Spatiotemporal patterns of forest pollinator diversity across the southeastern United States

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We sampled bees (Hymenoptera: Apoidea), hover flies (Diptera: Syrphidae), and butterflies (Lepidoptera) on nineteen National Forests across the southeastern U.S. and related their diversity to ecoregion, landscape context, canopy openness, and forest composition. Bee richness was negatively correlated with both the amount of conifer forest and the extent of wetlands in the surrounding landscape but was positively correlated with canopy openness. Hover flies and butterflies were less sensitive to landscape context and stand conditions. Pollinator communities differed considerably among ecoregions, with those of the Central Appalachian and Coastal Plain ecoregions being particularly distinct. Bee richness and abundance peaked two months earlier in Central Appalachia than in the Coastal Plain and Southeastern Mixed Forest ecoregions. Our findings suggest that hardwood forests may play a particularly important role in supporting forest-associated bees in the southeastern U.S. and that efforts to create more open forest conditions may benefit this fauna. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Despite growing concerns over declines in pollinating insects (Barendregt et al., 2022 ; Forister et al., 2021 ; Powney et al., 2019 ; Ulyshen & Horn, 2023 ), there is surprisingly little information on how the diversity of these organisms is distributed across time and space, especially at transregional scales. For conservation efforts to be effective over large areas, it is critical to understand how different ecoregions contribute to regional pollinator diversity, and how local diversity is influenced by landscape context and local conditions. Unfortunately, such fundamental information is lacking for many parts of the world, and even knowing which pollinator species are present remains a major knowledge gap for many land managers (Rivers et al., 2018 ). While museum records provide valuable insights into the distribution of species, such data are typically from specimens collected over many years using a variety of methods and rarely include detailed information on local habitat conditions (Orr et al., 2021 ). Furthermore, because pollinator populations exhibit strong inter-annual variation (Williams et al., 2001 ) and all sampling methods are more effective for some taxa than others (Cane et al., 2000 ; Joshi et al., 2015 ), there is a strong need for coordinated efforts to simultaneously and consistently sample pollinators over large geographic areas. There is increasing awareness that forests play key roles in supporting pollinator diversity, which in turn provides pollination services to adjacent habitats, including crops (Ulyshen et al., 2023 ). However, many questions remain about how forest composition and structure shape these communities. A growing number of studies indicate that pollinators are active within the canopies of temperate forests (Allen & Davies, 2023 ; Ulyshen et al., 2010 ; Urban-Mead et al., 2021 ; Urban-Mead et al., 2023 ) and utilize abundant pollen and nectar from flowering trees. Even wind-pollinated taxa, such as oaks ( Quercus spp.), can serve as important pollen sources for many bees (Kraemer & Favi, 2005 ; Saunders, 2018 ; Urban-Mead et al., 2023 ). By contrast, conifer-dominated forests provide little or no floral resources in the canopy, possibly resulting in less favorable conditions for pollinators. This is significant, as conifer forests are favored throughout the world for timber and pulp production (Kanninen, 2010 ). For example, the forests of the southeastern U.S. are the largest producer of industrial roundwood in the world and most of that production comes from densely stocked—often planted—stands of intensively managed pines native to the region (Prestemon & Abt, 2002 ). Forests of the southeastern U.S. were historically far more open than many are today (Carroll, Kapeluck, Harper & Van Lear, 2002 ; Van Lear et al., 2004 ). Pine dominance was greatest on the Coastal Plain where the frequent occurrence of fires played a key role in sustaining savanna-like conditions over large areas. Such open conditions support a high abundance and diversity of flowering plants in the herbaceous layer (Carr et al., 2010 ) which, in turn, benefit pollinator communities (Hanula et al., 2015 ; Moylett et al., 2019 ; Odanaka et al., 2020 ). Closed-canopy conditions, by contrast, often result in suppressed floral availability or concentrate flowering to the early spring months prior to canopy closure (Peterson & Reich, 2008 ; Taki et al., 2007 ; Watson et al., 2011 ). Despite strong interest in restoring open woodlands for a wide range of benefits including conserving endangered invertebrates and reducing the risk of bark beetle outbreaks (Dixon et al., 2022 ; Oluoch et al., 2021 ; Van Lear et al., 2004 ), relatively few of the region’s pine and oak-dominated forests currently have open stand structures (Gilliam & Platt, 1999 ). Those that do typically arose from mechanical thinning, wind damage, low initial stocking levels, or the application of prescribed fire (Hanberry et al., 2020 ). Previous researchers suggested that the closure of southeastern U.S. forests over the past century may have contributed to pollinator declines (Hanula et al., 2015 ). To address some of these concerns, we sampled bees, hover flies, and butterflies on nineteen U.S. Forest Service experimental forests which together provide a good representation of major forest types typical of the southeastern U.S. Our objectives were to 1) collect baseline information on the diversity of these pollinators across the region, 2) determine how the diversity and composition of pollinator communities differ among major ecoregions, 3) explore the relationship between pollinator diversity and landscape context (e.g., amount of surrounding conifer forest and wetland), and 4) compare pollinator seasonality among ecoregions. Methods Study sites The U.S. Forest Service Southern Research Station’s (SRS) experimental forest network (EFN) consists of 19 units (Table 1) across the southeastern U.S. (Figure 1). Established decades ago, these experimental forests were intended to address a variety of research needs, from silviculture to naval stores production to forest genetics, erosion control, and hydrology. The SRS EFN covers seven of the nine distinct southeastern ecoregions as defined by Bailey (2016), including the Central Appalachian Forest, Southeastern Mixed Forest, Outer Coastal Plain Mixed Forest, Eastern Broadleaf Forest, Lower Mississippi Riverine Forest, Ouachita Mixed Forest, and Ozark Broadleaf Forest. The first three of these were the most sampled in this study (including three or more experimental forests) and correspond to the Southern Appalachians, Piedmont, and Coastal Plain physiographic regions, respectively. In 2018-2019, a series of plots (coordinates) were systematically established on all experimental forests in the SRS EFN to provide a standardized and uniform framework for large scale analyses. The ultimate goal is to collect long-term forest data at these locations following the Forest Service’s Forest Inventory and Analysis (FIA) protocol (Bechtold & Patterson, 2005). However, such data have not yet been collected at most of the plots sampled in this study, and were thus not available for the current analysis. From this collection of plots, we randomly selected five from each experimental forest for use in this study. We selected two additional plots at Chipola EF because there was a possibility that some would be disturbed by planned management operations, and would therefore need to be dropped from the study. However, this did not happen, resulting in a total of 97 plots across the network. The distance between plots within experimental forests was at least 500 m while the distance between experimental forests ranged from ~20-1,400 km (Figure 1). All plots were forested, although canopy openness and successional stage differed considerably depending on recent disturbance events (e.g., Hurricane Michael) or management activities (e.g., mechanical thinning or prescribed burning). Based on National Land Cover Data from 2021, the mean and median percent forest cover within 500m of the plots were 86.8% and 91.0%, respectively, with a range of 31.3-100%. Pollinator sampling and identification Pollinators were sampled on all 19 experimental forests using colored pan traps. Although pan traps provide a standardized, simple, and effective method for sampling pollinators in forests (Ulyshen et al., 2022) they are known to be more effective at collecting some taxa than others (Baum & Wallen, 2011; Cane et al., 2000) and can also under-sample pollinators in areas with an abundance of flowers (Baum & Wallen, 2011). While netting off flowers would have improved the study (Roulston et al., 2007), we limited sampling to pan trapping given limits of time and personnel as well as the logistical challenges inherent to visiting remote sites across such a large geographic area. Although we did not conduct floral surveys in this study, we did measure canopy openness (see plot measurements, below) which correlates with flower availability (Chase et al., 2023) and include this term in models of pollinator richness (see statistical analysis, below). Sampling involved placing two sets of three pan traps at each plot, with the traps filled with soapy water during operation. Each set was situated 5 m to the north or south of plot center and consisted of a yellow, a blue and a white plastic bowl (15.5 cm opening, ~400 ml capacity) suspended 20-30 cm above the ground on wire stands. Sets were oriented east-west with a 5 m separation between traps. The traps were used as they were manufactured and were not painted. Traps were operated once a month for three days at a time from March to September 2021. Target dates for sampling were established at the beginning of the study. However, because weather conditions differed considerably across our study region, the exact dates of sampling varied somewhat (Table S1) to ensure that trapping took place at all locations during periods of clear weather when pollinators would be most active. All bees, hover flies, and butterflies were pinned and identified by the senior author (bees and butterflies) and SR and ADY (hover flies) to species (or rarely to morphospecies) using online (discoverlife.org) and printed resources (Gibbs, 2011; Gibbs et al., 2013; Glassberg et al., 2000; Mitchell, 1960, 1962; Skevington et al., 2019). Voucher specimens have been deposited in the senior author’s reference collection. Data were pooled by plot and sampling period for analysis. Plot measurements We used a convex densiometer to measure canopy openness at the center of each plot (Lemmon, 1956). Measurements were made while facing each cardinal direction in May, June, and July and we used the average of these readings as the percent canopy openness for each plot. Measurements at these times of year captured the maximum shade condition of overstory trees across our study area. We also collected information on stand basal area and overstory composition by recording the diameter and species of all trees (>20 cm at 1.4 m above the ground) within a 0.1 ha circular area centered on each plot. Statistical Analysis Our analysis, performed in R (R Core Team, 2022) and ArcGis Pro, consisted of two parts. First, we related the alpha diversity (i.e., local species richness at the plot level) and community composition of pollinators to land cover data and stand metrics across all plots. We then tested for differences in community composition, pollinator diversity (alpha, beta, and gamma as defined below), and seasonal patterns of alpha diversity and abundance among the most-sampled ecoregions (i.e., those represented by three or more experimental forests: Central Appalachian, Coastal Plain and Southeastern Mixed, Figure 1). Whereas alpha diversity is defined as the number of species observed in a plot, beta diversity represents the dissimilarity in diversity among plots within each experimental forest and consists of turnover (species replacement) and nestedness (species loss) components (Baselga, 2010). Finally, gamma diversity refers to the total diversity within each ecoregion. We used NLCD (National Land Cover Database, https://www.usgs.gov/centers/eros/science/national-land-cover-database) data from 2021 (Dewitz, 2023), with a resolution of 30 x 30 m, to calculate the percentage of each land cover category surrounding our plots. This was done at five spatial scales (radii of 250 m, 500 m, 1 km, 2 km, and 4 km). Land cover metrics tested for multicollinearity at each spatial scale were percentages of: non-wetland forest (i.e., deciduous forest + evergreen forest + mixed forest), non-wetland open land (i.e., grassland/herbaceous + shrub/scrub + pasture/hay + barren land + cultivated crops + developed high, medium, and low intensities + developed_open), wetland (i.e., woody wetlands + emergent herbaceous wetlands), and evergreen forest (hereafter referred to as conifer forest). Of these, non-wetland forest was dropped based on a high variance inflation factor (VIF) value, and non-wetland open land was dropped based on subsequent problems with model convergence. After confirming a lack of spatial autocorrelation for any of the response variables among experimental forests based on Moran’s I using the package ape (Paradis & Schliep, 2019), we used the lme4 package (Bates et al., 2015) to produce negative binomial regression models (glmer.nb) of pollinator richness. To determine which spatial scale to use in the final models of pollinator diversity, we first tested separate models for each of the five spatial scales mentioned above and compared the R-squared values, using the rsq.glmm function of the rsq package (Zhang & Zhang, 2022), i.e., the scale of effect: Holland et al., (2004). This was done separately for the richness of bees, hover flies, butterflies, and all pollinators combined. Based on this analysis, we determined landscape metrics to be most informative at the scale of 500 m for bee, butterfly, and total pollinator richness, and at the scale of 2 km for hover fly richness (Figure S1), so these were the scales used in the corresponding models. Our final model of pollinator richness included canopy openness, wetland, and conifer forest as fixed effects (all scaled to have a mean of 0 and a standard deviation of 1) and experimental forest as a random effect to account for the lack of independence among plots within each experimental forest. Additional terms for the number of tree genera and basal area were not included due to multicollinearity. Given our study’s large sample size, we used the default