Studies of habitat responses of bird species have sometimes produced inconsistent findings regarding sensitivity to habitat area, edges, and other aspects of habitat composition or fragmentation (Villard 1998, Thompson et al. 2002, Bayard and Elphick 2010, van der Hoek et al. 2013, Vetter et al. 2013). This inconsistency limits our ability to address both basic and applied questions, such as differences in population vulnerability in the face of landscape change, variation among species in habitat area requirements, plasticity in habitat use, and the likely effectiveness of conserving one habitat area versus another.
Multiple factors can help explain differences in findings, such as study design, regional variation in species abundance, or variation in habitat amount at the landscape scale. We focus here on the third of these. Amount of habitat in the landscape has long been understood to influence species occurrence, with landscape-scale effects operating simultaneously with proximate-scale effects (e.g., Cunningham et al. 2006, Desrochers et al. 2010, Zitske et al. 2011). Landscape-scale habitat amount also can influence local-scale edge effects (Thompson et al. 2002). We examine whether landscape-scale habitat amount influences habitat occupancy at the proximate scale. We also consider how the shape of response to habitat availability at the proximate scale, as visualized using loess plots, may respond to habitat availability at the landscape scale. This visualization can add important nuance to our understanding of species responses to habitat amount. We explore this question in a mixed savanna landscape, which allows us to explore these dynamics in landscapes that vary from largely wooded to largely open.
We proposed that landscape context could influence site-scale responses in one of two directions (Fig. 1). In landscapes where little suitable habitat is available, e.g., little tree cover, for woodland birds, a species could (1) become less selective and broaden its tolerance to occupy less-suitable sites (occupying areas that are lightly wooded at the site scale), or (2) become more selective, i.e., avoiding lightly wooded sites. For woodland birds, we consider mostly-wooded sites to be more suitable and lightly-wooded sites to be less suitable. Stated more generally, we consider the following competing hypotheses:
H0: No effect. Responses to site-scale habitat are similar in different landscapes;
H1: Tolerance hypothesis. When little habitat is available in the landscape, birds are more likely to occupy less-suitable sites; they discriminate little between abundant and sparse habitat at the local scale.
H2: Avoidance hypothesis. When little habitat is available in the landscape, birds are less likely to occupy less-suitable sites; they discriminate more strongly between abundant and sparse habitat at the local scale.
These alternatives have fundamental implications for conservation strategy. If H0 is true, then protecting all available habitat areas, including isolated ones, is equally important. If H1 is true, then conservationists should give special attention to species occupying less-suitable habitat areas: these may provide critical resources when other habitat is unavailable; they may also act as ecological traps. For example, area-sensitive species could be forced to occupy unsuitably edge-rich habitat, presumably with corresponding higher abundance of edge-dwelling predators, where expansive interior habitat is unavailable (see Vetter et al. 2013). In this case, controlling predators or reducing other risk factors would be a conservation priority. If H2 is true, then reduced habitat availability at the landscape or regional scale could make local habitat remnants less useful for species conservation. This would imply that fragmentation is a positive feedback process, in which habitat loss leads to reduced suitability of remaining fragments. In this case, conservation efforts should focus on strategies to prevent widespread habitat loss, such as attention to the economic and political drivers that influence landscape change.
These conservation implications are not academic questions. Conservation agencies with limited budgets frequently must prioritize spending among different habitat areas. Decisions to maintain or purchase a particular area of habitat sometimes depend on managers’ expectations of how well small or isolated areas are likely to support wildlife of concern.
