Looking beyond rare species as umbrella species: Northern Bobwhites (Colinus virginianus) and conservation of grassland and shrubland birds

Biological Conservation 186 (2015) 233–240

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Looking beyond rare species as umbrella species: Northern Bobwhites (Colinus virginianus) and conservation of grassland and shrubland birds Andrew D. Crosby a,⇑, R. Dwayne Elmore a, David M. Leslie Jr. b, Rodney E. Will a a

Department of Natural Resource Ecology and Management, Oklahoma State University, 008C Agriculture Hall, Stillwater, OK 74078, USA U.S. Geological Survey, Oklahoma Cooperative Fish and Wildlife Research Unit, Department of Natural Resource Ecology and Management, Oklahoma State University, 007 Agriculture Hall, Stillwater, OK 74078, USA b

a r t i c l e

i n f o

Article history: Received 22 July 2014 Received in revised form 6 March 2015 Accepted 13 March 2015 Available online 8 April 2015 Keywords: Avian community Bell’s vireo Dicksissel Bewick’s Wren Grassland birds Habitat restoration

a b s t r a c t Changes in land use and land cover throughout the eastern half of North America have caused substantial declines in populations of birds that rely on grassland and shrubland vegetation types, including socially and economically important game birds such as the Northern Bobwhite (Colinus virginianus; hereafter bobwhites). As much attention is focused on habitat management and restoration for bobwhites, they may act as an umbrella species for other bird species with similar habitat requirements. We quantified the relationship of bobwhites to the overall bird community and evaluated the potential for bobwhites to act as an umbrella species for grassland and shrubland birds. We monitored bobwhite presence and bird community composition within 31 sample units on selected private lands in the south-central United States from 2009 to 2011. Bobwhites were strongly associated with other grassland and shrubland birds and were a significant positive predictor for 9 species. Seven of these, including Bell’s Vireo (Vireo bellii), Dicksissel (Spiza americana), and Grasshopper Sparrow (Ammodramus savannarum), are listed as species of conservation concern. Species richness and occupancy probability of grassland and shrubland birds were higher relative to the overall bird community in sample units occupied by bobwhites. Our results show that bobwhites can act as an umbrella species for grassland and shrubland birds, although the specific species in any given situation will depend on region and management objectives. These results suggest that efficiency in conservation funding can be increased by using public interest in popular game species to leverage resources to meet multiple conservation objectives. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Changes in land use and land cover throughout the world have led to population declines in many native species and are a cause of concern over the general loss of biodiversity (Noss et al., 1995). A common strategy for combatting these declines over the last several decades has been the designation of umbrella species (Caro, 2003). An umbrella species is one whose habitat requirements encompass many other biodiversity components with similar habitat requirements and less extensive spatial requirements (Suter et al., 2002). Conservation of habitat for an umbrella species could therefore confer protection to many other species (Roberge and Angelstam, 2004). Although limitations of umbrella species have

⇑ Corresponding author at: Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, Room 13 Natural Resources Building, East Lansing, MI 48824, USA. Tel.: +1 517 432 0804; fax: +1 517 432 1699. E-mail addresses: [email protected] (A.D. Crosby), [email protected] (R.D. Elmore), [email protected] (D.M. Leslie Jr.), [email protected] (R.E. Will). http://dx.doi.org/10.1016/j.biocon.2015.03.018 0006-3207/Ó 2015 Elsevier Ltd. All rights reserved.

been pointed out (Andelman and Fagan, 2000; Berger, 1997) and the concept is by no means universally accepted (Roberge and Angelstam, 2004), it has shown potential as a conservation tool (Simberloff, 1998). Recent studies have suggested that conservation efforts for White-backed Woodpeckers (Dendrocopos leucotos) and Cappercaillie (Tetrao urogallus) can bolster declining forest bird species in Europe (Roberge et al., 2008; Suter et al., 2002), lending support for umbrella species as a viable concept in conservation planning. Typically, species considered as candidates for umbrella status are uncommon and highly sensitive to human disturbance (Fleishman et al., 2000), as reflected by the above mentioned species in Europe. Other species that have been proposed as umbrella species are the grizzly bear (Ursus arctos horribilus) and Northern Spotted Owl (Strix occidentalis), both of which are uncommon and protected (Caro, 2003). However, there are issues with the use of uncommon species as umbrellas, such as population viability (Berger, 1997) and resistance by the general public to conservation efforts due to economic and other concerns

