fat present) to five (high abundance of fat present) (Supplementary File 1) as per the pervious study [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Assessment of uterine tone score\u003c/h2\u003e \u003cp\u003eAt the same time as the intra-abdominal fat assessment, the tone of the uterus was scored as a subjective observation by the technicians and recorded as a value between one (pale, flaccid uterus) to five (bright pink turgid uterus) (Supplementary File 2) as per the previous study [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Assessment of AI time post-CIDR removal\u003c/h2\u003e \u003cp\u003eAs per the previous study [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], during oestrous synchronisation, each ewe was assigned a CIDR pull group to ensure ewes were inseminated within the optimal time frame. At the time of insemination in the cradle, the eID tag of each ewe was scanned using a Tru-Test XRS2 (\u003cem\u003eTru-Test Datamars, Australia\u003c/em\u003e) giving a time stamp for data collection in the cradle. To determine the time of AI post-CIDR removal, this was subtracted from the end time of the CIDR pull group each ewe was allocated. This was standardised across each CIDR pull group across all programs. Data was presented as hh:mm:ss post CIDR pull.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Advanced in vitro Assessment of Semen Characteristics Post-thaw\u003c/h2\u003e \u003cp\u003eA subset of the semen used from each sire was stored and thawed within the same breeding season and the AI program. Pellets (n\u0026thinsp;=\u0026thinsp;2) were thawed in a glass thawing tube for 2 minutes in a 37\u0026ordm;C water bath with agitation, while straws (n\u0026thinsp;=\u0026thinsp;4 straws) were thawed for 30 seconds in a 37⁰C water bath with agitation. The total volume per sample was recorded by suspending the sample in a pipette prior to being diluted 1:0.5 with PBS\u0026thinsp;+\u0026thinsp;0.3% BSA (Phosphate Buffed Saline\u0026thinsp;+\u0026thinsp;0.3% Bovine Serum Albumin; pH 7.4, osmolarity 297). This was then held at 37\u0026ordm;C over a 6h incubation period. Following an initial assessment of sperm concentration (described below; 3.5.1), an aliquot of each sample was taken at 0, 3 and 6h post-thaw and further diluted to 50\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL with PBS\u0026thinsp;+\u0026thinsp;0.3% BSA.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1. Assessment of sperm concentration, subjective motility, and percentage of abnormal morphology post-thaw\u003c/h2\u003e \u003cp\u003eThe concentration of each frozen sample was determined using a NucleoCounter SP-100 (ChemoMetec) immediately post-thaw. 50 \u0026micro;l of semen was diluted with S100 reagent (ChemoMetec) and analysed according to the manufacturer's instructions.\u003c/p\u003e \u003cp\u003eSubjective motility was first assessed after the initial 1:0.5 dilution with PBS\u0026thinsp;+\u0026thinsp;0.3% BSA as well as following dilution of the sample to 50\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL at each time point. The percentage of motile spermatozoa was subjectively assessed using a phase-contrast microscope (x100) described by Evans and Maxwell (1987). Samples (6 \u0026micro;L) were placed on slides and enclosed using a 22 x 22 mm coverslip warmed to 37⁰C. Values were obtained to the nearest 5% by examining five fields of each sample (kept on a heated slide and coverslip at 37⁰C).\u003c/p\u003e \u003cp\u003eAfter thawing, 10uL of semen was fixed with 190uL (1\u0026thinsp;+\u0026thinsp;10 dilution) in 3% NaCl. Within a 24h timeframe, the percentage of abnormal spermatozoa was subjectively assessed using a phase-contrast microscope (x400). Capturing a minimum of 200 cells, the results were converted into the percentage of the sample containing spermatozoa with abnormal morphology. Morphological defects include head defects (detached heads, acrosomes reacted, amorphic heads), damaged midpieces (proximal droplets, bent midpieces) and tail abnormalities (distal reflex, coiled tails, broken tails). As such, this was a measure of the proportion of abnormal spermatozoa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Assessment of sperm motility and kinetic analyses using a computer assisted sperm analysis (CASA)\u003c/h2\u003e \u003cp\u003eSperm motility was measured using the computer-assisted sperm analysis (\u003cem\u003eHT CASA IVOS II (Animal Breeder) Version 1.13.7; Hamilton-Thorne, USA\u003c/em\u003e) using the appropriate settings for ram spermatozoa (this includes amongst others; head size 10\u0026ndash;42 \u0026micro;m\u003csup\u003e2\u003c/sup\u003e, progressive motility thresholds of straightness 80% and average path velocity 75 \u0026micro;m/s). Samples were further diluted to a concentration of 25\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL with PBS\u0026thinsp;+\u0026thinsp;0.3% BSA before 6 \u0026micro;L was placed on slides warmed to 37⁰C (Cell Vu; Millennium Sciences, Mulgrave, Victoria, Australia) and enclosed with a 22 x 22 mm coverslip. For each sample, eight fields of video recordings were recorded, capturing a minimum of 200 cells (frame rate 60 Hz). Motility and kinematic parameters were subsequently calculated including total and progressive motility, ALH, BCF, LIN, STR, VAP, VCL, VSL, and WOB.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3. Flow cytometric analysis\u003c/h2\u003e \u003cp\u003eSamples were assessed for a range of membrane and metabolic indicators at a final concentration of 10\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL following staining with various fluorochromes. Using the CytoFLEX (\u003cem\u003eCytoFLEX and CytExport 2.0 Software Beckman Coulter; USA\u003c/em\u003e), three lasers were employed; 50 mW 488nm, 50 nW 638 nm and 80 mW 405 nm. All samples were stained with the DNA probe Hoechst 33342 (final concentration of 1\u0026micro;g/mL), which has a fluorescence detection filter of 450/45 BP, to gate any possible debris in the sample. Sperm cells were isolated from total events based on 488nm forward and side scatter profiles. For each of the below variables, 10,000 sperm cells were analysed, and a minimum of 1000 Hoechst 33342 positive events were required to obtain valid results.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003e2.5.3.1. Assessment of sperm acrosome integrity and viability\u003c/h2\u003e \u003cp\u003eSample preparation for sperm viability and acrosome integrity was performed as previously described [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], by staining a combination of Propidium Iodine (PI, final concentration 6\u0026micro;M) and Fluorescein isothiocyanate peanut agglutinin (FITC-PNA, final concentration 0.4 \u0026micro;g/mL) for 10 minutes at 37⁰C. PI and FITC-PNA fluorescence detection was on 690/50, and 525/40 nm bandpass (BP) filters, respectively. Cells were considered viable with intact acrosomes if cells were both PI and FITC-PNA negative.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section4\"\u003e \u003ch2\u003e2.5.3.2. Assessment of sperm membrane lipid fluidity\u003c/h2\u003e \u003cp\u003eChanges in lipid fluidity within the membrane of viable spermatozoa were assessed using a staining combination of both merocyanine 540 (M540, final concentration 0.83 \u0026micro;M) and Yo-Pro (final concentration 25nM) for 10 minutes at 37⁰C. The fluorescence of M540 and Yo-Pro was detected on a band-pass filter of 585/42 nm and 525/40 nm, respectively. A sperm population was considered viable if it was recorded as Yo-Pro negative. The median value for M540 fluorescence of this viable population was used to determine the relative membrane lipid fluidity. Results with a greater mean value corresponded to greater lipid destruction on the membrane, thus, greater membrane fluidity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section4\"\u003e \u003ch2\u003e2.5.3.3. Assessment of mitochondrial superoxide production\u003c/h2\u003e \u003cp\u003eMitochondrial superoxide production was assessed using a dual stain combination of Mitosox Red (final concentration 2.5 \u0026micro;M) and Sytox green (final concentration 30 Nm) for 20 minutes at 37⁰C. Mitosox Red fluorescence was detected at 585/42 and Sytox Green fluorescence