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A global-scale expert assessment of drivers and risks associated with pollinator decline global-scale expert assessment of drivers and risks associated with pollinator decline.

Pollinator decline has attracted global attention and substantial efforts are underway to respond through national pollinator strategies and action plans. These policy responses require clarity on what is driving pollinator decline and what risks it generates for society in different parts of the world. Using a formal expert elicitation process, we evaluated the relative regional and global importance of eight drivers of pollinator decline and ten consequent risks to human well-being. Our results indicate that global policy responses should focus on reducing pressure from changes in land cover and configuration, land management and pesticides, as these were considered very important drivers in most regions. We quantify how the importance of drivers and risks from pollinator decline, differ among regions. For example, losing access to managed pollinators was considered a serious risk only for people in North America, whereas yield instability in pollinator-dependent crops was classed as a serious or high risk in four regions but only a moderate risk in Europe and North America. Overall, perceived risks were substantially higher in the Global South. Despite extensive research on pollinator decline, our analysis reveals considerable scientific uncertainty about what this means for human society.

respond to pollinator decline 2 , it has proven more difficult to identify objectives to address 82 risk even at a regional scale, resulting in often potentially ineffective policies 23 . 83 Here, we use a structured expert elicitation technique and a globally representative group of 84 pollinator and pollination experts to evaluate the relative importance of eight major drivers of 85 pollinator decline, and the risks to human well-being associated with ten direct impacts of 86 pollinator decline defined by the IPBES report 4 (Table 1). We make a separate assessment for 87 each of six global regions, defined as geographic continents, with the exception that Pacific 88 Islands are grouped with Asia as 'Asia-Pacific', rather than with Australia and New Zealand 89 as 'Oceania' (see Methods; Figure S1). Indirect impacts, such as increased land conversion in 90 response to lower yields, were not assessed. We also do not consider interactions between 91 different drivers. While such interactions are clearly important in fully understanding 92 pollinator decline 24,25 , we consider current knowledge about interactions between drivers is 93 not sufficient to enable an assessment of the scale and scope presented here. 94 We take a scientific-technical approach to risk, in which a risk is understood as the 95 probability of a specific hazard or impact taking place. We use a semi-quantitative risk   While climate change was considered an 'important' or 'very important' driver in every 125 region, there was unanimous lack of confidence over its importance relative to other drivers: 126 median confidence scores were 'medium' in every region except Africa, for which seven of 127 the 10 scorers responded that climate change effects are 'unknown' ( Figure S1 and Extended 128 Data Table 2). Limited long-term data are available to demonstrate impacts of climate change 129 on pollinators, and studies available are restricted to a small number of taxa such as although for Australia/NZ and Africa, the degree of confidence was 'inconclusive', indicating 147 low amounts of evidence and low agreement among our experts (see Table 2 for definitions).

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This is a particular concern in Africa, where pollinated crops are both nutritionally 44 and 149 economically 45 important to livelihoods and well-being. Yield instability in pollinator-150 dependent crops, which is higher than that for non-dependent crops at global scale 46 , was 151 classed as a serious or high risk in four of the six regions but moderate in Europe and North 152 America, where the economic dependence and relative area of highly pollinator dependent 153 crops is lower. Direct impacts of wild fruit production losses had very low risk scores in 154 economically developed regions of North America, Europe and Australia/New Zealand 155 (median scores <6), but were highly polarized, classed as a serious risk in Africa, Asia-

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Risks were greatest in South America than in other regions (Extended Data Table 3: mean 160 risk score across all ten impacts = 48.2), with four 'high' risks (pollination deficits, yield 161 instability, food system resilience and wild pollinator diversity) and five 'serious' risks. The 162 high risks reflect the high diversity of insect pollinated crops grown and exported throughout 163 the region 48 , often by small holder farmers, in and around areas of natural habitats that 164 contain a high diversity of pollinating insects 49 . Continuing losses of pollinators are therefore 165 likely to destabilise both regional food production and international trade, affecting 166 livelihoods across the region. Like other regions of the Global South, South America is also 167 home to a high diversity of extant indigenous cultures and people, many of whom rely upon 168 subsistence agriculture and natural resources such as non-timber forest products 50 , increasing 169 the risks impacts from a decline in honey, wild fruits and cultural values.

