Philippa Kaur

Experimental designWithin the new varieties obtained in the market the variety “Emerald” is a good model for pollination service management. In Argentina the plant is vigorous and very productive, and can produce fruit in autumn without reducing the spring production through the long flowering period41. As of 2020, around 260 hectares are planted with this blueberry variety in Entre Ríos province.Field work was conducted during the 2017 flowering period (from July 17th to August 23rd, 2017) on 9 blueberry plots (Vaccinium corymbosum var. Emerald) located in the region of Concordia (Entre Rios, Argentina). Three plots were pollinated under “precision (precision) honey bee management”, while 6 other plots were pollinated under conventional (conventional) honey bee management (two conventional plots per precision plot). We decided to double the number of control plots per farm (conventional bee management) in order to reflect the variability that these agroecosystems present in the study region.Each plot comprised a 1-ha area, planted with the Emerald variety. The plants were all the same age and subjected to the same agricultural management in respect to fertilization, pruning, water, etc. Six of the nine plots (2 precision plots and the corresponding 4 conventional ones) were open air, while 3 plots (one precision and two conventional) were covered with mesh. This mesh is used to prevent hail and wind damage. All the plots were pollinated by honey bees (Apis mellifera L.) at a density of 10 hives per cultivated ha (Fig. 5).Figure 5Map of the plots of blueberries studied. The fields with conventional and precision management of bees are detailed in each farm, and the location of the hives used in each particular case. One Precision (yellow) and two Conventional (the numbers stand out C1 and C2, in red) plots were sampled per farm.Full size imagePrecision honey bee managementOne month before the blooming season, we carefully selected 30 beehives based on: (a) number of combs covered with adult bees, (b) number of combs with brood, and (c) the presence of an active egg-laying queen, to be prepared for the pollination service. The selected stock of beehives to be used in the precision plots came from the same stock of bees to be used in the control plots, and thus, we could expect that any differences regarding to bees’ performance in the field would be related to the different management (precision) and not due to differences regarding the stocks of bees, neither the ecotypes nor the beekeeping management previous to ours. During the month prior to the pollination service, the selected beehives were prepared for the blooming period by being fed with a dietary supplement (nutritional technology exclusively licensed by Beeflow S.A. and Beeflow Corporation) to boost their immune systems and strength. When blooming reached 5–10% in the precision plots, the beehives were placed so as to surround each plot and managed individually. Beehives were checked during the blooming period and, if it was necessary, they were also fed. We also coordinated pesticide spray applications with growers to avoid bee losses during the pollination service. When 95% of the female flowers were senescent the hives were retired from the field and returned to their original apiary.Conventional bee managementThis group of beehives represented the conventional management performed by beekeepers in Concordia Region, which consists of beehives placed in big groups, between 5 to 50, and located either next to or up to hundreds of meters from the field, with high variation in terms of hive quality (see above), and with infrequent servicing. This management style is similar to those performed by many beekeepers around the globe.In summary, the main differences between the management of bee hives for pollination services in blueberry are: Start of the incentive diet, the hives for precision management began to be fed before the blooming period with a dietary supplement (sugar syrup 1: 1) to encourage the collection of pollen; Protein feeding, the hives for precision handling were fed during the entire flowering period of the blueberry with a diet especially rich in protein and essential amino acids (i.e.,: alpha arginine); Spatial arrangement, strategic and homogeneous location of the hives individually in contrast to distribution in medium groups (5 hives) to large (30 hives) located in strategic places according to the production advisor; Management against agrochemical applications on the plot, Preventive management for pesticides, fungicides and fertilizers applied by means of aqueous suspension, in this case through an agricultural spraying turbine for a tractor, was carried out only in the hives for precision pollination.Data collectionThe visitation rate to flowers was estimated simultaneously in precision and conventional plots, and at different times of the day. To do this, pollinator censuses were performed by recording the number of flowers visited by pollinators for a period of 5 min (i.e., no. visits . flower−1 . 