Wald Z-test to assess significance. However, to confirm these conclusions, we also conducted likelihood ratio tests by dropping predictors one at a time (drop1) and using the chi-square test to assess significance. Using the same methods as described above, we calculated R-squared for each model and the contributions of fixed and random terms to the total. To further explore how the relationship between bee richness and conifer forest may vary among ecoregions, we re-ran the model after limiting to the Southeastern Mixed or Coastal Plain ecoregions. We did not examine this relationship for other ecoregions due to either low replication (e.g., some ecoregions were represented by just one experimental forest) or, as for the Central Appalachian ecoregion, a low coverage of conifer forests. Moreover, we did not repeat these analyses for hover flies or butterflies because conifer forest was not a significant predictor in the main models for those groups. For comparisons of pollinator community composition (all groups combined) among plots, we performed non-metric multidimensional scaling (NMDS) on a Bray-Curtis distance matrix using the vegan package (Oksanen et al., 2007). This was based on Hellinger-transformed abundance data which involves dividing each count by the sample total and taking the square root of each resulting proportion. We then conducted envfit tests for significant correlations between the resulting axes and the following variables of interest: canopy openness, wetlands within 500 m, conifer forests within 500 m, basal area, and the number of tree genera. Then we used the adonis2 function of the vegan package (Oksanen et al., 2007) to test for pairwise differences (PERMANOVA) in pollinator composition among the three most-sampled ecoregions. Finally, to determine if any taxa were strongly associated with one or more of the ecoregions, we performed indicator species analysis using the multipatt function in the package indicspecies (De Caceres et al., 2016). This test produces values ranging from 0 (no association) to 1 (complete association). To compare the alpha (plot-level) diversity of bees, hover flies, butterflies, and all pollinators among the most-sampled ecoregions, we ran negative binomial models consisting of ecoregion and experimental forest as fixed and random effects, respectively. Total beta diversity, along with its individual turnover and nestedness components, were calculated for each experimental forest using the beta.div.comp function of the adespatial package (Dray et al., 2023). These metrics were then compared among the most-sampled ecoregions using generalized linear models. To compare gamma diversity among the most-sampled ecoregions, we used the iNext package (Hsieh et al., 2022) to produce rarefaction and extrapolation curves for richness (q=0), with statistical significance indicated by non-overlapping confidence intervals at the maximum reference sample size (Chao et al., 2014). To better understand how landscape and stand metrics differed among the most-sampled ecoregions, and how these differences might help explain patterns in pollinator diversity, we tested linear mixed effects models (lmer) relating wetland, conifer forest, and canopy openness to ecoregion. Canopy openness was square-root transformed to meet assumptions of normality. Because assumptions could not be met for wetland data, we made pairwise comparisons between ecoregions using the non-parametric Wilcoxon signed-rank tests using the pairwise.wilcox.test function of the stats package. To test whether pollinator seasonality differed among ecoregions, we compared when the richness and abundance of bees, butterflies, and hover flies peaked among the most-sampled ecoregions. First, for each sampled plot, we calculated the richness or abundance of each taxon by month relative to the that taxon’s highest monthly richness or abundance from the same plot, resulting in a standardized value ranging from 0-1. We then plotted the mean ± SE of these values by ecoregion and month to visualize differences in seasonality among ecoregions. We used pairwise Wilcoxon signed-rank tests to determine if the peak month of response varied among the most-sampled ecoregions. Finally, we used the Chao1 estimator to estimate the richness of bees, butterflies, hover flies, and all pollinators combined that could be collected using pan traps across our studied forests. These calculations were made for the entire region as well as for each of the three most-sampled ecoregions using the rareNMtests package (Cayuela & Gotelli, 2014). The estimates are based on the corresponding lists of collected species and their abundances. Results Diversity patterns We collected 266 pollinator species, including 172 bee species, 50 hover fly species, and 44 butterfly species (Table S2). Based on the Chao1 estimator, this represents about 71% of the species that could have been captured in this study using the same traps and sampling locations (Table S3). Across the entire region, bee richness was negatively correlated with the extent of both conifer forests and wetlands in the surrounding landscape and was positively correlated with canopy openness (Table 2, Figure 2). These patterns held mostly true for above- and below-ground nesting bee richness with the exception of there being no significant correlation between the richness of above-ground nesters and canopy openness (Table 2). When bee richness data from the Southeastern Mixed or Coastal Plain ecoregions were analyzed separately, the negative relationship with conifer forests was only significant among Southeastern Mixed forests while the negative correlation with wetlands was only significant among forests of the Coastal Plain (Table 2). No factor in our models was a significant predictor of hover fly or butterfly diversity (Table 2). Overall, total pollinator richness was positively correlated with canopy openness, negatively correlated with wetlands, and not significantly related to conifer forests (Table 2). Likelihood ratio tests largely corroborated determinations of statistical significance from our models, with the only discrepancy being the detection of a weakly significant negative correlation between bee richness in the Southeastern Mixed ecoregion and the extent of wetlands (Table S4). There was no significant spatial autocorrelation in the richness of bees (I=-0.10, p=0.48), hover flies (I=-0.06, p=0.99), butterflies (I=0.06, p=0.12), or overall pollinator richness (I=-0.11, p=0.39) among experimental forests. Alpha diversity differed among the most-sampled ecoregions (Figure S3). Butterfly richness was significantly lower in Central Appalachian plots than in the other ecoregions. Moreover, hover fly richness was significantly lower in Central Appalachian plots than in those from the Southeastern Mixed ecoregion (Figure S3). Total beta diversity did not differ among the three most-sampled ecoregions: Central Appalachian vs. Coastal Plain (Estimate=-0.04, t=-0.965, p=0.35); Central Appalachian vs. Southeastern Mixed (Estimate=-0.06, t=-1.43, p=0.18); and Coastal Plain vs. Southeastern Mixed (Estimate=-0.02, t=-0.57, p=0.58). The turnover and nestedness components of beta diversity also did not differ among these ecoregions (results not shown). Finally, butterfly gamma diversity differed significantly among the most-sampled ecoregions, with the Coastal Plain and Southeastern Mixed ecoregions having 2-3 times more species than Central Appalachia (Figure S2). However, no differences in gamma diversity among these three ecoregions were detected for bees, hover flies, or all pollinators combined (Figure S2). For estimates of bee, butterfly, and hover fly richness by ecoregion, see Table S3. Community composition NMDS ordination (stress=0.18) revealed a particularly distinct separation between the Central Appalachian and Coastal Plain ecoregions, with the other ecoregions being intermediate in composition (Figure 3). All of our landscape and stand variables, except for extent of wetlands, were significantly correlated with the NMDS axes (Table S5). PERMANOVA comparisons among the three most-sampled ecoregions revealed significant differences in pollinator composition among ecoregions (F 2,62 = 14.5135, p < 0.001, R-squared=0.21) and experimental forests (F 12,62 = 3.7352, p < 0.001, R-squared= 0.33). All pairwise comparisons were significant: Central Appalachian vs. Coastal Plain (F 1,45 = 11.041, p < 0.001); Central Appalachian vs. Southeastern Mixed (F 1,43 = 11.508, p < 0.001); and Coastal Plain vs. Southeastern Mixed (F 1,60 = 8.2707, p < 0.001). Based on indicator species analysis, 72 taxa were associated with one or more of the most-sampled ecoregions (Table S6). Nearly half of these (33) were associated with the Central Appalachian ecoregion while a further six were associated with both the Central Appalachian and Southeastern Mixed ecoregions. Sixteen and 13 species were associated with the Coastal Plain and Southeastern Mixed ecoregions, respectively, and a further four were associated with both of those ecoregions (Table S6). No species was found to be associated with both the Central Appalachian and Coastal Plain ecoregions. Landscape and stand metrics Significant differences in landscape and stand metrics were detected among the most-sampled ecoregions (Figure S4). Canopy openness was 2-3 times higher in Coastal Plain forests than in those from the other two ecoregions. Similarly, the extent of wetlands on the Coastal Plain was over 5 times greater than in the Southeastern Mixed ecoregion and far greater than in Central Appalachia where wetlands were essentially absent (Figure S4). We also found Central Appalachia to have about a fifth as much conifer forest on the surrounding landscape as compared to the other two ecoregions (Figure S4). However, there was no difference in conifer forest cover between the Coastal Plain and Southeastern Mixed ecoregions. Seasonality patterns We detected notable differences in the seasonality of pollinators among ecoregions (Figures 4 and S5). Most notably, bee richness and abundance peaked about two months earlier in the Central Appalachian ecoregion than in the Coastal Plain or Southeastern Mixed ecoregions (Figure 4), and these differences were statistically significant (Figure S6). The only other significant difference in seasonality concerned butterfly abundance which peaked later in the season in the Southeastern Mixed than in the two other most-sampled ecoregions (Figure S6). Discussion We sought to better understand how forest pollinators are distributed across the southeastern U.S. and how they are affected by landscape context and local forest conditions. We detected strong differences in composition among the most-sampled ecoregions. Pollinator communities collected from the Central Appalachian and Coastal Plain ecoregions were particularly distinct, underscoring the value of these ecoregions to regional biodiversity, whereas those from the Southeastern Mixed and other ecoregions were roughly intermediate in composition. In addition, we found striking differences in pollinator seasonality among ecoregions, with bee richness peaking about two months earlier in Central Appalachian forests than in other ecoregions. Differences in forest structure and composition likely drove many of the observed differences in pollinator composition and seasonality among ecoregions. For example, the combination of hardwood-dominance and closed canopy conditions likely explains the distinct and spring-seasonal bee fauna collected from Central Appalachian forests. Floral availability in both the canopies and understories of temperate deciduous forests is known to peak in early spring and to decline rapidly following leaf expansion and canopy closure, with many bees exhibiting similar seasonal patterns (Chase et al., 2023 ; Harrison et al., 2018 ; Urban-Mead et al., 2021 ). By contrast, the largely pine-dominated forests of the Coastal Plain tend to be more open (Figure S4) and provide flowers throughout the growing season (Ulyshen et al., 2023 ). The abundance and variety of flowers peak later in the year (Platt et al., 1988 ), which likely explains the mid-summer peak in bee richness in Coastal Plain forests. Interestingly, the seasonality of bees in the Southeastern Mixed ecoregion shared similarities with both of the other ecosystems. There was a main summer peak coinciding with that observed on the Coastal Plain as well as a smaller spring peak coinciding with that seen in Central Appalachian forests. It is clear from our results that pollinator activity is not confined to the spring months in most forests of the southeastern U.S. Only Central Appalachian forests exhibited the springtime peak in bee diversity reported from forests of the northeastern U.S. (Harrison et al., 2018 ). We detected a negative relationship between bee richness and conifer forest cover across the southeastern U.S. However, this pattern was stronger within the Southeastern Mixed ecoregion, an area historically dominated by mixed hardwood forests, than on the Coastal Plain where it was not statistically significant. This discrepancy may reflect conifer-dominated areas in the Southeastern Mixed region being primarily high density, closed-canopy pine plantations. By contrast, conifer-dominated areas on the Coastal Plain are a mix of high-density pine plantations and open-canopy pine stands, the latter often maintained with prescribed fire (Cummins et al., 2023 ). It is well established that bee diversity increases as pine stands become more open with age and with certain natural or anthropogenic disturbances (Dixon et al., 2022 ; Hanula et al., 2015 ), and it is likely that such conditions differ between ecoregions given the relatively frequent occurrence of fire in open forests on the Coastal Plain (Pyne, 1982 ). However, the conclusion that hardwood-dominated areas play an important role in supporting bee diversity across the southeastern U.S. is supported by work documenting the value of flowering trees, including even wind-pollinated taxa such as oaks, to many bees (Kraemer & Favi, 2005 ; Saunders, 2018 ; Urban-Mead et al., 2023 ). Further support comes from Traylor et al. ( 2022 ) who reported a positive but weak relationship between flowering tree diversity and bee diversity in Georgia. Despite the negative effects of conifer forest cover on bee richness, no such patterns were observed for hover flies or butterflies. In fact, we detected positive, though non-significant, correlations between conifer cover and the richness of both taxa. These patterns may help explain why butterfly alpha and gamma diversities appear to be lower in Central Appalachian forests than in those from the Southeastern Mixed or Coastal Plain ecosystems (Figures S2-3). The alpha diversity of hover flies was also significantly