effectiveness of stand-level and landscape-level variables for explaining bird occurrence in an industrial forest, The
Hagan, John M
ABSTRACT. We evaluated the effectiveness of habitat variables derived at two spatial scales for explaining the presence or absence of 20 bird species at 363 point count stations in an extensive industrial forest in Maine. Sixteen stand-level (microhabitat) and seven landscape-level (within 1 km radius, or 3.14 km^sup 2^) variables were used in stepwise logistic regression models to evaluate the relative roles of these two spatial scales for explaining each species’ occurrence at a point count station. Species were classified as either early-successional (n = 6) or late-successional (n = 14) species for analysis. Three regressions were run for each of the 20 species: (1) stand variable only, (2) landscape variable only, and (3) stand and landscape variables combined. Regression models that included only stand-level variables averaged significantly higher model concordance scores than regression models that included only landscape variables. For 17 of 20 species, regressions that included only stand-level variables better explained the presence or absence of a species at a point than did regression that included only landscape-level variables. The presence of two species was better explained by landscape variables, and the presence of one species could not be explained by either stand- or landscape-level variables. Because many variables were positively associated with presence of some species and negatively associated with other species, management for particular stand- or landscape-level features must be preceded by a determination of which species are more important to conserve. FoR. Sci. 48(2):231-242.
Key Words: Avian, forestry, landscape, microhabitat, habitat loss, fragmentation, wildlife.
INCREASINGLY, FOREST MANAGERS ARE seeking to integrate management forbiodiversity into forest planning (Kohm and Franklin 1997, American Forest and Paper Association 2001). However, forest management, even for a single species, can be complicated by the fact that the presence of a species at any given location may be dependent on factors at work at multiple spatial scales (e.g., Turner 1989, Wiens et al. 1993). An array of typically unknown microhabitat and landscape factors can affect whether a species is found in any given forest stand. If forest managers are to manage successfully for biodiversity, or specific subsets of biodiversity (e.g., birds), it is important to understand the degree to which habitat factors at different spatial scales influence species occurrence.
Ecologists have long understood that microhabitat vegetation structure (in forestry terms, within-stand structure) influences whether a species will occupy a site (e.g., Hutchinson 1957, MacArthur 1958, James and Shugart 1970, James 1971, Willson 1974, Verner 1981, James and Warner 1982, Cody 1985). Much research has been devoted to describing and modeling wildlife-habitat relationships using microhabitat structure (e.g., Noon et al. 1980, papers in Verner et al. 1986, DeGraaf et al. 1998). In the last 2 decades, however, ecologists have begun to understand that landscape-level factors, such as abundance or configuration (fragmentation) of habitat, can influences a species’ presence or absence in a forest stand or patch (MacClintock et al. 1977, Whitcomb 1977, Lynch and Whitcomb 1978, Robbins 1979, Whitcomb et al. 1981, Lynch and Whigham 1984, Freemark and Merriam 1986, Robbins et al. 1989). For birds that breed in forests, the amount of forest habitat in the larger landscape also can be related to reproductive success (e.g., Galli et al. 1976, Robbins 1980, Ambuel and Temple 1983, Wilcove 1985, Blake and Karr 1987, Small and Hunter 1988, Gibbs and Faaborg 1990, Donovan et al. 1997). Much of the landscape-scale research of forest birds has been conducted in agricultural and urbanizing landscapes (e.g., Gates and Gysel 1978, Chasko and Gates 1982, Wilcove 1985, Gibbs and Faaborg 1990, Verboom et al. 1991, Robinson et al. 1995, Villard et al. 1999); the implications for managed forest landscapes still are poorly understood (e.g., Andren 1994).
Few studies have partitioned the relative effectiveness of microhabitat factors and landscape factors for explaining the presence or absence of birds at a location (Lynch and Whigham 1984, Robbins et al. 1989, Knick and Rotenberry 1995, Bolger et al. 1997, Donovan et al. 1997, Saab 1999), and even fewer studies have been conducted on this question in forest– dominated landscapes (Drapeau et al. 2000, Rodewald and Yahner 2000). Many recent studies of landscape or habitat fragmentation effects on birds do not record (or report) microhabitat data, implicitly assuming that microhabitat structure is either not a factor, or is an insignificant factor, for explaining whether a species occupies a site (e.g., McGarigal and McComb 1995, Trzcinski et al. 1999, Villard et al. 1999, Burke and Nol 2000). Indeed, microhabitat factors may be overwhelmed by a relatively greater importance of landscape factors on forest birds where forest patches are surrounded by clearly unsuitable habitat, such as agricultural fields. However, the relative contribution of microhabitat (stand-level) and landscape-level factors for explaining species occurrence may be much more relevant in landscapes that provide a diverse continuum of habitat types, such as managed forests. In addition, because forest managers actively manipulate habitat at multiple spatial scales, and can modify practices to meet specific objectives, it is of special interest to understand the relative importance of habitat factors derived from both the microhabitat and landscape scales in managed forests.