on 525/40 BP filters. A sperm population of Sytox Green negative, \u0026ldquo;live\u0026rdquo;, was used to determine the median Mitsox Red fluorescence value relative to the amount of mitochondrial superoxide production. A positive control was created by combining each sample with 5 \u0026micro;M hydrogen peroxide to stimulate mitochondrial superoxide production. The positive control was used to assess the effectiveness of the stain and determine the appropriate gating of stained populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section4\"\u003e \u003ch2\u003e2.5.3.4. Assessment of lipid peroxidation\u003c/h2\u003e \u003cp\u003eLipid peroxidation of the sperm membrane was assessed using Bodipy C11 (581/591). Samples were aliquoted and stained with the Bodipy C11 probe (final concentration 10 \u0026micro;M) at 37⁰C for the entirety of the 6h assessment. At each time point, the samples had a timed incubation for 30 minutes. Once staining was complete, samples were centrifuged for 10 minutes at 800 g. After removing the supernatant, each pellet was resuspended in a PBS\u0026thinsp;+\u0026thinsp;0.3% BSA buffer and counterstained with PI (final concentration 6 \u0026micro;M) for 10 minutes at 37⁰C before running on the CytoFlex. The detection of lipid peroxidation was measured by both 585/42 and 525/40 bandpass filters. The live population was first gated and used to determine the percentage of cells with positive Bodipy C11 fluorescence. This indicated the relative change in lipid peroxidation of a given sample. A positive control was made up by combining aliquots of all samples and incubated with 5 \u0026micro;M hydrogen peroxide to induce greater lipid peroxidation. The positive control was used to assess the effectiveness of the stain and determine the appropriate gating of stained populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section4\"\u003e \u003ch2\u003e2.5.3.5. Assessment of intracellular reactive oxygen species (ROS)\u003c/h2\u003e \u003cp\u003eTo determine the relative amount of oxygen species (ROS) within a cell, a staining combination of dichlorodihydrofluorescein diacetate acetyl ester (H\u003csub\u003e2\u003c/sub\u003eDCFDA final concentration 5 \u0026micro;M) and PI (final concentration 6 \u0026micro;M) was used. Samples were aliquoted and stained with the H\u003csub\u003e2\u003c/sub\u003eDCFDA at 37⁰C for the entirety of the 6h assessment. At each time point, the samples had a timed incubation of 1h before being centrifuged for 10 minutes at 800 g. After removing the supernatant, pellets are resuspended in a PBS\u0026thinsp;+\u0026thinsp;0.3% BSA buffer and counterstained with PI (final concentration 6 \u0026micro;M) for 10 minutes at 37⁰C before running on the CytoFlex. The H\u003csub\u003e2\u003c/sub\u003eDCFDA fluorescence was determined on the 525/40 bandpass filter. The H2DCFDA fluorescences in live cells (PI negative) was used to measure intracellular ROS production. A positive control was made up by combining aliquots of all samples and incubated with 5 \u0026micro;M hydrogen peroxide to induce greater ROS. The positive control was used to assess the effectiveness of the stain and determine the appropriate gating of stained populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section4\"\u003e \u003ch2\u003e2.5.3.6. Assessment of DNA integrity\u003c/h2\u003e \u003cp\u003eAt 0 and 6h post thaw, 40 \u0026micro;L of each sample (50\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL) was aliquoted for DNA assessment. Each sample was washed by resuspending the sample in 2mL of PBS\u0026thinsp;+\u0026thinsp;0.3% BSA and centrifuging for 10 minutes at 800 g. The supernatant was removed before resuspension and repeat centrifugation After the final spin, the supernatant was removed, and pellets (final concentration approx 2\u0026times;10\u003csup\u003e6\u003c/sup\u003e sperm) were snap-frozen in liquid nitrogen for 30 seconds before being stored at -80⁰C until assessment.\u003c/p\u003e \u003cp\u003eDNA fragmentation was measured using flow cytometry on a (\u003cem\u003eCytek Aroura 3L; Sydney Flow Cytometry\u003c/em\u003e) after staining with Acridine Orange (AO), as described by Evenson and Jost (2000), with some minor changes. In summary,, snap frozensamples were diluted to a concentration of 2\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL with a TNE buffer (0.15 M NaCl, 0.01 M Tris HCl, 1mM disodium EDTA pH 7.4). A 100uL aliquot of the sample was taken and diluted with 200uL of Acid Detergent Solution (0.08 NHCl, 0.15 M NaCl, 0.1% Triton X 100 pH 1.2), which was gently mixed by swirling in hand for 30 seconds. Once mixed, samples were stained with 600uL of Acridine Orange (final concentration 6 \u0026micro;g/mL) for 3 minutes before being assessed using flow cytometry. Green (B2) and Red (V11) fluorescence were detected using the 528/21 and 644/27 band pass filters, respectively. The flow rate was set to slow, and a minimum of 1000 cells were recorded per sample. DNA fragmentation was determined by the relative amount of single-stranded DNA (ssDNA) in proportion to the total amount of spermatozoa (dsDNA\u0026thinsp;+\u0026thinsp;ssDNA) and indicated by the amount of red fluorescence regarding the total amount of fluorescence.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Measure of Fertility\u003c/h2\u003e \u003cp\u003eDepending on the AI program and site, the pregnancy status per ewe was determined approximately 55 days post-insemination. Ewes were fasted 24hrs prior to scanning. A real-time cutaneous ultrasound (Oviscan 6 with a 3.5 MHz probe) was used to scan each ewe to determine the presence of fetuses and their number. As per the previous study [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], pregnancy from AI was recorded as either 1 (pregnant) or 0 (empty), while the number of fetuses observed was recorded as the exact number.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical Analysis\u003c/h2\u003e \u003cp\u003eEwe ID was matched between AI and pregnancy datasets while sire ID was matched between AI and \u003cem\u003ein vitro\u003c/em\u003e semen analysis datasets to create one Masterfile. All data was therefore compared within the individual ewe. Data was then cleaned to remove ewes without pregnancy data. All statistical analyses were performed on R Studio (Version 2023.09.1\u0026thinsp;+\u0026thinsp;494).\u003c/p\u003e \u003cp\u003eIn accordance with the previous study [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], the overall pregnancy data was assessed to determine average pregnancy and reproductive rates. AI success was determined by calculating the number of ewes pregnant over the total number of ewes inseminated. The reproductive rate was determined by calculating the number of offspring (fetal number) over the total number of ewes inseminated. Descriptive statistics were performed to evaluate the number of ewes inseminated and the percentage pregnant for each categorical factor level recorded, as well as site, sire ID and breeding season. All values included mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM) and were de-identified for anonymity.\u003c/p\u003e \u003cp\u003eAt 0, 3 and 6h post-thaw, the relationships between \u003cem\u003ein vitro\u003c/em\u003e semen traits were assessed by Spearman rank correlation. As the CASA measurements were highly correlated, measures of total motility, progressive motility, ALH, BCF, LIN, STR, VAP, VCL, VSL, and WOB were combined using a Principal Component Analysis (PCA, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to reduce multiple testing bias and collinearity. The first 3 principal components were significant (eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1), and together accounted for 92% of the variation in the data (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PCA1, accounted for 54.92% of the variation and was interpreted as a composite measure of sperm velocity, with positive loadings (\u0026lt;\u0026thinsp;\u0026plusmn;0.30) from WOB, VSL, VAP, STR, LIN and BCF (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PCA2, accounting for 24.73% of the variation, had a strong positive loading of VCL, VAP and ALH (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and again is interpreted as a measure of sperm velocity. PCA3 contributed 12.90% of the variation and had a strong negative loading from Total Motility and Progressive Motility (-0.70 and \u0026minus;\u0026thinsp;0.53, respectively), interpreted as a measure of sperm motility. The significant components were subsequently used in the regression analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEigenvalues and variances explained by the first 3 PCAs at 0h post-thaw, along with the loading of each measurement within the PCA for CASA motility and velocity traits.