Effects of stop-level habitat change on Cerulean Warbler detections along Breeding Bird Survey routes in the central Appalachians.
The Ohio Hills and Cumberland Plateau physiographic areas of the Appalachian Mountain Bird Conservation Region (BCR 28; http://www.nabci-us.org/map.html) are the core breeding range for Cerulean Warblers (Hamel 2000b, Sauer et al. 2008). Mesic upland forests, particularly ridgetops, are important habitat within the core range (Rosenberg et al. 2000, Weakland and Wood 2005). Cerulean Warblers require large tracts of mature forests with tall deciduous trees to sustain viable breeding populations (Oliarnyk 1996, Hamel 2000a).
Habitat loss and fragmentation from land use changes are thought to be major factors contributing to declining populations in the core breeding range (Hamel et al. 2004, Buehler et al. 2008). Donovan and Flather (2002) identified the effects of land use changes on Cerulean Warbler populations in the Appalachian Region as high priority for research. Recent research has begun to assess the effects of large-scale habitat change and fragmentation on Cerulean Warbler populations in the region (Weakland and Wood 2005; Wood et al. 2005, 2006). Analysis of the relationship of changes in Cerulean Warbler counts to habitats along BBS routes in the core breeding range may provide insight into the role habitat change has in long-term population declines.
The BBS, established in 1966 to monitor breeding bird populations, is the primary data source for population status of landbirds across North America (O'Connor et al. 2000). Each 40-km BBS route consists of 50 point-count stops, 0.8 km apart along secondary roads. Routes are surveyed once each year by volunteer observers, who identify and count all birds seen within 400 m or heard within a 3-min period at each stop (Robbins et al. 1986). Routes are distributed randomly and are assumed to reflect representative habitats (Donovan and Flather 2002). BBS counts used in analysis of population trends are summed across all 50 stops; they represent a composite of the habitats encountered and stop-specific changes in habitat are overshadowed (Sauer 1999). A more in-depth approach to examine how habitat changes affect Cerulean Warbler population trends on BBS routes is to analyze data at each stop instead of across the entire route. Small scale habitat characteristics, including slope position, aspect, and microhabitat features such as canopy gaps, can affect Cerulean Warbler abundance (Weakland and Wood 2005, Perkins 2006); these features are lost when habitat is analyzed at the route level.
We examined the association between change in the Cerulean Warbler population and habitat at stops along BBS routes in the core breeding range. We (1) analyzed the effects of land cover and forest fragmentation changes, measured from aerial photographs, on Cerulean Warbler populations over three time periods at stops on a subsample of BBS routes to examine long-term changes; and (2) quantified land cover and forest fragmentation metrics at BBS stops over two time periods using National Land Cover Data (NLCD) to examine effects of more recent habitat changes on Cerulean Warbler populations along a broader sample of BBS routes.
Study Area.--We used survey data from BBS routes within the West Virginia, Kentucky, and Ohio portions of BCR 28 (Fig. 1). The study area is within NLCD mapping zones 47, 53, 61, and 62 (Homer et al. 2004). The Ohio Hills, Northern Cumberland Plateau, and Mid Atlantic Ridge and Valley physiographic regions (www.partnersinflight. org/bcps/pifplans.htm) of BCR 28 comprise most of the study area. The Ohio Hills (~8 million ha) is characterized by dissected plateaus ranging from 150 to 450 m in elevation (Rosenberg 2000). The Northern Cumberland Plateau (~5.5 million ha) is a rolling hills tableland ranging from 300 to 580 m in elevation (Demarest 2003). The Mid Atlantic Ridge and Valley (~5 million ha) is dominated by long mountainous ridges and intervening valleys ranging from 100 to 1,100 m in elevation (Rosenberg 1999). Dominant land cover of each physiographic area is mixed mesophytic forests consisting primarily of oaks (Quercus spp.) and hickories (Carya spp.).
Data Collection.--We used data from BBS routes within the study area that met three criteria. (1) Selected routes had at least one stop with detections of Cerulean Warblers within at least one time period. Our study examined how change in land cover and habitat may have affected detections of Cerulean Warblers, and routes that did not have a single detection lacked this information. (2) We included only BBS routes with stop-level Global Positioning System (GPS) coordinates collected in recent years by route observers so stops could be mapped as accurately as possible. Route observers at times adjust stop locations to safer or quieter stopping points; stop locations mapped by third parties may not be accurate. Stops were not included if the route path was changed between time periods. (3) We only used BBS routes that were surveyed at least three times within the 5 years centered on each year of available land cover data. Survey data over multiple years better identify stops at which Cerulean Warblers actually were present but were missed in some years. Birds can be missed in a 3min counting period and, if not detected, cannot be assumed to be absent. The 3-5 year period also may provide a more accurate measure of the response of Cerulean Warblers to habitat changes than data from only 1 year, which would be more of a snapshot in time. Using BBS data from years that bracket the land cover data ensured that land cover was representative of conditions when the routes were surveyed.
