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Recent post-wildfire salvage logging benefits local and landscape floral and bee communities
Forest Ecology and Management 424 (2018) 267–275
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Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco
Recent post-wildfire salvage logging benefits local and landscape floral and bee communities Laura J. Heil, Laura A. Burkle
T
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Montana State University, Department of Ecology, Bozeman, MT 59717, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Disturbance-diversity relationships Floral resources Forest succession Greater Yellowstone Ecosystem Pollinator declines Timber harvest
Understanding the implications of shifts in disturbance regimes for plants and pollinators is essential for successful land management. Wildfires are essential natural disturbances that are important drivers of forest biodiversity, and there is often pressure to respond to wildfire with management like post-wildfire logging (i.e., removal of dead trees for economic value immediately following wildfire). We investigated how local floral and bee density, species richness, and community composition and dispersion were influenced by post-wildfire logging, and how these effects differed between an older (24 years-since-fire) and a more recent (8 years-sincefire) wildfire in the Gallatin National Forest, Montana USA. We also tested how these local patterns scaled up to influence landscape patterns in floral and bee diversity. After recent wildfire, local floral and bee density and species richness were higher in logged than in unlogged sites, and these effects were variable over the course of the growing season. There were no differences in community dispersion except that of bees in logged areas of the recent wildfire, which were less heterogeneous compared to unlogged areas. Despite this reduction in bee community heterogeneity, overall bee diversity was highest across logged areas of the recent fire. While the positive effects of post-wildfire logging on local density and richness did not persist in the older fire, compositional differences in flowers and bees between logged and unlogged areas were observed in both the recent and older wildfire. Together, these results suggest that the local benefits of post-wildfire logging for floral and bee richness are evident within a decade of these disturbances, but have diminished within a quarter century of fire. Nevertheless, compositional effects persist, resulting in higher overall landscape floral and bee diversity when both logged and unlogged areas of each wildfire were present. This study provides evidence for near-term benefits of post-wildfire logging for local and landscape floral and bee communities. Post-wildfire logging may be considered as a forest management option when balanced with the maintenance of unlogged areas to encourage biodiversity conservation at the landscape scale.
1. Introduction One of the greatest threats to biodiversity and pollination services includes anthropogenic shifts in disturbance regimes (Turner, 2010), and understanding the implications of these shifts for plants and pollinators is essential for successful land management. Wildfires play key roles in structuring terrestrial plant and animal communities worldwide and in driving spatial and temporal heterogeneity in biodiversity across multiple scales (Turner, 2010). By decreasing forest canopy cover and creating early successional habitat, wildfires can benefit floral resources and pollinator communities (Potts et al., 2003, 2005; Taki et al., 2013). Both local flower and pollinator diversity increase immediately after fire (1–15 years after burning), but then steadily decline with timesince-disturbance (e.g., Potts et al., 2003; Grundel et al., 2010; Dafni et al., 2012; Taki et al., 2013).
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Corresponding author. E-mail address: [email protected] (L.A. Burkle).
https://doi.org/10.1016/j.foreco.2018.05.009 Received 18 February 2018; Received in revised form 3 May 2018; Accepted 4 May 2018 0378-1127/ © 2018 Elsevier B.V. All rights reserved.
A common post-wildfire management practice is a type of salvage logging in which dead or damaged trees are removed specifically after wildfire disturbance (Lindenmayer et al., 2008). Post-wildfire logging reduces fuels for subsequent fires and stimulates regeneration in postwildfire systems (Sessions et al., 2004, but see Donato et al., 2006). Post-wildfire logging also alters forest structural complexity, community composition, and ecosystem processes and functions (Lindenmayer and Noss, 2006). Because it takes place during earlier successional stages compared to non-salvage logging, post-wildfire logging can have additional effects including greater soil compaction, increased erosion, greater reductions in canopy cover, harvesting of larger sized blocks of trees, harvesting trees at younger ages, and construction of more logging roads (McIver and Starr, 2000, Morissette et al., 2002; Lindenmayer et al., 2008). In light of the expected increase in intensity and frequency of
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older wildfire. Furthermore, given that disturbances in general can homogenize or diversify community composition, we expected that any patterns in community dispersion in logged and unlogged areas of the wildfire, in combination with local effects, would scale up to influence landscape-level patterns in the diversity of flowers and bees.
wildfires and post-wildfire logging (e.g., Lindenmayer et al., 2008), it is important to understand both near-term (0–10 years) and longer-term effects of these disturbances on biodiversity, ecosystem services, and ecosystem functioning (Parmenter et al., 2003; Turner, 2010). The combination of wildfire and post-wildfire logging, in particular, may influence succession of the logged region by depleting, or at times eliminating, biological legacies (i.e., the organisms and biotic structures that persist from the pre-disturbance ecosystem) (Turner et al., 2013; Lindenmayer et al., 2008). In post-disturbance ecosystems, biological legacies provide structural complexity for decades to centuries (Lindenmayer et al., 2008), which can be important specifically for the recovery of forb and pollinator species (Ponisio et al., 2016). For example, biological legacies in the form of dead trees and coarse woody debris (CWD) may provide increased diversity of micro-climates supporting a higher diversity of forbs (Franklin et al., 2000), as well as more nesting habitat that may support a greater abundance and diversity of cavity-nesting pollinators (Harmon and Sexton, 1996; Moretti et al., 2009; Williams et al., 2010; Mateos et al., 2011; Vázquez et al., 2011). Alternatively, post-wildfire logging may be beneficial to flowers and pollinators by increasing light availability for understory plants, though these potential effects have not been investigated. Such benefits to local floral and pollinator diversity have been shown to occur shortly following similar forest disturbances including logging (i.e., clearcutting and forest thinning) as well as a combination of prescribed burning and shrub removal (Romey et al., 2007; Pengelly and Cartar, 2010; Taki et al., 2013; Jackson et al., 2014; Campbell et al., 2007). While quantifying the influences of post-wildfire logging on local floral and pollinator communities is important for understanding the small-scale effects of these disturbances, investigating how these effects vary across space can provide more complete insights to the implications of post-wildfire logging on floral and pollinator diversity. Such insights are especially valuable at landscape scales relevant to conservation and management. For example, the effects of post-wildfire logging on alpha- (i.e., local diversity) and beta-diversity (i.e., site-tosite variation in community composition) will combine to influence landscape-level patterns of diversity (Burkle et al., 2015). Disturbances can homogenize the composition of communities across sites (e.g., Vellend et al., 2007), or increase among-community heterogeneity in composition (e.g., Myers et al., 2015), depending on the relative importance of different community assembly processes. The degree to which post-wildfire logging influences the beta-diversity of flowers and pollinators is unknown, but holds considerable potential to impact patterns of diversity across landscapes. Over two years in a mixed-conifer system in Montana, USA, we investigated (1) how post-wildfire logging influenced floral and bee density, species richness (i.e., alpha-diversity), composition, and community dispersion (i.e., beta-diversity); (2) how these effects of postwildfire logging differed between an older (24 years-since-burn) and a newer (8 years-since-burn) fire; and (3) how these effects of postwildfire logging scaled up to influence the landscape-level diversity of flowers and bees. We hypothesized that local floral and bee density and species richness could be higher in logged areas within the wildfire perimeters, if the increased sunlight availability accompanying logging is beneficial for plant and bee habitat. Alternatively, post-wildfire logging could reduce local floral and bee density and species richness through, for example, removal of biological legacies and soil compaction. Additionally, we hypothesized that any effects of logging on local floral and bee density and species richness would be more pronounced in the newer fire compared to the older fire. We also expected that the local composition of floral and bee communities would differ between logged and unlogged areas in response to potential differences in environmental conditions as well as in foraging and nesting habitat for bees. Because successional processes could result in a convergence of characteristics between logged and unlogged areas over time, we expected the composition of floral and bee communities to differ between logged and unlogged areas in the more recent wildfire but not in the