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In contrast to South America, Africa had very low risk scores for honey production and 171 managed pollinators (both 'low' risk; see Figure 2 and Extended Data Table 3). Beekeeping 172 is unique in Africa, since it is the only global region that has large, genetically diverse 173 populations of native honey bees still thriving in the wild 51 . There are few reports of colony 174 losses, numbers of managed hives are increasing in many African countries and managed 175 honey bee populations seem relatively resilient to Varroa mite 52 .

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The risk of loss of aesthetic values, happiness or well-being associated with wild pollinators 177 or wild plants dependent on pollinators was perhaps the most difficult to score, in all regions.

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In some contexts, one can make an argument that aesthetic values associated with pollinators 179 are increasing, as people become more aware of their roles, beauty and diversity. Discussions  to low confidence and low agreement among scorers.

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The availability of managed pollinators was only considered a serious risk to people in North globally, yet less is understood about the broader implications of these risks for human well-218 being. The case for action to address pollinator decline is most clearly made for South 219 America, while there is an urgent need for further research in Africa, to address the 220 substantial uncertainties around the risks to people from pollination deficits, and the 221 importance of changes in land cover and configuration, as a driver of pollinator decline.

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Acknowledgements 223 We thank the following people, who took part in an early scoping of this exercise, helping to     biogeographically and geopolitically (see Figure S1).

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For each region, experts individually assigned probability, scale and severity scores for each 262 of eight impacts of pollinator decline, and importance scores to each of 10 drivers of 263 pollinator declines defined by the IPBES 4 (Table 1), using the five-point Likert scales 264 described in Table S1. All scores were accompanied by a confidence score of low, medium or 265 high, enabling experts to qualify their judgements with a level of confidence, based on the 266 amount of evidence they were aware of, and its quality.

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The following definitions of probability, scale and severity were available for authors to 268 consult throughout the process:

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Probability: A high probability of impact suggests that the impact is already taking place or is 270 very likely, at least in some circumstances. Low probability implies that the impact is NOT 271 taking place or is unlikely. Unknown means there is not enough evidence to make a 272 judgement on whether or not the impact is happening, or likely to happen. Moderate means there is evidence for a moderate impact on people and livelihoods, and small 283 means a small impact. Unknown means there is not enough evidence on the severity of this 284 impact to make a judgement.

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Experts rated the importance of each driver in affecting pollinators in each specific region, on 286 a 1-5 scale from not important to the most important (Tables 1 and S1). 287 We set an a priori expectation of consensus as an interquartile distance of < 2 between scores 288 for a particular element (not including confidence). This still allows us to distinguish between 289 high and low agreement following criteria in Table 3, in which high agreement is denoted by  In an initial scoping phase, all experts were invited to comment on the proposed scoring 294 structure described above. Following this, the first round of scoring was conducted online in 295 October 2017. Each expert was asked to score for all regions, considering the evidence in the 296 IPBES report 4 alongside their own expertise. Experts could add comments to support their 297 scores, and were encouraged to cite parts of the IPBES report 4 and other specific literature.

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Scores and comments were compiled and summaries sent to the experts, detailing the median 299 and interquartile range of scores for each element, and the proportions of 'unknown' 300 responses.

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Each expert was then assigned a region (always one they were familiar with) and a driver, 303 and asked to play a cynic role, doing focused background research to challenge, refute or 304 support the scores from the first round, with evidence. Cynic roles were not made known 305 during later discussions, but cynics were invited to comment appropriately and to actively 306 introduce new evidence to the discussions.

