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This is an observational study with no intervention on flock and hatchery practices. None of the birds or eggs were exposed to experimental procedures. The study was based mainly on existing data provided by the hatchery company (DanHatch Denmark A/S) from five broiler breeder flocks in Denmark during the period from November 2018 to January 2019 when the breeders were 46 to 62 weeks of age, see details in Table 1. In addition, feed samples from the flock locations and eggs from grocery stores were acquired.Table 1 Flocks and production periods.Full size tableThe average age of breeders was 48–59 weeks (SD from 0.5 to 2.2) ranging from 46–50 weeks to 57–62 weeks (Table 1; Supplementary Fig. S1 online) with observation period ranging from 1.6 to 7.6 weeks in the five flocks. Average laying percent over observation days was 65% (SD = 5.4%) and average hatchability over deliveries was 79% (SD = 5.8%).Feed samplesTwenty-six feed samples were collected for analysis of glyphosate content, 3 to 10 feed samples per flock. The glyphosate concentration related to a given sampling date was assumed representative for the flock from this day and until next sampling. Average duration of the preceding samples were used as duration for the last sampling date within each flock. Glyphosate (N‐(phosphonomethyl) glycine) and the glyphosate degradation product, aminomethylphosphonic acid (AMPA) in feed samples were analysed by the method described by Nørskov et al.4.Production dataData on egg production and hatchability from periods following each feed sampling was obtained from the hatchery company. Daily information was available on laying percent (100% * number of eggs/number of breeders), breeder age (days) and egg weight. For the hatchability, this was calculated as the proportion of eggs placed in incubators from which a viable chicken hatched (but presented as a percentage, i.e. multiplied by 100%). Daily egg weight had been calculated as the average from approx. 30 randomly sampled eggs.Glyphosate concentration of the feed consumed by the breeders during the 10 days prior to laying was the explanatory variable of main interest. The weighted average of glyphosate concentrations across the 10 days of development from follicle to ovulation of egg was used with number of days each glyphosate sample is representative during these 10 days as weights. For hatchability, glyphosate concentrations were aggregated at the level of delivery by weighted averaging using number of hatch eggs as weights.Eggs from grocery storesNo eggs were obtained from the five flocks, however we acquired eight cartons of conventional as well as eight cartons of organic eggs from eight different grocery stores. Three eggs from each carton were selected and egg yolk were analysed for glyphosate by the microLC-MS/MS method as described by Nørskov et al.4 adjusted to the egg yolk matrix.Statistical analysisLaying percent and hatchability were analysed by linear mixed effects models, including a random effect of flock and a first order autoregressive correlation structure to account for the repeated measurements from each flock. Following two covariates were considered for both outcomes: average egg weight (g) and breeder age (decimal weeks). However, since egg weight and breeder age are highly correlated (Pearson’s correlation coefficient ranging from 0.73 to 0.95 in the five flocks; Supplementary Fig. S1 online), only breeder age was included in the models. An important reason for this choice being that average egg weight was missing for 24% and 43% of the days from flock 4 and 5, respectively. In the age range used for this study, laying percent decrease with breeder age (Supplementary Fig. S1 online) as substantiated by a correlation coefficient between − 0.38 and − 0.87. Hatchability also decrease with breeder age (Supplementary Fig. S1 online).In addition, storage time on farm until delivery (1 to 5 days) and storage time at hatchery until incubation starts (1 to 11 days) were included as covariates for hatchability. The incubation start date was determined as date of hatching minus 21 days. For hatchability, covariates obtained from flock production data were aggregated at the level of delivery by weighted averaging; using daily number of eggs as weights for the calculation of average egg weight, number of hatch eggs as weights for average storage time on farm, and current number of breeders as weights for average breeder age. Weighted average storage time on farm until delivery varied from 1.0 to 4.0 and was on average 2.1 days. For storage time at hatchery, deliveries had been split on one to four incubator start dates. Therefore, weighted average of storage days was calculated using number of delivered eggs as weights. Weighted average storage time at hatchery before incubation starts varied from 1.2 to 8.0 days and was on average 4.8 days.Final models were fitted with restricted maximum likelihood estimation using the lme function from the nlme package v. 3.1-152 in R version 4.0.45 and with a significance level of 0.05. Fixed effects were tested by χ2 likelihood ratio tests after maximum likelihood estimation. Model checking was carried out by examination of qq-plots for normality and scatter plots of residuals versus predicted values to look for uncovered trends and variance heterogeneity. More