lower in Central Appalachian than in Southeastern Mixed forests. One possible explanation for the observed patterns in butterfly diversity concern skippers (Hesperidae) which feed primarily on grasses which are less available in closed forests than in the more open stands typical of the Coastal Plain (Dixon et al., 2022 ; Knapp et al., 2014 ). Indeed, over half of the skippers found to be significantly associated with one or more ecoregions in this study were associated with Coastal Plain forests (Table S6). We detected a negative correlation between bee richness and the extent of surrounding wetlands. This pattern held true for all bees combined as well as for both below- and above-ground nesting bees. Although these results are consistent with past work that also trapped fewer pollinator species in wetlands than in upland habitats (Begosh et al., 2020 ), other studies report a positive effect of wetlands on the abundance and richness of bees (Evans et al., 2018 ). Wetlands may be perceived to be more important when compared to highly disturbed habitats, such as row crops, than when compared to other semi-natural areas. The negative effect of wetlands on below-ground nesting bees reported in the current study is perhaps not surprising given that most species prefer to nest in well-drained soils (Harmon-Threatt, 2020 ) and are likely to be negatively affected by saturated or flooded conditions. However, this cannot fully explain our results given that the richness of above-ground nesting bees was similarly, though less strongly, negatively correlated with wetlands. The possibility that wetlands provide fewer floral resources than other habitats and therefore support a lower diversity of bees regardless of nesting substrate is not well supported given that hover flies and butterflies were unaffected by wetlands in the surrounding landscape (discussed below). Moreover, Begosh et al. ( 2020 ) found hymenopteran pollinators to forage more in wetlands at certain times of the year than in upland habitats in Nebraska. It could be that wetland conditions reduce nesting success even for bees that nest above ground (but see Simanonok et al., 2022 ). The fact that no negative relationship was observed between wetland cover and hover fly or butterfly richness is not surprising given that these groups are generally less dependent on soil conditions than are bees for their reproductive success. In fact, some common species of hover flies have aquatic larval stages and may be associated with wetlands (Skevington et al., 2019 ). In Nebraska, Begosh et al. ( 2020 ) reported higher hover fly abundance from wetland habitats than from uplands. It is clear from our results that habitat requirements vary among major pollinator taxa and that, as a group, these insects will benefit most from a diverse mix of forest types. As shown in previous work (Chase et al., 2023 ), we found a strong positive correlation between both total bee richness and the richness of below-ground nesting bees and canopy openness. While this may relate in part to a greater availability of floral resources in more open forests (Hanula et al., 2016 ; Platt et al., 2006 ), the fact that above-ground nesting bees were unaffected by canopy openness requires a different explanation. Frequent disturbances such as fire that maintain open canopies and patches of relatively bare ground are known to benefit ground-nesting bees in the region (Ulyshen et al., 2021 ) but this benefit may not extend to other groups of bees including above-ground nesters and forest-dependent bees in general. More work is needed to understand the relative importance of different flowering tree taxa to bees and other pollinators in order to best guide conservation efforts. For example, the results from this and previous work suggests efforts to create more open conditions in Central Appalachian forests may benefit bees and other pollinators (Campbell et al., 2018 ; Ulyshen et al., 2022 ), especially later in the season. However, it is important to understand how the spring-active bee fauna will be impacted by such interventions. The utilization of fire, for instance, can be expected to alter tree composition by disadvantaging diffuse-porous taxa such as Acer and Prunus in favor of ring-porous, fire-hardy, taxa such as Quercus and Carya (Nowacki & Abrams, 2008 ; Kreye et al., 2013; Alexander et al., 2021 ). The implications of such changes for pollinators remain unknown. Similarly, it is important to consider the distinction between forest-dependent pollinators and habitat generalists. Research from the northeastern US suggests about a third of the bee fauna native to that region is forest-dependent and that those species are more sensitive to forest cover than other species (Smith et al., 2021 ). Given these findings, it is necessary to take community composition and species-level responses into account rather than simply looking at differences in the total richness or abundance of all pollinators between habitats. Otherwise, there is a risk of managing for generalists at the expense of forest-specialists, which may ultimately reduce the total diversity of pollinators across the region. References Adams, M., Loughry, L. H., & Plaugher, L. L. (2008). Experimental forests and ranges of the USDA Forest Service. Revised edition. USDA Forest Service General Technical Report NE-321 . Alexander, H. D., Siegert, C., Brewer, J. S., Kreye, J., Lashley, M. A., McDaniel, J. K., Paulson, A. K., Renninger, H. J., & Varner, J. M. (2021). Mesophication of oak landscapes: Evidence, knowledge gaps, and future research. BioScience, 71 , 531-542. Allen, G., & Davies, R. G. (2023). Canopy sampling reveals hidden potential value of woodland trees for wild bee assemblages. Insect Conservation and Diversity, 16 (1), 33-46. doi:https://doi.org/10.1111/icad.12606 Bailey, R. G. (2016). Bailey's ecoregions and subregions of the United States, Puerto Rico, and the U.S. Virgin Islands. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0003. Barendregt, A., Zeegers, T., van Steenis, W., & Jongejans, E. (2022). Forest hoverfly community collapse: Abundance and species richness drop over four decades. Insect Conservation and Diversity, 15 (5), 510-521. doi:https://doi.org/10.1111/icad.12577 Baselga, A. (2010). Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography, 19 (1), 134-143. doi:https://doi.org/10.1111/j.1466-8238.2009.00490.x Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67 , 1-48. Baum, K. A., & Wallen, K. E. (2011). Potential Bias in Pan Trapping as a Function of Floral Abundance. 84 Journal of the Kansas Entomological Society (2), 155-159, 155. Bechtold, W. A., & Patterson, P. L. (2005). The enhanced forest inventory and analysis program--national sampling design and estimation procedures (No. 80). USDA Forest Service, Southern Research Station. Begosh, A., Smith, L. M., Park, C. N., McMurry, S. T., & Lagrange, T. G. (2020). Effects of wetland presence and upland land use on wild hymenopteran and dipteran pollinators in the rainwater basin of Nebraska, USA. Wetlands, 40 (5), 1017-1031. doi:10.1007/s13157-019-01244-w Campbell, J. W., Vigueira, P. A., Viguiera, C. C., & Greenberg, C. H. (2018). The effects of repeated prescribed fire and thinning on bees, wasps, and other flower visitors in the understory and midstory of a temperate forest in North Carolina. Forest Science, 64 (3), 299-306. doi:10.1093/forsci/fxx008 Cane, J. H., Minckley, R. L., & Kervin, L. J. (2000). Sampling bees (Hymenoptera: Apiformes) for pollinator community studies: Pitfalls of pan-trapping. Journal of the Kansas Entomological Society, 73 (4), 225-231. Carr, S. C., Robertson, K. M., & Peet, R. K. (2010). A vegetation classification of fire-dependent pinelands of florida. Castanea, 75 (2), 153-189, 137. Carroll, W. D., Kapeluck, P. R., Harper, R. A., & Van Lear, D. H. (2002). Background paper: Historical overview of the southern forest landscape and associated resources. In D. N. Wear & J. G. Gries (Eds.), Southern Forest Resource Assessment. Gen. Tech. Rep. SRS-53. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station (pp. 635). Cayuela, L., & Gotelli, N. J. (2014). rareNMtests: Ecological and biogeographical null model tests for comparing rarefaction curves. R Package v1. Vienna: The R Foundation for Statistical Computing. Chao, A., Gotelli, N. J., Hsieh, T. C., Sande, E. L., Ma, K. H., Colwell, R. K., & Ellison, A. M. (2014). Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecological Monographs, 84 , 45–67. Chase, M. H., Fraterrigo, J. M., Charles, B., & Harmon-Threatt, A. (2023). Wild bee response to forest management varies seasonally and is mediated by resource availability. Forest Ecology and Management, 548 , 121426. doi:https://doi.org/10.1016/j.foreco.2023.121426 Cummins, K., Noble, J., Varner, J. M., Robertson, K. M., Hiers, J. K., Nowell, H. K., & Simonson, E. (2023). The southeastern U.S. prescribed fire permit database: Hot spots and hot moments in prescribed fire across the southeastern U.S.A. Fire, 6 (10), 372. De Caceres, M., Jansen, F., & De Caceres, M. M. (2016). Package ‘indicspecies’. Indicators, 8 (1). Dewitz, J. (2023). National Land Cover Database (NLCD) 2021 Products: U.S. Geological Survey Data Release. USA: U.S Geological Survey. Dixon, C. M., Robertson, K. M., Ulyshen, M. D., & Sikes, B. A. (2022). Pine savanna restoration on agricultural landscapes: The path back to native savanna ecosystem services. Science of The Total Environment, 818 , 151715. doi:https://doi.org/10.1016/j.scitotenv.2021.151715 Dray, S., Bauman, D., Blanchet, G., Borcard, D., Clappe, S., Guenard, G., Jombart, T., Larocque, G., Legendre, P., Madi, N., Wagner, H. H., & Siberchicot, A. (2023). Package ‘adespatial: Multivariate multiscale spatial analysis’. 2023 . Evans, E., Smart, M., Cariveau, D., & Spivak, M. (2018). Wild, native bees and managed honey bees benefit from similar agricultural land uses. Agriculture, Ecosystems & Environment, 268 , 162-170. doi:https://doi.org/10.1016/j.agee.2018.09.014 Forister, M. L., Halsch, C. A., Nice, C. C., Fordyce, J. A., Dilts, T. E., Oliver, J. C., Prudic, K. L., Shapiro, A. M., Wilson, J. K., & Glassberg, J. (2021). Fewer butterflies seen by community scientists across the warming and drying landscapes of the American West. Science, 371 (6533), 1042-1045. doi:10.1126/science.abe5585 Gibbs, J. (2011). Revision of the metallic Lasioglossum (Dialictus) of eastern North America (Hymenoptera: Halictidae: Halictini). Zootaxa, 3073 , 1-216. Gibbs, J., Packer, L., Dumesh, S., & Danforth, B. N. (2013). Revision and reclassification of Lasioglossum ( Evylaeus ), L.( Hemihalictus ) and L.( Sphecodogastra ) in eastern North America (Hymenoptera: Apoidea: Halictidae). Zootaxa, 3672 (1), 1-117. Gilliam, F. S., & Platt, W. J. (1999). Effects of long-term fire exclusion on tree species composition and stand structure in an old-growth Pinus palustris (Longleaf pine) forest. Plant Ecology, 140 (1), 15-26. doi:10.1023/A:1009776020438 Glassberg, J., Minno, M. C., & Calhoun, J. V. (2000). Butterflies through binoculars : Oxford University Press. Hanberry, B. B., Bragg, D. C., & Alexander, H. D. (2020). Open forest ecosystems: An excluded state. Forest Ecology and Management, 472 , 118256. doi:https://doi.org/10.1016/j.foreco.2020.118256 Hanula, J. L., Horn, S., & O’Brien, J. J. (2015). Have changing forests conditions contributed to pollinator decline in the southeastern United States? Forest Ecology and Management, 348 , 142-152. doi:https://doi.org/10.1016/j.foreco.2015.03.044 Hanula, J. L., Ulyshen, M. D., & Horn, S. (2016). Conserving pollinators in North American forests: A review. Natural Areas Journal, 36 (4), 427-439. Harmon-Threatt, A. (2020). Influence of nesting characteristics on health of wild bee communities. Annual Review of Entomology, 65 (1), 39-56. doi:10.1146/annurev-ento-011019-024955 Harrison, T., Gibbs, J., & Winfree, R. (2018). Forest bees are replaced in agricultural and urban landscapes by native species with different phenologies and life-history traits. Global Change Biology, 24 (1), 287-296. doi:https://doi.org/10.1111/gcb.13921 Holland, J. D., Bert, D. G., & Fahrig, L. (2004). Determining the spatial scale of species' response to habitat. BioScience, 54 (3), 227-233. Hsieh, T. C., Ma, K. H., & Chao, A. (2022). iNEXT: Interpolation and extrapolation for species diversity. R package version 3.0.0, http://chao.stat.nthu.edu.tw/wordpress/software_download/. Joshi, N. K., Leslie, T., Rajotte, E. G., Kammerer, M. A., Otieno, M., & Biddinger, D. J. (2015). Comparative trapping efficiency to characterize bee abundance, diversity, and community composition in apple orchards. Annals of the Entomological Society of America, 108 (5), 785-799. doi:10.1093/aesa/sav057 Kanninen, M. (2010). Plantation forests: global perspectives. In J. Bauhus, P. van der Meer, & M. Kanninen (Eds.), Ecosystem Goods and Services from Plantation Forests (pp. 1-15). London, UK: Earthscan. Knapp, B. O., Walker, J. L., Wang, G. G., Hu, H., & Addington, R. N. (2014). Effects of overstory retention, herbicides, and fertilization on sub-canopy vegetation structure and functional group composition in loblolly pine forests restored to longleaf pine. Forest Ecology and Management , 320 , 149-160. Kraemer, M. E., & Favi, F. D. (2005). Flower phenology and pollen choice of Osmia lignaria (Hymenoptera: Megachilidae) in central Virginia. Environ Entomol, 34 (6), 1593-1605. doi:10.1603/0046-225x-34.6.1593 Krey, J. K., Varner, J. M., Hiers, J. K., & Mola, J. (2013). Toward a mechanism for eastern North America forest mesophication: differential litter drying across 17 species. Ecological Applications, 23 , 1976-1986. Lemmon, P. E. (1956). A spherical densiometer for estimating forest overstory density. Forest Science, 2 (4), 314-320. Mitchell, T. B. (1960). Bees of the Eastern United States Volume 1 : The North Carolina Agricultural Experiment Station. Mitchell, T. B. (1962). Bees of the Eastern United States Volume 2 . The North Carolina Agriculture Experiment Station. Moylett, H., Youngsteadt, E., & Sorenson, C. (2019). The impact of prescribed burning on native bee communities (Hymenoptera: Apoidea: Anthophila) in longleaf pine savannas in the North Carolina sandhills. Environmental Entomology, 49 (1), 211-219. doi:10.1093/ee/nvz156 Nowacki, G. J., & Abrams, M. D. (2008). The demise of fire and “mesophication” of forests in the eastern United States. BioScience, 58 , 123-138. Odanaka, K., Gibbs, J., Turley, N. E., Isaacs, R., & Brudvig, L. A. (2020). Canopy thinning, not agricultural history, determines early responses of wild bees to longleaf pine savanna restoration. Restoration Ecology, 28 (1), 138-146. doi:https://doi.org/10.1111/rec.13043 Oksanen, J., Kindt, R., Legendre, P., O’Hara, B., Stevens, M. H. H., & Oksanen, M. J. (2007). The vegan package. Community Ecology Package, 10 (631-637), 719. Oluoch, S., Lal, P., Wolde, B., Susaeta, A., Soto, J. R., Smith, M., & Adams, D. C. (2021). Public preferences for longleaf pine restoration programs in the southeastern United States. Forest Science, 67 (3), 265-274. doi:10.1093/forsci/fxab008 Orr, M. C., Hughes, A. C., Chesters, D., Pickering, J., Zhu, C.