Research Paper

INTRODUCTION

Populations of nearly half of all bird species are thought to be declining globally (Lees et al. 2022), with 57% of species in North America showing declining trends since 1970 (Rosenberg et al. 2019). Climate change likely plays a major role in these declines (Jenouvrier 2013, Bateman et al. 2020) through both direct demographic effects and indirect effects of climate on local habitat (e.g., Roberts et al. 2019, Cadieux et al. 2020, Ceresa et al. 2021). Birds are especially sensitive to these indirect climate effects on microhabitat, defined here as habitat features at or below the scale of the breeding territory (i.e., second- and third-order habitat selection sensu Johnson 1980), because their microhabitat associations are tied to both local floristic composition (Lee and Rotenberry 2005, Adams and Matthews 2019) as well as vegetation structural features (MacArthur and MacArthur 1961, Culbert et al. 2013). Microhabitat can play an important role in explaining avian breeding occupancy (Hack et al. 2023) and reproductive success (Shew et al. 2019, Kuile et al. 2023), perhaps because finer-scale habitat features are more closely linked to food resource availability and nesting locations. Long-term changes to climate are leading to latitudinal and elevational shifts in plant communities (Boisvert-Marsh and de Blois 2021), however, as well as the creation of no-analog plant communities (Urban et al. 2012). In the western United States, extreme drought and wildfire events have also led to high tree mortality, especially of drought-sensitive species (Allen et al. 2010, Fettig et al. 2013, Clark et al. 2016), which, in turn, alters habitat structure. Predicting how bird species will respond to climate change therefore requires understanding their associations with these changing aspects of habitat. However, bird-microhabitat associations also tend to differ, often substantially, among geographic regions within a species’ range (Whittingham et al. 2007, Crosby et al. 2019, Zillig et al. 2023, Van Lanen et al. 2024), and predictive models of local habitat associations used for management decisions are more accurate when incorporating regional, rather than global, associations (Doherty et al. 2016, Crosby et al. 2019, Elliott et al. 2023, Schofield et al. 2023).

Climate-change effects are particularly pronounced in the Southwestern United States, which is currently in the grip of a 20-year megadrought (Overpeck and Udall 2020, Williams et al. 2022), leading to outsized negative effects on forest ecosystems (Buotte et al. 2019). One widespread Southwestern plant community of great ecological and cultural importance is pinyon-juniper woodlands, a mid-elevation, dwarf woodland dominated by pinyon pine (Pinus edulis or monophyla) and juniper (Juniperus spp.; Romme et al. 2009, Muldavin and Triepke 2020). These woodlands are home to many habitat-specialist birds (Paulin et al. 1999), which track their occurrence on the landscape (Van Lanen et al. 2023), yet climate-change mediated drought is profoundly altering the distribution, floristic composition, and structure of their habitat. Changing precipitation patterns and disturbance regimes have led to both upslope and downslope shifts in woodland distribution (Weisberg et al. 2007, Garbarino et al. 2020), part of a larger process of both contraction and infilling of open woodlands to dense stands (Amme et al. 2020, Filippelli et al. 2020) depending on climate and soil properties. These changes produce both higher and lower tree densities in existing woodlands as well as shifts in plant communities at the upper and lower ecotones. On a more immediate time scale, extreme drought events and associated outbreaks of bark beetles (Gaylord et al. 2013) have led to large-scale die-off and die-back (i.e., loss of foliage) of pinyon pines (Breshears et al. 2005, Hicke and Zeppel 2013, Meddens et al. 2015) and resulted in juniper-dominated woodlands (Mueller et al. 2005). In particular, P. edulis is projected to decline across its range with increasing regional drought (Shriver et al. 2022, Noel et al. 2025). These die-off events also lead to structural changes by reducing canopy cover (Clifford et al. 2011; Flake and Weisberg 2019), which in turn increases understory cover and changes understory plant composition (Flake and Weisberg 2021). Such changes are particularly concerning because many pinyon-juniper bird species are associated with specific structural elements such as canopy height and cover (Sedgwick 1987, Pavlacky and Anderson 2004) or tied to the presence of pinyon pines (Pavlacky and Anderson 2001, Fair et al. 2018) during the breeding season.

In addition to drought, management interventions are also changing habitat features of pinyon-juniper woodlands. With the increased prevalence and size of high-severity fires (Singleton et al. 2019), and infilling of woodlands due to historical fire suppression (Filippelli et al. 2020), managers have increasingly turned to mechanical thinning of pinyon-juniper to reduce both density-dependent drought mortality of trees (e.g., Greenwood and Weisberg 2008) and fuel loads (Huffman et al. 2009, Redmond et al. 2014a). Despite the widespread use of this management technique to increase pinyon-juniper fire resiliency, particularly near human infrastructure, questions remain regarding its effects on ecosystem dynamics and habitat-specialist wildlife (Hartsell et al. 2020, Redmond et al. 2023). In addition to reducing the density of pinyon and juniper, mechanical thinning appears to increase understory vegetation density over long time periods (Ernst-Brock et al. 2019, Almalki et al. 2023). While a recent review found that overall effects of pinyon-juniper thinning on wildlife were generally non-significant (Bombaci and Pejchar 2016), a growing body of evidence suggests thinning reduces the occupancy of pinyon-juniper specialist birds (Crow and van Riper 2010, Bombaci et al. 2017, Magee et al. 2019). For example, the Pinyon Jay (Gymnorhinus cyanocephalus), a habitat-specialist corvid that is a seed-dispersal mutualist of the pinyon pine, was found to avoid nesting in thinned pinyon-juniper stands (Johnson et al. 2018), and Gray Vireo (Vireo vicinior), another pinyon-juniper specialist, prefers to nest in areas with greater foliage density (Harris et al. 2020). Despite these apparent negative effects of thinning, the specific mechanisms that reduce habitat suitability (the ability for a habitat to support viable populations over ecological timescales; Kellner et al. 1992) for pinyon-juniper birds in thinned stands remain unknown and require an understanding of how specific floristic composition and physiognomy changes associated with thinning are impacting birds.