Based on the extensive camera trap study, the very first information about the occupancy, trapping rate, activity pattern, group size, social structure and vital rates of the critically endangered Western Derby eland in its last refugium, the NKNP in Senegal, is presented here. The first estimation of abundance since 2006 is also provided7.Spatiotemporal behaviour pattern of WDE in the parkThe results of the CT survey in the NKNP highlight the substantially lower occupancy and trapping rate of WDE in comparison to other large ungulates in the park. According to the current results, the WDE occupied less than 5% of the park area during the dry season, being exclusively within the zone of Mont Assirik, and more specifically the Mansa Fara marsh, which can be thus designated as the core area of the WDE distribution. The trapping rate of the roan antelope, which is considered the most abundant antelope species in the NKNP, was 4.04, i.e. more than 11 times higher than that of WDE in the present study. Even the Western hartebeest, which is considered a rare species in the NKNP, had a trapping rate of 0.61, which is ca. twice as high that that of the WDE (see Rabeil et al.8 for further details, and additional ungulate species).In the zone of Mont Assirik, the trapping rate of WDE increased to 2.42, but the trapping rates of other antelope species remained higher still (4.6 for roan antelope and 3.39 for Western hartebeest8). The WDE distribution is therefore strongly localised within an area which seems to also be attractive for the other species, including the incidental records of elephant. The Mont Assirik zone, and more specifically the Mansa Fara marsh area, is therefore the crucial zone within the park for the WDE, and it appears to support a larger number of other antelope species as well. This zone should therefore be considered as a key conservation area, potentially very sensitive to targeted poaching, and thus crucial for efficacy of targeted law enforcement actions.When looking at the diurnal activity pattern, the WDE were active before midnight, approximately 3 h after sunset, in the morning, approximately 2 h after sunrise, and then again in the afternoon, with the peak activity during the hottest part of the day. This activity pattern is different from the typical bimodal activity pattern, which has peaks at dawn and dusk, as reported for most African grazing and browsing herbivores, seen as a behavioural thermoregulation strategy to avoid heat stress41,42,43. Instead, the WDE, being a large body-sized browsing antelope19,44, must stay active throughout the day to seek discretely distributed food, and fulfil foraging requirements by feeding while moving. The WDE appears to be well-adapted to tolerate such high temperatures, similar to kudu45, roan46, and giraffe47. Such behaviour pattern enable the law enforcement patrols, as well to tourists, to detect herds of WDEs and monitor them, thereby increasing their protection against poaching.Individual identification and recapturesThe individual identification of animals was more successful during the daytime, as the light conditions mostly did not allow for the proper visualization of the stripes on the flanks during the night captures (as similarly reported in Jůnek et al.18). When the ID is targeted to be successful during the night (as for the leopards and tigers), the camera traps are often set to the video mode to ensure a higher possibility of identification48. However, the activity of the WDE is not predominantly nocturnal, and the captures were distributed over both the daylight and night hours, and therefore the results are considered representative for the whole period.The AD animals were more likely to be identified in the present study because of their larger body size, resulting in better visibility of their stripes. The higher identification rate of larger individuals also likely contributed to the higher probability of recaptures, which were only recorded for individuals of 2Y and older.Overall, the identification success rate was comparable, maybe even slightly higher, than the previous camera trap study performed on the Eastern Giant eland in Chinko, CAR, specifically in the dataset from the dry season13, which corresponds to the observation period in the present study as well.In the NKNP, recaptures of individuals were recorded, whereas there were none reported in Chinko13. The recapture rate of the WDE in NKNP, with mostly short distances between the capture-recapture sites, even after the long-time gaps between the captures, confirm again that the WDE likely inhabit a relatively limited area of the park.Group size and social structureThe mean group size recorded in the NKNP during the present study was slightly larger than that within Chinko; however, the maximum group size was smaller in NKNP (32 vs. 41 individuals). Mixed herds were the largest in terms of the number of individuals, in both studies. The average group size has been reported as 20–30 individuals49, but Derby elands may form large herds of over 100 individuals in the late dry season14. Similarly, a large herd was reported within NKNP in 2006, having 69 individuals7, and a herd of around 60 WDE was also recently reported by patrols in 2020 (GIE Niokolo, personal communications). It is important to highlight that the results from the present study reflect the number of individuals per event based on visible individuals within the scope of the camera, and that the real group sizes may actually be larger.No adult males were present in the mixed herd in two cases within the present study; however, there were always 2YM and a few unidentified individuals, suggesting that the herd should not be considered as a pure “nursery herd”, as known for sexually dimorphic antelope species50.Calves are born in the NKNP during the period comparable to that of Bandia, Fathala and Chinko, i.e. during the early dry season16. The higher proportion of calves in the dry season corresponds with the nursing period of six months for WDE44. Given a pregnancy length of nine months, the WDE mating season in NKNP peaks in January/February, which also corresponds with the formation of large herds with multiple males, as similarly seen in Chinko and Cameroon13,14.Vital ratesThe sex ratio of the WDE in the NKNP was female-biased. The skewed adult sex ratio reflects the