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Motility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgressive Motility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWOB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eALH, amplitude of lateral head displacement; BCF, beat-cross frequency; LIN, linearity; STR, straightness; VAP, average path velocity; VCL, curvilinear velocity; VSL, straight-line velocity; WOB, wobble; PCA, principal component.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA logistical binomial regression analysis was used to examine the influence of female and male fertility traits on the probability of pregnancy post-AI. Additionally, an intraclass correlation coefficient (ICC) analysis was conducted to explain the proportion of variance attributed to individual factors within the random model, encompassing Site, Sire ID including those that were Frozen-thawed, and Thaw Day. A preliminary univariate analysis was then run to determine the impact of each individual factor on pregnancy achievement. The logistical binominal regression model was refined by the backward selection process, eliminating non-significant fixed effect variables and interactions (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The final multivariable model included only significant factors and interactions (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For all variables within the model, an odds ratio was performed to determine the likelihood of change in pregnancy following a single unit change in the factor whilst keeping the model consistent. This included the odds ratio percentage change and 95% CL.\u003c/p\u003e \u003cp\u003eFor any fixed categorical effects, an Emmeans pairwise comparison was performed to assess the significant difference between groups within the variable. Groups were considered significantly different to each other if the comparison returned a p-value of \u0026lt;\u0026thinsp;0.05. All values are reported with mean \u0026plusmn;SEM.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Overall Descriptive Statistics of the Dataset\u003c/h2\u003e\n \u003cp\u003eData was collected from 30 254 ewes and 388 rams from 30 sites. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the total number of ewes and sires included in the dataset and the resultant fertility following laparoscopic artificial insemination across the 3 breeding seasons.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e displays the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM and range for each fertility factor recorded. For each \u003cem\u003ein vitro\u003c/em\u003e semen parameter, it tracks the change of each trait 0, 3, and 6h post-thaw. Additionally, Supplementary File 3 describes the percentage of ewes\u0026apos; pregnancy for each level within the categorical factors recorded in the data set.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Contribution of Variation Caused by Random Terms\u003c/h2\u003e\n \u003cp\u003eThe ICC analysis was performed on the random model to assess the proportion of total variance contributed by random terms. The proportion of variation in pregnancy success attributed to Site, Sire frozen-thawed, and Thaw Day was 53.57%, 22.02%, and 24.42%, respectively. The level of significance was not assessed on these variations.\u003c/p\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1. The proportion of variation in pregnancy success contributed by site\u003c/h2\u003e\n \u003cp\u003eFrom the multivariable model, the site where data was collected contributed 53.57% of the variation detected between the random terms. With 30 sites across the 3 breeding seasons, the pregnancy rate ranged from 49.89\u0026ndash;89.02% (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2. The proportion of variation in pregnancy success contributed by sire\u003c/h2\u003e\n \u003cp\u003eFrom the multivariable model, 24.42% of the variation detected between the random terms was contributed by the sires with frozen-thawed semen used for AI. Across the 388 sires, the pregnancy rate ranged from 15.88\u0026ndash;96.67%, averaging 68.54% (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Factors within the model found to influence pregnancy following laparoscopic AI of sheep\u003c/h2\u003e\n \u003cp\u003eDespite 33 factors returning significant p values when considered in a univariate logistic model (data not shown), only 7 remained significant when included in the same binomial logistic regression model. Sperm \u003cstrong\u003efreezing concentration\u003c/strong\u003e (\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL), the percent of \u003cstrong\u003emorphologically abnormal spermatozoa\u003c/strong\u003e, the proportion of \u003cstrong\u003eviable spermatozoa\u003c/strong\u003e with \u003cstrong\u003eintact acrosomes\u003c/strong\u003e at 6h post-thaw, \u003cstrong\u003eCASA PCA3\u003c/strong\u003e at 0h post-thaw, \u003cstrong\u003euterine tone\u003c/strong\u003e and \u003cstrong\u003eintra-abdominal Fat\u003c/strong\u003e of ewes, were found to significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;=\u0026thinsp;0.021, P\u0026thinsp;=\u0026thinsp;0.033, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;=\u0026thinsp;0.047, respectively, to influence the likelihood of pregnancy. There were no significant interactions between variables (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1. The impact of the number of sperm frozen on the probability of successful pregnancy\u003c/h2\u003e\n \u003cp\u003eThe average freezing concentration for a pellet and straw was 722\u0026times;10\u003csup\u003e6\u003c/sup\u003e \u0026plusmn; 15.27 spm/mL, and 270.75\u0026times;10\u003csup\u003e6\u003c/sup\u003e \u0026plusmn; 16.47 spm/mL, respectively. Freezing concentration ranged from 81.29 to 2283.75\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL and averaged 569.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06 spm/mL. Notably, there was no significant interaction observed between freezing concentration and package type within the model (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Therefore, freezing concentration was considered across package types. Following an odds ratio calculation, it was determined that an additional 100\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL frozen in either a pellet or straw corresponded to a 5.09% increase in pregnancy probability (OR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.05 to 1.05, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u003cem\u003e3.3.2 The impact of abnormal sperm morphology on the probability of successful pregnancy following laparoscopic AI of sheep\u003c/em\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe average number of morphologically abnormal spermatozoa per frozen-thawed sires sample was 15.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06%, ranging from 2.5 to 70% abnormal spermatozoa. The odds of a 1% increase in abnormal spermatozoa corresponded to a 1.07% decrease (OR\u0026thinsp;=\u0026thinsp;0.99, 95% CL: 0.98 to 1.00, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) in the probability of a ewe being pregnant.