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A risk assessment of health, production, and resource occupancy for 4 laying hen strains across the lay cycle in a commercial-style aviary system

Different strains of commercial laying hens have been molded by varying selection pressures, impacting their production, health, and behavior. Therefore, assumptions that all laying hen strains use the given resources within aviary systems similarly and maintain equal health and performance may be false. We investigated interactions among patterns of aviary resource use by 2 strains of white and 2 strains of brown laying hens (4 units per strain, 144 hens per unit) with daily egg production, location of egg laying, keel fractures, and footpad damage across the lay cycle. Hens' distribution among resources (litter, nest, wire floor, ledge, and perch) was recorded during light and dark periods at 28, 54, and 72 wk of age. Daily egg production and location were recorded, and 20% of hens per unit were randomly selected and assessed for keel bone damage, foot health, and plumage quality. Production and health risks associated with hens' resource use were assessed using multivariable regression. During the day, more brown hens occupied wire floors, while larger numbers of white hens were on perches and litter. More brown hens were on lower-tier wire floors in the dark, while more white hens occupied top tiers. Brown hens laid more eggs outside nests, showed lower incidence of keel fractures, and had better plumage quality than white hens. White hens had higher odds of keel fractures (4.2) than brown hens. Odds of keel fractures were 3.7 and 5.7 times higher at 54 and 72 wk than at 28 wk in all strains (P ≤ 0.05). Occupying the upper tier at night increased odds of keel fractures by 5.4 times. Occupying perches was associated with lower odds of foot lesions and poor plumage quality in all strains across the lay cycle (P ≤ 0.05). Finally, white hens were associated with lower odds of non-nest laying (0.76), whereas higher nest use by brown hens resulted in higher odds of non-nest egg laying (1.56) across the lay cycle (P ≤ 0.05). Distinct strain differences in resource use in an aviary were associated with different risks to hens' production, health, and welfare.

INTRODUCTION
Housing laying hens in noncage systems such as aviaries has considerably increased as the laying hen industry in North America is phasing out caged housing in an attempt to meet consumer and legislative demands for improved hen welfare. Aviary housing systems are specifically designed to allow hens to fulfill their natural behavioral needs to nest, perch, forage, explore, and dust bathe by providing the birds with resources they have high motivation to use (as reviewed by Cooper and Albentosa [2003]). For instance, aviaries provide hens a greater opportunity to perform species-specific behaviors such as roost on elevated perches and platforms; dust bathe, scratch, and forage in a littercovered floor area; jump and fly through vertical spaces across tiers; and lay into enclosed nests.
Providing such additional resources and spaces to fulfill these behaviors has resulted in complicated multiple, stacked aviary tiers connected through platforms and perches with feed, water, and nests distributed across that vertical space. Such complex configurations of aviary designs might impair even hen distribution throughout the available spaces, use of shared resources, and, in turn, hen health. For example, falls and collisions from tiers and perches in aviaries are a contributing factor to the high prevalence of keel bone fractures reported in commercial laying hens (Wilkins et al., 2011;Harlander-Matauschek et al., 2015;Stratmann et al., 2015). Moreover, flock synchrony might also impair hen distribution and use of shared resources within the complex design of aviaries (Webster and Hurnik, 1994;Duncan, 1998). Synchrony can arise from bird-based factors such as the hen's internal biological rhythm, which motivates them to perform certain behaviors at certain times of the day, such as prelay behavior and oviposition during the morning period. External factors such as social facilitation, when performance of a behavior by one hen encourages other hens to perform the same behavior, may also lead to synchrony. This flock synchrony might influence hens to use resources simultaneously, and when coupled together with the complexity of aviary designs, including distribution of resources across the multitier structure of aviaries, synchrony could potentially lead to overcrowding and subsequently aggression, frustration, and the economic concern of reduced productivity (Abrahamsson and Ragnar, 1995;Od en et al., 2002;Freire et al., 2003).
Adding to the complex aviary design and flock synchrony, interstrain variability of laying hens might also influence patterns of hen distribution and resource use in ways that subsequently impact their health and production. Several anecdotal reports from producers and scientists studying laying hen behavior suggest substantial differences exist among strains in their behavior and preferences, such as brown strains not using perches to the same degree that white strains do (Ali et al., 2016;Kozak et al., 2019). White Leghorns that were selected mainly for egg production were found to be less social and perform foraging behavior less intensively than a domesticated hen strain that was not selected for production traits (Sch€ utz and Jensen, 2001). Klein et al. (2000) reported differences in foraging behavior between Lohmann-selected Leghorns and DeKalb chicks, while Braastad and Katle (1989) concluded that layer strains selected for high feed conversion efficiency were less active, showed less foraging behavior, and were less aggressive than birds selected for low feed conversion efficiency. Recent research that was conducted on the same flock as that of the present study reported distinct strain influences in the distribution pattern of laying hens throughout tiers (Ali et al., 2016), use of different resources during the light period (Ali et al., 2019b) and during nighttime (Ali et al., 2019a), and nest use (Villanueva et al., 2017). For instance, DeKalb White and Hy-Line W36 hens occupied litter areas in larger numbers than Hy-Line Brown and Bovans Brown hens during midday and evening, whereas more hens of both brown strains were found in nests than white hens during the morning (Ali et al., 2019b). During the night, white hens occupied the highest locations within aviaries more frequently than brown hens (Ali et al., 2019a).
Such differences in patterns of distribution and resource use by various layer strains within tiered aviaries might also influence their production and health measures. For instance, particular strains of hens might aggregate near and inside nests in the morning to perform prelay behavior and oviposition; however, inadequate space for simultaneous access to nests by all hens may result in mislaid eggs (i.e., litter-or system-laid eggs) by individuals unable to access nests at this time (Villanueva et al., 2017). Similarly, hens preferentially roost on higher perches at night (Schrader and M€ uller, 2009;Brendler and Schrader, 2016;Campbell et al., 2016), simulating the roosting of ancestral jungle fowl high in trees to avoid predators (Wood-Gush and Duncan, 1976;Wood-Gush et al., 1978); thus, insufficient perching space might lead to overcrowding and occasional falls and subsequent keel bone damage (Stratmann et al., 2015). Moreover, not being able to perch might affect a hen's foot health (Hughes and Appleby, 1989;Knowles and Broom, 1990;Hughes et al., 1993) or plumage quality (Appleby and Hughes, 1995). Therefore, the main goal of this research was to investigate interactions between different patterns of resource use exhibited by different strains of laying hens (DeKalb White, Hy-Line W36, Bovans Brown, and Hy-Line Brown) when housed within the same aviary design with their production and health across the lay cycle. This study highlights the possible risks associated with housing laying hens in an aviary without considering the fit between their genetic predisposition and how resources are provided in the aviary. More frequent keel fractures were predicted to be associated with tendencies to use perches and roost in higher locations at night, and keel fractures were predicted to be more prevalent in white strains than in brown strains at 72 wk than at 28 wk. Risks of mislaying eggs were predicted to be associated with nest overcrowding and to be observed more in brown rather than in white strains.

Ethics
All research protocols were approved by the Michigan State University Institutional Animal Care and Use Committee before the start of data collection. were used. The hens were part of a larger study, from which results have been published with regard to the distribution of the strains throughout the various tiers and substrates of the aviary, during light and dark periods at 28 wk of age (Ali et al., 2016, Ali et al., 2019a. The present study focused specifically on describing the possible influences of different patterns of resource use exhibited by these different strains of laying hens within multitier aviaries on their production and health measures across the entire lay cycle.

General Description of Housing and System Management
In brief, 16 aviary units (Natura 60; Big Dutchman, Holland, MI) were used to house the 4 different strains of laying hens. The 16 units were divided equally among 4 rooms, and laying hen strains were allocated so that all strains were present in each of the rooms (1 unit per strain per room ! 4 strains ! 4 rooms 5 16 units in total). Each aviary unit consisted of a 3-tiered wire enclosure (each tier had an internal ceiling height of 61 cm for a total height of 240 cm from the floor to top of the tiered aviary structure) and a litter area with wood shavings (composed of a litter area underneath the tiered enclosure and an open litter area in front of the tiered enclosure). The floor of the top tier was 180 cm in height. Units were stocked at 144 hens per unit following United Egg Producers (2017) recommendations for cage-free egg layers. Hens were provided with a 5-cm feeder space and 88 cm 2 of nesting space. The nest ran the length of each unit in the upper tier, with one central partition creating 2 compartments of equal size. The colony nest was 52-cm wide, and each compartment was 122-cm long.
Each unit had 8 round metal perches extending the full length of the unit (244 cm) that allowed 89% of the hens to perch simultaneously (at 15 cm per hen). Each hen had 1,132 cm 2 of usable floor area consisting of 551 cm 2 per hen in the tiered enclosure (439 cm 2 on wire floors plus 112 cm 2 of solid metal ledge space) and 581 cm 2 per hen in the litter areas. Pin-metered (nipple) drinkers were provided at a rate of 1 per 9 hens. For further details on aviary design and available space per hen, see the study by Ali et al. (2016).
After the 25th week of age (when the target of w90% of egg production was achieved), doors on the lower tier of the aviary enclosures opened each morning at 11:30. These doors allowed hens daily access to litter-covered floor areas after egg laying. The doors closed again at 01:00, approximately 5 h after all the lights were off. For a full description of the lighting program and other details on system management, please see the study by Ali et al. (2016).

