Short-Term Benets of Prescribed Fire to Bird Communities of Dry Forests

Background: Low-severity prescribed fire is a tool used for reducing fuel loads on public lands, particularly in dry conifer forests of the western United States characterized by historically 16 mixed- and low-severity fire regimes. Understanding the ecological effects of prescribed fire treatments is important for predicting the impacts of these management actions on wildlife communities. But few studies have estimated small landbird responses to forest treatments at spatial scales relevant to their ecology or have examined potential differences in treatment 20 effects applied within historically mixed- vs. low-severity fire regimes. Therefore, we evaluated prescribed fire treatment effects and relationships with burn severity for avian communities in dry conifer forests dominated by ponderosa pine ( Pinus ponderosa ) located on seven National treatments at mixed-severity locations were unexpected because prescribed fire applications are 47 more similar to historical wildfires characterizing low-severity fire regimes. 48 49 Conclusions: Bird populations in historically low-severity locations may be relatively 50 unresponsive to prescribed fire because fire there is typically more frequent, expected, and 51 regular. By comparison, fire events are relatively rare historically in mixed severity locations, 52 potentially eliciting more responses to an infrequent opportunity, even by species that are 53 strongly associated with recently burned forests by wildfire. Our results suggest that fire 54 management activities intended to reduce fuels and lower the risk of high-severity wildfire can 55 also be effective in creating habitat for some fire specialists at least in the short term. 56


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By investigating impacts of fire treatments on individual species at individual sites, 95 however, results of most studies are limited for informing management activities. Single-species 96 management is frequently impractical except for species of conservation concern or status (e.g., 97 threatened or endangered). Studies that synthesize overall treatment impacts on wildlife    Here, we evaluated the influence of prescribed fire treatments on avian species 134 occupancy and richness at locations representing both mixed-and low-severity fire regimes in 135 dry mixed conifer forests across the interior western United States, known as the Birds and Burns 136 Network. Our plot sizes averaged 300 ha, a spatial scale appropriate for drawing inference about 137 7 landbirds with varying home range sizes, and we used a before-after-control-impact (BACI) 138 design for rigorous evaluation of treatment effects (Morrison et al. 2008;Popescu et al. 2012).

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Low-severity prescribed fire treatments on these study sites increased numbers of dead trees, 140 opened the forest canopy, and reduced shrub cover within two years of treatment (Saab et al. 141 2006). Based on these habitat changes, we predicted changes in species' occupancy rates 142 concurring with life history traits (Table 1). We also expected species richness and average 143 species responses to vary regionally depending on the different historical fire regimes (Latif et al. 144 2016b). Because prescribed fire is intended to burn at low severity, we predicted occupancy 145 changes to be more positive and of stronger magnitude at locations characterized by historically 146 low-severity fire regimes.

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Study system 150 We selected paired study units (treatments and controls) within areas identified by seven national 151 forests that planned to conduct fuel reduction treatments for the Birds and Burns Network 152 ( Figure 1). Each study unit was approximately 200-400 ha (Table 2) Figure 1). Thus, we established 12-unit pairs for a total of 24 study units (Table 2). USFS   Bird surveys 179 We surveyed birds for 1-4 years and 1-3 years before and after prescribed fire treatments at 180 mixed-severity and low-severity units, respectively. For comparability across fire regimes, we 181 restricted our primary analysis to data from 2 years before to 2 years after prescribed fire 182 treatments (Table 3)   Occupancy models 208 We used avian point count data in a hierarchical multi-species occupancy model (Dorazio et al.

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We excluded raptors, owls, and grouse because they were not readily detectable with our survey 220 methods, and we only included species breeding in our study areas. For mobile animals such as  We used occupancy patterns estimated from our analysis to evaluate predictions for 227 species based on their individual life histories (Table 1)  where the latent variable z ijt for occupancy given probability of occupancy ψ ijt was modeled as: ). 245 We analyzed changes in species occupancy patterns using a model that fully leverages treatments did not realize homogenous impacts on vegetation structure and composition. We 249 therefore measured shifts in occupancy from before to after treatment along a continuous burn 250 severity gradient (contra control-impact categories) represented by CBI to evaluate treatment 251 effects. We modeled occupancy (ψ ijt ) as a function of burn severity measured after treatment 252 (CBI j ), treatment period (PER jt = 0 or 1 for before or after site j was treated, respectively), and 253 the interaction between severity and period (CBI j × PER jt ). Thus, 3), 256 where β 0,il is the intercept and β ir parameters described additive or interactive effects of data at low-severity locations. We did include Markovian species persistence, however, in a 264 supplemental analysis of data from mixed-severity locations (described further below and in 265 Appendix A). 266 We primarily inferred species-specific prescribed fire effects from the extent to which 267 occupancy shifted towards or away from severely burned (or unburned) points following 268 treatment (hereafter treatment effect = × , ). We considered the evidence for prescribed 269 fire effects to be definitive for species with statistically supported treatment effects (90% BCI 270 excluded zero). We also examined support for differences in treatment effects between fire 271 regimes by deriving the 90% BCI for where β mixed and β low represent estimated treatment effects in mixed-and low-severity regimes, 274 respectively.

