Philippa Kaur

Plants increase their freezing resistance upon exposure to low temperatureThe freezing resistance (LT50 values) was found to vary ranging from − 6.9 °C (14-August-2017) to − 31.7 °C (04-November-2018) over the course of study period. The freezing resistance of leaves recorded during the 12 sampling time-points has been provided in Table 1 (also see39). The overlap of confidence intervals around the mean was examined for comparison of LT50 values for the different sampling time-points. Significant differences in freezing resistance were observed across the sampling time-points (Table 1). Leaves of R. anthopogon collected during summer [July and August (Air temperature and photoperiod was about 9.6 °C and 13 h day−1 respectively)] showed marginal resistance to freezing (LT50: − 7 °C) and thus, are more susceptible to freezing damage. Further, as the ambient air temperature and photoperiod decreased towards the end of growing season (i.e., October and November 2017 with air temperature and photoperiod of about − 1.1 °C and 10.5 h day−1 respectively), the plants acquired the highest freezing resistance (LT50: − 30 °C). Interestingly, a sharp increase in freezing resistance (− 29.4 °C) was observed in September 2018, when the daily mean air temperature decreased below 0 °C due to sudden snowfall (Supplementary Fig. S2). Comparison of LT50 values of all the leaf samples of R. anthopogon showed that cold de-acclimation occurred after the snowmelt during early spring in June (LT50: − 13.4 °C) with an increase in air temperature and photoperiod. These results demonstrated that R. anthopogon plants exhibit lowered freezing resistance during the warmer months [hence, these time-periods were referred as non-acclimation (NA)], progressively develop greater freezing resistance during the onset of winter season (hence, referred as cold acclimation) followed by an intermediate level of freezing resistance during the spring [hence, these time-periods were referred as de-acclimation (DA)].Table 1 The estimates of LT50, calculated by fitting sigmoidal curve to electrolyte leakage values of temperature treatments, recorded for leaves collected during the different sampling time-points (from August 22, 2017 to September 18, 2018).Full size tableDuring the acclimation period (i.e., late in the growing season), plants acquired the highest resistance to freezing (Fig. 1). The low electrolyte leakage (= high freezing resistance) observed during this period might be due to changes in cell wall properties (such as increase in lignification and suberization of cell walls), which provide resistance to diffusion of electrolytes from cells of the leaves to the extracellular water47. Moreover, high freezing resistance may also be attributed to high leaf toughness and sclerophyllous habit of this evergreen species48. Further, it was found that freezing resistance was the lowest during mid-summer period. This pattern could be explained by a trade-of between plant growth rates and freezing resistance, where warmer temperatures favour plant allocation to growth49. These observations corroborated well with earlier reports that showed a rapid increase in ‘freezing resistance’ during the transition from summer to early winter and vice versa50.Figure 1LT50 [black point (with solid fill) on the curve] calculated by fitting sigmoidal curve to relative electrolyte leakage (REL %) values recorded during the three different acclimation phases. GOF indicates ‘goodness of fit’ test values for the fitted sigmoidal curves.Full size imagePhotosynthetic rates are higher during non-acclimation and de-acclimation periodIt was found that PN of R. anthopogon varied in the range from 8.336 to 17.64 μmol(CO2)m−2 s−1 and E from 2.281 to 4.912 mol(H2O)m−2 s−1, throughout its growing season. The Gs of leaves was estimated to be in the range from 0.110 to 0.265 mol (H2O) m−2 s−1. WUE, a ratio of PN and E, varied between 52.21 and 87.68 (Table 2). The gas exchange parameters of R. anthopogon varied significantly among the sampling time-points [referred to here as different acclimation phases of the growing period of evergreen shrub (Fig. 2, Table 3)]. In particular, PN was significantly lower on 18-September-2018 (referred as cold acclimation phase), whereas it was higher on 31-August-2018 and 15-June-2018 (referred as NA and DA phases, respectively). Similarly, Gs of leaves was significantly lower during cold acclimation in comparison to the rest of the acclimation phases (i.e., NA and DA). Further, WUE was significantly higher during cold acclimation, while it was lower during both NA and DA (p ≤ 0.05) (Fig. 2).Table 2 Variability in leaf gas exchange parameters of R. anthopogon during the different acclimation phases (NA = Non-acclimation, LA = Late cold acclimation and DA = De-acclimation).Full size tableFigure 2Variability in leaf gas exchange parameters of R. anthopogon during the three acclimation phases [i.e., Non-acclimation (31 August, 2018), Cold acclimation (18 September, 2018) and De-acclimation (15 June, 2018)]. Different alphabets (a, b, c) represent statistically significant values (p  More

