Broad-scale impacts of an invasive native predator on a sensitive native prey species within the shifting avian community of the North American Great Basin

Biological Conservation 243 (2020) 108409

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Broad-scale impacts of an invasive native predator on a sensitive native prey species within the shifting avian community of the North American Great Basin

T

Peter S. Coatesa, , Shawn T. O'Neila, Brianne E. Brusseea, Mark A. Riccaa, Pat J. Jacksonb, Jonathan B. Dinkinsc, Kristy B. Howed, Ann M. Mosere, Lee J. Fosterf, David J. Delehantyg ⁎

a

U.S. Geological Survey, Western Ecological Research Center, Dixon, CA 95620, USA Nevada Department of Wildlife, Reno, NV 89511, USA Department of Animal and Rangeland Science, Oregon State University, Corvallis, OR 97331, USA d Nevada Natural Heritage Program, Carson City, NV 89701, USA e Idaho Department of Fish and Game, Boise, ID 83707, USA f Oregon Department of Fish and Wildlife, Hines, OR 97738, USA g Idaho State University, Department of Biological Sciences, Pocatello, ID 83209, USA b c

ARTICLE INFO

ABSTRACT

Keywords: Anthropogenic subsidies Nest predators Nest survival Overabundant native species Sagebrush Spillover predation

Human enterprise has modified ecosystem processes through direct and indirect alteration of native predators' distribution and abundance. For example, human activities subsidize food, water, and shelter availability to generalist predators whose subsequent increased abundance impacts lower trophic-level prey species. The common raven (Corvus corax; hereafter, raven) is an avian scavenger and predator, native to the northern hemisphere, that can become invasive when subsidized. Raven populations are increasing at unprecedented rates in many regions globally. Information regarding scale of impact and potential ecological thresholds is needed to guide conservation actions aimed at reducing adverse effects on sensitive prey. We conducted a multipart analysis to investigate broad-scale variation in raven densities and impacts on nesting greater sage-grouse (Centrocercus urophasianus), an indicator species for sagebrush ecosystems in western North America. We estimated raven densities using > 16,000 point surveys over 10 years within the Great Basin, USA, and examined associations with anthropogenic and environmental covariates. Average density was 0.54 ravens km−2 (95% CI: 0.42–0.70), with higher densities at lower relative elevations comprising increased agriculture and development. We then used a reduced dataset to estimate the effect of raven density on sage-grouse nest survival (nests = 737). We identified negative impacts to nesting sage-grouse, especially where raven density exceeded ~0.40 km−2, a potential ecological threshold. We mapped regions where elevated raven densities were predicted to depress sage-grouse population growth in the absence of compensatory demographic responses from other sage-grouse life-history stages, and found ~64% of sage-grouse breeding areas were adversely impacted by high raven density.

1. Introduction Humans have profoundly impacted ecosystems by disrupting a variety of ecological processes that influence biodiversity (Corlett, 2015). For example, while the diminishment of apex predators is a widely recognized conservation problem, the increase and redistribution of mesopredators also creates significant conservation dilemmas globally. An important ecological effect occurs when unintended anthropogenic resource subsidies accrue to mesopredators which then

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increase in abundance and distribution leading to cascading effects to prey species (Oro et al., 2013; Ritchie and Johnson, 2009). Resource subsidies can thus indirectly facilitate increased predation pressure on sensitive or endangered prey species by opportunistic native predators (Boarman, 2003; Kristan III and Boarman, 2003). We use the term ‘hyperpredation’, which typically describes increased predation pressure on sensitive species as a result of increased abundance of native predators due to introduced, non-native prey species (Smith and Quin, 1996). In this case, anthropogenic resource subsidies substitute for non-

Corresponding author at: 800 Business Park Rd., Suite D., Dixon, CA 95620, USA. E-mail address: [email protected] (P.S. Coates).

https://doi.org/10.1016/j.biocon.2020.108409 Received 1 August 2019; Received in revised form 24 December 2019; Accepted 3 January 2020 0006-3207/ © 2020 Published by Elsevier Ltd.