Some studies have investigated the interaction of proximate and landscape scales. Parker et al.’s (2005) meta-analysis of 33 separate studies found a nonsignificant tendency for patch size effects to diminish as the amount of surrounding forest increased. Radford et al. (2005) found that species richness responded differently to amount of tree cover in landscapes with aggregated versus dispersed tree cover. Ribic et al. (2009a) found that abundance of some avian species was positively associated with proportion of suitable habitat in the landscape around a site, and in a broader literature review of area sensitivity in grassland birds, Ribic et al. (2009b) reported that most studies found weaker evidence for area sensitivity in landscapes comprising a high proportion of grassland. In meta-analyses of edge effects on nest predation, both Batáry and Báldi (2004) and Vetter et al. (2013) found contrasting results among different study areas and concluded that landscape context, in particular forest cover, strongly influences the effect of edge density on nest predation. Our data set allowed us to address the issue directly and with 22 different species.
We do not focus here on the relative importance of habitat amount and habitat configuration, a question that has been explored extensively elsewhere (Thompson et al. 2002, Fahrig 2013, Villard and Metzger 2014) and that continues to be debated (Fahrig 2015, Hanski 2015). Indeed, Lindenmeyer and Fischer (2007) and Didham et al. (2012) have argued that while this dichotomy has become entrenched in our understanding of habitat change, it has not always been useful for furthering conservation goals (see also Villard and Metzger 2014). We focus on habitat amount as an explanatory variable, and we examine the interaction of its effects on species occurrence at the landscape and proximate scales.
Understanding the influence of landscape context on proximate-scale habitat responses is also useful for comparing population abundance and population trends among regions. Many eastern North American bird species, for example, occur from the Atlantic coast to the Great Plains. Monitoring efforts such as the North American Breeding Bird Survey (BBS) have found contrasting population trends in different parts of those ranges (Sauer et al. 2014). The BBS shows the value of incorporating data from throughout the continent to gain a broader understanding of population trends. Further studies that compare multiple study areas are important for improving our understanding of fundamental species ecology (van der Hoek et al. 2013, Vetter et al. 2013). For example, if one particular landscape factor, such as edge density or habitat area, is important in one region, is its influence similar in others? If not, does this imply plasticity in response to habitat, or are other factors in play? Integrating studies across multiple landscapes and regions would help elucidate general patterns in influences of habitat on population distributions and on population trends.
We assessed the consistency of site-scale responses in contrasting landscape contexts by examining responses of woodland birds to the amount of proximate-scale tree cover (within 100 m around sample locations) in open landscapes and in wooded landscapes. We did this comparison in a naturally fragmented oak savanna landscape of grassland and woodland that provided a range of landscape-scale tree cover.
Our study area was the Sheyenne National Grassland in southeastern North Dakota (97.5W, 46.5N), which comprises 28,400 ha of tallgrass prairie, mixed-grass prairie, wetlands, and woodland. Manske (1980) and Seiler and Barker (1985) have described the vegetation of the area. Plant communities include tallgrass and mixed-grass prairie on rolling upland topography, bur oak (Quercus macrocarpa) savanna and quaking aspen (Populus tremuloides) stands on upland dunes, and sedge meadows and wetlands in low-lying areas. Low (0.5 - 1 m) shrubs, primarily western snowberry (Symphoricarpos occidentalis), are scattered throughout the mixed-grass prairie. A riparian deciduous forest dominated by basswood (Tilia americana), cottonwood (Populus deltoides), and willow (Salix spp.) occurs on the northern end of the area. With its diversity of vegetation types, the Sheyenne National Grassland supports a rich variety of birds (Cunningham et al. 2006, Martin and Svingen 2010) and a diversity of landscape types minimally interrupted by human settlements or agriculture.
Indicated breeding pairs were counted (Stewart and Kantrud 1972, Igl and Johnson 1997, Desrochers et al. 2010, Pickens and King 2014) along belt transects 2 - 6 km long. We designated indicated breeding pairs, following Stewart and Kantrud (1972) and Igl and Johnson (1997): If sexes were alike, the number of singing males was counted. If no individuals were singing, then the number of observed individuals was halved and rounded up to derive indicated pairs. Birds flying over the segment were included only if they apparently were using the area for foraging.