In this study, we used point-count surveys of breeding-bird communities, paired with local measures of floristic composition and vegetation structure, to model habitat relationships in three national parks on the southern Colorado Plateau. We examined these relationships across an eleven-year period (2008-2018) in three parks encompassing a gradient of structural complexity and floristic composition, including a large area of one park that was mechanically thinned. Our objectives were to: (1) quantitatively describe key floristic and structural variables explaining microhabitat associations for pinyon-juniper bird species on the Colorado Plateau, and (2) use these relationships to understand prospective species responses to drought-related tree die-off and mechanical thinning. As in other habitat studies, we predicted that the most important floristic and structural drivers of breeding-season density would vary greatly among species based on their particular foraging and nesting ecologies.

METHODS

Study sites and sampling design

Bird and habitat data were collected at national parks and monuments in the Southern Colorado Plateau Inventory & Monitoring Network (SCPN), located near the Four Corners region of the Southwestern USA (Fig. 1). These data were collected as part of the network’s long-term vital signs bird monitoring program (Holmes et al. 2015) in three parks located in New Mexico, Arizona, and Colorado, respectively: Bandelier National Monument (Bandelier), Grand Canyon National Park (Grand Canyon), and Mesa Verde National Park (Mesa Verde). Bird and habitat variables were sampled in concurrent years, with sample visits to each park occurring every three years, starting in 2008 and running through 2018. Survey data at Bandelier were only collected once, however. The initial survey year varied by park, with habitat data initially gathered in 2008 at Bandelier, in 2009 at Mesa Verde, and in 2011 at Grand Canyon. The number of survey years was therefore variable across parks (N = 1 at Bandelier, 4 at Mesa Verde, 3 at Grand Canyon), and no survey data was collected at any park in 2010, 2013, and 2016. Monitoring efforts were limited to specific plant communities for each park; while a range of plant communities were monitored across the network, we only included pinyon-juniper monitoring points in our analysis. A spatial sampling frame for each plant community was originally created by overlaying National Park Service vegetation and soil classification maps to identify areas of overlap between the focal plant community and soil layers associated with that community (DeCoster et al. 2012, Appendix A). The sampling frame was further reduced by removing areas near roads, human structures, and archaeological sites; areas with a slope of > 30%; and areas more than 2 hours’ travel time from crew campsites. Field crews visited all survey points prior to establishment to ground truth habitat assignations. A more complete description of the sampling frame delimitation is provided in Holmes et al. (2015, Appendix A).

The sample replicate for the analysis consisted of individual point-count survey locations, though the spatial sampling regime varied by park. In parks with smaller sampling frames (Bandelier, Mesa Verde), survey points were placed as a regular 200 m grid across the focal habitat (Fig. 1). At Grand Canyon, the large sampling frame precluded this approach, and the sampling scheme consisted of regular clusters of survey points (Fig. 1) placed throughout the sampling frame using the GRTS algorithm (Stevens and Olsen 2004). Clusters comprised 3 × 3 blocks of point-count stations 200 m apart. To account for spatial autocorrelation of survey data in the model, we grouped spatially aggregated survey points at Bandelier and Mesa Verde into clusters. All of the pinyon-juniper study sites were located within natural landscapes and consisted of relatively homogenous persistent woodlands (sensu Romme et al. 2009), defined as having moderate to relatively dense canopy cover and a variable but often sparse understory of shrubs, subshrubs, and forbs. The Mesa Verde sites were old-growth woodlands dominated by Utah juniper (J. osteosperma) and two-needle pinyon pine (P. edulis) with an understory of Purshia tridentata, Artemisia tridentata, Cercocarpus montanus, and Amelanchier utahensis. The Grand Canyon sites, located on the South Rim near Pasture Wash, were also dominated by P. edulis and J. osteosperma, but with a higher basal area of pinyon pine. The understory consisted of Purshia stansburiana and A. tridentata.

The sites at Bandelier are part of the Pajarito Plateau and represent the most disturbed woodlands. A mass die-off of pinyon pines occurred on the plateau in 2002-2003, resulting in a near total loss (> 95%) of pinyon pines. In addition, Bandelier mechanically thinned ~2,000 ha of the remaining woodlands starting in the spring of 2007 and running through 2010 to prevent soil erosion near archaeological sites. These areas were treated by cutting all live trees < 15 cm in diameter at the root crown, then lopping and scattering the cut vegetation in bare areas. This treatment was designed to mimic the mortality associated with historical surface fires at the site. Of the point-count stations in the monument, 33 (47%) were sampled after the thinning treatment (i.e., sites thinned in 2007 or 2008), 11 were sampled prior to thinning, and 26 stations were never thinned (Appendix 1: Fig. S1). Survey sites at Bandelier therefore represent both thinned and un-thinned sites which experienced pinyon pine die-off. The point-counts and associated vegetation surveys at Bandelier occurred in late May and mid-June 2008, after that year’s thinning treatments took place. After treatments, the canopy cover was 10-20%, and the vegetation consisted of J. monosperma and very minimal P. edulis with a diverse understory of Quercus undulata, Cercocarpus montanus, and other shrubs and forbs.

Point-count surveys

Breeding-season densities of pinyon-juniper bird species were sampled using eight-minute, unlimited-radius point counts, following the methods described in Holmes et al. (2015, Standard Operating Procedure #4). During survey years, each point-count station was surveyed at least twice; the surveys at Bandelier in 2008 used three replicates, though we only included the first two surveys in analyses. The first surveys took place throughout May and the second surveys took place throughout June, depending on the park and year. In all cases, subsequent visits were separated by three or more weeks. Point-count surveys were conducted by a single trained observer, who identified all individuals seen or heard to species where possible. We subsequently updated the bird taxonomy to match the current American Ornithological Society checklist (Chesser et al. 2024). Surveys took place between a half hour before and four hours after local sunrise. Surveyors recorded the minute of first detection (1-8) and the estimated horizontal distance (in m) between the bird and the observer for each individual or flock detected. Before each survey, observers recorded the date, start time, and environmental factors likely to impact the detectability of birds. These included the estimated wind speed (0-6 on the Beaufort scale), background noise (not counting birdsong, 0-3 scale), and cloud cover (recorded to the nearest 10%). Prior to analysis, we removed any detections recorded as ‘flyovers’ (individuals flying over the point and not interacting with the local habitat). We also excluded aerially foraging species that are not strongly associated with the habitat variables that we measured: Violet-green Swallow (Tachycineta thalassina) and White-throated Swift (Aeronautes saxatalis).