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Our sampling design afforded inferences that were stronger than purely observational inference from treatment effects, and we also drew weaker but substantive inference from burn 292 severity relationships that were consistent with our predictions based on species life histories. 293 We also followed up our primary analysis with two supplemental analyses. For one, we included 294 data from additional years at mixed severity locations and a Markovian persistence parameter to 295 14 better account for variability among years (hereafter "extended sampling model"; Appendix A).

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For the other, we analyzed data from each fire regime separately and estimated separate 297 treatment effects for each post-treatment year (hereafter "yearly effect model"; Appendix A). We 298 examined species with statistically supported treatment effects or CBI relationships from our 299 primary analysis, and/or statistically supported treatment effects in supplemental analyses. We 300 evaluated the strength of evidence for prescribed fire effects based on the consistency of patterns 301 estimated across analyses and with biologically based predictions (Table 1). 302 We modeled detectability separately by location (fixed effect) and as a species-specific 303 normal random effect b 0,i : where p il is the probability of detecting species i at location l during a survey of a given point 306 count station in a given year when the species was present. We modeled heterogeneity in 307 detectability among species and assumed detectability did not change with treatment condition 308 (preliminary models with treatment effects on detection converged poorly and were therefore 309 abandoned). We modeled heterogeneity among species using a correlation term (ρ) between 310 species intercepts of detection probability (b 0,i ) with occupancy probability (β 0,i ) (Dorazio et al. Species-level prescribed fire effects and burn severity relationships 343 We identified 33 species for which we found statistically supported treatment effects and/or burn 344 severity (CBI) relationships (Figures 3, 4, 5). Treatment effects were supported for 4 species in American Robin, Western Bluebird, and Hairy Woodpecker; Figures 3, 4). For some species, 360 treatment effects and CBI relationships were not unequivocally supported in every analysis (i.e.,

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Positive treatment effects were also statistically supported for several species in the 364 mixed severity regime when data from additional years were considered (Black-backed 365 Woodpecker, American Three-toed Woodpecker, Brown Creeper, Western Wood-peewee, 366 House Wren, Dusky Flycatcher, and Gray Flycatcher; Figure 4). The yearly effect model showed 367 treatment effects primarily arose in the second year following treatment ( Figure 4D, 4E Community-wide patterns and differences between regimes were also apparent but limited.

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Treatment effects were generally stronger in magnitude (i.e., deviated further from zero) in the 389 mixed severity regime, where effects were more positive than negative (Figures 3, 4, 5). Despite 390 the apparent difference between regimes, we found no statistically supported difference in    including species of concern that rely on recent disturbance (e.g., Black-backed Woodpecker), 517 old/mature forest specialists (e.g., Red-faced Warbler), and species that require multiple seral We implemented a regional Interior Western U.S. study to estimate small landbird responses to 525 prescribed fire treatments at spatial scales relevant to their ecology. We examined differences in             Statistically supported occupancy parameter estimates (posterior median) and 90% BCIs describing treatment effects (β _(CBI×PER)) and post-treatment CBI relationships (β _CBI+β _(CBI×PER)) for 25 species observed at locations with historically mixed-severity re regimes. Estimates from a primary model (A, B) are compared with those from supplemental models that included data from additional years and a Markovian persistence effect (C) or separated effects by post-treatment year (D, E).
Treatment effects describe the extent to which occupancy shifted towards or away from burned sites with treatment application, whereas CBI (composite burn index) relationships quantify the post-treatment correlation only.

Figure 5
Statistically supported occupancy parameter estimates (posterior median) and 90% BCIs describing treatment effects (β _(CBI×PER)) and CBI relationships (β _CBI+β _(CBI×PER)) for 17 species observed at locations with historically low-severity re regimes. Estimates from our main model (A, B) are compared with those from a supplemental model that separated effects by post-treatment year (C, D). Treatment effects describe the extent to which occupancy shifted towards or away from burned sites with treatment application, whereas CBI (composite burn index) relationships quantify the post-treatment correlation only.

Figure 6
Predicted occupancy with burn severity (CBI) for example species showing treatment responses statistically supported in historically mixed severity regimes but not supported in low severity regimes. Relationships with CBI were estimated before (grey) and after (black) treatment in mixed severity regimes (left) and low severity regimes (right), and treatment responses are inferred from the change in slope between the two. Intercept terms for calculating model predictions were averaged (mean) across locations within each regime. Full species names are listed in Appendix E.

Supplementary Files
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