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u003cem\u003e3.3.3 The impact of sperm viability and acrosome integrity on the probability of successful pregnancy following laparoscopic AI of sheep\u003c/em\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eAt 6h post-thaw, the average percentage of acrosome intact and viable spermatozoa for frozen-thawed sires was 11.74% \u0026plusmn; 0.04%, ranging from 0 to 34.64%. An analysis of the data revealed that the odds of a 1% increase in the number of viable sperm with intact acrosomes corresponded to a 1.01% increase (OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 0.34 to 2.56) in the probability of ewes being pregnancy (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u003cem\u003e3.3.4 The impact of CASA motility and velocity traits (CASA PCA3) on the probability of successful pregnancy following laparoscopic AI of sheep\u003c/em\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eAs seen in Supplementary File 4, there is a clear and strong correlation between CASA traits at 0, 3 and 6h post-thaw. Following the formation of the PCA variable (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), Although PCA1 and PCA2 explained a significant amount of variation, only PCA3 remained significant in the final model (P\u0026thinsp;=\u0026thinsp;0.033). PCA3 had a strong negative loading from Total Motility and Progressive Motility (-0.70 and \u0026minus;\u0026thinsp;0.53, respectively), which is interpreted as a measure of sperm motility.\u003c/p\u003e\n \u003cp\u003eAt 0h post-thaw, the average total motility for frozen-thawed sires was 40.77% \u0026plusmn; 0.12%, ranging from 5.8 to 89.5%. The average CASA progressive motility for frozen-thawed sires was 30.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10%, ranging from 2.3 to 79.8%. The odds ratio analysis of CASA PCA3 indicated an inverse association with pregnancy outcomes, with the odds of pregnancy decreasing by 0.37% for every standard deviation away from the average for motility (OR\u0026thinsp;=\u0026thinsp;1.00, 95% Cl: 1.00 to 0.99, Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). This infers that reduced sperm motility characteristics, as represented by higher CASA PCA3 scores, are associated with reduced odds of pregnancy.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.5. The impact of uterine tone on the probability of successful pregnancy\u003c/h2\u003e\n \u003cp\u003eWithin the model, an increase in uterine tone had a positive effect on the probability of pregnancy rate. The average uterine tone score was 3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0005, ranging from 1 to 5. As seen in Supplementary File 3, a uterine tone score of 1\u0026thinsp;+\u0026thinsp;2 (57.88%, N\u0026thinsp;=\u0026thinsp;2780 ewes) scored a significantly lower AI success rate than a uterine tone score of 3 (65.20%, N\u0026thinsp;=\u0026thinsp;12387 ewes) and 4\u0026thinsp;+\u0026thinsp;5 (70.29%, N\u0026thinsp;=\u0026thinsp;7086 ewes) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, \u0026lt;\u0026thinsp;0.001, respectively). Additionally, ewes with a uterine tone score of 3 recorded significantly lower probability of pregnancy than ewes which scored a uterine tone score of 4\u0026thinsp;+\u0026thinsp;5 (P\u0026thinsp;=\u0026thinsp;0.0091).\u003c/p\u003e\n \u003cp\u003eThe odds ratio of achieving a successful pregnancy was 5.55% higher for ewes that scored a uterine tone score of \u0026ldquo;3\u0026rdquo; compared to a uterine tone score of \u0026ldquo;1\u0026thinsp;+\u0026thinsp;2\u0026rdquo; (OR\u0026thinsp;=\u0026thinsp;1.06, 95% Cl: 0.59 to 1.88, Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Similarly, the odds of AI pregnancy were 7.21% higher for the category \u0026quot;4\u0026thinsp;+\u0026thinsp;5\u0026quot; compared to \u0026quot;1\u0026thinsp;+\u0026thinsp;2\u0026quot; (OR\u0026thinsp;=\u0026thinsp;1.07, 95% Cl: 0.44 to 2.60, Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u003cem\u003e3.3.6 The impact of intra-abdominal fat on the probability of successful pregnancy following laparoscopic AI of sheep\u003c/em\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe model also calculated that an increase in intra-abdominal fat had a positive effect on the probability of pregnancy rate. The average intra-abdominal fat score was 3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005, ranging from 1 to 5. As seen in Supplementary File 3, an intra-abdominal fat score of 1\u0026thinsp;+\u0026thinsp;2 (59.36%, N\u0026thinsp;=\u0026thinsp;3895 ewes) scored a significantly lower AI success rate than an intra-abdominal fat score of 4\u0026thinsp;+\u0026thinsp;5 (71.94%, N\u0026thinsp;=\u0026thinsp;4748 ewes, P\u0026thinsp;=\u0026thinsp;0.038,). There was no significant difference in pregnancy rate between ewes which scored an intra-abdominal fat score of 3 (65.60%, N\u0026thinsp;=\u0026thinsp;13385 ewes) and 1\u0026thinsp;+\u0026thinsp;2 or 3 to 4\u0026thinsp;+\u0026thinsp;5 (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eThe odds ratio of achieving a successful pregnancy was 5.37% higher for ewes, with an intra-abdominal fat score of \u0026ldquo;3\u0026rdquo; compared to an intra-abdominal fat score of \u0026ldquo;1\u0026thinsp;+\u0026thinsp;2\u0026rdquo; (OR\u0026thinsp;=\u0026thinsp;1.05, 95% Cl: 0.89 to 1.25, Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Similarly, the odds of successful pregnancy were 6.81% higher for the intra-abdominal fat category \u0026quot;4\u0026thinsp;+\u0026thinsp;5\u0026quot; compared to \u0026quot;1\u0026thinsp;+\u0026thinsp;2\u0026quot; (OR\u0026thinsp;=\u0026thinsp;1.07, 95% Cl: 0.78 to 1.47, Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study investigated the impact of female factors recorded during AI and male \u003cem\u003ein vitro\u003c/em\u003e semen traits assessed post-thaw on the probability of pregnancy occurring in sheep. This study has considered male and female fertility traits simultaneously, comparing the pregnancy success of the individual ewe rather than across flock averages. The resultant fertility model indicated that an increase in the number of sperm frozen per pellet or straw, the percentage of viable spermatozoa with intact acrosomes and greater sperm motility will result in a positive linear increase in the probability of achieving pregnancy. Additionally, insemination of ewes with a uterine tone and intra-abdominal fat score of 4 or 5 are more likely to result in a successful pregnancy than ewes, which score 1 or 2. Conversely, a higher proportion of abnormal spermatozoa in a sample is associated with reduced chances of pregnancy. Notably, there is an ongoing contribution of variation by the site or location where the AI was performed, the sire used for AI, and the thaw date. As such, the influence of each fertility factor within the model is further modified depending on these random terms. Nevertheless, the identification of predictive \u003cem\u003ein vitro\u003c/em\u003e semen traits can now be used to pre-screen and select sires or frozen semen samples prior to use in a breeding program. This will help to improve the success rates of artificial breeding programs, offering producers an effective tool to increase genetic and production gains in a challenging industry. Further studies will help determine the accuracy and precision of the model to optimise thresholds as semen standards for the artificial breeding sheep industry.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe impact of the number of sperm frozen on the probability of successful pregnancy\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe concentration at which sperm is frozen has the potential to impact cryosurvival post-thaw as well as subsequent insemination dose. In the current study, the number of sperm frozen in either a pellet or straw was found to influence the likelihood of successful pregnancy following laparoscopic AI. For every additional 100\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL frozen, the probability of achieving pregnancy increased by 5.09% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, there was no interaction with package type. While there is currently no agreed-upon standard for the industry, it is generally assumed that a pellet should be frozen between 600\u0026ndash;800\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL and used to inseminate approximately 3 ewes. On the other hand, a straw should be around 200\u0026ndash;300\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL and equate to only 1 dose per ewe [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Pleasingly, in the current study, the average concentration of pellets and straws was 722\u0026times;10\u003csup\u003e6\u003c/sup\u003e \u0026plusmn; 15.27 and 270.75\u0026times;10\u003csup\u003e6\u003c/sup\u003e \u0026plusmn; 16.47 spm/mL, respectively, suggesting the data collected accurately reflected current protocols used throughout the sheep artificial breeding industry in Australia.