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Using Field Data and GIS-Derived Variables to Model Occurrence of Williamson’s Sapsucker Nesting Habitat at Multiple Spatial Scales

Using Field Data and GIS-Derived Variables to Model Occurrence of Williamson’s Sapsucker Nesting... Smith AK, Ohanjanian IP, Martin K (2015) Using Field Data and GIS-Derived Variables to Model Williamson's sapsucker (Sphyrapicus thyroideus) is a migratory woodpecker that breeds in Occurrence of Williamson’s Sapsucker Nesting Habitat at Multiple Spatial Scales. PLoS ONE 10(7): mixed coniferous forests in western North America. In Canada, the range of this wood- e0130849. doi:10.1371/journal.pone.0130849 pecker is restricted to three small populations in southern British Columbia, precipitating a Editor: Chao-Dong Zhu, Institute of Zoology, CHINA national listing as ‘Endangered’ in 2005, and the need to characterize critical habitat for its survival and recovery. We compared habitat attributes between Williamson’s sapsucker Received: January 29, 2015 nest territories and random points without nests or detections of this sapsucker as part of a Accepted: May 25, 2015 resource selection analysis to identify the habitat features that best explain the probability of Published: July 15, 2015 nest occurrence in two separate geographic regions in British Columbia. We compared the Copyright: © 2015 Drever et al. This is an open relative explanatory power of generalized linear models based on field-derived and Geo- access article distributed under the terms of the graphic Information System (GIS) data within both a 225 m and 800 m radius of a nest or Creative Commons Attribution License, which permits random point. The model based on field-derived variables explained the most variation in unrestricted use, distribution, and reproduction in any medium, provided the original author and source are nest occurrence in the Okanagan-East Kootenay Region, whereas nest occurrence was credited. best explained by GIS information at the 800 m scale in the Western Region. Probability of Data Availability Statement: The Williamson nest occurrence was strongly tied to densities of potential nest trees, which included open Sapsucker habitat plot data are available through the forests with very large (diameter at breast height, DBH, 57.5 cm) western larch (Larix occi- Government of Canada data portal(http://open. dentalis) trees in the Okanagan-East Kootenay Region, and very large ponderosa pine canada.ca/en/open-data) with directions on how to (Pinus ponderosa) and large (DBH 17.5–57.5 cm) trembling aspen (Populus tremuloides) access in the Methods section. trees in the Western Region. Our results have the potential to guide identification and pro- Funding: This research was funded by Environment tection of critical habitat as required by the Species at Risk Act in Canada, and to better Canada (support to MCD) and The Natural Sciences and Engineering Research Council of Canada (to manage Williamson’s sapsucker habitat overall in North America. In particular, manage- JN). Funding was provided for various phases of the ment should focus on the maintenance and recruitment of very large western larch and pon- field project to LWG by Canadian Wildlife Service, B. derosa pine trees. C. Ministry of Environment, Forest Investment Account (B.C. Ministry of Forests), B.C. Timber Sales, Weyerhaeuser Ltd., Pope and Talbot Ltd. (now PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 1/18 Williamson's Sapsucker Nesting Habitat Selection Interfor), Tembec Industries Ltd. (now Canadian Introduction Forest Products Ltd.), and Tolko Industries Ltd. The Williamson’s sapsucker (Sphyrapicus thyroideus) is a migratory woodpecker that breeds in the funders had a limited role in discussions of study montane mixed coniferous forests of western North America. Its breeding distribution extends design, and data collection, but no role in the analysis, decision to publish, or preparation of the from British Columbia to Baja California, Mexico [1]. In Canada, Williamson’s sapsucker is manuscript, and does not alter our adherence to restricted to the southern interior of British Columbia where it occurs primarily in mid-eleva- PLOS ONE policies on sharing data and materials. tion mixed coniferous forests containing either western larch (Larix occidentalis) or ponderosa Competing Interests: This work was supported by pine (Pinus ponderosa), along with Douglas-fir (Pseudotsuga menziesii) and often containing a Weyerhaeuser Ltd., Pope and Talbot Ltd. (now deciduous component of trembling aspen (Populus tremuloides)[2], [3]. In 