In this study we compare the relative contribution of microhabitat and landscape variables for explaining the occurrence (presence/absence) of 20 landbird species at point count stations in the extensive industrial forest of northern Maine. Our goal was to understand the relative importance of stand-scale and landscape-scale habitat variables in shaping bird populations in a forest-dominated landscape. Forest management offers great potential for manipulating both microhabitat and landscape patterns for particular species or groups of species. By comparing the relative contributions of microhabitat and landscape variables, we reveal at which scale management action might yield the greater contribution to maintaining avian biodiversity.
This study took place in a 2,000 km^sup 2^ section of private industrial forestland in north central Maine (Figure 1) owned at the time of the study primarily by two forest products companies (S.D. Warren Co. and Great Northern Paper). The study area is in the subboreal Acadian forest, a transition zone from temperate deciduous forest to higher latitude eastern boreal forest. Dominant deciduous species included maple (Acer rubrum, A. saccharum, and A. pensylvanicum), American beech (Fagus grandifolia), paper birch (Betula papyrifera), and yellow birch (B. alleghaniensis). Dominant coniferous species were balsam fir (Abies balsamea), spruce (Picea rubens, P. glauca, and P. mariana), and white pine (Pinus strobus).
The study area was mapped in a Geographic Information System (GIS) according to forest type by the industrial forest landowners. The basic unit of the mapping system is the forest stand, an area of forest that is represented by roughly homogeneous tree species composition, forest age, and canopy closure. Experienced photo-interpreters used 1:15,840 aerial photographs to delineate stands to a resolution of about 1 ha. For our study area, each stand was given a code that described its type, age, and crown closure. Four levels of type (H: >75% hardwood; HS: 50-75% hardwood with remainder softwood; SH: 50-75% softwood with remainder hardwood; S: >75% softwood), three levels of age [1 = young (
From late May to early July, 1992 and 1993, we used 50 m fixed-radius point counts to sample 363 points across all forest types, from clearcuts to mature hardwood, softwood, and mixedwood forest. Only birds detected within the point count circle were included in the analyses. Each sampled stand had only one point count station, except in a few instances where the stand was very large (>200 ha). In these instances no more than two points were sampled within the stand, and the points were separated by a minimum of 200 m, but usually greater than 500 m. Sampled stands ranged from 0.5 ha to 244.0 ha (mean = 36.8 ha).
At each point we conducted two point counts, one early (May 24 to June 14) and one later (June 15 to July 8) in the breeding season. A single point count lasted 10 min. All point counts were conducted between 0515 and 0930 hr. All birds seen or heard within 50 m of the point count center were recorded. For the purposes of this study, we only used presence/absence data for a species at a given point count station. Usually, no more than one individual of a species occurred within the 50 m radius point count circle, so it made no sense to analyze density as the dependent variable. A species was considered “present” at a station if it occurred during either of the two point count surveys.
Twelve observers, working in pairs, conducted the point counts during the 2 yr study. Early in the season, only experienced observers were allowed to observe, while the partner recorded data. To minimize bias, all observers trained intensively with highly experienced supervisors for 2 wk in the study area each season before formal sampling began. All observers sampled all habitats to further avoid observer bias in any particular habitat.