Land cover data can be measured from several remotely-sensed data sources; we used aerial photographs and the NLCD in our study. Aerial photographs have been taken occasionally within the study area over the lifetime of the BBS and are most suited to investigate long-term changes in land cover along BBS routes. However, aerial photographs are spatially limited, do not provide complete coverage of many BBS routes, and hand-digitizing land cover from them is time-consuming. The NLCD allows land cover to be quickly assessed for a broader extent of BBS routes, but was limited to 1992 and 2001 at the time of our study. Thus, we used aerial photographs to examine long-term land cover changes at a subset of BBS stops and NLCD data to quantify changes in land cover over a shorter time period for BBS stops along 28 routes.
Aerial photograph data were separated into three periods: 1967/1971 (early), 1982/1985 (middle), and 2000/2003 (late) to correspond with years that aerial photographs were available. We used aerial photographs taken during leaf-off (Oct to early May) when it is possible to distinguish coniferous from deciduous trees. Six of the 88 BBS routes in the study area (5 routes in West Virginia, 1 in Ohio) had aerial photographs available for appropriate years and met our three criteria for inclusion. Aerial photographs were available for 240 stops on all six routes for the middle to late time period but for only 68 stops on two routes for the early to middle time period (Table 1). Photographs from all time periods were rectified and digitized in ArcMap, Version 9.2 (ESRI, Redlands, CA, USA). Aerial photographs from different years or sources had different resolutions, and we digitized land cover at the minimum scale available (1:60,000). We digitized land cover into four types: a combined deciduous/ mixed forest type, coniferous forest, developed, and agriculture. We distinguished between deciduous/mixed and coniferous forests because Cerulean Warblers are not known to use coniferous forests (Hamel 2000a). The agriculture land cover collapsed all remaining types not included by the three other land cover types and was dominated by agricultural land.
[FIGURE 1 OMITTED]
Twenty-eight BBS routes with 1,375 stops (Table 1) met criteria to be included for comparison of the 1992 and 2001 NLCD data. Development is an important land cover class that is changing over time along BBS routes, but we were not able to use this class for the NLCD data sets. Small, secondary roads were not classified in the 1992 NLCD, but in 2001 they were classified as the developed type (Vogelmann et al. 2001, Homer et al. 2004). Thus, differences in developed area around a BBS stop could be the result of different classification methods rather than a change in land cover; thus, we combined developed and agriculture into a non-forest land cover class. Finer distinctions between specific land cover types are probably unnecessary and may not be valid (Thogmartin et al. 2004, Wickham et al. 2007) at a broad scale in assessing amount of potential Cerulean Warbler habitat.
Land cover was quantified within a 300-m buffer around each stop from aerial photographs and the NLCD. Cerulean Warblers rarely are detected beyond 100 m on point counts (Bosworth 2003) and their territories average 0.3-1.0 ha in size (Oliarnyk and Robertson 1996, Barg et al. 2005, Perkins 2006); thus, the 300-m (28.3 ha) buffer is sufficiently large to accommodate territories of several Cerulean Warblers. We calculated area of each land cover polygon, and summed area for each land cover type per BBS stop. The NLCD was reclassified to the three land cover types and the area was tabulated for each type around each stop. We used the percentage of each land cover type for analyses because the raster grid cells of the NLCD do not create a perfect circle around each of the route stops and, as a result, the total area around each buffered stop was ~28.3 ha.
We also calculated change over time in several forest fragmentation metrics for the aerial photograph and NLCD data sets. Cerulean Warblers prefer large tracts of unfragmented deciduous forests (Hamel 2000a, Weakland and Wood 2005), and we included maximum size of deciduous/mixed forest patch (ha), core area of deciduous/mixed forest (%), and edge density (m/ ha). Maximum size forest patch was the size of the largest deciduous/mixed forest polygon within the 300-m buffer around the stop. Core forest area was the amount of deciduous/mixed forest >60 m from an edge converted to percentage of core forest in each buffer. Edge effects are known to occur within 50 m of a forest edge (Paton 1994) and other studies have used 50 m in analyses (Hazier et al. 2006, Wood et al. 2006). Edge density was the amount of linear edge relative to the total land area at each buffered stop (McGarigal et al. 2002). We used the four land cover classes for the aerial photograph data set and weighted a coniferous forest edge as zero and a developed or agriculture edge as one. Forest-nonforest edge density for the NLCD data set was the amount of linear edge between deciduous/ mixed forest and non-forest patches; forest-forest edge density was the amount of linear edge created by a road splitting deciduous/mixed forest patches. We calculated forest-forest edge density because Cerulean Warblers may use gaps in forest created by roads (Weakland and Wood 2005, Perkins 2006). Roads were not included in the 1992 NLCD classification scheme (Vogelmann et al. 2001, Homer et al. 2004) and the 30-m cell size of the NLCD may not account for smaller roads in the 1992 and 2001 NLCD. Thus, we incorporated a general roads layer into both NLCD data sets, including roads as small as jeep trails from the U.S. Detailed Streets data set (http://www.esri. com/metadata/esriprof80.dtd) following McElhone (2009).
Count data for Cerulean Warblers at stops along the selected BBS routes were obtained from BBS staff (USGS, Patuxent Wildlife Research Center, Laurel, MD, USA). Stop-level count data after 1996 were downloaded from www.pwrc. usgs.gov/bbsapps/index.cfm. We extracted earlier data from the BBS field sheets. Cerulean Warbler detections were averaged within the 3-5 year time bracket surrounding each aerial photograph or NLCD year.