Local habitat covariates

We modeled the effects of ten habitat covariates (Table 1), describing both floristic composition and vegetation structure, on breeding-season bird densities. We selected habitat variables that we predicted would change with tree die-off and thinning, and therefore could help explain mechanisms driving changes to breeding-season densities. Species associated with greater sapling density, greater canopy and subcanopy foliar cover, greater canopy height, and higher pinyon pine basal area should be most at risk to future tree die-off and thinning in the region, while species associated with higher juniper basal area, greater shrub foliar cover and height, and greater snag basal area may benefit from future disturbance (refer to Table 1 for a full list of predicted responses to thinning and vegetation die-off). Habitat variables were collected once per survey year at each point-count station following the methods described in Holmes et al. (2015, Standard Operating Procedure #5). Some covariates were collected using a plotless sampling technique directly from the count station, while others were collected in four circular subplots of two different radii (11.3 and 5 m, respectively). The sampling methods used were consistent across sites and years, and for any given habitat measure only a single method was used. The first subplot was centered on the count station, while the center points of the other three were located 30 m from the count station at 0°, 120°, and 240° bearings. Therefore, all habitat measurements represent the local area within ~40 m of the count station and are relatively independent across count stations, which were separated by 200 m. Measures of pinyon pine and snag basal area were collected directly from the count station using an angle gauge as part of a variable area plot technique. In both cases, the observer spun around the count station center point and counted as ‘hits’ all visible pinyon pines or snags larger than the 10 basal area factor aperture on the angle gauge. The basal area was averaged across subplots to give a mean value for each count station.

Six covariates were collected at the 11.3-m-radius subplot level: foliage height diversity, canopy height class, subcanopy foliar cover class, shrub height class, shrub foliar cover class, and dominant shrub species. Foliage height diversity, a measure of the vertical complexity of foliage strata, was derived from presence-absence data of five vegetation strata: emergent trees, canopy, subcanopy, shrub layer, and dwarf shrub layer (shrubs < 0.5 m tall at maturity; Appendix 1: Fig. S2). In the field, the subcanopy was defined as a distinct stratum of trees (not shrubs) below the canopy stratum, while emergent trees were those with a crown higher than the contiguous canopy. For each subplot, each vegetation stratum was determined to be present or absent. We then determined the proportion of subplots at which each stratum was present and calculated the Shannon’s diversity index (H’) of these proportions. Higher diversity values are the result of greater proportions of more strata, and therefore represent a more complex vertical vegetation structure. To determine shrub and canopy layer height, observers calculated the average height of the stratum across the subplot using a clinometer and used this value to assign a height class to the subplot. Height classes consisted of 0.5, 1, 2, 5, 10, and 20, with each value representing the maximum height of that class in meters. For the subcanopy and shrub layers, the percentage foliar cover over each subplot was estimated using the Braun-Blanquet scale (1-7 values; Wikum and Shanholtzer 1978). In each case, we averaged the four subplot height and foliar cover class values to obtain a measurement for each count station. Lastly, observers listed the top three shrub species in each subplot in order of dominance (no shrubs were listed when no shrubs were present). We selected the modal most dominant shrub species for each count station (i.e., across the four subplots) in each year surveyed as a measure of understory plant composition.

Two additional covariates were collected within 5-m-radius subplots at each count station: percentage canopy closure and sapling density. Canopy closure was calculated in each cardinal direction from the center point of each subplot using a convex spherical densiometer; we averaged the canopy closure values from each cardinal direction at each subplot to calculate a measure for the count station as a whole. The total number of saplings with a DBH of 2.5 to < 10 cm were tallied for each subplot, and we calculated the stem density of saplings at each count station by dividing the total number of saplings counted by the total area surveyed.

Modeling habitat associations

We adapted a single-species Bayesian hierarchical mixture model developed by Amundson et al. (2014) to estimate bird density in each of the 3 parks using data from the first two surveys conducted at each count station in each survey year. Our model makes use of several sub-models including an N-mixture model of abundance (Royle, 2004) and models of detectability based on time removal (availability) and distance sampling (perceptibility; Farnsworth et al. 2002). This approach allows for the simultaneous estimation of availability (p_a; probability an individual was available for detection by signaling its presence), perceptibility (p_d; probability an available individual was perceived by an observer), and true abundance during survey k (N_k). We modeled N_k as a Poisson random variable with mean l_k (k in 1, ..., K surveys, where K = ∑ surveys/station/year) which was related to the survey-specific count (y_k) as

(1)

(2)

where n_k denotes the number of individuals available for detection.

We modeled survey-specific heterogeneity in p_a and p_d as detailed in Amundson et al. (2014). Briefly, the model assumes that individuals were available with probability a during each minute of an eight-minute survey period. As a result, the availability during interval j of survey k is given by π_jk = a_k(1 - a_k)^j-1. The corresponding conditional probability is given by

(3)

where p_a[k] = ∑_j π_jk represents the probability an individual is available during at least one interval during survey k. Following Jones et al. (2024), we modeled availability as logit(1-a_k) = b₀ + b₁minute of day_k + b₂ordinal date_k + b₃ordinal date²_k + b₄cloud cover_k. This model included fixed effects of minute of day, ordinal date (both linear and quadratic terms), and cloud cover (less than or greater than 50%). We modeled perceptibility using observed horizontal detection distances and considering the probability that an individual was detected in distance bin b = 1, ..., B of survey k as p_d[k] = ∑_b π_d[bk], with corresponding conditional probability

(4)

We modeled detection distances using a half-normal distribution,

(5)

where r_b is the midpoint distance of bin b, δ_b is the width of bin b, σ_k shapes the decline in detection probability with distance, and r_max is the species-specific maximum detection distance. We omitted either 5% or 10% of the farthest observations of each species to avoid fitting sparse data from the tail of the distribution of detection distances and to ensure that the detection interval and detection-distance bin were statistically independent. Where necessary, we truncated further distant detections until detection interval and detection-distance bin were statistically independent. We tested for independence using analysis of variance, making use of a conservative alpha value (0.10) where possible, or the standard alpha value (0.05) when necessary to avoid excessive truncation. Following Jones et al. (2024), heterogeneity in perceptibility was modeled as log(σ_k) = log(σ₀) + b₁noise_k + b_2k[surveyor], which included a fixed effect of environmental noise during the survey and a random effect of surveyor.

