Philippa Kaur

Deformation measurement accuracy
To evaluate the accuracy of ICT, synthetic deformation fields were added to the R-specimen datasets (Fig. 3a). For this purpose, constant strain was simultaneously introduced in both R and L directions ((varepsilon_{RR}),({ }varepsilon_{LL})). Absolute accuracy (hat{varepsilon }) was measured by adding synthetic deformation to the reference state #1 (#1 + synthetic), and then measuring with ICT strain of #1 + synthetic with respect to #1. Differential accuracy ({Delta }hat{varepsilon }) was measured by adding synthetic deformation to the deformed state #2 (#2 + synthetic), measuring with ICT strain of #2 + synthetic with respect to the reference state #1, and finally subtracting the ICT measured strain between #2 and #1.
Figure 3 shows ICT strain estimates for tracheids and wood rays. The accuracy is highest along the cell-cross section (RT for tracheids, LT for wood rays) and lowest in the cell longitudinal direction (L for tracheids, R for wood rays). Due to the tubular cell geometry (Fig. 2b,c) of both cell types, tracking is more challenging in the longitudinal cell axis, for which symmetry reduces the available landmarks (for instance, wood pits in Fig. 2) and deformation tracking accuracy. Absolute accuracy is limited by both cell segmentation and deformation parametrization. Differential accuracy is further influenced by experimental uncertainties between #1 and #2 acquisitions, such as vibration artifacts and sample relaxation strains. While absolute and differential accuracy are similar in cell-cross sections, differential accuracy is reduced with respect to absolute accuracy along the longitudinal cell axis. The sensitivity limit for strain measurements in tracheids is (varepsilon_{RR})  More

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The meso-scale habitat simulation model MesoHABSIM21 was used to assess the impact of low-head HPPs on fish populations. MesoHABSIM is a physical habitat modelling system developed for e-flow assessment and river channel restoration planning. It describes the utility of instream habitat conditions for aquatic fauna, allowing to simulate change in habitat quality and quantity in response to alterations of flow and river hydromorphology. Meso-scale habitats are defined as geomorphic units (GUs, such as pools, riflles, rapids, glides22) that can be used by species and life stages for a significant part of their diurnal routine23. A meso-habitat can be considered suitable or optimal when the configuration of hydraulic patterns, together with the attributes that provide shelter, create favourable conditions for survival and development of animals. MesoHABSIM approach is based on the aggregation of three models24:
1.
A hydromorphological model that describes the spatial mosaic of fish-relevant hydro-morphological features.

2.
A biological model describing the relationship between the presence and abundance of fish and the physical environment of the river.

3.
A habitat model quantifying the amounts, frequency and duration of the available habitat depending on the flow regime and local river morphology.