2. Methods 2.1. Study sites and experimental design This study was conducted in the Gallatin National Forest, Montana, USA (45°14′N, 110°33′W) in the Thompson Creek (6979 acres burned in 1991) and Wicked Creek (22,195 acres burned in 2007) wildfires. This region is characterized by montane mixed-conifer forest composed of Pseudotsuga menziesii, Pinus contorta, Picea engelmannii, and Abies lasiocarpa (elevation: 1913 m–2244 m) with mixed severity fires occurring every 35–200 years (Montana Field Guide, 2010; U.S. Department of Agriculture, 2012). We selected two different-aged, mixed-severity wildfires (i.e., consisting of low-, medium-, and high-severity burned areas; Monitoring Trends in Burn Severity program (Eidenshink et al., 2007; http://www.mtbs.gov/) that were similar to each other: they were located in immediately adjacent watersheds, contained similar spatial distributions of and proportions of salvaged-logged areas within the wildfire perimeter (see below), and were characterized by similar elevation, slope and aspect. This allowed us to compare forb and pollinator communities between these two wildfires to investigate the effects of time-since-fire using a space-for-time substitution. Post-wildfire salvage logging (in these cases, patch cutting of dead trees) occurred in 1993 in six stands (ca. 177 total acres; mean 35 acres) located within the Thompson Creek fire perimeter, and logging occurred in 2008 in 15 stands (ca. 654 total acres; mean 37 acres) within the Wicked Creek fire perimeter. Within each wildfire, we selected two 15-hectare areas that were logged and two 15-hectare areas that did not receive post-wildlife logging treatments (hereafter, unlogged). Within each fire, selected areas were 1–1.5 km of each other, and 5–6 km apart between the two fires. Data on the spatial location of logging were acquired from the U.S. Forest Service, and the presence of salvage logging was verified by observing logged stumps within the designated logged areas. Within each stand, we established nine sites that were randomly selected using a stratified design from the GRTS package in R (R Core Development Team, 2015), for a total of 72 sites. At each site, we established a 25 m-diameter circular plot (491 m2). There was no evidence of spatial autocorrelation of flowers or bees across plots within each fire-logging combination (Moran’s I: 0.13–0.07; 0.45 > z > 0.12; 0.45 > p > 0.33 in all cases), and we cannot distinguish whether positive spatial autocorrelations of flowers and bees across fire-logging combinations (Moran’s I: 0.38–0.22; 2.22 > z > 1.13; 0.13 > p > 0.027) are due to the effects of logging per se or because logging was necessarily patchy across the landscape. Thus, although the spatial arrangement of logged areas within burns were similar between the two wildfires, we highlight potential landscape influences when interpreting our results. 2.2. Quantification of site characteristics We used ArcGIS 10 to extract elevation, slope and aspect for each site. We included these site characteristics to account for any variability in environmental conditions between sites. However, none of these characteristics were included in our final model set (see below) as none of them were statistically significant predictors of floral or bee density or richness (P > 0.05 in all cases). We also quantified coarse woody debris (CWD; downed trees > 5 cm in diameter) volume at each site because of its known ecological importance. CWD is reduced by logging (Hopkins et al., 2014), suppresses forb habitat (Tinker and Knight, 2000; Vázquez et al. 2011), is an important nesting resource for cavity-nesting bees (Harmon and 268
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and bee density and richness, we used Akaike’s Information Criteria (AIC) model selection (random effects kept constant between models using REML = F; criteria for cutoff: delta AIC < 2). When only main effects and two-way interactions were included, the top models for floral and bee density and species richness did not explain expected variation in the effects of Logging, Fire ID, or the interaction between Logging and Fire ID. Thus, we investigated these relationships further and included additional two-way interactions (with Year, see Supp. Info) and three-way interactions (Fire ID × Logging × JD, and Fire ID × Logging × CWD) in AIC model selection to determine the new best-supported model for our data. Using these top models, we used the lsmeans R package for a post hoc assessment of multiple comparison of means, and we based all significant differences in means on non-overlapping 95% confidence intervals (Lenth, 2016). To examine the effects of post-wildfire logging, Fire ID (Thompson Creek versus Wicked Creek) and their interaction on floral and bee community composition and dispersion (i.e., beta-diversity), we used PERMANOVAs (adonis, Oksanen, 2015) and distance-to-centroid tests (betadisper) based on Bray-Curtis dissimilarities that included community data across the growing season over both years of sampling. Additionally, we used similarity percentage analysis (simper) to identify which species contributed most strongly to observed compositional differences between logged and unlogged areas and between the older and newer fire. Because we were interested in whether the observed compositional dissimilarities (see Results) reflected any differential effects of logging or Fire ID on bee species with particular nesting habits (i.e., ground- vs. cavity-nesting), we assigned each species to one of these nesting habits based on the literature of those or related species. We then calculated the proportion of individuals and proportion of species at each site belonging to those nesting habits, and tested the effects of logging, Fire ID, and their interaction using a MANCOVA. We excluded parasitic species from this analysis given that their nesting location is more related to the presence of their host species than the availability of nesting habitat per se. Finally, to better understand how the local effects of post-wildfire logging in an older and newer fire scaled up to influence the landscapelevel diversity of forbs and bees, we used species accumulation curves across sampling sites within each Logging × Fire ID combination. With these curves, we determined the regional species richness of flowers and bees measured across each of these landscapes. Thus, together with our understanding of compositional differences in flowers and bees (see analyses above), we assessed which combinations of wildfire disturbances and post-wildfire logging would lead to the highest floral and bee diversity at the landscape-level.
Sexton, 1996; Moretti et al. 2009; Williams et al. 2010; Mateos et al. 2011; Vázquez et al. 2011), and it is important for long-term ecosystem nutrient cycling (Tinker and Knight, 2000). Along a 25 m transect at each site, we measured the diameter of each piece of CWD (Lutes and Keane, 2006), and total CWD volume in m3 ha−1 was calculated using a standard method: V = 9.869 ∗ (∑d2)/8L, where d is the piece diameter in cm, and L is the length of the transect in meters (Harmon and Sexton, 1996). Although there were no differences in mean CWD between logged and unlogged sites in each wildfire, there was variability across the landscape and between the two fires (i.e., 2.5 times higher CWD in Thompson Creek than Wicked Creek), so we kept CWD in the model selection (below) (ANOVA, Logging: F1,68 = 0.38, P = 0.54; Fire ID: F1,68 = 22.2, P < 0.0001; Logging × Fire ID: F1,68 = 1.32, P = 0.25). 2.3. Plant sampling Throughout the 2014 and 2015 growing seasons (1 June–21 August), we visited each site once per week. During each site visit, we quantified floral densities, species richness, and composition by recording open flowers of each species along a 25 m × 2 m band transect. Although there were additional plant species present at these sites (see Burkle et al., 2015 for effects of wildfire severity on forb, grass, and tree diversity in this system), we focused on plant species in bloom as they represent floral resources for bee communities. 2.4. Pollinator sampling During each site visit, we quantified the densities, species richness, and composition of pollinators by hand-netting pollinators within a 25 m diameter circular plot for 20 min during sunny, calm weather and peak pollinator activity (0900-1630). Plots were visited in random order during these hours. We considered pollinators to be any insect floral visitor that was observed flying among flowers and contacting floral reproductive parts. Each plot was sampled for 3.7 h over each growing season, for a total of 264 h across all plots. The pollinators were collected individually in vials, immediately put on ice, and identified to species later in the lab. Given that the majority of specimens captured were bees (see Results), we focused on this group for analyses (below). 2.5. Statistical analyses To assess the effects of post-wildfire logging on local floral and bee density and species richness, we used generalized linear mixed-effects models. Additionally, we investigated whether these effects of postwildfire logging differed between two different-aged fires, Wicked Creek (the more recent fire) and Thompson Creek (the older fire). We accounted for repeated measures within and across two sampling years by including Julian day and sampling year as factors (see Supp Info, Figs. S1 and S2 for inter-annual variation). The inclusion of Julian day also allowed us to investigate how the effects of logging varied over the growing season. Logging (post-wildfire logging vs. no logging), Fire ID (Thompson Creek vs. Wicked Creek), CWD volume (CWD), Julian day (JD; representing the first day of each sampling week), and sampling year (2014 vs. 2015) were fixed effects. We also included two-way interactions between Fire ID and Logging, Fire ID and CWD, and Logging and CWD to address our original questions. Random factors included Site nested with Stand. Given that floral and bee density and species richness were correlated with one another (R2 > 0.3), we analyzed each of these responses separately and interpreted the results accordingly. A Poisson distribution was used for all response variables as they were all counts bounded at 0 and were not overdispersed. All analyses were performed in R 3.1.3 (R Core Development Team, 2015). Mixedeffects models were analyzed using the R package lme4 (Bates et al., 2015). To determine the best-supported models for predicting local floral
3. Results Over 264 h of observation, we sampled 2954 pollinator specimens of 189 species visiting 125 forb species: 86.7% of those pollinators were bees (Hymenopterans), 8.2% flies (Dipterans), 4.5% butterflies (Lepidopterans), and 0.6% beetles (Coleopterans). Bees represented 80% of total species caught, representing 152 species (Table S1). Rarely, there were sites in which no bees were observed. We also counted a total of 739,250 flowers representing 236 forb species, however not all forb species were visited by pollinators. 3.1. Local floral density and species richness Overall, post-wildfire logging influenced both local floral density and species richness, but these effects depended on Fire ID (i.e., recent vs. older wildfire) and varied seasonally (Tables 1 and 2; Figs. 1 and 2). Specifically, mean floral density was 28% lower in logged than unlogged sites in the older fire (Thompson Creek), whereas floral density was 39% greater in logged than unlogged sites in the newer fire (Wicked Creek) (Fig. 1A). There was no difference in mean floral species richness between logged and unlogged sites in the older fire, 269
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however, mean floral species richness was 23% higher in logged sites than in unlogged sites in the newer fire (Fig. 1B). Over the course of the growing season, there were minimal differences in local floral density (Fig. 2A) and species richness (Fig. 2C) between logged and unlogged sites in the older fire (Thompson Creek). By contrast, in the more recent fire (Wicked Creek), floral density (Fig. 2B) and species richness (Fig. 2D) in logged areas were higher than in unlogged areas throughout much of the summer. In Wicked Creek, the strongest positive effects of logging on floral density occurred in mid-summer, while the strongest positive effects of logging on species richness occurred during the first half of the growing season; during this time, floral richness was, on average, 89% higher in logged areas compared to unlogged areas.
Table 1 Summary table for final model of floral density showing fixed effects. See Table S2 for estimate values and standard errors.