We modeled mean population size as log(l_k) = b_{0k[cluster[station]]} + b_1k[year] + b_{2k[dominant shrub]} + b₃canopy closure_k + b₄sapling density_k + b₅snag basal area_k + b₆Pinyon pine basal area_k + b₇canopy height class_k + b₈subcanopy foliar cover_k + b₉shrub height class_k + b₁₀shrub foliar cover_k + b₁₁foliage height diversity_k. This model includes a random intercept term for station nested within cluster to account for spatial autocorrelation, as well as random intercept terms for the year and the dominant shrub species for each survey, and fixed slope effects for the other survey-specific habitat covariates (listed above). We chose to model the effects of both habitat structural features and plant species composition on abundance because birds are known to preferentially forage in specific plant species within a given stratum (e.g., Wood et al. 2012) and we were therefore interested in determining the relative importance of pinyon pine versus juniper in the canopy stratum and of specific shrub species in lower strata in shaping local abundance. We modeled the effect of the dominant shrub as a random effect due to the large number of taxa (19) and the fact we were already fitting a large number of fixed habitat effects in the abundance sub-model. While there are no tests of overfitting that have been developed for Bayesian hierarchical models, following the rough guideline of N/k ≥ 10 (662 replicate surveys for each species / 9 fixed effects), our models are not overfit. We did not include park-specific effects within our model because only one park was surveyed per year, and Bandelier was only surveyed once (in 2008), and as a result the year effect for 2008 and the park effect for Bandelier would be confounded. Given this study design, the survey year captures information about both the year of the survey, as well as the park which was surveyed. Therefore, we report approximate ‘park effects’ which were calculated by stacking the posterior distributions of the random year effects for the years a given park was surveyed (Bandelier: 2008 only; Grand Canyon: 2011, 2014, and 2017; Mesa Verde: 2009, 2012, 2015, and 2018).

For each park, we reported the mean breeding-season density (birds ha^-1) across the sampled area while accounting for the species-specific sampled area (determined by the species-specific r_max) as

(6)

We also calculated an overall density across parks as a weighted average of the proportion of the total sampling area of inference that occurred within each park (219.63 ha in Bandelier, 2138.68 ha in Grand Canyon, and 459.48 ha in Mesa Verde). Notably, individuals with home ranges that partially overlap the survey area violate the closure assumption of N-mixture models; therefore, our estimates of N_k, as well as our estimates of park-specific and overall density, should be interpreted as the number of individuals using the sampling area as opposed to the number permanently present therein (Latif et al. 2016).

Parameter estimation

We fit models in JAGS version 4.3.0 (Plummer 2003) using the ‘jagsUI’ package (Kellner 2024) in R version 4.3.3 (R Core Team 2024). We standardized all continuous covariates prior to analyses, and we only included complete cases in the analysis. Kendall’s rank correlation was less than 0.5 for all included covariates. The variance inflation factor for all fixed covariates was < 3 (mean = 1.48, range = 1.06-2.12), suggesting a lack of multicollinearity in the data. We modeled the priors for fixed effects using a normal distribution with mean of 0 and precision of 0.01. The prior for the random station within cluster intercept was a normal distribution with a mean given by each station’s cluster effect and precision t_station, with the prior for the cluster effects being normally distributed with a mean of 0 and precision t_cluster. Similarly, we assumed the random effects for year and dominant shrub species were normally distributed with a mean of 0 and precision t_year and t_shrub, respectively. We used a gamma distribution with both a shape and rate of 0.1 for the precision (t) of all random effects. We ran three chains for 110,000 iterations each, with a burn-in of 50,000 iterations. We thinned each chain by 30, yielding a joint posterior distribution of 6,000 samples. We assessed convergence of the chains via inspection of the MCMC summaries and the Gelman-Rubin statistic (R-hat; Gelman and Rubin 1992); chains were considered converged when R-hat < 1.2 (Kéry and Schaub 2012). Goodness-of-fit for the availability and perceptibility sub-models was assessed using posterior predictive checks in the form of Bayesian P-values derived from the posterior distributions, as suggested by MacKenzie et al. (2017) and as implemented in Jones et al. (2024). P-values near 0.5 indicate an adequate fit and P < 0.2 or P > 0.8 indicating an inadequate fit. We report the 90% Bayesian credible interval (BCI) for each parameter, which is thought to be more stable than the 95% credible interval (Kruschke 2015). We considered a relationship between a covariate and the relevant sub-model supported if the 90% BCI for its coefficient did not include zero. To obtain effect sizes for covariates across the full community, we stacked the joint posterior distributions of each parameter estimate for all species and derived the mean and 90% BCI.

To visualize patterns of species responses across covariates, we ran a principal components analysis (PCA) on the mean effect sizes of each species to the nine fixed habitat covariates. We excluded three outlier species (Black-chinned Hummingbird [Archilochus alexandri], Brown-headed Cowbird [Molothrus ater], and Dusky Flycatcher [Empidonax oberholseri]) from the PCA to avoid biasing the ordination.

RESULTS

Point-count surveys and associated habitat data were collected in eight of the 11 years of the 2008-2018 timeseries, during which time 16 observers conducted 1,324 point-count surveys (after removing incomplete cases of habitat data) at 246 unique count stations (N of count stations = 70 at Bandelier, 90 at Grand Canyon, and 86 at Mesa Verde). Overall, both vegetation structure and plant species composition varied considerably across the three parks (described in the Supplemental Materials). Bird surveys resulted in 14,126 unique detections, of which 14,025 could be identified to species. Surveyors identified 93 species across all surveys (Appendix 1: Table S1); of these we modeled habitat relationships for 31 species with sufficient sample size of detections, and models converged for 25 species (Table 2). R-hat values were ≈ 1 for the parameters of interest for all modeled species, though some random effects of site showed R-hat values between 1.1 and 1.2. We fit the same model for all species modeled. Distance sampling model parameterizations for each species are reported in Appendix 1: Table S2; overall, we truncated 11.60 ± 6.55% of detections (mean ± SD), though the maximum detection distance (mean ± SD = 107.36 ± 64.40 m; range = 25-300 m) and effective area surveyed (mean ± SD = 4.87 ± 6.66 ha; range = 0.20-28.30 ha) varied significantly across species (Appendix 1: Table S2). The modeled estimates of availability were generally high (0.59 ± 0.27; mean ± SD of mean values of p_a; Appendix 1: Table S3, Fig. S3), though estimated average perceptibility was much lower (0.40 ± 0.14; mean ± SD of median values of p_d). We found a high goodness-of-fit for both detectability sub-models, as measured by Bayesian P-values (Appendix 1: Table S3, Fig. S3). We found supported responses for each of the five fixed covariates fit on the p_a and p_d sub-models, though the number of species with supported responses varied considerably across covariates (Appendix 1: Table S4, Fig. S4).