Statistical Analyses.--Statistical analyses were conducted using SAS (SAS Institute Inc. 2004) with [alpha] = 0.10. Univariate analyses indicated variables were not normally distributed, and we transformed them using the most appropriate method to achieve normality. We used arcsine square root transformation on percent of each land cover type and percent forest core area, and a logarithmic transformation for maximum forest patch, edge density, forest-forest edge density, and forest-nonforest edge density. A square root transformation was applied to average and maximum Cerulean Warbler counts because of their poisson distribution (Zar 1996).
We examined long-term changes in habitat over the entire BBS survey period by comparing the four land cover classes (deciduous/mixed forest, coniferous forest, developed, agriculture) and three fragmentation metrics (max size forest patch, forest core area, edge density) between the early and middle time periods (68 stops on 2 routes) and between the middle and late time periods (240 stops on 6 routes) with ANOVA (Ritchie et al. 1998). Each ANOVA model included route, period, and stop within route.
We compared Cerulean Warbler detections between the early and middle time periods, and between the middle and late time periods using ANCOVA (Welsh and Ollivier 1998) with variables: route, period, and stop within route. Covariates were percentage of each land cover in each time period. Stop within route was the error term in the ANOVA and ANCOVA models for testing differences between time periods. We repeated comparisons between the middle and late time periods using only stops that had a Cerulean Warbler detected during at least one period (i.e., a presence-only analysis).
We examined recent habitat changes by comparing three land cover classes (deciduous/ mixed forest, coniferous forest, non-forest) and four fragmentation metrics (max size forest patch, core forest, forest-forest edge density, forest-nonforest edge density) between 1992 and 2001, and NLCD data for 1,375 stops on 28 BBS routes with ANOVA. We also compared land cover and fragmentation metrics between the 1992 and 2001 NLCD data, using only those stops where at least one Cerulean Warbler was detected (344 stops from 28 routes).
We compared mean and maximum Cerulean Warbler detections between time periods for all stops and for those stops at which the species was detected using ANCOVA with the variables: route, period, and stop within route. Covariates were percentage of each land cover in each time period. The analysis of all stops along a route allowed us to examine habitat changes across landscapes where Cerulean Warblers were known to occur. Analysis of stops that had at least one Cerulean Warbler detected allowed us to examine local conditions where Cerulean Warblers actually were detected.
Aerial Photograph Analysis for 1967/1971 vs. 1982.--Deciduous/mixed forest cover increased from the early to middle time period at 68 stops on two BBS routes (Table 2), whereas agriculture land cover decreased. The amount of coniferous forest and developed land cover did not change, and none of the fragmentation metrics differed between the early and middle time periods. Average and maximum Cerulean Warbler detections per stop decreased (Table 2) and Cerulean Warblers were detected at fewer stops (15% in the early period vs. 3% in the middle period).
Aerial Photograph Analysis for 1982/1985 vs. 2000/2003.--Amount of developed land cover increased and agriculture decreased around 240 stops on six BBS routes between the middle and late time period (Table 3). Core forest increased, but other forest metrics (forest land covers, max size forest patch, and edge density) did not change (Table 3). Average number of Cerulean Warbler detections per stop decreased, but the maximum number counted did not change between these two time periods and percent stops with detections was similar (27% stops in the middle period and 30% in the late period). Cerulean Warblers were detected at 76 of the 240 stops during either the middle (65 Cerulean Warblers detected) or late time period (71 Cerulean Warblers detected); 44 stops had detections in both time periods (18% of stops).
Developed land cover increased and agriculture land cover decreased (Table 3) from the middle to late period at 76 stops where Cerulean Warblers were detected, although both land covers were less abundant than for all stops. We found no change in deciduous/mixed or coniferous forest and none of the fragmentation metrics differed between the middle and late time periods. Average number of Cerulean Warbler detections per stop decreased more markedly than in the all stops analysis and the maximum number detected approached a significant decline (P = 0.10).
NLCD Analysis for 1992 vs. 2001.--The deciduous/mixed forest and coniferous forest land cover types decreased, whereas the non-forest type increased at 1,375 stops on 28 routes (Table 4). Forest patch size increased, whereas core forest, forest-forest edge density, and forest-nonforest edge density decreased. Cerulean Warblers were detected at 14% of stops in 1990-1994 and 17% in 1999-2003. Average and maximum Cerulean Warbler detections per stop were not different, although counts increased slightly and approached significance (P = 0.11).
Cerulean Warblers were detected at 344 of 1,375 stops in both time periods. Coniferous and deciduous/mixed forests decreased at these stops (Table 4), while non-forest land cover increased from 15 to 25%. Maximum size of forest patch and forest-nonforest edge density increased, forest-forest edge density decreased, and amount of core forest did not change. The mean and maximum number of Cerulean Warblers detected increased.