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native prey species. Increased abundance of native predators can lead to invasion of previously unoccupied adjacent habitats where their predatory effects are known as spillover predation (Kristan III and Boarman, 2003; Oro et al., 2013). Common ravens (Corvus corax; hereafter, ravens) are opportunistic generalist predators native to North America (Boarman and Heinrich, 1999). Raven abundance has increased substantially since the mid-20th century (Sauer et al., 2017), and landscapes subsidized with new, anthropogenic food resources and roosting and nesting structures now exhibit increased raven abundance as well as raven expansion into previously unoccupied habitat (Boarman et al., 2006; Leu et al., 2008; Bui et al., 2010). Negative consequences of increased raven abundance have been documented for a variety of threatened or endangered prey species listed by the U.S. Fish and Wildlife Service spanning multiple ecosystems, including the desert tortoise (Gopherus agassizii; Boarman, 2003), least tern (Sterna antillarum; Marschalek, 2011), marbled murrelet (Brachyramhus marmoratus; Manley et al., 1999), piping plover (Charadrius melodus; Manley et al., 1999), and snowy plover (Charadrius nivosus; Peterson and Colwell, 2014). Formulating management actions to directly control or reduce raven abundance is difficult for several reasons. Managers often consider ethical and cultural principles associated with lethal raven removal, societal resistance to valuing one native species over another, international treaty obligations that constitute raven protection under the federal Migratory Bird Treaty Act (16 U.S.C. §§ 703–712), and the behavioral capacity of ravens to learn quickly and avoid control efforts. One prey species of concern to wildlife managers is the greater sagegrouse (Centrocercus urophasianus, hereafter sage-grouse), a sagebrush obligate species occupying ~62 million ha of western North America (Schroeder et al., 2004). Unprecedented conservation efforts have been devoted to halting > 50 years of steady population decline across most of sage-grouse range. Sage-grouse are faced with a suite of threats including loss or degradation of habitat resulting from wildfire and invasive grass expansion (Coates et al., 2016b), conifer expansion (Baruch-Mordo et al., 2013), cropland development (Smith et al., 2016), and anthropogenic surface disturbance (Green et al., 2017). Concomitantly, raven population growth has occurred within the open landscapes of sagebrush ecosystems due to the expansion of human enterprise (Howe et al., 2014; O'Neil et al., 2018). Despite continued increases in raven abundance within areas overlapping sage-grouse habitat, only localized research has examined sage-grouse nesting productivity in relation to elevated raven numbers in sagebrush ecosystems, with negative relationships identified in these studies (Bui et al., 2010; Coates and Delehanty, 2010; Dinkins et al., 2016). The magnitude and scale of potential negative impacts on sage-grouse has not been measured. Prevailing evidence suggests potential for wideranging negative effects of overabundant raven populations on sagegrouse abundance (Peebles et al., 2017), with the underlying mechanism being suppressed sage-grouse reproduction due to nest failure from raven depredation (documented at local scales; Coates and Delehanty, 2010; Dinkins et al., 2016). Managers could benefit from improved regional- and landscape-level information to develop management strategies at the population level, such as when and where to implement actions that reduce interactions between ravens, sagegrouse, and other sensitive prey. If ravens pose a significant population-level threat to sensitive prey species, then variation in raven densities should correspond to the degree of adverse impacts across landscape scales. Obtaining information on this relationship is logistically challenging because it requires estimation of raven densities and in-depth monitoring of prey responses across multiple field sites. To assess population-level impacts most effectively on sage-grouse, raven density must be quantified at spatial scales large enough to overlap multiple sage-grouse subpopulations while resolution must be detailed enough to accurately estimate sagegrouse nest survival at local levels. Such information would inform the spatial extent of the threat posed by elevated raven abundance and

provide managers with estimated ecological thresholds of raven density that, when surpassed, predict negative impacts on sage-grouse reproduction. Our study consisted of four primary objectives that were carried out within the Great Basin of the western United States. Our objectives were to: 1) estimate raven density and abundance at multiple field site units independently of environmental effects; 2) evaluate factors driving variation in raven density and map predicted density across the broader study extent; 3) in a separate analysis, quantify population-level impacts of raven density on sage-grouse nest survival across this spatial extent and time period; and 4) map predicted impacts of raven densities on sage-grouse populations across sage-grouse range within the Great Basin. Importantly, we quantified raven density first in the absence of environmental or anthropogenic effects. This was done to isolate estimates of raven density from potentially confounding anthropogenic effects when conducting analyses of sage-grouse nest survival. We focus on interactions between sage-grouse and ravens, a current and relevant problem in the western USA, but our framework also serves as an exportable example of large-scale monitoring of any overabundant subsidized predator species and associated consequences for a sensitive prey species. 2. Methods 2.1. Study area Our study area encompassed sagebrush ecosystems within the Great Basin, focusing on Idaho, Nevada and portions of Oregon and California, USA (Fig. A.1; Supplementary Material). The Great Basin is a high, cold desert, with elevations reaching 3995 m. Land use was primarily rangeland in non-forested regions (grassland/shrubland), with some irrigated or dry cropland agriculture, and rangeland enrolled in the federal Conservation Reserve Program on private lands. Vegetation communities were dominated by Wyoming big sagebrush (Artemisia tridentata wyomingensis), black sagebrush (A. nova), and little sagebrush (A. arbuscula) at low elevations (< 2100 m), with mountain big sagebrush (A. t. vaseyana) common at high elevations (> 2100 m). We detail the study area further in Appendix A (Supplementary Material). 2.2. Raven density 2.2.1. Raven surveys We conducted a standardized 10-min point count survey protocol for ravens in sagebrush ecosystems. Across 10 years, from 2007 to 2016, we conducted 16,974 raven point count surveys (hereafter, surveys) within 43 different field site units (hereafter, units) representing typical sage-grouse habitat within sagebrush ecosystems of the Great Basin. Units varied in size and prevalence of anthropogenic development, and approximated the areas of local sage-grouse subpopulations (1192.0 km2 ± SE 165.7; Fig. A.1; Supplementary Materials). We conducted multiple surveys per unit and year between mid-March and mid-September (95% of surveys performed 31 March – 27 July) coinciding with sage-grouse nesting and brood-rearing life phases and habitats, which were identified using intensive GPS and radio-telemetry methods (see Sage-grouse nest monitoring). This ensured that density estimates from surveys represented areas of sage-grouse use. Survey locations within each unit were assigned using stratified random sampling to account for disproportionate availability of land cover types and variation in distances to potential sources of anthropogenic subsidies. We strove to conduct > 50 surveys for each unit-year combination (hereafter, unit-year), and exceeded this criterion at most units (n = 194.4 ± SE 19.1). Detailed survey methodology is reported in Appendix B (Supplementary Material). 2.2.2. Raven density estimation Raven density was estimated for each unit-year using point count 2