Observations, Measurements, and Data Collection
Observations were conducted over 3 consecutive days at each of the 3 periods relative to the level of production throughout the lay cycle. Peak lay observations were conducted when hens were 28 wk old (3 wk after first opening the aviary doors), whereas mid lay and end lay observations were conducted when hens were 54 and 72 wk old, respectively. These ages were selected before the start of the study, based on typical production levels for these strains. During each period, direct observations and video recording of hens' distribution across different resources within the tiers and litter areas were conducted during light and dark periods. A count of all hens in a unit took approximately 90 s during direct observations. During the light period (i.e., when the lights were on), a total of 3 observation sets per unit were conducted per day (3 sets ! 3 D). Morning observations were conducted 15 min after the lights were on (morning: starting at 5:15), during the middle of the light period (midday: starting at 12:15), and 2 h before the lights were off (evening: starting at 18:00). The observations conducted during midday after the aviary doors opened at 11:30 began 30 min later than the exact midpoint of the light period to allow hens to distribute throughout the tiers and litter areas of the units.
For the dark period (i.e., when the lights were off), direct observations of hen occupancy of different tiers inside the tiered enclosures were conducted over 3 consecutive days at each of the 3 periods. Two sets of observations were performed; the first was 30 min after full darkness (dark PM: starting at 21:30), and the second observation was conducted 2 h before lights were on (dark AM: starting at 3:00). During each observation, 2 counts of hens were made for each of the 16 units, with the second count made approximately 1 h after the first count.
Across the 3 D of each period, the rooms were visited in a different randomized order. Before the start of data collection, 3 observers were trained for 3 D to establish synchrony within observer pairs and ensure a high level of interobserver reliability. All observations were performed by a pair of observers (composed of 2 of the 3 previously trained observers). One observer was located in the human access aisle, whereas the second observer observed from the litter aisle. This placed one observer on each side of the tiered enclosure to allow for simultaneous recording of the birds' distribution within a unit from both human and litter aisles ( Figure 1). Counts of hens from the litter aisle were made when the observer was positioned at the end of the row or in the preceding unit to the one being observed.
During data collection, each observer counted the number of hens per location throughout the aviary unit, starting from the litter area or bottom tier, depending on whether hens had litter access, and then worked upward. During the light period, hens in the upper tier, including the perch and metal ledge, and nest were counted only by the observer in the litter aisle, as shown in Figure 1. A folding, 2-step stool was used to help the observer see into the nest. At night, when the nest was closed, the observer in the human aisle climbed on the enclosure to look down to count hens on the top tier, perch, and ledge. Feeders in the center of the 2 lowest tiers were used to divide the aviary in half to avoid double counting of hens on those levels. If the feed belt ran during data collection, observations were paused for 5 min to allow hens to settle down before attempting to count them. To mitigate effects of feeding activity on hen distribution, aviary units were observed in a random order each day, over 3 consecutive days at each age, and observed twice during each time of day. Thus, all units were equally likely to be observed during feeding, and by having multiple observations at each time of day and age, we were more likely to get a representative sample of the hens' use of each level and area of the aviary.
The observers used slow and calm movement to avoid disturbance to birds as they moved between units. At night, disturbance of hens was minimized by using green headlamps, which allowed observers to see hens in the darkened room without rousing them to movement (Ali et al., 2016;Campbell et al., 2016). During the observer training period, the hens also had the opportunity to acclimate to the presence of observers in the room performing the routine data collection.
Observations that were conducted while aviary doors were closed (i.e., morning and dark observations) were carried out by counting the number of birds within each location, as described previously. Observations that were conducted when aviary doors were open (midday and evening) also included data captured using a combination of video and direct observation of hens in the litter area. Ceiling-mounted high-resolution digital video cameras (VF450; Clinton Electronics, Loves Park, IL) were used to record hen distribution on the open litter area. Hens on the litter underneath the enclosure were counted by the observer in the human aisle, and their distribution was simultaneously recorded using a handheld video camera (VIXIA, HFM41; Canon, Oita, Japan). To ensure accurate counts were made of hens underneath the enclosure (as this was difficult owing to the narrow opening available for observers to look through), live counts were later confirmed using the video footage.
Egg number and location (nest area, system, and floor) were recorded daily along with mortality on each day of behavioral observation. During each period, 20% of the hens per unit were also assessed for basic health and well-being parameters including keel fractures and deviations, footpad health, and plumage quality following a modified version of the Welfare Quality (WQ) scoring system (Welfare Quality, 2009). Welfare assessments were conducted during the dark period of the third day of each period. Hens were randomly selected from different locations (i.e., wire floors, perches, and ledges) and different tiers of the aviary and temporarily kept in a spent hen transport cart (Alternative Design, Siloam Springs, AR). The hens were handled gently and individually scored for keel bone deformations (fractures and deviations), footpad condition, and plumage damage; the sampled hens per each unit were assessed for each measure (i.e., keel bone fractures) using a 2-point score (0 5 no lesion or not affected, 1 5 lesion or affected). Multiple assessors conducted the WQ evaluations. Before starting the WQ assessment in each room, 1 hen from that particular room was scored by all the assessors to ensure parity in scoring. Any disagreement among assessors was settled by discussion within the group before proceeding to data collection.

Data Processing and Statistical Analyses
Before analysis, all hen count data obtained from direct observations and video recordings were collated and converted to the percentage of hens per tier/ resource. Total daily egg production was calculated as a percentage for each unit by first dividing the total number of eggs laid in a day in that unit by the actual number of hens in that unit and multiplying by 100 (e.g., if 125 eggs were laid in a unit with 137 hens, then the daily production percentage would be 91.24%). The percentages of eggs laid each day in nests, litter, and the tiered enclosure in a unit were calculated based on the total number of eggs produced that day by hens in that unit (e.g., if 101 eggs were laid in nests of 125 total eggs, the percentage of nest-laid eggs for the unit would be 80.8%). For, welfare measures, the number of affected hens across the 4 units per strain (i.e., hens with keel fracture, keel deviation, footpad injury, and damaged plumage) was transformed into percentages of affected hens of the sampled hens per each strain for each period (e.g., if 21 hens showed keel bone fractures of the 29 hens assessed per unit, the percentage of affected hens for the unit would be 72.4%). For each of the 4 strains, there were 4 aviary unit replicates, and aviary unit was the experimental unit for all statistical tests. Each live observation set was composed of 2 counts of hen distribution within each unit for each of the 5 times of day (light period: morning, midday, and evening; dark period: dark PM and dark AM) across 3 time periods (peak lay, mid lay and end lay). There were 24 total observations for each strain during each individual time of day (e.g., for light: morning, midday, and evening); this was calculated as follows: 24 5 2 counts/unit per observation set * 3 D of observation for each time period of data collection (peak lay, mid lay, and end lay) * 4 units per strain.
Statistical analyses were performed using R software (version 3.3.1), package "stats" (R Core Team, 2013). Descriptive statistics were calculated using the psych package, and data are presented as mean 6 SEM; P 0.05 was considered significant. Generalized linear mixed models were developed with family set to "binomial" (because data were normal and met assumptions of equal variance), using the lme4 package (Bates et al., 2014), to describe the influence of laying hen strain in terms of resource use, tier occupancy within the aviaries, different times of day, different periods, and all possible interactions. Fixed effects in the models included strains of hen (B1, B2, W1, W2), times of day (morning, midday, and evening), periods (peak lay, mid lay, and end lay), and their interactions, whereas response variables included different tiers (bottom, middle, and top tier) and resources (wire floor, perch, ledge, nest, and litter).
Following the same design, generalized linear mixed models were developed to describe the influence of laying hen strains on egg production, physical measures of welfare, across different periods, and all possible interactions. Fixed effects in the models included strains of hen (B1, B2, W1, W2), periods, and their interactions, whereas response variables included egg production (eggs laid by hens [%] and location of eggs [nest, enclosure, and litter]) and health measures (keel fractures and deviation, footpad and plumage damage). Aviary unit and day of observation were included as random effects for all models, and P 0.05 was considered significant. Statistically significant effects were further analyzed using Tukey's honestly significant difference multiple comparison procedure using the "multcomp" package (Hothorn et al., 2008).
To explore production and health risks associated with hens' resource use across strains, multivariable, mixed-effects beta regression models using the "betareg" package (Hallgren et al., 2014) were deployed to analyze the data. Mixed-effects beta regression modeling fitted the bounded nature of proportions in our data set and could properly adjust to account for random factors and nonindependent observations. Four different regression models were generated to identify possible risks in keel fractures, footpad and plumage damage, and incidence of non-nest-laid eggs associated with hens' strains, stage of production, distribution across tiers, and resource use within aviaries across light and dark periods. Both strains of brown hens (B1 and B2) and of white hens (W1 and W2) were typically similar to each other in their patterns of occupancy of the various substrates and tiers in the aviary enclosure, egg production and location, and prevalence of health issues. Therefore, observations of resource use, egg production, and health parameters of both brown strains B1 and B2 and both white strains W1 and W2 were combined and used for calculations of regression coefficients and odd ratios. For each model, possible predictor variables were included, and a subset of these variables was selected using backward stepwise elimination, retaining variables with P 0.05 only. The Akaike information criterion was used to determine the most appropriate model that completely fit our data set (i.e., backward elimination was conducted until the lowest Akaike information criterion was achieved, while P-values were 0.05). Aviary unit and day of observation were included as random effects for all the models. Finally, coefficient estimates were transformed and presented as odd ratios (OR).
Finally, following Landis and Koch (1977), interobserver reliability was calculated using Cohen's kappa agreement coefficient (K), using the "Cohen. Kappa" function in the psych package of R. Interobserver reliability was measured when trainees were observing the same area of the same aviary simultaneously. Interobserver agreement was very high (kappa 5 0.96 [P , 0.001], CI 5 [0.90, 0.99]). Moreover, to examine the degree of hens' movement from the top tier to the middle or bottom tiers between dark PM and dark AM periods, kappa coefficients of agreement (K coefficients) were calculated. For each strain at each period (peak lay, mid lay, and end lay), the difference between the 2 dark PM observations and the 2 dark AM observations for the top tier within the unit was used to calculate the K coefficient (Ali et al., 2016).