-D., & Ascher, J. S. (2021). Global patterns and drivers of bee distribution. Current Biology, 31 (3), 451-458.e454. doi:https://doi.org/10.1016/j.cub.2020.10.053 Paradis, E., & Schliep, K. (2019). ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics, 35 (3), 526-528. Peterson, D. W., & Reich, P. B. (2008). Fire frequency and tree canopy structure influence plant species diversity in a forest-grassland ecotone. Plant Ecology, 194 (1), 5-16. doi:10.1007/s11258-007-9270-4 Platt, W. J., Evans, G. W., & Davis, M. M. (1988). Effects of fire season on flowering of forbs and shrubs in longleaf pine forests. Oecologia, 76 (3), 353-363. doi:10.1007/BF00377029 Platt, W. J., Carr, S. M., Reilly, M., & Fahr, J. (2006). Pine savanna overstorey influences on ground-cover biodiversity. Applied Vegetation Science, 9 (1), 37-50. Powney, G. D., Carvell, C., Edwards, M., Morris, R. K. A., Roy, H. E., Woodcock, B. A., & Isaac, N. J. B. (2019). Widespread losses of pollinating insects in Britain. Nature Communications, 10 (1), 1018. doi:10.1038/s41467-019-08974-9 Prestemon, J. P., & Abt, R. C. (2002). Chapter 13: Timber products supply and demand. In D. N. Wear & J. G. Greis (Eds.), Southern Forest Resource Assessment. USDA Forest Service General Technical Report SRS-53 (pp. 299-325). Pyne, S. J. (1982). Fire in America - a cultural history of wildand and rural fire . Seattle and London: University of Washington Press. R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Rivers, J. W., Galbraith, S. M., Cane, J. H., Schultz, C. B., Ulyshen, M. D., & Kormann, U. G. (2018). A review of research needs for pollinators in managed conifer forests. Journal of Forestry, 116 (6), 563-572. doi:10.1093/jofore/fvy052 Roulston, T. a. H., Smith, S. A., & Brewster, A. L. (2007). A comparison of pan trap and intensive net sampling techniques for documenting a bee (Hymenoptera: Apiformes) fauna. Journal of the Kansas Entomological Society, 80 (2), 179-181. Saunders, M. E. (2018). Insect pollinators collect pollen from wind-pollinated plants: implications for pollination ecology and sustainable agriculture. Insect Conservation and Diversity, 11 (1), 13-31. doi:https://doi.org/10.1111/icad.12243 Simanonok, M. P., Powley, M., & Otto, C. R. (2022). Cavity-nesting bee nesting success across gradients of floral resources and land cover. Prairie Naturalist, Special Issue (1), 65-77. Skevington, J. H., Locke, M. M., Young, A. D., Moran, K., Crins, W. J., & Marshall, S. A. (2019). Field guide to the flower flies of northeastern North America . Smith, C., Harrison, T., Gardner, J., & Winfree, R. (2021). Forest-associated bee species persist amid forest loss and regrowth in eastern North America. Biological Conservation, 260 , 109202. doi:https://doi.org/10.1016/j.biocon.2021.109202 Taki, H., Kevan, P. G., & Ascher, J. S. (2007). Landscape effects of forest loss in a pollination system. Landscape Ecology, 22 , 1575-1587. Traylor, C. R., Ulyshen, M. D., Wallace, D., Loudermilk, E. L., Ross, C. W., Hawley, C., Atchison, R. A., Williams, J. L., & McHugh, J. V. (2022). Compositional attributes of invaded forests drive the diversity of insect functional groups. Global Ecology and Conservation, 35 , e02092. doi:https://doi.org/10.1016/j.gecco.2022.e02092 Ulyshen, M., Elliott, K., Scott, J., Horn, S., Clinton, P., Liu, N., Miniat, C. F., Caldwell, P., Oishi, C., Knoepp, J., & Bolstad, P. (2022). Effects of Rhododendron removal and prescribed fire on bees and plants in the southern Appalachians. Ecology and Evolution, 12 (3), e8677. doi:https://doi.org/10.1002/ece3.8677 Ulyshen, M., Robertson, K., Horn, S., & Dixon, C. (2023). Effects of season of fire on bee-flower interaction diversity in a fire-maintained pine savanna. 13 (8), e10450. doi:https://doi.org/10.1002/ece3.10450 Ulyshen, M. D., & Horn, S. (2023). Declines of bees and butterflies over 15 years in a forested landscape. Current Biology, 33 , 1346-1350. Ulyshen, M. D., Soon, V., & Hanula, J. L. (2010). On the vertical distribution of bees in a temperate deciduous forest. Insect Conservation and Diversity, 3 , 222-228. Ulyshen, M. D., Urban-Mead, K. R., Dorey, J., & Rivers, J. W. (2023). Forests are critically important to global pollinator diversity and enhance pollination in adjacent crops. Biological Reviews, 98 , 1118-1141. Ulyshen, M. D., Wilson, A. C., Ohlson, G. C., Pokswinksi, S. M., & Hiers, J. K. (2021). Frequent prescribed fires favour ground-nesting bees in southeastern U.S. forests. Insect Conservation and Diversity, 14 (4), 527-534. doi:https://doi.org/10.1111/icad.12484 Urban-Mead, K. R., Muñiz, P., Gillung, J., Espinoza, A., Fordyce, R., van Dyke, M., McArt, S. H., & Danforth, B. N. (2021). Bees in the trees: Diverse spring fauna in temperate forest edge canopies. Forest Ecology and Management, 482 , 118903. doi:10.1016/j.foreco.2020.118903 Urban-Mead, K. R., van Dyke, M., Muniz, P., Young, A., Danforth, B. N., & McArt, S. H. (2023). Early spring orchard pollinators spill over from resource-rich adjacent forest patches. Journal Applied Ecology, 60 , 553-564. Van Lear, D. H., Harper, R. A., Kapeluck, P. R., & Carroll, W. D. (2004). History of piedmont forests: Implications for current pine management. In Gen. Tech. Rep. SRS 71. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station (pp. 127-131). Watson, J., Wolf, A., & Ascher, J. (2011). Forested landscapes promote richness and abundance of native bees (Hymenoptera: Apoidea: Anthophila) in Wisconsin apple orchards. Environmental Entomology, 40 , 621–632. Williams, N. M., Minckley, R. L., & Silveira, F. A. (2001). Variation in native bee faunas and its implications for detecting community changes. Conservation Ecology, 5 , 7. Zhang, D., & Zhang, M. D. (2022). Package ‘rsq’. R-Squared Related Measures. Available online: https://cran. r-project. org/web/packages/rsq/rsq. pdf . Tables Tables 1 to 2 are available in the Supplementary Files section Additional Declarations The authors declare no competing interests. Supplementary Files Tables.docx Cite Share Download PDF Status: Published Journal Publication published 03 Jun, 2024 Read the published version in Diversity and Distributions → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Oishi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chris","middleName":"","lastName":"Oishi","suffix":""},{"id":289799167,"identity":"14c3e62b-7416-4255-b71b-e196c05a312f","order_by":21,"name":"Shawna Reid","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shawna","middleName":"","lastName":"Reid","suffix":""},{"id":289799168,"identity":"e057c227-ba6c-4c1b-adb1-437a454b2860","order_by":22,"name":"Samm Reynolds","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Samm","middleName":"","lastName":"Reynolds","suffix":""},{"id":289799169,"identity":"536f2784-24e6-4e1b-895c-9d7ed30474e2","order_by":23,"name":"Kevin Robertson","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Robertson","suffix":""},{"id":289799170,"identity":"48fefcf1-e0ba-4b39-850b-6e1252405882","order_by":24,"name":"Dan Saenz","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Saenz","suffix":""},{"id":289799171,"identity":"d14753a3-3d22-45d3-b83c-0c43328fa15b","order_by":25,"name":"Nathan Schiff","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nathan","middleName":"","lastName":"Schiff","suffix":""},{"id":289799172,"identity":"58eff0ee-deaa-4fd0-b0b0-9ab54073fa52","order_by":26,"name":"Brian Scholtens","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"","lastName":"Scholtens","suffix":""},{"id":289799173,"identity":"648613bd-40c4-4c7a-a119-31789f24ab7a","order_by":27,"name":"Joel Scott","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Joel","middleName":"","lastName":"Scott","suffix":""},{"id":289799174,"identity":"f30bc67a-e056-4456-9290-bf08d1aef77a","order_by":28,"name":"Marty Spetich","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Marty","middleName":"","lastName":"Spetich","suffix":""},{"id":289799175,"identity":"5085edac-9721-4b69-a9f5-8fa3bcbe4ce1","order_by":29,"name":"Mary Sword","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mary","middleName":"","lastName":"Sword","suffix":""},{"id":289799176,"identity":"f4136ff7-2dda-44b0-ba39-24f6fea16132","order_by":30,"name":"Melanie Taylor","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"Taylor","suffix":""},{"id":289799177,"identity":"7694dd22-59d4-4ecc-b61f-f311fb561189","order_by":31,"name":"John Willis","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Willis","suffix":""},{"id":289799178,"identity":"82f343b8-0335-4a88-af9c-a1462c5595e8","order_by":32,"name":"Andrew Young","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Young","suffix":""}],"badges":[],"createdAt":"2024-04-10 16:03:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4248368/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4248368/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1111/ddi.13869","type":"published","date":"2024-06-03T07:43:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54472512,"identity":"28f5ba3d-151e-4b67-9d51-ff68f1c6a166","added_by":"auto","created_at":"2024-04-11 05:25:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":234089,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of the studied Experimental Forests (red dots) in relation to Bailey’s ecoregions across the southeastern U.S.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4248368/v1/b20fd28feaac6d0c06f83662.png"},{"id":54472513,"identity":"ce851d12-421a-4b1e-8bec-b199debaa473","added_by":"auto","created_at":"2024-04-11 05:25:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":200455,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between bee richness and significant predictors across the southeastern U.S. The variables were standardized to have a mean of 0 and a standard deviation of 1.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4248368/v1/7c6c0491fe24b4a8a3d07ddd.png"},{"id":54472515,"identity":"89f4dbca-b96a-4886-98ff-b156d3d788f7","added_by":"auto","created_at":"2024-04-11 05:25:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129817,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS ordination reflecting differences in pollinator communities (bees, butterflies, and hover flies combined) among ecoregions and in relation to significantly correlated landscape and stand metrics. The symbols represent the 97 plots from which pollinators were collected.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4248368/v1/3a6d6bb9982b3fb90d660a30.png"},{"id":54472516,"identity":"16d6cd74-0646-4c4e-8a9a-c25dc8f597c1","added_by":"auto","created_at":"2024-04-11 05:25:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":283552,"visible":true,"origin":"","legend":"\u003cp\u003eMean ± SE richness and abundance of bees, hoverflies, and butterflies by Ecoregion and month. Values were relativized by the maximum observed monthly value for each plot.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4248368/v1/fbe35fc2f3c2ba060990b19c.png"},{"id":58715325,"identity":"01138249-6cf8-48b6-b37d-e964d7379a16","added_by":"auto","created_at":"2024-06-20 07:43:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1233416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4248368/v1/935f48da-b594-4706-b901-bebd2edbbf54.pdf"},{"id":54472514,"identity":"8015a551-ae40-4425-a67e-72a46b7e0ec6","added_by":"auto","created_at":"2024-04-11 05:25:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21162,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4248368/v1/40eae2f39622d05cd312969c.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSpatiotemporal patterns of forest pollinator diversity across the southeastern United States\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite growing concerns over declines in pollinating insects (Barendregt et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Forister et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Powney et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ulyshen \u0026amp; Horn, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), there is surprisingly little information on how the diversity of these organisms is distributed across time and space, especially at transregional scales. For conservation efforts to be effective over large areas, it is critical to understand how different ecoregions contribute to regional pollinator diversity, and how local diversity is influenced by landscape context and local conditions. Unfortunately, such fundamental information is lacking for many parts of the world, and even knowing which pollinator species are present remains a major knowledge gap for many land managers (Rivers et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While museum records provide valuable insights into the distribution of species, such data are typically from specimens collected over many years using a variety of methods and rarely include detailed information on local habitat conditions (Orr et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, because pollinator populations exhibit strong inter-annual variation (Williams et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and all sampling methods are more effective for some taxa than others (Cane et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Joshi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), there is a strong need for coordinated efforts to simultaneously and consistently sample pollinators over large geographic areas.