Historic Habitat and Cerulean Warbler Changes: 1967/1971 vs. 1982.--Several land covers changed at 68 stops along the two BBS routes for which we analyzed aerial photographs from the early to middle time periods. The increase in deciduous/ mixed forest and decline in agriculture land cover probably was the result of agricultural field abandonment and forest succession as suggested by others (Keller and Scallan 1999, Betts et al. 2007). Similarly, Bart et al. (1995) found an increase in forest cover from 1963 to 1988 within 280 m of roads in western Ohio. Many agricultural fields may have been abandoned during the early time period, but the successional woody vegetation was not distinguishable from active agricultural fields on aerial photographs. Sufficient time had elapsed by the middle time period for abandoned agricultural fields to develop into early stage forests that could be differentiated on aerial photographs, but these young forests were likely less suitable Cerulean Warbler habitat. Young forests consist mainly of shrubs and pole-sized trees, lacking the large mature trees (Hamel 2000b) and horizontal and vertical structural diversity that Cerulean Warblers prefer (Weakland and Wood 2005, Perkins 2006), although young forests are not completely avoided by Cerulean Warblers (Wood et al. 2005).
Cerulean Warbler abundance decreased during this same time period and their distribution became more restricted, changing from 15 to 3% of BBS stops. Regional BBS analyses (Sauer et al. 2008) found Cerulean Warblers declined from 1967 to 1982 in two of the three physiographic regions that comprised our study area (-4.8% in Ohio Hills, -5.2% in Cumberland Plateau). The lack of a positive response to increased young forest cover in breeding areas suggests events or conditions during migration or in wintering areas may contribute to population declines.
Recent Habitat and Cerulean Warbler Changes: 1982/1985 vs. 2000/2003.--The agriculture land cover lost from the middle to late periods was replaced by developed land cover for all stops and the set of stops at which Cerulean Warblers were detected. Agricultural fields appear to have been converted to development instead of being abandoned during this time period.
Core forest increased between the middle and late time periods with the all-stops data. Agricultural fields abandoned in the early time period and which were early stage deciduous/mixed forests by the middle time period, may have developed a more contiguous canopy cover by the late time period perhaps contributing to increases in core forest. Core forest is considered an important habitat characteristic for Cerulean Warblers (Oliarnyk 1996, Hamel 2000a); however, detections again decreased, but less than in the early versus middle time period. Regional BBS analyses (Sauer et al. 2008) similarly found a less steep decline in 1982-2003 in the Ohio Hills (-2.2) and the Cumberland Plateau (-2.1) physiographic regions.
Cerulean Warblers were detected at 27% of all stops in the middle and 30% in the late time period; however, more were detected per stop in the middle than the late time period (Table 3). The decrease in abundance but not distribution, the large decrease in abundance at stops with Cerulean Warbler presence, but lack of change in broad-scale fragmentation metrics considered important to Cerulean Warblers suggests there may be other small-scale factors (e.g., canopy gaps/heterogeneity) influencing population trends of Cerulean Warblers in breeding areas along the BBS routes examined.
Short-term Habitat and Cerulean Warbler Changes: 1992 vs. 2001.--Several NLCD land cover variables believed to be important to Cerulean Warblers declined from 1992 to 2001 at BBS stops. Deciduous/mixed forest declined and was replaced by non-forest land cover, primarily development, in both the all stops and presence-only analyses (Table 4).
The amount of deciduous/mixed forest and core forest was greater at stops where Cerulean Warblers occurred than at all stops. Cerulean Warbler abundance increased at presence-only stops and did not change at all stops despite the decline in deciduous/mixed forest. This, in concert with the increase in edge density, suggests microhabitat features within large, continuous deciduous/mixed forests also are important and provides support that, at a local scale, Cerulean Warblers are able to tolerate some edge habitat which may increase structural diversity in the canopy (Weakland and Wood 2005). The smaller amount of non-forest land cover at presence-only stops supports Cerulean Warbler avoidance of large-scale habitat disturbance (Wood et al. 2006).
Cerulean Warbler abundance may have increased at presence-only stops despite the decrease in deciduous/mixed forest and forest-forest edge density because their density increased in the remaining suitable habitat. A key habitat factor for Cerulean Warblers includes interior, unfragmented forests (Ofiarnyk 1996, Hamel 2000a); however, there was little interior forest (9% for all stops and 13% for presence-only stops) in 2001 within 300 m of BBS stops. The maximum size forest patches within 300 m of a BBS stop increased for presence-only stops and all stops in our study area (Table 4), and were sufficiently large to contain several Cerulean Warbler territories (range = 0.21 ha [Roth 2004] to 1.04 ha [Oliarnyk and Robertson 1996]).
Cerulean Warbler detections did not change between 1992 and 2001 across all 1,375 BBS stops examined (Table 4); they were detected at 14% of all stops in 1992 and 17% in 2001. Sauer et al. (2008) reported declines of 4.1-6.8% during this time period for the three physiographic regions intersecting our study area. This disparity in trends may relate to which routes were included. We used data from 28 BBS routes because of lack of stop-level GPS coordinates and routes that were run inconsistently, whereas Sauer et al's. (2008) trend analysis was based on 75 routes. Additionally, we focused on the core breeding range of Cerulean Warblers (Fig. 1), whereas Sauer et al. (2008) included more BBS routes near the periphery of the range.
