In areas where major land-use changes have gone in the opposite direction, however, bee abundance has tended to increase . These changes may represent detectable effects from the US Department of Agriculture Conservation Reserve Program, which compensates farmers for retiring marginal lands . Given the clear patterns in Fig. 1 A–C, supported by other studies at finer spatial scales, this initial assessment can help set management priorities to maintain populations of wild bees and other wildlife amid agricultural intensification .We put estimates of relative wild bee supply in the context of nationwide demand for pollination services, by comparing predicted bee abundance to county-level information on crops. A total of 139 counties contain almost half of pollinator dependent crop area but support relatively low wild bee abundance. In these counties, there seems to be a significant mismatch between the supply of wild bees and demand for pollination services. Because our estimates are relative indices, they do not permit absolute comparisons of supply and demand that would determine whether pollinator abundances are adequate to pollinate crops fully. A more robust approach to locate regions of mismatch, therefore, is to identify counties in which supply and demand are changing in opposite directions . This comparison of trends pinpoints many of the same counties as Fig. 1D, and adds others. In these counties,blueberry container regardless of whether demand for pollination services has already overtaken the ability of wild bees to supply them, recent trends indicate that the risk is growing over time .
Growers of crops dependent on bees for pollination will need to depend more heavily on managed honey bees to supply pollination in the absence of abundant wild bee populations. We predict increasing demand over time for honey bees in those regions highlighted in Fig. 1F, but a test of that prediction is beyond the scope of this paper. We also suggest that efforts to manage pollinator habitats, monitor bee populations, and evaluate pollen limitation in crops are most important in these regions. Crop expansion probably contributed to the declining quality of bee habitats between 2008 and 2013; indeed, we find a negative correlation between changes in crop demand and bee abundance across all US counties . Studies from northern Europe have shown that mass-flowering crops can enhance wild bee abundance in surrounding landscapes , but our analyses indicate the opposite relationship and emphasize the need for more careful assessment of North American systems. Analysis of individual crops provides another perspective on potential mismatches between US wild bee supply and demand . Crops that have decreasing wild bee abundance and increasing cultivated area tend to be those that are more dependent on bee-mediated pollination . Pollination supply and demand are therefore mismatched for precisely the crops that most require pollination. Variability in US crop yields has been found to increase with greater dependence on pollinators , so these trends, if they continue, may destabilize crop production over time. To maintain stability in yields, farmers may need to maintain habitats for wild bees on and around their farms or invest more heavily in managed pollinators.
We consider our estimates of uncertainty to be as informative as the bee abundance predictions themselves. All assessments involve uncertainty, but few report this crucial information with sufficient clarity and rigor . We are encouraged to note that our model validation supports the uncertainty estimates; expert-derived parameters improved model fit to a greater degree in areas where experts reported more certainty . Quantifying uncertainty allows us to make initial predictions about the status and trends of pollinator abundance using uneven and incomplete information. It also helps identify regions where additional studies will most effectively improve our estimates and strengthen the national assessment over time. Highly uncertain regions are also those where the precautionary principle would be appropriate in land management strategies to prevent pollinator loss. In practice, uncertainty in our model can increase for three reasons: First, experts may not be certain about the resource quality of a particular land-cover type ; next, individual experts are certain but disagree about the quality of resources available ; and finally, experts acknowledge that a land-cover type is heterogeneous in its resource quality . In our case, experts were less certain about the quality of nesting resources than of floral resources; this suggests a need to increase effort to understand the nesting biology of wild bees . Experts were also more certain about the quality of crops than of non-crop land covers ; this could reflect relative expertise among experts or greater spatial and temporal heterogeneity of natural land covers. Although our approach carefully captured expert uncertainty, three other sources of uncertainty arise from the data themselves. First, the Cropland Data Layer , like all land-cover and land-use data, contains classification errors , which contribute to the uncertainty in our estimates.
For example, apparent land-use conversion from deciduous forest into woody wetlands contributed to predicted declines in bee abundance between 2008 and 2013, especially in Minnesota . Conversely, apparent conversion from grasslands into shrubs was the major driver in areas of increased pollinator abundances . Both changes, however, are partly a result of inconsistent classification, which led to apparent changes when none occurred. In addition, urban gardens could support a significant abundance of wild pollinators, but the CDL does not capture these specific features within “developed” categories . Despite such inaccuracies, the CDL is the only available national coverage of land-uses/covers in agricultural as well as natural areas . Second, for our measures of pollination demand , for each crop we rely on Klein et al. for estimates of pollinator dependence . These estimates consist of simple percentages of yield and have been widely used in studies of pollination services . They also contain some uncertainty, however, because each percentage represents the midpoint of a range reported originally in Klein et al. , whereas dependencies vary among crop varieties, climates, field settings, and cultivation practices. Because we focus on analyses of relative demand among crops and counties, our findings are likely robust to this uncertainty. Finally, we elicited expert parameters on nesting resources for different guilds and for floral resources at different seasons. However, we combined these estimates to produce a single probability distribution for each habitat type, which increased the uncertainty of our estimates . In the future, more detailed assessments could integrate information on bee communities, nesting habits, and flight seasons to develop more refined probability distributions for each pixel. Indeed, our model predicted bumble bee abundances more accurately when we used parameters relevant to this genus ,growing blueberries in containers compared with averaged parameters . Although we have focused on bees, other taxa can be important crop pollinators . For simplicity in this initial nationwide assessment, we have also pooled all bee species into an overall abundance index, but bee taxa clearly vary in their importance as crop pollinators and their response to land use . Future work should distinguish pollinator taxa or guilds to model the trends and importance of each separately. Beyond these uncertainties, three additional caveats deserve mention. First, our assessment is based on a simple landscape model that predicts relative abundance of bees based on nesting resources, floral resources, and foraging distance. Although this model has proved to be informative in a variety of settings , it neither captures abundances of individual bee species nor reports visitation rates, pollination efficiency, or other variables important for realized pollination services. Second, although the model validation explained significant amounts of variance in field data, substantial variance remained unexplained. Clearly, other factors influence bee abundance in landscapes, but this study is intended as an initial national assessment of wild pollinators in general. Third, we evaluate trends over only 5 y; analysis of longer-term changes in both wild bee populations and land cover will provide a more robust assessment.
