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- Cultivation of bioenergy feedstocks is a growing land-use world-wide, yet we have a poor understanding of how bioenergy crop management practices affect biodiversity. This knowledge gap is particularly acute for candidate cellulosic bioenergy feedstocks, such as tree plantations, and for organisms that provide important ecosystem services, such as pollinators.
- We examined bee communities in 83 sites across three states in the southeastern United States—Alabama, Florida and Georgia. We compared bee abundance and diversity in 66 pine plantation sites that reflect management with and without potential bioenergy feedstock production. At least three bioenergy feedstock production methods have been proposed for this region: (a) converting conventional timber stands to short-rotation bioenergy plantations; (b) harvesting feedstock by thinning conventional plantations; and (c) harvesting of woody debris residues after plantations have been clear-cut.
- We found that bioenergy-associated management practices including younger plantations (relative to older) and woody debris removal (relative to debris unremoved) in clear-cut plantations were associated with reduced bee diversity. Removing ground debris in clear-cut plantations also drastically increased bee abundance, though this effect was largely driven by strong dominance of just two bee species. Clear-cut plantations had lower beta diversity than standing plantations.
- Synthesis and applications. Management practices associated with bioenergy feedstock production can have negative effects on bee community diversity. In particular, harvesting of debris in clear-cut plantations dramatically reduces bee diversity. Large-scale bioenergy feedstock production that increases the prevalence of young and clear-cut stands may cause landscape-level beta diversity to decline. Nevertheless, bioenergy pine plantations likely support higher bee diversity than corn fields, an alternative bioenergy feedstock.
- 大量生产生物燃料已改变对土地的利用并普及全世。但其種植与管理能源作业对自然生物的多样性, 特别是对提供重要生态系统服务的生物, 如传粉动物所造成的影响却无深入了解。现代纤维素生物燃料作业尤其如此。
- 我们在美国东南部 - 阿拉巴马州, 佛罗里达州和佐治亚州 - 83 个地点测度蜂系群落。三州的大片地区目前以松树种植园为主, 主要生产木材, 但有计划将部分转变为生产纤维素生物燃料, 以达到美国生物能源生产目标。至少有以下三种生物能源原料生产方案:1)将常规木材松树园改为短轮伐期松树园, 以加速原料产量; 2) 利用常规木材松树园减薄林木作业时收取修剪后的原材料; 3)利用砍伐松树园后 留下的木质碎片作为生物能源原料。我们比较 66 个松树种植园, 在有和没有生物 能源原料生产的管理情况下对蜂系的多度和多样性所造成的影响进行研究。
- 研究结果显示生物能源相关的管理实践, 包括较年轻的种植园(类似短轮伐期相对于较老的松树园)和清除砍伐松树园后留下的木质碎片(相对于未清除碎片砍伐后的松树园)与蜂系辛普森多样性指数(Simpson diversity index)的减少有关。清除砍伐松树园地面碎片大大增加了蜂系的多度, 但这主要是由 126 种蜂系中的 2 种所驱动, 因此减少群落即均度。砍伐松树园中的 β 多样性低于未砍伐的松树园。
- 合成和应用:将美国东南部常规木材松树园改为生物能源生产可能对该地区蜂系群落的多样性有负面影响, 特别是在砍伐松树园后收集木质碎片可能会大大减少蜜蜂的多样性。大量生产生物能源原料可能会导致区域 β 多样性下降。然而, 比较目 前较常用的玉米生物燃料作业, 常规木材和生物燃料的松树园作业较可能维持更高 度多样性的蜂系群落。
Bioenergy production is a significant driver of land-use change in many parts of the world, and currently constitutes 2% (30–35 million hectares) of global cropland area (Popp et al., 2016). Bioenergy cultivation is predicted to increase substantially in the coming decades, given governmental mandates, tax exemptions and incentives (Sorda, Banse, & Kemfert, 2010), as well as the production of ‘next-generation’ or cellulosic bioenergy, which can potentially produce greater energy yields per unit area than traditional starch- or sugar-derived fuels. While the conversion of pristine habitats for the cultivation of perennial bioenergy crops poses a clear threat to biodiversity (Fletcher et al., 2011; Immerzeel, Verweij, Hilst, & Faaij, 2014), the ecological impact of introducing perennial bioenergy plantations to existing agricultural landscapes is less clear. How such perennial plantations are managed may significantly impact their capacity to support biodiversity (Bonham, Mesibov, & Bashford, 2002; Kerr, 1999; Mazurek & Zielinski, 2004).