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hierarchical distance sampling models (Royle et al., 2004; Sillett et al., 2012) with ‘unmarked’ (Fiske and Chandler, 2011) in R 3.5.0 (R Core Team, 2018). Distance sampling uses the relationship between probability of detection and distance to an observer to model counts of individual animals (Buckland et al., 2001), correcting for declining detection probability as distance from the observer increases. We righttruncated distance observations beyond 1.125 km, the approximate distance at which detection probability declined below 0.1 (Buckland et al., 2001; Burnham et al., 2004). We aggregated observations into 5 distance classes with breakpoints at 225, 450, 675, 900, and 1125 m (e.g., Sillett et al., 2012; Kéry and Royle, 2015). We specified a halfnormal key function for the inverse monotonic and curvilinear relationship between distance and detection (Thomas et al., 2010; Fiske and Chandler, 2011). We considered the hazard-rate key function as well (Thomas et al., 2010; Fiske and Chandler, 2011), but this key frequently produced unrealistic detection parameters or convergence failures. Density was modeled using a negative binomial abundance distribution with unit and year effects on abundance (Royle et al., 2004; Sillett et al., 2012). For the detection model component, we pooled units and years together to obtain a minimum of 60 raven observations for each unit- or unit-year-specific distance-detection function (Buckland et al., 2001). If < 60 observations occurred at a given unityear combination, we first combined years within the unit to estimate a unit-specific detection curve (e.g., Buckland et al., 2001, p. 231). If 60 observations did not occur overall for a unit, we pooled its surveys with those of its neighboring unit(s) until the minimum number was achieved. We fit covariates on the detection function to capture variation in detectability across space and time (Marques et al., 2007). At each survey location, we used zonal statistics and visibility analysis in ArcMap 10.4 (Environmental Systems Research Institute [ESRI], Redlands, CA) to model components of the landscape that likely influenced detection. Specifically, we calculated the proportion of forested land cover (% forest) and the area of the viewshed (km2) within 1.125 km of the observer. We used GIS land cover data from the National Land Cover Database (Homer et al., 2015) to estimate forested cover. The viewshed estimated the visible area of the landscape based on topography surrounding the observer, evaluated using a Digital Elevation Model (DEM; 30-m spatial resolution). We used the ArcPy module for Python 2.7 (Python Software Foundation, http://www.python.org) to automate the visibility analysis. Because we did not conduct surveys under poor conditions (fog, rain, or excessive wind; Appendix B; Supplementary Material), we did not include weather covariates. We zstandardized area of viewshed and % forest prior to analysis. We derived unit-year estimates of raven density at each unit-year by specifying categorical effects (unit and year), which were conditioned on the modeled parameters for detection (Kéry and Royle, 2015; Sillett et al., 2012).