Year Logging Fire ID Julian Day Year × Logging Year × Fire ID Year × Julian Day Fire × Julian Day Logging × Julian Day Logging × Fire Logging × Fire × Julian Day
Df
F-value
P-value
1 1 1 11 1 1 10 10 11 1 10
3.15 4.38 1.52 8134.1 117.62 1772.95 2200.46 607.93 1570.17 1.27 270.20
0.086 0.045 0.23 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.27 < 0.0001
3.2. Floral community composition and beta-diversity Table 2 Summary table for final model of floral species richness showing fixed effects. See Table S3 for estimate values and standard errors. P-value
1 1 1 11 1 1 1 10 10 11 1 1 1 10 1
0.51 12.44 0.40 26.64 0.40 57.85 124.05 4.58 4.57 0.76 2.08 0.54 0.59 2.95 1.47
0.48 0.0016 0.53 < 0.0001 0.53 < 0.0001 < 0.0001 0.0009 0.0009 0.67 0.16 0.47 0.45 0.013 0.24
c
A
Unlogged Logged
800 b
ab
600 a 400 200 0
Mean bee density (# bees per 491m2 per 20min)
TC 4
C
B
10
3
b a a
1
0 TC
WC
a
a
a b
8 6 4 2 2D Graph 6
0 TC
c
2
12
WC
Mean bee species richness (# species per 491m2 per 20 min)
Mean floral density (# flowers per 50m2)
1000
F-value
Mean floral species richness (# species per 50m2)
Year Logging Fire ID Julian Day log(CWD) Year × Logging Year × Fire ID Year × Julian Day Fire ID × Julian Day Logging × Julian Day Logging × Fire ID Fire ID × log(CWD) Logging × log(CWD) Logging × Fire ID × Julian Day Logging × Fire ID × log(CWD)
Df
Floral community composition differed between logged and unlogged areas (F = 5.36; R2 = 0.062; DF = 1,68; P < 0.001) and between Wicked Creek and Thompson Creek (F = 6.96; R2 = 0.081; DF = 1,68; P < 0.001) (Fig. 3A). Additionally, the effect of postwildfire logging on floral community composition differed between Wicked Creek and Thompson Creek (Fire ID × Logging interaction: F = 5.93; R2 = 0.069; DF = 1,68; P < 0.001), and numerous floral species contributed to these compositional dissimilarities. For instance, within both Thompson Creek and Wicked Creek, Lupinus sericeus flowers were more common in unlogged areas and Symphoricarpos albus flowers were more common in logged areas. While Achillea millefolium flowers were more common in unlogged areas of Thompson Creek, they were more common in logged areas of Wicked Creek. Across all sites, the beta-diversity (i.e., dispersion) of flowers was lower in logged areas (F = 6.20, DF = 1,70, P = 0.015), and the betadiversity of flowers was not different between Wicked Creek and Thompson Creek (F = 2.65, DF = 1,70, P = 0.11). However, within fires, there were no differences in beta-diversity among logged and unlogged areas (Fig. 3B; F = 0.58, DF = 3,68, P = 0.63).
WC
D
c
2
b a a
1
0 TC
Fire
WC
Fire 270
Fig. 1. Mean floral density (A), floral species richness (B), bee density (C) and bee species richness (D) between post-wildfire logged (unfilled) and unlogged (filled) areas in Thompson Creek (TC; older fire) and Wicked Creek (WC; newer fire). Error bars represent 95% confidence intervals around model estimates. Significant differences are indicated by letters.
Forest Ecology and Management 424 (2018) 267–275
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Mean floral density (# flowers per 50m2)
2500
Thompson Creek (older fire)
A
2000
2500
Unlogged Logged
*
1500
1000
1000
500
500
Mean floral species richness (# flowers per 50 m2)
16
160
170
180
190
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210
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230
0 150 16
C
14
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10
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8
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4
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2
0 150
160
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190
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230
* * * * *
* *
2000
1500
0 150
Wicked Creek (newer fire)
B
160
170
180
190
200
D
0 150
* * * * * *
160
170
180
190
210
*
200
210
*
220
230
* *
220
230
Julian Day Fig. 2. Mean floral density (top panels: A and B) and floral species richness (bottom panels: C and D) for Thompson Creek (left panels: A and C) and Wicked Creek (right panels: B and D) between logged (unfilled symbols) and unlogged (filled symbols) sites across the growing season (Julian Day). Each Julian day represents the first day of the sampling week from June through August. Significant differences between logged and unlogged on a given day are indicated by asterisks. Error bars represent 95% confidence intervals around model estimates. Fig. 3. Ordination (NMDS) of floral (A) and bee (C) species composition of post-wildfire logged (unfilled symbols) and unlogged (filled symbols) sites in Thompson Creek (squares) and Wicked Creek (circles). Boxplots of community dispersion (distance-to-centroids) of flowers (B) and bees (D) in logged and unlogged areas of Thompson Creek and Wicked Creek. Letters indicate significant differences in community dispersion (P < 0.05) based on Tukey’s tests; missing letters indicate no differences. Distanceto-centroids were calculated using Bray-Curtis dissimilarities.
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However, there is no evidence to indicate that the proportions of individuals or species with different nesting habitats were influenced by Logging or Fire ID (Logging: F2,64 = 1.87, P = 0.16; Fire ID: F2,64 = 2.05, P = 0.14; Logging × Fire ID interaction: F2,64 = 1.74, P = 0.18). Across all sites, the beta-diversity (i.e., dispersion) of bees was lower in logged areas (F = 16.90, DF = 1,67, P = 0.0001), and bee beta-diversity was not different between Wicked Creek and Thompson Creek (F = 0.21, DF = 1,67, P = 0.65). However, within each fire, the pattern of reduced bee beta-diversity in logged areas compared to unlogged areas was only present in Wicked Creek (F = 41.04, DF = 1,32, P < 0.0001), and there was no difference in beta-diversity between logged and unlogged areas of Thompson Creek (F = 1.80, DF = 1,33, P = 0.19) (Fig. 3D).
Table 3 Summary table for final model of bee density showing fixed effects. See Table S4 for estimate values and standard errors.
Year Logging Fire ID Julian Day Year × Logging Year × Fire ID Year × Julian Day Fire ID × Julian Day Logging × Julian Day Logging × Fire ID Logging × Fire ID × Julian Day
Df
F-value
P-value
1 1 1 11 1 1 10 10 11 1 9
539.37 6.65 6.37 41.39 16.18 12.76 24.94 2.20 5.59 1.16 3.79
< 0.0001 0.015 0.017 < 0.0001 0.0003 0.0012 < 0.0001 0.045 < 0.0001 0.29 0.0026
3.5. Floral and bee landscape diversity Table 4 Summary table for final model of bee species richness showing fixed effects. See Table S5 for estimate values and standard errors.
Year Logging Fire ID Julian Day Year × Logging Year × Fire ID Year × Julian Day Fire ID × Julian Day Logging × Julian Day Logging × Fire ID Logging × Fire ID × Julian Day
Df
F-value
P-value
1 1 1 11 1 1 10 10 11 1 9
188.9 18.60 23.36 10.40 29.02 5.30 13.60 1.62 2.85 6.54 2.07
< 0.0001 0.0002 < 0.0001 < 0.0001 < 0.0001 0.028 < 0.0001 0.15 0.011 0.016 0.064
For forbs, the landscape-level species richness was lowest in unlogged areas of Wicked Creek (43 total forb species), and that of logged areas of Wicked Creek was almost twice as high (75 forb species) (Fig. 5A). The landscape-level forb species richness was highest in Thompson Creek, with similar richness between logged and unlogged areas (98 and 95 forb species, respectively). Similar to forbs, the landscape-level species richness of bees was lowest in unlogged areas of Wicked Creek (30 bee species), and logged and unlogged areas of Thompson Creek had similar landscape-level bee richness (50 and 51 bee species, respectively) (Fig. 5B). However, patterns in the spatial scaling of bee diversity in logged areas of Wicked Creek were different from those of forbs: landscape-level bee richness was highest in logged areas of Wicked Creek (80 bee species). Overall, because the differentaged burns and both logged and unlogged areas within each burn all supported different suites of forb and bee species, the greatest biodiversity occurred at the landscape level, composed of a mosaic of the full combination of disturbances and land management types.
3.3. Local bee density and species richness Post-wildfire logging also influenced local bee density and species richness, and these effects again depended on Fire ID (i.e., recent vs. older wildfire) and varied seasonally (Tables 3 and 4; Figs. 1 and 3). Specifically, mean bee density and mean bee species richness were 37% and 22% higher, respectively, in logged areas compared to unlogged areas in the more recent wildfire (Wicked Creek, Fig. 1C and D). While there were trends for bee density and richness to be higher in logged areas than unlogged areas in the older wildfire, these effects were not significant. Over the growing season, there were minimal differences in bee density (Fig. 4A) or richness (Fig. 4C) between logged and unlogged areas of the older fire (Thompson Creek). By contrast, in Wicked Creek, bee density (Fig. 4B) and richness (Fig. 4D) were consistently higher during the first half of the growing season, reaching up to 1.9 times higher in logged areas compared to unlogged areas. Later in the growing season (i.e., Julian days 223–232), bee richness was up to twice as high in logged areas than in unlogged areas.