Trends in Cerulean Warbler populations did not relate well to forest metrics, and loss of suitable forested habitat is still considered a major cause for Cerulean Warbler population declines in the core breeding range (Hamel et al. 2004). Our study illustrates the potential importance of microhabitat features such as small, isolated canopy gaps (Perkins 2006) that we were unable to detect with our coarse land cover analysis. Broad habitat features such as deciduous/mixed forest and forest-forest edge density decreased over time, while Cerulean Warbler detections increased, but none of these habitat variables account for canopy gaps or vegetation structure.
We thank the U.S Fish and Wildlife Service, West Virginia Division of Natural Resources, National Fish and Wildlife Foundation, and the National Council for Air and Stream Improvement for financial support. We especially thank Keith Pardieck and Dave Ziolkowski, national coordinators of the Breeding Bird Survey, for access to and assistance with BBS data. We thank the many BBS observers whose participation was instrumental to the success of our study. Matthew Shumar, Molly McDermottt, Jackie Strager, Brandon Miller, and Sandy Taylor helped with data collection and organization. Fekedulegn Desta provided statistical support and Michael Strager provided logistical and technical support. The West Virginia Division of Natural Resources and Kentucky Department of Fish and Wildlife Resources provided off-road point-count data. Michael Strager, Keith Pardieck, and Dan McAuley provided valuable comments on this manuscript. Mention of trade names or commercial products does not imply endorsement by the U.S. Government.
BARG, J. J., J. JONES, AND R. J. ROBERTSON. 2005. Describing breeding territories of migratory passerines: suggestions for sampling, choice of estimator, and delineation of core areas. Journal of Animal Ecology 74:139-149.
BART, J., M. HOFSCHEIN, AND B. G. PETERJOHN. 1995. Reliability of the Breeding Bird Survey: effects of restricting surveys to roads. Auk 112:758-761. BETTS, M. G., D. MITCHELL, A. W. DIAMOND, AND J. BETY. 2007. Uneven rates of landscape change as a source of bias in roadside wildlife surveys. Journal of Wildlife Management 71:2266-2273.
BOSWORTH, S. B. 2003. Cerulean Warbler relative abundance and frequency of occurrence relative to large-scale edge. Thesis, West Virginia University, Morgantown, USA.
BUEHLER, D. A., J. J. GIOCOMO, J. JONES, P. B. HAMEL, C. M. ROGERS, T. A. BEACHY, D. W. VARBLE, C. P. NICHOLSON, K. L. ROTH, J. BARG, R. J. ROBERTSON, J. R. ROBB, AND K. ISLAM. 2008. Cerulean Warbler reproduction, survival, and models of population decline. Journal of Wildlife Management 72:646-653.
DEMAREST, D. 2003. Northern Cumberland Plateau, Physiographic Area 21-Executive Summary. Partners in Flight. Cornell Laboratory of Ornithology, Ithaca, New York, USA. www.partnersinflight.org/bcps/ pl_21sum.htm
DONOVAN, T. M. AND C. H. RATHER. 2002. Relationships among North American songbird trends, habitat fragmentation, AND landscape occupancy. Ecological Applications 12:364-374.
HAMEL, P. B. 2000a. Cerulean Warbler (Dendroica cerulea). The birds of North America. Number 511. HAMEL, P. B. 2000b. Cerulean Warbler status assessment. USDI, Fish and Wildlife Service, Washington, D.C., USA. www.fws.gov/Midwest/eco_serv/soc/birds/ cerw/cewa_sa.html
HAMEL, P. B., D. K. DAWSON, AND P. D. KEYSER. 2004. How we can learn more about the Cerulean Warbler (Dendroica cerulea). Auk 121:7-14.
HAZLER, K. R., A. J. AMACHER, R. A. LANCIA, AND J. A. GERWIN. 2006. Factors influencing Acadian Flycatcher nesting success in an intensively managed forest landscape. Journal of Wildlife Management 70:532-538.
HOMER, C., C. HUANG, L. YANG, B. WYLIE, AND M. COAN. 2004. Development of a 2001 national land-cover database for the United States. Photogrammetric Engineering and Remote Sensing 70:829-840.
KELLER, C. M. E. AND J. T. SCALLAN. 1999. Potential roadside biases due to habitat changes along Breeding Bird Survey routes. Condor 101:50-57.
MCELHONE, P. M. 2009. Cerulean Warbler population and habitat changes along Breeding Bird Survey routes in the central Appalachians. Thesis. West Virginia University, Morgantown, USA.
MCGARIGAL, K., S. A. CUSHMAN, M. C. NEEL, AND E. ENE. 2002. FRAGSTATS: spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst, USA. www.umass.edu/landeco/ research/fragstats/fragstats.html
O'CONNOR, R. J., E. DUNN, D. H. JOHNSON, S. L. JONES, D. PETIT, K. POLLOCK, C. R. SMITH, J. L. TRAPP, AND E. WELLING. 2000. A programmatic review of the North American Breeding Bird Survey. Report of a peer review panel. USGS, Patuxent Wildlife Research Center. Laurel. Maryland, USA. www.pwrc.usgs.gov/ BBS/bbsreview/bbsfinal.pdf
OLIARNYK, C. J. 1996. Habitat selection and reproductive success in a population of Cerulean Warblers in southeastern Ontario. Thesis. Queen's University, Kingston, Ontario, Canada.
