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1 doi: /nature16532 Contents Supplementary Methods Supplementary Method 1. Modelling the flowering phenologies Supplementary Method 2. Modelling the annual nectar productivity of the unsurveyed species Supplementary Method 3. Estimating habitat land covers in the 1930s Supplementary Method 4. Computing indexes of habitat nectar diversity Supplementary Results Supplementary Result 1. Flower-visiting insects of the main nectar provider plants Supplementary Result 2. Functional nectar diversity historical trends Supplementary Result 3. Sensitivity analyses with alternative datasets Supplementary Result 3.1. Sensitivity analyses with alternative management practices in improved grasslands (grazed white flower, Trifolium repens) Supplementary Result 3.2. Sensitivity analyses with the alternative nectar rectangular productivity database Supplementary Result 3.3. Sensitivity analyses with only empirical nectar productivity database Supplementary Result 4. Validation of the datasets Supplementary Result Correlation between the two surveyed sites for nectar, flower density and peak flower density values Supplementary Result 4.2. Relationships between phenology parameters used and those derived from Insect Pollinators Initiative (IPI) AgriLand floral transects Supplementary Result 4.3. Relationships between empirical and published values for nectar at flower and vegetative scales Supplementary Result 4.4. Relationships between flower number and vegetative cover 1

2 Supplementary Discussion Supplementary Discussion 1. Potential drivers and trends of changes in nectar productivity per unit area from 1930s to 1970s Supplementary Discussion 2. Comparing early losses to recent increases in floral resources Supplementary Tables Supplementary Table 1. Habitat nectar productivity, diversity and national provision in Supplementary Table 2. Historical changes in national nectar provision considering only shifts in land covers among habitats in England & Wales ( ) and in Great Britain ( ). Supplementary Table 3. Historical changes in national nectar provision considering both shifts in land covers among habitats and shifts in nectar productivity within habitats in England & Wales ( ) and in Great Britain ( ). Supplementary Table 4. Drivers of changes in nectar over time. Supplementary Table 5. Summary of land cover values of habitats (Great Britain and England & Wales). Supplementary Table 6. Effect of plant traits on the log10 (x + 1) transformed empirical annual nectar sugar productivity. Supplementary Table 7. Summary of the nectar dataset scaling-up from the flower to the vegetative scales. Supplementary Table 8. Habitat description. Supplementary Table 9. Summary of countryside vegetation datasets used for the study. Supplementary Table 10. Contribution of species to the nectar provision shifts over recent decades (1978 to 2007). 2

3 Supplementary Methods Supplementary Method 1. Modelling the flowering phenologies 1) Estimating flower density at the peak of the flowering season of the surveyed species Flower density ( actual flower density ) is a snapshot measure of the number of flowers per square meter which has been surveyed at a specific date ( actual date of the survey ). To avoid underestimations of flower density of the species for which the date of the survey is delayed from the peak of the flowering season, we estimated the flower density ( peak flower density ) expected at the peak of the flowering season ( peak date ) for each species. To do this we assumed that flower density varies according to a triangular function across the flowering season (see Supplementary Figure 1). We estimated the flower density expected at the peak of the flowering season ( peak flower density ) as follows: If the actual date of the survey was before the peak of the flowering season (days lag 0); then peak flower density = peak date start date actual date start date x actual flower density; If the actual date of the survey was after the peak of the flowering season (days lag > 0); then peak flower density = end date peak date end date actual date x actual flower density; With: Start date = date of the start of the flowering season (first day of the first flowering month recorded from the EcoFlora database 1 ) End date = date of the end the flowering season (last day of the last flowering month recorded from the EcoFlora database 1 ) Peak date = date corresponding to the middle of the flowering season or when possible (42 species), date of the peak of the flowering season recorded from the EcoFlora database 1 Actual date of the survey = date of the survey of flower density Days lag = difference between the actual date and the peak date 3

4 Actual flower density = flower density counted at the actual date of the survey (number of open flowers per m²) Peak flower density = flower density estimated at the peak of the flowering season (number of open flowers per m²) When the actual flower density was surveyed outside from the theoretical flowering season (30 occasions) or when the ratio between the peak flower density and the actual flower density was >10 (5 occasions), we did not rely on these flower peak flower density estimated values for further calculations (the values for 13 species out of the initial list of 175 were discounted due to this type of mismatch with published phenology). 2) Estimating annual flower density and nectar productivity of the surveyed species Knowing the estimated flower density at the peak of the flowering period ( Peak flower density ), we were able to calculate the annual flower density as the triangular area of flower density across the flowering season: Annual flower density = With: Peak flower density flowering period length 2 Peak flower density = flower density estimated at the peak of the flowering season (number of open flowers per m²) Flowering period length = start date end date of the flowering season (number of flowering days in a year) Annual flower density = flower density estimated over the whole year (number of open flowers per m² over the year) Then, the annual nectar productivity (kg of sugars/ha cover/year) was calculated by multiplying the nectar productivity per flower (µg of sugars/flower/24h) and the annual flower density (number of open flowers per m² over the year). We were able to provide empirical nectar productivity values for 162 amongst the 175 species initially surveyed in the field; and we assigned a default zero value of one of them (Brassica rapa harvested before flowering) leading to a list of 161 species with empirical nectar values (Supplementary Table 11). 3) Estimating monthly nectar productivity of the surveyed and unsurveyed species To estimate nectar productivity of each month, we first calculated nectar productivity at the start ( start month nectar productivity ) and at the end of each month ( end month nectar productivity ) from nectar productivity estimated at the peak of the flowering season as follows: 4