Transects allowed us to efficiently acquire a relatively large data set remote from roads. There were 24 belt transects. Bird counts were conducted between half an hour before sunrise and four hours after sunrise, between late May and early July from 2002 to 2005. One observer walked these transects slowly (1 km/hour), recording all birds seen or heard within 50 m on either side. The same observer did surveys in all years. We used this conservative distance to reduce variation in detectability: although detection varies among species and habitat types, especially at distances of 100 m or more (Matsuoka et al. 2012), detections do not tend to decline appreciably within 50 m (Simons et al. 2007, Koper et al. 2016), and previous studies have found that a 50 m distance provides reliable data for a broad range of species in wooded as well as open habitats (Matsuoka et al. 2012). Studies of auditory-only detections have shown that detection distances are subject to error (Alldredge et al. 2007), especially in windy or noisy conditions (Koper et al. 2016). To reduce these risks, we sampled only in weather conditions with little wind and no rain; other noise sources were minimal. A 50-m distance also ensured that we were sampling local habitat use, rather than landscape-scale habitat. Field methods are described more fully by Cunningham et al. (2006).
A global positioning system (GPS) unit was used to divide transects into 100-m segments and to record bird counts by these segments, which later were georeferenced to land-cover data. All analysis was done on these 100-m transect segments, for which we calculated amount of tree cover (X) and the presence or absence of a species (Y).
Definitions of “habitat” vary in ecological studies and may include factors as diverse as vegetative density, maturity, species composition, vertical structure, and other features; moreover, for many birds habitat includes multiple types of vegetation, such as edges, shrubs, or grassland, as well as trees. In studies aiming to maximize explanation of site selection in individual species, detailed descriptors of habitat and fragmentation can be essential. For comparisons across a number of species and environments, however, or where exact details of habitat preferences are unclear, a more generalized approach can provide useful insights. Thus many studies of woodland bird responses to landscapes generalize habitat in terms of the extent or amount of tree cover (e.g., Andrén 1994, Freemark and Collins 1992, Parker et al. 2005, Radford et al. 2005, Desrochers et al. 2010). We follow this convention in the present study.
Landscape composition and fragmentation also can be described with many measures, such as patch shape, isolation, core area, or edge density, or other metrics (McGarigal et al. 2002). In preliminary analysis, we used FRAGSTATS (McGarigal et al. 2002) and the FragStatsBatch utility in ArcGIS 9.2 (Mitchell 2007, ESRI 2004) to calculate these different metrics. We calculated these metrics using land cover data that was digitized from 1-m resolution digital air photos and converted to raster format with a cell size of 5 m. We then compared explanatory effects among metrics to evaluate which were most useful for explaining species presence/absence. Habitat amount is widely understood to be more informative than configuration factors (Fahrig 2013, Villard and Metzger 2014). For example, in some studies it has been only at low levels of habitat availability in the landscape that configuration variables (size or proximity of patches) have become important (e.g., Villard and Metzger 2014, Hanski 2015). We compared the explanatory effect of landscape metrics in our study area and found that overall habitat amount provided as good as or better explanation than other fragmentation metrics (Fig. 2). This measure is increasingly recognized as influential for proximate-scale habitat occupation in fragmented landscapes (Dunford and Freemark 2005, Ribic et al. 2009b, Desrochers et al. 2010, Cunningham and Johnson 2011, 2012, Vetter et al. 2013).
Habitat extent (amount) and configuration (such as edge density or cohesion) are often considered to be different approaches to evaluating fragmented landscapes. But if we consider fragmentation in terms of the difference between extensive, unbroken habitat and more mixed landscapes (see Andrén 1994, Wiens 1995), then the contrast between 90% and 40% tree cover (within 100 m, for example) does serve to distinguish expansive habitat from mixed habitat (see Fig. 1). Amount of tree cover is also less sensitive to scale than many configuration measures, such as edge density, core area, shape, or cohesion (McGarigal et al. 2002). Therefore we used tree cover measured within a 100-m radius to represent habitat extent at the proximate, or site, scale.