Daytime Resource Occupancy
Interactions between strain and observational period during the light periods were found for wire floor (Z 5 9.86; P 5 0.023), perch (Z 5 6.85; P 5 0.031), and litter (Z 5 5.25; P 5 0.039; Table 1) occupancy. Specifically, more brown hens (B1 and B2) than white hens (W1 and W1) were observed on the wire floors of all tier levels across different observational periods (peak lay: P 5 0.011, mid lay: P 5 0.023, and end lay: P 5 0.029). Contrarily, more white hens occupied perches across different observational periods (peak lay: P 5 0.021, mid lay: P 5 0.031, and end lay: P 5 0.025). White hens (W1 and W2) also occupied the litter area in larger numbers than brown hens (B1 and B2) during peak lay (P 5 0.020) and mid lay (P 5 0.024) periods, whereas such differences were not significant during the end lay period. Nest occupancy was always higher for brown hens than for white hens in the peak lay (P 5 0.031) and mid lay (P 5 0.038) periods but not in the end lay period, whereas no differences were detected in ledge occupancy by different strains across different observational periods, as shown in Table 1.

Nighttime Resource Occupancy and Movement
Generally, more brown hens (B1 and B2) occupied the wire floor (Z 5 10.23; P 5 0.001) than white hens All parameters are expressed as the mean percentage of hens 6 SEM of the 4 strains (B1 5 Hy-Line Brown, B2 5 Bovans Brown, W1 5 DeKalb White, and W2 5 Hy-Line W36) occupying wire floor, perch, ledge, nest, and litter space during the light period, throughout the aviary, and across different stages of the lay cycle. (W1 and W2), whereas the latter occupied perches (Z 5 8.96; P 5 0.011) and ledges (Z 5 7.52; P 5 0.019) in larger numbers during the dark period across different stages of the lay cycle (Table 2). Specifically, brown hens (B1 and B2) occupied wire floors of the bottom and middle tiers in greater numbers than white hens across different stages of the lay cycle (peak lay: P 5 0.014, mid lay: P 5 0.020, and end lay: P 5 0.0285), whereas more white hens occupied top-tier wire floors across the lay cycle (Z 5 12.52; P 5 0.001). Similarly, white hens consistently occupied top-tier perches in larger numbers than brown hens across the lay cycle (peak lay: P 5 0.001, mid lay: P 5 0.013, and end lay: P 5 0.023). In addition, more white hens (W1 and W2) occupied ledges of the middle and top tier than brown hens during the peak and mid lay periods (P 5 0.021, 0.026) as well as the top tiers during the end lay period (P 5 0.034; Table 2). A considerable degree of tier-to-tier movement between dark PM and dark AM periods was recorded, with white hens consistently showing more tier-to-tier movement (lower K values) than brown hens across different stages of the lay cycle (Table 3).

Egg Production and Location
An interaction was also observed between hen strains, egg production, and location for egg laying across different stages of the lay cycle (Table 4; Z 5 2.52, 5.36; P 5 0.021, 0.002). Both strains of brown hens laid more eggs throughout the tiered enclosure than W1 and W2 hens during the peak lay period (P 5 0.031), whereas W1 and W2 hens laid a higher percentage of their eggs inside the nests than B1 and B2 hens across the lay cycle (peak lay: P 5 0.010, mid lay: P 5 0.023, and end lay: P 5 0.039). B1 and B2 hens laid a higher percentage of their eggs in the litter than the W1 and W2 hens across the lay cycle (peak lay: P 5 0.012, mid lay: P 5 0.019, and end lay: P 5 0.021). Finally, average daily egg production differed by strains as hens of both white strains laid more eggs than the hens of both brown strains (peak lay: P 5 0.024, mid lay: P 5 0.031; Table 4).

Health Measures
An interaction was observed between hen strains and different stages of the lay cycle in the prevalence of keel fractures (Z 5 9.36; P 5 0.001), keel deviations (Z 5 6.36; P 5 0.025), footpad quality (Z 5 12.85; P 5 0.001), and plumage damage (Z 5 6.96; P 5 0.009), as shown in Table 5. Explicitly, higher percentages of keel fractures and deviations were recorded in W1 and W2 hens than in B1 and B2 hens across the lay cycle (keel fractures: peak lay: P 5 0.031, mid lay: P 5 0.035, and end lay; P 5 0.029), and differences in keel deviations were significant during the mid lay period (P 5 0.036). Contrarily, B1 and B2 hens showed a higher prevalence of footpad lesions than the white strains across the lay cycle (peak lay: P 5 0.001, mid lay: P 5 0.004, and end lay: P 5 0.021). B1 and B2 hens also showed a higher prevalence of plumage damage than white hens particularly during the mid and end lay periods (P 5 0.024, P 5 0.009), as shown in Table 5.
In this aviary system and under the reported management practices, white hens (W1 and W2) had increased odds of experiencing keel fractures (OR 5 4.21) compared with brown hens (B1 and B2). Hens of all 4 strains were more prone to keel fractures during the mid lay (OR 5 3.69) and end lay (OR 5 5.63) periods than during the peak lay period; however, hens of white strains (OR 5 7.63) were more susceptible to keel fracture than hens of brown strains (OR 5 4.25; Table 6) during the end lay period. Using the top tier more frequently for roosting during the dark period was associated with 5 times higher odds of experiencing a keel fracture, and roosting on the upper-tier perch specifically was associated with an increase in the odds of keel bone fractures (OR 5 2.66). Moreover, roosting on ledges was associated with increasing the odds of keel fractures (OR 5 2.21), particularly roosting on the top-tier ledge (OR 5 3.23; Table 6). Finally, the white strains showed the highest tier-to-tier movement during the dark period, which was associated with increasing the odds (OR 5 7.23) of keel fracture more than brown hens.
Generally, white hens (W1 and W2) in this system were less prone to footpad damage (OR 5 0.33) than brown hens; however, hens of all 4 strains were more susceptible to footpad damage during the end lay period (OR 5 3.23), but particularly brown hens (OR 5 4.13; Table 6). In addition, more occupancy of litter areas Results are expressed as K coefficients (degree of agreement), with higher values indicating higher agreement among percentage of hens and, therefore, less movement of hens from the top tier within an aviary unit (B1 5 Hy-Line Brown, B2 5 Bovans Brown, W1 5 DeKalb White, and W2 5 Hy-Line W36). by hens during the light periods and more hens roosting on perches during the dark period were associated with lower odds of footpad damage (OR 5 0.53, 0.72). Finally, higher occupancy of the wire floor during the dark period was associated with increasing the odds of footpad damage (OR 5 3.56).
Decreased odds of showing plumage damage were found for white hens (OR 5 0.78), whereas hens of all 4 strains were more prone to plumage damage during the end lay period (OR 5 2.69; Table 6) than during the peak lay period. During the end lay period, brown hens were more susceptible (OR 5 2.72) to plumage damage than white hens. Occupying the wire floor in larger numbers (OR 5 1.96), particularly during the dark period (OR 5 2.58), was associated with increased odds of plumage damage. On the other hand, higher occupancy of litter areas during the light periods (OR 5 0.69) and of perches for roosting at night (OR 5 0.63) was more associated with reduced incidence of plumage damage.
White hens tended to lay more in the colony nests than brown hens; therefore, white hens were associated with decreased odds of non-nest laying in this system (OR 5 0.76). However, during the end lay period, the odds of non-nest laying were reduced across all strains (OR 5 0.45). Surprisingly, occupying nests in larger numbers during the light period was associated with higher odds of non-nest laying (OR 5 1.56; Table 6), and occupying litter in larger numbers during the same period was also associated with a higher incidence of non-nest laying (OR 5 1.25).