relationships and complex interactions implicitly using a machinelearning classification tree algorithm (Cutler et al., 2007; Shoemaker et al., 2018). We weighted each unit-density estimate by number of years surveyed to correct for unbalanced effort across time. We used variable selection to minimize the number of predictors and meansquared error (MSE) while maximizing the percentage of variation explained (Murphy et al., 2010). We generated raven density surface predictions across the Great Basin based on the GIS covariates that were included in the selected model (Breiman, 2001). To approximate the scale of analysis of the input data, we smoothed the surface by averaging predictions using neighborhood analysis (10-km radius circular focal statistics) in ArcGIS™ Spatial Analyst (ArcMap 10.4; ESRI, Redlands, CA, USA). Because our goal was to use estimated parameters for map predictions, we evaluated these models 1000 times, performing leave-one-out cross-validation (Hastie et al., 2009) and using the subsequent distribution of plausible outcomes to quantify uncertainty. We randomly omitted one unit at each iteration and generated a prediction for the omitted unit. Each time, we sampled from the estimated distribution of raven density from distance-detection models in ‘unmarked,’ where density ~N (μ, σ2). For each model, we also generated a spatial prediction of density and an estimate of total abundance, henceforth obtaining bootstrapped confidence intervals (i.e., pixel-level uncertainty) for our raven density surface and total abundance estimate. We report estimates of variance explained, bias, and MSE associated with the random forest models. We summarized variable importance by storing model results and reporting the proportion of models each variable appeared in and its median importance ranking. We stored partial dependence plots from each model to demonstrate predictors' influences on raven density (Fig. C.1; Supplementary Material). To estimate raven abundance within sagebrush ecosystems across the Great Basin, we specified a spatial extent that was restricted to sagebrush cover types from the Sagebrush Assessment Project (Comer et al., 2002). Using the raven density surface, we derived the estimated number of ravens per pixel, and subsequently summed the estimated number of ravens across the restricted area of interest to obtain an estimate of annual raven abundance averaged across years of the study. 2.3. Impacts on sage-grouse nest survival 2.3.1. Sage-grouse nest monitoring To estimate the effects of raven density on sage-grouse nest survival across subpopulations, we monitored sage-grouse nests at 19 units (unit-year combinations = 50) in Nevada and California. We captured sage-grouse using spotlighting techniques (Wakkinen et al., 1992) during spring, summer, and fall of 2009–2016, and fitted them with VHF (< 3% body mass; Advanced Telemetry Systems, Isanti, Minnesota) or GPS transmitters (< 4% body mass; GeoTrak, North Carolina). We sought to locate sage-grouse ≥2 times weekly during nesting (March – May). Visually-confirmed nests were visited frequently and determined as successful (≥1 chick hatched) or failed.

2.2.3. Mapping raven density After obtaining unit-year estimates of raven density, we conducted a retrospective spatial analysis to explore relationships between these densities and environmental covariates. A secondary purpose of this analysis was to generate a spatially-explicit surface of raven density within sagebrush ecosystems of the Great Basin and to derive estimates of total abundance. To accomplish this, we identified 15 a priori candidate predictors that represented landscape variation in terms of climate, vegetation, topography, and anthropogenic footprint (Table C.1; Supplementary Material). Using GIS, we measured each variable for each unit by generating a 95% kernel density isopleth polygon from survey unit locations, and calculated zonal averages for each covariate within the polygon. Then, we used the density estimates from the distance sampling model as a response variable in a random forest regression analysis with landscape covariates (Breiman, 2001; Cutler et al., 2007). Random forest models do not require distributional assumptions about the response variable and accommodate non-linear

2.3.2. Estimating raven density effects on sage-grouse nest survival We estimated daily nest survival as a function of environmental covariates (Dinsmore et al., 2002) using ‘RMark’ (Laake and Rexstad, 2008), which implements program MARK (White and Burnham, 1999) through R (3.5.0). We examined spatiotemporal variation in raven density (i.e., represented by unit-year estimates) as a predictor of sagegrouse nest survival across subpopulations using a multivariate nest survival analysis to account for other relevant environmental effects. To account for uncertainty in raven density within each unit-year, we performed Monte Carlo simulations (m = 1000), and sampled from the distribution of raven density at each iteration. We considered linear, log-transformed (pseudo-threshold), and quadratic effects of raven density on nest survival, and stored the results of the best-fitting functional form based on AIC with second-order bias correction (AICc; 3

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Fig. 1. Average and standard deviation (pixel-level variation) of annual predicted common raven (Corvus corax) density (ravens km−2) derived from random forest models given field site unit-specific estimates of raven density that were obtained from hierarchical distance sampling models at 43 field site units within the Great Basin region, USA. Fifteen landscape-level predictors summarizing climate, vegetation, topography and anthropogenic footprint were used to predict average raven density at each pixel.

Anderson, 2008). To account for effects of other environmental covariates that may influence nest survival, we measured percent total shrub cover, percent sagebrush cover, percent herbaceous understory cover (i.e., grasses and forbs), and percent annual grass using National Land Cover Database Shrublands Products (900-m2 resolution; Xian et al., 2015). We considered an effect of cumulative burned area because increasing wildfire impacts have been shown to negatively influence sage-grouse populations, partly through reduced nest survival (Coates et al., 2016b; Foster et al., 2019). We included elevation and topographic roughness (variance in elevation; Riley et al., 1999) from a 900-m2 DEM. These covariates were measured at multiple spatial extents from nests: 75 m (1.77 ha) based on movement distances during recess periods when hens leave the nest to forage (Dudko et al., 2019), as well as 167.9 m (8.7 ha), 439.5 m (61.5 ha), and 1451.7 m (661.4 ha) (minimum, mean, and maximum daily movement distances for sagegrouse, respectively; Coates et al., 2016a). We also included Palmer