To characterize landscape composition, we used amount of tree cover within 400-m radii around bird observations. This is a relatively small area to represent landscape conditions, but our aim was to test for differences, not to characterize landscape effects per se. The choice of scales was therefore arbitrary, and similar analysis could be done at different scales. In preliminary analysis we tested larger “landscapes” of 800 m and 1200 m radius around bird observations, and these produced results similar to those at 400 m. However variation in tree cover at those scales was reduced, because with larger radii we had few areas with a high percentage of tree cover. Thus we had lower confidence in comparisons of open and wooded landscapes at those larger radii than we did with the smaller 400-m radius. Because our purpose was to test whether contrasting landscape conditions had an effect, then, we used a 400-m radius to represent the landscape scale, which ensured that we had a reasonably large sample of “wooded” landscapes for analysis.
We analyzed data first by comparing strength of response of species’ occurrence to 100-m scale tree cover in open landscapes and in wooded landscapes, using logistic regression. In the logistic models we were interested in the relative strength of one model over another, not in the absolute explanatory power of our models, which contained only one explanatory variable (amount of tree cover). We then graphed frequency of occurrence on a gradient of percentage tree cover, again in open landscapes and wooded landscapes, to assess whether patterns of occurrence differed between the two landscape contexts.
Because we were interested in differences between landscape conditions, rather than in examining particular threshold values or landscape scales, we used a threshold of 30% tree cover to distinguish wooded versus open landscapes (within a 400-m radius around bird observations). A higher threshold value was not used because the study area was largely open grassland, and relatively few landscapes had abundant tree cover within a 400-m radius. Thus “wooded” landscapes had at least 30% tree cover within 400 m (N = 562 transect segments, range = 30.1 to 75.4% tree cover, median = 38% tree cover). “Open” landscapes had less than 30% tree cover within 400 m (N = 2699 segments, range = 0 to 29.9% tree cover, median 5% tree cover). In all, 22 species with affinities for woodland habitat occurred at least 20 times in both open and wooded landscape groups (Table 1).
For each species, for each landscape condition, we evaluated the strength of response to 100-m-scale tree cover using logistic regression (Bayard and Elphick 2010, Desrochers et al. 2010), with site-scale percentage tree cover as the explanatory variable and presence/absence as the response variable. Preliminary analysis indicated that quadratic models produced results that were equivalent to or stronger than linear models for all species. Thus for each species we used the following model: Probability of occurrence = 1 - 1/(exp(β0 + β1X + β2X²)), where X is percentage tree cover within 100 m of a segment. Results are reported in terms of R²L, an analog of the usual multiple correlation coefficient (R²) appropriate for logistic regression (Menard 2000, Quinn and Keogh 2002). Our data set represented multiple years, so we tested for variation by year on each species’ responses to habitat composition. This analysis indicated that year effects and interactions between year and other variables were not significant, so for subsequent analysis we pooled data from the four years (Appendix 1, Methods). This analysis was done using JMP software (SAS Institute 2010). We did this analysis first for the entire data set and then for independent subsets.
Field data were nonindependent, adjacent observations gathered on belt transects, so to reduce dependence among observations we extracted 10 subset groups of transect segments by grouping every 10th segment from the complete data set. All segments in a group were thus separated by at least 900 m. We then analyzed each of the 10 subset groups separately. To assess whether there was a difference between open landscapes and wooded landscapes, we compared the mean responses (R²L values) of 10 open-landscape groups and 10 wooded-landscape groups. (Of 440 groups, 86 groups with fewer than 4 observations of a species were excluded from calculation of means).