5 If the start or the end of the considered month was before the peak of the flowering season (days lag 0); then Start month nectar productivity = End month nectar productivity = start month date start date peak date start date end month date start date peak date start date x peak nectar productivity x peak nectar productivity If the start or the end of the considered month was after the peak of the flowering season (days lag > 0); then Start month nectar productivity = end date start month date end date peak date x peak nectar productivity End month nectar productivity = end date end month date end date peak date x peak nectar productivity With: Start date = date of the start of the flowering season (first day of the first flowering month recorded from the EcoFlora database 1 ) End date= date of the end the flowering season (last day of the last flowering month recorded from the EcoFlora database 1 ) Peak date= date corresponding to the middle of the flowering season or when possible (42 species), date of the peak of the flowering season recorded from the EcoFlora database 1 Start month date = date of the start of the considered month (first day of each month) End month date = date of the end of the considered month (last day of each month) Days lag = difference between the considered month date (start or end) and the peak date Peak nectar productivity = nectar productivity estimated at the peak of the flowering season (kg of sugars/ha/24h) Start month nectar productivity = nectar productivity estimated at the start month date (kg of sugars/ha/24h) End month nectar productivity = nectar productivity estimated at the end month date (kg of sugars/ha/24h) 5

6 We calculated the nectar productivity of each month ( monthly nectar productivity ) according to the trapezium area representing the sum of daily nectar productivities of the considered month (see Supplementary Figure 1) as follows: Monthly nectar productivity start month nectar productivity + end month netcar productivity = x number of days in the month 2 With: Start month nectar productivity = nectar productivity estimated at the start month date (kg of sugars/ha/24h) End month nectar productivity = nectar productivity estimated at the end month date (kg of sugars/ha/24h) When the peak of the flowering season falls on the middle of the considered month, we added to the monthly nectar productivity computed from the trapezium area, the crest of the triangle. Supplementary Figure 1. Schematic representation of flowering phenology modelling assuming a triangular function of flower density across the flowering season. a, Schematic representation of the 6

7 estimation of flower density at the peak of the flowering season. b, Schematic representation of estimation of nectar productivity for one month of the flowering season. The dashed line indicates the peak date of the flowering season (middle of the flowering period when peak date was not recorded in the EcoFlora database 1 ) and the black trapezium indicates an example of monthly nectar productivity estimated from the sum of daily nectar productivities of the month of interest. Supplementary Method 2. Modelling the annual nectar productivity of the unsurveyed species The modelling of nectar productivity for the unsurveyed species relies on values predicted from their plant traits. The plant traits were mainly recorded from the BiolFlor database 2. Flower shape is derived from Müller flower classification system which has been condensed to five classes: 1) pollen rewarding flowers (where pollen is the main reward, or flowers are occasionally visited by insects); 2) open (for flowers with open nectaries, plus fly pollinated flowers); 3) partly-hidden (partly hidden nectaries); 4) hidden (completely hidden nectaries), and 5) bee flowers (for bee, bumblebee, and hymenoptera pollinated flowers). When the flower shape was not documented, classification from closely related species from the same genus was used. Breeding system is classified into five classes: allogamous, facultative allogamous, mixed, facultative autogamous and autogamous mating systems. When the breeding system was not documented, it was completed using information found in the literature. Life span is classified into five modalities: annual, annual/biennial, biennial, biennial/pluriennial and pluriennial. When the life span was not documented, it was completed by information found on a variety of botanical internet databases (mainly EcoFlora database 1 ). The degree of dicliny is classified in three modalities: hermaphroditic, monoecious (for monoecious, gynomonoecious, andromonoecious and trimonoecious) and dioecious (for dioecious and gynodioecious species). The maximum height is condensed into three classes: small (max height <1 m), medium (1 m max height <10 m) and tall (max height 10 m). When the maximum height was not documented, information from a variety of Flora was used. The flowering period is the number of days between the start and the end of the flowering period (see Supplementary Method 1). Family comprises 26 classes: family or higher clade origin. When a family was represented by three or fewer members, family was replaced by a higher clade origin (monocot, core eudicot, rosids and asterids). In total, we managed to gather a complete combination of plant traits for 153 species amongst the 161 with empirical annual nectar productivity. We analysed the annual nectar sugar productivity of the surveyed species (log transformed (x+1) of the empirical nectar productivity) according to the seven traits listed above (flower shape, breeding system, life span, dicliny, height, flowering period and family) with a linear model in R The main effects were tested, but not the interactions as there was a lack of statistical power for some combinations of traits in specific families. The normality and homoscedasticity of residuals were plotted and visually checked (all assumptions are satisfied with the exception of variance which is low in some families due to the small number of representatives). The most parsimonious model was selected on the basis of AIC criterion (stepaic from MASS package in R 3 ). The estimates from this statistical model (Supplementary Table 6, N=153; Adjusted 7