For the modelling, the time series of daily water discharge data in natural and altered (downstream HPPs) conditions were created for wet, normal and dry years in order to describe the habitat suitability in all possible hydrological conditions. Conditional habitat suitability criteria (CHSC) were developed to define the relationship between fish distribution and physical environment. Physical spatial measurements of river hydraulic and fish shelter attributes (current velocity, depth, discharge, sediments, woody debris, boulders, etc.) were conducted on a scale of mesohabitat during field surveys. SimStream plugin of QGIS25 was used to organize collected data for mesohabitat modelling.
Hydrological data and hydromorphological surveys
The daily time series of discharge data of three water gauging stations (WGSs; Bartuva-Skuodas, Venta-Leckava and Mūša-Ustukiai) were taken from the hydrological yearbook of the Lithuanian Hydrometeorological Service for the periods of 1970–2000 (period before construction of HPPs) and of 2001–2015 (period after construction). The WGSs are located downstream the selected HPPs, and their data were used for the assessment of the altered discharge conditions and the impact of HPPs on fish communities. Two additional WGSs of Minija River-Kartena (for the Bartuva and Venta rivers) and Nemunėlis River-Tabokinė (for the Mūša River) were chosen for the restoration of natural conditions of river discharge at case study sites according to the analogy method26. The selection of a river analogue was based on the same hydrological region, similar catchment area, similarity in physico-geographical and hydrometeorological characteristics, and absence of anthropogenic structures which interrupt the continuity of the river, e.g. dams. The regression equation between case study river and river-analogue was prepared using daily water discharge data of 1970–2000 (period before construction of HPPs). The natural regime of investigated rivers after construction of HPPs (2001–2015) was restored using regression equations. In this way, we obtain the annual hydrographs of the investigated rivers in natural and altered conditions. In order to evaluate the habitat suitability in all possible hydrological conditions, hydrographs were prepared for wet, normal and dry hydrological years (probability of 5, 50 and 95%, respectively), according to average discharge data in the period of 2001–2015.
Four different discharge values (from minimal to average) were defined for hydromorphological measurements in each site of the selected river. These discharges represented the minimum, average and maximum low flow discharges of 30 consecutive days (Q30_min, Q30_ave, Q30_max) in the warm period (May–September), and multi-annual mean water discharge (Qannual_mean) in 1970–2000 (before HPPs construction). According to the Lithuanian law, environmental flow (Qenv) is defined at each HPP as 80% or 95% probability of the mean minimum discharge of 30 consecutive days of the warm period11. A Laser Rangefinder (distance, inclination, azimuthal measurements) connected via Bluetooth with the field tablet was used for the mapping of hydromorphological units (HMUs, also called mesohabitats). The maps of HMUs polygons were digitized in the .shp format using MapStream plugin of QGIS25,27. The length of an analysed river reach was defined as 20 times the mean river width28. The depth and flow velocity measurements in each defined HMU were done using a propeller-type flow meter mounted on a wading rod. Depending on the polygon area, from 5 to 30 measurements were carried out in each HMU, while the measurement density (point/m2) was kept as constant as possible in each case study considering its size (on average one point per 6 m2 in the Bartuva, 20 m2 in the Mūša and 25 m2 in the Venta rivers).
The presence/absence of fish shelters and vegetation were assessed visually (see21 for details). All measurements were carried out as close as it is possible to four defined discharges (minimum low flow (Q30_min), average low flow (Q30_ave), maximum low flow (Q30_max) and annual mean (Qannual_mean)) of each selected case study (Table 1).
Fish data and conditional habitat suitability criteria
Four Cyprinidae fish species, which are common in cyprinid-dominated lowland rivers of Lithuania20, but differ in rheophily and reproduction habitat were selected for the assessment of HPPs impact: lithophilic rheophilic schneider Alburnoides bipunctatus and dace Leuciscus leuciscus, phyto-lithophilic eurytopic roach Rutilus rutilus, and diadromous lithophilic eurytopic vimba Vimba vimba (fish guilds according to29). Based on the classification of fish species in European rivers according to their overall resistance to habitat degradation30, the selected species also represent different guilds of tolerance capacity: schneider is intolerant species, dace and vimba are intermediate, and roach is tolerant31. These four species are all benthopelagic, and in this respect they are similar, but due to their different preferences for rheophilic conditions, spawning habitat and overall habitat quality, it was expected that their response to changes in flow conditions should also be different. Currently access for diadromous vimba to most rivers is limited by dams; therefore, habitat availability for vimba was modelled only in the Venta River, which is still accessible for this species and contains its spawning grounds.
To define conditional habitat suitability criteria (CHSC)21, the river monitoring database for 2008–2015 was used. Data on the physical, chemical and hydrological characteristics of river sites was collected by the Lithuanian Environmental Protection Agency (EPA). Fish monitoring and assessment of hydromorphological characteristics of the site at the time of sampling was carried out by the Nature Research Centre under agreement with EPA. Standardized single-pass electric fishing took place in mid-July–September on river sections with a minimum length of at least 10 times the wetted width (but not less than 50 m) using backpack pulse current electrofisher (type IG200-2; HANS GRASSL GmbH) with a maximum output of 800 V and a maximum power of 10.0 kW per pulse.