Drought Severity Index (PDSI; Palmer, 1965) to represent temperature and precipitation effects, as well as distance to power lines (medium voltage = 55–230 kV, high voltage = ≥ 230 kV, or combined; Platts, 2018), distance to roads (primary/secondary roads, small roads and trails, or combined; U.S. Census Bureau TIGER/Line Shapefiles), and proportion of agricultural land cover (within each respective spatial extent) to represent anthropogenic effects. Individual covariates were nest initiation date, transmitter type (GPS/VHF), and age of hen (yearling/adult). We implemented an initial variable reduction analysis to eliminate uninformative variables (e.g., Arnold, 2010) by developing single-variable models and comparing each to an intercept-only model using AICc. Variables were then included in a full multivariate model if their inclusion resulted in 2 AICc units lower than the null. If a variable was supported across more than one spatial extent, we retained the extent with lowest AICc. We excluded variables that co-varied (|r| ≥ 0.65) by including only the representation with lowest AICc to 4

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reduce effects of multicollinearity. Lastly, we derived cumulative 38day survival probability using the delta method (Powell, 2007) and predicted nest survival across a range of raven density values while setting other environmental covariates at their mean values.

Table 1 Importance rankings of landscape predictors used to predict common raven (Corvus corax) density within the Great Basin region, USA, 2007–2016. Predictors were ranked first by overall ‘Importance’ (median ranking from 1000 models within a cross-validation routine, 25th and 75th percentiles in parentheses) and second by ‘Inclusion’ (proportion of models including the predictor).

2.3.3. Mapping areas with predicted hyperpredation effects The final objective of this study was to map areas where raven density was predicted to impact sage-grouse nest survival. First, we defined an ecological threshold value as the density of ravens that corresponded to average nest survival across all unit-year combinations. Exceeding this threshold would result in expected below average nest survival, assuming other covariates represented average conditions. We demarcated the continuous raven density map into high and low raven density classes based on this ecological threshold value. Lastly, we intersected high raven density areas (e.g., polygons) with sage-grouse concentration areas that were based on resource selection functions from sage-grouse telemetry locations combined with predictive modeling of space use and abundance of breeding sage-grouse (described in Coates et al., 2016b and Doherty et al., 2016). The resulting delineations reflect areas where ravens have the greatest projected impacts on breeding sage-grouse.

Landscape predictor

Importance

Inclusion (%)

Top-ranked (%)

Distance to developed area Elevation Distance to agricultural field Distance to transmission line (≥55 kV) Palmer Drought Severity (PDSI) Distance to landfill Avg. road density % Open cover type Compound Topo. Index (CTI) Distance to water % Pinyon Juniper CC1 Normalized difference vegetation (NDVI)

1 2 3 4

(1,1) (2,2) (3,4) (3,4)

1.000 0.999 0.851 0.897

96.9 3.0 0.001

6 (5,6) 6 (5,6) 7 (6,8) 7 (6,8) 9 (8,10) 9 (8,11) 10 (8,11) 10 (9,12)

0.699 0.655 0.532 0.514 0.368 0.337 0.321 0.300

3. Results We obtained 8920 observations of ravens at distance ≤1125 m across all surveys of field site units during the study (Table D.1 and Fig. D.1; Supplementary Material), with ≥1 raven observed at 3845 surveys (22.7%).

most commonly ranked 3rd and 4th, respectively. Raven density was greatest at units with lower average elevation with closer proximity to developed areas, agricultural fields, and transmission lines (Fig. 2). Other landscape predictors frequently appeared in random forest models but had less influence (Table 1).

3.1. Raven density and abundance

3.2. Raven density and sage-grouse nest survival

We fit distance-detection models for 17 distinct regions comprising 43 field site units and 10 years grouped by geographic location (Table D.2; Supplementary Material). Detection functions included unit or year covariates when supported by the data, thereby accounting for spatiotemporal variation in raven detectability (Table D.2 and Fig. D.1; Supplementary Material). Landscape covariates influenced detection for nearly all regions (n = 16). Area of viewshed positively influenced detection probability at 14 regions (Table D.2; Supplementary Material). Proportion of forested cover was supported as a covariate for 7 regions, but its effect on detection probability varied (Table D.2; Supplementary Material). Raven density estimates ranged from 0.00–1.86 ravens km−2 ( x = 0.510, SD = 0.382) across all units and years (Table D.3 and Fig. D.2; Supplementary Material). Raven density was 0.54 ravens km−2 (95% CI = 0.42–0.70) throughout the Great Basin and adjoining shrub-steppe ecoregions encompassed by our study, based on projections from random forest models averaged across years (Fig. 1), corresponding to total abundance of 403,346 ravens (95% CI = 310,783–522,803). Restricting abundance estimation to sagebrush ecosystems, we estimated 0.53 (0.44–0.65) ravens km−2, corresponding to total abundance of 165,186 (136,874–201,581) ravens. Random forest models of raven density reliably explained variation in unit-level raven density (R2RF = 0.83 [0.79–0.84]), and accurately recovered unit-level estimates when predicting to new data. Based on simulation and cross-validation, predictions from training data occurred within the uncertainty regions of testing data 78% of the time, with overall R2 between predicted and observed estimates of 0.74. Average model bias was minimal (0.001), with MSE = 0.12. Uncertainty varied spatially in response to presence or absence of strong local predictors (Fig. 1). Elevation and proximities to developed areas, agricultural fields, and transmission lines had the strongest influences on raven density. Distance to developed area was included in all 1000 model runs and was the top-ranked predictor in 969 models (Table 1). Elevation occurred in 999 model runs, and was the top ranked predictor in 30 models. Distance to agricultural field and distance to transmission line occurred in 851 and 897 models and were