Pine plantations in the United States represent a bioenergy feedstock system of key importance because these plantations—both modified from extensive existing plantations and also expansion of these areas—are expected to form the bulk of the nation's mandated bioenergy production goal of 36 billion gallons of liquid bioenergy to be produced annually by 2022 (Sissine, 2007). There have already been shifts in the use of these plantations from producing only conventional timber products to producing woody pellets that are currently used in bioenergy systems (Duden et al., 2018; Galik & Abt, 2016). Within the United States, the southeastern coastal plain that extends from southeastern Virginia to southeastern Texas is expected to be the primary contributor to pine bioenergy feedstock. Three major bioenergy feedstock production methods have been proposed (Munsell & Fox, 2010): (a) converting conventional timber stands to short-rotation bioenergy plantations; (b) harvesting feedstock through thinning conventional plantations not primarily grown for bioenergy; and (c) harvesting of woody debris residues after plantations have been clear-cut. We need to understand the ecological impacts of changes in plantation management, especially relative to alternative bioenergy production systems such as annual crops like corn (Gonzalez et al., 2012), as well as how they compare to natural ‘reference’ habitats (Fletcher et al., 2011; Immerzeel et al., 2014), in this case naturally occurring stands of longleaf pine (e.g. Gottlieb et al., 2017).
The capacity for pine plantations to support wildlife is strongly dependent on management (Bonham et al., 2002; Kerr, 1999; Mazurek & Zielinski, 2004) and specific differences in bioenergy versus conventional timber plantation management may variably impact biodiversity. For example, in conventional timber plantations individual trees are allowed time to attain a critical bole diameter to maximize timber volume (Thompson & Pitt, 2003). In contrast, plantations managed for bioenergy feedstock are focused on efficiently amassing overall plantation biomass and so may benefit from more rapid crop turnover (Klepac & Mitchell, 2016). Resulting young plantations may have scant understorey vegetation and lack vertical structure, and thus lower habitat heterogeneity to support biodiversity (Riffell, Verschuyl, Miller, & Wigley, 2011b). A second means of obtaining biomass for bioenergy feedstock could involve little change from the status quo. Plantations managed for conventional timber products are usually thinned to reduce competition among trees and maximize growth in the remaining individuals (Thompson & Pitt, 2003). The biomass collected from thinning can be used as bioenergy feedstock. Pine plantations in the southeast United States managed for solid timber can be thinned after 10–15 years of growth, with landowners removing between 250 and 750 trees/ha (Stokes & Watson, 1996). Thinning may lead to increases in biodiversity. A study conducted in a U.S. national forest in Georgia observed greater bee species richness in thinned compared to dense pine forests (Hanula, Horn, & O'Brien, 2015; but see Breland, Turley, Gibbs, Isaacs, & Brudvig, 2018). Fallen woody debris is another by-product of timber production that may be harvested for biomass (Riffell, Verschuyl, Miller, & Wigley, 2011a). A meta-analysis of bioenergy harvest methods shows that removal of plantation ground debris differentially affects various taxa (Riffell et al., 2011b), as ground debris provides a valuable resource for a variety of organisms (Castro & Wise, 2010; Gottlieb et al., 2017; Mengak & Guynn Jr, 2003; Rodríguez & Kouki, 2015).
When considering the potential ecological impacts of bioenergy cultivation, it is particularly critical to examine taxonomic groups that provide vital ecosystem services. One such group is biotic pollinators, most notably bees (Hymenoptera: Apoidea), which provide pollination services that are vital to the productivity of both wild and cultivated plants. Habitat destruction and land-use change are thought to be the most important drivers of declines in pollinators such as bees (Dicks et al., 2016; Potts et al., 2010). Managed conifer forests can contribute significantly to the conservation of diverse species and ecosystem services, yet the extent to which they support pollinators and pollination is poorly known (Rivers, Galbraith, et al., 2018). Given potentially significant impacts of bioenergy production on land-use change (Wright, Larson, Lark, & Gibbs, 2017), it is important to understand how management practices pertaining to bioenergy production could impact pollinator taxa.