\u003c/p\u003e \u003cp\u003eThe authors interpret the above result as the number of spermatozoa frozen as a proxy for the insemination dose. For laparoscopic AI, it is recommended that each ewe should receive approximately 25\u0026times;10\u003csup\u003e6\u003c/sup\u003e motile sperm or 12.5\u0026times;10\u003csup\u003e6\u003c/sup\u003e motile sperm/horn [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Assessing the sperm concentration of a pellet or straw post-thaw ensures a more reliable insemination dose, equal to or more than 25\u0026nbsp;million motile sperm. The recommended laparoscopic insemination dose in sheep has remained largely unchanged since the mid-80s, when it first emerged as a reproductive tool for sheep [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Previous studies have demonstrated that an increase in motile sperm dose from 0.5 to 50\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm resulted in lambing rates of 27\u0026ndash;62% [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Which then later, a sperm dose of 20\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL achieved a rate of 76.8% [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In contrast, other studies have also found no difference in conception rates when doses were reduced from 52.2\u0026times;10\u003csup\u003e6\u003c/sup\u003e to 13\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The lack of significant difference in these studies may be related to the number of ewes used per treatment structure, which limits statistical power, or the compounding effects of sperm type (fresh, liquid stored, or frozen) or diluents used, ultimately making it difficult to compare results across studies.\u003c/p\u003e \u003cp\u003eIt's important to emphasise that while higher numbers of sperm per pellet or straw frozen theoretically offer increased insemination doses, it's important to find the balance between optimal freezing conditions to ensure sperm survival and effective insemination doses. This concept has been studied abundantly in previous literature [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] across livestock species. Studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] found that freezing at concentrations above 600\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL reduced sperm viability, acrosome integrity and motility. Attributed to the excessive build-up of free radicals, it\u0026rsquo;s proposed to cause changes in the sperm: cryoprotective agents [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The higher the amount of cryoprotectant per sperm cell, the higher the percentage of microdomains (unfrozen water channels), leading to better quality post-thaw [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Alternate studies [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] reported lambing rates of 57.1% when sperm was frozen at 800\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL and inseminated at 160\u0026times;10\u003csup\u003e6\u003c/sup\u003e spermatozoa. This was compared to 81.2% when sperm was frozen at 200\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL and inseminated at 40\u0026times;10\u003csup\u003e6\u003c/sup\u003e spermatozoa. Similar results were recorded by standardising doses to 25\u0026nbsp;million sperm [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Sperm frozen at 200 and 400\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/ml recorded a higher lambing rate of 57.5% compared to sperm frozen at 800\u0026times;10\u003csup\u003e6\u003c/sup\u003e spm/mL, which returned a lambing rate of 45.5%.\u003c/p\u003e \u003cp\u003eIn any event, it is clear that the concentration at which sperm is frozen directly impacts the insemination dose, which can then further alter pregnancy results. Semen must be frozen at an appropriate concentration to not only mitigate the impacts of freeze-thaw damage on the sperm cell but also optimise the number of sperm per ewe per insemination dose. With an increase in the accuracy and number of technologies currently available on the market that can objectively measure sperm concentration, it should be easier to ensure samples are frozen at accurate concentrations, regardless of package type. Further studies must now take the spread of data collected in the current study and establish thresholds that could be used as standards in the industry. When considered with other factors in the model, this would help to standardise the dose of semen used for artificial insemination and increase the chances of pregnancy success.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe impact of the percentage of abnormal spermatozoa on the probability of successful pregnancy\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe assessment of sperm morphology is common practice during routine basic semen assessment for a number of species, including stallions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], bulls [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] and boars [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], yet its correlation with the fertility of frozen-thawed ram spermatozoa has been contradictory [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. In the current study, results reported that for every 1% increase in the percentage of abnormal spermatozoa within a frozen sample, a 1.07% decrease in the probability of a ewe falling pregnant would be observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In our study, sperm morphology was classified as either abnormal or normal with this approach aligning with the methods and results reported by previous ram studies [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In this study, a significant difference in the percentage of abnormal ram spermatozoa was reported between groups that exhibited high and low fertility (4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30% compared to 13.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37%, respectively). Our current study builds upon these results by directly comparing the morphology of samples inseminated per ewe rather than the fertility average of a group of individuals.\u003c/p\u003e \u003cp\u003eAs reviewed [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], the morphology of an individual spermatozoon is an important indicator of its fertilising potential and has been proven in a number of species including; deer [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], bull [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] and stallions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and rams [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In some species, studies have attempted to further classify abnormalities into different morphological structures, such as head, acrosome, midpiece and tail, to further link types of abnormalities with causes of infertility [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. In stallions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], observed aberrations of the head, acrosome, and midpiece, including detached heads, coiled and bent tails, and premature germ cells however, only percent of normal sperm was found to have a significant impact on the percentage of pregnant mares per cycle. This could suggest that better methods of standardised assessment are needed, which consider the protocol and equipment used as well as the assessor's experience before more detailed classifications of morphological abnormalities can be used to predict the fertility of samples.\u003c/p\u003e \u003cp\u003eAt least, in the cattle industry, standards to measure bull sperm morphology are frequently used to assess and grade the quality of bull samples [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], To date, nothing of this detail exists for the sheep industry. Thus, the results of the current study are a positive step forward for the industry, providing a comprehensive data set that accurately reflects a negative relationship between increasing morphology abnormalities and sheep fertility following AI. Even more so, the results provide evidence that even basic morphology assessment is a key parameter that should be considered when assessing the quality of ram samples during fertility assessment.