Twenty species occurred at 10% or more of the 363 point count stations and were used in logistic regression analyses. Fourteen species were categorized as late-successional species and six as early-successional species (Table 2). For 17 of 20 species, regression models that included only stand-level variables produced a greater concordance score than regressions that included only landscape-level variables (Table 2). For one species [northern parula (scientific names of all species are listed in Table 2)] there were no significant variables in either the stand- or landscape-variable regressions. Only the blue-headed vireo and the chestnut-sided warbler had higher concordance scores for the landscape– variable regressions (Table 2). The blue-headed vireo was a late-successional species, and the chestnut-sided warbler was an early-successional species. The mean concordance of regression models that used only stand-level variables [mean = 73.8%, 2.6 (SE), n = 20 species] was significantly higher than models that used only landscape-level variables [mean = 63.7%, 2.3 (SE), n = 20 species, P = 0.019] (Figure 2). The mean concordance of regressions that included stand- and landscape-level variables [mean = 76.6%, 2.3 (SE), n = 20 species] was not significantly greater than the mean concordance value for stand-level only regressions (P = 0.42). There was a significant correlation between concordance scores for stand-level and landscape-level regressions (r = 0.47, P = 0.04, n = 20), indicating that when the presence or absence of a species was better predicted with stand-level variables, presence/absence was also better predicted with landscape– level variables.
The regression including both stand- and landscape-variables for the Canada warbler produced the greatest model concordance (93.6%, Table 2). That is, the presence or absence of the Canada warbler at a point count station was more reliably predicted than for any other species [the significant variables, in order of importance, were dead tree basal area (positive relationship), understory stems 2-4 m tall (positive), understory stems 6+ in tall (negative), landscape contrast index for forest type (positive), and landscape contrast index for forest age (negative)] (see Appendix I for a list of all species and associated significant regression variables).
To understand which variables best explained the presence or absence of species at a point, we tallied the number of times independent variables were significant in species regressions. Hardwood basal area was the most commonly significant variable (for six species, Table 3). However, this variable was positively correlated with the presence of three species (black-throated green warbler, ovenbird, red-eyed vireo) and negatively correlated with the presence of three other species (bay-breasted warbler, magnolia warbler, winter wren). The density of understory stems 2-4 m tall was related positively to the presence of four species (American redstart, black-and-white warbler, magnolia warbler, and Swainson’s thrush). The density of understory stems 4-6 m tall was related positively to the presence of one species (Canada warbler) and negatively to three species (blueheaded vireo, blackburnian warbler, and white-throated sparrow). This latter result demonstrates that the presence of both early- and late-successional species could be related positively to the same vegetation variable.
Generally, landscape variables related poorly to species presence or absence when included in regressions with stand variables (Appendix I). However, landscape variables emerged as significant for some species, despite the dominating effect of stand-level variables. The total area of earlysuccessional forest was positively related to the presence of two species (American redstart, magnolia warbler) and negatively related to the presence of one species (red-eyed vireo). This result might indicate that the red-eyed vireo is sensitive to loss of late-successional forest at the landscape scale. The contrast index for forest age was positively related to the presence of two species (chestnut-sided warbler, blue-headed vireo) and negatively related to the presence of one species (Canada warbler). Because the Canada warbler was an earlysuccessional species, this result suggests that this species may be negatively affected by the amount of late-successional forest in the landscape. One species was positively related to the amount of late-successional forest in the surrounding landscape (bay-breasted warbler), suggesting sensitivity to loss of late-successional forest at the landscape scale.
To further explore possible landscape effects, we tallied the number of species that showed significance to each landscape variable in the landscape-variable-only regressions. This functionally removed the effect of stand-level vegetation variables on species presence or absence. Three landscape variables tied for most commonly significant (each showing significance for six species) (Table 4). The total area of early-successional forest within 1 km of the point count station was positively related to the presence of three species (white-throated sparrow, common yellowthroat, and magnolia warbler), and negatively related to the presence of three species (Canada warbler, red-eyed vireo, and blackburnian warbler). Two of the three species positively related to the amount of early-successional habitat are early-successional species. The magnolia warbler was classified as a late-successional species, but it was abundant in early-successional forest as well. Two of the three species negatively related to the amount of early-successional forest are late-successional species (red-eyed vireo and blackburnian warbler).
The weaker contribution of landscape variables relative to stand variables cannot be attributed to a narrow range of values for landscape metrics. There was much variance available within independent variables for explaining presence or absence of species, both for early-successional and late-successional points (Appendix 2). For example, the amount of late-successional forest ranged from 17 ha to 293 ha in the 314 ha (1 km radius) landscape circles among latesuccessional points; the amount of early-successional habitat ranged from 10 ha to 294 ha for early-successional points. Refer to Appendix 2 for summary statistics for all variables.