For CHSC construction, only river sites in natural conditions (from good to high ecological status according to the European Water Framework Directive) with a catchment area of 100–5000 km2 and sampled by wading were selected from the database. In total, 245 river sites were selected. 160 sites in 75 rivers (2/3 of the selected sites) were randomly selected and used to build CHSC. The remaining 85 locations in 53 rivers (1/3 of all locations) were used for calibration. Once the locations were selected, their depth and current velocity were classified into intervals of 0.15 m and 0.15 m s-1 following the MesoHABSIM protocol (up to 0.15, 0.15–0.3, 0.3–0.45, etc.). The preference of schneider, dace and roach for depth and current velocity was determined by their frequency of occurrence in each of the intervals. In order to minimize the impact of random catches, species were considered present only when the number of individuals exceeded 25th percentile of the number of individuals in all places where they were found. Species were considered abundant when the number of individuals was greater than the median abundance in all places where they were found. A species was considered present in a particular interval of depth or current velocity only when its frequency of occurrence was  > 40%. Accordingly, a species was considered abundant only in those groups of depth and velocity where the number of individuals was greater than the median in more than 50% of the sites. The preference for the type of substrate and shelters was determined according to the analysis of these environmental variables in the river sites where the species should be present based on the criteria of depth and current velocity. According to the geomorphological and ecological definition of mesohabitat21,22, 10 m2 was considered the minimum surface that an HMU must have to be considered a suitable (species present) or optimal (species abundant) habitat for fish. When tested on an independent dataset (85 sites), CHSC were considered satisfactory for the presence of species when the species were present in  > 60% of the sites meeting the criteria (total accuracy  > 0.6). CHSC were considered satisfactory for the abundance of species when the species were present in  > 60% of the sites meeting the abundance criteria and the abundance of individuals was higher than the median in at least 50% of these sites.
CHSC for vimba were selected by an expert judgement, analysing common features of the river sites where this species was observed. Migration of vimba to the majority of former spawning grounds is currently restricted by dams. Therefore, this species is constantly found in a limited number of rivers, in which vimba is present not only during spawning in spring, but is also common in specific habitats in summer and autumn.
For the validation of CHSC for schneider, dace and roach, a single-pass electric fishing was performed in 42 HMUs of 4 natural rivers (Minija, Dubysa, Šventoji and Merkys), in river stretches with a length of 150–400 m, a maximum depth up to 1.5 m, and a catchment size of 315–3040 km2, during the low flow season, with high transparency of water. Fish were sampled by wading by a team of 3 persons using a backpack pulse current unit of a similar type as for fish monitoring (IG200-2D; HANS GRASSL GmbH). CHSC verification for vimba was carried out only in 14 out of 42 HMUs, since this species is constantly found in only one of the natural rivers selected for verification. A single-pass electric fishing was also conducted in all HMUs which were identified in the studied river stretches below HPPs at the low flow. Fish sampling was accomplished by wading and using pulse current backpack electric fishing gear. A single-pass electric fishing strategy was used, as the CHSC criteria were also developed based on single-pass sampling data. Studies show that in most cases species composition and rank abundance of common species do not change significantly after the first pass32,33,34.
To assess the predictive performance of CHSC, correctly classified instances, sensitivity, specificity, and true skill statistic were calculated based on confusion matrix analysis35.
Assessment of HPPs impact
The habitat area available for the species was modelled at different discharges of rivers. The impact of HPPs on habitat availability was assessed based on the comparison of the modelled available habitat area (i) at reference conditions during a dry year, (ii) under HPPs functioning in dry, normal and wet years, and (iii) at environmental Qenv. The flow value that exceeded 97% of the time at reference conditions (Q97)36 during a dry year and the corresponding area of species habitat (expressed in m2, hereafter, the minimum threshold area) were used as common denominators. Deviation of temporal availability of suitable habitats for modelled fish species due to HPPs functioning at different flows was assessed based on relative increase in the cumulative continuous duration of days when the area of the habitat falls below the minimum threshold values (hereafter, the stress days alteration; SDA). SDA analysis is based on the assumption that minimum habitat availability is a limiting factor for fish species, and events occurring rarely in nature create stress to aquatic fauna and shape the community. Therefore, for the selected minimum habitat threshold (expressed in m2), the number of habitat stress days that occur under those conditions was calculated and used as a benchmark for comparative analysis using the SDA metric, (see e.g.28,36,37 for details). Finally, we normalize SDA values between 0 and 1 by using the index of temporal habitat availability (ITH) as it is described by Rinaldi et al.28.
The relative abundance of fish species that are common in the cyprinid-dominated rivers of Lithuania (the frequency of occurrence in the natural river sites is  > 50%) was also compared in river reaches with natural (42 sites, 85 fishing occasions) and regulated (below HPPs; 20 sites, 39 fishing occasions) flows, which met at least good water quality criteria and fell within the same range of catchment size and slope as the rivers selected for modelling did. The sites were selected from the same river monitoring database for 2008–2015, which was used for selection of sites for CHSC development. The significance of identified differences was assessed using the Mann–Whitney U test. More