5 min−1). Each census involved a different randomly selected group of flowers (between 10 and 30 floral units per census), within a randomized location within the plantation. These visits censuses were carried out in all the lots studied on the same day between 10 am and 6 pm, periodically changing the visiting hours to each lot to cover the entire hourly range throughout the 15 sampling dates distributed in between June 17 and August 23, 2017. Census of visits to flower were carried out on days with a favorable climate for foraging bees, sunny to partially sunny days at times where the temperature it is suitable for bee activity ( > 10° C). In total, we performed 70 pollinator censuses per plot distributed in 15 days, totaling 52.5 h of observation (i.e. 5.8 hs per plot × 9 plots).In each plot, 5 plants were tagged (i.e., 5 plants per plot * 9 plots, 45 plants in total), and within each plant 3 branches were randomly selected and tagged with paper tape (a total of 225 experimental branches). The number of open flowers between the tag and the end of the branch were counted in order to determine the number of flowers that produced a fruit (i.e., fruit set). This experimental design allowed the estimation of variation in flowers within and among plants and plantations, subjected to the different pollination practices.When the time of the harvest arrived, the number of fruits (corresponding to each tagged branch) developed from each plant was counted in relation to the total number of flowers originally present on the branch to estimate fruit-set. Then, the quality of all tagged fruits harvested was evaluated by quantifying individual weight and firmness. A total of ~ 6700 flowers and ~ 5200 fruits were analyzed.To estimate the firmness of the fruits we used the “compression force,” which is a widely used indicator, using a texture analyzer TA.XT Plus (Stable Micro Systems Ltd., United Kingdom) equipped with a 5 kg load cell and a 75 mm cylinder aluminum probe. It is defined as the force necessary to deform the fruit by 10% and is used as a quantifiable measure equivalent to the force produced when a fruit is pressed between the thumb and the index finger. Finally, to estimate individual fruit weight we used an electronic scale.Statistical analysisWe analyzed the effect of honey bee management practice (conventional vs. precision) on: (i) visitation frequency to blueberry flowers, (ii) fruit set, (ii) fruit weight, and (iii) fruit firmness, using general and generalized mixed-effects models. Data analysis was carried out using the lmer function from the “lme4” package42,43 of R software (version 2.15.1), applying separate analyses and distributions for each response variable (e.g. visits frequency, fruit set, and firmness, see below).For (i) visitation frequency (i.e., bee visits) as the response variable the model assumed a Poisson error distribution with a log-link function. We included the number of flowers observed in each census as an offset (i.e., a fixed predictor known in advance to influence insect visitation)44. For (ii) fruit set (i.e., proportion of flowers setting fruit) as response variable the model assumed a Binomial error distribution with a logit-link function. And finally, for (iii) fruit weight and (iii) fruit firmness as response variables the models assumed a Gaussian error distribution. For all models, honey bee management practice (conventional vs precision) was included as a fixed effect, each “plant” was nested within “field”, and each “field” was nested within “farm” as a random effect, following the hierarchical design of our experimental design (i.e. 3 farms, 3 fields within each farm, and 5 plants within each field). More

Magnesium soil contents and relationships to livestock healthMagnesium (Mg) deficiency (hypomagnesaemia) in ruminant livestock is a serious issue for the agricultural sector and accounts for a significant number of animal deaths annually. It is caused by a diet deficient in Mg, or due to an imbalance in the supply of Mg in comparison to other mineral cations1. Hypomagnesaemia is likely to be responsible for lower-productivity and diminished well-being in more animals in a herd compared to those displaying acute symptoms, given that herds/flocks generally receive a common diet2,3. If Mg deficiency could be prevented, it would be of benefit to both animal welfare and economic productivity. Recent research has confirmed that whilst hypomagnesaemia is commonly reported by UK farmers, the reported use of preventative measures is low, and the use of pasture interventions is lower still4. Pasture interventions can include the application of Mg-rich fertiliser or lime products, or selection of sward species with a propensity to take-up elevated Mg concentrations4,5.One aspect of dietary supply is the geographic control of pasture and farm-produced fodder. It is known that total Mg and plant-available Mg concentrations in soil are controlled by geological and geographic factors6,7,8,9 and that there is little evidence for any changes in pasture soil Mg concentrations through time6,10. The magnesium content of soil relates to that of the bedrock, where it is high in the bedrock it is high in soil and vice versa. Thus, the composition of all pasture and farm-grown fodder will be influenced by this natural environmental endowment as well as pasture management decisions.Grass productivity and soil pHA key pasture management activity is that of soil pH—which here is reported as measured in water (pHw), consistent with standard agronomic laboratory