At the scale of our landscape assessment (3.14 km^sup 2^), this study showed that stand-level structural characteristics exceeded landscape characteristics in their ability to explain the presence or absence of most species at a particular location. All species taken together, based on mean regression concordance scores, the inclusion of landscape variables with stand variables did not significantly improve the ability of models to predict the presence or absence of species. Landscape variables did significantly relate to the presence or absence of some species, but stand-level variables were much more commonly significant.
Only a few studies have examined simultaneously the contribution of microhabitat vegetation structure and landscape variables for explaining presence/absence of bird species at a point. Lynch and Whigham (1984) studied forest birds in 270 forest fragments of different sizes in the coastal plain of Maryland and found that 30 of 31 species studied showed a significant relationship to one or more microhabitat or landscape variables. Eighteen species showed significant relationships with one or more microhabitat variables, 18 species to patch isolation variables, and 8 species to patch area. Robbins et al. (1989), in a similar study of forest patches in Maryland, Virginia, Pennsylvania, and West Virginia, found that degree of patch isolation and patch area were more commonly significant predictors of avian occurrence than were microhabitat variables. Although both studies (Lynch and Whigham 1984, and Robbins et al. 1989) reported significant relationships between species’ abundance and both microhabitat and landscape variables, there was a notable difference in the conclusions about the relative importance of microhabitat and landscape variables. Two studies conducted in shrubland ecosystems (southwestern Idaho and southern California) concluded that the addition of landscape variables in regression models significantly improved the ability to predict the presence or absence of a bird species at a point (Knick and Rotenberry 1995, Bolger et al. 1997). A study of birds in riparian zones in Idaho found that landscape patterns dominated microhabitat variables in explaining the occurrence of 32 small land bird species (Saab 1999).
A key difference in our study, and those noted above, was the degree of suitability of habitat between study plots. For example, the Bolger et al. (1997) study took place in San Diego County in southern California and included a strong landscape gradient from large patches of native coastal shrub habitat to small patches embedded in residential and commercial development. The Lynch and Whigham (1984) and Robbins et al. (1989) studies were conducted on forest birds, but in landscapes with a dominant agricultural component. The unsuitability of the matrix habitat could be a key factor in explaining the apparently stronger landscape effects on avian occurrence in these other studies. Northern Maine’s industrial forest landscape had virtually no human habitation and was dominated by medium-age and mature forest at very large spatial scales (1000s of km^sup 2^). It is mostly a mosaic of differentially suitable habitat. Whether a species perceives the landscape as “black or white” (use, not use) or as “shades of gray” (a gradation of use) might have major effects on results and interpretations of landscape patterns (Addicott et al. 1987, Andr6n 1997). Many recent landscape studies of forest birds make no effort to evaluate (or report) variability in vegetation structure among forest patches; rather, habitat is simply categorized as suitable or unsuitable. Failure to evaluate microhabitat could lead to mistaken interpretation of landscape effects, as pointed out by Lynch and Whigham (1984). Lack of landscape effects on avian abundance, occurrence, or reproductive success has been a common result of many studies in forest-dominated landscapes, such as managed forests (e.g., Helle 1984, Rosenberg and Raphael 1986, Haila et al. 1987, 1993, Bayne and Hobson 1997; but see also Small and Hunter 1988, Hagan et al. 1996, King et al. 1996).
We found only two studies in forest-dominated landscapes that evaluated both microhabitat and landscape variables on avian occurrence or reproductive success. Rodewald and Yahner (2000) concluded that pairing success of ovenbirds in central Pennsylvania was determined primarily by microhabitat factors and not by the amount of habitat loss (e.g., mature forest cover) within a 1 km radius of the nesting location. Drapeau et al. (2000) studied avian community structure in three landscape types in northwestern Quebec: an agricultural landscape, a managed forest landscape, and a natural forest landscape. Of the variation that could be explained in bird community structure (25%), 38% was attributed to local habitat variables and 29% to landscape context variables. However, this analysis combined the data from all three landscape types, and thus the relative explanatory power of microhabitat and landscape variables in only the managed forest landscape could not be discerned. Moreover, Drapeau et al. (2000) studied avian community diversity and richness, and did not attempt to predict the occurrence of individual species. Schmiegelow et al. (1997) indirectly examined the importance of local vegetation variables on bird communities in Alberta by conducting a before-and-after study. The same 50 m radius point count stations were sampled both before and after harvesting took place in the surrounding landscape, but the sample locations were not disturbed. The study found a small decrease in Neotropical migrant species several years after the harvest. Presumably, these changes were related to factors at work in the landscape, rather than changes in microhabitat vegetation.