lower survival rate of males in comparison with females, typical for polygynous species51. This result also corresponds with the findings from other Derby eland populations, namely from Chinko, where the bias towards females in the adult sex ratio was even more pronounced (0.67:113). A similar ratio was found in the hunting reserves within Cameroon35, but also in the semi-captive population, without hunting and without predators34. As the ratio in NKNP was less skewed than that within Chinko and Cameroon, a lower or zero selectivity for males by hunters/poachers is expected.The population of WDE in the NKNP showed a lower proportion of adults versus other age categories compared to the demographic structure of the WDE in the semi-captive breeding facilities of the Bandia and Fathala reserve33,52, and to those of the Eastern subspecies of Derby eland in the Central African Republic13 (see Table 3). The data from the present study also showed a surprisingly high breeding rate (likely close to 100%), as well as a high survival rate of yearlings. This combination of demographic characteristics should be highly favourable, and likely to lead to a significant population growth rate; however, this does not seem to be the case of the WDE population in the NKNP (please refer to further discussion about population size).In this context, the population of WDE in the NKNP was explored deeper, to examine possible scenarios of changes within the population structure. The changes in vital rates between two years of monitoring (2017 and 2018) were examined, by taking advantage of the possible recognition of the age category until two years of age, and the knowledge of the life tables of the enclosed, non-predated WDE population in the Bandia reserve34. Life tables were created for each year, and for males (M) and females (F) separately, according to the standard structure2, and based on two scenarios: a) only the observed number of JUV and 1Y (nx), and modelled 2Y (model ‘JUV + 1Y’); b) the observed number of JUV and 2Y (nx) (model ‘JUV + 2Y’). Then, estimations of animals in age categories based on two parameters were calculated: (i) based on the mortality rate (qx) known from the Bandia reserve (Senegal), and (ii) based on the recorded number of animals (NAD), to calculate the estimation of mortality rate (for details, see Additional file 1: Table S2).The resulting values demonstrated that with survival rates comparable to a population without predation and poaching, the number of adults would be twice or three times higher than currently detected in the present study. Yet, considering the recorded number of adult individuals, the annual adult survival rate was considerably low, i.e. 59–69% in males and 67–82% for females. To conclude, the demographic structure of WDE in NKNP showed a high breeding rate, moderate juvenile survival, high survival rate of yearlings, and a low survival rate of adults.Juvenile survival is one of the most fluctuating vital rate parameters, sensitive to population density, stochastic environmental variation, and predation53,54,55. Given the high proportion of juveniles within the population, and the breeding rate higher than that in Cameroon (74%14) and within the captive population (77%34), the juvenile survival rate does not seem to negatively affect the population growth in the NKNP. High breeding rates could be a more robust determinant of population change than AD mortality53, and it is therefore possible that the WDE population size is stable in the NKNP, or even increasing, despite the low adult survival rates. On the other hand, the relatively low numbers of AD individuals in the population indicates low survival rates, which may lead to the decline and final crash of the population54. It is acknowledged that data from two consecutive years was used in the present study, which were not comparable due to different CT settings, and that long-term monitoring, which accounts for variability in vital rates, would be a conservation essential to identify the trend and population change.Based on the present findings of WDE spatiotemporal behaviour and estimates of vital rates, several explanations about multiple processes interacting in the environmental, anthropogenic and conservation context of the park, which inherently affect the small population of WDE, can be inferred. One explanation may suggest that a low proportion of AD WDE and higher JUV survival rates may reflect the influence of growing populations of apex predators in the NKNP, specifically the population of lions56, which may preferentially target the adult individuals57. The age-sex structure also encourages the interpretation that the adult animals are exposed to human-related factors, which prevents them from expanding from the core area of their distribution, exacerbating male-male competition in the limited space34. The poaching activity was also highlighted as an existing threat to WDE populations35. However, law enforcement has been substantially intensified in the core and south-eastern part of the NKNP since 201758, and lion-conservation actions are specifically supported. Thus, the predator populations may have started to grow, which is confirmed by the relative high trapping rate of lions in this core area8. Hence, increased predation may interfere with other environmental factors and consequently affect the WDE population dynamics at the level of AD individuals55,59.A complementary scenario may highlight other factors, specifically, those which maintain the WDE population within a certain spatial extent of the park, i.e. Mont Assirik and Mansa Fara marsh zone. This area can be delimited either ecologically by specific unidentified resources, or by anthropogenic factors, namely a highly frequented trade road crossing the park, wild bushfires, and intensive livestock encroachment in a large band from the borders