8 r²=0.55) were used to predict the annual nectar sugar productivity for the initial list of species on the basis of their traits. In order to check the validity of the predicted values, we compared these modelled values with the empirical ones for the surveyed species. We performed a standardized major axis regression on the log10 (x+1) empirical nectar values and log10 (x+1) modelled nectar values of the surveyed species. Major axis regression is designed to assess relationships between continuous variables both of which are measured with error and it is standardized when variances of the two datasets are unequal. Because this method involves the use of empirical values both as the start for the modeling approach and for the validity check, we implemented a leave-one-out method. Here we successively removed each species from the empirical dataset and re-ran the model to predict the nectar productivity for the excluded species. We repeated this step to get modeled values for 149 surveyed species (153 species minus 4 species for which the annual nectar productivity can t be modelled due to their being only a single member in a family after removal). We performed a standardized major axis regression (sma function from smatr package in R 3 ) on the log10 (x+1) empirical nectar values and log10 (x+1) modelled nectar values of the surveyed species (N=149, r²=0.44, slope= 0.83 [95% CI=0.74; 0.94], intercept=0.26 [95% CI=0.07; 0.46], p<.0001; see Extended Data Figure 6). The slope being close to 1, this indicates that the relationship between the two datasets is almost linear. In total, we statistically modelled the values of annual nectar productivity for 252 species on the basis of their plant traits (see Supplementary Table 11). Negative modelled values (between -1 and 0; e.g. Papaver dubium) were considered as equivalent to 0. Ilex aquifolium and Populus tremula showed surprising high modelled values due to their belonging to families with highly rewarding members (Aquifoliaceae from the Asterids clade and Salicaceae), consequently we did not include these two values in the final nectar productivity database. Thus we thus used 94 modelled values as 156 species amongst the 252 were already characterized either by empirical or zero-default nectar values and two modelled species were not included in the analysis (see Supplementary Table 11). Supplementary Method 3. Estimating habitat land covers in the 1930s Land cover values of habitats in the 1930s come from the Dudley Stamp land utilization survey maps, the ground surveys of which were carried out between 1925 and These have now been digitized and are available through the UK Environment Agency. More up-to-date land cover information is available from the Centre for Ecology and Hydrology s UK Land Cover Map for , which was derived from the semi-automated classification of satellite images. Meadow/Grass, Forest/Woodland, and Arable land classes were refined in order to match with the countryside surveys habitats described from 1978 to The Land class meadow/grass land class was refined as acid grassland, neutral grassland and calcareous grassland on the basis of the fraction of grasslands found on these soil types (acid 41.96%, neutral 32.79%, calcareous 24.99% and undefined soils 0.25%). The estimations of the area of improved grasslands in the 1930s vary between 0 4 to 600 Mha 6. We used the value of 0 ha of improved grasslands in the 1930s, so the contribution of this habitat is not considered for further nectar national estimations in order to be conservative (i.e. potential underestimation of nectar provision in the 1930s). The Forest/Woodland class was refined 8

9 as broadleaf woodland and conifer woodland on the basis of the fraction of forest types estimated by the Forestry Commission in (68.48% of broadleaved woodlands versus 31.52% of conifer woodlands). The broadleaf woodland category comprises standing and felled broadleaved forests, mixed forests and scrubs in England and Wales in The conifer woodland category comprises standing and felled conifer forests in England and Wales in The arable land category comprised the area of arable and orchards. The areas of Bog, Bracken, Fen were not available for 1930 s, so the contribution of these habitats are not considered for further nectar national estimations. Again, this could lead to a potential underestimation of nectar provision in 1930 s, so it is conservative. Because the Dudley Stamp land utilization data are not available for the whole Great Britain (only England and Wales), we compared them to habitat land cover values in 1978, 1990, 1998 and 2007 in England and Wales and estimates of nectar productivity derived from plots in England and Wales for further national nectar estimations. As habitat nectar productivity in the 1930s can t be assessed, we are not able to investigate the effects of potential changes in nectar productivity between 1930s and Nevertheless, in view of the potential drivers involved in post- 1930s, we can expect a decrease in nectar productivity per unit area within several of the commonest habitats (see Supplementary Discussion 1), which would be again conservative in regards to 1930s trends observed considering only changes in land cover. Supplementary Method 4. Computing indexes of habitat nectar diversity The diversity of nectar sources in habitats was estimated via two indexes: the species nectar diversity (which considers proportional nectar produced per each species) and the functional nectar diversity (which considers proportional nectar produced per each floral morphology group). These two indexes are derived from the Shannon index based on the total nectar output of each entity (species or floral morphology group), which is itself the product of the abundance of the entity and the per capita nectar production. We use the proportional nectar production in place of the proportional abundance figure that would usually be used when applying the Shannon index in a conventional species diversity study. The Shannon diversity indexes encompass both richness and evenness in term of nectar; sites with both a large number of nectar sources and a more even spread of nectar amongst those sources will get higher scores. Species nectar diversity (Shannon index based on the nectar contribution of each species) is calculated as follows: H S sp = i=1 p i ln(p i ) where p i is the proportional nectar contribution of plant species i and S is the total number of plant species in each plot. Functional nectar diversity (Shannon index based on the nectar contribution of each floral morphology group) is calculated as follows: 9