2005, it was classi- Interfor), Tembec Industries Ltd. (now Canadian fied as ‘Endangered’ by the Committee on the Status of Endangered Wildlife in Canada, based Forest Products Ltd.) and Tolko Industries Ltd., and primarily on low population numbers and projected rates of habitat loss, particularly for forests coauthor A. Kari Stuart-Smith’s affiliation with with mature western larch [4]. Consequently, identification and protection of Williamson’s Canadian Forest Products Ltd. does not alter the authors' adherence to PLOS ONE policies on sharing sapsucker critical habitat is required for its conservation in Canada. data and materials. Habitat suitability models can help identify critical habitat, defined in the Species at Risk Act as ‘habitat that is necessary for the survival or recovery of a listed wildlife species’ [5]. These models can provide evidence for habitat attributes selected and used to a greater extent than suggested by their availability, and can thus be used to identify habitat features particularly important for management of species at risk. Sousa [6] developed a Habitat Suitability Index (HSI) for Williamson’s sapsuckers breeding in western North America, which involved four variables: percentage tree canopy closure, percentage of area dominated by aspen, average diameter at breast height (DBH) of overstory aspen, and number of suitable soft snags (DBH >30.5 cm, or DBH >45.7 cm for ponderosa pine) within a 4 hectare (ha) area around the nest [6]. When Sousa’s model was applied in Arizona [7], the HSI correctly indicated Williamson’s sapsucker selected nest territories in snow-melt drainages over areas on ridge tops, but incor- rectly classified 63.6% of plots not used for nesting as optimal breeding habitat. Redefining var- iables and making the model region-specific may improve the degree of this misclassification. Nesting habitat attributes used and selected by Williamson’s sapsucker appear to vary geo- graphically. In Arizona, snag densities (i.e., number of dead trees) were the most important fac- tor influencing nest location in stands dominated by aspen [7]. In Oregon, the sapsucker preferred to nest in forest areas with <75% canopy cover and <34 m /ha basal area [8], with recommended estimates of 3.71 snags/ha with a DBH >30.5 cm to maximize a sapsucker pop- ulation [9]. In British Columbia, densities of 20 trees/ha with a DBH >57 cm and 60–150 trees/ha with a DBH >22 cm were recommended to maintain Williamson’s sapsucker breeding habitat [10]. This variation indicates the sapsucker has flexible nesting requirements and may be selecting (or avoiding) particular habitat features in different systems. Habitat selection by nesting Williamson’s sapsucker is likely influenced by both nesting and foraging requirements operating at multiple spatial scales from territories to the surrounding landscape. Crockett and Hadow [11] suggested nest trees in aspen were chosen based on prox- imity to foraging areas instead of nest tree features. Williamson’s sapsucker in Colorado had a minimum breeding territory size of 4 ha [12], while in British Columbia the minimum breed- ing territory was ~16 ha [10]. Territories must provide foraging opportunities for sapsuckers, including substantial quantities of sap and ants. Sap trees are typically coniferous trees (DBH 23–47 cm) within 100 m of the nest tree [13]. After eggs hatch, adults augment their diet with carpenter ants (Camponotus spp.) and other ants, which they also feed nestlings [14]. As car- penter ants depend on logs for nest substrate, the provision of downed logs within territories was considered an essential habitat attribute for nesting Williamson’s sapsucker [14]. While field measurements from the nest territory provide precise habitat use information at the terri- tory scale, models derived from landscape-level attributes can be used to extrapolate probabil- ity of occurrence outside sampled areas [15]. Therefore, a comparison of habitat selection PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 2/18 Williamson's Sapsucker Nesting Habitat Selection models using field- and GIS-derived variables may provide insights both into the scales at which Williamson’s sapsucker selects habitat attributes and into methods for characterizing habitat selection by the sapsucker. In this paper, our primary objective was to identify variables influencing Williamson’s sap- sucker nesting habitat selection at the spatial scale of breeding territories and, where possible, to identify associated threshold values. We performed a resource selection analysis [16], which compared habitat features