It is worth noting that independence among samples was not a necessary condition of analysis: Previous studies have demonstrated the usefulness of nonindependent data when conclusions do not rest on estimates of significance in parametric tests, which underestimate variance in nonindependent data and thus overestimate the significance of results in hypothesis testing (Pan 2001, Diniz-Filho et al. 2003). Repeating tests on independent subsamples, however, does increase confidence that we were not repeatedly measuring or evaluating the same observations.
In addition to regression results, the shape of a species’ response to tree cover is useful in indicating levels of tree cover at which the species is most likely to occur. We used loess (locally weighted estimation and scatterplot smoothing; Cleveland and Devlin 1988, Cohen 1999) to define curves showing changes in the probability of occurrence (incidence) as site-scale tree cover increased. We created scatter plots separately for open landscapes and for wooded landscapes as follows: We sorted all segments by percentage tree cover within 100 m. We then grouped the sorted observations into even-sized groups, and for each group we calculated the observed frequency (probability) of occurrence of a species. Thus, by aggregating the segments, we created continuous data, representing the frequency of occurrence for a group, from binary presence/absence observations. For each group we also calculated the average percentage tree cover. We then plotted frequency of occurrence values against the average percentage tree cover. For the 2699 open-landscape segments, we used 44 groups of 60 observations and one group of 59; for the 562 wooded-landscape segments, we used 20 groups of 27 and one group of 22.
To visualize patterns in the incidence plots, we then used SAS PROC LOESS (SAS Institute 1999), with smoothing parameter of 0.5, which showed patterns while reducing noise. The resulting curves indicated patterns such as thresholds, peaks, and asymptotes in responses to amount of tree cover at the 100-m scale. A flat line would indicate no response to proximate tree cover. A curve rising to the right would indicate selection for abundant proximate tree cover. Peaks would indicate a tendency to occur most frequently at intermediate levels of tree cover at the 100-m scale (Cunningham and Johnson 2012). In plots comparing open and wooded landscapes, we adjusted Y-axes to the data range, to show the relative shape of the patterns.
For loess plots, we used the entire data set to show patterns of response. To test whether results were similar with the entire data set or with subsets (as in logistic regression analysis above), we tested the influence of nonindependence in our study by comparing incidence plots for the entire (nonindependent) data set to plots calculated for five separate subsamples of the data, in which all sites were separated by at least 400 m (see Appendix 1). For all species, subsamples produced similar patterns but, because they were smaller samples, had more variability than did the entire data set. Loess plots with all observations were effectively an average of the different subsamples for a species. Because it is not possible to know which of the subsamples is most “correct” for a species, the most reliable pattern is that of the entire data set. Results are shown for all species in Appendix 1.
Regression models were significant (p < 0.001) for all 22 species in open landscapes but for only 9 of the 22 species in wooded landscapes (Table 1). In a comparison of independent subsets of the data, with 10 open-landscape groups and 10 wooded-landscape groups (Fig. 3), small sample sizes led to reduced differences between open and wooded landscapes, but the overall pattern was the same: 16 of 22 species had significantly stronger responses to proximate-scale tree cover in open landscapes, as indicated by nonoverlapping standard error intervals. Several species were strongly different between the two contexts, e.g., Yellow-bellied Sapsucker (Sphyrapicus varius), Eastern Wood-Pewee (Contopus virens), House Wren (Troglodytes aedon). Several edge or generalist species showed little or no difference, e.g., Mourning Dove (Zenaida macroura), Eastern Kingbird (Tyrannus tyrannus), American Robin (Turdus migratorius).