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(Montgomery et al., 1994). Alternatively, in some situations habitat conservation for game species may offer an effective means of conserving co-occurring species. This is because there is generally a great deal of support, both socially and financially, for their conservation and many game species require large spatial areas if hunted populations are to be maintained. Therefore, certain game species may offer expanded and perhaps more realistic opportunities for facilitating conservation of declining species that rely on similar habitats This has implications for many hunted and nonhunted species throughout the world. In North America, open vegetation types such as grasslands and shrublands have undergone large-scale loss and degradation since the time of European settlement (Noss et al., 1995). Loss of grasslands in North America has been estimated to exceed 80% (Knopf, 1994; Noss et al., 1995). Development by humans and fire exclusion have led to extensive invasion of grasslands and shrublands by tree species such as eastern redcedar (Juniperus virginiana), and subsequent conversion to closed-canopy forest (Bidwell et al., 2002). Afforestation, extensive agriculture, and intensive silviculture have also led to the loss of fire-maintained open woodlands and other early-successional communities (Brennan and Kuvlesky, 2005). The extensive loss of these vegetation types has been coincident with severe distribution-wide declines in bird species that rely on them such as Bell’s Vireo (Vireo bellii), Dicksissel (Spiza americana), and Prairie Warbler (Setophaga discolor) (Sauer et al., 2014). This list also includes the Northern Bobwhite (Colinus virginianus; hereafter bobwhite), a species of great interest to many hunters, landowners, and wildlife managers. Bobwhites are a socially and economically important game bird whose geographical distribution extends throughout most of eastern North America. Bobwhites require grass and forbs with interspersed shrub cover, and these requirements can be met by a wide range of vegetation types from open woodland and brushy prairie (hereafter shrubland) to nearly open grassland (Cram et al., 2002; Guthery, 2002). Therefore, bobwhites can be associated with a wide range of bird community assemblages that rely on open and semi-open plant structure. Despite the fact that bobwhites are one of the most widely managed-for species in North America, their populations have experienced distribution-wide declines of >4% per year from 1966 to 2012 (Sauer et al., 2014) due, in large part, to the large-scale loss of areas with the appropriate vegetation structure and composition (Brennan, 1991; Williams et al., 2004). There is widespread support for large-scale habitat restoration as an effective method for stopping the decline of bobwhite populations (Dimmick et al., 2002; Williams et al., 2004), and restoration is being implemented throughout the species’ distribution through both state and federal programs (Crosby et al., 2013; Dimmick et al., 2002; Howell et al., 2009). Brennan and Kuvlesky (2005) have suggested that restoring habitat for bobwhites would support many other declining grassland and srhubland bird species. Although there is extensive research on the effect of habitat management on bobwhite populations (Guthery, 1997, 2002), we could find only one study (Gruver and Guthery, 1986) that examined the impact on other bird species. In order to examine the potential of a game bird to act as an umbrella for other declining species, we evaluated the relationship between bobwhite occurrence and bird community composition. This case study could serve as a model for other game species across the world where similar patterns emerge. We conducted our study on private lands in the central hardwoods and cross timbers regions of the southcentral United States. Our first objective was to describe the change in bird community composition along a gradient of open grassland to closed canopy forest and ascertain the primary vegetation structural features driving the change. Our second objective was to determine which bird species were associated with bobwhites in community analysis, and our third objective was to

test if bobwhite presence was a positive predictor for the occurrence of any other bird species and of species richness in general, particularly for grassland and shrubland birds of conservation concern. We hypothesized that bobwhites would be most strongly associated with grassland and shrubland communities, and their presence would be a significant positive predictor of occurrence and richness of species that rely on these vegetation types. 2. Materials and methods 2.1. Study area We conducted our surveys in the central hardwoods and cross timbers regions of eastern and south-central Oklahoma, USA (Fig. 1). The private lands that comprised our study area were all P80 ha and chosen because they contained existing bobwhite habitat and/or had ongoing bobwhite habitat management efforts. This ensured that we were able to sample a wide range of vegetation types and bird community assemblages. The central hardwoods area has rolling topography and is dominated by oak (Quercus spp.) and hickory (Carya spp.) forests interspersed with areas of native and introduced pasture, hayfield, and row crops. The cross timbers region is mainly flat lands occasionally dissected by small drainages leading to larger rivers. It is characterized by a mosaic of tallgrass prairie and cross timbers forest. Dominant tree species are post oak (Q. stellata), blackjack oak (Q. marilandica), and hickories, and the most prominent grasses include big bluestem (Andropogon gerardii), little bluestem (Schizachyrium scoparium), and indiangrass (Sorghastrum nutans). The main land uses in both regions are ranching and row-crop agriculture (Natural Resource Conservation Service, 2012). In the central hardwoods region, we conducted our research on 10 private ranches and the T.J. Nickel Preserve owned by The Nature Conservancy. In the cross timbers region, we conducted research on 6 private ranches and the Pontotoc Ridge Preserve owned by The Nature Conservancy. These properties provided a mosaic of vegetation types that represented a gradient of closed-canopy forest, woodland, savannah, and open prairie. 2.2. Sample unit selection We located sample units non-randomly within the properties to include a range of forest canopy cover from 0% to >90% and ensure robust comparisons that included the entire range of the forestgrassland vegetation gradient. Each sample unit was a 400-m-radius circle encompassing 50 ha. This size was chosen for two reasons: it represented an accepted radius of audibility for bobwhites (Hansen and Guthery, 2001; Stoddard, 1931), and the area was sufficient to encompass a typical bobwhite home range (Janke and Gates, 2013; Lohr et al., 2011). Sample units were a minimum of 800 m apart. We established 31 sample units across 13 properties in 2009 and resampled 30 of them in 2010 and 29 in 2011. The number of sample units on each property depended on the size of the property and availability of appropriate vegetation types. The difference in sample units between years was due to management activities on one sample unit and the loss of access to one property after the first two surveys in 2010. Within each sample unit, we systematically located four 100-m-radius point count stations in a design modified from Wilson et al. (1995). The first station was at the center point of the sample unit, and the remaining 3 were located 250 m away at angles of 90°, 210°, and 330°. 2.3. Point counts and habitat measurements

or

Our sampling scheme was designed both to establish presence absence of bobwhites and estimate bird community