Breeding-season density across parks

We found an average overall breeding-season density of ~1 bird ha^-1 (1.07 ± 1.60; mean ± SD of mean values), though density varied considerably among species, and, for some species, among parks (Appendix 1: Table S5, Fig. S5). The species with the highest mean densities across parks were Black-chinned Hummingbird (7.75 birds ha^-1), Black-throated Gray Warbler (Setophaga nigrescens; 3.28 birds ha^-1), Bushtit (Psaltriparus minimus; 2.14 birds ha^-1), Juniper Titmouse (Baeolophus ridgwayi; 1.90 birds ha^-1), and Gray Flycatcher (Empidonax wrightii; 1.89 birds ha^-1). We found large effect sizes, and numerous supported responses, of park effects on breeding-season density (Appendix 1: Table S6, Fig. S6); we estimated park effects by stacking the posterior distributions of all year effects for years in which a park was surveyed (only one park was surveyed per year). Notably, we found a relatively large negative effect size of Bandelier on density (-0.75 [-4.24, 1.01]; mean and 90% BCI of the stacked joint posterior distributions across species), with nine supported negative effects (4 supported positive effects). Six of the modeled species were either not detected (Mountain Chickadee [Poecile gambeli] and Gray Vireo) or detected in very low numbers (< 15 detections; Plumbeous Vireo [Vireo plumbeus], Pinyon Jay, Bushtit, and White-breasted Nuthatch [Sitta carolinensis]) at Bandelier. The park effect for Grand Canyon was also negative (-0.22 [-2.40, 1.62] mean and 90% BCI of the stacked joint posterior distributions across species), though there were fewer supported effects on density (5 supported negative effects, 3 supported positive effects). The Mesa Verde park effect had the smallest average effect size (-0.13 [-2.27, 1.52] mean and 90% BCI of the stacked joint posterior distributions across species), though species-level effect sizes were still generally large (3 negative supported effects, 4 supported positive effects).

Effects of floristic composition and vegetation structure on breeding-season density

Overall, we found supported responses to all ten microhabitat variables, and 18 of 25 species (72%) showed a supported effect of at least one of these covariates, though effect sizes on density were generally small. The effects of vegetation structure on density were generally better supported than those of floristic composition (Fig. 2, Fig. 3, Tables S7 and S8), though community mean effect sizes were near zero in most cases. In spite of the thinning and large-scale pinyon-pine die-off at Bandelier, we found few supported effects of canopy cover (-0.005 [-0.23, 0.26], mean and 90% BCI effect size of the stacked joint posterior distributions across species; 4 positive, 2 negative supported effects) and canopy height (1 positive, 3 negative supported effects) on breeding-season densities, though there was a trend of higher densities with decreasing canopy height (-0.04 [-0.31, 0.22]; mean and 90% BCI effect size across species). Instead, we found that a lower foliage stratum more strongly predicted density. Increasing foliage cover in the shrub layer (0.08 [-0.20, 0.41]; mean and 90% BCI effect size across species; 5 positive, 3 negative supported effects), but not the subcanopy (0.04 [-0.29, 0.44]; mean and 90% BCI effect size across species; 1 positive, 0 negative supported effects), was positively associated with breeding-season densities of some species, though community responses were variable (Fig. 2). While foliage cover in species-specific strata had supported effects on densities, the overall number of strata with foliage present (foliage height diversity) did not strongly influence densities for most species (0.02 [-0.31, 0.37]; mean and 90% BCI effect size across species; 1 positive, 1 negative supported effects). Other structural variables also had little effect on density, including shrub height (-0.02 [-0.28, 0.18]; mean and 90% BCI effect size across species; 2 positive, 1 negative supported effects), snag basal area (-0.003 [-0.23, 0.25]; mean and 90% BCI effect size across species; 1 positive, 0 negative supported effects), and sapling density (-0.02 [-0.30, 0.24]; mean and 90% BCI across species; 1 positive, 2 negative supported effects). Overall, species responses to structural variables were linear and generally small (Appendix 1: Fig. S8, Fig. S9), with the exception of non-linear positive responses to increasing shrub foliage cover (Appendix 1: Fig. S10).

We also found supported effects of floristic composition on breeding-season densities at the microhabitat scale (Fig 3 and Appendix 1: Fig. S6, Table S8). There was a small but negative overall effect of two-needle pinyon pine (P. edulis) basal area on bird densities (-0.05 [-0.52, 0.21]; mean and 90% BCI effect size across species) and many supported effects among individual species (5 negative, 3 positive supported effects). However, we found only 7 supported effects (4 negative, 3 positive) of dominant shrub species on breeding-season densities. Species responses were generally idiosyncratic, though there was a weak community trend of lower densities where antelope bitterbrush (P. tridentata) dominated (-0.05 [-0.58, 0.43]; mean and 90% BCI effect size across species) and higher densities where big sagebrush (A. tridentata) dominated (0.05 [-0.45, 0.53]; mean and 90% BCI effect size across species). Average effect sizes across species were near zero for most shrub species, however (Fig. 3, Appendix 1: Fig. S7). Blue-gray Gnatcatcher (Polioptila caerulea) responded most strongly to shrub composition, showing a supported negative effect of antelope bitterbrush (-0.37 [-0.71, -0.03]) and positive supported and near-supported effects of Utah serviceberry (Amelanchier utahensis; 0.74 [0.19, 1.31]) and alderleaf mountain mahogany (C. montanus; 0.36 [-0.04, 0.78]), respectively. Because other shrub species had low sample sizes of points at which they were classified as dominant (Appendix 1: Table S9), we do not report their effect sizes on bird density.