Patterns of responses in incidence plots also differed between open and wooded landscapes for most species (Figs. 4 and 5). For example, the probability of Mourning Dove occurrence increased with tree cover at the 100-m scale in open landscapes, but in wooded landscapes Mourning Doves were more likely to occur in moderately wooded areas and avoided abundant tree cover at the 100-m scale (as shown by the loess curve peaking in the middle range of tree cover). All species except the Eastern Kingbird had generally positive responses to increasing tree cover in open landscapes. In wooded landscapes, in contrast, most species shifted to a weaker or even negative pattern. Some species had a relatively flat response in wooded landscapes, e.g., House Wren, American Robin, Baltimore Oriole (Icterus galbula). Other species changed to patterns with an asymptote at intermediate levels of proximate tree cover, e.g., Yellow-bellied Sapsucker, Eastern Wood-Pewee. Still others shifted to declining patterns, e.g., Vesper Sparrow (Pooecetes gramineus), Lark Sparrow (Chondestes grammacus). A small group of species retained a clearly positive trend even in wooded landscapes; these were species with strongest interior-habitat affinities, Least Flycatcher (Empidonax minimus), Red-eyed Vireo (Vireo olivaceus), Ovenbird (Seiurus aurocapilla).
Only the Ovenbird had a more sharply positive response to proximate tree cover when in wooded landscapes: here the weaker response in open landscapes reflects the small number of observations in open landscapes. This species did not occur at all in sites with less than 20% tree cover at the proximate scale (Figs. 4 and 5). Three species had thresholds of occurrence in open landscapes, Eastern Wood-Pewee, Yellow-throated Vireo (Vireo flavifrons), and Red-eyed Vireo. In the wooded landscapes none of these species had thresholds of proximate-scale tree cover, and one (Eastern Wood-Pewee) tended to occupy moderately wooded sites when in a wooded landscape.
For the woodland species examined here, the results support our hypothesis 2, that birds show increased selectiveness and use a narrower range of proximate-scale tree cover in landscapes where tree cover is not abundant. In regression analysis, the strength of explanation was stronger in open landscapes than in wooded landscapes for most species; in incidence plots, species that favored abundant tree cover when observed in open landscapes were frequently nonselective or even avoided abundant tree cover when observed in relatively wooded landscapes. Previous work (Parker et al. 2005, Ribic et al. 2009a) has indicated similar patterns.
If a study examined the Eastern Wood-Pewee or White-breasted Nuthatch (Sitta carolinensis) only in open landscapes, both species could be described as strongly preferring abundant tree cover. A study examining the same species in a wooded landscape could describe them as indifferent to increasing tree cover or even avoiding heavily wooded habitat at the proximate scale (Fig. 4). Other species that frequent habitat edges, such as the Eastern Kingbird or Eastern Bluebird (Sialia sialis), might appear indifferent to increasing proximate tree cover when observed in open landscapes. These same species, studied in a wooded environment, might show an aversion to heavy tree cover. Still sharper contrasts might occur for the Vesper Sparrow and American Goldfinch (Spinus tristis), both of which responded positively to increasing tree cover in open landscapes but negatively in wooded landscapes (Fig. 5).
There were species that showed little difference among landscapes: The Ovenbird, for example, showed strong responses to increasing tree cover even in the wooded landscapes. For other species, similar responses in open and wooded landscapes may reflect the particular range of available landscapes in our study area: the Least Flycatcher, for example, is not an interior woodland species in all environments, but in this landscape, at this low range of landscape-scale tree cover, this species strongly preferred greater amounts of proximate tree cover. A comparison with more heavily wooded environments might yield more variation in results. Some edge species or generalist species also may have little difference in their responses in different environments, presumably owing to a wide tolerance of habitat conditions, for example, Cedar Waxwing (Bombycilla cedrorum). In addition, it is important to note that we used only tree cover as an explanatory variable, and many species select for shrubs, woodland edges, or other habitat features, e.g. Gray Catbird (Dumetella carolinensis), Yellow Warbler (Setophaga petechia). A similar approach to modeling other habitat types might produce responses with stronger patterns in loess plots.