This first national assessment of status and trends of wild bee abundance will be valuable as a response to the recent federal mandates to direct additional research and management attention toward pollinators. A national program to detect future changes in bee populations has been estimated to cost $2,000,000 and to require 5–10 y. Our national assessment can be used to focus such a costly effort, targeting bee and habitat surveys on regions that show high uncertainty, especially where agricultural demand for pollination services is high. Counties with mismatched levels of relative pollinator “supply” and “demand” warrant priority efforts to conserve and restore habitats for pollinators as well as other actions that can affect bees. As such efforts proceed, national assessments can be repeated with new information to update estimates, revise priorities, and track progress toward sustainable management of our nation’s wild pollinators.In this genome release, we report on the first assembled genome of a member of the genus Arctostaphylos. Our genome assembly is part of the California Conservation Genomics Project , the goal of which is to establish patterns of genomic diversity across the state of California and its many habitats. The CCGP will sequence the complete genomes of approximately 150 carefully selected species projects. Many of these taxa are threatened or endangered, and therefore in need of conservation management in the face of rapidly accelerating biodiversity decline. The combined reference genome plus landscape genomics approach of the CCGP, based on the resequencing of many individuals of each target species across the state, will allow the identification of hotspots of diversity across California and provide a framework for informed conservation decisions and management plans. Manzanitas are among the most conspicuous and dominant native chaparral species in the California Floristic Province , a biodiversity hotspot characterized by a Mediterranean-type climate with hot, dry summers and cool, wet winters. These plants comprise the most diverse woody genus in the CFP , and their diversity has long fascinated taxonomists. Manzanitas serve essential roles in their native ecosystems, including rapidly regenerating in fired-disturbed areas, and providing food resources for pollinators and fruit-eating animals . In addition, these plants are of great importance for conservation management: over half of the more than 100 morphologically defined manzanita species and subspecies are narrow endemics with highly restricted distributions and are considered rare and/or endangered . In contrast to their importance in ecology, evolution, and conservation studies, genomic resources for manzanitas are nearly nonexistent beyond investigations into karyotypes of diploid and tetraploid species . In this study, we present the first genome sequence of a manzanita. Big berry manzanita, Arctostaphylos glauca , is a widespread diploid species common in northern Baja California and across southern and coastal central California that is hypothesized to be the progenitor of several potential hybrid manzanita species . With funding and support from the CCGP, we created this scaffold-level assembly using a hybrid de novo assembly approach that combines Hi-C chromatin-proximity and PacBio HiFi long-read sequencing data. This genome assembly will provide a robust basis for studying the diversification and evolutionary history of Arctostaphylos in the CFP.We selected for sequencing a naturally occurring big berry manzanita adult located in the San Gabriel Mountains, Angeles National Forest, Los Angeles County, California. We collected floral buds and inflorescences from this individual on February 19, 2018, and January 20, 2020. When possible, we extracted from floral tissue, which was easier to grind and gave higher yields of DNA than other tissues. Tissues were flash-frozen in liquid nitrogen and stored at −80 °C prior to DNA extraction. A voucher is deposited at the herbarium of University of California, Riverside .A Dovetail Hi-C library was prepared in a similar manner as previously described . For each library, chromatin was fixed in place with formaldehyde in the nucleus. Extracted, fixed chromatin was digested with DpnII, the 5′ overhangs were filled in with biotinylated nucleotides, and free blunt ends were ligated. After ligation, cross links were reversed, and the DNA purified from protein. Purified DNA was treated to remove biotin that was not internal to ligated fragments. The DNA was then sheared to ~350 bp mean fragment size and sequencing libraries were generated using NEBNext Ultra enzymes and Illumina-compatible adapters. Biotin-containing fragments were isolated using streptavidin beads before PCR enrichment of each library.The classification of repeat elements generated by RepeatMasker is shown in Table 3.Our assembly is the first sequenced genome in the genus Arctostaphylos, representing the initial step toward understanding genetics and adaptation in this highly diversified genus.