Patterns of species responses across habitat variables

Based on an examination of a scree plot and the axis Eigenvalues, we retained and plotted the first two PCA axes, representing 42.8% of the variance (Fig. 4). While this represents a smaller percentage of the total variance, the PCA did not include the year effects, which had much larger effect sizes on abundance. The first axis had an Eigenvalue of 2.16 and explained 24.0% of the variance (Appendix 1: Table S10). The species responses to pinyon pine basal area (0.43), foliage height diversity (0.25), snag basal area (-0.44), subcanopy foliage cover (-0.54), and sapling density (-0.37) loaded heavily on this axis (Appendix 1: Table S11), which we interpret as a measure of species’ associations with early-successional or die-off associated woodlands characterized by high snag densities and many young conifers (negative values) or late-successional pinyon-pine-dominated (positive values) woodlands. The second axis had an Eigenvalue of 1.69 and explained 18.7% of the variance, with high positive loadings of responses to canopy height (0.49), foliage height diversity (0.48), and shrub foliage cover (0.30) and high negative loadings of response to shrub height (-0.58). We, therefore, interpreted this axis as a measure of association with greater vertical foliage structure (positive values), typically characterized by greater presence of foliage strata in the shrub layer, midstory, and canopy. Overall, species scores were concentrated in the lower left quadrant (Appendix 1: Table S12; Fig. 4).

DISCUSSION

We found numerous supported responses to microhabitat variables across 18 of 25 species, though park-level effects were an order of magnitude larger than microhabitat effects. Our results suggest that pinyon-juniper specialist birds are relatively resilient to loss of canopy cover at local scales, with only generalist species of forests and woodlands showing a supported association with greater canopy cover (Fig. 2a). There were no clear threshold values in canopy cover affecting breeding densities (Appendix 1: Fig. S8), though nine species, including many insectivorous foliage gleaners, showed a supported negative effect of Bandelier on density after a large-scale pinyon pine die-off (and associated reduction in canopy cover; Appendix 1: Fig. S6), suggesting potential landscape-level canopy cover effects not captured in our models. Species also showed limited associations, positive or negative, with pinyon-pine basal area (Fig. 3), perhaps indicating a selection for both tree species. Exceptions to this trend included positive associations with greater pinyon pine basal area of cavity-nesting birds (3 species), and negative associations of species associated with canopy gaps (5 species). Our results suggest that the extent of shrub foliage cover in the woodland understory is a major factor in shaping the functional composition of the insectivorous bird guild in pinyon-juniper woodlands. Foliage gleaning bird species (5 species), of both canopy and understory, showed positive supported associations with shrub foliage cover, while aerial insectivores (2 species) and bark foragers (1 species) were negatively associated with this structural feature (Fig. 2b). An ordination of species’ responses to all microhabitat features (Fig. 4) suggests that the community segregated along a woodland successional gradient, and generally preferred a simplified vertical vegetation structure.

Resilience of PJ-specialist birds to canopy cover loss, but potential landscape effects

We found few supported effects of canopy cover or height on density across the range of variables included in the study (~10-40% canopy cover), and estimated species densities showed gradual and largely linear changes across the gradient of canopy cover (Appendix 1: Fig. S9a, Fig. S10). When evaluating habitat associations over larger intervals of canopy cover (i.e., from mature woodland to treeless shrubland), other studies have found most pinyon-juniper bird species prefer more wooded conditions (Sedgwick 1987, Pavlacky and Anderson 2004, Knick et al. 2017). However, our results agree with Magee et al. (2019) in that most of the pinyon-juniper community was relatively resilient to moderate, local reductions in canopy cover. This may be due to the large variability inherent in pinyon-juniper woodlands, which encompass everything from open shrub-dominated savanna to closed-canopy woodlands (Romme et al. 2009, Muldavin and Triepke 2020). The persistent woodlands sampled in this study likely represent the more mesic, closed-canopy end of this gradient, and, therefore, lower canopy cover in this environment is likely well within the natural range of variation for the habitat. More open canopy structures may also have existed prior to European settlement (Landis and Bailey 2005), and open canopies may harbor a greater diversity of canopy arthropods (Müller et al. 2014). Unlike pinyon-juniper specialist species, we found supported effects of canopy cover on the densities of five generalist species of conifer forest and woodlands. Magee et al. (2019) also found that generalist forest species, including Mountain Chickadee and White-breasted Nuthatch, showed reduced occupancy in thinned pinyon-juniper landscapes, suggesting a sensitivity to reduced canopy cover. This relationship is unusual, because habitat-specialist birds are typically more sensitive to disturbance than generalists (e.g., Devictor et al. 2008), and may reflect adaptations by pinyon-juniper specialists to exploit the variable nature of their habitat.

Extreme thinning (removal of > 90% of tree cover), however, has resulted in local extirpations or reduced densities of many pinyon-juniper bird species (Crow and van Riper 2010, Bombaci et al. 2017, Johnson et al. 2018), and suggests that a minimum threshold of canopy cover may be necessary for maintaining pinyon-juniper woodland species. Although we found no evidence for such a minimum canopy cover threshold, local-scale habitat associations may also not be good predictors of landscape-level occupancy (Farrell et al. 2019), and negative effects of canopy cover loss on pinyon-juniper birds appear to be more pronounced at the landscape level (Magee et al. 2019). We found nine supported negative effects of Bandelier on breeding-season densities, including pinyon-juniper specialists, where a landscape-scale die-off resulted in > 95% loss of pinyon pines five years prior to sampling. While it is impossible to definitively conclude that the die-off, and resulting loss of canopy cover, was responsible for local declines and extirpations, this interpretation is supported by a concurrent long-term study on the adjacent Los Alamos National Laboratory property that found similar declines and extirpations during this time period (Fair et al. 2018). The pinyon pine die-off event and thinning are partially confounded at the Bandelier study sites, but Fair et al. (2018) found that declines occurred in both thinned and un-thinned stands following the die-off, with abundance and richness declining faster at the thinned sites. Therefore, the landscape-level loss of canopy cover may be important in driving declines in addition to any effects of the loss of pinyon pines per se. However, breeding bird density was significantly correlated with pinyon pine density in at least one study (Masters 1979).