of the park, inwards (up to 10 km). There is also a vast area in the central part of the park that offers an important space with a supposed carrying capacity for large herbivore populations. This area is, however, outside of the zone of intensified law enforcement, and suffers from inadequate surveillance in the long-term, due to the absence of tracks and therefore being difficult for rangers to access. This area certainly represents an attractive zone for targeted illegal hunting actions. These limiting factors constrain large mammals to concentrate within the zone of Mount Assirik and Mansa Fara marsh, which, in turn, makes animal populations vulnerable to any potential environmental or man-induced incidents, like bush fire.Population sizeThe estimated population size of 195 individuals corresponds with the range of most recent estimates of the WDE population size in the NKNP, i.e. 100–200 (approximately 170) individuals6,7,60. Given the fact that the model contains only the data for AD animals (as no other age category had recapture records), it may be considered that this estimate refers to the number of adult individuals in the population. With regards to Table 3, showing that adults are likely to form 43 to 44% of the whole population, it may be inferred that the actual number of WDE in the NKNP could be higher, even up to 300 individuals, if the data are corrected for the 22% of unidentified individuals. The WDE density estimate of 0.138 individuals/km2 was comparable to densities of Eastern Derby eland in CAR (densities ranging between 0.04 and 0.16 individuals/km2), in Chinko13, and ranging between 0.002 and 0.1 individuals/km2 in the northern CAR61, as well as in Cameroon, with densities ranging between 0.002 and 0.08 individuals/km262. On the other hand, in comparison to other antelope species, the estimated WDE density falls within the range of densities of large herbivores reported from many other sites in African protected areas63, where lower values correspond to the larger areas and are also associated with large browsers, i.e. to the type of diet. Maximum densities of a healthy undisturbed DE population were estimated at about 0.5 individuals/km249, and can reach up to 1.19 individuals/km2 in intensively surveyed hunting zones in Northern CAR61. Thus, the density of WDE in the NKNP could be potentially higher. More

From the phytosociological relevés performed in each sampling area it is evident that hives were positioned in grasslands rich in Alpine herbaceous species (Table S1). In fact, among the 169 identified species, 85% were herbaceous species common in meadows (of Arrhenatherion elatioris and Triseto flavescentis-Polygonion bistortae phytosociological alliance) and acidophilus pastures (Siversio-Nardetum). 15% of the species were trees and shrubs (not abundant in the floristic relevés of the apiary areas considered), including some of beekeeping interest such as: Rhododendron ferrugineum, Castanea sativa and Rubus idaeus. From the MDS biplot (Fig. 2) elevation is the main ecological variable that differentiates sampling areas. In particular, the relevés of stations B and F are characterized by a floristic composition which is different from the areas at higher elevation (characterized by a higher presence of microthermal alpine species). This is due to the separation between the sub-montane belt and the high mountain belt vegetation on the 1.300 m a.s.l. line in the study area25.Figure 2MDS of the phytosociological relevés. Capital letters indicate the six sampling areas, the 1.300 m a.s.l. contour line that separates sub-montane belt and high mountain belt vegetation is highlighted in red.Full size imageAlthough the beehives were positioned in mountain grasslands, melissopalynological analysis presented a different picture. The pollen of numerous species detected through the floristic relevés were found in the honey samples via melissopalynological analysis, although the latter did not totally overlap with the floristic characterization of the area, in particular from a “quantitative” point of view. In fact, the floristic relevés showed a relative richness of herbaceous species (Table S1) peculiar of mountain grasslands that would seem promising for the production of wildflower honeys. Conversely, in the melissopalynological analysis the species considered interesting but not predominant in the botanical description were relevant (Fig. 3 and Table S2).Figure 3MDS of the melissopalynological analysis of the six samples (dots) of mountain wildflower honeys produced in the stations considered. The crosses are the pollens found in the honey samples, the most important are indicated.Full size imageThe premises to produce wildflower honey is that the botanical species contributing must be different and sometimes very numerous, without any of them assuming a dominant character. However, this was not fully evident in our research: although it was possible to identify more than seventy species through melissopalynological analysis and even more through the floristic characterization of the areas, most of them were defined as minor or sporadic pollen (Table S2). Even though apiaries were in mountain grasslands, the most relevant role was played by some woody species/shrubs: Rubus (presumably Rubus idaeous L., identified in the floristic relevés) and rhododendron (Rhododendron ferrugineum L.) for the mountain/subalpine belt and Castanea and Ericaceae (heather) in the submountain belt. Following the rules to define ‘‘unifloral honey’’, three of the wildflower honeys could be defined unifloral or bifloral:

Rhododendron unifloral: honey A (Rhododendron 47.18%), and honey C (Rhododendron 62.93%);

Raspberry unifloral: honey B (Rubus 67.12%)

Raspberry and Rhododendron bifloral: honey D (Rhododendron 34.27% and Rubus 34.74%) as well as honey E (Rubus 44.25%, Rhododendron 34.14%).