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In November 2017, all experts attended a workshop in Reading, UK. Experts were divided 309 into two groups, which each discussed the results from the first round, and the evidence that 310 supports them, for three regions. Group 1 discussed and scored in rounds 2 and 3 for Europe, 311 North America and Africa; Group 2 discussed and scored South America, Asia Pacific and 312 Australia/New Zealand. Discussions were facilitated and notes taken throughout. Facilitators 313 kept in contact and discussed any specific issues arising about how to score, to ensure that 314 both groups responded in the same way. At the end of each part of the discussion, participants 315 scored again for each element of risk, and each driver, for each region in turn. Scoring was 316 conducted independently and anonymously, using Excel spreadsheets on personal laptops.

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All members of a group were encouraged to score for each region discussed in their group, 318 with the following guidance: "Score if you can (but you don't have to). If you feel confident 319 to score for a region outside your own personal knowledge, please do so. These issues are 320 complex and open to interpretation. This is why we employ a subjective scoring process, with 321 anonymous scoring. Listen to the discussion, and then score as you understand it."

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These round 2 results were compiled as before, and any scores with interquartile range (IQR) 323 2 (our a priori criterion for consensus), progressed to round 3 for rescoring.

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Round 3 scoring took placed on the second day of the workshop in a plenary discussion. This 325 allowed a further opportunity for any consistent differences in scoring or approach across 326 groups to be revealed, although this was not the case. Second round scores were presented 327 and made the subject of debate and discussion. Experts scored again anonymously and 328 independently, using laptops, for the regions they scored for in round 2, although the Median scores following the third round of scoring were used to derive risk scores (the 341 product of probability, scale and severity scores) and associated risk categories (boundaries 342 described in Figure 2), importance scores for drivers, and confidence categories for all final 343 scores, following criteria given in Table 3. In assigning confidence categories, the quantity 344 and quality of evidence was based on assigned confidence scores for each risk or driver. The 345 confidence score is the percentage of the maximum possible confidence score (9 for risks, 3 346 for drivers), represented by the median confidence scores from the final round, with the three 347 medians summed in the case of impacts (confidence score for risk = ( Confidence scores for 348 probability, scale and severity/9) * 100)).

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Overall global scores for the importance of drivers were calculated as a median of the six 350 region-level scores and confidence scores, to ensure equal weight was given to each region 351 (although the numbers were unchanged if individual scores across all six regions were used). 352 We did not calculate overall global risk scores for different impacts of pollinator decline, 353 because these scores were based on assessments of probability, scale and severity for 354 different global regions and it does not make sense to average these across regions. All 355 figures were drawn using the ggplot2 package 58 , in R version 4.0.0 60 . 356 We hypothesized that the scores participants gave for each component of the risk, or driver 357 importance, were dependent on the impact, or driver, being scored, and on the region being 358 scored, rather than reflecting individual scorer differences or differences emerging from the 359 style of discussion between the two groups. We tested this hypothesis using Cumulative Link impacts, and importance of drivers. 365 We consider the effect of Region and Impact, or Region and Driver, on score, for each of four

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For each model, we tested the proportional odds assumption, that the effects of region or 371 impact group were the same, regardless of where the cut-off points were placed across the 372 five score categories, using the nominal test and scale test functions, which use likelihood 373 ratio tests. When this assumption was violated, we used partial proportion odds models where 374 possible, given our data structure. Independent variables that failed the tests were examined, 375 with scale (dispersion of latent variable) allowed to vary among levels of the dependent 376 variable (failure of scale_test) or effects of the relevant factor assumed to be nominal rather 377 than ordinal (failure of the nominal_test).