DISCUSSION
Comparative analyses of hens' distribution across tiers and occupancy of various substrates (Tables 1-3), egg production and location (Table 4), and prevalence of keel fractures and deviations, footpad and plumage damage (Table 5) among 4 strains of laying hens in the aviary system revealed almost no differences between strains of Table 4. Total egg production and location of eggs laid by 4 strains of laying hens in an aviary system across different stages of the lay cycle. Location of eggs laid each day is expressed as a percentage of the total eggs laid that day (100%) in each unit. All parameters are presented as means 6 SEM of percentage of hens of the 4 strains (B1 5 Hy-Line Brown, B2 5 Bovans Brown, W1 5 DeKalb White, and W2 5 Hy-Line W36) with keel deformation, footpad and plumage damage, calculated as the percentage of affected hens within a 20% randomly selected sample of hens per each aviary unit. the same color across the lay cycle. The 2 white strain (W1 and W2) hens were similar to each other, except for a single case when the 2 white strains of hens differed in their use of the wire floor during the day at the end lay period (Table 1) while the 2 brown strains (B1 and B2) were similar to each other. However, differences in patterns of distribution and resource use were present between white and brown hens across the lay cycle and were associated with the variability of their health and production parameters. As all birds were raised in the same manner, difference in rearing environments is not a likely explanation for the differences observed in adult hens.

Resource Occupancy Across the Lay Cycle
Distinct differences were detected between brown and white hens in their daytime pattern of resource occupancy during the peak lay period, and to some extent, these were preserved across the mid and end lay periods. Specifically, more brown hens were observed on wire floors than white ones, whereas the latter occupied perches and litter areas in greater numbers across the lay cycle. Differences between brown and white strains in wire floor, litter, and perch occupancy may be attributed to some innate dissimilarities in preferences to using either graspable perches or flat surfaces during the day. Similar findings were reported by Faure and Jones (1982), with white Leghorn hens observed using perches of different types and heights more frequently than brown Leghorn hens during the day. White hens may also have used perches for undisturbed resting (Brendler and Schrader, 2016), whereas brown hens distributed themselves more evenly through the system. Hens use perches to enhance their perception of safety (Newberry et al., 2001); therefore, the lower daytime perch use exhibited by brown hens may be due to being less fearful than white hens (Fraisse and Cockrem, 2006; Table 6. Results of multivariable mixed-effects beta regression models used to investigate the effect of different patterns of resource use exhibited by hens of the 4 strains in a multitier aviary system on the prevalence of keel fractures, footpad and plumage damage, and incidence of non-nest laying across the peak, mid, and end lay periods. De Haas et al., 2014). On the other hand, white hens occupied litter areas in greater numbers than brown hens, which seems to suggest they were not fearful of accessing the litter area. White hens may have a stronger instinct to roost or simply be more physically capable of readily accessing higher levels in the aviary. Brown birds, with a lower wing-to-body ratio, heavier body weights, and larger body sizes, may find it more difficult to move around levels in tiered aviary systems. Brown hens may also have preferences for maintaining interbird distances appropriate for behaviors they are performing (Keeling, 1994). If brown hens were performing more dust bathing on the open litter areas, fewer hens might be expected in this area as these behaviors require more space than standing, sitting, or preening (Mench and Blatchford, 2014). A follow-up study of these hens found that brown hens of these strains maintained greater distance between themselves and conspecifics on the litter while dust bathing than the white hens, which also showed higher levels of simultaneous dust bathing activity (Grebey et al., 2020). However, the motivation behind the white and brown hens' differential occupancy of the perches and litter area is purely speculative at this point.
More brown hens were counted in nests during the light period in the peak lay period, which is consistent with findings of our previous study on the same flock at 36 wk of age (Villanueva et al., 2017). In the present study, differences in daytime nest occupancy were found to extend to mid lay when hens were 54 wk old. Such differences in nest occupancy between brown and white hens may reflect differences in their circadian drive to lay eggs in the morning (Yeates, 1963;Vestergaard, 1982;Channing et al., 2001) as it appears this drive is more robust in the brown strains we studied. Another explanation for such differences might be genotypic variation in strains' typical oviposition time (T umov a et al., 2007;T umov a and Gous, 2012). However, differences between brown and white hens in nest occupancy during the light period faded by the end lay period as levels of egg laying decreased, and the hens' need to use nests was subsequently reduced.
When the nighttime occupancy of different resources of these hens was examined, more white than brown hens were observed on top-tier wire floors, perches, and ledges across all periods, and more brown than white hens were observed on bottom-tier wire floors. Such differences in roosting patterns between brown and white hens were maintained across the middle and end stages of the lay cycle to a large extent. The white hens' pattern of roosting at height vs. preferring a particular type of substrate may be attributed to their tendency to prioritize roosting at height above the type of substrate on which they roost (Schrader and M€ uller, 2009). On the other hand, the dispersed roosting pattern across tiers exhibited by brown hens, regardless of the height of the roosting location, might be explained by a drive to maintain a greater interbird distance. Previous studies in litter-based systems found that ISA Brown hens tended to disperse across the space provided to them (Channing et al., 2001;Od en et al., 2002), which may explain the large number of brown hens observed on wire floors and lower regions of the aviary at night in the present study. Recording hen movement across tiers through comparing hen roosting locations between dark PM and AM observations revealed a higher movement frequency from the top tier by white hens during the peak lay period, and the incidence of such movements increased during the mid and end lay periods.
The higher incidence of nighttime movement by white hens is undoubtedly related to the larger number of hens that tended to roost on top-tier resources, than brown hens that tended to disperse across tiers. Crowding of the top-tier resources by white hens during the dark period might lead hens to either voluntarily switch to another roosting location or to fall involuntarily from their original location (Ali et al., 2019a). However, further investigation is required to distinguish what causes tier-to-tier movements in the dark period.

