Hooded Warbler
Ecoregional Scale Conservation Planning

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Northern Bobwhite (Colinus virginianus)
The northern bobwhite is a resident gamebird that occurs throughout the eastern United States and Great Plains. Survey-wide, populations have declined by 3 percent per year since 1966 (Sauer and others 2005). Declines in the CH and WGCP have been equally dramatic (3.1 and 4.4 percent per year, respectively) during this same period Table 005 (Table 005) . As a resident gamebird, the species is not afforded special status by the USFWS (protection is relegated to state wildlife agencies). Nevertheless, PIF designates the species one needing management attention in both the CH and WGCP (regional combined scores = 16 and 15, respectively; Table 001 Table 001) . To address range-wide declines in population numbers, the Northern Bobwhite Conservation Initiative was drafted in 2002.
Relative abundance of Northern Bobwhite, derived from Breeding Bird Survey data, 1994 - 2003.
image courtesy of www.whatbird.com

Natural History:

The northern bobwhite is an economically important gamebird in the southern and central United States (Brennan 1999). The bobwhite is associated with early-successional vegetation, making use of agricultural fields, grasslands, grass-shrub rangelands, park-like pine forests and mixed pine-hardwood forests. At a county scale, the area in cultivated lands and livestock density shows a curvilinear relationship to bobwhite population indices in Texas (Lusk and others 2002a). In Oklahoma, bobwhite indices decrease with the proportion of the landscape in mature woodland, but increase with the proportion of brushy prairie or early-successional habitat (Guthery and others 2001). Guthery and others (2001) found the highest populations in areas lacking cropland agriculture. However, Williams and others (2000) found bobwhites selecting cropland when it comprised a small portion of the landscape. Patterns of use and survival differ between crop-dominated areas and rangeland-dominated areas during the hunting season in Kansas (Williams and others 2000). Bobwhite densities vary across the range depending on habitat quality but are highest in areas in areas of small (0.5-5.0 ha) interspersed patches of habitat.

Frequency and intensity of disturbance is important for this species, especially in southern pine forests where prescribed burning is a useful management tool. Cram and others (2002) found higher bobwhite abundance in pine-grassland restoration areas in Arkansas as conifer basal area and hardwood basal area decreased and woody structure <2 m tall increased. Bobwhite also occur in cottonwood reforestation plots <4 years old in Mississippi and Louisiana (Twedt and others 2002). Most management for bobwhite has historically occurred at a local scale, but Guthery (1999) showed that optimal configuration of patch types and sizes has variability (i.e., “slack”), and Williams and others (2004) promoted a regional management strategy focused on useable space (i.e., more patches of native prairies, savanna, and other favored vegetation types).

Weather affects bobwhite populations, including positive effects of summer temperature and fall precipitation (Lusk and others 2002a) and negative effects of spring flooding and low winter temperatures (Applegate and others 2002). Bridges and others (2001) found a negative correlation between drought indices in dry regions and bobwhite abundance, but this pattern did not hold in wetter regions of Texas. Lusk and others (2002b) also found climatic variables to be more important than landscape variables for predicting bobwhite abundance in Oklahoma.

Nests are constructed of litter (grass or pine needles) in areas of high structural complexity (Townsend and others 2001); brood cover is found in open areas that provide chicks greater mobility at ground level. Nevertheless, Taylor and others (1999) did not find any habitat attributes associated with higher probabilities of adult survival or clutch success. White and others (2005) examined multiple landscape buffers (250-1000 m radius, or 19.6-314.1 ha) around nest sites and random points to examine landscape effects on nest site selection. Bobwhite were found to respond to both composition and configuration of landscapes, including proportions of open-canopy planted pine and fallow fields, interspersion-juxtaposition index, and patch density. A model containing all four of these variables applied at the largest landscape had the best predictive ability, but was closely followed by a model containing only proportion of open-canopy planted pine applied at the smallest landscape size. Several other types of habitat models have been developed for the bobwhite: HSI (Schroeder 1985), PATREC (Roseberry and Sudkamp 1998), and logistic regression (Burger and others 2004). Tests of several models have found they perform poorly (Roseberry and Sudkamp 1998, Burger and others 2004).

Model Description:

Habitat quality for bobwhite is affected by many parameters that are not easily measured at any scale: the proportion of forbs or open areas in grasslands, herbaceous vegetation height, grasslands and crop field management, and intra- and inter-annual climatic variations. Therefore, we restricted our habitat suitability model to aspects of landscape composition and forest structure that we could quantify from available datasets. Our final model contains seven factors: +

  • landform
  • landcover
  • successional age class
  • hardwood basal area
  • evergreen basal area
  • grass landcover
  • interspersion of open and forest habitats.

The first suitability function combines landform, landcover, and successional age class into a single matrix (SI1) to define unique combinations of these classes Table 087 (Table 087) . We then directly assigned suitability index scores to these combinations based on northern bobwhite habitat associations outlined in Hamel (1992).

Forested sites utilized by northern bobwhites are typically woodlands with low hardwood and pine basal area (SI2 and SI3, respectively). We used data from Cram and others (2002) and Tall Timbers Research Station to inform inverse logistic functions that predict suitability index scores for bobwhites at various basal area levels Table 088 (Table 088 and Table 089 Table 089 ; Figure 051 Figure 051 and Figure 052 Figure 052 , respectively).

We directly assigned suitability index scores to grass landcover (SI4) classes based on their potential to provide feeding, nesting, and brood-rearing habitat Table 090 (Table 090) . We assumed natural grassland-herbaceous had the greatest potential to provide these habitats, though it is likely that any given patch can only satisfy two of the three requisites at any point in time (Stoddard 1931). We assumed areas in small grain production provided foraging opportunities but had little residual value for nesting or brood-rearing. Similarly, fallow fields provide marginal nest and brood habitat but no forage. Lastly, pasture-hay and row crops may provide some foraging, nesting, and brood-rearing habitat, but the value of these landcovers is likely limited due to management practices that produce unsuitable vegetative structure during most of the breeding season.

Bobwhites rely on landscapes comprised of interspersed vegetation types (White and others 2005, Guthery 2000). We used the composition of open and forest landcovers within a 1-km landscape (SI5) to index the interspersion of these cover types. Guthery (1999, 2000) and others before him (see Schroeder 1985 and references therein) have noted that bobwhites can tolerate a broad range of landscape configurations, a property Guthery termed “slack.” Based on suggestions from Guthery (pers. comm.), we assumed optimal proportions (suitability index score = 1.000) ranged from 10–40 percent forestland and 50–90 percent open habitat Table 091 (Table 091) .

We calculated the overall suitability index score by first determining the geometric mean of SI scores for forest structure attributes (SI1, SI2, and SI3). Open habitats lacking forest structure were assigned suitability index score independently (SI4). The landscape context of these forest and open habitats were then considered by determining the geometric mean of these site-level and landscape-level variables (SI5) together.

Overall SI = (((SI1 * SI2 * SI3)0.333 + SI4) * SI5)0.500