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These models do not account for the random effects of scorer or group, because the scorers 379 were divided among two separate groups, each of which only scored half of the regions. We 380 ran Cumulative Link Mixed Models separately for each group, including scorer as a random 381 effect to account for differences between individual scorers. The effects of group cannot be 382 analysed as a random factor with this study design, because there are only two levels. The 383 effect of Group cannot be separated from the effect of Region in a single model. 384 We used McFadden's pseudo R 2 value ( 2 ) to provide an indication of goodness of fit for all 385 models, as recommended by Menard (2002) 61 . This is calculated relative to a null model

Short Form Definitions from IPBES pollinators and pollination assessment report 4 Direct drivers of pollinator decline
Pollinator management Management of bees (honey bees, bumblebees, stingless bees and solitary bees) for honey production, and of bees or other insects for pollination. "Two major Apis species are managed around the world: the western honey bee Apis mellifera and the eastern honey bees Apis cerana and Apis indica." (Section 2.4.2.1) "Five species of bumble bees are currently used for crop pollination, the major ones being Bombus terrestris from Europe and Bombus impatiens from North America." (Section 2.4.2.2). "Bee management is a global and complex driver of pollinator loss." (Section 2.4.3).

Pests and Pathogens
Parasites, pathogens and disease of all pollinating animals are included, both naturally circulating in populations and those associated with human management. "Bee diseases by definition have some negative impacts at the individual bee, colony or population level. Parasites and pathogens can be widespread in nature but may only become problematic when bees are domesticated and crowded." (Section 2.4.1) Pesticide use "Pesticides (fungicides, herbicides, insecticides, acaricides, etc.) are primarily used in crop and plant protection against a range of pests and diseases and include synthetic chemicals, biologicals, e.g., Bacillus thuringiensis (Bt) or other chemicals of biological origin such as spider venom peptides." (Section 2.3.1.) Veterinary medicines are also included. Land management Land management refers to "arrangements, activities and inputs people undertake in a certain land cover type to produce, change or maintain it" (Section 2.2.1). "Land management such as agricultural and conservation practices has a great influence at both landscape and local scales on the nesting and foraging environment of pollinators." (Section 2.2.2) Land cover and configuration "Land cover has been defined by the UN FAO as the observed (bio)physical cover on the earth's surface". (Section 2.2.1.) This includes the extent of different habitat and land use types, and their spatial configuration at landscape scale. Invasive alien species "Alien species' are defined as a (non-native, non-indigenous, foreign, exotic) species, subspecies, or lower taxon occurring outside of its natural range (past or present) and dispersal potential (i.e. outside the range it occupies naturally or could occupy without direct or indirect introduction or care by humans) and includes any part, gametes or propagule of such species that might survive and subsequently reproduce. 'Alien invasive species' are alien species that become established in natural or semi-natural ecosystems, and are an agent of change, threatening native biological diversity" (Section 2.5.1) GMOs "Genetically modified (GM) organisms (GMOs) are organisms that have been modified in a way that does not occur naturally by mating and/or natural recombination. One of the most common methods to do this is by bioengineering transgene(s) into the new organism. The most common plant transgenes confer herbicide tolerance (HT), or toxicity towards herbivores (insect resistance, IR), although other characteristics have been also engineered (e.g., drought resistance in wheat, nutritional values in sorghum)." (Section 2.3.2.) Climate change "a change in the state of the climate that can be identified … by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer." (Section 2.6) Direct impacts of pollinator decline Pollination Deficits Crop pollination deficit leading to lower quantity or quality of food (and other products).
Reduction in the quantity or quality of food, fibre, fuel or seed that can be produced, as a result of pollinator loss.

Crop yield instability
Crop yields becoming less stable or predictable between years, or locations. Honey Production Fall in honey production (and other hive products) Reduction in the amount of honey or hive products that can be produced, as a result of pollinator loss Food system Resilience Decline in long term resilience of food production systems Resilience is the ability of the food production system to withstand or recover from shocks or adverse effects, such as changes in climate.    Table 2.      individual scorers who tended to score more highly than others across the board scored 481 particularly highly on this aspect.

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There is a significant effect of discussion group, if Region is replaced by Group in the basic  for independent variables where the proportional odds assumption is not met (shown by 543 failure of nominal_test, results in Ext Data Table 6). Standard errors in brackets. Regions 544 organised by discussion group (Group). Effects are shown relative to an arbitrary base