Interaction Between Resource Occupancy, Production, and Health Across the Lay Cycle
A high prevalence of keel fractures was recorded across the 4 strains of hens in the present study, particularly toward the end of the lay cycle. Similar reports of a high prevalence of keel damage in noncage systems have been identified in several studies (Sherwin et al., 2010;K€ appeli et al., 2011;Tarlton et al., 2013;Petrik et al., 2015). In the present study, a higher incidence of keel fractures was associated with hens of both white strains than with hens of the brown strains, and a possible explanation might be the white hens' pattern of resource occupancy during the dark period. Such an explanation is further supported through the interactions we identified between occupancy of specific resources and incidence of keel damage. For instance, roosting on the perch and ledge of the top tier was strongly associated with a higher incidence of keel fractures, and white hens roosted on the top tier in larger numbers than brown hens. Furthermore, top-tier movement during the dark period was strongly associated with a higher incidence of keel fractures in white hens. Thus, the higher incidence of keel fractures in white hens may be attributed to a higher likelihood of falls from higher areas in the aviary. A similar association between keel fractures and resource use was reported by Stratmann et al. (2015), who suggested that installing ramps could potentially reduce keel bone damage. Several reports have confirmed the strong association between keel damage and collisions with housing structures and the pressure load on keel bones due to prolonged perching (Scholz et al., 2008;Sandilands et al., 2009;Pickel et al., 2010;Toscano et al., 2013). It is also possible that the incidence of keel fracture associated with both white strains could be attributed to a genetic predisposition ( propensity for genetic regulation of fracture susceptibility, with clear differences in the frequency of experimental fractures across crossbred and pure lines. They further concluded that given the control of environmental differences in their experimental design, such differences were exclusively related to a genetic predisposition. As expected, the risk of keel fractures increased in all strains during the mid lay and end lay periods compared with the peak lay period, with a higher risk in white over brown hens. Progressive bone resorption due to high calcium demands of egg laying and subsequent osteoporosis is a rational explanation for a high prevalence of keel fractures during the later stages of the production cycle (Whitehead, 2004;Regmi et al., 2016). However, the lower body weights and subsequently lower breast muscle coverage in the white hens, along with their preference for roosting on higher locations, even during the late stage of the lay cycle, might exacerbate differences in keel bone fractures among strains. Fleming et al. (2004) also related a high prevalence of keel fractures in modern white hybrid hen strains to a low amount of breast muscle, which leaves the keel bone relatively unprotected and vulnerable to fractures. Moreover, Gregory and Devine (1999) reported an association between the emaciation and subsequent reduction in breast muscle mass that happens to laying hens later in production to a subsequent higher incidence of keel bone fractures. This association could explain the higher incidence of keel fractures in white vs. brown hens and particularly during the late stages of the lay cycle.
Variable prevalences of footpad lesions in noncage systems have repeatedly been reported from different housing designs and across different stages of production (Simonsen et al., 1980;Abrahamsson and Tauson, 1995;Gunnarsson et al., 1995). In the present study, white hens were associated with less risk of footpad lesions, which can be explained by their pattern of resource use. For instance, white hens were recorded to be more frequently occupying the litter area during the day and roosting on perches during the night. A possible explanation could be that using litter and perches is associated with relieving pressure on the footpad and reducing risk of injury as a result of being in contact with the metal surfaces of wire floors. These findings are consistent with previous reports by Weitzenb€ urger et al. (2006) and Abrahamsson and Tauson (1997), who concluded that prolonged standing and walking on wired floors made hens more prone to footpad lesions. Similarly, Appleby et al. (1992) reported greater footpad damage in caged hens without perches than in those with perches; the more time the hens spent perching, the less the footpad damage. By the end of the lay cycle, increased risks of footpad lesions were found for hens of all strains, which match the findings reported by Blatchford et al. (2016) and Regmi et al. (2018) of increased foot lesions in aviary-housed hens as the hens' age progressed. However, these risks increased more for brown hens, wherein their pattern of resource use (i.e., occupying wire floor in greater numbers than perches and the litter area) might be responsible for aggravating the incidence of foot lesions during the late stages of the production cycle.
Although white hens were observed occupying perches and litter areas in greater numbers than brown hens, brown strains were more susceptible than white hens to inferior plumage quality, particularly during the end lay period. Hens on litter exhibit foraging and dust bathing, which are associated with less feather pecking (Vestergaard, 1994;Johnsen et al., 1998) and the removal of lipids and ectoparasites, resulting in healthier plumage (Van Liere, 1992;Sandilands, 2001). Similarly, higher occupancy of perches was associated with better plumage quality, which could possibly be due to the fact that perching hens soil hens on wire floors, as perches in the present study were located above the wire floors.
Overall, white hens laid more eggs in nests than brown hens across the lay cycle, while the latter tended to lay in the litter and enclosure more frequently than white hens. This is consistent with the findings of Singh et al. (2009) who reported a higher incidence of floor laying in Lohmann brown than in Lohmann white hens and findings from this same flock at 36 wk of age (Villanueva et al., 2017). As the nests in the present study were located in the top tier, it is possible that the heavier brown hens found movement upward to the nests more difficult than white hens (Moinard et al., 2004).
Given that hens typically lay most of their eggs in the morning, not having enough space for hens to nest synchronously could be expected to result in either adaptation by hens to either lay eggs in nests at other times of the day or to lay eggs in other areas of the system. Villanueva et al. (2017) reported that brown hens adapted by laying more eggs outside the nest but maintained a strong circadian rhythm of morning egg laying, whereas white hens extended their occupancy of and egg laying in nests through the early afternoon and laid a higher percentage of their eggs in nests. Use of nests by white hens could have drawn more birds to continue using nests, which is in agreement with the findings of Tahamtani et al. (2018), who described hens' preference for laying in crowded nests. The incidence of floor laying for all strains of hens decreased during the mid and end lay periods, paralleling the overall reduction in egg production approaching the end lay period.

CONCLUSION
Distinct differences in terms of distribution and resource occupancy between brown and white hen strains within aviaries were associated with different risks to hens' production and health. White hens' tendency to occupy perches and litter during the day, roost on the top tier at night, and exhibit more movement during the night put them at more risk of keel damage but less risk of footpad lesions and plumage damage than brown hens. On the other hand, more hens of brown strains occupied wire floors, which was associated with a higher incidence of footpad lesions and lower plumage quality but less keel damage. Such knowledge is helpful for determining how to account for variability between strains and highlights the possible consequences of mismatching between a particular strain's needs and how resources are provided within a given aviary style. Although our specific conclusions can only be applied to the 4 strains and aviary style examined in the present study, our findings coupled with those of others suggest that white hens need more space for roosting at height in aviaries or simultaneous access to litter, whereas brown hens may need larger nest space allowances or nests provided at lower levels in aviaries. Calculations to estimate space needs of brown hens may also need to factor in preferences for distribution or more interbird space. Mismatches between aviary design and hen requirements could have consequences that are a threat to the welfare and productivity of laying hens in aviaries and counteract the intended benefits of transitioning hens to cage-free housing.


extfieldsofstudy: Biology Medicine

provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2020.jsonl.gz.gz

text:
Expression of Gibberellin Metabolism Genes and Signalling Components in Dwarf Phenotype of Breadfruit (Artocarpus altilis) Plants Growing on Marang (Artocarpus odoratissimus) Rootstocks

Breadfruit (Artocarpus altilis) is a traditional staple tree crop throughout the tropics. The species is an evergreen tree 15–20 m; there are currently no size-controlling rootstocks within the species. Through interspecific grafting, a dwarf phenotype was identified in breadfruit plants growing on Marang (Artocarpus odoratissimus) rootstocks, which displayed ~60% reduction in plant height with ~80% shorter internodes. To gain insight into the molecular mechanism underlying rootstock-induced dwarfing, we investigated the involvement of gibberellin (GA) in reduction of stem elongation. Expression of GA metabolism genes was analysed in the period from 18 to 24 months after grafting. In comparison to self-graft and non-graft, scion stems on marang rootstocks displayed decrease in expression of a GA biosynthetic gene, AaGA20ox3, and increase in expression of a GA catabolic genes, AaGA2ox1, in the tested 6-month period. Increased accumulation of DELLA proteins (GA-signalling repressors) was found in scion stems growing on marang rootstocks, together with an increased expression of a DELLA gene, AaDELLA1. Exogenous GA treatment was able to restore the stem elongation rate and the internode length of scions growing on marang rootstocks. The possibility that GA deficiency forms a component of the mechanism underlying rootstock-induced breadfruit dwarfing is discussed.