10 H S fun = i=1 p i ln(p i ) where p i is the proportional nectar contribution of flower type i and S is the total number of flower types in each plot. Flower types are derived from Müller flower classification system, mainly recorded from the BiolFlor database 2, which has been condensed to five classes: 1) pollen rewarding flowers (where pollen is the main reward, or flowers are occasionally visited by insects); 2) open (for flowers with open nectaries, plus fly pollinated flowers); 3) partly-hidden (partly hidden nectaries); 4) hidden (completely hidden nectaries) and 5) bee flowers (for bee, bumblebee, and hymenoptera pollinated flowers). When the flower shape was not documented, data from closely related species from the same genus (or the commonest flower shape type of the family) was used. Given that this measure of diversity incorporates floral morphology of nectar sources, it reflects the diversity of nectar sources in terms of resource accessibility for flower-visiting insects. Supplementary Results Supplementary Result 1. Flower-visiting insects of the main nectar provider plants In order to investigate the insect species supported by the main nectar provider plants (Trifolium repens, Calluna vulgaris, Cirsium palustre and Erica cinerea), we combined published and unpublished plant-pollinator interaction data from Memmott s group and a review of literature (33 published and unpublished sources in total, see Supplementary Table 12). Insects from Bombus lucorum and Bombus terrestris were combined in a single class Bombus lucorum/terrestris to homogenize the level of identification among sources. We observed consistent results with regards to the flower morphology we measured on the field (depth and width of flower tubes for flowers per species; see Extended Data Table 2). Given the important number of references collected for Trifolium repens, the number of species visiting this species is low (54 species from 21 published and unpublished sources) including 13 species of Bombus. Flower morphology of Trifolium repens flowers (high depth and low width of flower tubes) is likely to restrict the number of species. Calluna vulgaris flowers are visited by a much larger range of insect species (139 species in total from 9 sources) belonging to Diptera, Hymenoptera and Lepidoptera. Flowers from this species (low depth and high width) are accessible to many insect species. We gathered less data for Cirsium palustre and Erica cinerea but again the morphology seems determinant in the number and the identity of visiting insects (Erica flowers are accessible to a larger range of insect species compared to Cirsium flowers). These results suggest that Trifolium repens, a dominant flowering species in improved grassland that contributes 30% of the national nectar supply, does not support a high diversity of insects due to the morphology of flowers. The gain of nectar from this species may not benefit to a large diversity of pollinators, but it is important to long-tongues bees. 10

11 Supplementary Result 2. Functional nectar diversity historical trends We repeated the statistical analyses performed on the species nectar diversity (Shannon index based on nectar contribution of species), this time considering the functional nectar diversity index (Shannon index based on the nectar contribution of floral morphology groups). From the most recent Countryside survey (2007), we found significant differences among habitats in term of their functional nectar diversity index (DF=10, F=24.15, P<.0001). Arable land is the habitat with the lowest functional nectar diversity index (it is the also habitat with the lowest species nectar diversity) whereas broadleaved woodland (followed by neutral grassland and calcareous grassland) is the habitat with the highest functional nectar diversity index (Figure 1 and Supplementary Table 1). This suggests that the diversity of flower shapes of nectar producing plants in these habitats might provide nectar resources that would be accessible for a wide range of pollinator species. From the temporal series of Countryside Surveys (1978 to 2007), we investigated the historical changes in functional nectar diversity within habitats (significant interaction between Year and Habitat: DF=30, F=1.72, p=0.009). Significant overall decreases in functional nectar diversity were detected in arable land and improved grassland from 1978 to 2007 (Extended Data Figure 2). However, significant decreases of species nectar diversity in conifer and broadleaved woodlands presented in the main paper were no longer detected, but the same decreasing trend remained. Neutral grassland showed gradual increase in functional nectar diversity (significant increase of functional nectar diversity from 1990 to 2007). Arable land showed similar temporal trends to those presented in the main paper for species nectar diversity: functional nectar diversity decreased from 1978 to 1998 and rebounded from 1998 to 2007 (Extended Data Figure 2). Supplementary Result 3. Sensitivity analyses with alternative datasets Supplementary Result 3.1. Sensitivity analyses with alternative management practices in improved grasslands (grazed white flower, Trifolium repens) As noted in the text, floral resources in improved grasslands may be sensitive to management, as their most important floral resource species, the white clover Trifolium repens, may not have much opportunity to flower in heavily grazed fields. In order to test for potential effects of changes in management practices on nectar resource provision of improved grasslands, we repeated the analyses of nectar productivity and diversity after removing the contribution of Trifolium repens. Considering the most recent Countryside Survey (2007), habitats showed significant differences in term of nectar productivity (DF=10, F=70.62, p<.0001) and species nectar diversity (DF=10, F=18.84, p<.0001). As in the main results presented, calcareous grassland remains the best habitat in terms of both nectar productivity per unit area and diversity of nectar sources, and arable remains the poorest habitat. However, nectar productivity per unit area of improved grassland is much lower after removing the contribution of Trifolium repens (13.92 kg of sugars/ha cover/year without Trifolium repens versus kg of sugars/ha cover/year with Trifolium repens). Species nectar diversity of improved grassland after removing the contribution of Trifolium repens is similar to the value of 11