Here we examine how managing pine plantations for bioenergy production in the southeastern United States may impact bee communities. Pine plantations grown for timber, paper and pulp (mainly Pinus taeda and Pinus elliottii) currently dominate large areas of the states of Georgia, Florida and Alabama. When cellulosic bioethanol technology becomes industrially viable, these plantations—after management changes—are expected to contribute a large portion of the United States’ mandated bioenergy production goals, and are already contributing substantially to the global woody pellet biomass market (Dale et al., 2017). Examining biodiversity impacts of possible bioenergy management practices is necessary for policy makers and the bioenergy industry to make informed decisions that minimize potential ecological impacts. We examine how bee abundance and species diversity respond to three strategies for obtaining biomass for bioenergy: (a) a shift towards younger, denser plantations for more rapid biomass feedstock accumulation; (b) harvesting feedstock by thinning older plantations; (c) harvesting of woody debris after plantations have been clear-cut. We also examine bee communities in natural remnants, as a reference for unmanaged forest, and corn fields, which represent an alternative annual bioenergy crop type known to impact biodiversity (Fletcher et al., 2011; Immerzeel et al., 2014). We consider both bee richness and evenness, as the latter reflects the uniformity of species responses. We expect younger plantations to have developed less habitat heterogeneity than mature plantations, resulting in lower bee diversity. Thinning plantations should increase light and warmth in the understorey to encourage more flowering plants and greater bee diversity. As many bee species nest in woody debris and on the ground, debris removal should disturb nesting habitat to reduce bee diversity. Finally, we expected that managed pine stands would support greater bee diversity than corn fields.
2 MATERIALS AND METHODS
2.1 Study sites and strata
We sampled bee communities from 83 sites, including 66 pine plantations, 10 natural reference-condition sites (longleaf pine forest remnants), and 7 corn production sites, distributed across the U.S. states of Florida, Georgia and Alabama, which are expected to be key bioenergy states. The sites were the same as those sampled for birds in prior work (Gottlieb et al., 2017) and clustered into three geographic ‘strata’ that did not follow state lines (Table 1). In pine plantations, we focus on three key attributes that reflect potential management changes for bioenergy feedstock production: (a) younger plantation age; (b) plantation thinning; and (c) harvesting of ground debris after plantations are clear-cut. We examined the effect of plantation age on bee communities by comparing young, unthinned plantations of about 8–12 years old (simulating harvest-ready bioenergy plantations) to more mature plantations that are 24–25 years old and have already been thinned. We also compared bee communities in plantations soon after thinning (simulating harvesting for bioenergy feedstock) to unthinned plantations of similar age (12–16 years). Finally, we examined the impact of harvesting plantation ground debris by comparing recently clear-cut plantations (felled within the last 2 years) with and without debris harvest. Each of the six ‘plantation types’ was represented in 9–12 sites (Table 1); all sites were >16 ha and spaced at least 2.5 km apart.
|Plantation Attributes||Resembling bioenergy production||Total number of sites||Strata||No. of sites||Without bioenergy production||Total number of sites||Strata||No. of sites|
|Younger plantations||Young (8–12 years), unthinned||11||
Mature (24–25 years), thinned
|Harvesting feedstock by thinning||Thinned (12–16 years)||11||
|Unthinned (12–16 years)||11||
|Harvesting debris after clear-cut||Trees clear-cut, debris harvested||9||
|Trees clear-cut, debris uncleared||12||
|Non-plantation land-uses||Corn fields||7||
|Natural longleaf pine stands||10||
2.2 Measuring local bee and plant communities
We surveyed bee communities over the spring and summer seasons of 2013, 2014 and 2015. Sites were not sampled repeatedly across years. In each site, we marked out two 2 × 200 m sampling transects at least 50 m from the plantation edge and at least 100 m away from one another.