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe influence of the percentage of viable, acrosome intact spermatozoa on the probability of successful pregnancy\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe current model revealed that for every 1% increase in the number of viable sperm with intact acrosomes at 6h post-thaw, a 1.01% increase in the probability of pregnancy occurred (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A viable, acrosome intact spermatozoon is one that maintains a constant, functioning plasma membrane around the entire cell and contains the presence of acrosomal enzymes along the cellular membrane of the sperm head prior. Combined, it is essential for the normal functioning of the cell, implying that sperm are capable of transitioning to the site of fertilisation, fusing with the zona pellucida, undergoing the acrosome reaction [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] and achieving successful fertilisation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between sperm viability and fertility following insemination has been extensively proven across multiple species, recording a correlation \u0026lsquo;r\u0026rsquo; score of r\u0026thinsp;=\u0026thinsp;0.32, 0.64, 0.64, 0.05, 0.68 and 0.28 in bulls [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], dairy bulls [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], stallions [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] and boars [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e], respectively. Briefly, these studies agreed with that presented in the current study where, the greater the proportion of viable sperm and acrosomal integrity, the greater the probability of pregnancy occurring. Of the literature above, only one paper [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] successfully measured the viability of bull sperm at 0 and 4h post-thaw. However, as the current study measured viability at 6h, this could more closely imitate the environment sperm are exposed to following deposition within the female tract. In general, sperm are inseminated just prior to a ewe ovulating; therefore, they are required to survive for up to 6-12h before interacting with an oocyte. At least for laparoscopic AI, measuring the level of viability or live: dead with intact acrosomes at 6h would ensure the population of sperm deposited in the uterus was capable of achieving fertilisation after incubation at 37\u0026ordm;C (artificially post-thaw in a water bath or \u003cem\u003ein vivo\u003c/em\u003e within the reproductive tract).\u003c/p\u003e \u003cp\u003eOpposing this trend, [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] measured no importance or significance of ram sperm viability to pregnancy rates. Yet importantly, in contrast to the current study, this paper compared sperm viability to previously recorded fertility following cervical AI with a very low number of samples used [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It also raises the question of whether the predictive power of sperm traits can be used interchangeably across insemination methods. Given the path sperm take to achieve fertilisation after deposition in the cervix differs greatly from being deposited directly into the uterine horns, the reliance on specific sperm traits would be altered. This suggests a similar study should be conducted for other reproductive technologies to ensure all methods of AI and physiological responses to sperm transport are accounted for when predicting the fertility of ram sperm. Nonetheless, these results have the potential to establish industry standards which would be crucial for enhancing reproductive outcomes.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe impact of motility and velocity traits assessed using CASA (PCA3) on the probability of successful pregnancy\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe use of a principal component analysis (PCA) for CASA variables underscores the positive impact of sperm motility and kinetic traits on pregnancy likelihood while considering the extreme correlation between factors. Of all the PCAs considered within the current dataset, PCA3 exhibited the greatest influence on pregnancy, primarily driven by total and progressive motility (-0.70 and \u0026minus;\u0026thinsp;0.53, respectively). Analysis of the odds ratios revealed a 0.37% increase in the odds of pregnancy occurring for every negative standard deviation away from the average (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This indicates that as total and progressive motility values increase, the PCA3 loadings or value decreases. Thus, as PCA3 values decline, the likelihood of pregnancy occurring increases. In other words, the model makes biological sense given a sample with high total and progressive motility is also likely to exhibit efficient metabolism of substrates and be more capable of achieving fertilisation [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] so the probability of achieving pregnancy increases.\u003c/p\u003e \u003cp\u003eGiven the importance of sperm motility to sperm function, extensive research in several species has focused on the relationship between motility as determined by CASA and fertility. Studies across livestock species, including bulls [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], stallions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], deer [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], rats [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], human [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], salmon [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e] and rams [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], have all shown similar results to the current study where, as total sperm motility and average path velocity increase, the likelihood of pregnancy also increases [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Previous research [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] has demonstrated the relationship between motility assessed by CASA and fertility of cryopreserved ram sperm. They saw a direct relationship between an increase in the average-path velocity (VAP), the curvilinear velocity (VCL) and the head beat-cross frequency (BCF), correlated with the percentage of ewes pregnant following AI (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.678, 0.745, 0.852, respectively). This is not always the case in all literature, and some studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], have reported a lack of correlation between kinematic parameters and fertility following laparoscopic AI in sheep. The contradictory results in previous ram studies are unsurprising, given the different protocols and diluents used for assessment, making it difficult to compare across studies. As such, the need for standardisation across the industry is vital.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe Impact of Uterine Tone on the Probability of Successful Pregnancy\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe present study revealed a clear linear relationship between the uterine tone of ewes at the time of AI and the resultant pregnancy. Pregnancy rates increased from groups 1\u0026thinsp;+\u0026thinsp;2 (58.13%), 3 (65.30%) and 4\u0026thinsp;+\u0026thinsp;5 (71.04%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Changes in uterine tone have been previously studied in mares [\u003cspan additionalcitationids=\"CR87\" citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e] and dairy cows [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], and more recently in ewes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The current fertility model presented above confirmed the results of our previous study, highlighting the importance of uterine tone even when semen factors are also considered in the same model. The probability of pregnancy occurring in the current study was 7.21% higher for ewes with a uterine score of \u0026ldquo;4\u0026thinsp;+\u0026thinsp;5\u0026rdquo; compared to those ewes observed to score a uterine tone of \u0026ldquo;1\u0026thinsp;+\u0026thinsp;2\".