12 species nectar diversity obtained with Trifolium repens included (0.79 for the Shannon index of nectar species without Trifolium repens versus 0.73 with Trifolium repens). Historical shifts in nectar productivity and diversity from 1978 to 2007 still vary depending on the habitat type and time period considered (interaction between habitat and year: DF=30, F=1.87, P=0.003 for nectar productivity and DF=30, F=2.96, P<.0001 for species nectar diversity) and the same patterns across time are observed (Extended Data Figure 4). However, the decrease in species nectar diversity of improved grassland from 1978 to 2007 becomes non-significant after removing the contribution of Trifolium repens. After taking into account the national land cover of habitats, the historical trends in national nectar supply remain close to those presented in the main text. Nationally, we obtain respectively 501, 472, 498 and 601 in terms of millions of kg of sugars/year in 1978, 1990, 1998 and 2007 after the discounting of grazed Trifolium repens. This corresponds to -4.93% changes from 1978 to 1990, +5.93% from 1990 to 1998, % from 1998 to 2007 in terms of the average national nectar productivity. It is worth noting that the increase in nectar provision from 1998 to 2007 remains but it is less and a slight decrease in nectar provision from 1978 to 1990 is now detected. This suggests that management practices in improved grassland that would give the give the opportunity for Trifolium repens to flower could compensate, at least quantitatively, the national decline of nectar resources from the other plant species. Supplementary Result 3.2. Sensitivity analyses with the alternative nectar rectangular productivity database To test whether our assumption of a triangular phenology overly influenced our results, we repeated the analyses assuming a rectangular phenology curve. We built an alternative nectar database with flower density kept constant across the flowering season (ie. rectangular phenology). We then combined it with the Countryside Survey vegetation data and compared the results with those presented in the main paper obtained from a triangular nectar database. Similar to the results presented in the main paper, annual nectar productivity (DF=10, F=61.878, p<.0001) and species nectar diversity (DF=10, F=26.171, p<.0001) significantly differed according to habitats when using the alternative nectar rectangular database with the most recent countryside survey (2007). We observed similar trends with both nectar databases: calcareous grassland remains the best habitat in term of nectar productivity and species nectar diversity whereas arable land is the poorest in both respects. The two databases give the same overall result with respect to the historical shifts in annual nectar productivity and diversity in habitats per time period (significant interaction between Year and Habitat considering the rectangular database: DF=30, F=2.30, p= for nectar productivity and DF=30, F=2.66, p<.0001 for species nectar diversity). Historical shifts in species nectar diversity after using the alternative nectar rectangular database were close to those presented in the main text. The exception is the shift in broadleaf woodland from 1978 to 2007 which became not significant and shifts in acid grassland from 1978 to 2007 that became significant after considering the rectangular database; other habitats showed similar historical trends that those presented in the main text (Extended Data Figure 4). 12

13 After taking into account the national land cover of habitats, the historical trends in national nectar supply remain close to those presented in the main text when using a rectangular function. Nationally, we obtain respectively 651, 636, 666 and 797 in terms of millions of kg of sugars/year in 1978, 1990, 1998 and 2007 with the alternative nectar rectangular database. This corresponds to -1.45% change from 1978 to 1990, +5.34% from 1990 to 1998, % from 1998 to 2007 in terms of the average national nectar productivity. Supplementary Result 3.3. Sensitivity analyses with only empirical nectar productivity database To test whether the use of modelled nectar values for the unsurveyed species was problematic, we repeated the analyses of nectar productivity and diversity considering only the contribution of species with empirical nectar values. We combined the empirical nectar values of 161 species (instead of 260 species with empirical and modelled values in the main text) with the Countryside Survey vegetation data and compared the results with those presented in the main paper. Similar to the results presented in the main paper, annual nectar productivity (DF=10, F=75.06, p<.0001) and species nectar diversity (DF=10, F=22.38, p<.0001) significantly differed according to habitats when using only nectar empirical values with the most recent Countryside Survey (2007). We observed similar trends with both nectar databases: calcareous grassland remains the best habitat in term of nectar productivity and species nectar diversity whereas arable land remains the poorest. The two databases gave the same overall result with respect to the historical shifts in annual nectar productivity and diversity in habitats per time period (significant interaction between Year and Habitat considering empirical values only: DF=30, F=1.57, p=0.02 for nectar productivity and DF=30, F=2.52, p<.0001 for species nectar diversity). Historical shifts in nectar productivity and diversity using only empirical nectar values were analogous to those presented in the main text (Extended Data Figure 4). Habitat mean nectar productivities computed when considering only empirical values were systematically slightly below those obtained in the main text considering both empirical and modelled values. However they are very close to those obtained in the main text considering both empirical and modelled values because we collected empirical data for the majority of the commonest species. After taking into account the national land cover of habitats, the historical trends in national nectar supply remain close to those presented in the main text. Nationally, we obtain respectively 595, 593, 595 and 737 in terms of millions of kg of sugars/year in 1978, 1990, 1998 and 2007 considering only the empirical nectar productivity values. This corresponds to +0.54% change from 1978 to 1990, +0.89% from 1990 to 1998, % from 1998 to 2007 in terms of the average national nectar productivity. 13

14 Supplementary Result 4. Validation of the datasets Supplementary Result Correlation between the two surveyed sites for nectar, flower density and peak flower density values For the species surveyed in at least two distinct sites (112 species for nectar and 114 for flower density), we compared the values obtained in these two locations in order to check the validity of the measured variables. We performed a major axis linear regression model on the values of log10 (x+1) transformed nectar sugar content, flower density and estimated peak flower density from the two surveyed sites (ma function from smatr package in R 3 ). Major axis regression is designed to assess relationships between continuous variables both of which are measured with error. Even with the relatively low sampling intensity at each location (median= 10 flowers sampled for nectar; 5 quadrats for flower density), we found a strong correlation between the two locations for the log10 (x+1) transformed nectar sugar content (slope = 1.07 [0.99;1.16]; intercept=-0.17 [-0.32;-0.01]; r²=0.85, p<.0001, Extended Data Figure 6), the flower density (slope = 1.02 [0.92; 1.12]; intercept=-0.11 [- 0.43; 0.22]; r²=0.78, p<.0001, Extended Data Figure 6) and the flower density estimated at the peak of the flowering season (slope = 1.00 [0.90; 1.12]; intercept=-0.17 [-0.57; 0.22]; r²=0.77, p<.0001, Extended Data Figure 6). The three slopes being not different to 1; this indicates that the relationship between the two locations is linear for the three parameters. Supplementary Result 4.2. Relationships between phenology parameters used and those derived from Insect Pollinators Initiative (IPI) AgriLand floral transects In order to check the robustness of phenology parameters used to scale up nectar resources temporally (from day to year), we compared our phenology parameters with alternative ones. The IPI AgriLand national field survey was designed to study the main factors involved in pollinator decline (96 sites in Great Britain surveyed in 2012 and 2013). On each site, flower counts of insect-pollinated species in flower were made to estimate floral resources occurring along transects. A total of m 2 was surveyed per site on three occasions during each survey season (April-May, June-July and August- September). 1000m 2 of this area was proportionately stratified per sub-broad habitat areas of the site. The remainder was assigned to linear features of the site (roadside verges, water feature edges, hedgerows and stone walls and fencelines), with 2 x 20m 2 of transect allocated to each linear feature type present. Transects were selected at random and split into manageable sub-sections when they were 60m or longer. To sample flower abundance, all floral units of each animal-pollinated species were counted in a 0.5 x 1m quadrat placed at 10m intervals along each transect (a floral unit being operationally defined as the cluster of flowers over which a honeybee could walk, rather than fly, to reach all nectaries, e.g. capitulum, sub-umbel etc.). Vegetative cover of each species was also recorded. The number of flowers per floral unit was estimated separately by counting the flowers on three randomly selected floral units outside the transect areas on each survey date. We used these data (unpublished data) to indirectly estimate some phenology parameters (length of flowering period and date of the peak of flowering) assuming that species seen in a transect in one survey but not in another, were present but not in flower in the second survey. The flowering period length corresponds to the 14