Introduction
Trees with reduced stature allow high-density planting and facilitate tree management and harvesting. In many species, tree dwarfing has been achieved through the widespread use of dwarfing rootstocks. The mechanism underlying rootstock-induced dwarfing has been extensively studied but remains poorly understood [1][2][3][4]. Breadfruit (Artocarpus altilis) is a traditional staple tree crop throughout the tropics. The species is an evergreen tree from 15 to 20 m. Breadfruits comprise fertile and sterile diploids and sterile triploids and has hundreds of cultivars [5], but there is currently no size-controlling rootstock within the species. Through interspecific grafting, a dwarf phenotype was recently identified in breadfruit plants growing on marang (Artocarpus odoratissimus) rootstocks [6]. Under the same genus of Artocarpus, marang is also a large tropical fruit tree to 25 m; no dwarf phenotype has been identified [7]. Little is known about the intriguing interaction by which marang greatly reduces the tree size of grafted scions when used as rootstocks.
The molecular mechanism by which dwarfing is conferred may differ between species as well as between annuals and perennials [4]. Several mechanisms have been proposed to explain how rootstocks cause dwarfing in scions. These include reduction of water and solute transport across graft union [3,8], anatomical change [2], and altered hormone signalling between scions and rootstocks [1,9]. There is considerable evidence to suggest that disruption in gibberellin (GA) metabolism plays a role in rootstock-induced dwarfing [4]. Previously dwarfing apple interstocks were found to limit the supply of [ 3 H]GA 4 to scion shoot tips as compared to non-dwarfing interstocks [10]. Dwarfing apple rootstocks also reduce the supply of the root-produced GA precursor, GA 19 , to scions [9,11]. Application of GA to scions on apple dwarfing rootstock restores the node number of both the primary axis and secondary shoots [12]. In ungrafted apple rootstocks, the level of GAs is the lowest in the dwarfing rootstock M.9 and the highest in the non-dwarfing rootstock MM111 [13]. Transcriptomic analysis revealed downregulation of GA biosynthetic genes in scions of apple trees gowning on dwarfing rootstocks [14] and upregulation of a GA catabolic gene together with decreased GA level in persimmon scion stems grafted on dwarfing interstocks [15]. Furthermore, a dwarf plum hybrid with elevated transcript levels of a major GA catabolic gene, GA2ox, exhibits shorter internodes and reduced stem elongation, and when used as rootstocks, it reduces the level of bioactive GAs in scions and reduces scion vigor [16].
GAs are a family of diterpenoid plant hormone involved in a wide range of plant growth and development [17]. In higher plants, the flux of active GAs is regulated by the balance between the rates of biosynthesis and deactivation [18]. Biosynthesis of GAs starts from geranylgeranyl diphosphate, a C20 precursor [18]. GA20-oxidase (GA20ox) is a multifunctional enzyme that converts GA 12 or GA 53 to GA 9 or GA 20 through three sequential oxidations, therefore representing one of the key enzymes controlling GA biosynthetic flux [18]. Suppression of GA20ox genes reduces endogenous active GA content and produces dwarfism in many species [17,19,20]. On the other hand, the main route for GA deactivation is through 2β-hydroxylation catalysed by 2-oxyglutarate dependent GA2-oxidases (GA2ox), leading to formation of biologically inactive GAs [17]. Overexpression of GA2ox genes therefore enhances GA deactivation and produces dwarf phenotype [16,21]. In parallel with the direct regulation of endogenous GA concentration mediated by GA biosynthetic and catabolic genes, GA signalling is regulated by the negative regulators, DELLA proteins [22]. DELLA proteins are characterized by a highly conserved N-terminal DELLA domain essential for GA-induced proteolysis [23]. Binding of GA molecule to its receptor, GA-INSENSITIVE DWARF1 (GID1) results in rapid degradation of DELLA proteins via the ubiquitin-proteasome pathway, as a result, it releases the DELLA repression of GA responses [23,24]. Mutants with over-accumulated DELLA display dwarf phenotype [23,25]. In plants, GA20-oxidase and GA2-oxidase are encoded by gene families [17], and DELLAs in dicot species are encoded by small gene families of various sizes [23]. While the overlap expression pattern suggests functional redundancy, the tissue-specific expression patterns among family members of GA2-orxidase genes in both poplar [26] and Arabidopsis [27] reflect specialised and functional divergence in their relative contribution to GA metabolism and signalling in a subset of organs.
Three predicted functional GA20-oxidases genes were isolated in breadfruit (Artocarpus altilis cv. Cannonball), with two genes, AaGA20ox1 and AaGA20ox3, predominantly expressed in green vegetative organs [28]. A cohort of four GA2-oxidase genes, AaGA2ox1-AaGA2ox4, was also cloned in the same cultivar, with three highly expressed in vegetative organs [29]. There are two DELLA genes, AaDELLA1 and AaDELLA2, isolated in "Mason" breadfruit species [30]. However, the lack of dwarf varieties and the scarcity of dwarfing rootstocks for breadfruits has limited our understanding of how vegetative growth is controlled by rootstocks in the species and any potential role of these genes in conferring breadfruit dwarfism.
Our current work focused on the involvement of GA in the scion stem elongation of breadfruits growing on marang rootstocks in relation to dwarf phenotype. We investigated GA response and the expression of GA biosynthetic and catabolic genes together with DELLA protein abundance and transcript levels in breadfruit scion stems in the period from 18 to 24 months after grafting. Our evidence suggests that GA deficiency may form a component in breadfruit dwarfing mechanism induced by marang rootstocks. The current work provided insight into the molecular mechanism modulating dwarfing in breadfruit through interspecific rootstocks.

Effect of Rootstocks on Stem Elongation of Breadfruit Scions
Plants 20 months old after grafting were used for stem elongation analysis. The measurement was started from the emergence of an internode until the cessation of extension in the same segment. Significantly shorter internodes were observed in breadfruit plants growing on marang rootstocks, with 79.8% reduction in final internode length compared to those on self-grafts ( Figure 1a). As a result, breadfruit plants on marang rootstocks displayed short stature with height reduction by 52.0% at 12 months and by 59.6% at 24 months after grafting (Figure 1b,c). There was no significant difference in both internode length and final plant height between the self-grafts and the non-graft ( Figure 1). The results were consistent with previous growth observation in the 18-month period after grafting [6].

Effect of Rootstocks on the Expression of GA Metabolic Genes
Six GA20-oxidase genes, AaGA20ox1-AaGA20ox6, were previously isolated in breadfruits, with three, AaGA20ox1-AaGA20ox3, predicted to encode functional GA20-oxidase and the other three, AaGA20ox4-AaGA20ox6, predicted to be unprocessed pseudogenes [28]. The expression of the three AaGA20oxs, AaGA20ox1-AaGA20ox3, were measured in scion stems, but only two genes, AaGA20ox1 and AaGA20ox3, showed expression in the current experiment condition. Similarly, of the four GA2ox genes previously identified in breadfruit [29], only two genes, AaGA2ox1 and AaGA2ox2, were detected in scion stem tissues under the current growth condition. All the detectable genes were analysed monthly in the period from 18 to 24 months after grafting. It was shown that the overall expression levels of AaGA20ox3 were over 1000 times higher than those of AaGA20ox1 and that the expression levels of AaGA2ox1 were over 10 times higher than those of AaGA2ox2 for all samples (Figure 2a-c). Over the six month period, scion stems on marang rootstocks showed no significant change in transcript levels of AaGA20ox1 but showed decreased transcript levels of AaGA20ox1 at serval time points, including 20, 21, 23, and 24 months after grafting when compared to those on self-grafts and non-grafts (Figure 2a,b). There was no significance difference between the self-graft and non-graft in the expression of these two genes (Figure 2a,b). For GA2-oxidase genes, higher levels of expression were detected every month from 18 to 24 months except for the 19-month time point for AaGA2ox1, but no significant change for AaGA2ox2 compared to those on self-grafts and non-graft was observed (Figure 2c

Effect of Rootstocks on DELLA Protein Abundance and Transcript Levels
Two DELLA genes, AaDELLA1 and AaDELLA2, were previously cloned from "Mason" breadfruit [30]. To confirm that the two genes or potentially more DELLA genes were present in stems of "Gold Noli" breadfruit, DELLA genes were first cloned from "Gold Noli" breadfruit by degenerate PCR using primers corresponding to two conserved regions of all known DELLAs, MDELLA(V/A) and AHFTANQA [23]. An expected fragment of 700 bp was amplified, and 40 degenerate PCR clones were sequenced. Only two distinct groups of cDNA clones were identified with one aligned to AaDELLA1 and the other aligned to AaDELLA2. Both alignments had 100% similarity to the previous AaDELLAs. Full lengths of the two DELLA genes were then isolated using

Effect of Rootstocks on DELLA Protein Abundance and Transcript Levels
Two DELLA genes, AaDELLA1 and AaDELLA2, were previously cloned from "Mason" breadfruit [30]. To confirm that the two genes or potentially more DELLA genes were present in stems of "Gold Noli" breadfruit, DELLA genes were first cloned from "Gold Noli" breadfruit by degenerate PCR using primers corresponding to two conserved regions of all known DELLAs, MDELLA(V/A) and AHFTANQA [23]. An expected fragment of 700 bp was amplified, and 40 degenerate PCR clones were sequenced. Only two distinct groups of cDNA clones were identified with one aligned to AaDELLA1 and the other aligned to AaDELLA2. Both alignments had 100% similarity to the previous AaDELLAs. Full lengths of the two DELLA genes were then isolated using primers corresponding to the 5' and 3' end of the AaDELLA1 and AaDELLA2 genes. Sequencing of the resulting full-length cDNA clones confirmed that "Mason" breadfruit had two DELLA genes, one identical to AaDELLA1 and the other identical to AaDELLA2 at the nucleic acid level.
To measure the DELLA protein abundance, a polyclonal anti-AaDELLA was raised which targeted the highly conserved region of AaDELLA1 and AaDELLA2 (see Materials and Methods). DELLA protein abundance was examined in scion stems growing on different rootstocks every 3 months in the period of 18 to 24 months after grafting. Compared to the self-graft and non-graft at the corresponding times, levels of immunologically detectable DELLA were obviously higher in scion stems grafted on marang rootstocks, indicating an increase in the accumulation of DELLA proteins at all the tested time points (Figure 3). The transcript levels of AaDELLA1 and AaDELLA2 were further analysed in scion stems growing on different rootstocks. When compared to those on the self-graft and non-graft over the 6-month period, scion stems on marang rootstocks were found to have increases in the expression of AaDELLA1 at three time points, including 18, 21, and 24 months, but to have relatively stable expression of AaDELLA2 ( Figure 4). The expression levels between the self-graft and the non-graft were not significantly different for both genes (Figure 4).