practice in the UK11. Grassland mineral soil is recommended to be maintained at pHw ≥ 6.0 in Britain12,13. In Ireland, where grass-clover pasture is more widely practiced, a pHw threshold of 6.5 is recommended14. However, multiple lines of evidence exist that indicate pasture soil is frequently below these pHw recommendations. Private sector on-farm sample data summaries from the UK consistently show pH typically below recommendations in pasture soil: the most recent annual data synthesis reports 57% of grassland soil with pHw ≤ 5.99, and 27% with pHw 6.00–6.498. This is consistent with systematically collected public sector data across the north of Ireland, where 84% of pasture samples were below the clover-grass recommended threshold of pHw 6.515.Grassland production is widespread in Wales and western England (Fig. 1). Two environmental factors jointly contributing to the lower pH in these regions are (1) geological—these areas are most often on soils which are developed over rocks with low concentrations of base cations (Figs. 1 and 2); and, (2) these areas are also often upland areas, associated with typically higher rainfall16 which will further leach base cations. Added to these environmental factors are the application of nitrogen fertilisers which have an acidifying effect17. Thus, many pastoral areas require treatment using agricultural lime in order to optimize soil pH for grass growth18.The use of liming materialsThe opportunity to improve grazing livestock Mg nutrition through use of Mg-rich lime is identified in guidance available to farmers12. This can have the dual benefits of maintaining soil pH for grass growth and ensuring Mg levels in livestock feed is at sufficient levels19,20,21. The combination of soil treatment for pH and Mg would therefore appear to be an efficient solution to solve issues surrounding Mg deficiency22. Conversely, for many soils with existing high Mg levels it may be important to treat with low Mg liming materials to ensure an optimal Ca–Mg balance to preserve the soil structure23.The use of Mg lime is only one of many methods of controlling Mg levels in livestock feed. Other methods, such as direct additions to feeds, salt licks, pelletised fertilisers products are also effective in reducing incidences of hypomagnesaemia and need to be considered as part of holistic review of a individual farms requirements, this is discussed in Kumssa et al.5.Maintaining optimal soil pH will directly affect the productivity of grass used for grazing, and will increase fertiliser use efficiency14. However, in some cases, for example upland sheep farming, a low investment—low return approach, with minimum interventions such as liming, may be entirety sensible and appropriate to the farm business and local landscape24,25.The extent to which agricultural lime is used in Britain is captured through the annual British Survey of Fertiliser Practice (BSFP) and can also be inferred from commodity production statistics. Production can be regarded as a good proxy for consumption since due to its high bulk and low price it is not exported in significant quantities.The BSFP, is an annual Department for Environment, Food and Rural Affairs (DEFRA) survey26, which representatively samples fertiliser and lime use across the British farming sector. This captures information on lime use in three geological material categories as used in arable and pastoral systems, as well as use of sugar beet lime and ‘other’ options. Sugar beet lime use is very low on grassland (generally unrecorded on ‘permanent’ pasture); ‘other’ categories are generally on a par with Mg-lime, but more detailed liming characteristics are not reported. Figure 3 shows a clear trend in decreasing production of agricultural liming material over the last 40 years. Lime use in the UK peaked in the late 1950s and mid 1960s likely due to a subsidy for agricultural lime in place at the time, this ended in 1978, causing prices to increase and subsequently lime use to decrease27. The use of agricultural lime has continued on a declining, or flat trend, likely due to reluctance to engage in soil treatments that are seen to be costly and a lack of knowledge over its potential benefits.Figure 1Classes of land cover considered by this study, based on Land Cover Map 201533. Some features of this map are based on digital spatial data licensed from the UK Centre for Ecology and Hydrology. Created using ArcMap 10.7.1, ESRI, 2019.Full size imageFigure 4 shows the use of geological lime products to be low at present in respect of the proportion of fields to which lime is reported to be applied, and that this is particularly pervasive on permanent grassland, with a 10-year average to 2019 of 2.9% (range 2.0–4.1%). Of this, a 10-year average of 0.4% of fields had Mg-lime applied, with 1.8% of fields having limestone applied. The limited use of chalk (0.1% of fields) probably reflects the distance between the majority of pasture and the outcrop of the chalk. Recent grassland ( More