Honey F, due to the contribution of pollen from Tilia genus (that was detected only in this sample as an important sporadic pollen, 3.5%) Castanea (96.4% in honey F, but it should be noted that chestnut pollen is an overrepresented pollen) and in the second count Ericaceae (32.45%, that was considered a secondary pollen together with Rubus, with a percentage of 38.59% in honey F) differed from the other honeys (Fig. 3).Rubus pollen was anyway present in good amounts in all the samples considered, and was a dominant pollen in honey B, a secondary pollen in honeys C, D, E and F and a minor pollen in honey A. Sorbus and Tilia pollens were detected only in honey F, while no rhododendron was detected in honey F. Honey D was characterized by a percentage higher than the “rare pollen” category of some important alpine essences, such as Liliaceae, Centaurea, Campanulaceae, Anthyllis f., Polygonum bistorta, Lotus alpinus and Potentilla/fragaria (Table S2).Although wildflower honeys are intrinsically characterized by a high variability compared with unifloral honey, this shows the importance of the formal characterization of honey to obtain a product which satisfies consumer expectations, and it was demonstrated that the botanical origin of honey cannot be based on the claims of local beekeepers by considering the predominant flowers surrounding the hive.Although honeybees are considered supergeneralists in their foraging choices, there are certain key species or plant groups that are particularly important in honeybee foraging2, and many were identified in the botanical characterization of the area, including Rubus idaeus L., Calluna vulgaris L., rhododendron and some present in the broad-leaved woods mentioned such as chestnut (Castanea sativa Mill.) or plants of Tilia genus. In the research work by Hawkins et al.2, Rubus fruticosus L. was among the frequently found species and tree pollen belonging to Castanea sativa L. as well as, for example, species of Malus, Salix and Quercus spp, was frequently seen. These kinds of preferences could relate to the ease of availability and abundance of the plant, the quality and abundance of the nectar and pollen and/or specific nutrients or trace elements provided by these species or neurological aspects (as will be discussed further). As referred by beekeepers, over the last decades the production of mountain wildflower honey, that often does not meet the characteristics expected and presents flavours that are reminiscent of other kinds of honey such as rhododendron or linden or chestnut, is becoming more and more critical and this was absolutely confirmed by this study.This could be linked to the fragmentation of an important habitat of the Alps—mountain grasslands (meaning pastures and meadows) for anthropic and climatic reasons8,9. Honeybees from the same colony forage across areas spanning up to several hundred square kilometres, and at linear distances as far as 9 km from the hive41. Onlooker bees are those in charge of finding nectar sources and of giving instructions to the employed bees, the other foraging bees, that communicate the necessity to look for new resources of food to the onlookers through continuous dance communication42. Among the onlookers, there is a difference between the bees that scout for different nectar sources or recruit to well known floral resources43 and there is an optimal ratio of scouts to recruits, for the most effective collective foraging41. However, this balance may change based on the structure of the landscape in which the bees forage for food44,45,46. Theoretical models47,48 and empirical tests49 suggest that when resources are concentrated into a small number of highly rewarding patches, colonies perform best with few scouts and many recruits, while when resource patches are small, evenly distributed, and easy to locate, successful colonies invest more in scouting than in recruitment. This is strictly linked to climate and social changes in the mountains: mountain grasslands are no longer evenly distributed and easily localizable, as they are scattered among expanding areas of shrublands and forests9 and, for the above-mentioned reasons, it is more efficient for the colony to invest in more recruiters than scouters, as recruiters will identify a small number of highly rewarding patches, such as raspberry or rhododendron shrublands or linden and chestnut woods, that are highly rewarding and very different in quality.This overlaps with individual and collective honeybee behaviour driven by proximate physiological mechanisms that involve the tryptophan metabolism via kynurenine pathway that is one of main neuroprotective mechanisms. In this research, many of the differences/similarities among the samples might be attributed to metabolic alterations within this pathway, represented by relative amounts of kynurenic acid. However, different quinoline structures have also been identified (Fig. 4). Neurotransmitters play a central role in several of the biological processes that honeybees require to perform activities such as foraging behaviour50. A considerable amount of literature highlights