15 number of days between the first and the last date a species have been seen in flower in any site. The peak flowering date (converted as a numerical variable) corresponds to breakpoint estimates from piecewise regressions ( segmented package in R 3 ) on flower count data. These two parameters were computed separately for 2012 and 2013 surveys, and means were calculated only for species surveyed both in 2012 and We performed standardized major axis linear regressions (sma function from smatr package in R 3 ) between our phenological parameters and the IPI AgriLand national survey ones for the two phenological parameters. Major axis regression is standardized when variances of the two datasets are unequal. For both peak flowering date (17 species in common) and log10 (x+1) flowering period length (149 species in common), we found positive significant relationships between our estimations and the national AgriLand ones (slope=1.92 [1.38; 2.70], intercept=-38461[-65873; ], r²=0.61, p=0002 for peak flowering dates; and slope=1.67[1.45; 1.93], intercept=-1.29 [-1.78; -0.81], r²=0.22, p<.0001 for log10 (x+1) flowering period lengths, Extended Data Figure 6), suggesting that the flowering parameters we used are robust. However, given that the Agriland survey lasted from April to September (6 months), the duration of the flowering periods we used are higher to those found in the national AgriLand survey. The use of national default estimations of the duration of flowering period for each species may have introduced some sources of errors (r² = 0.22) that may be reduced in future with more detailed geographical phenological data. Supplementary Result 4.3. Relationships between empirical and published values for nectar at flower and vegetative scales In order to check the robustness of our nectar results we compared our estimated nectar productivity values to previously published data. We performed major axis linear regression (ma function from smatr package in R 3 ) between our empirical values and the published data for nectar at flower scale (µg sugars/flower/day) and standardized major axis linear regression (sma function from smatr package in R 3 ) for nectar at the vegetative scale across the year (kg of sugars/ha cover/year). For the flower scale, we used the nectar database from Raine & Chittka 8 for 33 shared species between our two studies (values in µg/flower/day). We found a strong correlation of the log10 (x+1) transformed nectar values between Raine & Chittka and our data (slope=0.80 [0.65; 0.98], intercept=0.22 [-0.16; 0.60], r²=0.77, p<.0001, Extended Data Figure 6); with a slope close to 1 indicating a quasi-linear relationship between the two datasets. At the vegetative scale, we compiled nectar data from published studies for 128 species, including 78 species shared with our survey (values in kg/ha cover/year; see Supplementary Table 13 for references to the specific studies used). Where values were available from more than one source, an average was calculated. Where values were given only as honey potential in the literature, these values were multiplied by 0.8 to give sugar potential. This ratio has been reported in the majority of the published sources (See supplementary Table 13 for references). We found a correlation between the log10 (x+1) transformed nectar values between published studies and our nectar data at the vegetative scale (slope=1.58 [1.35; 1.86], intercept=-0.87 [-1.37; -0.38], r²=0.49, p<.0001, Extended Data Figure 6); suggesting that the two datasets are 15

16 consistent despite the large pool of uncertainties. The propagation of errors from various sources (from nectar at the flower scale, from flower density, from the estimated phenology) throughout the scalingup process also leads to some variability and thus to a lower fit (r²=49 at the vegetative scale compared to 0.77 at the flower scale). Supplementary Result 4.4. Relationships between flower number and vegetative cover The estimation of habitat nectar productivity has been made by combining Countryside Survey vegetation data (vegetative cover; in %) with our floral resources database (species nectar productivity; in kg of nectar per unit area per year). This assumes that for each species, vegetative cover can be used to predict floral abundance. In order to check that vegetative cover and the number of flowers are positively correlated, we used data from the IPI AgriLand national field survey (unpublished data, see Supplementary Result 4.2 for details). In each of the 96 sites, flower counts and vegetative cover of species in flower have been recorded in 0.5 m² quadrats along transects from April to September (data from 2012 survey only available). We plotted the number of open flowers according to the vegetative cover (in %) for 23 out of the 35 species that together contribute to 95% of the national nectar supply (11 species were not surveyed in the IPI AgriLand survey for analyses and Brassica napus did not show enough variation in vegetative cover; Extended Data Figure 7). In order to analyze the relationship between the number of flowers and the vegetative cover for each species, we performed negative binomial generalized linear models adapted for count data with overdispersion (glm.nb function with identity link from the MASS package in R 3 ). Because the month of the survey is likely to influence the relationship between flower number and vegetative cover (as we assumed through the triangular function of flower density across the flowering period), we included the month of the survey as a categorical covariate. Only data from months with at least five observations were included. For each species, we thus analyzed the number of flowers according to the vegetative cover ( Cover ), the month of the survey ( Month ) and the interaction between these two terms ( Cover:Month ). We detected a significant interaction between vegetative cover and the month of the survey or a significant effect of vegetative cover for 20 out of 23 species. Whatever the month considered, most of regression slopes between flower number and vegetative cover were positive (only 8 out of 63 slope estimates in total showed negative values none of which was significantly different from 0); indicating an overall positive linear relationship between these two parameters (Extended Data Figure 7). The month that corresponds to the flowering peak date often showed a higher slope compared to earlier or later months of the flowering period (Extended Data Figure 7). However the relationships between cover and flower density were often noisy, leading to poor fits in some cases. Some of that noise is likely to be due to many of the common species having been recorded in several habitats across geographical regions, which is likely to influence the flower: vegetative cover ratio. Overall, however our assumption that flower density rises with area of vegetative cover is validated. The month of the survey is an important covariate for many species, underlying the importance of considering flowering phenology in the scaling-up of floral resources. 16