\u003c/p\u003e \u003cp\u003eThere is increasing awareness that forests play key roles in supporting pollinator diversity, which in turn provides pollination services to adjacent habitats, including crops (Ulyshen et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, many questions remain about how forest composition and structure shape these communities. A growing number of studies indicate that pollinators are active within the canopies of temperate forests (Allen \u0026amp; Davies, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ulyshen et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Urban-Mead et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Urban-Mead et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and utilize abundant pollen and nectar from flowering trees. Even wind-pollinated taxa, such as oaks (\u003cem\u003eQuercus\u003c/em\u003e spp.), can serve as important pollen sources for many bees (Kraemer \u0026amp; Favi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Saunders, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Urban-Mead et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By contrast, conifer-dominated forests provide little or no floral resources in the canopy, possibly resulting in less favorable conditions for pollinators. This is significant, as conifer forests are favored throughout the world for timber and pulp production (Kanninen, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For example, the forests of the southeastern U.S. are the largest producer of industrial roundwood in the world and most of that production comes from densely stocked\u0026mdash;often planted\u0026mdash;stands of intensively managed pines native to the region (Prestemon \u0026amp; Abt, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eForests of the southeastern U.S. were historically far more open than many are today (Carroll, Kapeluck, Harper \u0026amp; Van Lear, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Van Lear et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Pine dominance was greatest on the Coastal Plain where the frequent occurrence of fires played a key role in sustaining savanna-like conditions over large areas. Such open conditions support a high abundance and diversity of flowering plants in the herbaceous layer (Carr et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) which, in turn, benefit pollinator communities (Hanula et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Moylett et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Odanaka et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Closed-canopy conditions, by contrast, often result in suppressed floral availability or concentrate flowering to the early spring months prior to canopy closure (Peterson \u0026amp; Reich, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Taki et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Watson et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Despite strong interest in restoring open woodlands for a wide range of benefits including conserving endangered invertebrates and reducing the risk of bark beetle outbreaks (Dixon et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Oluoch et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Van Lear et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), relatively few of the region\u0026rsquo;s pine and oak-dominated forests currently have open stand structures (Gilliam \u0026amp; Platt, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Those that do typically arose from mechanical thinning, wind damage, low initial stocking levels, or the application of prescribed fire (Hanberry et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Previous researchers suggested that the closure of southeastern U.S. forests over the past century may have contributed to pollinator declines (Hanula et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address some of these concerns, we sampled bees, hover flies, and butterflies on nineteen U.S. Forest Service experimental forests which together provide a good representation of major forest types typical of the southeastern U.S. Our objectives were to 1) collect baseline information on the diversity of these pollinators across the region, 2) determine how the diversity and composition of pollinator communities differ among major ecoregions, 3) explore the relationship between pollinator diversity and landscape context (e.g., amount of surrounding conifer forest and wetland), and 4) compare pollinator seasonality among ecoregions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy sites\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe U.S. Forest Service Southern Research Station\u0026rsquo;s (SRS) experimental forest network (EFN) consists of 19 units (Table 1) across the southeastern U.S. (Figure 1). Established decades ago, these experimental forests were intended to address a variety of research needs, from silviculture to naval stores production to forest genetics, erosion control, and hydrology. The SRS EFN covers seven of the nine distinct southeastern ecoregions as defined by Bailey (2016), including the Central Appalachian Forest, Southeastern Mixed Forest, Outer Coastal Plain Mixed Forest, Eastern Broadleaf Forest, Lower Mississippi Riverine Forest, Ouachita Mixed Forest, and Ozark Broadleaf Forest. The first three of these were the most sampled in this study (including three or more experimental forests) and correspond to the Southern Appalachians, Piedmont, and Coastal Plain physiographic regions, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn 2018-2019, a series of plots (coordinates) were systematically established on all experimental forests in the SRS EFN to provide a standardized and uniform framework for large scale analyses. The ultimate goal is to collect long-term forest data at these locations following the Forest Service\u0026rsquo;s Forest Inventory and Analysis (FIA) protocol (Bechtold \u0026amp; Patterson, 2005). However, such data have not yet been collected at most of the plots sampled in this study, and were thus not available for the current analysis. From this collection of plots, we randomly selected five from each experimental forest for use in this study. We selected two additional plots at Chipola EF because there was a possibility that some would be disturbed by planned management operations, and would therefore need to be dropped from the study. However, this did not happen, resulting in a total of 97 plots across the network. The distance between plots within experimental forests was at least 500 m while the distance between experimental forests ranged from ~20-1,400 km (Figure 1). All plots were forested, although canopy openness and successional stage differed considerably depending on recent disturbance events (e.g., Hurricane Michael) or management activities (e.g., mechanical thinning or prescribed burning). Based on National Land Cover Data from 2021, the mean and median percent forest cover within 500m of the plots were 86.8% and 91.0%, respectively, with a range of 31.3-100%.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePollinator sampling and identification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePollinators were sampled on all 19 experimental forests using colored pan traps. Although pan traps provide a standardized, simple, and effective method for sampling pollinators in forests (Ulyshen et al., 2022) they are known to be more effective at collecting some taxa than others (Baum \u0026amp; Wallen, 2011; Cane et al., 2000) and can also under-sample pollinators in areas with an abundance of flowers (Baum \u0026amp; Wallen, 2011). While netting off flowers would have improved the study (Roulston et al., 2007), we limited sampling to pan trapping given limits of time and personnel as well as the logistical challenges inherent to visiting remote sites across such a large geographic area. Although we did not conduct floral surveys in this study, we did measure canopy openness (see plot measurements, below) which correlates with flower availability (Chase et al., 2023) and include this term in models of pollinator richness (see statistical analysis, below). Sampling involved placing two sets of three pan traps at each plot, with the traps filled with soapy water during operation. Each set was situated 5 m to the north or south of plot center and consisted of a yellow, a blue and a white plastic bowl (15.5 cm opening, ~400 ml capacity) suspended 20-30 cm above the ground on wire stands. Sets were oriented east-west with a 5 m separation between traps. The traps were used as they were manufactured and were not painted.\u003c/p\u003e\n\u003cp\u003eTraps were operated once a month for three days at a time from March to September 2021. Target dates for sampling were established at the beginning of the study. However, because weather conditions differed considerably across our study region, the exact dates of sampling varied somewhat (Table S1) to ensure that trapping took place at all locations during periods of clear weather when pollinators would be most active. All bees, hover flies, and butterflies were pinned and identified by the senior author (bees and butterflies) and SR and ADY (hover flies) to species (or rarely to morphospecies) using online (discoverlife.org) and printed resources (Gibbs, 2011; Gibbs et al., 2013; Glassberg et al., 2000; Mitchell, 1960, 1962; Skevington et al., 2019). Voucher specimens have been deposited in the senior author\u0026rsquo;s reference collection. Data were pooled by plot and sampling period for analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePlot measurements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used a convex densiometer to measure canopy openness at the center of each plot (Lemmon, 1956). Measurements were made while facing each cardinal direction in May, June, and July and we used the average of these readings as the percent canopy openness for each plot. Measurements at these times of year captured the maximum shade condition of overstory trees across our study area. We also collected information on stand basal area and overstory composition by recording the diameter and species of all trees (\u0026gt;20 cm at 1.4 m above the ground) within a 0.1 ha circular area centered on each plot.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis, performed in R (R Core Team, 2022) and ArcGis Pro, consisted of two parts. First, we related the alpha diversity (i.e., local species richness at the plot level) and community composition of pollinators to land cover data and stand metrics across all plots. We then tested for differences in community composition, pollinator diversity (alpha, beta, and gamma as defined below), and seasonal patterns of alpha diversity and abundance among the most-sampled ecoregions (i.e., those represented by three or more experimental forests: Central Appalachian, Coastal Plain and Southeastern Mixed, Figure 1). Whereas alpha diversity is defined as the number of species observed in a plot, beta diversity represents the dissimilarity in diversity among plots within each experimental forest and consists of turnover (species replacement) and nestedness (species loss) components (Baselga, 2010). Finally, gamma diversity refers to the total diversity within each ecoregion.\u003c/p\u003e\n\u003cp\u003eWe used NLCD (National Land Cover Database, https://www.usgs.gov/centers/eros/science/national-land-cover-database) data from 2021 (Dewitz, 2023), with a resolution of 30 x 30 m, to calculate the percentage of each land cover category surrounding our plots. This was done at five spatial scales (radii of 250 m, 500 m, 1 km, 2 km, and 4 km). Land cover metrics tested for multicollinearity at each spatial scale were percentages of: non-wetland forest (i.e., deciduous forest + evergreen forest + mixed forest), non-wetland open land (i.e., grassland/herbaceous + shrub/scrub + pasture/hay + barren land + cultivated crops + developed high, medium, and low intensities + developed_open), wetland (i.e., woody wetlands + emergent herbaceous wetlands), and evergreen forest (hereafter referred to as conifer forest). Of these, non-wetland forest was dropped based on a high variance inflation factor (VIF) value, and non-wetland open land was dropped based on subsequent problems with model convergence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter confirming a lack of spatial autocorrelation for any of the response variables among experimental forests based on Moran\u0026rsquo;s I using the package ape (Paradis \u0026amp; Schliep, 2019), we used the lme4 package (Bates et al., 2015) to produce negative binomial regression models (glmer.nb) of pollinator richness. To determine which spatial scale to use in the final models of pollinator diversity, we first tested separate models for each of the five spatial scales mentioned above and compared the R-squared values, using the rsq.glmm function of the rsq package (Zhang \u0026amp; Zhang, 2022), i.e., the scale of effect: Holland et al., (2004). This was done separately for the richness of bees, hover flies, butterflies, and all pollinators combined. Based on this analysis, we determined landscape metrics to be most informative at the scale of 500 m for bee, butterfly, and total pollinator richness, and at the scale of 2 km for hover fly richness (Figure S1), so these were the scales used in the corresponding models.\u003c/p\u003e\n\u003cp\u003eOur final model of pollinator richness included canopy openness, wetland, and conifer forest as fixed effects (all scaled to have a mean of 0 and a standard deviation of 1) and experimental forest as a random effect to account for the lack of independence among plots within each experimental forest. Additional terms for the number of tree genera and basal area were not included due to multicollinearity. Given our study\u0026rsquo;s large sample size, we used the default Wald Z-test to assess significance. However, to confirm these conclusions, we also conducted likelihood ratio tests by dropping predictors one at a time (drop1) and using the chi-square test to assess significance. Using the same methods as described above, we calculated R-squared for each model and the contributions of fixed and random terms to the total.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further explore how the relationship between bee richness and conifer forest may vary among ecoregions, we re-ran the model after limiting to the Southeastern Mixed or Coastal Plain ecoregions. We did not examine this relationship for other ecoregions due to either low replication (e.g., some ecoregions were represented by just one experimental forest) or, as for the Central Appalachian ecoregion, a low coverage of conifer forests. Moreover, we did not repeat these analyses for hover flies or butterflies because conifer forest was not a significant predictor in the main models for those groups.\u003c/p\u003e\n\u003cp\u003eFor comparisons of pollinator community composition (all groups combined) among plots, we performed non-metric multidimensional scaling (NMDS) on a Bray-Curtis distance matrix using the vegan package (Oksanen et al., 2007). This was based on Hellinger-transformed abundance data which involves dividing each count by the sample total and taking the square root of each resulting proportion. We then conducted envfit tests for significant correlations between the resulting axes and the following variables of interest: canopy openness, wetlands within 500 m, conifer forests within 500 m, basal area, and the number of tree genera. Then we used the adonis2 function of the vegan package (Oksanen et al., 2007) to test for pairwise differences (PERMANOVA) in pollinator composition among the three most-sampled ecoregions. Finally, to determine if any taxa were strongly associated with one or more of the ecoregions, we performed indicator species analysis using the multipatt function in the package indicspecies (De Caceres et al., 2016). This test produces values ranging from 0 (no association) to 1 (complete association).