the involvement of the neuroprotective kynurenine pathway (KP) final product kynurenic acid (KinA) in the regulation of the stress-related hormone dopamine in the honeybee as well as in other animal species51,52. The major known source of dietary KynA are pollen and nectar produced by sweet chestnuts53,54 and it has been verified that this compound is found in high concentrations in chestnut flowers55. This is coherent with the results of this study: chestnut pollen was found in honey F, produced in the lower station where chestnuts also appear in the floristic relevés, and KynA was found to be a dominant compound in honey F. Interestingly, chestnut pollen was found as sporadic pollen in all the other samples, even those produced in the highest apiary stations (Table S2).Figure 4Kinurenic acid and 3-hydroxyquinaldic acid structure and content in the six honey samples, performed in triplicate. The box diagram representing the median with distribution interval between 25 and 75%.Full size imageFurther, KinA may possess positive properties in a number of pathologies of the gastrointestinal tract, especially colitis, colon obstruction or ulceration56,57. It has been proposed that KinA may also possess antioxidative properties56,57,58,59. This was confirmed by this study, since the wildflower honey with a high component of chestnut pollen was the one with the highest antioxidant properties at the FRSA test (66.61 ± 4.77%), even if lower than manuka honey (84.21 ± 1.04%), a dark honey that is a well-known nutraceutical product and has recently attracted attention for its biological properties, especially for its antioxidant and anti-microbial capacities60. Honey A showed the lowest power (22.40 ± 0.28%) while the other honeys ranked around 40% (Fig. 5). Interestingly, metabolomic analysis revealed the presence of 3-hydroxyquinaldic acid (Fig. 4), which is a kynurenic acid isomer and, although its function has not been elucidated in detail, a few literature data indicate its role as a precursor of naturally occurring peptide antibiotics from the quinomycin family61.Figure 5Results of the FRSA test. Capital letters represent the six honey samples considered. Manuka honey was used as a control.Full size imageIn order to evaluate the ability of honey to induce wound closure, a scratch wound assay was performed (Fig. 6)62. Scratch assay creates a gap in confluent keratinocyte monolayer to mimic a wound. It has already been demonstrated that honeys are able to induce wound closure63 to different extents depending on honey origins and properties.Figure 6The scratch wound test in keratinocytes, HaCaT cells, exposed to honeys. (a) The digitalized pictures of scratched cells after 24 h exposure to 0.5% (w/v) of honeys. (b) The closing percentage wound values after 24 h exposure. Statistics on bars indicate differences compared to the control (CTRL) condition determined by a One-Way ANOVA followed by Dunnett’s test (****p  More

Soil and litter samplingMineral soil (0–15 cm) was collected at the Elizabeth Woods site, a 120-year-old deciduous forest in West Virginia, US (39° 32′ 50.6″ N, − 80° 00′ 00.4″ W). Soils were collected from four 20 × 20 m plots dominated by either AM-associated trees (i.e. Liriodendron tulipifera and Acer saccharum), or ECM-associated trees (i.e. Quercus rubra, Quercus velutina and Carya ovata). These sites have been characterized previously as Culleoka-Westmoreland silt loam soils at the AM sites and Dormont and Guernsey silt loams at the ECM sites40. Soils were also characterized by C:N ratios 11.7 and 14.1 for the AM and ECM soils respectively, with a pH of 6.8 for both soils. Soils with the same mycorrhizal status were pooled and homogenized, air-dried at room temperature for ~ 24 h and sieved through 2.0 mm mesh before the initiation of the experiment. Uniformly 13C labeled litter ( > 97 atom % 13C) from Quercus robur (i.e., ECM substrate) and Liriodendron tulipifera (i.e. AM substrate) leaves (Isolife BV, Wageningen, NL) were incubated in soil mesocosms in a factorial design with five replicates for each treatment combination (2 soil types × 2 substrate types), along with five replicate controls (no 13C substrate addition) for each soil type. The 13C enriched substrates were dried and ground to a powder and added in a suspension of 0.5 ml sterile water to 20 g of soil at a concentration of 400 ug 13C g−1 soil. The control soils received 0.5 ml sterile water additions. These incubations were well mixed and kept at 60% water-holding capacity for the 21-day period at room-temperature18. Chemical characteristics of soils and plant substrates are provided in Table S1.DNA processing and qSIPFor quantitative stable isotope probing, DNA was extracted, quantified, ultracentrifuged, fractionated and sequenced as described in18,26. DNA was extracted using a MoBio PowerSoil HTP Kit following the manufacturer’s instructions. For stable isotope probing, 5 ug of DNA was loaded into a 5-ml ultracentrifuge tube with ~ 3.5 ml of a saturated cesium chloride (CsCl) solution and ~ 900 ml gradient buffer (200 mM Tris, 200 mM KCl, 2 mM EDTA). DNA was separated