at nest territories to areas where the sapsucker was confirmed to be absent (no detections of nests, sap feeding, or individuals), hereafter ‘random point’ or ‘no nest’. We used habitat measurements collected in the field and through a Geographic Informa- tion System (GIS) from the Vegetation Resource Inventory (VRI) databases maintained by the British Columbia Ministry of Forests, Lands and Natural Resource Operations. We compared the following sets of variables to assess which best explain nesting habitat selection: field mea- surements at 225 m radius and GIS information within a 225 m, 400 m, and 800 m radius of the nest. We then assessed the relative effectiveness of using available GIS databases to using field-derived data. Study Area This study encompassed the three disjunct geographic regions of the Williamson’s sapsucker breeding range in southern British Columbia [3]: the Western Region, west of the Okanagan valley principally near Merritt and Princeton; the Okanagan Region, east of Okanagan Lake and Okanagan Valley, south of Penticton and near the United States border; and the East Koo- tenay Region near Cranbrook (Fig 1). Forests cover much of the study area, dominated by Douglas-fir, western larch, ponderosa pine, lodgepole pine (Pinus contorta), trembling aspen, and hybrid spruce (Picea glauca x engelmannii). The forests of the Okanagan and East Koote- nay Regions have similar species compositions, dominated by Douglas-fir and western larch; the Western Region is dominated by ponderosa pine and aspen, and lacking western larch. We therefore analyzed the Okanagan and East Kootenay Regions as one group (the Okanagan-East Kootenay Region), separate from the Western Region. Fig 1. Map of the study area, indicating locations of Williamson’s sapsucker nests (black circles) and random points without detections (white circles) for three geographic regions where the species occurs in southern British Columbia, Canada. doi:10.1371/journal.pone.0130849.g001 PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 3/18 Williamson's Sapsucker Nesting Habitat Selection Methods Ethics statement The surveys were conducted primarily on public lands and private land with permission by landowner. No birds were handled and no samples collected. The Williamson’s Sapsucker Recovery Team (Canada) approved the data collection for avian nest and habitat monitoring. All surveys of nesting habitat were conducted to minimize disturbance on birds, usually after nesting activities were completed. All field activities were in agreement with federal and provin- cial legislation. Data used in this analysis can be freely accessed on the Government of Canada Open Data Portal (http://open.canada.ca/en/open-data) under the name ‘Williamson Sap- sucker habitat plot data’. Field methods We located Williamson's sapsucker nests during three field seasons (2006–2008) by using sys- tematic call- and drum-playback (CPB) surveys to detect the presence of sapsuckers and later searching these areas for nests, following the methods in Gyug et al. [2]. In 2008, we chose ran- dom points within the area of known occupancy, and conducted 25–60 minute CPB surveys. We included at least five well-spaced CPB points within a 225 m radius of each accessible point in May-June to confirm the presence or absence of Williamson’s sapsucker [3]. We characterized forest habitat around nests and random points with no detection of Wil- liamson’s sapsucker using circular vegetation plots in a 16 ha area (225 m radius). The 16 ha sampling area assumed a minimum territory size in which a breeding pair could meet its forag- ing requirements during the nesting period, based on mean nearest-neighbour nest distances of 450 m in the Okanagan-East Kootenay Region [2]. We used protocols from the Birds and Burns Network [17] for vegetation sampling of standing live and dead trees, with similar meth- ods used by Nielsen-Pincus and Garton [18]. At each nest tree or plot center, we sampled a 50 m variable-width transect in each of the four cardinal directions, with north and south tran- sects starting at the center and east and west transects starting 10 m from the center. In the remaining area around the nest or random point (60–225 m radius), we randomly sampled fif- teen additional 50 m transects, with the constraint that they lay within the boundaries of forest stands mapped by the provincial Vegetation Resource Inventory (VRI). A random point within the 60–225 m radius was selected as the centre point for each random transect, and the orienta- tion of each transect was determined by randomly selecting a bearing