In contrast to the lack of effect of canopy cover, we found supported associations with lower canopy height for three pinyon-juniper specialist species: Juniper Titmouse, Woodhouse’s Scrub-Jay (Aphelocoma woodhouseii), and Pinyon Jay. These species are thought to be year-round residents in pinyon-juniper woodlands, and their association with lower canopies may reflect a preference for drier microhabitats on south and east facing slopes. South-facing aspects in pinyon-juniper woodlands are known to have lower soil moisture and conifer cover (Westerband et al. 2015), lower primary productivity (Huang et al. 2012), and less conifer recruitment (Greenwood and Weisberg 2009), potentially leading to lower canopy heights and larger areas of bare ground. Lower canopy heights were also associated with less productive soil types in the Great Basin (Greenwood and Weisberg 2009). Woodlands with lower conifer cover have higher solar radiation and soil temperatures (Royer et al. 2012), likely reducing snow cover in winter and leading to warmer microclimates during the non-breeding season. Juniper Titmouse had a significantly higher occupancy on south-facing slopes in Wyoming (Pavlacky and Anderson 2001), and Pinyon Jay has been documented to use more open habitats during the non-breeding season (Johnson et al. 2016). In addition, all of these species likely engage in seed-caching behavior during the fall and winter months, with the two corvids in particular caching large numbers of pinyon pine seeds in bare ground (Vander Wall and Balda 1981, Marzluff and Balda 1992). Sites with low canopy cover may correspond with preferred caching and foraging locations, which tend to be south-facing, open microhabitats (Marzluff and Balda 1992, Boone et al. 2021, Sicich et al. 2025). A better understanding of how topography, particularly aspect and slope position, affect habitat suitability for pinyon-juniper specialist birds is needed.

Associations with pinyon pines are driven by nesting ecology and habitat preferences

At the microhabitat scale, pinyon-juniper bird species showed few associations with a specific floristic composition within persistent woodlands, with neither tree nor shrub species composition showing many supported effects on density. At the larger landscape scale, pinyon-juniper species are associated with pinyon pines and junipers over other conifer species (Zillig et al. 2023), and many obligate and semi-obligate species are not found in other habitat types throughout their western North American range (Paulin et al. 1999, Van Lanen et al. 2023). Within pinyon-juniper woodlands, however, both tree (typically two intermixed species) and shrub (dominated by one or two species; Appendix 1: Fig. S7) species richness at our sites was low, perhaps providing fewer opportunities for selection of individual plant species. While bird species are not strongly associated with pinyon pines over junipers at our relatively homogenous study sites, many pinyon-juniper birds showed a significant association with pinyon pine presence at a site in southwestern Wyoming where this tree is scarce (Pavlacky and Anderson 2001). Therefore, relative preferences for tree species may be tied to landscape-level tree distributions. Alternatively, species may be selecting for a balance of juniper and pinyon pine on the breeding territory. The vast majority of pinyon-juniper bird species studied in New Mexico preferentially nested in junipers relative to their abundance (Goguen et al. 2005, Francis et al. 2011). By contrast, studies of foraging substrates have found that many pinyon-juniper birds preferentially forage in pinyon pines (Laudenslayer and Balda 1976, Masters 1979). Keane and Morrison (1999) found, for example, that Black-throated Gray Warblers foraged more than twice as often in pinyon pines as in junipers, and that pinyon pines tended to contain higher arthropod abundances than junipers. Pinyon pines may also contain a greater foliage surface area (Laudenslayer and Balda 1976), and harbor a distinct arthropod community relative to junipers (Riskas 2021). Therefore, selecting for a mix of pinyon pine and juniper on the breeding territory may facilitate an optimization between nesting and foraging microhabitats.

The three species that showed supported responses to higher pinyon pine basal area were all cavity-nesting species: Mountain Chickadee, Juniper Titmouse, and Ash-throated Flycatcher (Myiarchus cinerascens; Table 2). Nest cavities may be a limiting resource on the breeding densities of these species, and Masters (1979) also found that pinyon pine densities explained the density of cavity-nesting species. Pinyon pines are more drought sensitive than junipers (Mueller et al. 2005) and are, therefore, more likely to be weakened by bark beetles or disease, facilitating cavity excavation. These bird species are secondary cavity nesters which frequently use woodpecker-excavated nest cavities (Youkey 1990), and the most common woodpecker species in this habitat (Hairy Woodpecker, Dryobates villosus) may preferentially nest in pinyon pines (Francis et al. 2011). Five species of woodland edge or treefall gaps also showed supported associations with lower pinyon pine basal area: Mourning Dove (Zenaida macroura), Black-chinned Hummingbird, Dusky Flycatcher, Common Raven (Corvus corax), and Brown-headed Cowbird. These species may select for canopy gaps that are often caused by local-scale pinyon-pine die-offs from fungal or beetle infestations (Floyd et al. 2003), and which, therefore, tend to have lower pinyon pine basal area. Alternatively, these species may prefer early-successional (e.g., stage I and stage II) woodlands, which tend to be juniper-dominated (Huffman et al. 2012, Miller et al. 2019).

Shrub foliage density shapes functional composition of the insectivore guild

We found numerous supported responses of insectivorous birds to the foliage cover of shrubs, with both positive and negative responses. Five species of foliage gleaning insectivores showed non-linear positive associations with greater shrub cover (Appendix 1: Fig. S8c, Fig. S10). Increasing shrub foliage cover from < 1% to 25-50% lead to estimated density increases of 205% for Blue-gray Gnatcatcher, 179% for Black-headed Grosbeak, 166% for Brown-headed Cowbird, 147% for Plumbeous Vireo, 136% for Spotted Towhee, and 114% for Gray Vireo, among others. While some birds prefer shrub-dominated shrubland-woodland ecotones in the Great Basin, the species that showed positive associations with shrubs at our sites are generally species that occur in woodland interior (Sedgwick 1987, Pavlacky and Anderson 2004). Blue-gray Gnatcatcher breeding densities are known to be positively associated with Rosaceae shrub density (Pavlacky and Anderson 2001) and Gray Vireo densities also increase with increasing shrub cover of big sagebrush (Schlossberg 2006). Both canopy and understory foliage gleaners (Table 2) were associated with greater understory shrub foliage, suggesting that species forage across a greater range of vertical foliage bands in this dwarf woodland. Understory shrub species are often the only deciduous vegetation in persistent pinyon-juniper woodlands, and arthropod community composition in this habitat is in part driven by plant primary productivity (Uhey et al. 2020). Keane and Morrison (1999) found that arthropod densities were significantly higher on understory bitterbrush and sagebrush than on canopy pinyon pines and junipers in May, and, therefore, shrubs may provide key foraging habitat for foliage gleaners during the peak of the breeding season.