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Fig. 1. Map of study areas and property locations for bird community surveys in the south-central United States in 2009–2011.

composition within the sample unit, and we used survey protocols for songbirds and bobwhites simultaneously. To estimate bird community composition, we conducted standardized point counts (Ralph et al., 1995) at each point count station 1–3 times per year (usually 3, rarely 1) during the breeding season (mid-May–late July) from 2009 to 2011, during which we counted all birds seen or heard within 100 m of the station in a 5-min period. Every effort was made to re-visit each station 3 times; however, the number of visits per year could vary due to weather, accessibility, or time constraints. In addition to point counts, we conducted bobwhite call counts concurrent with point counts at the centerpoint of each sample unit whereby we counted all bobwhites seen or heard within the sample unit (Crosby et al., 2013; Hansen and Guthery, 2001). This allowed us to take advantage of the greater audibility of bobwhite calls to improve our detection probability and survey the entire sample unit area, which was necessary because bobwhite presence was used as an explanatory variable. We recorded date and time of day at the beginning of each point count. All surveys were done between 0.5 h before sunrise and 4.5 h after sunrise, and we did not survey when it was raining or when wind speeds exceeded 20 km/h (Ralph et al., 1995). Counts of each species were reduced to detection/non-detection data for analysis and, because vegetation structure and composition were being actively modified within some sample units but not others, we pooled all years and treated each year as an independent set of observations. We established 4 vegetation measurement points within each point count station, with the first point being at the station centerpoint and the remaining points located 63 m away at angles of 90°,

210°, and 330°. Our vegetation measurements included overstory canopy and mid-story structure (Zipkin et al., 2010). We measured the proportion of overstory canopy cover at each vegetation sampling point, beginning in June 2009, using a hemispheric camera and WinSCANOPYÓ canopy analysis software (Regents Instruments Inc., Canada). Canopy was remeasured in 2010–2011 only if it had visibly changed due to disturbance (usually habitat restoration activities). To avoid including ground-level vegetation in the photograph, all photographs were taken from a height of 1 m above the ground. We extended a 20-m transect in a random direction from each vegetation sample point and measured the proportion of shrub cover using the line-intercept method (Canfield, 1941) as modified by Harrell and Fuhlendorf (2002). We also measured visual obstruction at 10 m and 20 m along the transect using a profile board (Guthery et al., 1981). Our visual obstruction metric was the average of visual obstruction values at each stratum of the profile board as an index of vegetation height (Harrell and Fuhlendorf, 2002). 2.4. Data analysis 2.4.1. Multi-species occurrence model We applied a modified version of the multi-species occurrence model of Dorazio et al. (2006) as presented by (Zipkin et al., 2010), whereby we estimated community-level occupancy at each sample unit while accounting for spatial dependence among point count stations within sample units and species-level detectability during each survey (Mordecai et al., 2011). This hierarchical model

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simultaneously accounted for occurrence and detection probability, where temporally replicated surveys at each point count station allowed the formal distinction between true species absence and the species being present but not detected (MacKenzie et al., 2002). The approach of modeling the entire community at once allowed for improved precision of occurrence estimates, particularly for rare or elusive species, by using all community data (Zipkin et al., 2010). In this framework, the occurrence z of species i at sample unit j was a Bernoulli random variable where zij  Bernðwij Þ. To account for dependence among point count stations within sample units, we added the use parameter suggested by Mordecai et al. (2011), in which the species use y of point count station k, given that it occurs in the sample unit, is yijk  Bernðhijk Þ. Finally, detection d during survey l at the point count station, given use, was dijkl  Bernðpijkl Þ. We incorporated variables that described species-specific detection probability to increase precision of occupancy estimates for each sample unit. These variables were time of day (as minutes from sunrise) and date (as Julian day), so that the model for detection was:

logitðpijkl Þ ¼ gi þ b1i dayl þ b2i timel

ð1Þ

The community-level parameters in the model assume that probabilities of occurrence, use, and detection for each species were random effects described by community-level hyperparameters drawn from a normal distribution (Royle and Dorazio, 2009). To avoid artificially truncating the potential number of species in the community, we applied the data augmentation procedure described by Royle and Dorazio (2009) whereby an arbitrarily large number of all-zero encounter histories was added to the dataset, and each species’ availability for detection, w, was a random variable where wi  BernðXÞ. Therefore, the ‘‘true’’ number of species, N, was assumed to have a uniform (0, M) prior distribution where M was chosen to be much larger than the observed number of species (n), so that the posterior distribution was not truncated (Royle and Dorazio, 2009). Our purpose was to model resident species that could be indicative of vegetation types and communities. Therefore, we excluded raptors (Families: Accipitridae, Cathartidae, Falconidae, Strigidae) and large corvids (Corvus spp.) from the analysis because their large home ranges and ubiquitous distribution make their status as residents difficult to verify and may cause them to be unreliable as indicators of vegetation structure and composition or community. We also considered species with <5 total detections over the entire study period to be incidentals and excluded them from the analysis. We conducted a Bayesian analysis of the data with programs R version 2.15 and WinBUGS version 1.4.3 (Spiegelhalter et al., 2007), using uninformative prior distributions for all community-level parameters. (R and WinBUGS code provided in Appendix A; Supplementary Material). We ran 3 chains of 500,000 iterations each with a burn-in of 200,000, and thinned the posterior chains by 1000. We assessed convergence of the posterior chains using the R-hat statistic (Brooks and Gelman, 1998). 2.4.2. Ordination and cluster analysis We used nonmetric multidimensional scaling (NMDS) on the community occurrence probabilities to determine which species most commonly occurred with bobwhites and the range of the primary vegetation gradients along which bobwhites may occur. NMDS is an unconstrained ordination technique that uses an iterative process to preserve the ordering relationship among objects along a specified number of axes (Legendre and Legendre, 1998). Species that tend to occur together at sample units will be closer together in ordination space than species that do not, reflecting similar habitat requirements. Likewise, sample units with similar bird communities will be closer together, reflecting similar

vegetation composition and structure. Because very low detection probabilities can lead to unreasonably high estimates of occupancy, we only included species for which the mean estimate of detection probability in the community analysis was >0.15 (MacKenzie et al., 2002). To quantify the association between community composition and vegetation, we used a Spearman’s rank correlation test between the axis 1 and 2 species scores and measured vegetation variables (Chapman et al., 2004). We used partitioning around medoids (PAM), a non-hierarchical clustering technique, to divide sample units into groups based on species composition and species into groups based on sample unit occurrence probabilities. We conducted a Kendall’s W coefficient of concordance test on species groups to test the significance of each species correlation with other group members (Borcard et al., 2011). We used the Bray–Curtis distance in NMDS and PAM analyses. 2.4.3. Bobwhite presence as a predictor of occurrence and richness To test whether bobwhites were a significant predictor for the presence of any bird species, we applied the same multi-species occurrence model as above but specified the verified presence of bobwhites within the sample unit as a predictor variable for species occurrence. Verified presence meant that bobwhites were detected at least once during point counts or call counts within the sample unit during that year. Our criterion for significance was whether the coefficient for bobwhite presence was positive and the 95% Posterior Interval (PI) did not include 0. We ran 3 chains of 20,000 iterations each with a burn-in of 10,000. We again assessed convergence of the posterior chains using the R-hat statistic. To test for a correlation between bobwhite presence and species richness, we used the Wilcoxon Rank-Sum Test to compare estimates of mean species richness and occurrence probability between sample units where bobwhites were present and sample units where they were not. We did this for all species combined and for species identified as being associated with grassland and shrubland habitats (DeGraaf et al., 1991; Rosenberg, 2004). 3. Results 3.1. Bird community composition We detected 78 bird species among all sample units and years, of which 61 qualified as residents for inclusion in the community occurrence analysis. The model estimated 65 species present among all sample units in all years (95% PI: 61–73). There were 33 species with an estimated detection probability >0.15 used in the NMDS and cluster analyses (Table 1). Species locations along NMDS axis 1 indicated a gradient of overstory canopy cover, and locations along axis 2 indicated a gradient of understory structure (Fig. 2). The Spearman’s rank correlation test showed significant negative correlations between overstory canopy and axis 1 site scores (q = 0.493, P < 0.005) and between vegetation height and axis 2 site scores (q = 0.434, P < 0.005), suggesting that these are the primary vegetation structural gradients influencing bird community composition in our sample units. The cluster analysis on sample units differentiated sample units along axis 1, with sample unit group 1 primarily associated with high and intermediate canopy cover, and sample unit group 2 with low canopy cover (Fig. 2a and b). There were known bobwhite occurrences at 80% of the group-2 sample units and 16% of group-1 sample units (Fig. 2a). We detected bobwhites at 24 sample units among all sample units and years, giving a naïve occupancy estimate of 0.267. Bobwhites were most strongly associated with low to intermediate levels of overstory canopy and higher levels of understory

A.D. Crosby et al. / Biological Conservation 186 (2015) 233–240 Table 1 Common names, scientific names, American Ornithologist’s Union (AOU) codes, group membership based on partitioning around medoids analysis, and P from Kendall’s W correlation test for 33 bird species from surveys in eastern Oklahoma, USA (2009– 2011). In bold are species for which Northern Bobwhite presence within the sample unit was a positive predictor.