Ethical considerations
This study was carried out in accordance with the guidelines for ethical treatment of animals of the International Society of Applied Ethology. It was approved by the Institutional Animal Care and Use Committee of the Institute of Animal Science and the Czech Central Committee for Protection of Animals, Ministry of Agriculture (Permit Number 27356/2016-MZE-17214). Calves on the low-milk schedule received a milk allowance of approx. 12% of their body weight, equalling the traditional calf feeding practices26.
Animals and housing
The study was conducted at the Netluky research station of the Institute of Animal Science in Prague, Czech Republic. Data were collected from August 2016 until April 2017.
Seventy-two Holstein Friesian dairy calves (31 heifers and 41 bulls) were included in the study. Calves were separated from their dams at approximately 12 h after birth and housed individually either in outdoor hutches or in individual pens in a naturally ventilated open barn equipped with curtains. In both cases, the area available for each calf was 1.4 m × 1.4 m straw-bedded lying area and 1.2 m × 1.2 m solid walking area. While in individual housing, calves were fed 3 l of milk twice per day through teat-buckets at 06.00 and 18.00 and received concentrates and water ad libitum. Calves entered the experiment at an average age of 13.3 ± 3.1 days (mean ± S.D.) and were then housed in groups of three. Calves were allocated to groups balanced by sex, age and weight. Groups entered the experiment consecutively with 1–2 groups per week. Groups were housed in a naturally ventilated open barn with curtains. Group pens were 10.1 m2 consisting of a straw-bedded lying area (4.2 m × 1.4 m; approx. 2.0 m2 per calf) and a concrete walking and feeding area (3.5 m × 1.2 m). The group pens were covered with visual barriers in order to avoid direct visual contact of other calves. The visual barriers in front of the respective pens were removed in order to allow video recording; however, groups that were recorded simultaneously were allocated in the barn in a way that precluded visual contact without the front visual barriers of the pens. The calves received water, hay and concentrates ad libitum, offered in buckets and were provided with fresh straw bedding three times per week. All routine farm work was done before 10.00. Calves were hot-iron disbudded at 24.4 ± 3.1 days of age (mean ± S.D.). Disbudding wounds are painful for more than three weeks27, however no difference in play behaviour after disbudding was found after 27 hours23, thus we do not expect an effect of disbudding on play in our study. On the recording days, the air temperature in the barn ranged between -4.5 °C and 29 °C with the average (± S.D.) being 7.9 (± 8.6) °C.
Experimental design and procedures
Experimental design
Groups were allocated to treatments balanced by sex composition, age, weight and point of time entering the experiment. Milk allowance, group composition and number of groups assigned to each of the treatments are displayed in Fig. 1. Calves in all treatments received three milk meals per day at approximately 06.00, 12.00 and 18.00. All calves were offered 6 l of milk per day at the beginning of week 3. For UHigh and MHigh calves, the offered milk was gradually increased to 9 l of milk per day in week four and 12 l of milk per day in week six (Fig. 1). Therefore, the total milk amount offered from the start of the experiment until the end of week eight (42 days) was 240 l for ULow and MLow calves and 420 l for UHigh and MHigh calves. Milk was offered in teat buckets. Calves were tethered for the duration of the milk meal using neck collars and were released when all calves of the group had finished their meals (i.e. calves had either emptied the buckets or stopped drinking milk; approx. 5 min). If calves did not finish the offered milk meal, the volume of the remaining milk amount was measured. The volume of unconsumed milk was then summed from the point of entering the experiment until the respective day of behaviour recording. For ULow and MLow the total volume of unconsumed milk amounted to 0.3 ± 0.9 l (mean ± S.D.; median/interquartile range: 0/0 – 0). For UHigh and MHigh the total volume of unconsumed milk amounted to 16.7 ± 18.0 l (median/interquartile range: 10/2.5–25). The average daily amount of milk refusal was 0.1 ± 0.3 l and 0.3 ± 0.6 l, when calves were four weeks and eight weeks old, respectively.
Figure 1