4. Discussion To our knowledge, this research is the first to elucidate the nearand long-term impacts associated with post-wildfire logging on native floral and pollinator communities. Recent post-wildfire salvage logging benefited flowers and bees at both local and landscape scales. Both floral and bee density and richness were higher in post-wildfire logged sites than in unlogged sites in a recent wildfire (Wicked Creek), but not in an older wildfire (Thompson Creek), indicating that the benefits of post-wildfire logging for local-scale floral and bee communities diminish over successional time. Despite lower beta-diversity of bees across logged sites of the recent fire, overall bee diversity was highest across this area, suggesting that the positive local effects of logging on bee richness were strong. Moreover, because logged and unlogged areas within both burns supported different suites of forb and bee species, a combination of managed (logged post-wildfire) and unmanaged (burned only) patches led to the highest biodiversity of forbs and bees at the landscape level. While post-wildfire logging similarly enhanced two interacting trophic levels—local floral and bee communities—in the near-term (Wicked Creek), the strongest positive effects associated with postwildfire logging occurred at somewhat different times during the growing season for flowers and bees. For example, bee densities were particularly high in logged sites during the first half of the growing season, whereas local floral and bee species richness saw additional enhancements in logged sites late in the growing season as well. Uniquely, floral density appeared to benefit most from logging in midsummer, due to several species blooming especially prolifically at logged sites. Bumble bees are abundant and important generalist pollinators (e.g., Cameron et al., 2011) in this system, and their social nature likely contributed to these patterns. For example, the early-
3.4. Bee community composition and beta-diversity Bee community composition differed between logged and unlogged areas (F = 3.37, R2 = 0.05, DF = 1,65, P = 0.001), between Thompson Creek and Wicked Creek (F = 2.43; R2 = 0.03, DF = 1,65; P = 0.001), and the effect of post-wildfire logging on bee community composition differed between Thompson Creek and Wicked Creek (Fire ID × Logging interaction: F = 2.08; R2 = 0.03, DF = 1,65; P = 0.004) (Fig. 3B). Numerous bee species contributed to the compositional dissimilarities between logged and unlogged areas and between Wicked Creek and Thompson Creek. For example, Bombus bifarius, Bombus rufocinctus, and Lasioglossum (Dialictus) spp. were more common in logged areas of both Thompson Creek and Wicked Creek, while Andrena topazana was more common only in unlogged areas of Thompson Creek. 272
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Julian day Fig. 4. Mean bee density (top panels: A and B) and bee species richness (bottom panels: C and D) in Thompson Creek (left panels: A and C) and Wicked Creek (right panels: B and D) between logged (unfilled symbols) and unlogged (filled symbols) sites across the growing season (Julian Day). Each Julian day represents the first day of each sampling week. Significant differences between logged and unlogged on a given day are indicated by asterisks. Error bars represent 95% confidence intervals around model estimates.
Fig. 5. Species accumulation curves with 95% confidence intervals for floral (A) and bee (B) species richness of post-wildfire logged (lighter gray) and unlogged (darker gray) sites in Thompson Creek and Wicked Creek.
summer enhanced availability of diverse floral resources in logged areas of the recent wildfire may have allowed for higher worker production in these colonies, and, as the benefits of a larger colony workforce accrued over the growing season, could have contributed to higher colony success and abundances in late summer (sensu Williams et al., 2012). Such differences in the timing of benefits of post-wildfire logging for forbs versus bees may influence forb-bee interactions, with potential but unknown implications for forb and bee reproduction. The diminished positive effects associated with logging in the older fire on local floral and bee communities were consistent with our predictions, as were the overall lower floral and bee densities and species richness in the older fire compared to the newer fire. Both of these
patterns can emerge through a variety of mechanisms associated with forest succession. For example, forest development after disturbance results in increased canopy cover over time and reduced light availability for understory flowering plant growth (Winfree et al, 2007; Taki et al., 2013), to which bees respond directly through decreased foraging (e.g., Potts et al., 2003; Grundel et al., 2010; Pengelly and Cartar, 2010; Taki et al., 2013; Jackson et al., 2014; Rubene et al., 2015). Our results are also partially consistent with studies showing that floral and bee abundance and richness from burned or logged areas become more similar to undisturbed sites over time (from 8 to 178 years post-disturbance) (Potts et al., 2003; Pengelly and Cartar, 2010; Taki et al., 2013). In addition to successional processes, the presence of higher 273
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levels of CWD in the older fire compared to the newer fire may have contributed to the differences that we observed in flowers and bees between Thompson Creek and Wicked Creek. For example, copious CWD could reduce germination and rooting space for flowering plants (e.g., Tinker and Knight, 2000; Vázquez et al., 2011). Of note, local floral species richness did not decline as expected with time-since-disturbance in our study, highlighting that although the composition of flowering plant species appeared to change with time, forest succession had not yet proceeded to point where the richness of understory flowering species was affected. We would expect the rate of attenuation of logging effects to vary across systems with factors that influence the speed of succession, such as precipitation, productivity, typical fire return interval, and characteristics of the dominant trees. In particular, we would expect the responses of flowering plants and bees to post-wildfire logging to persist longer in systems with lower productivity and slower succession, and responses to diminish more quickly in systems with higher productivity and faster succession (sensu Huston, 2014). Further, although other studies have documented negative effects of logging and clearcutting on understory herbaceous communities that persist for over a century in Appalachian hard-wood forests (Wyatt and Silman, 2010, Duffy and Meier, 1992, but see Belote et al., 2012), similar legacies of postwildfire logging appear to be much weaker in the fire-adapted conifer forests in this study. While the effects of post-wildfire logging on floral and bee alphaand beta-diversity were mostly diminished a quarter century after disturbance in this system, not all effects of logging in the older wildfire had disappeared. First, the lower density of flowers in logged sites compared to unlogged sites of the older fire may reflect environmental conditions less conducive for flower production. This pattern was mainly driven by three dominant plant species (i.e., Achillea millefolium, Lupinus sericeus, Spiraea betulifolia), though many other sub-dominant species flowered more prolifically in unlogged sites as well. Second, perhaps surprisingly, although landscape species richness of flowers and bees was similar between logged and unlogged areas of the older fire, compositional differences remained, providing evidence that logged and unlogged areas together supported more species than either alone. Therefore, our results indicate that management of burned conifer landscapes for biodiversity will include a mosaic of logged and unlogged areas. This study sets the foundation for understanding the effects of postwildfire salvage logging on floral and bee communities, and we highlight several areas where additional research would be particularly useful. The uncontrolled nature and large spatial extents of wildfires necessitated an observational approach to this study, and the patches within the wildfire perimeter that were originally selected for logging were likely not randomly placed. Thus, to better understand the mechanisms underlying the patterns resulting from post-wildfire logging, experimental approaches can provide additional insights. For example, the enhanced floral density and richness in post-wildfire logged areas could be due to environmental conditions (e.g., soil moisture, soil nutrients, sunlight), seedbank viability, dispersal patterns (including differences in landscape composition between the two fires), or reproductive success, among others, which could be investigated experimentally. Furthermore, while this study focused on the effects of post-wildfire logging on bee foraging habitat (i.e., floral density and species richness) and we did not find any evidence that logging differentially influenced ground- vs. cavity-nesting bees or CWD (i.e., potential nesting habitat for cavity-nesting bees), we did not directly assess bee usage of potential nesting habitat or reproductive success. The effects of post-wildfire logging on foraging habitat may not be the same (in magnitude and/or direction) as the effects on nesting habitat since bee foraging and nesting habitat can be spatially separated (Westrich, 1996). The availability of nesting habitat has been found to be important for bee community structure (Potts et al., 2005), and timber harvesting likely disrupts the soil and removes biomass
(Lindenmayer et al., 2008), which may influence potential nest sites for ground-nesting bees and cavity-nesting bees, respectively. Lastly, examination of additional areas in the chronosequence of time-sincedisturbance (Hutto and Belote, 2013), especially in the years immediately following post-wildfire logging, will aid in understanding how this type of forest management influences temporal patterns in biodiversity. As changes in climate lead to more frequent and severe fires (e.g., Westerling et al., 2006), the pressure for post-wildfire logging is likely to increase (Lindenmayer et al., 2008). Lindenmayer and Noss (2006) urge that ecologically informed forest management policies regarding post-wildfire logging must be set in place before major natural disturbances occur so that haphazard decision making can be avoided. Current forest management, particularly post-wildfire management, focuses largely on tree growth and regeneration (Belote and Aplet, 2014). Despite the fact that insect and understory plant species make up a large majority of forest biodiversity, and the most diverse areas experience the most logging, little attention is given to managing for biodiversity (Hagar, 2007). However, given the importance of early successional habitat to floral and bee communities (Taki et al., 2013), it is imperative that ecological assessments of logging consider additional indices of biodiversity (Belote and Aplet, 2014), including floral and pollinator community dynamics. To our knowledge, this study provides the first assessment of the responses of floral and bee communities associated with post-wildfire logging and lays critical groundwork for understanding the effects of forest management practices on biodiversity. Post-wildfire logging positively affected local floral and bee density and species richness in a recent wildfire, suggesting that there are near-term benefits of postwildfire logging. While these benefits diminished with time, it is important to note that there were few observable negative long-term effects of post-wildfire logging on forb and bee communities in this system. At a landscape scale, however, the highest floral and bee diversity was achieved after wildfires with a mix of both logged and unlogged areas because they each supported different suites of species. Author’s contributions LB conceived the idea. LB and LH designed the methodology. LH collected the data. LH and LB analyzed the data, wrote the manuscript, contributed critically to the drafts, and gave final approval for publication. Data accessibility Data will be made available on Dryad Digital Repository. Acknowledgements We thank E. Ringer and S. Durney for help in the field and lab; E. Reese, C. Dolan, C. Delphia, H. Ikerd, S. Burrows, and T. Griswold for assistance with bee identification; and T. Belote, D. McWethy, W. Glenny, M. Simanonok, A. Slominski, and S. Durney for helpful discussions and feedback on earlier drafts of this manuscript. A NSF GRFP (DEB-2013168225) to L. Heil provided funding. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.foreco.2018.05.009. References Bates, D., Maechler, M., Bolker, B.M., Walker, S.C., 2015. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. Belote, R.T., Jones, R.H., Wieboldt, T.F., 2012. Compositional stability and diversity of
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Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco
Recent post-wildfire salvage logging benefits local and landscape floral and bee communities Laura J. Heil, Laura A. Burkle
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Montana State University, Department of Ecology, Bozeman, MT 59717, USA
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A B S T R A C T
Keywords: Disturbance-diversity relationships Floral resources Forest succession Greater Yellowstone Ecosystem Pollinator declines Timber harvest
Understanding the implications of shifts in disturbance regimes for plants and pollinators is essential for successful land management. Wildfires are essential natural disturbances that are important drivers of forest biodiversity, and there is often pressure to respond to wildfire with management like post-wildfire logging (i.e., removal of dead trees for economic value immediately following wildfire). We investigated how local floral and bee density, species richness, and community composition and dispersion were influenced by post-wildfire logging, and how these effects differed between an older (24 years-since-fire) and a more recent (8 years-sincefire) wildfire in the Gallatin National Forest, Montana USA. We also tested how these local patterns scaled up to influence landscape patterns in floral and bee diversity. After recent wildfire, local floral and bee density and species richness were higher in logged than in unlogged sites, and these effects were variable over the course of the growing season. There were no differences in community dispersion except that of bees in logged areas of the recent wildfire, which were less heterogeneous compared to unlogged areas. Despite this reduction in bee community heterogeneity, overall bee diversity was highest across logged areas of the recent fire. While the positive effects of post-wildfire logging on local density and richness did not persist in the older fire, compositional differences in flowers and bees between logged and unlogged areas were observed in both the recent and older wildfire. Together, these results suggest that the local benefits of post-wildfire logging for floral and bee richness are evident within a decade of these disturbances, but have diminished within a quarter century of fire. Nevertheless, compositional effects persist, resulting in higher overall landscape floral and bee diversity when both logged and unlogged areas of each wildfire were present. This study provides evidence for near-term benefits of post-wildfire logging for local and landscape floral and bee communities. Post-wildfire logging may be considered as a forest management option when balanced with the maintenance of unlogged areas to encourage biodiversity conservation at the landscape scale.