For each sampling day, we collected bees using pan traps and aerial netting, which work effectively in tandem (Westphal et al., 2008). Pan traps consisted of small, plastic cups (3.25 oz., model P325, SOLO Cup Company) painted with ultraviolet-bright blue, white or yellow paint, and filled with a dilute detergent-water solution that drowns the bees (Kearns & Inouye, 1993; Westphal et al., 2008). Fifteen pan traps were held approximately 40 cm above the ground on wire stakes (VIGORO plant props, model 611872, Spectrum Brands Holdings Inc., bent to better hold traps) so as to be visible above herbaceous vegetation, and positioned in alternating colours evenly along the centre 100 m of the sampled transect. Pan traps were set up in the morning and collected after 24 hr. During each sampling day, we also performed targeted aerial netting of bees along the entire length of a transect for 30 min, excluding handling time for every successful capture with a stopwatch. Bee surveys were postponed on cloudy or rainy days, and each transect was sampled up to four times on separate days, amounting to up to 8 sampling days per site. Due to unforeseen weather and logistical difficulties, however, 9 of the 66 sites are represented by only one transect or by fewer than three samples. These sites were spread across two of the three strata and across land-use categories. Still, we employed statistical methods robust to imbalances in sampling effort across our analyses. Collected bees were preserved in ethanol or pinned, brought back to the laboratory and identified to species or occasionally genus.
We measured available floral resources at each site once during the study on a sampling day, within 1 m of the central 100 m of each transect, by counting and identifying to species all understorey non-grass plants that were in bloom. We surveyed floral resources among sites within a stratum as close together in time as possible to maximize comparability. A separate analysis of pollen loads present on some of the bee specimens from this work is covered in Bell et al. (2017), which describes interactions between bee and plant species.
2.3 Bee specimen preparation and identification
We usually pinned bee specimens on the day of sampling. We keyed out all specimens to genus, and 92% of taxa to species, using Discover Life online keys (https://www.discoverlife.org), in conjunction with Michener (2000) and Michener, McGinley, and Danforth (1994). Our identifications were verified by Ismael Hinojosa (Universidad Autonoma de Mexico) and Sam Droege (USGS); Sam Droege identified many specimens (particularly Lasioglossum) to species.
2.4 Data analysis
All statistical analyses were conducted in r (R Core Team, 2016).
2.4.1 Bee abundance
We analysed bee abundance per daily survey across plantation management types using generalized linear mixed-effects models implemented in the glmmADMB package for r (Skaug, Fournier, Bolker, Magnusson, & Nielsen, 2016) with negative binomial errors (count data, overdispersed relative to a Poisson distribution). In all cases we included ‘site’ nested within ‘strata’ (sites were clustered in three geographic strata) and as random effects, and examined two explanatory variables: (a) plantation management/land-use types; and (b) understorey plant richness. Understorey plant richness served as a single metric describing potentially available floral resources for bees for all 3 years of this study because flower abundance data from 2015 was of poor resolution. Based on 2013 and 2014 data, floral richness and flower abundance were strongly correlated (Pearson's product–moment correlation, r = .73, p < .001, df = 37, removing one outlier). We specified a priori pair-wise contrasts of plantation management types that simulate management practices with and without changes for bioenergy feedstock production, using general linear contrasts within our mixed-effects models with the r package multcomp (Hothorn, Bretz, & Westfall, 2008). We also considered bee diversity in longleaf pine forest remnants (natural reference) and corn fields (alternative annual bioenergy crop type).
2.4.2 Bee species diversity
We analysed species diversity in each plantation type using both individual- and sample-based rarefaction to account for differences in bee individuals caught and sampling effort. Rarefaction allows for comparisons of the diversity between two sites as if they had the same number of sampled individuals or events (e.g. Chazdon, Colwell, & Denslow, 1998; Colwell et al., 2012). We constructed rarefied and extrapolated diversity estimates analyses with the r package iNEXT (Hsieh, Ma, & Chao, 2016), which calculates bootstrapped diversity values with 95% confidence intervals (rep = 50) for 40 evenly spaced ‘knots’ between the first sample or individual. The extrapolation extends to double the minimum empirical sampling effort. These curves are plotted with ggiNEXT (Hsieh et al., 2016), an extension of the r-package ggplot2 (Wickham, 2016). We examined species richness with sample-based rarefaction, and both species richness and inverse Simpson index (henceforth ‘inverse Simpson’) with individual-based rarefaction. The inverse Simpson is sensitive to dominance (Morris et al., 2014) and estimates ‘effective richness’ by penalizing true richness based on decreasing community evenness.