17 Supplementary Discussion Supplementary Discussion 1. Potential drivers and trends of changes in nectar productivity per unit area from 1930s to 1970s In the main text, we show a 32% decline in floral resources in England and Wales between the 1930s and 1970s on the basis of land-use changes only. Changes in nectar productivity cannot be assessed for that period due to the lack of vegetation surveys for this period. However, this decline is likely to have been even higher if changes in nectar productivity within habitats would have been incorporated. The dominant habitats probably saw large decreases in nectar production per unit area in the post-1930s period due to post-war changes in habitat management. We can expect higher nectar productivity and diversity in arable land in 1930s than in 1978: arable lands were probably managed in similar way to organic arable lands today (i.e. a low level of herbicide input). Many studies have demonstrated that organic farm support higher plant richness and higher non-crop plant cover than conventional farms We can similarly expect higher nectar productivity per unit area in woodlands in 1930s than in the 1970s. The Forestry Commission mentioned in the Forest Census 1 7 that planted conifers will have progressively suppressed the ground vegetation reaching peak shading within more or less 20 years of planting. When felled at years many areas will have been replanted quickly afterwards leaving little chance for floriferous vegetation to re-establish. The broadleaf woodlands will generally have become more shaded from post-ww2 onwards also suppressing any floriferous understorey vegetation compared to open woodlands widely clear-felled post-ww2. In addition, wide-spread practice of coppicing broadleaf woodland (which helped maintain a diverse and floriferous herbaceous layer) has largely been abandoned post-war Finally, by favoring grasses over forbs 19,20, we can expect that post-war nitrogen deposition in herbaceous habitats may have played a role in decreasing nectar productivity and diversity of grasslands. See supplementary Table 4 for a summary of potential drivers involved in nectar changes over historical time periods. Supplementary Discussion 2. Comparing early losses to recent increases in floral resources In the main text, we show that land cover change suggests a 32% decline in floral resources in England and Wales between the 1930s and 1970s. Subsequently after a period of relative stability in the 1980s and 1990s, we find evidence of a 51% increase in resource provision post On face value, that appears to suggest that earlier losses have been more than compensated for by recent gains; but that is unlikely to be true. First of all, the percentage figures are from different baselines: a decline of onethird from a high baseline is precisely the same mathematically as a rise of one-half from the subsequent lower value. More importantly, the two figures are not directly comparable: the earlier decline in resources is based on change in land covers alone (as those are the only data available pre- 1978), whereas the later increase is based on shifts in both land covers and in the floral resources per unit area in each habitat. Assessed in terms of land cover change alone (Figure 4a), the post-1998 increase in resources has been only 4.6%, far less than the earlier decline. Thus the recent increase in overall resources has been almost entirely caused by increased resources per unit area in many of the habitats (especially improved grassland, neutral grassland, arable land and broadleafed 17

18 woodland). These same habitats almost certainly saw large decreases in nectar production per unit area in the post-1930s period due to post-war changes in habitat management (e.g. herbicide use in arable land, cessation of coppicing in woodlands, nitrogen deposition in grasslands), as noted in the main text and discussed in Supplementary Discussion 1. This implies that the actual post-1930s decline in British floral resources was considerably more than the 32% figure we derive from land cover change alone. Consequently, the recent increase in floral resources, while substantial, is almost certainly less than the mid-20th century decline. Moreover, the continuing erosion in floral resource diversity may further constrain the value of these resources to pollinators 18

19 Supplementary Tables Supplementary Table 1 Supplementary Table 1. Habitat nectar productivity, diversity and national provision in Habitat nectar productivity values and 95% confidence intervals corresponding to back-transformed (10^x 1) estimates of the linear mixed model fitted to log10 (x+1) nectar productivity of all non-linear plots surveyed in Species nectar diversity values and 95% confidence intervals correspond to estimates of the linear mixed model fitted to species nectar diversity of all non-linear plots surveyed in Functional nectar diversity values and 95% confidence intervals correspond to estimates of the linear mixed model fitted to functional nectar diversity of all non-linear plots surveyed in Habitat nectar provision values correspond to habitat nectar productivity multiplied by areas of land cover in Great Britain (see Supplementary Table 5 for habitat land cover values). 19