A contrast in conclusions about the nature and strength of habitat selection in different study areas does not mean we cannot compare study areas or understand habitat responses; it means that comparisons should be explicit about context. General observations regarding habitat requirements should pay explicit attention to extent of suitable habitat in the landscape and region surrounding a study area. Studies of fragmentation and the effects of patch area, isolation, shape, or other metrics should account for, or even control for, landscape context in the study design. This conclusion should not be surprising, as it has long been clear that birds respond to landscapes at a range of scales (Wiens 1995, Lee et al. 2002), and that species occurrence is influenced by the nature of the matrix composition, as well as by a habitat area itself (Ricketts 2001, Haila 2002). Our results further indicate that it is insufficient to describe habitat responses without reference to habitat availability in the larger context.
The variation in shape of our loess plots also indicates that caution should be employed in widely used designations such as “interior” and “edge” species, because context can influence patterns of habitat selection. Several woodland-dependent species selected for abundant tree cover in an open landscape, showing preferences for interior habitat, but selected more edge-rich sites when the surrounding landscape was wooded. Examples include Yellow-bellied Sapsucker, Eastern Wood-Pewee, Blue Jay (Cyanocitta cristata), and White-breasted Nuthatch (Fig. 4). Previous studies have suggested that contrasting study areas could account for such variation in observed fragmentation sensitivity (see, for example, Chan and Ranganathan 2005, Vetter et al. 2013). It may seem contradictory that species would prefer trees at the landscape scale but avoid them at the proximate scale, but some of these species may use both edge and interior woodland features: for example, they might benefit from both the better cover of dense woodlands and greater invertebrate prey density at open edges.
This study designated landscape scale at a small radius of 400 m, and it distinguished open and wooded landscapes at a relatively low threshold of 30% tree cover within that 400-m radius. We used these values because our landscape was largely open. It was beyond the scope of this study to identify the scales and percentage tree cover at which contrasts might emerge or diminish for different species, but we do know from this study that responses are not always the same in different landscape contexts. Repeating this analysis in other study areas could help identify further the influence of scale and of thresholds in tree cover on results.
Similarly, there is a possibility that differences in tree species, height, density, and other aspects of tree cover could influence bird species distributions. Clearly bird species respond to many aspects of habitat composition and configuration at a range of scales. In particular, riparian forests were taller and more dense, with more basswood and cottonwood and fewer oaks than in the drier savanna landscapes. These contrasts are difficult to control for in natural environments. However, these contrasts do not diminish the importance of our findings: Suppose, for example, an open landscape that has more oaks and a wooded landscape with more basswood. In general, Red-eyed Vireos, which occupy both basswood and oak forests, were more likely to occur in densely wooded locations within the open landscapes. An Eastern Wood-Pewee, in contrast, which also occupies both types of tree species, was more likely to occur at intermediate levels of tree cover when in the wooded landscape. Is this because Eastern Wood-Pewees prefer oaks to basswood, while Red-eyed Vireos prefer basswood to oaks? Those contrasts were not clearly evident in this study, although detailed analysis of responses to tree species is beyond the scope of this paper. The fact remains that despite variations in habitat composition, the amount of tree cover had an effect, both in strength of explaining species occurrence and in the shape of responses as tree cover in the landscape increased.
For purposes of species conservation, a lesson to be taken from these results is that when suitable habitat is not readily available at the landscape scale, then birds can be increasingly sensitive in habitat requirements. Increased sensitivity to proximate habitat conditions could exacerbate the challenge of species conservation as general habitat availability declines: For species that strongly prefer abundant tree cover, or those with large home ranges, fragmentation in the larger landscape could make species less likely to occupy a local conservation area. Conversely, small conservation areas may become more useful or effective if regional processes and incentives lead to broad-scale habitat regeneration (Renfrew and Ribic 2008). In either case, a piecemeal approach to conservation is likely to be less effective than more regional strategies that address the larger processes of habitat loss.
We appreciate the support of D. Svingen, B. Stotts, E. Euliss, and T. Finkle. This work was supported by funding and assistance from the U.S. Forest Service, Dakota Prairie Grasslands, USGS Northern Prairie Wildlife Research Center, and Vassar College.
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