Andren (1994) reviewed studies of forest fragmentation effects on vertebrates and concluded that densities within suitable habitat patches generally were unaffected in landscapes with >30% of suitable habitat. He found that when landscapes lost 70% to 90% of original habitat, effects of forest edge and/or isolation began to appear, suggesting that there may be a threshold at which fragmentation effects develop. The notion that such landscape thresholds exist has been challenged (Fahrig 1997, Bender et al. 1998, Monkkonen and Reunanen 1999, With and King 1999). For example, Villard et al. (1999) found a continuous relationship (rather than step function) between abundances of 15 bird species and forest cover in 33 landscapes in eastern Ontario. Fahrig (1998) concluded that population survival would be in jeopardy when
The scale at which the landscape is examined could be critical to whether landscape effects are observed. Twenty-percent suitable habitat at a 1 km radius (3.14 km^sup 2^) scale may be irrelevant if the 5 km radius (78.5 km^sup 2^) landscape contains, say, 50% suitable habitat. If we had scaled our landscape to a 5 km radius unit of study, we would have concluded that the landscape was >50% covered by late-successional forest for most point count stations (we could not do this because it would have required access to adjacent landowner GIS data, which we did not have). Thus, landscapes that had been heavily harvested (last 10 yr) at the 1 km radius scale were embedded in only moderately harvested 5 km radius landscapes. If relevant population dynamics of the species we studied functioned at a 5 km radius scale, or larger, then our 1 km scale analyses would have been inappropriate. We have little knowledge of the scale at which different species’ population processes operate. Even though many studies of landscape-scale effects on birds use a 3-6 km2 landscape unit for evaluation (e.g., Askins and Philbrick 1987, Askins et al. 1987, Robbins et al. 1989, Knick and Rotenberry 1995, McGarigal and McComb 1995, Bolger et al. 1997, Villard et al. 1999, Bergin et al. 2000, Rodewald and Yahner 2000), our results beg the question “at what scale (or scales) should we be looking for landscape-scale effects on species?”
Despite the dominance of stand-level variables for explaining species presence or absence, landscape variables were significant for some species. For example, the red-eyed vireo (late-successional) was negatively related to the amount of early-successional forest in the 1 km radius landscape. The red-breasted nuthatch (late-successional) was negatively related to the total area of clearcut in the 1 km landscape. The bay-breasted warbler (late-successional) was positively related to the amount of late-successional forest in the 1 km landscape. Some early-successional species were related to landscape metrics as well: the chestnut-sided warbler was positively related to the landscape contrast in forest age, the Canada warbler was negatively related to the landscape contrast in forest age, and the Nashville warbler was negatively related to landscape contrast in forest type (e.g., coniferous-deciduous contrast). Therefore, we caution against generalizing that landscape variables are not important to bird occurrence in managed forests.
We only evaluated 20 of about 90 landbird species that occur in the industrial forest of Maine. Although stand variables better explained the occurrence of 17 species than did landscape variables, landscape factors could be critically important to species we did not study. We studied species that were relatively common in the study area (occurred at 10% or more of the point count stations). Less abundant species may be differentially sensitive to landscape variables, as might be expected if uncommon species have more trouble locating and colonizing suitable habitat.
In addition, we studied occurrence of species, not productivity. We know that abundance or occurrence is not always related to productivity in bird populations (Van Home 1983, Vickery et al. 1992, Hagan et al. 1996). However, most studies of avian reproductive success in forest-dominated landscapes have failed to find edge or fragmentation-related effects on avian productivity (Hanski et al. 1996, Rudnicky and Hunter 1993, DeGraaf 1995, King et al. 1996, Rodewald and Yahner 2000, but see Small and Hunter 1988, Hagan et al. 1996). If reproductive success was not substantially diminished in our managed forest landscape, this could partly explain why landscape variables often were not related to species occurrence in this study. In addition, given the dominance of late-successional forest in the larger landscape, dispersal throughout the landscape was not likely limiting occurrence of late-successional species. Early-successional species probably viewed our study area as more fragmented than did late-successional species (all else being equal, such as dispersal capacity).
Finally, other species groups might be more sensitive to landscape factors. For example, in Maine, the American marten (Martes americana) appears to be sensitive to both microhabitat and landscape factors; this species prefers forest patches that are at least 6 m tall and several hundred hectares in extent (Chapin et al. 1998). Hargis et al. (1999) suggested that to maintain marten populations in Utah, the combination of natural and man-made forest openings should not exceed 25 % of a 9 km^sup 2^ landscape unit. We are therefore careful to restrict our conclusions about the lack of landscape relationships to the bird species we studied.