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Overall strategyThe aim of this project was to synthesize publicly available data on land use, pesticide use, and toxicity to generate a ‘toolkit’ of data resources enabling improved landscape-scale research on pesticide-pollinator interactions. The main outcomes are several novel datasets covering ten major crops or crop groups in each of the 48 contiguous U.S. states:

I)

Average application rate (kg/ha/yr) of >500 common pesticide active ingredients (1997–2017),

II)

Aggregate bee toxic load (honey bee lethal doses/ha/yr) of all insecticides combined (1997–2014), (Note that this dataset ends in 2014 because after that year, data on seed-applied pesticides were excluded29, and these contribute significantly to bee toxic load21)

III)

Reclass tables relating these pesticide-use indicators to land use/land cover classes to enable the creation of maps predicting annual pesticide loading at 30–56 m resolution.

An overview of the steps, inputs, and outcomes are provided in Fig. 1.Fig. 1Overview of the data synthesis workflow described in this paper.Full size imageData inputsA summary of input datasets is provided in Table 1.Table 1 Data inputs used in this study.Full size tablePesticide dataPesticide use data were last downloaded from the USGS National Pesticide Synthesis Project30,31 in June 2020. This dataset reports total kg applied of 508 common pesticide active ingredients by combinations of state, crop group, and year for the contiguous U.S. from 1992–2017 (crop groups explained in Table 2). The data are derived primarily from farmer surveys conducted by a private firm (Kynetec). For California, USGS obtains data from the state’s pesticide use reporting program32. USGS then aggregates and standardizes both data sources into a common national dataset that is released to the public and was used in this effort. The USGS dataset includes both a ‘high’ and a ‘low’ estimate of pesticide use, varying based on the treatment of missing values in the source data31. Because previous work on this dataset suggested that the ‘low’ estimate more closely matches independent pesticide estimates33, we used the ‘low’ estimate throughout, but assess the influence of this choice on the resulting estimates (see Technical Validation). While we focus on the ‘low’ estimate for the data and outputs presented in this manuscript, the workflow we developed can accommodate both the low and high estimates.Table 2 USGS crop categories in pesticide source data, based on metadata from USGS30,31 and personal communication with USGS staff scientists.Full size tableCrop area dataTo translate pesticide use estimates into average application rates, it was necessary to divide total kg of pesticide applied by the land area to which it was potentially applied. Crop area data were last downloaded from the Quick Stats Database of the USDA34 in May 2020, using data files downloaded from the ‘developer’ page. This USDA dataset contains crop acreage estimates generated from two sources: the Census of Agriculture (Census), which is comprehensive but conducted only once every five years35 and the crop survey conducted by the National Agricultural Statistics Service (NASS), which is an annual survey based on a representative sample of farmers in major production regions for a more limited subset of crops36.Honey bee toxicity dataTranslating insecticide application rates into estimates of bee toxic load (honey bee lethal doses/ha/yr) required toxicity values for each insecticide active ingredient in the USGS dataset. We used LD50 values for the honey bee (Apis mellifera) because this is the standard terrestrial insect species used in regulatory procedures, and so has the most comprehensive data available. This species is also of particular concern as an important provider of pollination services to agriculture. As previously reported21, the LD50 values were derived from two sources, the ECOTOX database37 of the U.S. Environmental Protection Agency (US-EPA), and the Pesticide Properties Database (PPDB)038. ECOTOX was queried in July 2017, by searching for all LD50 values for the honey bee (Apis mellifera) that were generated under laboratory conditions. Acute contact and oral LD50 values for the honey bee were recorded manually from the PPDB in June 2018.Land cover dataMapping pesticides to the landscape requires land use/land cover data indicating where crops are grown. We used the USDA Cropland Data Layer (CDL)39, a land cover dataset at 30–56 m resolution produced through remote sensing. This dataset is available starting in 2008 for states in the contiguous U.S., with some states (primarily in the Midwest and Mid-South) available back to the early 2000s.Data preparationRelating datasetsA major challenge in this data synthesis effort was relating the various data sources to each other, given that each dataset has unique nomenclature and organization. We created the following keys (summarized in Table 3) to facilitate joining datasets:

I)

USGS-USDA crop keys – Using documentation and metadata associated with the USGS pesticide dataset31,33,40, we created keys relating the USGS surveyed crop names (‘ePest’ crops) and the ten USGS crop categories to the large number of corresponding crop acreage data items in the Census and NASS datasets. For annual crops and hay crops we used ‘harvested acres,’ and for tree crops we used ‘acres bearing & non-bearing.’ These choices were made to maximize data availability and to correspond as closely as possible to the crop acreage from which the pesticide data were derived31. A separate key was developed for California because California pesticide data derives from different source data and covers a larger range of crops.

II)

USGS-CASRN compound key – Using USGS documentation as well as background information on pesticide active ingredients38,41, we generated keys relating USGS active ingredient names to chemical abstracts service (CAS) registry numbers to facilitate matching compounds to the ECOTOX and PPDB databases.

III)

USGS compound-category key – In this key we classified active ingredients into major groups (insecticides, fungicides, nematicides, etc.) and into mode-of-action classes on the basis of information from pesticide databases and resistance action committees38,41,42,43,44.

IV)

USGS-USDA compound key – To facilitate our data validation effort, we generated a key relating USGS compound names to USDA compound names, on the basis of information from several pesticide databases38,41.

V)

USGS-CDL land use-land cover keys – Using documentation from the USGS pesticide dataset describing the crop composition of each of the ten crop categories31, we created a key that matches these categories to land cover classes in the CDL. A separate key was developed for California given the differences in surveyed crops in this state, noted above.