2.4.3 Beta diversity
To compare bee community composition among different plantation types, we created a Bray–Curtis dissimilarity matrix (r-package vegan). We then plotted means and 95% confidence intervals of similarity of sites within a plantation type against its similarity to all other plantation types. This allows us to visualize the degree of species turnover among sites (beta diversity) of a given type relative to the global diversity of study sites.
2.4.4 Spatial autocorrelation
To test the assumption that sites in a stratum are independent, we used Mantel tests for spatial independence in community composition (r package vegan, Faith, Minchin, & Belbin, 1987; Oksanen et al., 2016) and Moran's I for abundance and species richness (r package ape, Paradis, Claude, & Strimmer, 2004). We tested for spatial autocorrelation within the three strata: S1, sites in Alabama; S2, sites in southern Georgia and the Florida panhandle; S3, sites in north-central Florida (Table 1).
3.1 Sampling overview
We sampled a total of 5,737 bees representing 126 species or morphospecies, comprising 1,480 individuals, 82 species from Alabama; 976 individuals, 71 species from Florida; and 3,281 individuals, 78 species from Georgia. We caught 1,105 bees of 86 species by netting and 4,644 bees of 104 species from pan traps. Of the 126 morphospecies, we were able to identify all but nine to the species level. Those identified only at the genus level comprise just 48 of the 5,737 bee individuals in the study. Bee species and flower species recorded in this study can be found in the (Tables S1–S3). Flower richness at each site type is plotted in Figure S1. The most common flower species recorded were Verbena brasiliensis, Justicia ovata, Callicarpa americana, Erigeron annuus and Rhexia virginica. Results from spatial autocorrelation analyses are presented in Table S4. We proceeded with statistical analyses assuming spatial independence between sites, though we included site and stratum as random effects in our mixed-effects models to account for potential dependence in repeated measures within the strata and sites.
3.2 Bee abundance
Management type but not understorey plant richness had significant effects on bee abundance. Bee abundance did not change with plantation age or thinning (Figure 1a) but was dramatically higher in clear-cut sites where plantation debris was harvested relative to where it was left in place (contrast: est. = 0.791, SE = 0.172, p < .05). In these debris-removal clear-cut sites, bee abundance was on average at least double that of any other site type. However, when the two most numerically dominant bee species, Lasioglossum nymphale and L. floridanum, were excluded from the dataset, abundances across all site types, including forest remnants and corn fields, were no longer significantly different (Figure 1).
3.3 Bee species diversity—Sample-based rarefaction
Bee species richness differed significantly among the managed plantation forests, forest remnants and corn fields. Forest remnants and cornfields had significantly lower richness curves than managed plantation forests but did not differ significantly from one another (Figure 2). When making comparisons only within managed plantation forests, however, sample-based rarefaction curves of species richness were not substantially different (i.e. overlapping confidence intervals) in our a priori pairwise contrasts (Figure 3).
3.4 Bee species diversity—Individual-based rarefaction
We only detected significant differences in diversity between different plantation ages and treatment of debris after tree harvest (Figure 4). Younger plantations had similar richness accumulation curves to mature plantations but significantly lower inverse Simpson curves based on non-overlapping 95% confidence intervals. Clear-cut plantations with debris removed had much lower species richness and inverse Simpson curves compared to clear-cut plantations with debris left intact.
3.5 Beta diversity
The bee communities among site were generally dissimilar (mean Bray–Curtis dissimilarity = 0.826; Figure 5, dashed line). Comparisons of mean pairwise community dissimilarity among our a priori contrasts did not show differences in beta diversity (non-overlapping 95% confidence intervals) in terms of young versus old or thinned versus unthinned plantations (Figure 5). By contrast, in clear-cut sites, debris removal treatments had substantially lower beta-diversity relative to sites without debris removal and across all land-use types we studied. Beta diversity was also significantly lower in pine forest remnants.