Group 1

Common name

Scientific name

AOU code

Blue Grossbeak Blue-gray Gnatcatcher Brown-headed Cowbird Carolina Chickadee Carolina Wren

Passerina caerulea Polioptila caerulea Molothrus ater

BLGR BGGN BHCO

0.489 0.224 0.602

Poecile carolinensis Thryothorus ludovicianus Spizella pusilla Myiarchus crinitus

CACH CARW

0.602 <0.005*

FISP GCFL

0.623 0.014*

Passerina cyanea Cardinalis cardinalis Passerina ciris Vireo olivaceus Piranga rubra Baeolophus bicolor Sitta carolinensis

INBU NOCA PABU REVI SUTA TUTI WBNU

0.114 0.350 0.602 0.459 0.602 0.007* 0.224

Coccyzus americanus

YBCU

<0.005*

Vireo bellii Thryomanes bewickii Aimophila cassinii Geothlypis trichas Spiza americana Sialia sialis Sturnella magna Dumatella carolinensis Chondestes grammacus Colinus virginianus Mimus polyglottos

BEVI BEWR

<0.005* <0.005*

CASP COYE DICK EABL EAME GRCA

<0.005* <0.005* <0.005* <0.005* <0.005* <0.005*

LASP

<0.005*

NOBO NOMO

<0.005* <0.005*

Parula americana Dendroica discolor Melanerpes erythrocephalus Agelaius phoeniceus Archilochus colubris

NOPA PRAW RHWO

<0.005* <0.005* <0.005*

RWBL RTHU

<0.005* <0.005*

Vireo griseus Icteria virens

WEVI YBCH

0.012* <0.005*

Field Sparrowa,b,c Great-crested Flycatcher Indigo Bunting Northern Cardinal Painted Buntingb Red-eyed Vireo Summer Tanager Tufted Titmouse White-breasted Nuthatch Yellow-billed Cuckoo Group 2

b,c

Bell’s Vireo Bewick’s Wrenb,c Cassin’s Sparrowb,c Common Yellowthroat Dickisselb,c Eastern Bluebirdb Eastern Meadowlarkb,c Gray Catbird Lark Sparrowb,c Northern Bobwhiteb,c Northern Mockingbirdb Northern Parula Prairie Warblerb,c Read-headed Woodpeckerb Red-winged Blackbird Ruby-throated Hummingbird White-eyed Vireob,c Yellow-breasted Chatb,c

P-value

*

Statistically significant correlation with other group members at a = 0.05. Showed a negative correlation to other group members, so likely misclassified. b Species associated with grassland or shubland vegetation types (DeGraaf et al., 1991; Rosenberg, 2004). c Considered grassland or shrubland priority species by Partners in Flight (Rosenberg, 2004). a

structure (Fig. 2b). The cluster analysis on species indicated 2 groups, with group 1 being primarily forest- and open woodlandassociated species and group 2 being almost exclusively grassland and shrubland species, including bobwhites (Table 1). Among those in group 2, nine were considered priority species by Partners in Flight, such as Bell’s Vireo, Dicksissel, Bewick’s Wren (Thryomanes bewickii), and Prairie Warbler (Rosenberg, 2004). One of the group-1 species (Field Sparrow, Spizella pusilla) showed a negative correlation with other group members in the Kendall’s W test and so was likely misclassified (i.e. placed in the wrong group). Eleven species (including Field Sparrow) did not show a statistically significant correlation with other group members, all of which were classified in group 1.

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3.2. Direct association with Bobwhites We found that verified bobwhite presence was a significant positive predictor for 9 bird species, all of which were grassland or shrubland species and 7 of which were considered priority species by Partner’s in Flight (Rosenberg, 2004). Of those, 6 were categorized in group 2 by the PAM cluster analysis and 1 (Field Sparrow) was categorized in group 1 (Table 1). Two species— Grasshopper Sparrow (Ammodramus savannarum) and Scissortailed Flycatcher (Tyrannus forficatus)—were not included in the PAM analysis due to low detection probabalities. Species richness among all birds was not significantly different between sample units where bobwhites were present and where they were not (W = 519.5; P = 0.644), while species richness of grassland and shrubland birds was significantly higher in sample unites where bobwhites were present (W = 1051.5; P < 0.005; Fig. 3). Similarly, mean occurrence probability among all birds was not significantly different between sample units where bobwhites were present and where they were not (W = 955; P = 0.1382), while occurrence probability of grassland and shrubland birds was significantly higher in sample unites where bobwhites were present (W = 1435; P < 0.005; Fig. 3).