OLIARNYK, C. J. AND R. J. ROBERTSON. 1996. Breeding behavior and reproductive success of Cerulean Warbiers in southeastern Ontario. Wilson Bulletin 108:673-684.
PATON, P. W. C. 1994. The effect of edge on avian nest success: how strong is the evidence? Conservation Biology 8:17-26.
PERKINS, K. A. 2006. Cerulean Warbler selection of forest canopy gaps. Thesis. West Virginia University, Morgantown, USA.
RITCHIE, M. E., D. TILMAN, AND J. M. H. KNOPS. 1998. Herbivore effects on plants and nitrogen dynamics in oak savanna. Ecology 79:165-177.
ROBBINS, C. S., D. A. BYSTRAK, AND P. H. GEISSLER. 1986. The Breeding Bird Survey: its first fifteen years, 1965-1979. Resource Publication 157. USDI, Fish and Wildlife Service, Washington, D.C., USA.
ROSENBERG, K. 1999. Mid-Atlantic Ridge and Valley Physiographic Area 12-Executive Summary. Partners in Flight. Comell Laboratory of Ornithology, Ithaca, New York, USA. www.partnersinflight.org/bcps/ pl_12sum.htm
ROSENBERG, K. 2000. Ohio Hills, Physiographic Area 22-Executive Summary. Partners in Flight. Cornell Laboratory of Ornithology, Ithaca, New York, USA. www.partnersinflight.org/bcps/pl_22sum.htm
ROSENBERG, K. V., S. E. BARKER, AND R. W. ROHRBAUGH. 2000. An atlas of Cerulean Warbler populations. Final Report to the USDI, Fish and Wildlife Service: 1997-2000 breeding seasons. Cornell Laboratory of Ornithology, Ithaca, New York, USA.
ROTH, K. L. 2004 Cerulean Warbler breeding biology in Big Oaks National Wildlife Refuge, Madison, Indiana. Thesis. Ball State University, Muncie, Indiana, USA. SAS INSTITUTE INC. 2004. SAS software. Version 9.1. SAS Institute Inc., Cary, North Carolina, USA.
SAUER, J. R. 1999. Combining information from monitoring programs: complications associated with indices and geographic scale. Pages 124-126 in Strategies for bird conservation: the Partners in Flight planning process (R. Bonney, D. N. Pashley, R. J. Cooper, and L. Niles, Editors.). Cornell Laboratory of Ornithology, Ithaca, New York, USA.
SAUER, J. R., J. E. HINES, AND J. FALLON. 2008. The North American Breeding Bird Survey, results and analyses 1966-2007. Version 5.15.2008. USGS, Patuxent Wildlife Research Center, Laurel, Maryland, USA. www.mbr-pwrc.usgs.gov/bbs/bbs.html
THOGMARTIN, W. E., A. L. GALLANT, M. G. KNUTSON, T. J. Fox, AND M. J. SUAREZ. 2004. Commentary: a cautionary tale regarding use of the National Land Cover Data set 1992. Wildlife Society Bulletin 32:970-978.
U.S. DEPARTMENT OF INTERIOR (USDI). 2006. Endangered and threatened wildlife and plants; 12-month finding on a petition to list the Cerulean Warbler (Dendroica cerulea) as threatened with critical habitat. Federal Register 71:70717-70733.
VOGELMANN, J. E., S. M. HOWARD, AND C. R. LARSON. 2001. Completion of the 1990s National Land Cover Data set for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources. Photogrammetric Engineering and Remote Sensing 67:650-662.
WEAKLAND, C. A. AND P. B. WOOD. 2005. Cerulean Warbler (Dendroica cerulea) microhabitat and landscape-level habitat characteristics in southern West Virginia. Auk 122:497-508.
WELSH JR., H. H. AND L. U. OLLIVIER. 1998. Stream amphibians as indicators of ecosystem stress: a case study from California's redwoods. Ecological Applications 8:1118-1132.
WICKHAM, J. D., K. H. RIITTERS, T. G. WADE, M. COAN, AND C. HOMER. 2007. The effects of Appalachian mountaintop mining on interior forest. Landscape Ecology 22:179-187.
WOOD, P. B., J. P. DUGUAY, AND J. V. NICHOLS. 2005 Cerulean Warbler use of regenerated clearcut and two-age harvests. Wildlife Society Bulletin 33:851-858.
WOOD, P. B., S. BOSWORTH, AND R. DETTMERS. 2006. Cerulean Warbler abundance and occurrence relative to large-scale edge and habitat characteristics. Condor 108:154-165.
ZAR, J. H. 1996. Biostatistical analysis. Third Edition. Prentice Hall, Upper Saddle River, New Jersey, USA.
PATRICK M. McELHONE, (1,4) PETRA BOHALL WOOD, (2) AND DEANNA K. DAWSON (3)
(1) West Virginia Cooperative Fish and Wildlife Research Unit, Division of Forestry and Natural Resources, West Virginia University, Morgantown, WV 26506, USA.
(2) U.S. Geological Survey, West Virginia Cooperative Fish and Wildlife Research Unit, Division of Forestry and Natural Resources, West Virginia University, Morgantown, WV 26506, USA.