via ultracentrifugation at 127,000g for 72 h using a TLN-100 rotor in an Optima Max bench top ultracentrifuge (Beckman Coulter, Fullerton, CA, USA). Tubes were fractionated into ~ 25 fractions of 150 µl each, and the density of each fraction was measured with a Raichart AR200 digital refractometer. DNA was purified using an isopropanol precipitation method. The 16S rRNA gene was subsequently quantified and sequenced in samples containing DNA, within the density range 1.660–1.735 gml−1 (~ 10 fractions per sample). To quantify the 16S rRNA gene, quantitative PCR was performed in triplicate using a QuantStudio 5 applied biosystems (Thermo Fisher Scientific) and primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACVSGGGTATCTAAT-3′)41. The PCR program used was as follows: 95 °C for 2 min followed by 45 cycles of 95 °C for 30 s, 64.5 °C for 30 s and 72 °C for 1 min. Libraries were sequenced on an Illumina MiSeq instrument (Illumina, Inc., San Diego, CA, USA) using a 300-cycle v2 reagent kit. Fungal 18S rRNA gene copies in each fraction were also quantified using primers 1380F (5′-CCCTGCCHTTTGTACACAC-3′) and 1510R (5′-CCTTCYGCAGGTTCACCTAC-3′). The PCR program used was as follows: 98 °C for 3 min followed by 40 cycles of 98 °C for 45 s, 60 °C for 45 s and 72 °C for 30 s. DNA fractions were amplified for fungal ITS rRNA genes using primers ITS4F (5′-AGCCTCCGCTTATTGATATGCTTAART-3′) and 5.8SF (5′-AACTTTYRRCAAYGGATCWCT-3′)42 and 300-bp paired-end read chemistry on an IlluminaMiSeq (Illumina, Inc., San Diego, CA, USA). The PCR program used was as follows: 95 °C for 6 min followed by 35 cycles of 95 °C for 15 s, 55 °C for 30 s, and 72 °C for 1 min. DNA fractions were then sequenced using a 500 cycle v2 reagent kit.Files came pre-split and joined multiple paired ends that we combined to pick operational taxonomic units (OTU). Open reference OTUs were picked at 97% identity using SILVA 128 release database for Bacteria and RDP database for Fungi. Taxa were analyzed at the ‘OTU’ level from the QIIME L7 table. Calculation of 13C excess atom fraction (EAF) was performed for each taxon as described previously18,19. Briefly, using the CsCl density gradient data, a weighted average density (WAD) was computed for each taxon’s DNA extracted from control soils that did not receive an isotopically enriched substrate. This natural abundance WAD was then compared to the taxon’s WAD following incubation with the 13C enriched material. The change in WAD can be used to quantify the amount of isotope incorporated into the DNA17,18. Preliminary data analysis revealed an effect of ultracentrifuge tube on estimation of phylotype weighted average density, probably a consequence of slight differences in CsCl density gradients between tubes. This technical error was corrected as previously described18,19. In addition to the samples subjected to qSIP analysis we also extracted and analyzed fungal and bacterial OTU’s from control soils where the DNA was extracted prior to incubation.FTICR-MS and lipidomic analysesSoil from substrate-incubated and controls mesocosms were processed and analyzed with Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), using a 12 T Bruker SolariX FTICR mass spectrometer at the Environmental Molecular Sciences Laboratory in Richland, WA, as described in Fudyma et al.43. Briefly, 100 mg of dried soil or litter substrate was extracted using an adjusted Folch extraction44. Extraction was performed on each sample by sequentially adding 2 ml MeOH, followed by a 5 s vortex; 4 ml CHCl3, followed by a 5 s vortex; sonication at 25 °C for 1 h (CPX3800 Ultrasonic Bath, Fisherbrand); addition of 1.25 ml of H2O, followed by a slight mix to achieve bi‐layer separation; and incubated at 4 °C overnight. The top, aqueous layer (metabolite—polar) was pipetted off into 1 ml glass vials and stored at − 80 °C until FTICR‐MS. The bottom, chloroform layer was dried down and stored in 50:50 methanol:chloroform until lipidomics analysis.A standard Bruker electrospray ionization (ESI) source was used to generate negatively charged molecular ions in the metabolite fraction. Samples were then introduced directly to the ESI source. The instrument settings were optimized by tuning on a Suwannee River fulvic acid (SRFA) standard, purchased from International Humic Substances Society (IHCC). Blanks (HPLC grade methanol) were analyzed at the beginning and end of the day to monitor potential carry over from one sample to another. The instrument was flushed between samples using a mixture of water and methanol. One hundred and forty‐four individual scans were averaged for each sample and internally calibrated using an organic matter homologous series separated by 14 Da (CH2 groups). The mass measurement accuracy was less than 1 ppm for singly charged ions across a broad m/z range (m/z 300– 800). Data analysis software (Bruker Daltonik version 4.2) was used to convert raw spectra to a list of m/z values, applying the FTMS peak picker module with a signal-to noise ratio (S/N) threshold set to 7 and absolute intensity threshold set to the default value of 100. Chemical formulae were then assigned