1. Introduction One of the greatest threats to biodiversity and pollination services includes anthropogenic shifts in disturbance regimes (Turner, 2010), and understanding the implications of these shifts for plants and pollinators is essential for successful land management. Wildfires play key roles in structuring terrestrial plant and animal communities worldwide and in driving spatial and temporal heterogeneity in biodiversity across multiple scales (Turner, 2010). By decreasing forest canopy cover and creating early successional habitat, wildfires can benefit floral resources and pollinator communities (Potts et al., 2003, 2005; Taki et al., 2013). Both local flower and pollinator diversity increase immediately after fire (1–15 years after burning), but then steadily decline with timesince-disturbance (e.g., Potts et al., 2003; Grundel et al., 2010; Dafni et al., 2012; Taki et al., 2013).
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Corresponding author. E-mail address: [email protected] (L.A. Burkle).
https://doi.org/10.1016/j.foreco.2018.05.009 Received 18 February 2018; Received in revised form 3 May 2018; Accepted 4 May 2018 0378-1127/ © 2018 Elsevier B.V. All rights reserved.
A common post-wildfire management practice is a type of salvage logging in which dead or damaged trees are removed specifically after wildfire disturbance (Lindenmayer et al., 2008). Post-wildfire logging reduces fuels for subsequent fires and stimulates regeneration in postwildfire systems (Sessions et al., 2004, but see Donato et al., 2006). Post-wildfire logging also alters forest structural complexity, community composition, and ecosystem processes and functions (Lindenmayer and Noss, 2006). Because it takes place during earlier successional stages compared to non-salvage logging, post-wildfire logging can have additional effects including greater soil compaction, increased erosion, greater reductions in canopy cover, harvesting of larger sized blocks of trees, harvesting trees at younger ages, and construction of more logging roads (McIver and Starr, 2000, Morissette et al., 2002; Lindenmayer et al., 2008). In light of the expected increase in intensity and frequency of
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older wildfire. Furthermore, given that disturbances in general can homogenize or diversify community composition, we expected that any patterns in community dispersion in logged and unlogged areas of the wildfire, in combination with local effects, would scale up to influence landscape-level patterns in the diversity of flowers and bees.
wildfires and post-wildfire logging (e.g., Lindenmayer et al., 2008), it is important to understand both near-term (0–10 years) and longer-term effects of these disturbances on biodiversity, ecosystem services, and ecosystem functioning (Parmenter et al., 2003; Turner, 2010). The combination of wildfire and post-wildfire logging, in particular, may influence succession of the logged region by depleting, or at times eliminating, biological legacies (i.e., the organisms and biotic structures that persist from the pre-disturbance ecosystem) (Turner et al., 2013; Lindenmayer et al., 2008). In post-disturbance ecosystems, biological legacies provide structural complexity for decades to centuries (Lindenmayer et al., 2008), which can be important specifically for the recovery of forb and pollinator species (Ponisio et al., 2016). For example, biological legacies in the form of dead trees and coarse woody debris (CWD) may provide increased diversity of micro-climates supporting a higher diversity of forbs (Franklin et al., 2000), as well as more nesting habitat that may support a greater abundance and diversity of cavity-nesting pollinators (Harmon and Sexton, 1996; Moretti et al., 2009; Williams et al., 2010; Mateos et al., 2011; Vázquez et al., 2011). Alternatively, post-wildfire logging may be beneficial to flowers and pollinators by increasing light availability for understory plants, though these potential effects have not been investigated. Such benefits to local floral and pollinator diversity have been shown to occur shortly following similar forest disturbances including logging (i.e., clearcutting and forest thinning) as well as a combination of prescribed burning and shrub removal (Romey et al., 2007; Pengelly and Cartar, 2010; Taki et al., 2013; Jackson et al., 2014; Campbell et al., 2007). While quantifying the influences of post-wildfire logging on local floral and pollinator communities is important for understanding the small-scale effects of these disturbances, investigating how these effects vary across space can provide more complete insights to the implications of post-wildfire logging on floral and pollinator diversity. Such insights are especially valuable at landscape scales relevant to conservation and management. For example, the effects of post-wildfire logging on alpha- (i.e., local diversity) and beta-diversity (i.e., site-tosite variation in community composition) will combine to influence landscape-level patterns of diversity (Burkle et al., 2015). Disturbances can homogenize the composition of communities across sites (e.g., Vellend et al., 2007), or increase among-community heterogeneity in composition (e.g., Myers et al., 2015), depending on the relative importance of different community assembly processes. The degree to which post-wildfire logging influences the beta-diversity of flowers and pollinators is unknown, but holds considerable potential to impact patterns of diversity across landscapes. Over two years in a mixed-conifer system in Montana, USA, we investigated (1) how post-wildfire logging influenced floral and bee density, species richness (i.e., alpha-diversity), composition, and community dispersion (i.e., beta-diversity); (2) how these effects of postwildfire logging differed between an older (24 years-since-burn) and a newer (8 years-since-burn) fire; and (3) how these effects of postwildfire logging scaled up to influence the landscape-level diversity of flowers and bees. We hypothesized that local floral and bee density and species richness could be higher in logged areas within the wildfire perimeters, if the increased sunlight availability accompanying logging is beneficial for plant and bee habitat. Alternatively, post-wildfire logging could reduce local floral and bee density and species richness through, for example, removal of biological legacies and soil compaction. Additionally, we hypothesized that any effects of logging on local floral and bee density and species richness would be more pronounced in the newer fire compared to the older fire. We also expected that the local composition of floral and bee communities would differ between logged and unlogged areas in response to potential differences in environmental conditions as well as in foraging and nesting habitat for bees. Because successional processes could result in a convergence of characteristics between logged and unlogged areas over time, we expected the composition of floral and bee communities to differ between logged and unlogged areas in the more recent wildfire but not in the
2. Methods 2.1. Study sites and experimental design This study was conducted in the Gallatin National Forest, Montana, USA (45°14′N, 110°33′W) in the Thompson Creek (6979 acres burned in 1991) and Wicked Creek (22,195 acres burned in 2007) wildfires. This region is characterized by montane mixed-conifer forest composed of Pseudotsuga menziesii, Pinus contorta, Picea engelmannii, and Abies lasiocarpa (elevation: 1913 m–2244 m) with mixed severity fires occurring every 35–200 years (Montana Field Guide, 2010; U.S. Department of Agriculture, 2012). We selected two different-aged, mixed-severity wildfires (i.e., consisting of low-, medium-, and high-severity burned areas; Monitoring Trends in Burn Severity program (Eidenshink et al., 2007; http://www.mtbs.gov/) that were similar to each other: they were located in immediately adjacent watersheds, contained similar spatial distributions of and proportions of salvaged-logged areas within the wildfire perimeter (see below), and were characterized by similar elevation, slope and aspect. This allowed us to compare forb and pollinator communities between these two wildfires to investigate the effects of time-since-fire using a space-for-time substitution. Post-wildfire salvage logging (in these cases, patch cutting of dead trees) occurred in 1993 in six stands (ca. 177 total acres; mean 35 acres) located within the Thompson Creek fire perimeter, and logging occurred in 2008 in 15 stands (ca. 654 total acres; mean 37 acres) within the Wicked Creek fire perimeter. Within each wildfire, we selected two 15-hectare areas that were logged and two 15-hectare areas that did not receive post-wildlife logging treatments (hereafter, unlogged). Within each fire, selected areas were 1–1.5 km of each other, and 5–6 km apart between the two fires. Data on the spatial location of logging were acquired from the U.S. Forest Service, and the presence of salvage logging was verified by observing logged stumps within the designated logged areas. Within each stand, we established nine sites that were randomly selected using a stratified design from the GRTS package in R (R Core Development Team, 2015), for a total of 72 sites. At each site, we established a 25 m-diameter circular plot (491 m2). There was no evidence of spatial autocorrelation of flowers or bees across plots within each fire-logging combination (Moran’s I: 0.13–0.07; 0.45 > z > 0.12; 0.45 > p > 0.33 in all cases), and we cannot distinguish whether positive spatial autocorrelations of flowers and bees across fire-logging combinations (Moran’s I: 0.38–0.22; 2.22 > z > 1.13; 0.13 > p > 0.027) are due to the effects of logging per se or because logging was necessarily patchy across the landscape. Thus, although the spatial arrangement of logged areas within burns were similar between the two wildfires, we highlight potential landscape influences when interpreting our results. 2.2. Quantification of site characteristics We used ArcGIS 10 to extract elevation, slope and aspect for each site. We included these site characteristics to account for any variability in environmental conditions between sites. However, none of these characteristics were included in our final model set (see below) as none of them were statistically significant predictors of floral or bee density or richness (P > 0.05 in all cases). We also quantified coarse woody debris (CWD; downed trees > 5 cm in diameter) volume at each site because of its known ecological importance. CWD is reduced by logging (Hopkins et al., 2014), suppresses forb habitat (Tinker and Knight, 2000; Vázquez et al. 2011), is an important nesting resource for cavity-nesting bees (Harmon and 268