Median daily survival for sage-grouse nests was 0.966 (95% CI: 0.959–0.971). Cumulative 38-day sage-grouse nest survival was 0.223 (0.140–0.320) for yearling females (first breeding season), and 0.273 (0.223–0.325) for adult females (≥ second breeding season). Sagegrouse nest survival was negatively associated with corresponding unityear estimates of raven density (Fig. 3). The best-fitting functional form of this relationship was a linear effect (870/1000 model iterations; = −0.465 [−0.864 to −0.066]). Evidence for the effect remained strong after accounting for variation explained by vegetation, land cover, topography, and potentially confounding anthropogenic variables (e.g., shrub and sagebrush cover, proximity to roads, agriculture), and individual effects of nest initiation date, hen age, and transmitter type (Tables E.1 and E.2; Supplementary Material). Sage-grouse nest survival increased with later initiation date, more shrub cover, greater topographic roughness, and better moisture conditions indicated by PDSI (Fig. 4, Table E.2; Supplementary Material). In addition to negative influences of raven density, nest survival was reduced by increasing amounts of burned area and more annual grass cover, though these effects' 95% CI partially overlapped 0 (Fig. 4, Table E.2; Supplementary Material). Proximity to transmission line and elevation effects were included in the final model but had very little overall influence (Table E.2; Supplementary Material). The modeled relationship between raven density and nest survival indicated that average sage-grouse nest survival occurred at raven densities of ~0.40 ravens km−2 (0.37–0.42; Fig. 3). Based on overlapping raven density maps with sage-grouse breeding concentration areas, we found that 63.9% (41,414 km2) of total sage-grouse breeding concentration area (64,858 km2) exceeded the value of 0.40 ravens km−2. This means a majority of projected sage-grouse breeding concentration areas across the Great Basin and adjoining ecoregions exhibit raven densities associated with below average sage-grouse nest survival (Fig. 5). Spatially-explicit data output maps are available online (Coates et al., 2020). 5

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Fig. 2. Bivariate partial-dependence plots of landscape predictors from random forest models explaining field site unit-specific estimates of common raven (Corvus corax) density that were derived from distance sampling models (ravens × km−2) at 43 units within the Great Basin 2007–2016 combined with landscape-level predictors summarized at each unit.

4. Discussion

scales. Resource subsidies to ravens (e.g. food, water, and perch substrate availability) have been previously associated with agriculture, anthropogenic development, and vertical structures (Kristan and Boarman, 2007; Howe et al., 2014, Coates et al., 2014, 2016a). In our study, the potential for anthropogenic subsidies stemming from agriculture and infrastructure was strongly associated with higher raven

Our findings support the prevailing hypothesis that human activities indirectly increase nest predation on sage-grouse, a species of great conservation concern, by providing ravens, a generalist avian mesopredator, access to beneficial resource subsidies across broad spatial 6