using in-house software following the compound identification algorithm that was described in Tolić et al.45. Peaks below 200 and above 800 were dropped to select only for calibrated and assigned peaks. Chemical formulae were assigned based on the following criteria: S/N  > 7 and mass measurement error  800 were not detected in our samples. The m/z values represent the molecular mass (in Dalton) of the detected ions since all detected ions were singly charged ions. While our results do not represent a quantitative characterization of OM, the values presented are relative differences and should be representative of the samples. Finally, we would like to acknowledge that we were not able to see any clear evidence of 13C label in our FTICR-MS analysis of the soil samples. The lack of 13C label in our FTICR-MS analysis of the soil samples even though they received labeled substrate could be either due to the fact that most of the labeled substrates produced by microbial activities were of low molecular weight, which cannot be detected by FTICR-MS and/or the leftover labeled substrate was of low abundance compared to the organic compounds previously present in the soil matrix. As such, we used the FTCIR-MS data to identify shifts in the overall composition of the chemical compounds in each soil.Lipids in the chloroform fraction were analyzed by LC‐MS/MS in both positive and negative ESI modes using a linear trap quadropole (LTQ) Orbitrap Velos mass spectrometer (Thermo Fisher Scientific), as described in detail previously46. Lipid species were identified using the LIQUID tool46 followed by manual data inspection. Confidently identified lipid species were quantified using MZmine47 and the peak intensities were normalized by linear regression and central tendency (i.e., identifying a central or typical value for a probability distribution) using InfernoRDN.Statistical analysisAll data analyses were performed using R 3.2.048. To examine the effects of soil type, substrate type and their interaction in the bacterial, fungal and chemical composition of DOM and the lipid pool; Bray–Curtis distance matrices were compared with permutational multivariate analysis of variance (PerMANOVA) and visualized with Principle Coordinate Analysis (PCoA) using vegan package49. PerMANOVA analysis were run on the relative abundance and on the 13C EAF of individual microbial taxa, separately for both bacterial and fungal communities.The analyses for FTICR-MS were performed separately for control and incubated soils using all assigned molecular formulae remaining after quality filtering31. In all cases, we applied a Z-score standardization before calculating Bray–Curtis distance matrices49. We analyzed the results from FTICR-MS as resulting from the decomposition of the added substrates for two reasons. First, this is a fully factorial design where individual soil samples were split to either receive AM poplar or ECM oak litter substrate. Thus, each soil sample starts with the same characteristics and the changes at the end of the incubation period should reflect the processing of litter. Second, we excluded molecular formulae present in the litters and thus, the differences we report in each soil type are derived from this processing (or the lack of it).We calculated aggregated indices that characterize both the composition and the physicochemical properties of the microbial (both bacteria and fungi) and the SOM and lipid pool34,36. For bacterial and fungal communities, we quantified Shannon–Weaver diversity index for each sample H′ = (-{sum }_{i=1}^{S} pi ln(pi)) (where pi is the proportion of species I) using the relative abundance of individual microbial taxa50. To find the percent of substrate assimilation by individual taxa, we calculated the proportion of C assimilated by each group as previously described18,51 as a percent. For SOM and lipid molecular formulae, we separately calculated weighted means of formula-based characteristics (i.e. m/z, Aromaticity Index—AI; H/C, O/C, and Nominal Oxidation State of Carbon-NOSC) as the sum of the product of the single-formula information (i.e. m/zi, AIi, H/Ci and NOSCi) and the relative intensity (Ii) divided by the sum of all intensities (e.g., m/z sample1 = ({sum }_{i=1}^{S})(m/zi ·Ii)/Σ(Ii)). With these metrics we obtained sample-level information related to the molecular size (i.e. m/z), the molecular bioavailability (i.e. higher H/C ratio), the molecular reactiveness (i.e. lower AI) and the energetic rewards from molecular oxidative degradation (i.e. higher NOSC) of the SOM, which allows to infer the potential of decomposition products to form stable SOM12,31,35. Detailed information of the calculated indices can be found in the literature31,35,36.We further tested the effects of soil type, substrate type and their interaction on each index using the “lm” function from the “stats” package. In these analyses, P values were approximated by an F test using Type II ANOVA tests with Kenward-Roger Degrees of Freedom52. When interactions between soil and substrate type were found at P  More

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