By contrast, we found that two other foraging guilds, aerial and bark foragers, were negatively associated with increasing shrub cover, though these relationships were more gradual and linear (Appendix 1: Fig. S11). Both Gray Flycatcher and Ash-throated Flycatcher are aerially foraging species that prefer to capture insect prey at lower foraging heights, either on the ground or on low outer foliage of conifers (Schlossberg and Sterling 2020, Cardiff and Dittmann 2020). Therefore, extensive understory shrub foliage cover may reduce foraging microhabitat for these species. White-breasted Nuthatch is a bark-foraging specialist which prefers conifer trunks and large branches as foraging substrate (Grubb Jr. and Pravosudov 2020). Conifer biomass tends to be inversely correlated with shrub biomass in pinyon-juniper woodland (Roundy et al. 2014), so areas of high shrub foliage cover may contain less trunk foraging area. These responses suggest that small-scale tree die-off and moderate canopy thinning may change the composition of the insectivore guild by increasing understory shrub cover (Albert et al. 2004, Roundy et al. 2014, Ernst-Brock et al. 2019, Huffman et al. 2019). Such effects may be lagged by 10+ years (Huffman et al. 2019), with declines in shrub cover occurring in the first 2-3 years after thinning (Redmond et al. 2014b, Havrilla et al. 2017). Prescribed fire treatments in pinyon-juniper understories have also been shown to reduce shrub cover over long (10+ year) time periods (Roundy et al. 2014, Huffman et al. 2019). Additional assessments of a diversity of thinning treatments on wildlife habitat suitability are increasingly necessary at long timescales.

Community segregation by woodland successional stage and vegetation complexity

When we examined species responses to the microhabitat variables in aggregate, species showed segregation by woodland successional stage (PC axis 1, Fig. 4). Species segregated into those associated with early-successional (stage I and II) and die-off affected woodlands (negative values on PC 1) with a greater presence of snags, saplings, and young conifers and those associated with mature (stage III) woodlands with a higher canopy height, pinyon pine basal area, and foliage height diversity. These habitat features correlate with pinyon-juniper woodland maturity stages (Harper et al. 2003, Huffman et al. 2012, Miller et al. 2019), though tree die-off and die-back may act to reset the successional clock in mature woodlands (Clifford et al. 2011). Few studies have assessed bird community response to pinyon-juniper woodland succession, but our results mirror similar findings of changes to community composition along gradients of conifer cover (Sedgwick 1987, Rosenstock and Van Riper III 2001, Pavlacky and Anderson 2004, Knick et al. 2017). Of the pinyon-juniper bird community, roughly half of the species showed stronger associations with early-successional habitats, though these species were often also present in late-successional woodlands. Species also responded differentially to the vertical complexity of vegetation (PC axis 2), with higher densities associated with simplified vegetation structure for most species. Persistent pinyon-juniper woodlands have infrequent fire-related disturbance, with stand-replacing fires occurring at average fire return intervals of 300-400 years (Romme et al. 2009). Late-successional persistent woodlands also show decreasing understory vegetation components with increasing tree dominance (Roundy et al. 2014). Therefore, persistent pinyon-juniper woodlands tend toward even-aged stands where foliage is generally concentrated in one or two vertical strata (understory or canopy), and few species may have, therefore, evolved to exploit a more complex vertical vegetation structure. Finally, Pinyon Jay showed a unique response to microhabitat variables, with higher densities in late-successional woodlands with low vertical vegetation complexity. This response could be tied to the species’ need for both closed-canopy woodlands for nesting and more open habitats for seed caching and arthropod foraging (Johnson and Sadoti 2023, Van Lanen et al. 2024, Sicich et al. 2025). Pinyon Jay may serve as a poor umbrella species for pinyon-juniper bird communities given its highly divergent local habitat associations.

Management implications

Our results suggest that most pinyon-juniper specialist birds are relatively resilient to tree mortality and thinning at the microhabitat (40-m-radius) scale, showing limited association with higher canopy cover or greater basal area of pinyon pine. Changes to density in response to canopy cover, both positive and negative, were gradual and linear, with no evidence of canopy cover thresholds (Figs. S9, S10). However, extreme (< 10% canopy cover remaining) removal of conifers has been shown to result in local declines and extirpations of pinyon-juniper specialist birds (Crow and van Riper 2010, Bombaci et al. 2017, Johnson et al. 2018). These negative effects could be due to a landscape loss of canopy cover, which may more adversely affect densities than local-scale losses (Magee et al. 2019). Evidence for positive effects of thinning on woodland health is sparse (Redmond et al. 2023), and negative effects of thinning and tree mortality may be amplified at landscape scales, so managers seeking to maximize habitat for birds should thin moderately and patchily where necessary (Darr et al. 2022), leaving a mosaic of dense and more open stands on the landscape. More landscape-level studies on the effects of thinning and conifer removal on bird communities are needed to inform pinyon-juniper management. Thinning and tree die-off may also affect bird communities through increases in understory shrub cover, which can shift the composition of the insectivore guild. Managers can increase shrub cover over long timescales through moderate thinning, which can dramatically increase the densities of some foliage gleaning insectivores, though shrub cover typically decreases in the 2-3 years after thinning. Persistent pinyon-juniper woodlands are not thought to be fire adapted (Romme et al. 2009), and prescribed fire treatments have led to the long-term (10+ year) loss of shrub cover, indicating that prescribed fire treatments may not be advisable if maintaining understory bird species is a management objective. Methods of thinning that leave understory shrubs in place are likely to increase bird densities immediately after treatment, but a better understanding of how treatment types affect understory plants is needed. Unlike pinyon-juniper specialists, many generalist birds of forest and woodland were more sensitive to local changes in canopy cover. Of these, Mountain Chickadee is most at risk to thinning and tree die-off, with higher densities associated with high canopy cover, greater pinyon pine density, and greater vertical vegetation structure. This species is, therefore, especially at risk to climate-change induced tree die-off and even moderate thinning, with local extirpations from pinyon-juniper woodlands likely following such events. Where Mountain Chickadee represents a species of conservation concern, managers may wish to conduct pre-treatment surveys for this species to identify the degree to which it is using areas that will be thinned.

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