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo compare the alpha (plot-level) diversity of bees, hover flies, butterflies, and all pollinators among the most-sampled ecoregions, we ran negative binomial models consisting of ecoregion and experimental forest as fixed and random effects, respectively. Total beta diversity, along with its individual turnover and nestedness components, were calculated for each experimental forest using the beta.div.comp function of the adespatial package (Dray et al., 2023). These metrics were then compared among the most-sampled ecoregions using generalized linear models. To compare gamma diversity among the most-sampled ecoregions, we used the iNext package (Hsieh et al., 2022) to produce rarefaction and extrapolation curves for richness (q=0), with statistical significance indicated by non-overlapping confidence intervals at the maximum reference sample size (Chao et al., 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo better understand how landscape and stand metrics differed among the most-sampled ecoregions, and how these differences might help explain patterns in pollinator diversity, we tested linear mixed effects models (lmer) relating wetland, conifer forest, and canopy openness to ecoregion. Canopy openness was square-root transformed to meet assumptions of normality. Because assumptions could not be met for wetland data, we made pairwise comparisons between ecoregions using the non-parametric Wilcoxon signed-rank tests using the pairwise.wilcox.test function of the stats package.\u003c/p\u003e\n\u003cp\u003eTo test whether pollinator seasonality differed among ecoregions, we compared when the richness and abundance of bees, butterflies, and hover flies peaked among the most-sampled ecoregions. First, for each sampled plot, we calculated the richness or abundance of each taxon by month relative to the that taxon\u0026rsquo;s highest monthly richness or abundance from the same plot, resulting in a standardized value ranging from 0-1. We then plotted the mean \u0026plusmn; SE of these values by ecoregion and month to visualize differences in seasonality among ecoregions. We used pairwise Wilcoxon signed-rank tests to determine if the peak month of response varied among the most-sampled ecoregions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, we used the Chao1 estimator to estimate the richness of bees, butterflies, hover flies, and all pollinators combined that could be collected using pan traps across our studied forests. These calculations were made for the entire region as well as for each of the three most-sampled ecoregions using the rareNMtests package (Cayuela \u0026amp; Gotelli, 2014). The estimates are based on the corresponding lists of collected species and their abundances.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eDiversity patterns\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe collected 266 pollinator species, including 172 bee species, 50 hover fly species, and 44 butterfly species (Table S2). Based on the Chao1 estimator, this represents about 71% of the species that could have been captured in this study using the same traps and sampling locations (Table S3). Across the entire region, bee richness was negatively correlated with the extent of both conifer forests and wetlands in the surrounding landscape and was positively correlated with canopy openness (Table 2, Figure 2). These patterns held mostly true for above- and below-ground nesting bee richness with the exception of there being no significant correlation between the richness of above-ground nesters and canopy openness (Table 2). When bee richness data from the Southeastern Mixed or Coastal Plain ecoregions were analyzed separately, the negative relationship with conifer forests was only significant among Southeastern Mixed forests while the negative correlation with wetlands was only significant among forests of the Coastal Plain (Table 2). No factor in our models was a significant predictor of hover fly or butterfly diversity (Table 2). Overall, total pollinator richness was positively correlated with canopy openness, negatively correlated with wetlands, and not significantly related to conifer forests (Table 2). Likelihood ratio tests largely corroborated determinations of statistical significance from our models, with the only discrepancy being the detection of a weakly significant negative correlation between bee richness in the Southeastern Mixed ecoregion and the extent of wetlands (Table S4). There was no significant spatial autocorrelation in the richness of bees (I=-0.10, p=0.48), hover flies (I=-0.06, p=0.99), butterflies (I=0.06, p=0.12), or overall pollinator richness (I=-0.11, p=0.39) among experimental forests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlpha diversity differed among the most-sampled ecoregions (Figure S3). Butterfly richness was significantly lower in Central Appalachian plots than in the other ecoregions. Moreover, hover fly richness was significantly lower in Central Appalachian plots than in those from the Southeastern Mixed ecoregion (Figure S3). Total beta diversity did not differ among the three most-sampled ecoregions: Central Appalachian vs. Coastal Plain (Estimate=-0.04, t=-0.965, p=0.35); Central Appalachian vs. Southeastern Mixed (Estimate=-0.06, t=-1.43, p=0.18); and Coastal Plain vs. Southeastern Mixed (Estimate=-0.02, t=-0.57, p=0.58). The turnover and nestedness components of beta diversity also did not differ among these ecoregions (results not shown). Finally, butterfly gamma diversity differed significantly among the most-sampled ecoregions, with the Coastal Plain and Southeastern Mixed ecoregions having 2-3 times more species than Central Appalachia (Figure S2). However, no differences in gamma diversity among these three ecoregions were detected for bees, hover flies, or all pollinators combined (Figure S2). For estimates of bee, butterfly, and hover fly richness by ecoregion, see Table S3.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCommunity composition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNMDS ordination (stress=0.18) revealed a particularly distinct separation between the Central Appalachian and Coastal Plain ecoregions, with the other ecoregions being intermediate in composition (Figure 3). All of our landscape and stand variables, except for extent of wetlands, were significantly correlated with the NMDS axes (Table S5). PERMANOVA comparisons among the three most-sampled ecoregions revealed significant differences in pollinator composition among ecoregions (F\u003csub\u003e2,62\u0026nbsp;\u003c/sub\u003e= 14.5135, p \u0026lt; 0.001, R-squared=0.21) and experimental forests (F\u003csub\u003e12,62\u0026nbsp;\u003c/sub\u003e= 3.7352, p \u0026lt; 0.001, R-squared= 0.33). All pairwise comparisons were significant: Central Appalachian vs. Coastal Plain (F\u003csub\u003e1,45\u0026nbsp;\u003c/sub\u003e= 11.041, p \u0026lt; 0.001); Central Appalachian vs. Southeastern Mixed (F\u003csub\u003e1,43\u003c/sub\u003e = 11.508, p \u0026lt; 0.001); and Coastal Plain vs. Southeastern Mixed (F\u003csub\u003e1,60\u0026nbsp;\u003c/sub\u003e= 8.2707, p \u0026lt; 0.001). Based on indicator species analysis, 72 taxa were associated with one or more of the most-sampled ecoregions (Table S6). Nearly half of these (33) were associated with the Central Appalachian ecoregion while a further six were associated with both the Central Appalachian and Southeastern Mixed ecoregions. Sixteen and 13 species were associated with the Coastal Plain and Southeastern Mixed ecoregions, respectively, and a further four were associated with both of those ecoregions (Table S6). No species was found to be associated with both the Central Appalachian and Coastal Plain ecoregions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLandscape and stand metrics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSignificant differences in landscape and stand metrics were detected among the most-sampled ecoregions (Figure S4). Canopy openness was 2-3 times higher in Coastal Plain forests than in those from the other two ecoregions. Similarly, the extent of wetlands on the Coastal Plain was over 5 times greater than in the Southeastern Mixed ecoregion and far greater than in Central Appalachia where wetlands were essentially absent (Figure S4). We also found Central Appalachia to have about a fifth as much conifer forest on the surrounding landscape as compared to the other two ecoregions (Figure S4). However, there was no difference in conifer forest cover between the Coastal Plain and Southeastern Mixed ecoregions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSeasonality patterns\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe detected notable differences in the seasonality of pollinators among ecoregions (Figures 4 and S5). Most notably, bee richness and abundance peaked about two months earlier in the Central Appalachian ecoregion than in the Coastal Plain or Southeastern Mixed ecoregions (Figure 4), and these differences were statistically significant (Figure S6). The only other significant difference in seasonality concerned butterfly abundance which peaked later in the season in the Southeastern Mixed than in the two other most-sampled ecoregions (Figure S6).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe sought to better understand how forest pollinators are distributed across the southeastern U.S. and how they are affected by landscape context and local forest conditions. We detected strong differences in composition among the most-sampled ecoregions. Pollinator communities collected from the Central Appalachian and Coastal Plain ecoregions were particularly distinct, underscoring the value of these ecoregions to regional biodiversity, whereas those from the Southeastern Mixed and other ecoregions were roughly intermediate in composition. In addition, we found striking differences in pollinator seasonality among ecoregions, with bee richness peaking about two months earlier in Central Appalachian forests than in other ecoregions.\u003c/p\u003e \u003cp\u003eDifferences in forest structure and composition likely drove many of the observed differences in pollinator composition and seasonality among ecoregions. For example, the combination of hardwood-dominance and closed canopy conditions likely explains the distinct and spring-seasonal bee fauna collected from Central Appalachian forests. Floral availability in both the canopies and understories of temperate deciduous forests is known to peak in early spring and to decline rapidly following leaf expansion and canopy closure, with many bees exhibiting similar seasonal patterns (Chase et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Harrison et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Urban-Mead et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By contrast, the largely pine-dominated forests of the Coastal Plain tend to be more open (Figure S4) and provide flowers throughout the growing season (Ulyshen et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The abundance and variety of flowers peak later in the year (Platt et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), which likely explains the mid-summer peak in bee richness in Coastal Plain forests. Interestingly, the seasonality of bees in the Southeastern Mixed ecoregion shared similarities with both of the other ecosystems. There was a main summer peak coinciding with that observed on the Coastal Plain as well as a smaller spring peak coinciding with that seen in Central Appalachian forests. It is clear from our results that pollinator activity is not confined to the spring months in most forests of the southeastern U.S. Only Central Appalachian forests exhibited the springtime peak in bee diversity reported from forests of the northeastern U.S. (Harrison et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe detected a negative relationship between bee richness and conifer forest cover across the southeastern U.S. However, this pattern was stronger within the Southeastern Mixed ecoregion, an area historically dominated by mixed hardwood forests, than on the Coastal Plain where it was not statistically significant. This discrepancy may reflect conifer-dominated areas in the Southeastern Mixed region being primarily high density, closed-canopy pine plantations. By contrast, conifer-dominated areas on the Coastal Plain are a mix of high-density pine plantations and open-canopy pine stands, the latter often maintained with prescribed fire (Cummins et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is well established that bee diversity increases as pine stands become more open with age and with certain natural or anthropogenic disturbances (Dixon et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hanula et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and it is likely that such conditions differ between ecoregions given the relatively frequent occurrence of fire in open forests on the Coastal Plain (Pyne, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). However, the conclusion that hardwood-dominated areas play an important role in supporting bee diversity across the southeastern U.S. is supported by work documenting the value of flowering trees, including even wind-pollinated taxa such as oaks, to many bees (Kraemer \u0026amp; Favi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Saunders, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Urban-Mead et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Further support comes from Traylor et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) who reported a positive but weak relationship between flowering tree diversity and bee diversity in Georgia.\u003c/p\u003e \u003cp\u003eDespite the negative effects of conifer forest cover on bee richness, no such patterns were observed for hover flies or butterflies. In fact, we detected positive, though non-significant, correlations between conifer cover and the richness of both taxa. These patterns may help explain why butterfly alpha and gamma diversities appear to be lower in Central Appalachian forests than in those from the Southeastern Mixed or Coastal Plain ecosystems (Figures S2-3). The alpha diversity of hover flies was also significantly lower in Central Appalachian than in Southeastern Mixed forests. One possible explanation for the observed patterns in butterfly diversity concern skippers (Hesperidae) which feed primarily on grasses which are less available in closed forests than in the more open stands typical of the Coastal Plain (Dixon et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Knapp et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Indeed, over half of the skippers found to be significantly associated with one or more ecoregions in this study were associated with Coastal Plain forests (Table S6).