We examined how bee communities responded to three potential bioenergy production practices in pine plantations. We focused on: (a) stand age, (b) plantation thinning and (c) harvesting of woody debris after plantations are clear-cut. We found effects of plantation management on both bee abundance and diversity. Harvesting woody debris for bioenergy feedstock in clear-cut sites increased bee abundance, though this response is driven by just two hyper-abundant species. Indeed, while bee species richness was not markedly different when woody debris was harvested or in younger bioenergy plantations, both had lower inverse Simpson—lower ‘effective richness’ due to low community evenness. Decreases in community evenness suggest that the impact of plantation management varies among bee species. Beta diversity was also significantly lower in young and clear-cut plantations, suggesting that shorter harvest rotations may drive convergence of local species assemblages towards more disturbance-tolerant or open habitat taxa. Plantations tended to be similar in bee species composition, with species such as Melissodes communis, L. floridanum, L. reticulatum and L. puteulanum being common. However, plantations collectively supported higher bee diversity than corn fields, an alternative bioenergy crop type. This is perhaps due to the variety of habitat conditions created through the different stages of plantation management and harvest. Thus, plantations may collectively also have greater habitat diversity than forest remnants, which may explain lower species diversity and community dissimilarity in the latter. Nevertheless, some bee species were more common in forest remnants, such as L. imitatum, L. apopkense and Augochloropsis metallica.
4.1 Plantation age
Appropriately managed plantations can over time develop diverse communities resembling those of native forests (Gallé, Torma, & Maák, 2016; Pawson, Brockerhoff, & Didham, 2009; Pawson, Brockerhoff, Meenken, & Didham, 2008) and can even include rare or endangered taxa (Berndt, Brockerhoff, & Jactel, 2008; Humphrey, Newton, Peace, & Holden, 2000). Longer harvest rotations, while less economically beneficial for timber production in many contexts, often benefit conservation objectives (Brockerhoff, Jactel, Parrotta, Quine, & Sayer, 2008; Humphrey, 2005). We found that bee communities in older pine plantations had significantly higher inverse Simpson. However, that they did not support greater bee abundance or rarified species richness suggests greater community evenness. Alternatively, young stands may experience more invasion by open-habitat specialists (Koivula, Kukkonen, & Niemelä, 2002; Pawson et al., 2008, 2009) that could reduce their relative evenness. Community dissimilarity among young plantations was significantly lower than dissimilarity among other plantation types, indicating that younger plantations tended to share more similar species.
4.2 Plantation thinning
The biomass collected from thinning conventional timber plantations can be a source of bioenergy feedstock. Thinning creates canopy gaps, which changes understorey microclimate and vegetation (Ares, Neill, & Puettmann, 2010; Nunes, Oliveira, Cabral, Branquinho, & Correia, 2014; Son, Lee, Jun, & Kim, 2004) and has been known to increase abundance and/or diversity of bees in other forests (Proctor, Burke, & Crins, 2012; Taki et al., 2010). Furthermore, another study that thinned longleaf pine forests close to our study region saw increased bee abundance and richness (Breland et al., 2018). Surprisingly, we failed to find consistent impacts of tree thinning practices on bee abundance, alpha diversity or beta diversity. In theory, thinning encourages the growth of flowering plants in the understorey, though understorey plant richness did not correlate with bee communities in this study. It is plausible that benefits of thinning could be slow to accumulate for some taxa if (e.g.) time is needed for the understorey to develop floral resources for bees. Alternatively, tree thinning alone may not create quality bee habitat if understorey shrubs are not also removed to encourage proliferation of diverse, herbaceous plant communities (Proctor et al., 2012).