(3) U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, MD 20708, USA.
(4) Corresponding author; e-mail: firstname.lastname@example.org
TABLE 1. BBS routes used in aerial photograph and NLCD analyses. Aerial photographs were compared between the early (1967/1971) and middle (1982) time periods, and between the middle (1982/1985) and late (2000/2003) time periods. BBS Route State Name 84039001 KY Cumberland Gap 84039011 KY Monticello 84066043 OH Meadowbrook 84066049 OH Dresden 84066072 OH Ray 84066073 OH New Marshfield 84066074 OH Omega 84066087 OH Scioto Run 84066181 OH South Olive 84066901 OH Wayne National Forest 84066903 OH Zaleski State Forest 84066904 OH Tar Hollow 84090005 WV Bramwell 84090014 WV Spencer 84090016 WV Nicut 84090022 WV Canaan 84090024 WV Three Forks 84090026 WV Cedarville 84090028 WV Strange Creek 84090037 WV Moundsville 84090038 WV Monongah 84090039 WV McDonald 84090041 WV Bebee 84090044 WV Greer 84090051 WV Martinsburg 84090052 WV Clinton 84090053 WV RuthBelle 84090147 WV Bismark Aerial photograph analysis NLCD analysis BBS Route Early/Middle Middle/Late 1992-2001 84039001 X 84039011 X 84066043 X 84066049 X X X 84066072 X 84066073 X 84066074 X 84066087 X 84066181 X 84066901 X 84066903 X 84066904 X 84090005 X 84090014 X 84090016 X 84090022 X 84090024 X X 84090026 X X 84090028 X 84090037 X X 84090038 X X X 84090039 X 84090041 X X 84090044 X 84090051 X 84090052 X 84090053 X 84090147 X TABLE 2. Land cover and fragmentation metrics based on aerial photographs for the early (1967/1971) and middle (1982) time periods and Cerulean Warbler detections during 5 years surrounding each aerial photograph year for 68 stops on two BBS routes in West Virginia and Ohio (df = 66). Early Middle Category [bar.x] [+ or -] SE [bar.x] [+ or -] SE Landcover Deciduous/mixed forest 36.3 [+ or -] 3.1 49.3 [+ or -] 3.40 Coniferous forest 1.5 [+ or -] 0.6 2.3 [+ or -] 1.15 Developed 16.8 [+ or -] 2.4 14.7 [+ or -] 2.11 Agriculture 45.4 [+ or -] 3.1 33.9 [+ or -] 3.16 Fragmentation metrics Max forest patch (ha) 6.8 [+ or -] 0.6 8.8 [+ or -] 0.6 Core forest (%) 7.8 [+ or -] 1.2 10.6 [+ or -] 1.8 Edge density (m/ha) 138.6 [+ or -] 5.3 136.8 [+ or -] 4.6 Cerulean Warblers detections/stop Average 0.05 [+ or -] 0.02 0.01 [+ or -] 0.007 Maximum 0.16 [+ or -] 0.05 0.03 [+ or -] 0.020 Category F P Landcover Deciduous/mixed forest 3.82 0.06 Coniferous forest 0 0.98 Developed 0.32 0.57 Agriculture 5.30 0.02 Fragmentation metrics Max forest patch (ha) 1.10 0.30 Core forest (%) 0.68 0.41 Edge density (m/ha) 0 0.99 Cerulean Warblers detections/stop Average 5.25 0.03 Maximum 5.48 0.02 TABLE 3. Land cover and fragmentation metrics based on aerial photographs for the middle (1982/1985) and late (2000/2003) time periods and Cerulean Warbler detections during 5 years surrounding each aerial photograph year for all 240 stops (df = 234) and 76 (df = 70) presence-only stops on six BBS routes in West Virginia and Ohio. All stops Middle Late [bar.x] [bar.x] [+ or -] SE [+ or -] SE Landcover % Deciduous/mixed forest 58.9 [+ or -] 1.7 62.0 [+ or -] 1.6 Coniferous forest 1.0 [+ or -] 0.4 0.8 [+ or -] 0.3 Developed 9.7 [+ or -] 1.0 14.7 [+ or -] 1.4 Agriculture 30.5 [+ or -] 1.6 22.5 [+ or -] 1.4 Fragmentation metrics Max forest patch (ha) 13.4 [+ or -] 0.3 10.4 [+ or -] 0.3 Core forest (%) 14.8 [+ or -] 0.9 16.6 [+ or -] 0.8 Edge density (m/ha) 142.1 [+ or -] 3.0 144.8 [+ or -] 3.3 Cerulean Warblers detections/stop Average 0.09 [+ or -] 0.02 0.06 [+ or -] 0.01 Maximum 0.23 [+ or -] 0.03 0.21 [+ or -] 0.03 All stops F P Landcover % Deciduous/mixed forest 0.97 0.32 Coniferous forest 0.16 0.69 Developed 