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and bee density and richness, we used Akaike’s Information Criteria (AIC) model selection (random effects kept constant between models using REML = F; criteria for cutoff: delta AIC < 2). When only main effects and two-way interactions were included, the top models for floral and bee density and species richness did not explain expected variation in the effects of Logging, Fire ID, or the interaction between Logging and Fire ID. Thus, we investigated these relationships further and included additional two-way interactions (with Year, see Supp. Info) and three-way interactions (Fire ID × Logging × JD, and Fire ID × Logging × CWD) in AIC model selection to determine the new best-supported model for our data. Using these top models, we used the lsmeans R package for a post hoc assessment of multiple comparison of means, and we based all significant differences in means on non-overlapping 95% confidence intervals (Lenth, 2016). To examine the effects of post-wildfire logging, Fire ID (Thompson Creek versus Wicked Creek) and their interaction on floral and bee community composition and dispersion (i.e., beta-diversity), we used PERMANOVAs (adonis, Oksanen, 2015) and distance-to-centroid tests (betadisper) based on Bray-Curtis dissimilarities that included community data across the growing season over both years of sampling. Additionally, we used similarity percentage analysis (simper) to identify which species contributed most strongly to observed compositional differences between logged and unlogged areas and between the older and newer fire. Because we were interested in whether the observed compositional dissimilarities (see Results) reflected any differential effects of logging or Fire ID on bee species with particular nesting habits (i.e., ground- vs. cavity-nesting), we assigned each species to one of these nesting habits based on the literature of those or related species. We then calculated the proportion of individuals and proportion of species at each site belonging to those nesting habits, and tested the effects of logging, Fire ID, and their interaction using a MANCOVA. We excluded parasitic species from this analysis given that their nesting location is more related to the presence of their host species than the availability of nesting habitat per se. Finally, to better understand how the local effects of post-wildfire logging in an older and newer fire scaled up to influence the landscapelevel diversity of forbs and bees, we used species accumulation curves across sampling sites within each Logging × Fire ID combination. With these curves, we determined the regional species richness of flowers and bees measured across each of these landscapes. Thus, together with our understanding of compositional differences in flowers and bees (see analyses above), we assessed which combinations of wildfire disturbances and post-wildfire logging would lead to the highest floral and bee diversity at the landscape-level.
Sexton, 1996; Moretti et al. 2009; Williams et al. 2010; Mateos et al. 2011; Vázquez et al. 2011), and it is important for long-term ecosystem nutrient cycling (Tinker and Knight, 2000). Along a 25 m transect at each site, we measured the diameter of each piece of CWD (Lutes and Keane, 2006), and total CWD volume in m3 ha−1 was calculated using a standard method: V = 9.869 ∗ (∑d2)/8L, where d is the piece diameter in cm, and L is the length of the transect in meters (Harmon and Sexton, 1996). Although there were no differences in mean CWD between logged and unlogged sites in each wildfire, there was variability across the landscape and between the two fires (i.e., 2.5 times higher CWD in Thompson Creek than Wicked Creek), so we kept CWD in the model selection (below) (ANOVA, Logging: F1,68 = 0.38, P = 0.54; Fire ID: F1,68 = 22.2, P < 0.0001; Logging × Fire ID: F1,68 = 1.32, P = 0.25). 2.3. Plant sampling Throughout the 2014 and 2015 growing seasons (1 June–21 August), we visited each site once per week. During each site visit, we quantified floral densities, species richness, and composition by recording open flowers of each species along a 25 m × 2 m band transect. Although there were additional plant species present at these sites (see Burkle et al., 2015 for effects of wildfire severity on forb, grass, and tree diversity in this system), we focused on plant species in bloom as they represent floral resources for bee communities. 2.4. Pollinator sampling During each site visit, we quantified the densities, species richness, and composition of pollinators by hand-netting pollinators within a 25 m diameter circular plot for 20 min during sunny, calm weather and peak pollinator activity (0900-1630). Plots were visited in random order during these hours. We considered pollinators to be any insect floral visitor that was observed flying among flowers and contacting floral reproductive parts. Each plot was sampled for 3.7 h over each growing season, for a total of 264 h across all plots. The pollinators were collected individually in vials, immediately put on ice, and identified to species later in the lab. Given that the majority of specimens captured were bees (see Results), we focused on this group for analyses (below). 2.5. Statistical analyses To assess the effects of post-wildfire logging on local floral and bee density and species richness, we used generalized linear mixed-effects models. Additionally, we investigated whether these effects of postwildfire logging differed between two different-aged fires, Wicked Creek (the more recent fire) and Thompson Creek (the older fire). We accounted for repeated measures within and across two sampling years by including Julian day and sampling year as factors (see Supp Info, Figs. S1 and S2 for inter-annual variation). The inclusion of Julian day also allowed us to investigate how the effects of logging varied over the growing season. Logging (post-wildfire logging vs. no logging), Fire ID (Thompson Creek vs. Wicked Creek), CWD volume (CWD), Julian day (JD; representing the first day of each sampling week), and sampling year (2014 vs. 2015) were fixed effects. We also included two-way interactions between Fire ID and Logging, Fire ID and CWD, and Logging and CWD to address our original questions. Random factors included Site nested with Stand. Given that floral and bee density and species richness were correlated with one another (R2 > 0.3), we analyzed each of these responses separately and interpreted the results accordingly. A Poisson distribution was used for all response variables as they were all counts bounded at 0 and were not overdispersed. All analyses were performed in R 3.1.3 (R Core Development Team, 2015). Mixedeffects models were analyzed using the R package lme4 (Bates et al., 2015). To determine the best-supported models for predicting local floral
3. Results Over 264 h of observation, we sampled 2954 pollinator specimens of 189 species visiting 125 forb species: 86.7% of those pollinators were bees (Hymenopterans), 8.2% flies (Dipterans), 4.5% butterflies (Lepidopterans), and 0.6% beetles (Coleopterans). Bees represented 80% of total species caught, representing 152 species (Table S1). Rarely, there were sites in which no bees were observed. We also counted a total of 739,250 flowers representing 236 forb species, however not all forb species were visited by pollinators. 3.1. Local floral density and species richness Overall, post-wildfire logging influenced both local floral density and species richness, but these effects depended on Fire ID (i.e., recent vs. older wildfire) and varied seasonally (Tables 1 and 2; Figs. 1 and 2). Specifically, mean floral density was 28% lower in logged than unlogged sites in the older fire (Thompson Creek), whereas floral density was 39% greater in logged than unlogged sites in the newer fire (Wicked Creek) (Fig. 1A). There was no difference in mean floral species richness between logged and unlogged sites in the older fire, 269
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however, mean floral species richness was 23% higher in logged sites than in unlogged sites in the newer fire (Fig. 1B). Over the course of the growing season, there were minimal differences in local floral density (Fig. 2A) and species richness (Fig. 2C) between logged and unlogged sites in the older fire (Thompson Creek). By contrast, in the more recent fire (Wicked Creek), floral density (Fig. 2B) and species richness (Fig. 2D) in logged areas were higher than in unlogged areas throughout much of the summer. In Wicked Creek, the strongest positive effects of logging on floral density occurred in mid-summer, while the strongest positive effects of logging on species richness occurred during the first half of the growing season; during this time, floral richness was, on average, 89% higher in logged areas compared to unlogged areas.