\u003c/p\u003e \u003cp\u003eWe detected a negative correlation between bee richness and the extent of surrounding wetlands. This pattern held true for all bees combined as well as for both below- and above-ground nesting bees. Although these results are consistent with past work that also trapped fewer pollinator species in wetlands than in upland habitats (Begosh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), other studies report a positive effect of wetlands on the abundance and richness of bees (Evans et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Wetlands may be perceived to be more important when compared to highly disturbed habitats, such as row crops, than when compared to other semi-natural areas. The negative effect of wetlands on below-ground nesting bees reported in the current study is perhaps not surprising given that most species prefer to nest in well-drained soils (Harmon-Threatt, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and are likely to be negatively affected by saturated or flooded conditions. However, this cannot fully explain our results given that the richness of above-ground nesting bees was similarly, though less strongly, negatively correlated with wetlands. The possibility that wetlands provide fewer floral resources than other habitats and therefore support a lower diversity of bees regardless of nesting substrate is not well supported given that hover flies and butterflies were unaffected by wetlands in the surrounding landscape (discussed below). Moreover, Begosh et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found hymenopteran pollinators to forage more in wetlands at certain times of the year than in upland habitats in Nebraska. It could be that wetland conditions reduce nesting success even for bees that nest above ground (but see Simanonok et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fact that no negative relationship was observed between wetland cover and hover fly or butterfly richness is not surprising given that these groups are generally less dependent on soil conditions than are bees for their reproductive success. In fact, some common species of hover flies have aquatic larval stages and may be associated with wetlands (Skevington et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Nebraska, Begosh et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported higher hover fly abundance from wetland habitats than from uplands. It is clear from our results that habitat requirements vary among major pollinator taxa and that, as a group, these insects will benefit most from a diverse mix of forest types.\u003c/p\u003e \u003cp\u003eAs shown in previous work (Chase et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we found a strong positive correlation between both total bee richness and the richness of below-ground nesting bees and canopy openness. While this may relate in part to a greater availability of floral resources in more open forests (Hanula et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Platt et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), the fact that above-ground nesting bees were unaffected by canopy openness requires a different explanation. Frequent disturbances such as fire that maintain open canopies and patches of relatively bare ground are known to benefit ground-nesting bees in the region (Ulyshen et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) but this benefit may not extend to other groups of bees including above-ground nesters and forest-dependent bees in general. More work is needed to understand the relative importance of different flowering tree taxa to bees and other pollinators in order to best guide conservation efforts. For example, the results from this and previous work suggests efforts to create more open conditions in Central Appalachian forests may benefit bees and other pollinators (Campbell et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ulyshen et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), especially later in the season. However, it is important to understand how the spring-active bee fauna will be impacted by such interventions. The utilization of fire, for instance, can be expected to alter tree composition by disadvantaging diffuse-porous taxa such as \u003cem\u003eAcer\u003c/em\u003e and \u003cem\u003ePrunus\u003c/em\u003e in favor of ring-porous, fire-hardy, taxa such as \u003cem\u003eQuercus\u003c/em\u003e and \u003cem\u003eCarya\u003c/em\u003e (Nowacki \u0026amp; Abrams, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kreye et al., 2013; Alexander et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The implications of such changes for pollinators remain unknown.\u003c/p\u003e \u003cp\u003eSimilarly, it is important to consider the distinction between forest-dependent pollinators and habitat generalists. Research from the northeastern US suggests about a third of the bee fauna native to that region is forest-dependent and that those species are more sensitive to forest cover than other species (Smith et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given these findings, it is necessary to take community composition and species-level responses into account rather than simply looking at differences in the total richness or abundance of all pollinators between habitats. Otherwise, there is a risk of managing for generalists at the expense of forest-specialists, which may ultimately reduce the total diversity of pollinators across the region.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdams, M., Loughry, L. H., \u0026amp; Plaugher, L. L. (2008). \u003cem\u003eExperimental forests and ranges of the USDA Forest Service. Revised edition. USDA Forest Service General Technical Report NE-321\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eAlexander, H. D., Siegert, C., Brewer, J. S., Kreye, J., Lashley, M. A., \u0026nbsp;McDaniel, J. K., Paulson, A. K., Renninger, H. J., \u0026amp; Varner, J. M. (2021). Mesophication of oak landscapes: Evidence, knowledge gaps, and future research. \u003cem\u003eBioScience, 71\u003c/em\u003e, 531-542.\u003c/li\u003e\n \u003cli\u003eAllen, G., \u0026amp; Davies, R. G. (2023). Canopy sampling reveals hidden potential value of woodland trees for wild bee assemblages. \u003cem\u003eInsect Conservation and Diversity, 16\u003c/em\u003e(1), 33-46. doi:https://doi.org/10.1111/icad.12606\u003c/li\u003e\n \u003cli\u003eBailey, R. G. (2016). Bailey\u0026apos;s ecoregions and subregions of the United States, Puerto Rico, and the U.S. Virgin Islands. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0003.\u003c/li\u003e\n \u003cli\u003eBarendregt, A., Zeegers, T., van Steenis, W., \u0026amp; Jongejans, E. (2022). Forest hoverfly community collapse: Abundance and species richness drop over four decades. \u003cem\u003eInsect Conservation and Diversity, 15\u003c/em\u003e(5), 510-521. doi:https://doi.org/10.1111/icad.12577\u003c/li\u003e\n \u003cli\u003eBaselga, A. (2010). Partitioning the turnover and nestedness components of beta diversity. \u003cem\u003eGlobal Ecology and Biogeography, 19\u003c/em\u003e(1), 134-143. doi:https://doi.org/10.1111/j.1466-8238.2009.00490.x\u003c/li\u003e\n \u003cli\u003eBates, D., M\u0026auml;chler, M., Bolker, B., \u0026amp; Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. \u003cem\u003eJournal of Statistical Software, 67\u003c/em\u003e, 1-48.\u003c/li\u003e\n \u003cli\u003eBaum, K. A., \u0026amp; Wallen, K. E. (2011). Potential Bias in Pan Trapping as a Function of Floral Abundance.\u003cem\u003e\u0026nbsp;84 Journal of the Kansas Entomological Society\u003c/em\u003e(2), 155-159, 155.\u003c/li\u003e\n \u003cli\u003eBechtold, W. A., \u0026amp; Patterson, P. L. (2005). \u003cem\u003eThe enhanced forest inventory and analysis program--national sampling design and estimation procedures (No. 80). USDA Forest Service, Southern Research Station.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eBegosh, A., Smith, L. M., Park, C. N., McMurry, S. T., \u0026amp; Lagrange, T. G. (2020). Effects of wetland presence and upland land use on wild hymenopteran and dipteran pollinators in the rainwater basin of Nebraska, USA. \u003cem\u003eWetlands, 40\u003c/em\u003e(5), 1017-1031. doi:10.1007/s13157-019-01244-w\u003c/li\u003e\n \u003cli\u003eCampbell, J. W., Vigueira, P. A., Viguiera, C. C., \u0026amp; Greenberg, C. H. (2018). The effects of repeated prescribed fire and thinning on bees, wasps, and other flower visitors in the understory and midstory of a temperate forest in North Carolina. \u003cem\u003eForest Science, 64\u003c/em\u003e(3), 299-306. doi:10.1093/forsci/fxx008\u003c/li\u003e\n \u003cli\u003eCane, J. H., Minckley, R. L., \u0026amp; Kervin, L. J. (2000). Sampling bees (Hymenoptera: Apiformes) for pollinator community studies: Pitfalls of pan-trapping. \u003cem\u003eJournal of the Kansas Entomological Society, 73\u003c/em\u003e(4), 225-231.\u003c/li\u003e\n \u003cli\u003eCarr, S. C., Robertson, K. M., \u0026amp; Peet, R. K. (2010). A vegetation classification of fire-dependent pinelands of florida. \u003cem\u003eCastanea, 75\u003c/em\u003e(2), 153-189, 137.\u003c/li\u003e\n \u003cli\u003eCarroll, W. D., Kapeluck, P. R., Harper, R. A., \u0026amp; Van Lear, D. H. (2002). Background paper: Historical overview of the southern forest landscape and associated resources. In D. N. Wear \u0026amp; J. G. Gries (Eds.), \u003cem\u003eSouthern Forest Resource Assessment. Gen. Tech. Rep. SRS-53. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station\u003c/em\u003e (pp. 635).\u003c/li\u003e\n \u003cli\u003eCayuela, L., \u0026amp; Gotelli, N. J. (2014). rareNMtests: Ecological and biogeographical null model tests for comparing rarefaction curves. R Package v1. Vienna: The R Foundation for Statistical Computing.\u003c/li\u003e\n \u003cli\u003eChao, A., Gotelli, N. J., Hsieh, T. C., Sande, E. L., Ma, K. H., Colwell, R. K., \u0026amp; Ellison, A. M. (2014). Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. \u003cem\u003eEcological Monographs, 84\u003c/em\u003e, 45\u0026ndash;67.\u003c/li\u003e\n \u003cli\u003eChase, M. H., Fraterrigo, J. M., Charles, B., \u0026amp; Harmon-Threatt, A. (2023). Wild bee response to forest management varies seasonally and is mediated by resource availability. \u003cem\u003eForest Ecology and Management, 548\u003c/em\u003e, 121426. doi:https://doi.org/10.1016/j.foreco.2023.121426\u003c/li\u003e\n \u003cli\u003eCummins, K., Noble, J., Varner, J. M., Robertson, K. M., Hiers, J. K., Nowell, H. K., \u0026amp; Simonson, E. (2023). The southeastern U.S. prescribed fire permit database: Hot spots and hot moments in prescribed fire across the southeastern U.S.A. \u003cem\u003eFire, 6\u003c/em\u003e(10), 372.\u003c/li\u003e\n \u003cli\u003eDe Caceres, M., Jansen, F., \u0026amp; De Caceres, M. M. (2016). Package \u0026lsquo;indicspecies\u0026rsquo;. \u003cem\u003eIndicators, 8\u003c/em\u003e(1).\u003c/li\u003e\n \u003cli\u003eDewitz, J. (2023). National Land Cover Database (NLCD) 2021 Products: U.S. Geological Survey Data Release. USA: U.S Geological Survey.\u003c/li\u003e\n \u003cli\u003eDixon, C. M., Robertson, K. M., Ulyshen, M. D., \u0026amp; Sikes, B. A. (2022). Pine savanna restoration on agricultural landscapes: The path back to native savanna ecosystem services. \u003cem\u003eScience of The Total Environment, 818\u003c/em\u003e, 151715. doi:https://doi.org/10.1016/j.scitotenv.2021.151715\u003c/li\u003e\n \u003cli\u003eDray, S., Bauman, D., Blanchet, G., Borcard, D., Clappe, S., Guenard, G., Jombart, T., Larocque, G., Legendre, P., Madi, N., Wagner, H. H., \u0026amp; Siberchicot, A. (2023). Package \u0026lsquo;adespatial: Multivariate multiscale spatial analysis\u0026rsquo;.\u003cem\u003e\u0026nbsp;2023\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eEvans, E., Smart, M., Cariveau, D., \u0026amp; Spivak, M. (2018). Wild, native bees and managed honey bees benefit from similar agricultural land uses. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment, 268\u003c/em\u003e, 162-170. doi:https://doi.org/10.1016/j.agee.2018.09.014\u003c/li\u003e\n \u003cli\u003eForister, M. L., Halsch, C. A., Nice, C. C., Fordyce, J. A., Dilts, T. E., Oliver, J. C., Prudic, K. L., Shapiro, A. M., Wilson, J. K., \u0026amp; Glassberg, J. (2021). Fewer butterflies seen by community scientists across the warming and drying landscapes of the American West. \u003cem\u003eScience, 371\u003c/em\u003e(6533), 1042-1045. doi:10.1126/science.abe5585\u003c/li\u003e\n \u003cli\u003eGibbs, J. (2011). Revision of the metallic Lasioglossum (Dialictus) of eastern North America (Hymenoptera: Halictidae: Halictini). \u003cem\u003eZootaxa, 3073\u003c/em\u003e, 1-216.\u003c/li\u003e\n \u003cli\u003eGibbs, J., Packer, L., Dumesh, S., \u0026amp; Danforth, B. N. (2013). Revision and reclassification of \u003cem\u003eLasioglossum\u003c/em\u003e (\u003cem\u003eEvylaeus\u003c/em\u003e), L.(\u003cem\u003eHemihalictus\u003c/em\u003e) and L.(\u003cem\u003eSphecodogastra\u003c/em\u003e) in eastern North America (Hymenoptera: Apoidea: Halictidae). \u003cem\u003eZootaxa, 3672\u003c/em\u003e(1), 1-117.\u003c/li\u003e\n \u003cli\u003eGilliam, F. S., \u0026amp; Platt, W. J. (1999). Effects of long-term fire exclusion on tree species composition and stand structure in an old-growth Pinus palustris (Longleaf pine) forest. \u003cem\u003ePlant Ecology, 140\u003c/em\u003e(1), 15-26. doi:10.1023/A:1009776020438\u003c/li\u003e\n \u003cli\u003eGlassberg, J., Minno, M. C., \u0026amp; Calhoun, J. V. (2000). \u003cem\u003eButterflies through binoculars\u003c/em\u003e: Oxford University Press.\u003c/li\u003e\n \u003cli\u003eHanberry, B. B., Bragg, D. C., \u0026amp; Alexander, H. D. (2020). Open forest ecosystems: An excluded state. \u003cem\u003eForest Ecology and Management, 472\u003c/em\u003e, 118256. doi:https://doi.org/10.1016/j.foreco.2020.118256\u003c/li\u003e\n \u003cli\u003eHanula, J. L., Horn, S., \u0026amp; O\u0026rsquo;Brien, J. J. (2015). Have changing forests conditions contributed to pollinator decline in the southeastern United States? \u003cem\u003eForest Ecology and Management, 348\u003c/em\u003e, 142-152. doi:https://doi.org/10.1016/j.foreco.2015.03.044\u003c/li\u003e\n \u003cli\u003eHanula, J. L., Ulyshen, M. D., \u0026amp; Horn, S. (2016). Conserving pollinators in North American forests: A review. \u003cem\u003eNatural Areas Journal, 36\u003c/em\u003e(4), 427-439.