Restoration of Stem Elongation by GA Treatment
To confirm that the rootstock-induced short stature is due to GA deficiency, breadfruit plants growing on marang rootstocks were sprayed with GA3. For GA treatment, the plants were 18 months old (after grafting) with a single stem, characterised by the main active stem elongation from the terminal buds and minimal growth at the lower part of the stems. Following GA application, it was shown that their stem elongation rate was significantly increased, with 7.5-fold higher in the second month and 6.4-fold higher in the third month compared to those of the untreated plants on marang rootstocks at the same time (Figure 5a). By the 3rd month after exogenous GA application, the stem elongation rate was fully restored to normal with no significant difference to those on self-graft

Restoration of Stem Elongation by GA Treatment
To confirm that the rootstock-induced short stature is due to GA deficiency, breadfruit plants growing on marang rootstocks were sprayed with GA 3 . For GA treatment, the plants were 18 months old (after grafting) with a single stem, characterised by the main active stem elongation from the terminal buds and minimal growth at the lower part of the stems. Following GA application, it was shown that their stem elongation rate was significantly increased, with 7.5-fold higher in the second month and 6.4-fold higher in the third month compared to those of the untreated plants on marang rootstocks at the same time (Figure 5a). By the 3rd month after exogenous GA application, the stem elongation rate was fully restored to normal with no significant difference to those on self-graft ( Figure 5a). Furthermore, the internode length was increased by 2.2-fold after 3 months following GA treatment ( Figure 5) and was also restored to nearly normal compared to those on self-graft (Figure 5b). There was no significant change in the number of internodes and the stem thickness within the four months after GA 3 treatment.
Plants 2020, 9, x 8 of 14 ( Figure 5a). Furthermore, the internode length was increased by 2.2-fold after 3 months following GA treatment ( Figure 5) and was also restored to nearly normal compared to those on self-graft ( Figure  5b). There was no significant change in the number of internodes and the stem thickness within the four months after GA3 treatment.  BM, breadfruit plants growing on marang rootstocks. All values represent mean ± SE from three biological replicates, with ** significant difference (p < 0.05) from the rest of the samples (non-graft and self-graft) and * significant difference from the non-graft only.

Discussion
Breadfruit plants growing on marang rootstocks were characterised by dwarf stature with~60% reduction in total plant height and~80% reduction in the length of internodes at the end of 24 months after grafting (Figure 1), consistent with the previous observation in the first 18-month period [6]. The phenotypes resemble the typical signs of GA deficiency [21,31]. The expression of GA biosynthetic genes and catabolic genes in scion stems growing on different rootstocks was compared over the period from 18 to 24 months after grafting. Of the two GA20ox genes expressed in stems, the predominant gene, AaGA20ox3, showed significantly reduced levels of expression several times during the six-month period (Figure 2). These suggest that the capacity of GA biosynthesis in scions of marang rootstocks may be affected by the reduced expression of the AaGA20ox3 gene during the period. Our results are in agreement with another study, where downregulation of the GA biosynthetic genes was found in scions gowning on dwarfing apple rootstocks of both M.9 and M.27 [14]. Suppression of GA20ox expression has been shown to produce plants with dwarf stature as a result of reduced endogenous GA contents in many species, inducing Arabidopsis, potato, tobacco, [17], apple trees [19], and citrus trees [20]. These suggest that downregulation of AaGA20ox3 may contribute to the dwarf phenotype of breadfruit plants grafted on marang rootstocks. Of the two GA catabolic genes, AaGA2ox1 and AaGA2ox2, a more consistent increase in the transcript levels of the major gene, AaGA2ox1, in the period suggests an upregulation of GA deactivation in scion stems on marang rootstocks in this period ( Figure 2). Upregulation of a GA2ox gene accompanying a decreased GA level was previously reported in scions of persimmon trees grafted on dwarfing interstocks [15]. Overexpression of GA2ox enhances GA deactivation and produces dwarf phenotype in many species [16,17,21]. These results suggest that upregulation of the main GA catabolic gene, AaGA2ox1, may contribute to the rootstock-induced dwarf phenotype in breadfruit plants over the period from 18 to 24 months after grafting.
The combination of upregulation of a GA biosynthetic gene and downregulation of a GA catabolic gene suggests a decrease in GA signals in scion stems growing on marang rootstocks. In support of this hypothesis, an increase in the accumulation of DELLA proteins (GA-signalling repressors) was found in scion stems on marang rootstocks (Figure 3). Various evidences have previously indicated that dwarfing rootstocks reduce GA concentration in scions [9][10][11]; the change of DELLA protein abundance has rarely been reported. This work provides insight into the functional significance of the GA deficiency in relation to the repression of GA response. In plants, DELLA proteins are rapidly degraded in response to GA via the ubiquitin proteasome pathway [23,24]; an increased DELLA protein abundance therefore reflects an increased repression of GA response in scions grafted on marang rootstocks. DELLA proteins act as negative regulators of plant growth [23]. Plants carrying over-accumulated DELLA display dwarf phenotype [25,32,33]. DELLAs also interact with multiple transcription factors and key regulators of other pathways through direct protein-protein interactions [22]. These include integrating GA with brassinosteroid, ethylene, jasmonate, abscisic acid, and auxin signalling pathways [22,[34][35][36]. Some of these signal pathways have long been proposed to be implicated in rootstock-induced dwarfism of other species [4]. In this context, the role of GA and its interaction with other growth regulators in regulating the dwarf traits of breadfruit on marang rootstocks deserves further investigation. Collectively, our results suggest that an increased repression of GA response contribute to the dwarf phenotype in breadfruit plants growing on marang rootstocks. These may be due to reduced GA signals as a result of the downregulation of GA biosynthesis and/or upregulation of GA deactivation. On the other hand, the increase in DELLA protein levels was reflected by the increased transcript levels of a DELLA gene, AaDELLA1, at all three occasions (Figure 4), suggesting that AaDELLA1 may be regulated at the transcription level. While most previous work has demonstrated the posttranscriptional regulation of DELLA protein [22,23], the transcriptional regulation of DELLA in response to various environmental signals has also been reported [35,37]. Upregulation of a DELLA gene was found in scions growing on persimmon dwarfing interstocks. In this context, it may be possible that transcriptional regulation of the AaDELLA1 plays a coordinative role in regulating GA response, thus contributing to the mechanism of rootstock-induced dwarfism in breadfruit plants.
Stem elongation of scions on marang rootstocks was restored to near normal by exogenous GA application as determined by both the stem elongation rate and stem internode length ( Figure 5). The results provide evidence that GA deficiency may play a role in rootstock-induced dwarfing of breadfruit plants and support the association of scion GA metabolism genes and signalling genes, including AaGA20ox3, AaGA2ox1, and AaDELLA1, with the development of dwarf phenotype in breadfruit plants. The combined expression profiles of these genes therefore may represent potential markers for breadfruit dwarfing. Breadfruit tree height can be controlled though the use of the GA inhibitor paclobutrazol [29]. For woody species, this generally involves a long-term, repeated application of the chemicals in order to achieve effective tree-size reduction. Our current work may provide an opportunity to develop various size-controlling rootstocks through fast screening of GA-related gene markers in scions, leading to environmentally sustainable solutions for breadfruit dwarfing.
In the current study, we focused on stem elongation as a key phenotype in breadfruit plants on marang rootstocks. However, the rootstock-induced dwarf traits in breadfruits involve other morphological and biochemical components [6] which are not examined in the current experiment. Future characterisation of the roles of GA and other hormones/factors in these dwarf traits is required for the unravelling of the molecular mechanism underlying rootstock-induced breadfruit dwarfing. GA is also produced in roots [18], although the majority of the studies have suggested that GA precursors rather than active GA are involved in long-distance transport [38]. Previously, dwarfing apple rootstocks were shown to limit the root-produced GA precursor, GA 19 , to scions [9][10][11]. While our study suggests a reduction of GA response in scion stems as determined by the increase of DELLA protein abundance, the nature of root-derived GAs and their contribution to the GA response in scion stems need further investigation. On the other hand, phloem transport of DELLA mRNA from rootstocks to scions has been reported [39]. The possibility of marang DELLA gene transfer to breadfruit scions cannot be ruled out. The sequence of marang DELLA gene is not available, but it is possible that the polyclonal anti-DELLA in the current study also binds marang-derived DELLAs, given that the target region shares high homology to DELLA sequences of other species (NCBI BLAST search).
In conclusion, breadfruit scion stems growing on marang rootstocks displayed decreased expression of a major GA biosynthetic gene, AaGA20ox3, at several times over the 6-month period from 18 to 24 months after grafting, and had persistently higher expression of a major GA catabolic gene, AaGA2ox1. Increased DELLA protein abundance was shown in scion stems on marang rootstocks together with an increase in the expression of a DELLA gene, AaDELLA1. Exogenous GA treatment was able to restore the stem elongation rate and the internode length of scions growing on the dwarfing rootstocks.