Table 3 Keys generated to relate datasets.Full size tableProcessing crop area dataBecause of differences in the crops included in pesticide use estimates, crop acreage data were processed separately for California and for all other states, and then re-joined, as follows: Acreage data were first filtered to include only data at the state level, reporting total annual acreage for states in the contiguous U.S. after 1996. Acreage data were joined to the appropriate USGS-USDA crop key and only those crops represented in the pesticide dataset were retained. We then generated an acreage dataset with single rows for each combination of crop, state, and year using data from the Census when available (1997, 2002, 2007, 2012, 2017), data from NASS in non-Census years, and temporal interpolation to fill in remaining missing values (i.e. linear interpolation between values in the same state and crop in the nearest surrounding years). This process was repeated for California, using acreage data for only that state in combination with the CA crop key. Finally, acreage data in the two datasets were recombined, converted to hectares, and summed by USGS crop group.Processing honey bee toxicity dataProcessing for the honey bee toxicity data has been described in detail elsewhere21. Briefly, toxicity values were categorized as contact, oral, or other and standardized where possible into µg/bee. Records were retained if they represented acute exposure (4 days or less) for adult bees representing contact or oral LD50 values in µg/bee. To generate a consensus list of contact and oral LD50 values for all insecticides reported in the USGS dataset, we gave preference to point estimates and estimates generated through U.S. or E.U. regulatory procedures, taking a geometric mean if multiple such estimates were available. Unbounded estimates (“greater than” or “less than” some value) were only used when point estimates were unavailable, using the minimum (for “less than”) or the maximum (for “greater than”). If values for a compound were unavailable in both datasets, we used the median toxicity value for the insecticide mode-of-action group. And finally, in rare cases (n = 1/148 compounds for contact toxicity and 8/148 compounds for oral toxicity) we were still left without a toxicity estimate for a particular insecticide. In those cases, we used the median value for all insecticides.Data synthesisCompound-specific application rates for state-crop-year combinationsUSGS data on pesticide application were joined to data on crop area. Average pesticide application rates were calculated by dividing kg applied by crop area (ha) for each combination of compound, crop group, state, and year.Aggregate insecticide application rates for state-crop-year combinationsThe dataset from the previous step was filtered to include only insecticides, and then joined to LD50 data by compound name. Bee toxic load associated with each insecticide active ingredient was calculated by dividing the application rate by the contact or oral LD50 value (µg/bee) to generate a number of lethal doses applied per unit area. These values were then summed across compounds to generate estimates of kg and bee toxic load per ha for combinations of crop group, state, and year.Missing values were estimated using temporal interpolation, where possible (i.e. linear interpolation between values in the same state and crop group in the nearest surrounding years). This dataset ends in 2014 because after that year seed-applied pesticides were excluded from the source data29, and they constitute a major contribution to bee toxic load21.We focused bee toxic load on insecticides for three reasons. First, quality of LD50 data is highest for insecticides and uneven for fungicides and herbicides. Point estimates make up the majority of LD50 values for insecticides, whereas  100 µg/bee”, increasing the uncertainty of downstream estimates). Second, insecticides tend to have greater acute toxicity toward insects than fungicides and herbicides (median [IQR] LD50 = 100 [44–129] µg/bee for fungicides, 100 [75–112] µg/bee for herbicides, and 1.36 [0.16–12] µg/bee for insecticides). As a result, insecticides account for > 95% of bee toxic load nationally, even when herbicides and fungicides are included (and even though insecticides make up only 6.5% of pesticides applied on a weight basis). Third, focusing these values on insecticides increases their interpretability, reflecting efforts directed toward insect pest management, rather than a mix of insect, weed, and fungal pest management (which often have distinct dynamics and constraints for farmers).While we chose to include only insecticides in this aggregate value, users are welcome to adjust the workflow to include fungicides and herbicides if desired. To this end, we provide our best estimates for LD50 values for fungicides and herbicides in the USGS dataset (Table 4).Table 4 Data outputs generated by this study.Full size tableReclassification tablesTo generate reclassification tables for the CDL, the pesticide datasets described above were joined by crop group to CDL land use categories. The output of these processes was a set of reclassification tables for combinations of compound, state, and year. Also generated was a set of reclassification tables for aggregate insecticide use for combinations of state and year.Of the 131 land use categories in the CDL, 16 represent two crops grown sequentially in the same year (double crops, found on ~2% of U.S. cropland in 201245), which required a modified accounting in our workflow. Pesticide use practices on double crops are not well described, but one study suggested that pesticide expenditures on soybean grown after wheat were similar to pesticide expenditures in soybean grown alone46. Therefore, we assumed that pesticide use on double crops would be additive (e.g. for a wheat-soybean double crop, the annual pesticide use estimate was generated by summing pesticide use associated with wheat and soybean).Missing values in the reclassification tables resulted from several distinct issues. Some values were missing because a particular crop was not included in the underlying pesticide use survey (e.g. oats was not included in the Kynetec survey), or because the land use category was not a crop at all (e.g. deciduous forest). These two issues were indicated with values of ‘1’ in columns called ‘unsurveyed’ and ‘noncrop,’ respectively. For double crops, a value of 0.5 in the ‘unsurveyed’ column indicates that one of the crops was surveyed and the other was not. For compound-specific datasets, missing values may reflect that a given compound was not used in a state-crop group-year combination. For the aggregate insecticide dataset, even after interpolation there were some missing values, usually when a state had very little area of a particular crop or crop group.Finally, missing data for double crops were treated slightly differently in the aggregate vs. compound-specific reclassification tables. For the aggregate insecticide dataset, estimates for double crops were only included if estimates were available for both crops; otherwise the value was reported as missing. For the compound-specific datasets, estimates for double crops were included if there was an estimate for at least one of the crops, since specific compounds may be used in one crop but not another. More