Table 1 Summary table for final model of floral density showing fixed effects. See Table S2 for estimate values and standard errors.
Year Logging Fire ID Julian Day Year × Logging Year × Fire ID Year × Julian Day Fire × Julian Day Logging × Julian Day Logging × Fire Logging × Fire × Julian Day
Df
F-value
P-value
1 1 1 11 1 1 10 10 11 1 10
3.15 4.38 1.52 8134.1 117.62 1772.95 2200.46 607.93 1570.17 1.27 270.20
0.086 0.045 0.23 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.27 < 0.0001
3.2. Floral community composition and beta-diversity Table 2 Summary table for final model of floral species richness showing fixed effects. See Table S3 for estimate values and standard errors. P-value
1 1 1 11 1 1 1 10 10 11 1 1 1 10 1
0.51 12.44 0.40 26.64 0.40 57.85 124.05 4.58 4.57 0.76 2.08 0.54 0.59 2.95 1.47
0.48 0.0016 0.53 < 0.0001 0.53 < 0.0001 < 0.0001 0.0009 0.0009 0.67 0.16 0.47 0.45 0.013 0.24
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Mean floral species richness (# species per 50m2)
Year Logging Fire ID Julian Day log(CWD) Year × Logging Year × Fire ID Year × Julian Day Fire ID × Julian Day Logging × Julian Day Logging × Fire ID Fire ID × log(CWD) Logging × log(CWD) Logging × Fire ID × Julian Day Logging × Fire ID × log(CWD)
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Floral community composition differed between logged and unlogged areas (F = 5.36; R2 = 0.062; DF = 1,68; P < 0.001) and between Wicked Creek and Thompson Creek (F = 6.96; R2 = 0.081; DF = 1,68; P < 0.001) (Fig. 3A). Additionally, the effect of postwildfire logging on floral community composition differed between Wicked Creek and Thompson Creek (Fire ID × Logging interaction: F = 5.93; R2 = 0.069; DF = 1,68; P < 0.001), and numerous floral species contributed to these compositional dissimilarities. For instance, within both Thompson Creek and Wicked Creek, Lupinus sericeus flowers were more common in unlogged areas and Symphoricarpos albus flowers were more common in logged areas. While Achillea millefolium flowers were more common in unlogged areas of Thompson Creek, they were more common in logged areas of Wicked Creek. Across all sites, the beta-diversity (i.e., dispersion) of flowers was lower in logged areas (F = 6.20, DF = 1,70, P = 0.015), and the betadiversity of flowers was not different between Wicked Creek and Thompson Creek (F = 2.65, DF = 1,70, P = 0.11). However, within fires, there were no differences in beta-diversity among logged and unlogged areas (Fig. 3B; F = 0.58, DF = 3,68, P = 0.63).
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Fig. 1. Mean floral density (A), floral species richness (B), bee density (C) and bee species richness (D) between post-wildfire logged (unfilled) and unlogged (filled) areas in Thompson Creek (TC; older fire) and Wicked Creek (WC; newer fire). Error bars represent 95% confidence intervals around model estimates. Significant differences are indicated by letters.
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Julian Day Fig. 2. Mean floral density (top panels: A and B) and floral species richness (bottom panels: C and D) for Thompson Creek (left panels: A and C) and Wicked Creek (right panels: B and D) between logged (unfilled symbols) and unlogged (filled symbols) sites across the growing season (Julian Day). Each Julian day represents the first day of the sampling week from June through August. Significant differences between logged and unlogged on a given day are indicated by asterisks. Error bars represent 95% confidence intervals around model estimates. Fig. 3. Ordination (NMDS) of floral (A) and bee (C) species composition of post-wildfire logged (unfilled symbols) and unlogged (filled symbols) sites in Thompson Creek (squares) and Wicked Creek (circles). Boxplots of community dispersion (distance-to-centroids) of flowers (B) and bees (D) in logged and unlogged areas of Thompson Creek and Wicked Creek. Letters indicate significant differences in community dispersion (P < 0.05) based on Tukey’s tests; missing letters indicate no differences. Distanceto-centroids were calculated using Bray-Curtis dissimilarities.
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However, there is no evidence to indicate that the proportions of individuals or species with different nesting habitats were influenced by Logging or Fire ID (Logging: F2,64 = 1.87, P = 0.16; Fire ID: F2,64 = 2.05, P = 0.14; Logging × Fire ID interaction: F2,64 = 1.74, P = 0.18). Across all sites, the beta-diversity (i.e., dispersion) of bees was lower in logged areas (F = 16.90, DF = 1,67, P = 0.0001), and bee beta-diversity was not different between Wicked Creek and Thompson Creek (F = 0.21, DF = 1,67, P = 0.65). However, within each fire, the pattern of reduced bee beta-diversity in logged areas compared to unlogged areas was only present in Wicked Creek (F = 41.04, DF = 1,32, P < 0.0001), and there was no difference in beta-diversity between logged and unlogged areas of Thompson Creek (F = 1.80, DF = 1,33, P = 0.19) (Fig. 3D).
Table 3 Summary table for final model of bee density showing fixed effects. See Table S4 for estimate values and standard errors.
Year Logging Fire ID Julian Day Year × Logging Year × Fire ID Year × Julian Day Fire ID × Julian Day Logging × Julian Day Logging × Fire ID Logging × Fire ID × Julian Day
Df
F-value
P-value
1 1 1 11 1 1 10 10 11 1 9
539.37 6.65 6.37 41.39 16.18 12.76 24.94 2.20 5.59 1.16 3.79
< 0.0001 0.015 0.017 < 0.0001 0.0003 0.0012 < 0.0001 0.045 < 0.0001 0.29 0.0026
3.5. Floral and bee landscape diversity Table 4 Summary table for final model of bee species richness showing fixed effects. See Table S5 for estimate values and standard errors.
Year Logging Fire ID Julian Day Year × Logging Year × Fire ID Year × Julian Day Fire ID × Julian Day Logging × Julian Day Logging × Fire ID Logging × Fire ID × Julian Day
Df
F-value
P-value
1 1 1 11 1 1 10 10 11 1 9
188.9 18.60 23.36 10.40 29.02 5.30 13.60 1.62 2.85 6.54 2.07
< 0.0001 0.0002 < 0.0001 < 0.0001 < 0.0001 0.028 < 0.0001 0.15 0.011 0.016 0.064
For forbs, the landscape-level species richness was lowest in unlogged areas of Wicked Creek (43 total forb species), and that of logged areas of Wicked Creek was almost twice as high (75 forb species) (Fig. 5A). The landscape-level forb species richness was highest in Thompson Creek, with similar richness between logged and unlogged areas (98 and 95 forb species, respectively). Similar to forbs, the landscape-level species richness of bees was lowest in unlogged areas of Wicked Creek (30 bee species), and logged and unlogged areas of Thompson Creek had similar landscape-level bee richness (50 and 51 bee species, respectively) (Fig. 5B). However, patterns in the spatial scaling of bee diversity in logged areas of Wicked Creek were different from those of forbs: landscape-level bee richness was highest in logged areas of Wicked Creek (80 bee species). Overall, because the differentaged burns and both logged and unlogged areas within each burn all supported different suites of forb and bee species, the greatest biodiversity occurred at the landscape level, composed of a mosaic of the full combination of disturbances and land management types.
3.3. Local bee density and species richness Post-wildfire logging also influenced local bee density and species richness, and these effects again depended on Fire ID (i.e., recent vs. older wildfire) and varied seasonally (Tables 3 and 4; Figs. 1 and 3). Specifically, mean bee density and mean bee species richness were 37% and 22% higher, respectively, in logged areas compared to unlogged areas in the more recent wildfire (Wicked Creek, Fig. 1C and D). While there were trends for bee density and richness to be higher in logged areas than unlogged areas in the older wildfire, these effects were not significant. Over the growing season, there were minimal differences in bee density (Fig. 4A) or richness (Fig. 4C) between logged and unlogged areas of the older fire (Thompson Creek). By contrast, in Wicked Creek, bee density (Fig. 4B) and richness (Fig. 4D) were consistently higher during the first half of the growing season, reaching up to 1.9 times higher in logged areas compared to unlogged areas. Later in the growing season (i.e., Julian days 223–232), bee richness was up to twice as high in logged areas than in unlogged areas.