4.3 Treatment of woody debris
Woody residues from tree thinning and harvesting (e.g. branches, stumps etc.) can be collected for bioenergy feedstock in plantations grown primarily for timber (Riffell et al., 2011a). When left uncleared, however, forest woody debris is utilized by a variety of wildlife and its absence has been linked to lower species diversity in a variety of taxa (Castro & Wise, 2010; Gottlieb et al., 2017; Mengak & Guynn Jr, 2003; Rodríguez & Kouki, 2015). We found that harvesting plantation woody debris correlated with higher bee abundances but on a per-individual basis, and species richness and community evenness were markedly lower. This is perhaps unsurprising, given that coarse woody debris has been associated with wood-nesting bee diversity in other managed forests (Rivers, Mathis, Moldenke, & Betts, 2018; Rodríguez & Kouki, 2017). Physical soil disturbance from the collection of debris may also impact ground nesters (Vázquez, Alvarez, Debandi, Aranibar, & Villagra, 2011). Even so, debris removal appeared to benefit two species: L. floridanum and Lasioglossum nymphale, which accounted for approximately 60% (34.5% and 26.3% respectively) of the 1,666 individuals (out of 75 species) collected from debris-cleared sites. These two species accounted for <15% (14.2% and <1% respectively) of the 1,031 bees collected from sites where debris was left uncleared (out of 60 species). Many Lasioglossum species are widespread and highly tolerant of disturbed habitats, such as corn fields (Wheelock & O'Neal, 2016), so they may not be good indicators of habitat quality. When these two species were excluded from the analysis, the effect of debris removal on bee abundance became undiscernible. Given that clear-cut sites had significantly lower beta diversity, our findings may instead suggest a convergence in species assemblages in clear-cut sites, perhaps due to reduced habitat heterogeneity caused by disturbance (Karp et al., 2012; Maaß, Migliorini, Rillig, & Caruso, 2014; Myers, Chase, Crandall, & Jiménez, 2015).
We must weigh the relative gains of various land-use practices against their impacts on biodiversity. Debris removal in clear-cuts may generate the lowest biomass yields of any of the practices examined (Munsell & Fox, 2010, also see Gottlieb et al., 2017), as debris tends to be thinly dispersed per hectare and usually has a high bark content that makes for poor bioethanol feedstock (Kimbell, Maness, Brown, Bowyer, & Argow, 2009). Furthermore, debris is a valuable resource to bees and other taxa (Ulyshen, 2018), making debris harvest in clear-cut plantations least sustainable for biomass feedstock production. Relative to mature plantations, younger stands and clear-cut plantations have experienced relatively recent major disturbance during tree harvest and may thus favour bee species that are disturbance-tolerant or prefer open habitats, though potential negative impacts on late-succession specialists or wood- and ground-nesting species are currently unclear. Overall, sites tended to have high beta diversity, suggesting that maintaining landscape heterogeneity, including plantations in various stages of production, could benefit diversity of bees and other pollinators (Miljanic et al., 2019; Rodríguez & Kouki, 2017).
We thank numerous field technicians and volunteers for their assistance with data collection, and the land owners and managers for access to their properties, especially Joe Butler, Charles Stripling, Loncala Inc., and Resource Management Service LLC. We thank Bridget Bradshaw, James Cox, Joe Seufert, Mauricio M. Nuñez-Regueiro, Olivia Macowski, Philip Chaon, Rachel Gardner, Rusty Reynolds, Stephen Doucette-Riise, Zachary Nolen and Zenda Iannetti for field assistance, and Ismael Hinojosa (Universidad Autónoma de México) and Sam Droege (USGS) for assistance with bee identification, and Joyce Seah for editing the Chinese-translated abstract. The Jones Center (Ichauway) and the Tall Timbers Research Station & Land Conservancy provided excellent fieldwork support. We also thank the U.S. Department of Agriculture, USDA-NIFA Initiative Grant No. 2012-67009-20090, the University of Florida's School of Natural Resource and Environment and Emory University's SIRE program and ENVS Lester Fund for support.
R.J.F., B.J.B., H.K.O., L.S. and I.G.W.G. conceived and designed the study; I.G.W.G., D.G., E.K.D., A.S.M., J.B. and B.L. collected the data; X.L. and D.G. conducted the statistical analyses with guidance from B.J.B.; X.L. interpreted the results and wrote the first draft of the manuscript. All authors contributed critically to drafts and approved the final for publication.
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