Experimental design of treatments, group composition and milk allowance. Sample size is the number of calves included in statistical analysis.

Full size image

Data from two groups were excluded from statistical analysis: in one ULow group, a calf died from health issues unrelated to the experiment and one UHigh group was treated for severe diarrhoea for a prolonged time and therefore was not offered 12 l of milk in order to avoid further digestive problems.
Health and weight assessment
Calves’ health state was assessed once per week by two assessors. The following indicators of compromised health were recorded: diarrhoea, coughing/sneezing and increased respiratory rate (adapted from Gratzer, et al.28; Supplementary Table 1). The overall health score was set to 0 when calves showed no or one symptom of diarrhoea or coughing/sneezing and 1 when calves showed either combined diarrhoea and coughing/sneezing or increased respiratory rate. The ratio of calves with a health score of 1 is shown in Table 1.
Table 1 Ratio of calves with a health score of 1 by treatment and age group.
Full size table

Calves were weighed once per week between Monday and Thursday. To allow for comparison, daily weight gain for every respective week was calculated and weights were subsequently corrected for Monday as reference weighing day. Body weights from the start until the end of the experiment (three to eight weeks of age) are presented in Supplementary Figure S1. These data show that higher milk provision in MHigh and UHigh calves resulted in faster growth.
Quantification of play behaviour
Data recording
Locomotor play behaviour of calves was quantified through leg-attached accelerometers, using a previously validated method29. In this study, accelerometers were used to record running, turning and bucking/buck-kicking, as defined in Größbacher, et al.30. The data used to validate accelerometer recordings for these behaviours30 were a subset of the data used in this study. Accelerometers (HOBO Pendant G Acceleration Data Logger, Onset Computer Corporation, Pocasset, MA, USA; product specifications described in detail in Luu, et al.29) were attached to calves’ hind legs with elastic cohesive bandages. The accelerometers were oriented with the x-axis perpendicular to the ground. Acceleration was measured on the vertical axis at 1 Hz, i.e. with one measurement per second, from 05.00 until 23.04 on two consecutive days (Tuesday and Wednesday) when calves were four and eight weeks of age and recordings were stored on the device. Accelerometers were fitted to calves from the evening before until the morning after recording days, after being programmed with an optical infrared base station with USB interface and the HOBOware Pro Software (Version 3.7.8; Onset Computer Corporation, Pocasset, MA, USA).
Behaviour classification
Data processing was performed in SAS 9.4. Always 10 acceleration measurements, representing a period of 10 s each, were evaluated. These 10 s periods were categorized into lying, standing or play behaviour using quadratic discriminant analysis. This categorization was based on six predictor variables, which were calculated for each period with the respective 10 values: mean of two highest acceleration measurements, mean of two lowest acceleration measurements, variance, maximum of absolute value of change in acceleration measurement, mean change in acceleration measurements, and total sum of absolute values of change in acceleration measurements30.
In order to develop the discriminant function, a reference data set was created with randomly selected short sections of accelerometer data obtained from recordings of calves in this study. This reference data set consisted of 52 recordings with a mean (± S.D.) duration of 37.8 ± 16.8 min. Lying, standing and play behaviour were visually identified from video of these recordings applying one-zero-sampling of the respective 10 s periods, for which predictor variables were calculated. This was used as the gold standard.
The discriminant function was then applied to the entire data set in two steps to identify periods that contained locomotor play, i.e. included events of running, turning and/or bucking30, based on the six predictor variables: The first discriminant function classified the acceleration data into lying and standing, based on equal prior probabilities (50:50 chance of both behaviours occurring). The second discriminant function classified all standing-periods according to their presence or absence of locomotor play, based on prior probabilities of the reference data set (3:97 chance of play occurring across all treatments). The transitions from lying to standing and vice versa were almost always falsely classified as playing, as identified from video, and reclassified into standing.
The validation of processing the raw acceleration data was accomplished through checking the agreement between the acceleration-based method and visually identified play of the reference data set30. It proved that although the absolute play-levels were overestimated with the acceleration method, the method was able to truthfully quantify the inter-individual differences in locomotor play in dairy calves30.
Data analysis
Data processing
The last four minutes of each recording were omitted to obtain observation durations of exactly 18 h. The number of 10 s periods of locomotor play was converted into minutes of locomotor play per recording day (18 h). Recordings were excluded for the duration of disturbance when any calf in the barn escaped their pen or a person entered the pen. If more than 1 h was missing or compromised, the entire recording day was excluded for the calves affected. If less than 1 h of the recording was missing, locomotor play was calculated on a per hour basis and extrapolated to the ‘standard’ duration of 18 h. Out of 264 recordings (4 recordings per calf overall with 2 recordings at the age of four and eight weeks, respectively), 17 recordings were excluded or missing and in 13 recordings a mean (± S.D.) of 23.9 ± 13.0 min were missing and locomotor play duration and bout frequency were extrapolated. This resulted in 245 recordings, i.e. data points, included in the model. Play bouts were assessed by counting standalone periods of play, i.e. single 10 s periods not proceeded and not followed by periods classified as play were counted as one play bout, or by counting consecutive periods of play, i.e. two or more play periods occurred in a row as one play bout. Mean bout durations were assessed by recording the duration of each bout, e.g. a play bout consisting of one play period was recorded as 10 s and a play bout consisting of 3 play periods was recorded as 30 s.
Individual play was defined as one calf performing play in a 10-s-period when no other calf in the group was performing play. Dyadic play was defined as one calf performing play in the same 10-s-period as any one other calf of the group. Individual and dyadic play were calculated as minutes per recording day (18 h). Data were only included when observations of all three calves of the group were available. If data of one of the calves in the group were partially missing, observations of the other calves for the same period of time were excluded. Then the duration of individual and dyadic play was extrapolated on a per hour basis as described above. 237 recordings, i.e. data points, were included in the analysis, whereof 15 recordings were extrapolated.
The observed and randomly expected proportion of dyadic synchronized play were calculated on the basis of dyads, i.e. the combination of always two calves of a group, according to Šilerová, et al.31. Both were calculated for each pair combination:

$$ Sync_{obs} = frac{{left( {2*C_{sync} } right)}}{{left( {C_{A} + C_{B} } right)}} $$

$$ Sync_{exp} = frac{{(2* C_{A} *C_{B} *1/P_{dyad} )}}{{left( {C_{A} + C_{B} } right)}} $$

where Csync is the number of synchronous play periods of the pair, CA is the total number of play periods of calf A, CB is the total number of play periods of calf B and Pdyad is the total number of recorded periods for each dyad. The randomly expected proportion of play is the proportion of synchronized play occurring by chance if the calves played independently of each other31.
Triadic play was defined as all three calves of a group performing play in the same 10-s-period and calculated as minutes per recording day. Only periods in which data for all calves of the group were available were included. Extrapolation of play duration (due to partially missing data) was done in 10 out of 79 recordings.
Statistical analysis
All data was analysed in SAS Version 9.4. Five separate linear mixed effects models were run with total duration of play, frequency of play bouts, mean bout duration, duration of dyadic play or duration of individual play as dependent variables. Treatment (ULow, MLow, MHigh, UHigh), age (week four, week eight) and overall health score (0,1) were included as fixed class effects, while volume of unconsumed milk and maximum daily temperature were included as fixed quantitative effects, i.e. as covariates. Age (week), nested in calf and group were included as random effects. Furthermore, the date of recording was included as a crossed random effect. The same model was used for all dependent variables. Initially, the full model contained the interaction effect of treatment and age, however this was never significant and therefore removed from the model. An auto-regressive covariance structure was selected based on the Akaike Information Criterion (AIC). All models were visually inspected for normal distribution of residuals devoid of skewness. More

The growing interest in understanding the complex bovine udder microbiome has resulted in several published studies in recent years. These studies aimed to uncover how this microbiome influences the udder health and possibly has an important role during mastitis. In this study, we contribute to this knowledge by exploring the milk microbiota with a cross-sectional study of the milk microbiota as elucidated from over 400 quarter milk samples obtained from 60 lactating cows.
Sampling of udder milk for microbiome analysis is difficult and challenging15,16. In order to accomplish a more representative overview of the milk microbiota that colonize the upper interior part of the udder, the sampling method used in this study was different to those previously used to study milk microbiota2,6. The sampling was performed after the cow was regularly milked with the intent to remove microbial contaminations from the teat apex and to avoid the sampling of milk present in the cistern which might contain bacteria able to enter the udder between milking. The complete removal of contaminations from the teat apex or the environment cannot be ensured by the results obtained in this study, and additional experiments are needed to compare microbiota from milk samples obtained pre- and post-milking. However, although some taxa associated with the environment, such as Bacillaceae and Pseudomonadaceae, were detected in the dataset their abundance was much lower ( More

We thank the Bren School of Environment Science and Management of the University of California Santa Barbara for support leading to the initial publication. This work was also supported by a grant overseen by the French ‘Programme Investissement d’Avenir’ as part of the ‘Make Our Planet Great Again’ programme (reference: 17-MPGA-0004) and by a National Science Foundation grant (LTER-1831944). More