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We confirm that Cuba is home to the world’s largest European honeybee population that has naturally become Varroa-resistant, with an estimated 220,000 colonies being maintained without any form of chemical treatment for over two decades19 although some drone-trapping occurred during the early years of the transition period This is despite the presence of the K-haplotype of the mite20 and the widespread occurrence of DWV19 throughout Cuba. Hence, the Cuban honeybee population is the first major case of Varroa-resistant European bees occupying an entire country of a large size (109,884 km2). In Europe the proportion of varroa-resistant honeybee populations in each country is highly variable21,22, but they still consist of small, isolated populations within any country. For example, the second largest known area of European Varroa-resistant honeybees is in North Wales, UK where 104 beekeepers have managed around 500 honey bee colonies over an area of 2500 km2 without treatment for over a decade23.It has long been established that sub-Sharan African and Africanised honeybees are Varroa-resistant and both populations cover much larger areas than Cuba, but these honeybee races are not capable of thriving in temperate regions or are rejected by beekeepers in Northern hemispheres. However, previous studies on African/Africanised and European honeybees4,5,6,9 all appear to have evolved with the same resistance mechanism7 and Cuban honeybees follow this pattern showing high recapping behaviour, high mite removal behaviour and low mite reproduction (Figs. 1, 4, Table 1).The strongest evidence that increased recapping behaviour is a direct response to the presence of Varroa, is the very low recapping rates in Varroa-naïve colonies. This is evidenced by the recapping baseline data that has now been collected from four different Varroa-naïve (Varroa free) honeybee populations (Australia, UK [two populations] and Hawaii [this study]) all producing similar results (Fig. 1). Across the four populations, a total of 9542 worker cells from 15 colonies have been studied with an average recapping rate of 2.0% (+ SD 3.2). Interestingly, only two of the colonies had atypical recapping rates of 8.5% and 10.7%, from Australia and Kauai respectively. This may suggest increased sensitivity in these colonies as no obvious causes e.g., wax moth or dead pupa, were detected in either colony. The data summary in Fig. 1 indicates that even in Varroa-treated populations the workers are still able to detect mite infested cells, but the average consistently falls significantly below that found in resistant populations. That is, in non-infested worker cells recapping rates are significantly higher in resistant populations in comparison to susceptible populations (Fig. 1) t4, 5 = − 4.185, p = 0.0023 as well as for infested cells t4, 5 = − 6.905, p = 0.00007.The ability of Cuban honeybees to detect infested cells causes not only high recapping levels but also high removal rates of artificially mite-infested cells. A mean removal rate of 81% is among one of the highest recorded in Apis mellifera7. The average control rate of 45% is driven by three colonies that all removed more than 75% of the controls, while the average of the remaining seven colonies was 28%. During the mite-removal studies in March 2022 natural Varroa infestation was 23%, whereas in December 2021 it was only 13%. This is due to decreasing worker brood rearing, caused by a shortage of nectar during the annual dry season. During this time there is an increase in hygienic behaviour in the colonies24, which could help explain the higher-than-expected removal of control cells.The reproductive ability of Varroa to produce viable i.e., mated, female offspring (r) in infested worker cells in resistant colonies in South Africa4 (r = 0.9), Brazil4 (r = 0.8), Mexico18 (r = 0.73), Europe3 (r = 0.84) is similar to the 0.87 found in Cuba (this study). In Cuba ‘r’ reduces to 0.77 when both single and multiple infested cells are considered. This reduction in mite reproduction, relative to susceptible colonies that have values of r greater than one, is directly linked to the increased ability of resistant workers to both detect and remove, by cannibalisation, the infested pupa. Hence, this ensures the invading mite fails to reproduce7 or reduces mite fertility due to the recapping process4. Although, in this study no significant difference was found in the reproduction of Varroa in recapped or non-recapped cells, supporting the findings of two previous studies5,9. Therefore, recapping may be playing a minor role in resistance. However, recapping remains the best indicator or ‘proxy’ of resistance within the vast majority of honeybee populations since it’s