\u003c/p\u003e \u003cp\u003eThe relationship between uterine tone, sperm quality and fertility is hypothesised to be related to the response of the ewe to the oestrous synchronisation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and coinciding deposition of semen artificially with subsequent ovulation in the ewe [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. An increase in uterine tone corresponds to a surge in oestrogen levels, causing epithelial uterine gland cells and rough endoplasmic reticulum to expand [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e] as well as an increase in blood flow to the area [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This, in turn, results in an increase in hypertrophy and contractions, aiding sperm transport from the cervix to the oviduct [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. It also implies ovulation is imminent, signposting optimal insemination time and increased chances of fertilisation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] should sperm quality be appropriate. To prove this theory, it would be of interest to conduct further studies to track hormone profile changes, uterine tone alterations and follicular development across the ewe oestrous cycle via laparoscope to pinpoint the exact time between a uterine tone score 4 or 5 and ovulation. Overlaid with the ability of different sperm types to survive incubation, this would enable us to accurately predict the optimum time of insemination in relation to uterine tone, increasing pregnancy success after AI.\u003c/p\u003e \u003cp\u003eFor now, the results above suggest that uterine tone could be a useful tool for screening ewes prior to insemination. A technician could observe the tone of ewes subjectively (once standardised) and exclude those ewes with a uterine tone lower than 3.5. A more stringent assessment of uterine tone at the time of AI could, therefore, reduce the variability in pregnancy and improve the overall AI success rate.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe Impact of Intra-abdominal Fat on the Probability of Successful Pregnancy\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSimilar trends were also observed in the intra-abdominal fat level of ewes undergoing AI. The study revealed a clear linear relationship between intra-abdominal fat and AI success in sheep, showing an increase in pregnancy rate from groups 1\u0026thinsp;+\u0026thinsp;2 (59.77%), 3 (65.70%) and 4\u0026thinsp;+\u0026thinsp;5 (72.56%, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). From these findings, the probability of a ewe with an intra-abdominal fat score of 4 or 5 was 6.81% higher than ewes\u0026rsquo;, which scored an intra-abdominal fat score of \u0026ldquo;1\u0026thinsp;+\u0026thinsp;2\u0026rdquo;. When considered with the other factors in the model, this would increase the success rate of laparoscopic AI in sheep.\u003c/p\u003e \u003cp\u003eThe inclusion of intra-abdominal fat in a fertility model is a novel parameter yet to be fully explored in livestock species. Previous research has focused heavily on the use of a ewes\u0026rsquo; body condition score (BCS) as the gold standard for pre-breeding soundness or fertility assessments [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] given it identifies a ewes\u0026rsquo; nutritional status, health, and reproductive potential.\u003c/p\u003e \u003cp\u003eSeveral studies in sheep have been able to successfully standardise BCS scores and link to fertility success [\u003cspan additionalcitationids=\"CR95\" citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e], yet limited studies have linked internal fat levels with fertility [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. A small proportion of papers have demonstrated a significant correlation between BCS, internal fat deposits [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e] and ultrasound subcutaneous fat depth within the abdominal region of the ewe [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Studies have recommended maintaining a BCS 3\u0026ndash;3.5 will help optimise reproductive performance (conception rate, little size, weaning rate and oestrus cycles to conception) and flock profitability [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. The current study saw an increase in the likelihood of pregnancy when ewes had an intra-abdominal fat score of 4 or 5. This suggests that while BCS and internal fat score are likely correlated to some degree, the scale used to assess internal fat score means at the upper limits, intra-abdominal fat score should still be considered individually and likely acts as a more accurate predictor for pregnancy following AI.\u003c/p\u003e \u003cp\u003eTo provide a recommendation or standard for the industry, the current results suggest that intra-abdominal fat levels should be assessed at the time of AI, with those below 3 likely excluded from insemination. When matched with the correct sperm type of adequate quality, this will maximise AI success.\u003c/p\u003e \u003cp\u003e \u003cem\u003eImplications for Industry and Future Research Objectives\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis study is the first to successfully combine both male and female fertility traits into a single logistic regression model to predict the likelihood of pregnancy occurring in a ewe following laparoscopic AI. While this fertility model is a critical first step for industry, the impact of the environment on the success of laparoscopic AI programs must still be considered. Extensive research has investigated the role of the environment, such as heat stress, humidity, cold snaps and rainfall, on sheep fertility [\u003cspan additionalcitationids=\"CR101 CR102 CR103\" citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], yet its explicit role during AI has yet to be fully elucidated when male and female factors are considered in the same study. This is likely due to the difficulty involved in accurately and reliably investigating this research question around Australia. Environmental studies involving temperature-controlled rooms are expensive and logistically challenging when considering the current study needed to examine fertility records of over 30,000 sheep to reach significance. Whatever the solution, environmental conditions such as those accounted for in the random model used above (site and day) must be examined in future studies to understand all factors known to influence sheep fertility.\u003c/p\u003e \u003cp\u003eTo establish thresholds for each variable in our predictive paradigm, the model must first be validated against a collection of \u0026lsquo;unseen data\u0026rsquo;. Future studies will aim to collect data in the same way as described above and enter it into the model to not only calculate measures of discrimination and calibration but also determine whether there is an overall cumulative effect of these predictors on the probability of pregnancy occurring in a ewe. From here, the spread of data for each variable will be used to set accurate, concise, and viable semen standards to improve the likelihood of pregnancy occurring.\u003c/p\u003e \u003cp\u003eUltimately though, the results presented in the current study now offer the ability to pre-screen rams, their semen samples and ewes prior to use in an artificial breeding program. This will help remove sub-fertile individuals, increasing the chances of pregnancy and the cost-benefits associated with reproductive technologies. With industry consultation, these results could help improve the efficiency of artificial breeding in sheep globally.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eEfficient use of superior genetics is paramount to maximise production benefits. This study has finally revealed the coveted link between \u003cem\u003ein vitro\u003c/em\u003e semen parameters, ewe traits, and pregnancy following laparoscopic AI, increasing the potential of artificial breeding programs to reduce breeding inefficiencies and improve sheep reproductive potential.