9.4 0.002 Agriculture 9.88 0.002 Fragmentation metrics Max forest patch (ha) 2.5 0.12 Core forest (%) 2.96 0.09 Edge density (m/ha) 0.08 0.77 Cerulean Warblers detections/stop Average 4.6 0.03 Maximum 1.07 0.30 Presence-only stops Middle Late [bar.x] [+ or -] SE [bar.x] [+ or -] SE Landcover % Deciduous/mixed forest 69.9 [+ or -] 2.5 74.4 [+ or -] 2.1 Coniferous forest 1.1 [+ or -] 0.6 0.7 [+ or -] 0.6 Developed 5.4 [+ or -] 1.2 8.6 [+ or -] 1.4 Agriculture 23.7 [+ or -] 2.2 16.3 [+ or -] 1.8 Fragmentation metrics Max forest patch (ha) 11.2 [+ or -] 0.1 12.0 [+ or -] 0.5 Core forest (%) 18.9 [+ or -] 1.4 20.9 [+ or -] 1.3 Edge density (m/ha) 139.9 [+ or -] 5.6 139.3 [+ or -] 6.1 Cerulean Warblers detections/stop Average 0.28 [+ or -] 0.04 0.19 [+ or -] 0.02 Maximum 0.71 [+ or -] 0.08 0.67 [+ or -] 0.07 Presence-only stops F P Landcover % Deciduous/mixed forest 0.73 0.39 Coniferous forest 0.92 0.34 Developed 5.08 0.03 Agriculture 4.28 0.04 Fragmentation metrics Max forest patch (ha) 1.33 0.25 Core forest (%) 0.92 0.34 Edge density (m/ha) 0.04 0.85 Cerulean Warblers detections/stop Average 7.77 0.007 Maximum 2.70 0.10 TABLE 4. Land cover and fragmentation metrics based on 1992 and 2001 NLCD and Cerulean Warbler detections for 1990-1994 and 1999-2003 for 1,375 stops (df = 1,346), and for 344 presence-only stops (df = 319) along 28 BBS routes in West Virginia, Ohio, and Kentucky (FF edge density = Forest-forest edge density, FNF edge density = Forest-nonforest edge density). All stops 1992 2001 [bar.x] [+ or -] SE [bar.x] [+ or -] SE Landcover (%) Deciduous/Mixed forest 64.3 [+ or -] 0.8 59.4 [+ or -] 0.7 Coniferous forest 3.3 [+ or -] 0.2 1.5 [+ or -] 0.1 Non-forest 32.4 [+ or -] 0.8 39.1 [+ or -] 0.7 Fragmentation metrics Max forest patch (ha) 7.6 [+ or -] 0.1 8.0 [+ or -] 0.1 Core forest (%) 9.7 [+ or -] 0.3 9.1 [+ or -] 0.3 FF edge density (m/ha) 20.5 [+ or -] 0.4 7.5 [+ or -] 0.2 FNF edge density (m/ha) 187.8 [+ or -] 2.8 172.9 [+ or -] 1.8 Cerulean Warbler detections/stop Average 0.08 [+ or -] 0.01 0.10 [+ or -] 0.01 Maximum 0.19 [+ or -] 0.01 0.30 [+ or -] 0.02 F P Landcover (%) Deciduous/Mixed forest 38.94 <0.001 Coniferous forest 161.09 <0.001 Non-forest 79.08 <0.001 Fragmentation metrics Max forest patch (ha) 7.2 0.007 Core forest (%) 10.99 0.001 FF edge density (m/ha) 423.04 <0.001 FNF edge density (m/ha) 11.34 0.001 Cerulean Warbler detections/stop Average 2.54 0.11 Maximum 1.81 0.18 Presence-only stops 1992 2001 [bar.x] [+ or -] SE [bar.x] [+ or -] SE Landcover (%) Deciduous/Mixed forest 81.6 [+ or -] 1.0 74.4 [+ or -] 0.8 Coniferous forest 3.8 [+ or -] 0.3 0.9 [+ or -] 0.2 Non-forest 14.6 [+ or -] 1.0 24.8 [+ or -] 0.8 Fragmentation metrics Max forest patch (ha) 9.0 [+ or -] 0.2 9.7 [+ or -] 0.2 Core forest (%) 13.4 [+ or -] 0.5 13.5 [+ or -] 0.6 FF edge density (m/ha) 25.8 [+ or -] 0.7 9.2 [+ or -] 0.4 FNF edge density (m/ha) 159.9 [+ or -] 5.7 179.1 [+ or -] 3.2 Cerulean Warbler detections/stop Average 0.31 [+ or -] 0.03 0.41 [+ or -] 0.03 Maximum 0.76 [+ or -] 0.05 0.99 [+ or -] 0.05 F P Landcover (%) Deciduous/Mixed forest 45.88 <0.001 Coniferous forest 125.09 <0.001 Non-forest 110.07 <0.001 Fragmentation metrics Max forest patch (ha) 3.49 0.063 Core forest (%) 0.15 0.698 FF edge density (m/ha) 255.08 <0.001 FNF edge density (m/ha) 54.24 <0.001 Cerulean Warbler detections/stop Average 5.45 0.03 Maximum 5.60 0.02
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|Author:||McElhone, Patrick M.; Wood, Petra Bohall; Dawson, Deanna K.|
|Publication:||The Wilson Journal of Ornithology|
|Date:||Dec 1, 2011|
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