easier, quicker, and it requires less skill to measure recapping rates than mite removal rates. However, recapping is a highly variable trait7, hence both many cells (200–300) per colony and many colonies ( > 10) per population ideally need to be studied to help reduce the variablity, also in temperate countries measuring recapping when mite-infestation rates peak in autumn maximises detecting infested cells since the recapping of cells is spatially associated with infested cells11.Despite the current focus on what is happening in worker cells, studies focusing on the role of recapping in drone brood are still in their infancy with. Currently, data is only available from South Africa9 (Fig. 1) and now Cuba (this study). Interestingly, both studies indicate no significant difference in recapping rates between infested and non-infested brood. This is caused by some colonies performing no recapping of drone brood, while some colonies do recap cells but in a non-targeted manner. Whereas there is a significant increase in the size of the recapped area between infested (3.1 mm) and non-infested (2.3 mm) worker cells (Fig. 3), this does not occur in drone brood, as it appears that the holes are entirely exploratory. However, the lack of removal of infested drone brood may be playing an important role in mite-resistance (see below).The mite infestation of worker cells currently varies between 23 and 13% in Cuba (this study), roughly 25 years after it was first detected (1996). Whereas, in Mexico and Brazil, infestation rates of worker brood have fallen from around 20% in 1996/1999 down to 4% in 2018/197. Although, Varroa was first detected in Brazil much earlier, in 197225 and the Africanised honeybees adapted to the mite and spread northward replacing the susceptible European colonies. Therefore, we predict that the worker infestation rate in Cuba will continue to fall over the next 20 years, especially if high mite-removal rates persist. Correspondingly, we would expect to see the infestation rates of the drone brood (currently at 40%) to remain high as mites potentially avoid reproduction in worker cells. This potentially is a key, but currently overlooked part, of the resistance mechanism. Since an empirical model26 indicated that negative mite population growth occurs in (resistant) Africanised honeybee colonies only when the initial drone cells are present. This is thought to arise because mites also show a tenfold preference to reproduce in drone cells (which comprises only 1–5% of all the honeybee brood) and they soon become overcrowded as the mite population increases. This leads to inter-mite competition for the limited food and space, causing an increase in mite mortality27, resulting in negative reproductive success for mites entering these overcrowded drone cells. Thus, mite population growth in drone brood cells is limited by a density-dependent mechanism. In Cuba it has been observed that strong colonies typically with drone brood do not weaken during the drought season, whereas colonies without drone brood are weak and often die during the drought (APP personal comm).Although Cuban beekeepers have been aware of their mite-resistant honeybees for 15 to 20 years’, Cuba’s situation has only recently come to light16,18. The main reason for Varroa-resistance in Cuba is due to the centralised decision to allow natural resistance to evolve, as also was done successfully in South Africa3, rather than becoming locked into using miticides, as has happened throughout the Northern hemisphere. The CIAPI and Veterinarian Services central decision to ‘not treat’ was greatly assisted by all Cuban beekeepers being professional, registered and embedded within a strong locally based beekeeping community where colony movement and exchange of queens is within each province.There is also a large feral population and due to Cuba’s sub-tropical climate, queens are replaced annually in managed colonies because of almost continuous egg-laying, similar to honeybees in Hawaii. This rapid queen turnover speeds up natural selection relative to honeybee populations in more temperate climates. Finally, Cuba’s 60-year ban on honeybee importation has helped isolate the country from been invaded by Africanised bees which has occurred in many nearby regions (eg. Mexico, Southern USA, Puerto Rico, neighbouring Dominican Republic13 and Haiti (D. Macdonald, Apiary Inspector, Min. of Agi BC, Canada, pers. Comm.). Cuba has many managed European colonies coupled with many queen rearing stations. These colonies are productive and mild mannered. Thus, Cuba is an excellent example of the power of natural selection in honeybees when they are allowed to adapt naturally to Varroa with minimal human interference. More

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