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et al., 2012; Dahlgren et al., 2016). The potential for such fundamental demographic effects on sage-grouse further reinforces the need for management action within sagebrush ecosystems to conserve remaining sage-grouse populations and other sensitive prey species. To help inform raven management decisions, we estimated a critical raven density to be approximately 0.40 ravens km−2, where raven densities exceeding this value corresponded to below average sagegrouse nest survival rates from multiple field site units across the Great Basin study extent. This value corroborates conclusions from a recent study in Wyoming, where sage-grouse nest survival doubled after raven densities were reduced from ~0.47 to 0.25 ravens km−2 (Dinkins et al., 2016). We note, however, that this may be a conservative estimate, and that resident ravens occurring at lower densities can still have impacts on sage-grouse nest survival (e.g., Bui et al., 2010), especially where shrub cover is sparse (Coates and Delehanty, 2010). A linear relationship between estimates of raven density and sage-grouse nest survival had the most statistical support in our analysis, indicating that this was likely the case in our study region. Integrating an ecological threshold of 0.40 ravens km−2 with mapped sage-grouse concentration areas provides initial baseline information to identify where sage-grouse are most likely threatened by hyperpredation from ravens, which can guide actions aimed at mitigating this threat. Based on our predictive models of raven density, elevated raven densities (≥0.40 ravens km−2) are evidently affecting at least 64% of the most important breeding concentration areas for sage-grouse in the Great Basin. Although a suite of factors affects sagegrouse populations within the Great Basin, wildlife managers often view the most critical threat to be wildfire and resulting land cover type conversion from native sagebrush and perennial grassland to annual grasslands (U.S. Fish and Wildlife Service, 2015), resulting in projections of long-term population declines under current wildfire cycles (Coates et al., 2016b). Impacts of wildfire notwithstanding, we note that < 10% of sage-grouse breeding concentration areas have been impacted by wildfire since the mid-1980s (Coates et al., 2016b), which is considerably less than our estimate of ~64% of breeding concentration areas currently impacted by elevated raven densities. While the effects of wildfire on an impacted population may be more immediate and severe, resulting in direct loss of habitat and potential for localized extinctions, the ubiquity and long-term persistence of the raven density threat to sage-grouse merits substantial consideration. Though not yet investigated, the long-term negative effects of wildfire on sage-grouse population growth may be exacerbated by elevated raven densities, where loss of shrub cover following wildfire leads to more effective predation by ravens because nest concealment is reduced (e.g., Coates and Delehanty, 2010). Substantial research implicates anthropogenic activity in fostering raven populations through resource subsidies, especially in remote landscapes with otherwise limited resource availability for ravens (Restani et al., 2001; Kristan and Boarman, 2007; O'Neil et al., 2018). Our findings illuminate landscape-level anthropogenic factors (developed areas, agriculture, and presence of transmission lines) that explain raven abundance across expansive, remote landscapes of the broader Great Basin and also corroborate previous research findings. For example, another large-scale study evidenced similar influences of agriculture and anthropogenic infrastructure (i.e., road density) on raven occupancy (O'Neil et al., 2018). Such anthropogenic activities can result in resource subsidies that provide a variety of feeding opportunities in the form of crops, food waste, increased carrion from livestock, and roadkill. We also found higher raven densities to be associated with presence of transmission lines which provide nesting (Howe et al., 2014) and perching (Coates et al., 2014) substrate for ravens and accommodate large communal raven roosts (Engel et al., 1992). Lower elevation habitats further corresponded with greater raven density and were characterized by valleys between mountain ranges with gentle topography (Omernik and Griffith, 2014). These landscape characteristics often are sites of greater human activity and infrastructure; lower relative

Fig. 3. Modeled relationship between estimated greater sage-grouse (Centrocercus urophasianus) cumulative (38-day) nest survival and common raven (Corvus corax) density estimates from 19 field site units in Nevada and California, USA, 2009–2016, given other environmental predictors occurring at their mean values. Vertical and horizontal lines intersect at mean nest survival, which occurred at raven density ≈ 0.40 km-2. Unit- and year-specific estimates of raven density were derived from hierarchical distance sampling models of raven density. Nest survival models were iteratively fit to the associated distributions of raven density using RMark to generate bootstrapped uncertainty bands that account for variation in raven density among unit-years in the study.

density in remote environments where, concurrently, sage-grouse were exhibiting below average nest survival. We witnessed this relationship across numerous field site units spanning mesic and xeric sagebrush community types. Although this study did not specifically report direct observations of ravens depredating sage-grouse nests, previous studies that employed video-monitoring techniques concluded that ravens were the primary nest predator of sage-grouse in areas of the Great Basin (Lockyer et al., 2013; Coates and Delehanty, 2010), and predation by ravens was a function of ravens' local abundance (Coates and Delehanty, 2010). Here, we quantified the relationship between raven density and sage-grouse nest survival on a region-wide scale and found a substantial, negative effect. Thus, our overall findings may reflect a significant, region-wide shift of the relationship between an opportunistic native predator and a seasonal prey item occupying semi-arid environments of the American west, wherein prevailing human land use promotes raven populations and suppresses sage-grouse. The extent and magnitude of potential raven impacts on sage-grouse populations has not previously been captured by typical monitoring programs or research studies of limited spatial extent and short duration. However, a large body of evidence has indicated that predation by overabundant ravens can cause population-level effects at smaller relative scales, leading to concern about the ability of rare and sensitive prey to sustain their populations under these conditions (Boarman, 2003; Esque et al., 2010; Shields et al., 2019). Yet, despite awareness of increasing raven abundance over the past four decades within the Great Basin (Sauer et al., 2017), estimates of actual raven abundance and/or density have been rare and previously were unavailable at regional and landscape scales (Dinkins et al., 2016). Similarly, few studies have evaluated impacts of ravens on nesting sage-grouse, though all have documented negative relationships (Coates and Delehanty, 2010; Bui et al., 2010; Dinkins et al., 2016). If sage-grouse cannot compensate for reduced nest survival through increases in other demographic rates, and no such mechanism has yet been identified, then increased nest predation could lead to suppressed population growth (e.g., Taylor 7

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Fig. 4. Modeled relationship between estimated greater sage-grouse (Centrocercus urophasianus) cumulative (38-day) nest survival and landscape-level predictors of % shrub cover (A), % annual grass cover (B), topographic roughness (C), cumulative burned area (D), drought severity index (E), and nest initiation date (F).

elevation may serve as a proxy for relevant landscape effects that are difficult to quantify at large spatial scales, such as ranching operations, livestock density, and water sources (Coates et al., 2016a). Evidence from standardized count data (Sauer et al., 2017) suggests that raven distribution is expanding into previously unoccupied areas, while abundance has increased within areas already occupied. Thus, sage-grouse not only lose habitat to the same human enterprise that appears to aid raven populations, but remaining sage-grouse nesting habitat now contains elevated densities of ravens, an intelligent and behaviorally innovative predator that can have novel effects on sage-grouse recruitment. Continued increases in raven abundance across the Great Basin region likely will affect many sensitive prey species like sage-grouse in areas that historically have not supported large raven populations.