4. Discussion Our research showed multiple lines of evidence indicating a strong association between bobwhites and the occurrence and richness of grassland and shrubland bird species, many of which are species of conservation concern. The fact that bobwhite presence was a significant positive predictor for occurrence of many of these suggests that bobwhites may act as a conservation umbrella for a number of declining species. Specifically, our research shows that areas where bobwhites occur are much more likely to contain many declining grassland and shrubland birds than areas that do not, and that the richness of these species is much higher in areas where bobwhites occur as well. Our research, then, supports the claim that restoring habitat for bobwhites would be beneficial to other bird species that rely on grassland and shrubland vegetation (Brennan and Kuvlesky, 2005). Our NMDS analysis showed that bobwhites occurred across a spectrum of the primary vegetation structural gradients. In the NMDS plot (Fig. 2), roughly, the upper left quadrant represented species and sample units associated with closed-canopy forest, the lower left represented forest with a higher level of understory structure, the lower right represented brushy prairie, and the upper right represented more open grassland. This suggested that a wide range of grassland and shrubland species may be promoted through habitat restoration for bobwhites, depending on the area and management objectives. For example, species such as Prairie Warbler and Red-headed Woodpecker (Melanerpes erythrocephalus) could benefit from open woodland management (Wilson et al., 1995), while Bell’s Vireo and Field Sparrow (Spizella pusilla) would be better served by management that promoted brushy prairie, and both types would be beneficial to bobwhites. Because habitat restoration for bobwhites can look different depending on area and objectives, it may be possible for such management to benefit grassland and shrubland birds locally while enhancing avian biodiversity at larger scales. Our research considered only co-occurrence as a relative measure of the benefits that secondary species may incur from bobwhite habitat restoration, as opposed to more detailed metrics such as habitat quality as estimated through demographic parameters. In their meta-analysis of conservation schemes using the umbrella species concept, Roberge and Angelstam (2004) stated that describing patterns of co-occurrence did not constitute an

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Fig. 2. Non-metric Multidimensional Scaling (NMDS) plots showing: (a) site scores with cluster analysis (Partitioning Around Medoids) results displayed as different symbols (group 1 = circles, group 2 = diamonds), and sites where Northern Bobwhites (Colinus virginianus) were detected circled; and (b) species scores. Species in bold in (b) are those for which Northern Bobwhite presence was a positive predictor. Species codes are standard American Ornithologists Union (AOU) four-letter codes (see Table 1). Five common species: WBNU, SUTA, BGGN, BHCO, and TUTI were clustered at (0, 0) and were removed from the plot for clarity.

Fig. 3. Comparison of average species richness and occupancy probability in sample units where Northern Bobwhite (Colinus virginianus) were present (Yes) and not present (No), among all bird species (top figures) and only grassland and shrubland species (bottom figures).

evaluation of a species’ umbrella function. They further stated that any such study must provide a quantitative measure of benefits to other species, such as richness and population viability of beneficiary species (Roberge and Angelstam, 2004). Our research suggests that restoring habitat for bobwhites has the potential to increase the total area occupied by other grassland and shubland species. Past studies have shown that, with few exceptions, occurrence rate has a strong and consistent positive relationship with

regional abundance (Zuckerberg et al., 2009). Co-occurrence may therefore be considered a relative quantification of a species potential to act as a conservation umbrella. We note that habitat restoration for bobwhites (or any species) is not likely to create ideal habitat for co-occurring species because each species has unique habitat requirements and none precisely overlap (Hutchinson, 1957). Bobwhites are considered a facultative grassland species rather than a grassland obligate (Vickery et al., 1999); meaning that, although they occur in open grasslands, they are not dependent on them and actually require a certain amount of woody cover that can be detrimental to some grassland obligates (Coppedge et al., 2001; Ellison et al., 2013). As some species are area sensitive, such as grasshopper sparrow, and may be prone to increased rates of nest parasitism in a woodland matrix, not all grassland avian species may benefit from bobwhite habitat restoration, at least in some landscapes. Despite the limitations caused by varying habitat requirements between species, our data indicate that bobwhite habitat restoration can benefit many avian species that require open woodland, shrubland, or grassland plant structure by providing more potential usable space. Future studies could focus on the impacts of specific restoration activities on demographic parameters of other species to better quantify the umbrella capacity of game species where resources are directed disproportionally to such species. Many of the criticisms of the umbrella species concept relate to the fact that one species can never perfectly represent the habitat needs of another, and that management for an umbrella species cannot offer coverage for entire communities (Roberge and Angelstam, 2004). Such criticisms suggest the implicit assumption that umbrella species are supposed to provide an ideal way to manage the ecosystem, an assumption that needs to be set aside because there is no such ideal (Fleishman et al., 2000; Simberloff, 1998). Rather, umbrella species can be used to meet certain objectives regarding conservation of species and communities of special concern. We know that managing habitat for certain species can provide habitat for other species that share similar requirements, as has been demonstrated by this and other research (e.g. Pakkala et al., 2003; Roberge et al., 2008). Therefore, if specific management objectives require the conservation of a community of species supported by a specific set of vegetation types, then the designation of an umbrella species that will represent enough