\u003c/li\u003e\n \u003cli\u003eHarmon-Threatt, A. (2020). Influence of nesting characteristics on health of wild bee communities. \u003cem\u003eAnnual Review of Entomology, 65\u003c/em\u003e(1), 39-56. doi:10.1146/annurev-ento-011019-024955\u003c/li\u003e\n \u003cli\u003eHarrison, T., Gibbs, J., \u0026amp; Winfree, R. (2018). Forest bees are replaced in agricultural and urban landscapes by native species with different phenologies and life-history traits. \u003cem\u003eGlobal Change Biology, 24\u003c/em\u003e(1), 287-296. doi:https://doi.org/10.1111/gcb.13921\u003c/li\u003e\n \u003cli\u003eHolland, J. D., Bert, D. G., \u0026amp; Fahrig, L. (2004). Determining the spatial scale of species\u0026apos; response to habitat. \u003cem\u003eBioScience, 54\u003c/em\u003e(3), 227-233.\u003c/li\u003e\n \u003cli\u003eHsieh, T. C., Ma, K. H., \u0026amp; Chao, A. (2022). iNEXT: Interpolation and extrapolation for species diversity. R package version 3.0.0, http://chao.stat.nthu.edu.tw/wordpress/software_download/.\u003c/li\u003e\n \u003cli\u003eJoshi, N. K., Leslie, T., Rajotte, E. G., Kammerer, M. A., Otieno, M., \u0026amp; Biddinger, D. J. (2015). Comparative trapping efficiency to characterize bee abundance, diversity, and community composition in apple orchards. \u003cem\u003eAnnals of the Entomological Society of America, 108\u003c/em\u003e(5), 785-799. doi:10.1093/aesa/sav057\u003c/li\u003e\n \u003cli\u003eKanninen, M. (2010). Plantation forests: global perspectives. In J. Bauhus, P. van der Meer, \u0026amp; M. Kanninen (Eds.), \u003cem\u003eEcosystem Goods and Services from Plantation Forests\u003c/em\u003e (pp. 1-15). London, UK: Earthscan.\u003c/li\u003e\n \u003cli\u003eKnapp, B. O., Walker, J. L., Wang, G. G., Hu, H., \u0026amp; Addington, R. N. (2014). Effects of overstory retention, herbicides, and fertilization on sub-canopy vegetation structure and functional group composition in loblolly pine forests restored to longleaf pine. \u003cem\u003eForest Ecology and Management\u003c/em\u003e, \u003cem\u003e320\u003c/em\u003e, 149-160.\u003c/li\u003e\n \u003cli\u003eKraemer, M. E., \u0026amp; Favi, F. D. (2005). Flower phenology and pollen choice of \u003cem\u003eOsmia lignaria\u003c/em\u003e (Hymenoptera: Megachilidae) in central Virginia. \u003cem\u003eEnviron Entomol, 34\u003c/em\u003e(6), 1593-1605. doi:10.1603/0046-225x-34.6.1593\u003c/li\u003e\n \u003cli\u003eKrey, J. K., Varner, J. M., Hiers, J. K., \u0026amp; Mola, J. (2013). Toward a mechanism for eastern North America forest mesophication: differential litter drying across 17 species. \u003cem\u003eEcological Applications, 23\u003c/em\u003e, 1976-1986.\u003c/li\u003e\n \u003cli\u003eLemmon, P. E. (1956). A spherical densiometer for estimating forest overstory density. \u003cem\u003eForest Science, 2\u003c/em\u003e(4), 314-320.\u003c/li\u003e\n \u003cli\u003eMitchell, T. B. (1960). \u003cem\u003eBees of the Eastern United States Volume 1\u003c/em\u003e: The North Carolina Agricultural Experiment Station.\u003c/li\u003e\n \u003cli\u003eMitchell, T. B. (1962). \u003cem\u003eBees of the Eastern United States Volume 2\u003c/em\u003e. The North Carolina Agriculture Experiment Station.\u003c/li\u003e\n \u003cli\u003eMoylett, H., Youngsteadt, E., \u0026amp; Sorenson, C. (2019). The impact of prescribed burning on native bee communities (Hymenoptera: Apoidea: Anthophila) in longleaf pine savannas in the North Carolina sandhills. \u003cem\u003eEnvironmental Entomology, 49\u003c/em\u003e(1), 211-219. doi:10.1093/ee/nvz156\u003c/li\u003e\n \u003cli\u003eNowacki, G. J., \u0026amp; Abrams, M. D. (2008). The demise of fire and \u0026ldquo;mesophication\u0026rdquo; of forests in the eastern United States. \u003cem\u003eBioScience, 58\u003c/em\u003e, 123-138.\u003c/li\u003e\n \u003cli\u003eOdanaka, K., Gibbs, J., Turley, N. E., Isaacs, R., \u0026amp; Brudvig, L. A. (2020). Canopy thinning, not agricultural history, determines early responses of wild bees to longleaf pine savanna restoration. \u003cem\u003eRestoration Ecology, 28\u003c/em\u003e(1), 138-146. doi:https://doi.org/10.1111/rec.13043\u003c/li\u003e\n \u003cli\u003eOksanen, J., Kindt, R., Legendre, P., O\u0026rsquo;Hara, B., Stevens, M. H. H., \u0026amp; Oksanen, M. J. (2007). The vegan package. \u003cem\u003eCommunity Ecology Package, 10\u003c/em\u003e(631-637), 719.\u003c/li\u003e\n \u003cli\u003eOluoch, S., Lal, P., Wolde, B., Susaeta, A., Soto, J. R., Smith, M., \u0026amp; Adams, D. C. (2021). Public preferences for longleaf pine restoration programs in the southeastern United States. \u003cem\u003eForest Science, 67\u003c/em\u003e(3), 265-274. doi:10.1093/forsci/fxab008\u003c/li\u003e\n \u003cli\u003eOrr, M. C., Hughes, A. C., Chesters, D., Pickering, J., Zhu, C.-D., \u0026amp; Ascher, J. S. (2021). Global patterns and drivers of bee distribution. \u003cem\u003eCurrent Biology, 31\u003c/em\u003e(3), 451-458.e454. doi:https://doi.org/10.1016/j.cub.2020.10.053\u003c/li\u003e\n \u003cli\u003eParadis, E., \u0026amp; Schliep, K. (2019). ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. \u003cem\u003eBioinformatics, 35\u003c/em\u003e(3), 526-528.\u003c/li\u003e\n \u003cli\u003ePeterson, D. W., \u0026amp; Reich, P. B. (2008). Fire frequency and tree canopy structure influence plant species diversity in a forest-grassland ecotone. \u003cem\u003ePlant Ecology, 194\u003c/em\u003e(1), 5-16. doi:10.1007/s11258-007-9270-4\u003c/li\u003e\n \u003cli\u003ePlatt, W. J., Evans, G. W., \u0026amp; Davis, M. M. (1988). Effects of fire season on flowering of forbs and shrubs in longleaf pine forests. \u003cem\u003eOecologia, 76\u003c/em\u003e(3), 353-363. doi:10.1007/BF00377029\u003c/li\u003e\n \u003cli\u003ePlatt, W. J., Carr, S. M., Reilly, M., \u0026amp; Fahr, J. (2006). Pine savanna overstorey influences on ground-cover biodiversity. \u003cem\u003eApplied Vegetation Science, 9\u003c/em\u003e(1), 37-50.\u003c/li\u003e\n \u003cli\u003ePowney, G. D., Carvell, C., Edwards, M., Morris, R. K. A., Roy, H. E., Woodcock, B. A., \u0026amp; Isaac, N. J. B. (2019). Widespread losses of pollinating insects in Britain. \u003cem\u003eNature Communications, 10\u003c/em\u003e(1), 1018. doi:10.1038/s41467-019-08974-9\u003c/li\u003e\n \u003cli\u003ePrestemon, J. P., \u0026amp; Abt, R. C. (2002). Chapter 13: Timber products supply and demand. In D. N. Wear \u0026amp; J. G. Greis (Eds.), \u003cem\u003eSouthern Forest Resource Assessment. USDA Forest Service General Technical Report SRS-53\u003c/em\u003e (pp. 299-325).\u003c/li\u003e\n \u003cli\u003ePyne, S. J. (1982). \u003cem\u003eFire in America - a cultural history of wildand and rural fire\u003c/em\u003e. Seattle and London: University of Washington Press.\u003c/li\u003e\n \u003cli\u003eR Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.\u003c/li\u003e\n \u003cli\u003eRivers, J. W., Galbraith, S. M., Cane, J. H., Schultz, C. B., Ulyshen, M. D., \u0026amp; Kormann, U. G. (2018). A review of research needs for pollinators in managed conifer forests. \u003cem\u003eJournal of Forestry, 116\u003c/em\u003e(6), 563-572. doi:10.1093/jofore/fvy052\u003c/li\u003e\n \u003cli\u003eRoulston, T. a. H., Smith, S. A., \u0026amp; Brewster, A. L. (2007). A comparison of pan trap and intensive net sampling techniques for documenting a bee (Hymenoptera: Apiformes) fauna. \u003cem\u003eJournal of the Kansas Entomological Society, 80\u003c/em\u003e(2), 179-181.\u003c/li\u003e\n \u003cli\u003eSaunders, M. E. (2018). Insect pollinators collect pollen from wind-pollinated plants: implications for pollination ecology and sustainable agriculture. \u003cem\u003eInsect Conservation and Diversity, 11\u003c/em\u003e(1), 13-31. doi:https://doi.org/10.1111/icad.12243\u003c/li\u003e\n \u003cli\u003eSimanonok, M. P., Powley, M., \u0026amp; Otto, C. R. (2022). Cavity-nesting bee nesting success across gradients of floral resources and land cover. \u003cem\u003ePrairie Naturalist, Special Issue\u003c/em\u003e(1), 65-77.\u003c/li\u003e\n \u003cli\u003eSkevington, J. H., Locke, M. M., Young, A. D., Moran, K., Crins, W. J., \u0026amp; Marshall, S. A. (2019). \u003cem\u003eField guide to the flower flies of northeastern North America\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eSmith, C., Harrison, T., Gardner, J., \u0026amp; Winfree, R. (2021). Forest-associated bee species persist amid forest loss and regrowth in eastern North America. \u003cem\u003eBiological Conservation, 260\u003c/em\u003e, 109202. doi:https://doi.org/10.1016/j.biocon.2021.109202\u003c/li\u003e\n \u003cli\u003eTaki, H., Kevan, P. G., \u0026amp; Ascher, J. S. (2007). Landscape effects of forest loss in a pollination system. \u003cem\u003eLandscape Ecology, 22\u003c/em\u003e, 1575-1587.\u003c/li\u003e\n \u003cli\u003eTraylor, C. R., Ulyshen, M. D., Wallace, D., Loudermilk, E. L., Ross, C. W., Hawley, C., Atchison, R. A., Williams, J. L., \u0026amp; McHugh, J. V. (2022). Compositional attributes of invaded forests drive the diversity of insect functional groups. \u003cem\u003eGlobal Ecology and Conservation, 35\u003c/em\u003e, e02092. doi:https://doi.org/10.1016/j.gecco.2022.e02092\u003c/li\u003e\n \u003cli\u003eUlyshen, M., Elliott, K., Scott, J., Horn, S., Clinton, P., Liu, N., Miniat, C. F., Caldwell, P., Oishi, C., Knoepp, J., \u0026amp; Bolstad, P. (2022). Effects of \u003cem\u003eRhododendron\u003c/em\u003e removal and prescribed fire on bees and plants in the southern Appalachians. \u003cem\u003eEcology and Evolution, 12\u003c/em\u003e(3), e8677. doi:https://doi.org/10.1002/ece3.8677\u003c/li\u003e\n \u003cli\u003eUlyshen, M., Robertson, K., Horn, S., \u0026amp; Dixon, C. (2023). Effects of season of fire on bee-flower interaction diversity in a fire-maintained pine savanna.\u003cem\u003e\u0026nbsp;13\u003c/em\u003e(8), e10450. doi:https://doi.org/10.1002/ece3.10450\u003c/li\u003e\n \u003cli\u003eUlyshen, M. D., \u0026amp; Horn, S. (2023). Declines of bees and butterflies over 15 years in a forested landscape. \u003cem\u003eCurrent Biology, 33\u003c/em\u003e, 1346-1350.\u003c/li\u003e\n \u003cli\u003eUlyshen, M. D., Soon, V., \u0026amp; Hanula, J. L. (2010). On the vertical distribution of bees in a temperate deciduous forest. \u003cem\u003eInsect Conservation and Diversity, 3\u003c/em\u003e, 222-228.\u003c/li\u003e\n \u003cli\u003eUlyshen, M. D., Urban-Mead, K. R., Dorey, J., \u0026amp; Rivers, J. W. (2023). Forests are critically important to global pollinator diversity and enhance pollination in adjacent crops. \u003cem\u003eBiological Reviews, 98\u003c/em\u003e, 1118-1141.\u003c/li\u003e\n \u003cli\u003eUlyshen, M. D., Wilson, A. C., Ohlson, G. C., Pokswinksi, S. M., \u0026amp; Hiers, J. K. (2021). Frequent prescribed fires favour ground-nesting bees in southeastern U.S. forests. \u003cem\u003eInsect Conservation and Diversity, 14\u003c/em\u003e(4), 527-534. doi:https://doi.org/10.1111/icad.12484\u003c/li\u003e\n \u003cli\u003eUrban-Mead, K. R., Mu\u0026ntilde;iz, P., Gillung, J., Espinoza, A., Fordyce, R., van Dyke, M., McArt, S. H., \u0026amp; Danforth, B. N. (2021). Bees in the trees: Diverse spring fauna in temperate forest edge canopies. \u003cem\u003eForest Ecology and Management, 482\u003c/em\u003e, 118903. doi:10.1016/j.foreco.2020.118903\u003c/li\u003e\n \u003cli\u003eUrban-Mead, K. R., van Dyke, M., Muniz, P., Young, A., Danforth, B. N., \u0026amp; McArt, S. H. (2023). Early spring orchard pollinators spill over from resource-rich adjacent forest patches. \u003cem\u003eJournal Applied Ecology, 60\u003c/em\u003e, 553-564.\u003c/li\u003e\n \u003cli\u003eVan Lear, D. H., Harper, R. A., Kapeluck, P. R., \u0026amp; Carroll, W. D. (2004). History of piedmont forests: Implications for current pine management. In \u003cem\u003eGen. Tech. Rep. SRS 71. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station\u003c/em\u003e (pp. 127-131).\u003c/li\u003e\n \u003cli\u003eWatson, J., Wolf, A., \u0026amp; Ascher, J. (2011). Forested landscapes promote richness and abundance of native bees (Hymenoptera: Apoidea: Anthophila) in Wisconsin apple orchards. \u003cem\u003eEnvironmental Entomology, 40\u003c/em\u003e, 621\u0026ndash;632.\u003c/li\u003e\n \u003cli\u003eWilliams, N. M., Minckley, R. L., \u0026amp; Silveira, F. A. (2001). Variation in native bee faunas and its implications for detecting community changes. \u003cem\u003eConservation Ecology, 5\u003c/em\u003e, 7.\u003c/li\u003e\n \u003cli\u003eZhang, D., \u0026amp; Zhang, M. D. (2022). Package \u0026lsquo;rsq\u0026rsquo;. \u003cem\u003eR-Squared Related Measures. Available online: https://cran. r-project. org/web/packages/rsq/rsq. pdf\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 2 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4248368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4248368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEfforts to understand how pollinating insect diversity is distributed across large geographic areas are rare despite the importance of such work for conserving regional diversity. We sampled bees (Hymenoptera: Apoidea), hover flies (Diptera: Syrphidae), and butterflies (Lepidoptera) on nineteen National Forests across the southeastern U.S. and related their diversity to ecoregion, landscape context, canopy openness, and forest composition. Bee richness was negatively correlated with both the amount of conifer forest and the extent of wetlands in the surrounding landscape but was positively correlated with canopy openness. Hover flies and butterflies were less sensitive to landscape context and stand conditions. Pollinator communities differed considerably among ecoregions, with those of the Central Appalachian and Coastal Plain ecoregions being particularly distinct. Bee richness and abundance peaked two months earlier in Central Appalachia than in the Coastal Plain and Southeastern Mixed Forest ecoregions. Our findings suggest that hardwood forests may play a particularly important role in supporting forest-associated bees in the southeastern U.S. and that efforts to create more open forest conditions may benefit this fauna.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal patterns of forest pollinator diversity across the southeastern United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-11 05:25:20","doi":"10.21203/rs.3.rs-4248368/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6137bdc-f510-4ab0-89c1-ec85fbbff487","owner":[],"postedDate":"April 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-20T07:43:14+00:00","versionOfRecord":{"articleIdentity":"rs-4248368","link":"https://doi.org/10.1111/ddi.13869","journal":{"identity":"diversity-and-distributions","isVorOnly":true,"title":"Diversity and Distributions"},"publishedOn":"2024-06-03 07:43:14","publishedOnDateReadable":"June 3rd, 2024"},"versionCreatedAt":"2024-04-11 05:25:20","video":"","vorDoi":"10.1111/ddi.13869","vorDoiUrl":"https://doi.org/10.1111/ddi.13869","workflowStages":[]},"version":"v1","identity":"rs-4248368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4248368","identity":"rs-4248368","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}