Plant Materials and Treatments
Breadfruit (Artocarpus altilis cv. Cannonball, also called "Noli Gold") and marang (Artocarpus odoratissimus) plants were obtained from a commercial nursery at Cairns, northern Queensland. Breadfruit plants as rooted cuttings and marang plants as seedlings were grown under glasshouse condition at 25 to 28 • C with natural daylight and daily water supply. Plants were grown in pots containing vermiculite and soil mixture as described previously [29]. Breadfruit scions selected from breadfruit plants 30 to 50 cm tall were grafted onto marang seedlings of similar sizes through approach grafting [40]. As a control, the breadfruit scions were also grafted onto breadfruit rootstocks of the same cultivar (self-graft). There were at least eight replicates for each grafting combination. Each established grafted plant was transferred to an 85-Litre pot six months after grafting and continued to grow under the same condition. The self-rooted breadfruit plants (non-graft) were grown alongside under the same condition. Plants were monitored for total height and stem elongation within the 24 months after grafting. Plants on marang rootstocks 18 months after grafting were used for GA treatment. For GA experiment, both foliage and soil surface were sprayed with 500 mg L −1 GA 3 (Sigma, Sydney, NSW, Australia, dissolved in 0.1% ethanol, 0.1% Triton X-100). The bioactive form GA 3 was chosen according to the previous experiment, showing a fast positive response to the chemical in stem growth of breadfruit trees [29]. Plants sprayed with the same concentration of ethanol and Triton X-100 were used for control. The treatment was applied once a week for 3 weeks. Three replicates were used for each treatment and control. Plants were monitored for stem elongation, and internode length in the second internode was counted from the top, with the first internode being defined as the one below the uppermost leaf.

Quantitative Real-Time PCR
Upon removal from plants, stem tissues (including epidermis, vascular tissues, and pith) in the second internodes were immediately immersed in RNAlater (Life Technologies, VIC, Australia) before stored at −80 • C. Total RNA was extracted by using RNeasy kit (Qiagen, VIC, Australia) and reverse transcribed with SuperScript reverse transcriptase and oligo(dT) (Life Technologies, VIC, Australia). Real-time PCR was performed on a Corbett Research Rotor-Gene 6000 cycler with the QuantiFast SYBR Green PCR Kit (Qiagen, VIC, Australia) as previously described [29]. Thermocycling was initiated with a 5-min incubation at 95 • C, followed by 40 cycles (95 • C for 10 s; 60 • C for 30 s). The specificity of amplification was confirmed by high-resolution melt curve analysis at the end of each run. The efficiency of each primer set was evaluated by standard curves using serial dilutions of plasmid DNA containing its amplified regions. Each reaction was carried out in duplicate (technical repeat) with non-reverse-transcribed cDNA (RT − ) as negative controls (non-template control). Transcripts of AaGA20oxs and AaGA2oxs were amplified using their gene-specific primers as previously described [28,29]. Gene-specific primers were 5 -GAA AAA GATCAGAAGAAGAAGAATCATCATG-3 and 5 -CCC AAA ACG GCC AGG AGCTCG-3 for AaDELLA2 for AaDELLA1 and 5 -CAT CAG AAG AAGAAT CAT GAA AAG GGA AC-3 and 5 -CAT GTC GGATGA CCT GAC CTT GTA G-3 for AaDELLA2. Two housekeeping genes, actin and elongation factor 1-α (AaEFα-1) were tested for stability across the time points according to a previous protocol [41]. The actin gene was amplified using primers 5'-AATGGAACTGGAATGGTGAAG GC-3' and 5'-TGCCAGATCTTCTCCATGTCATCC-3', and the AaEFα-1 was amplified using primer 5'-GAAGCTCTTCGTCAAGAGAA-3' and 5'-GAAATCTCTTGAAGTAACCATC-3'. The specificity of the primers was confirmed by amplicon sequencing. The actin gene, a more stable housekeeping gene compared to the elongation factor 1-α gene, was chosen to normalize to the expression of transcript abundance. The expression of each gene was an average of five biological replicates.

Cloning of DELLA cDNAs from "Gold Noli" Breadfruit
Total RNA was extracted from stem tissues of breadfruit plants by using RNeasy kit RNeasy kit (Qiagen, VIC, Australia) and reverse transcribed with reverse transcriptase and oligo(dT) (Life Technologies, VIC, Australia). The resulting cDNA was subjected to degenerate PCR using primers 5 -ATGGAYGARYTIYTIGCNG-3 and 5 -GCNCAYTTYACNGCNAAYCARGCN-3 as previously described [30]. The PCR reactions were performed at 35 cycles with annealing temperature at 52 • C. The PCR products were cloned into pGEMT vector (Promega, NSW, Australia) and sequenced. Full-length cDNA clones of AaDELLA genes were amplified with Advantage DNA polymerase (Takara Clontech, CA, USA). The thermocycling was initiated with a 2-min incubation at 92 • C, followed by 32 cycles (92 • C for 30 s and 68 • C for 2 min) and a final extension of 7 min at 68 • C. The gene-specific primers were 5 -GAAAAAGATCAGAAGAAGAAGAATCATCATG-3 and 5 -GACCCGACTTAGCGAGCCACAG-3 for AaDELLA1 and were 5 -GTGTTTGAGGAAAAAGAGGGCCTGTG-3 and 5 -GGGTCCGGCCCGA CTCAGAG-3 for AaDELLA2. These primers were designed from the sequence information of AaDELLA genes isolated from the "Mason" breadfruit [30]. PCR products were cloned into pGEMT vector and sequenced as above. The resulting sequences were analysed by Sequencher (version 5.4.1, Gene Codes Corporation, Ann Arbor, MI, USA).

Western Blotting
The highly conserved sequence of AaDELLA1 and AaDELLA2 [30] between residues 37 and 59 (KMWEEDDGGMDELLAVLGYKVR) was synthesized, and the resulting peptide was used to raise antibodies in a rabbit (Mimotopes, Melbourne, Australia). Polyclonal anti-AaDELLA was purified by affinity chromatography. The titres of the affinity-purified antibodies were determined by enzyme-linked immunosorbent assay (ELISA). Proteins were extracted from the full stem tissues in the second internodes of scions according to the method previously described [42], and protein concentration was determined by a bicinchoninic acid protein assay (Sydney, NSW, Australia). The protein extracts were resolved on 10% SDS-PAGE gels (20 µg protein per lane) and electroblotted onto nitrocellulose membranes followed by immuno-detection [43]. Blots were probed with pre-immune serum as negative controls. Colour development was performed using an alkaline phosphatase-conjugated secondary antibody with Western blue (Promega, NSW, Australia). Each blot was first probed with polyclonal anti-AaDELLA; then stripped with 0.2 N glycine, pH 2.5; and re-probed with anti-actin (Sydney, NSW, Australia). The ratio of the band intensity of the DELLA to that of the corresponding actin was analysed by Quantity One 1-D analysis software in the Gel Doc system (BioRad, NSW, Australia).

Statistical Analyses
Significant differences were tested using analysis of variance (ANOVA) followed by Tukey's multiple comparison test at p < 0.05 (IBM SPSS Statistics version 24).