We would like to recommend to forest managers either stand- or landscape-level prescriptions that might help maintain avian biodiversity. Even for the limited number of species we studied, this task is complicated by the fact that many variables were positively related to the occurrence of some species and negatively related to the occurrence of other species. Specific management recommendations therefore would benefit some species at the expense of others (Lynch and Whigham 1984). Unless some species are of higher conservation value (e.g., at much greater risk of extirpation), it is impossible to make specific recommendations that provide a net benefit to biodiversity (as opposed to benefiting one or a few species at the expense of others). Even though many stand variables were related to the occurrence of a species, it would not be practical or reasonable for forest managers to manipulate specific variables unless there was a species of unusual concern, such as an endangered or threatened species. There are no endangered bird species in the industrial forest of Maine at present. In addition, just because a species shows a statistical relationship to stand or landscape variables does not mean that management action is warranted. For example, the presence of the redeyed vireo, a late-successional forest species, was related to four stand variables and one landscape variable (Appendix 1). However, this species is one of the most abundant species in Maine’s industrial forest; its populations are not presently threatened by forest practices, and no special management action is warranted.
If we assume a credible social goal for the industrial forest of Maine is to maintain self-sustaining, well-distributed populations of native species, we can make recommendations to managers. Maine is divided into political units called townships. Most townships in northern Maine are approximately 10 x 10 km (and essentially unpopulated by humans). To maintain well-distributed populations, we recommend that mature forest be well distributed as well. If managers could keep a portion (at least 25%) of each township in mature forest cover (60 yr old, or older), it is likely that mature forest bird species would remain well-distributed across northern Maine. Another recommendation is to consolidate harvesting over the long term so that larger, unfragmented units of forest will be present in the landscape in the future (Li et al. 1993, Hagan and Boone 1996, Hagan et al. 1997, Chapin et al. 1998, Hargis et al. 1999). It is much easier to create age-class heterogeneity in the industrial forest landscape of northern Maine than it is to maintain (or create) large tracts of mature forest. Moreover, once heterogeneous harvest patterns are established on the landscape, it can take many decades for unfragmented mature forest habitat to be reestablished. This ecological strategy may also yield economic benefits by increasing efficiency of operations.
We encourage researchers not to ignore microhabitat when exploring landscape effects on species occurrence. Not all forest patches are equal in terms of habitat structure and floristics, and this has consequences for use of a patch by different species (irrespective of landscape issues). In addition, the event of becoming a patch or forest fragment can precipitate changes in within-patch vegetation structure and dynamics (Malcolm 1994, Laurance et al. 1998). Measuring vegetation as a part of landscape habitat studies can be time consuming and labor intensive, but the assumption that all habitat patches are equally suitable in terms of microhabitat could confound interpretation of landscape effects.
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John M. Hagan, Senior Scientist, Manomet Center for Conservation Sciences, 14 Maine Street, Suite 404, Brunswick, Maine 04011-Phone: (207) 721-9040; Fax: (207) 721-9144; E-mail: email@example.com. Amy L. Meehan, Biologist, Maine Department of Inland Fisheries and Wildlife, 650 State St., Bangor, Maine 04401-Phone: (207) 941-4483; E-mail: amy.Meehan@state.me.us.
Acknowledgments: The authors thank S.D. Warren and Great Northern Paper Companies for access to their property. C. Haag (S.D. Warren) and M. McKeague (Great Northern) helped facilitate this project. J. Hatch (S.D. Warren) and D. Boss (Great Northern) both contributed essential GIS expertise. P. McKinley played a critical role in coordinating field operations. We thank our field assistants: T. Bodeo, T. Elmeer, C. Fosdick, M. Orrell, R. Sladek, A. Sulzer, S. Woltman, P. Weisberg, and R. Williams. We are grateful to our financial supporters, including The Jessie B. Cox Charitable Trust, The John Merck Fund, the National Fish and Wildlife Foundation, the National Council of the Forest Products Industry for Air and Stream Improvement, and members and donors of Manomet Center for Conservation Sciences. We thank J.M. Reed, two anonymous reviewers, and the Associate Editor for greatly improving an earlier draft of the manuscript.
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