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Effects of storage and technical variationWe first validated our methods by assessing the effect of storage and technical variation on microbiome composition. To quantify the effect of the two storage methods on bacterial composition in fresh samples, we performed a separate pilot study with nine faecal samples sourced from nine captive meerkats at Zurich University. Samples were immediately frozen after collection, and then either freeze-dried or kept frozen at −80 °C for seven days. Microbiome composition clustered strongly by sample identity in their beta diversity (Supplementary Fig. 1b), and storage did not significantly affect composition (Weighted Unifrac: F = 0.7, p = 0.52; Unweighted Unifrac: F = 1.0, p = 0.37). Across samples analysed in this study, storage had significant yet small effects on estimated bacterial load, with frozen samples overall having slightly lower estimated abundance (t = 7.2, p  More

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Altogether, we found that, while traffic volume declined by  > 7% during the pandemic year (with a maximum monthly decline of nearly 40%), the absolute number of annual WVCs was largely unchanged. This resulted in significant increases of  > 8% in collision rates between vehicles and wildlife during the pandemic year, peaking at a  > 27% nationwide increase in April 2020. Other studies from the first several months of the pandemic documented similar transient declines in the number of WVCs when the pandemic began which then reversed in many jurisdictions as the pandemic progressed and traffic rebounded26,27. We observed a similar pattern over the first five months of the pandemic at the national scale (Fig. 2): WVCs initially declined during the pandemic in step with declines in traffic volume, but then started to increase to baseline levels at a faster rate than traffic, possibly due to behavioral lags by wildlife following traffic-mediated increases in wildlife road use. Though based on coarse-scale data, our research aligns with assertions from studies during27 and prior to the pandemic3,15,16,28,29 that the relationship between traffic volume and WVCs is non-linear.We postulate that the observed non-linear relationship between traffic volume and WVCs is the result of greater use of roads and roadsides by certain wildlife species, namely large mammals (Table S1), in response to decreasing traffic volume, as prior research has suggested3,14,15,16. This explanation is consistent with accounts of various wildlife species making increased use of human spaces during the pandemic17,20,21: with less cars on the roads, wildlife might be less deterred from roads by the noise and light pollution that accompany high traffic volumes9,10,11,20 and perceive roads as less risky, thereby increasing their willingness to attempt road crossings3,8,15,16. Beyond incidentally crossing roads while moving about the landscape8,9, wildlife might be attracted to roads for travel, mates, or other resources8,10,11. Many animals are shown to utilize roads to move efficiently across the landscape11,12, and roads and the surrounding areas are comparatively open, such that wildlife might select roads and roadsides for enhanced visibility to find mates, detect predators, or locate prey10,13. Roadsides also can provide foraging opportunities and essential nutrients for wildlife via abundant, high-quality early successional vegetation and high salt concentrations10,11. As such, decreased road traffic during the pandemic might have caused certain wildlife species to tolerate the risks associated with roads in order to access the benefits of roads and roadsides.An alternative explanation for the observed increases in collision rates is that human driving behavior, rather than animal behavior, changed during the pandemic. With fewer cars on the road, people might drive faster35, rendering it more difficult for both humans and wildlife to avoid collisions3. Preliminary studies from throughout the United States have indeed suggested changes to human driving behavior during the pandemic, with several jurisdictions reporting increased vehicle speeds35,36. Despite reported increases in vehicle speeds, however, the total number of vehicle collisions (the sum of both wildlife and non-wildlife collisions) mirrored trends in traffic volume and declined considerably during the pandemic37,38. Thus, because changes to human behavior appear to have had a minimal effect on vehicle collisions overall, it is unlikely that the observed changes in collision rates are due to increased vehicle speeds alone. Still, we cannot discount the possibility that changes to human driving behavior contributed to the patterns documented here, and future work should more explicitly test the relative effects of changes in traffic volume on both human driving behavior and wildlife space-use, as well as the resultant impacts on WVCs.A greater understanding of human driving behavior would also help explain our findings regarding changes in traffic patterns during the pandemic. Nationwide, the severity of COVID-19 restrictions accounted for a large amount of the variation in changes in monthly traffic volume (R2 = 0.968), but the severity of restrictions was less influential on changes in yearly traffic across states (Tables S3 and S4). Restrictions implemented throughout the pandemic were largely enacted for the purpose of minimizing travel, and other research has demonstrated that these restrictions were effective at reducing human mobility18,21. Our state-level findings, however, imply that it was