20 Supplementary Table 2 Supplementary Table 2. Historical changes in national nectar provision considering only shifts in land covers among habitats in England & Wales ( ) and in Great Britain ( ). a, England & Wales nectar provision in 1930, 1978, 1990, 1998 and Default habitat productivity values based on 1978 CS vegetation surveys were assumed to be constant from 1930 to Habitat nectar productivity values and 95% confidence intervals were extracted from the back-transformed estimates (10^x 1) of the linear mixed model fitted to log10 (x+1) annual nectar productivity of shared plots in England and Wales across years. Habitat nectar provision values correspond to habitat nectar productivity multiplied by the habitat s land cover in England and Wales at each time period (see Supplementary Table 5 for habitat land cover values). b, Great Britain nectar provision in 1978, 20

21 1990, 1998 and Default habitat productivity values based on 1978 CS vegetation surveys were assumed to be constant from 1978 to Habitat nectar productivity values and 95% confidence intervals were extracted from the back-transformed estimates (10^x 1) of the linear mixed model fitted to log10 (x+1) annual nectar productivity of shared plots across years. Habitat nectar provision values correspond to habitat nectar productivity multiplied by the habitat s land cover in Great Britain at each time period (see Supplementary Table 5 for habitat land cover values). The overall relative changes (CHANGE %) for each period are given both for the total amounts of nectar produced and for the average nectar productivities (sum of nectar divided by sum of land cover at each date). 21

22 Supplementary Table

23 Supplementary Table 3. Historical changes in national nectar provision considering both shifts in land covers among habitats and shifts in nectar productivity within habitats in England & Wales ( ) and in Great Britain ( ). a, England & Wales nectar provision in 1978, 1990, 1998 and Habitat nectar productivity values and 95% confidence intervals were extracted from the back-transformed estimates (10^x 1) of the linear mixed model fitted to log10 (x+1) annual nectar productivity of shared plots in England & Wales across years. b, Great Britain nectar provision in 1978, 1990, 1998 and Habitat nectar productivity values and 95% confidence intervals were extracted from the back-transformed estimates (10^x 1) of the linear mixed model fitted to log10 (x+1) annual nectar productivity of shared plots in Great Britain across years. c, Great Britain nectar provision in 1978, 1990, 1998 and 2007 after discounting the contribution of Trifolium repens in improved grassland. Habitat nectar productivity values and 95% confidence intervals were extracted from the back-transformed estimates (10^x 1) of the linear mixed model fitted to log10 (x+1) annual nectar productivity of shared plots (after the removal of Trifolium repens in improved grassland) in Great Britain across years. Habitat nectar provision values correspond to habitat nectar productivity multiplied by the habitat s land cover in Great Britain (or England & Wales) at each time period (see Supplementary Table 5 for habitat land cover values). The overall relative changes (CHANGE %) for each period are given both for the total amounts of nectar produced and for the average nectar productivities (sum of nectar divided by sum of land cover at each date). 23

24 Supplementary Table 4 Supplementary Table 4. Drivers of changes in nectar over time. Drivers potentially involved in changes in nectar productivity and diversity from the 1930s to The period, the habitat affected and the mechanisms by which the driver may act on nectar are summarized. 24

25 Supplementary Table 5 Supplementary Table 5. Summary of land cover values of habitats (Great Britain and England & Wales). a, Land cover values of habitats in 1978, 1990, 1998 and 2007 in Great Britain. Values in 1978 were extracted from Countryside Survey 1978 Data Rescue report 21. Values in 1990, 1998 and 2007 were extracted from Countryside Survey 2007 results 22. The mean value corresponds to the 25

26 parametric estimate and the confidence intervals are calculated by bootstrapping across the areas of the habitats in countryside squares within each Land class. b, Land cover values of habitats in 1978, 1990, 1998 and 2007 in England and Wales. Values in 1978 were extracted from Countryside Survey 1978 Data Rescue report 21. Values in 1990, 1998 and 2007 were extracted from Countryside Survey 2007 results 22. The mean value corresponds to the parametric estimate and the confidence intervals are calculated by bootstrapping across the areas of the habitats in countryside squares within each Land class. c, Land cover values of habitats in 1930 s in England and Wales. Values were estimated from the Dudley Stamp land utilization survey maps 4, and meadow/grass, forests/woodlands, and arable land classes were refined to match with countryside surveys habitat types (see Supplementary Method 3). 26

27 Supplementary Table 6 Supplementary Table 6. Effect of plant traits on the log10 (x + 1) transformed empirical annual nectar sugar productivity. Estimates of the terms included in the most parsimonious model are listed (see Supplementary Method 2). 27

28 Supplementary Table 7 Supplementary Table 7. Summary of the nectar dataset scaling-up from the flower to the vegetative scales. For each step of the scaling-up process, we give the sample sizes (median and range) of the variable measured in the field and the means and standard errors of both empirical and modelled variables computed on all species characterized. The assumptions leading to potential sources of errors are also listed for each step. 28

29 Supplementary Table 8 Supplementary Table 8. Habitat description. Habitat descriptions were adapted from Jackson et al and Morton et al

30 Supplementary Table 9 Supplementary Table 9. Summary of countryside vegetation datasets used for the study. *X for main non-linear squared plots; L for linear plots, H for hedgerows, W for watersides and R for road verges; ENG for England, SCO for Scotland, WAL for Wales and GB for Great Britain. 30

31 Supplementary Table 10 Supplementary Table 10. Contribution of species to the nectar provision shifts over recent decades (1978 to 2007). Species contribution to the national nectar provision changes from 1978 to 1990, from 1990 to 1998 and from 1998 to The panels a-c show the values of species contribution calculated on the basis of an overall actual nectar provision change for each period (i.e. 31