\u003c/p\u003e \u003cp\u003eThe results of this multivariate logistic regression model can now be used to explain the variation detected in pregnancy success following laparoscopic AI. An increase in freezing concentration (and thus sperm per insemination dose), percentage of viable, acrosome intact spermatozoa and motility kinematics will result in a positive linear increase in the probability of pregnancy occurring in a ewe. In addition, ewes with a uterine tone and intra-abdominal fat score of 4 or 5 are more likely to result in pregnancy than ewes with a score of 1 or 2. Finally, a decrease in the percentage of spermatozoa in a sample with morphological abnormalities will increase pregnancy probability. However, it is still important to consider the overarching variation contributed by the environment, site or location where AI occurred, as well as the sire used, as these factors will further modify the influence of the predictive factors mentioned above. Nevertheless, this fertility model is an important first step in revolutionising the artificial breeding sheep industry, providing a comprehensive dataset to mine trends and establish thresholds to troubleshoot \u003cem\u003ein vitro\u003c/em\u003e sperm processing and artificial insemination protocols. Following validation and if used effectively, these results will be able to predict the likelihood of pregnancy occurring following laparoscopic AI, restoring industry confidence and increasing the adoption within the sheep industry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The authors would like to acknowledge the ongoing support, time and donation of data from Merino stud breeders and artificial breeding companies around Australia including Livestock Breeding Services (NSW), Central West Genetics (NSW), Westbreed (WA), Genstock Australia (NSW), Genstock (WA) and Brecon Breeders (SA). They also acknowledge Dr Michelle Humphries, Livestock Breeding Services for her assistance in obtaining imaging for the standardisation of uterine tone and intra-abdominal fat scores. Staff and students within the Animal Reproduction Group, The University of Sydney are also thanked for their dedication, time and assistance towards data collection and advanced semen assessment. Sydney Informatics Hub (SIH), University of Sydney are also acknowledged for their guidance and support on the statistical analysis of data contained within this project.\u0026nbsp;The\u0026nbsp;Australian Wool Innovation (AWI)\u0026nbsp;invests in research, development, innovation and marketing activities along the global supply chain for Australian wool. AWI is grateful for its funding, which is primarily provided by\u0026nbsp;Australian wool-growers through a wool levy and by the Australian Government\u0026nbsp;which provides a matching contribution for eligible R\u0026amp;D activities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e This is the original work of the authors. \u003cstrong\u003eE.A. Spanner:\u003c/strong\u003e Conceptualisation, Data collection, Statistical analysis, Writing – original draft, Writing – review and editing. \u003cstrong\u003eS.P. de Graaf:\u003c/strong\u003e Conceptualisation, Writing – review and editing. \u003cstrong\u003eJ.P. Rickard:\u0026nbsp;\u003c/strong\u003eSupervision\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eConceptualisation, Data collection, Project administration, Writing – review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability Statement:\u003c/strong\u003e The dataset generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Disclosure Statement:\u003c/strong\u003e Dr J.P. Rickard and Miss E.A. Spanner were supported by funding from the McCaughey Memorial Institute.\u0026nbsp;This work was supported by the Australian Wool Innovation [ON-00837] and NSW Merino Breeders’ Association Trust.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement:\u003c/strong\u003e The authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eK. G. Geenty \u003cem\u003eet al.\u003c/em\u003e, \"Reproductive performance in the Sheep CRC Information Nucleus using artificial insemination across different sheep-production environments in southern Australia,\" Anim Reprod Sci, vol. 54, no. 6, pp. 715\u0026ndash;726, 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Eppleston and W. M. C. 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(accessed.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sheep, Sperm, Morphology, Concentration, Acrosome, Viability, Uterine tone, Intra-abdominal fat, Motility, Laparoscopic","lastPublishedDoi":"10.21203/rs.3.rs-4821205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4821205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe causes of variation in the success of laparoscopic artificial insemination (AI) in sheep are not well understood. As such, this study incorporated the contributions of multiple male and female factors relevant to the success of AI into a comprehensive prediction model for pregnancy success. Data from Merino ewes (N\u0026thinsp;=\u0026thinsp;30 254) including age, uterine tone (1; pale/flaccid-5; turgid/pink), intra-abdominal fat (1; little to no fat present-5; high fat), time of insemination and sire used, were recorded during AI. A subset of semen per sire (N\u0026thinsp;=\u0026thinsp;388) was thawed and assessed for volume, subjective motility, sperm concentration, and morphology. Sperm motility (CASA), viability and acrosome integrity (FITC-PNA/PI), membrane fluidity (M540/Yo-Pro), mitochondrial superoxide production (Mitosox Red/Sytox Green), lipid peroxidation (Bodipy C11), level of intracellular reactive oxygen species (H\u003csub\u003e2\u003c/sub\u003eDCFDA) and DNA fragmentation (Acridine Orange) were also assessed 0, 3 and 6h post-thaw. Logistic binomial regression revealed sperm concentration (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CASA parameters at 0h (PCA3; P\u0026thinsp;=\u0026thinsp;0.03), viable acrosome intact sperm at 6h (P\u0026thinsp;=\u0026thinsp;0.02), abnormal morphology (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), uterine tone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and intra-abdominal fat (P\u0026thinsp;=\u0026thinsp;0.03) of ewes influenced likelihood of pregnancy. Results generated will help standardise the pre-screening and selection of semen and ewes prior to artificial breeding programs, reducing variation in the success of sheep AI.\u003c/p\u003e","manuscriptTitle":"A multivariate model for the prediction of pregnancy following laparoscopic artificial insemination of sheep.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-09 14:37:44","doi":"10.21203/rs.3.rs-4821205/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-23T11:57:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-20T18:28:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116499277373147444826557542102704960737","date":"2024-09-16T16:41:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-11T11:50:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310195127679194742854781483671695643471","date":"2024-09-04T06:50:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-02T13:10:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-02T12:58:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-12T10:51:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-12T10:39:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-29T10:28:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a26a2c74-7643-4d45-a29a-f3b011d0796f","owner":[],"postedDate":"September 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":37254981,"name":"Biological sciences/Biotechnology/Animal biotechnology"},{"id":37254982,"name":"Biological sciences/Genetics/Animal breeding"}],"tags":[],"updatedAt":"2024-11-18T19:18:19+00:00","versionOfRecord":{"articleIdentity":"rs-4821205","link":"https://doi.org/10.1038/s41598-024-79253-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-11 15:57:59","publishedOnDateReadable":"November 11th, 2024"},"versionCreatedAt":"2024-09-09 14:37:44","video":"","vorDoi":"10.1038/s41598-024-79253-x","vorDoiUrl":"https://doi.org/10.1038/s41598-024-79253-x","workflowStages":[]},"version":"v1","identity":"rs-4821205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4821205","identity":"rs-4821205","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}