not only the restrictions themselves that reduced travel, but possibly also the associated anxiety regarding the risk of contracting the SARS-CoV-2 virus, as has been suggested in other studies21,22,23,24; although we observed the greatest declines in traffic volume early in the pandemic (Fig. 2A) when restrictions were most stringent (Fig. S2)21, there was widespread anxiety about the risks posed by SARS-CoV-2 during this time22,23, which likely motivated people to stay home independent of restrictions24. Indeed, anxiety and risk perception might explain the relationship between traffic volume and the other covariates in our top models (Table S4). Declines in traffic were greatest in the most densely populated states (Fig. 4A) and in states that had the highest and the lowest disease burdens (Fig. 4B). The risk of SARS-CoV-2 transmission is greater in more densely populated states due to the close proximity of and frequent interactions amongst people21. As such, people may have altered their road use more in densely populated states as compared to sparsely populated ones due to differing perceptions of disease transmission risk23—though differences in infrastructure in relation to population density likely contributed to this pattern as well39. Similarly, declines in traffic volume in states with larger outbreaks of SARS-CoV-2 might have been driven by increases in the perceived risk of contracting the virus21,23. Alternatively, traffic reductions in states with low disease burdens might reflect increased compliance with stay-at-home orders, and therefore less opportunity for disease spread40,41; essentially, reductions in traffic volume might be the cause of locally low disease burdens therein, rather than a consequence. Altogether, we posit that the observed heterogeneity in traffic volume between states is, at least in part, attributed to differences in the perceived risk posed by the SARS-CoV-2 virus.Regardless of the mechanisms underlying changes in traffic volume and WVCs, our observation that the annual number of WVCs was largely unchanged despite substantive declines in traffic volume has implications for mitigating WVCs going forward. Most directly, the lack of a directional change in WVCs suggests that road traffic levels in the United States are currently such that even large decreases in traffic volume would have minimal long-term effects on the absolute number of WVCs. As such, decreasing collisions by reducing traffic volume would require even larger and longer-lasting changes in traffic than those observed during the pandemic. Since such massive and sustained reductions in traffic are unlikely4,5,6, WVCs in the United States essentially represent a fixed cost as of now, both for human society and wildlife populations. As such, these transient decreases in traffic likely provided minimal reprieve to large mammals from collision-induced mortality, in contrast to speculation that changes in human mobility during the COVID-19 pandemic had substantial positive effects for wildlife populations by freeing wildlife from the pervasive direct and indirect effects of humans17,18,19,20,26,27,42.Indeed, it is possible that short-term decreases in traffic volume might ultimately be harmful to those wildlife species that increased their road use. Although the increases in collision rates we observed at the beginning of the pandemic were rapid and corresponded to nationwide declines in traffic volume (see also26,27), collision rates remained elevated even as traffic approached baseline levels in July (Fig. 2B). If wildlife responses to changes in traffic are asymmetric (i.e., increases in wildlife road use following declines in traffic occur more rapidly than decreases in wildlife road use in response to increased traffic), then short-term declines in traffic volume might lead to net increases in the number WVCs over longer timeframes, ultimately proving detrimental to certain wildlife populations1,3. Future work should evaluate the long-term effects of the pandemic on wildlife populations, specifically with regards to collision-induced mortality17,20,26,27,42.Although the COVID-19 pandemic provided an opportunity to examine the short-term effects of transient decreases in traffic volume on WVCs, the longer-term effects of expanding human populations, greater road densities, and altogether higher traffic volumes on WVCs are less clear. Similar to the increases in wildlife road use in response to decreases in traffic volume theorized here, steady increases in traffic might reduce wildlife road use long-term3,14,15,16; since road traffic is indeed increasing through time4,5,6, we might therefore see declines in WVCs as roads become more effective at repelling wildlife1,3,14. Although these reductions in vehicle-induced wildlife mortality are welcome, this would see roads increasingly serve as barriers to animal movement and gene flow43, further fragmenting already disconnected wildlife populations8. Thus, policy makers and urban planners should invest in infrastructure such as overpasses, underpasses, and fencing that enables wildlife to cross high-traffic roads safely or directs wildlife towards low-risk areas8,9. Even substantive short-term declines in road traffic are not sufficient to mitigate wildlife-vehicle conflict on their own. More