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of those vegetation types (along with the appropriate disturbance regimes) to maintain the community would be a legitimate pathway toward meeting the objective of community or ecosystem conservation. This is not the same as offering a panacea for ecosystem management, and should not be seen as such (Simberloff, 1998). Researchers have also suggested that to be effective as a conservation umbrella a species should be associated with higher overall levels of biodiversity (Andelman and Fagan, 2000). There are two main problems with this idea: the first is that the community the umbrella species is supposed to protect may have low diversity to begin with; and the second is that biodiversity is a scale-dependent measurement, so that restoring a community that has low diversity at small scales may increase overall biodiversity at larger scales if other communities are taken into account (Caro, 2003). Our results reflect those of Suter et al. (2002), who found that occurrence of Capercaillie, a European grouse that relies on structurally complex conifer forests, was associated with higher species richness and relative density of mountain birds of conservation concern in the Swiss Prealps. As with bobwhites, Capercaillie occurrence did not predict higher species richness of all birds in the region but only those associated with specific communities. This indicates that both species can be beneficial in meeting conservation objectives associated with specific communities which, as noted above, may be a more realistic measure of the umbrella species concept. Seddon and Leech (2008) proposed 7 criteria for identifying umbrella species candidates and bobwhites seem to qualify under these criteria (Table 2). There are very few species that have received as much research and management attention as bobwhites (criterion 1; Guthery, 1997). The average breeding-season home range of bobwhites is 25–40 ha (Janke and Gates, 2013; Lohr et al., 2011), which is much larger than the breeding territory of most passerines (criterion 2). Although bobwhite populations have been declining throughout most of their geographical distribution, extinction is unlikely (criterion 3). The bobwhite response to human disturbance is moderate and variable (criterion 6). Bobwhites seem to thrive in open agricultural areas such as farms and ranches, although the type of management can be important. For example, modern clean farming practices that eliminate fence rows and field borders, as well as fire suppression in many ranching areas, have led to declines in bobwhite populations as well as populations of many other grassland and shrubland bird species (Brennan and Kuvlesky, 2005). Finally, bobwhites are easily detected and survey methods are well-established (criterion 7; Hansen and Guthery, 2001). Their co-occurrence with other species has been demonstrated by our research (criterion 4), suggesting that habitat restoration for bobwhites can benefit Table 2 Consolidated umbrella species criteria as proposed by Seddon and Leech (2008), and Northern Bobwhite qualifications for meeting those criteria. Criteria

Northern Bobwhite qualification

Natural history and ecology well known Large home range size

One of the most well-studied species in North America Large breeding season home range relative to passerines of conservation concern Although populations are in decline, they are unlikely to go extinct in the near future Demonstrated by our research

High probability of population persistence Co-occurrence with other species Management needs benefit other species Moderate sensitivity to human disturbance Easily sampled or observed

Suggested by our research, but particular species that benefit will depend on region and management objectives Can coexist with certain agricultural activities, but other activities can cause declines Occurrence is easily determined

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these species (criterion 5), although the form of that benefit remains to be tested. Other hunted species across the world could provide similar opportunities to conserve a suite of associated species, such as with the afore-mentioned Cappercaillie in old-growth forests of Europe. As additional examples, Atlantic Forests in Brazil and Korean Pine Forests in the Russian Far East represent biologically rich ecosystems that have undergone severe loss and fragmentation, and both are home to an array of declining species, some of which qualify as game species (Cullen et al., 2000; Ogureeva et al., 2012). Incentive-based conservation and restoration of habitat for regulated hunting provides an opportunity for game species to act as umbrella species in these ecosystems as well, while ensuring that some benefits of conservation are conferred locally as well as globally. Therefore, we suggest that the umbrella concept should not solely be focused on uncommon or protected species, but instead could be used opportunistically to further broad biological diversity and conservation goals. A more inclusive use and application of this concept can well increase conservation opportunities globally. 5. Conclusions The use of game species as umbrella species has seen little discussion in the conservation literature, but where possible offers several advantages over rare and threatened species. First and foremost, there is a great deal of interest among the general public for conservation of game species, particularly by those who hunt and thereby provide much of the funding that goes toward wildlife conservation in many places in the world such as North America, parts of Europe, and parts of Africa. If these funds could be leveraged to conserve imperiled species, communities, or ecosystems then a great deal of efficiency in conservation funding could be gained. Secondly, private landowners are often willing to undertake land management for game species on their properties for personal and financial benefit. This is critically important because the vast majority of land throughout the world is under private ownership, and therefore effective conservation requires the willingness of private landowners to participate. Finally, an appreciation for the role of game species in meeting larger conservation objectives could generate a better working relationship among the multiple stakeholders. The promotion of a more positive relationship between the hunting public, private landowners, and the larger conservation community in this sense is likely to be beneficial in terms of both funding, participation, and legislation. By using these advantages to meet larger conservation goals, game species could play a heretofore underappreciated role in the conservation of other declining species and communities throughout much of the world. Acknowledgments Funding was provided by the Pittman-Robertson Federal Aid to Wildlife Restoration Act under project W-161-R (F10AF00180) of the Oklahoma Department of Wildlife Conservation and Oklahoma Agricultural Experiment Station at Oklahoma State University with additional support from The Nature Conservancy’s Weaver Grant Program, Oklahoma Ornithological Society, and Payne County Audubon Society. The project was administered through the Oklahoma Cooperative Fish and Wildlife Research Unit (Oklahoma Department of Wildlife Conservation, Oklahoma State University, U.S. Geological Survey, U.S. Fish and Wildlife Service, and the Wildlife Management Institute cooperating). We thank all of the landowners who allowed us to conduct research on their property. Additionally, we thank M.G. Sams and