4. Discussion To our knowledge, this research is the first to elucidate the nearand long-term impacts associated with post-wildfire logging on native floral and pollinator communities. Recent post-wildfire salvage logging benefited flowers and bees at both local and landscape scales. Both floral and bee density and richness were higher in post-wildfire logged sites than in unlogged sites in a recent wildfire (Wicked Creek), but not in an older wildfire (Thompson Creek), indicating that the benefits of post-wildfire logging for local-scale floral and bee communities diminish over successional time. Despite lower beta-diversity of bees across logged sites of the recent fire, overall bee diversity was highest across this area, suggesting that the positive local effects of logging on bee richness were strong. Moreover, because logged and unlogged areas within both burns supported different suites of forb and bee species, a combination of managed (logged post-wildfire) and unmanaged (burned only) patches led to the highest biodiversity of forbs and bees at the landscape level. While post-wildfire logging similarly enhanced two interacting trophic levels—local floral and bee communities—in the near-term (Wicked Creek), the strongest positive effects associated with postwildfire logging occurred at somewhat different times during the growing season for flowers and bees. For example, bee densities were particularly high in logged sites during the first half of the growing season, whereas local floral and bee species richness saw additional enhancements in logged sites late in the growing season as well. Uniquely, floral density appeared to benefit most from logging in midsummer, due to several species blooming especially prolifically at logged sites. Bumble bees are abundant and important generalist pollinators (e.g., Cameron et al., 2011) in this system, and their social nature likely contributed to these patterns. For example, the early-
3.4. Bee community composition and beta-diversity Bee community composition differed between logged and unlogged areas (F = 3.37, R2 = 0.05, DF = 1,65, P = 0.001), between Thompson Creek and Wicked Creek (F = 2.43; R2 = 0.03, DF = 1,65; P = 0.001), and the effect of post-wildfire logging on bee community composition differed between Thompson Creek and Wicked Creek (Fire ID × Logging interaction: F = 2.08; R2 = 0.03, DF = 1,65; P = 0.004) (Fig. 3B). Numerous bee species contributed to the compositional dissimilarities between logged and unlogged areas and between Wicked Creek and Thompson Creek. For example, Bombus bifarius, Bombus rufocinctus, and Lasioglossum (Dialictus) spp. were more common in logged areas of both Thompson Creek and Wicked Creek, while Andrena topazana was more common only in unlogged areas of Thompson Creek. 272
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Julian day Fig. 4. Mean bee density (top panels: A and B) and bee species richness (bottom panels: C and D) in Thompson Creek (left panels: A and C) and Wicked Creek (right panels: B and D) between logged (unfilled symbols) and unlogged (filled symbols) sites across the growing season (Julian Day). Each Julian day represents the first day of each sampling week. Significant differences between logged and unlogged on a given day are indicated by asterisks. Error bars represent 95% confidence intervals around model estimates.
Fig. 5. Species accumulation curves with 95% confidence intervals for floral (A) and bee (B) species richness of post-wildfire logged (lighter gray) and unlogged (darker gray) sites in Thompson Creek and Wicked Creek.
summer enhanced availability of diverse floral resources in logged areas of the recent wildfire may have allowed for higher worker production in these colonies, and, as the benefits of a larger colony workforce accrued over the growing season, could have contributed to higher colony success and abundances in late summer (sensu Williams et al., 2012). Such differences in the timing of benefits of post-wildfire logging for forbs versus bees may influence forb-bee interactions, with potential but unknown implications for forb and bee reproduction. The diminished positive effects associated with logging in the older fire on local floral and bee communities were consistent with our predictions, as were the overall lower floral and bee densities and species richness in the older fire compared to the newer fire. Both of these
patterns can emerge through a variety of mechanisms associated with forest succession. For example, forest development after disturbance results in increased canopy cover over time and reduced light availability for understory flowering plant growth (Winfree et al, 2007; Taki et al., 2013), to which bees respond directly through decreased foraging (e.g., Potts et al., 2003; Grundel et al., 2010; Pengelly and Cartar, 2010; Taki et al., 2013; Jackson et al., 2014; Rubene et al., 2015). Our results are also partially consistent with studies showing that floral and bee abundance and richness from burned or logged areas become more similar to undisturbed sites over time (from 8 to 178 years post-disturbance) (Potts et al., 2003; Pengelly and Cartar, 2010; Taki et al., 2013). In addition to successional processes, the presence of higher 273
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levels of CWD in the older fire compared to the newer fire may have contributed to the differences that we observed in flowers and bees between Thompson Creek and Wicked Creek. For example, copious CWD could reduce germination and rooting space for flowering plants (e.g., Tinker and Knight, 2000; Vázquez et al., 2011). Of note, local floral species richness did not decline as expected with time-since-disturbance in our study, highlighting that although the composition of flowering plant species appeared to change with time, forest succession had not yet proceeded to point where the richness of understory flowering species was affected. We would expect the rate of attenuation of logging effects to vary across systems with factors that influence the speed of succession, such as precipitation, productivity, typical fire return interval, and characteristics of the dominant trees. In particular, we would expect the responses of flowering plants and bees to post-wildfire logging to persist longer in systems with lower productivity and slower succession, and responses to diminish more quickly in systems with higher productivity and faster succession (sensu Huston, 2014). Further, although other studies have documented negative effects of logging and clearcutting on understory herbaceous communities that persist for over a century in Appalachian hard-wood forests (Wyatt and Silman, 2010, Duffy and Meier, 1992, but see Belote et al., 2012), similar legacies of postwildfire logging appear to be much weaker in the fire-adapted conifer forests in this study. While the effects of post-wildfire logging on floral and bee alphaand beta-diversity were mostly diminished a quarter century after disturbance in this system, not all effects of logging in the older wildfire had disappeared. First, the lower density of flowers in logged sites compared to unlogged sites of the older fire may reflect environmental conditions less conducive for flower production. This pattern was mainly driven by three dominant plant species (i.e., Achillea millefolium, Lupinus sericeus, Spiraea betulifolia), though many other sub-dominant species flowered more prolifically in unlogged sites as well. Second, perhaps surprisingly, although landscape species richness of flowers and bees was similar between logged and unlogged areas of the older fire, compositional differences remained, providing evidence that logged and unlogged areas together supported more species than either alone. Therefore, our results indicate that management of burned conifer landscapes for biodiversity will include a mosaic of logged and unlogged areas. This study sets the foundation for understanding the effects of postwildfire salvage logging on floral and bee communities, and we highlight several areas where additional research would be particularly useful. The uncontrolled nature and large spatial extents of wildfires necessitated an observational approach to this study, and the patches within the wildfire perimeter that were originally selected for logging were likely not randomly placed. Thus, to better understand the mechanisms underlying the patterns resulting from post-wildfire logging, experimental approaches can provide additional insights. For example, the enhanced floral density and richness in post-wildfire logged areas could be due to environmental conditions (e.g., soil moisture, soil nutrients, sunlight), seedbank viability, dispersal patterns (including differences in landscape composition between the two fires), or reproductive success, among others, which could be investigated experimentally. Furthermore, while this study focused on the effects of post-wildfire logging on bee foraging habitat (i.e., floral density and species richness) and we did not find any evidence that logging differentially influenced ground- vs. cavity-nesting bees or CWD (i.e., potential nesting habitat for cavity-nesting bees), we did not directly assess bee usage of potential nesting habitat or reproductive success. The effects of post-wildfire logging on foraging habitat may not be the same (in magnitude and/or direction) as the effects on nesting habitat since bee foraging and nesting habitat can be spatially separated (Westrich, 1996). The availability of nesting habitat has been found to be important for bee community structure (Potts et al., 2005), and timber harvesting likely disrupts the soil and removes biomass
(Lindenmayer et al., 2008), which may influence potential nest sites for ground-nesting bees and cavity-nesting bees, respectively. Lastly, examination of additional areas in the chronosequence of time-sincedisturbance (Hutto and Belote, 2013), especially in the years immediately following post-wildfire logging, will aid in understanding how this type of forest management influences temporal patterns in biodiversity. As changes in climate lead to more frequent and severe fires (e.g., Westerling et al., 2006), the pressure for post-wildfire logging is likely to increase (Lindenmayer et al., 2008). Lindenmayer and Noss (2006) urge that ecologically informed forest management policies regarding post-wildfire logging must be set in place before major natural disturbances occur so that haphazard decision making can be avoided. Current forest management, particularly post-wildfire management, focuses largely on tree growth and regeneration (Belote and Aplet, 2014). Despite the fact that insect and understory plant species make up a large majority of forest biodiversity, and the most diverse areas experience the most logging, little attention is given to managing for biodiversity (Hagar, 2007). However, given the importance of early successional habitat to floral and bee communities (Taki et al., 2013), it is imperative that ecological assessments of logging consider additional indices of biodiversity (Belote and Aplet, 2014), including floral and pollinator community dynamics. To our knowledge, this study provides the first assessment of the responses of floral and bee communities associated with post-wildfire logging and lays critical groundwork for understanding the effects of forest management practices on biodiversity. Post-wildfire logging positively affected local floral and bee density and species richness in a recent wildfire, suggesting that there are near-term benefits of postwildfire logging. While these benefits diminished with time, it is important to note that there were few observable negative long-term effects of post-wildfire logging on forb and bee communities in this system. At a landscape scale, however, the highest floral and bee diversity was achieved after wildfires with a mix of both logged and unlogged areas because they each supported different suites of species. Author’s contributions LB conceived the idea. LB and LH designed the methodology. LH collected the data. LH and LB analyzed the data, wrote the manuscript, contributed critically to the drafts, and gave final approval for publication. Data accessibility Data will be made available on Dryad Digital Repository. Acknowledgements We thank E. Ringer and S. Durney for help in the field and lab; E. Reese, C. Dolan, C. Delphia, H. Ikerd, S. Burrows, and T. Griswold for assistance with bee identification; and T. Belote, D. McWethy, W. Glenny, M. Simanonok, A. Slominski, and S. Durney for helpful discussions and feedback on earlier drafts of this manuscript. A NSF GRFP (DEB-2013168225) to L. Heil provided funding. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.foreco.2018.05.009. References Bates, D., Maechler, M., Bolker, B.M., Walker, S.C., 2015. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. Belote, R.T., Jones, R.H., Wieboldt, T.F., 2012. Compositional stability and diversity of
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