As widespread anthropogenic development continues within habitats of sensitive prey species, we anticipate that monitoring of raven population trends, range expansion, and space use will become imperative to guide effective adaptive management aimed at reducing hyperpredation of sensitive prey. Our approach represents the broader Great Basin, particularly sagebrush ecosystems, and surveys covered remote areas occurring in conjunction with sage-grouse monitoring. The capacity to monitor raven populations through rigorous surveys in both remote and developed areas will be central to informing future potential raven management actions, such as sensitive species habitat improvements (Coates and Delehanty, 2010), anthropogenic subsidy reduction and reduced access to subsidies (Boarman, 2003), non-lethal actions that reduce raven reproduction (e.g., egg-oiling [Brussee and 8

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Visualization. Kristy B. Howe: Resources, Writing - review & editing, Funding acquisition. Ann M. Moser: Resources, Writing review & editing, Supervision, Project administration, Funding acquisition. Lee J. Foster: Resources, Writing - review & editing, Project administration, Funding acquisition. David J. Delehanty: Conceptualization, Methodology, Investigation, Resources, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition. Declaration of competing interest We do not have any actual or potential conflict of interest including any financial, personal or other relationships with other people or organizations within three years of beginning the submitted work that could inappropriately influence, or be perceived to influence, our work. Acknowledgements We are indebted to the numerous biologists, volunteers, and technicians who spent countless hours surveying ravens and conducting lek counts that made this modeling effort possible within the Nevada Department of Wildlife (NDOW), Oregon Department of Fish and Wildlife, California Department of Wildlife, Idaho Department of Fish and Game, U.S. Geological Survey (USGS), and U.S. Fish and Wildlife Service (USFWS). In particular, we thank B. Prochazka, J. Dudko, K. Andrle, Z. Lockyer, K. Howe, G. Gillette, T. Gettelman, J. Ragni, J. Delehanty, A. Merritt, C. Hoffman, T. Tran, D. Mackell, B. Lowe, D. Disbrow, C. Tuliemro, M. Meyerpeter, B. Cunningham, I. Dudley, M. Falcon, G. Thompson, S. Reibman, C. Bowman, R. Gardner, E. Tyrell, A. Mohr, S. Burns, J. Brooks, J. Herrick, J. Malinowski, J. Dolphin, E. Hamblin, A. Anderson, J. Brockman, and C. Bottom. We are grateful for the assistance by J. Cupples (USFWS) in modifying the sampling design for Oregon and M. Casazza (USGS) for initial thoughts, concepts, and design. Work was funded by the NDOW, grant number USGS-011. We are appreciative of the NDOW and the Nevada Board of Wildlife Commissioners for their financial grants to support this work. All activities within this manuscript have followed appropriate animal care use protocol. Data and associated metadata are archived and will be made available at the USGS ScienceBase website https://www. sciencebase.gov/catalog/item/5e20f4cce4b014c85301fd67 (Coates et al., 2020). Use of trade or product names does not imply endorsement by the U.S. Government.

Fig. 5. Predicted common raven (Corvus corax) impacts within greater sagegrouse (Centrocercus urophasianus) concentration areas across the Great Basin, USA, 2007–2016. Predicted impacts were based on a raven density of ≥0.40 km−2 which corresponded to below-average survival rates of sagegrouse nests.

Coates, 2018, Shields et al., 2019]), and lethal removal applications (Dinkins et al., 2016; Conover and Roberts, 2017) if deemed appropriate by resource managers or other decision-makers. To further aid conservation efforts for sage-grouse, our spatially explicit maps depict areas where high potential of excess predation from ravens overlaps with sage-grouse breeding concentration areas. These maps could be used to identify areas of concern for sage-grouse populations and, more broadly, benefit initial planning of where and when to monitor and implement actions that mitigate the cascading indirect effects of a native but overabundant generalist predator species on sensitive prey.

Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biocon.2020.108409.

Role of funding source The study sponsors had no role in the study design, data collection, analysis, and interpretation of the data, in the writing of the report; and in the decision to submit the paper for publication.

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CRediT authorship contribution statement Peter S. Coates: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition. Shawn T. O'Neil: Methodology, Validation, Formal analysis, Writing - original draft, Writing - review & editing. Brianne E. Brussee: Methodology, Validation, Formal analysis, Writing - original draft, Writing - review & editing. Mark A. Ricca: Conceptualization, Methodology, Investigation, Writing - review & editing, Project administration, Funding acquisition. Pat J. Jackson: Investigation, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition. Jonathan B. Dinkins: Formal analysis, Writing - review & editing, 9

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