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E.M. Bartholomew of the Oklahoma Department of Wildlife Conservation, D.S. Wilson of the Department of Natural Resource Ecology and Management, Oklahoma State University; E. F. Zipkin of the Ecology, Evolutionary Biology, and Behavior program at Michigan State University; R.J. Cervantes, C. Griffin, C.E. Chappell, and N. Hillis for assistance in the field; C. Park and M. Hough for assistance with data analysis; and M. Payton of the Department of Statistics, Oklahoma State University for statistical consulting help. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although the software WinBUGS has been used by the U.S. Geological Survey (USGS), no warranty, expressed or implied, is made by the USGS or the U.S. Government as to the accuracy and functioning of the program and related program material nor shall the fact of distribution constitute any such warranty, and no responsibility is assumed by the USGS in connection therewith. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.biocon.2015.03. 018. References Andelman, S.J., Fagan, W.F., 2000. Umbrellas and flagships: efficient conservation surrogates or expensive mistakes? Proc. Natl. Acad. Sci. USA 97, 5954–5959. Berger, J., 1997. Population constraints associated with the use of black rhinos as an umbrella species for desert herbivores. Conserv. Biol. 11, 69–78. Bidwell, T.G., Engle, D.M., Mosely, M.E., Masters, R.E., 2002. Invasion of Oklahoma rangelands and forests by eastern redcedar and ashe juniper. Oklahoma Cooperative Extension Service Circular E-947, Stillwater, Oklahoma, USA. Borcard, D., Gillet, F., Legendre, P., 2011. Numerical Ecology with R. Springer, New York, New York, USA. Brennan, L.A., 1991. How can we reverse the northern bobwhite population decline? Wildl. Soc. Bull. 19, 544–555. Brennan, L.A., Kuvlesky, W.P., 2005. North American grassland birds: an unfolding conservation crisis? J. Wildl. Manage. 69, 1–13. Brooks, S.P., Gelman, A., 1998. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455. Canfield, R.H., 1941. Application of the line interception method in sampling range vegetation. J. Forest. 39, 388–394. Caro, T.M., 2003. Umbrella species: critique and lessons from East Africa. Anim. Conserv. 6, 171–181. Chapman, R.N., Engle, D.M., Masters, R.E., Leslie, D.M., 2004. Tree invasion constrains the influence of herbaceous structure in grassland bird habitats. Ecoscience 11, 55–63. Coppedge, B.R., Engle, D.M., Masters, R.E., Gregory, M.S., 2001. Avian response to landscape change in fragmented southern Great Plains grasslands. Ecol. Appl. 11, 47–59. Cram, D.S., Masters, R.E., Guthery, F.S., Engle, D.M., Montague, W.G., 2002. Northern bobwhite population and habitat response to pine-grassland restoration. J. Wildl. Manage. 66, 1031–1039. Crosby, A.D., Elmore, R.D., Leslie, D.M., 2013. Northern bobwhite response to habitat restoration in eastern Oklahoma. Wildl. Soc. Bull. 37, 733–740. Cullen Jr., L., Bodmer, R.E., Valladares Pádua, C., 2000. Effects of hunting in habitat fragments of the Atlantic forests, Brazil. Biol. Conserv. 95, 49–56. DeGraaf, R.M., Scott, V.E., Hamre, R.H., Ernst, L., Anderson, S.H., 1991. Forest and rangeland birds of the United States: natural history and habitat use. U.S. Dept. of Agriculture, Agricultural Handbook AH-688. Dimmick, R.W., Gudlin, M.J., McKenzie, D.F., 2002. The Northern Bobwhite Conservation Initiative. Miscellaneous publication of the Southeastern Association of Fish and Wildlife Agencies, South Carolina, USA. Dorazio, R.M., Royle, J.A., Söderström, B., Glimskär, A., 2006. Estimating species richness and accumulation by modeling species occurrence and detectability. Ecology 87, 842–854. Ellison, K.S., Ribic, C.A., Sample, D.W., Fawcett, M.J., Dadisman, J.D., 2013. Impacts of tree rows on grassland birds and potential nest predators: a removal experiment. PLoS ONE 8. Fleishman, E., Murphy, D.D., Brussard, P.F., 2000. A new method for selection of umbrella species for conservation planning. Ecol. Appl. 10, 569–579. Gruver, B.J., Guthery, F.S., 1986. Effects of brush control and game-bird management on nongame birds. J. Range Manag. 39, 251–253. Guthery, F.S., 1997. A philosophy of habitat management for northern bobwhites. J. Wildl. Manage. 61, 291–301. Guthery, F.S., 2002. The Technology of Bobwhite Management: The Theory Behind the Practice. Iowa State Press, Ames, Iowa, USA.

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