between 1 and 360 degrees. Widths of sampling swaths across the centre line of a 50 m transect were specific to the DBH classes of trees: 2 m width for small trees (DBH 7.5–22.4 cm), 6 m width for large live trees (DBH 22.5–57.4 cm), and 20 m width for both large dead standing trees (‘snags’, DBH 22.5 cm) that were 1.4 m in height and for very large live trees (DBH 57.5 cm). These widths resulted in plot sizes of 0.01, 0.03 and 0.1 ha respectively, which we used to convert counts to stem densities (number per ha). We recorded the following for each tree within a plot: species, DBH within size classes outlined in British Columbia silvicultural guidelines (17.5–22.4, 22.5–37.4, 37.5–52.4, 52.5–57.4, 57.5–67.4, 67.5 cm) [19], tree condition (alive or dead), the presence of nest cavities, and if the tree was known to contain a Williamson's sap- sucker cavity. We counted downed logs along all 50 m transects, and measured their large end diameter (LED), length within the plot, and diameter at the large and small end within the plot. We determined downed log volume (m /ha) using the plot method in 2006 and 2007 [20]. For most sites in 2007, we used the line intercept method [21] in addition to the plot method. PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 4/18 Williamson's Sapsucker Nesting Habitat Selection Estimates of downed log volume calculated using the plot method and line method were strongly correlated (Pearson’s r = 0.93, N = 30, P < 0.001), so we averaged the values in 2007 for plots where both methods were applied. In 2008, we used only the line intercept method. We counted stumps if their diameter at stump height (DSH) was 22.5 cm. Geographic Information System (GIS) data We extracted information from the Vegetation Resource Inventory (VRI) of the British Columbia Ministry of Forests and Range about vegetation cover within 225 m, 400 m and 800 m radius circles around the locations of nest trees and random points [22]. The VRI provided vegetation polygon characteristics derived from aerial photography calibrated with ground plots, from which we determined the total proportions of different habitat attributes. A prelim- inary evaluation revealed that the 400 m scale was strongly correlated with the 225 m scale (all Pearson’sr >0.85). Therefore, we only used the 225 m and 800 m radii in our analyses to cap- ture the nest territory (i.e., minimum territory size for the sapsucker to meet breeding and for- aging requirements) and an arbitrarily determined scale that was larger than this minimum territory scale. We defined habitat types by leading tree species, which included Douglas-fir, western larch, ponderosa pine, lodgepole pine, other coniferous species (total sum of hybrid spruce, subalpine fir, and other coniferous species observed), and broadleaf species, in each of four stand age classes based on available VRI information (0–39, 40–79, 80–119, and 120 yrs). In addition, we calculated the area consisting of cut blocks (<40 yrs old) in each polygon. We calculated two distance measures: the distance from a nest tree or random point to the nearest forest polygon aged 80 yrs (assigned a value of 0 if the point was in a forested polygon 80 yrs), and the distance from the nest or random point to the nearest edge of a forest cut block <40 yrs (assigned a value of 0 if the point was in a forest cut block <40 yrs). Data Analyses We compiled a total of 211 initial field and GIS variables representing habitat attributes that may influence Williamson’s sapsucker nest territory selection (S1 Table). To examine whether and how the probability of Williamson’s sapsucker nest occurrence varied with these forest habitat features, we constructed a series of Generalized Linear Models (GLMs) with ‘nest’ or ‘no nest’ as a binary response variable (where nest territories received a value of 1, and areas with no nests a value of 0). We used habitat variables as explanatory variables in each GLM, with a binomial error distribution and logit link function [23]. We divided the measured explanatory variables into the Okanagan-East Kootenay Region (n = 166) and Western Region (n = 69) datasets, and removed habitat attributes with no varia- tion. We screened these variables for correlations in a preliminary analysis, where we retained the more biologically relevant variable in a correlated pair (Pearson’sr >0.75) and excluded the other to avoid issues of multi-collinearity. This preliminary variable reduction resulted in 72 variables considered in model construction for the Okanagan-East Kootenay Region and 55 variables for the Western Region (Table 1). We first conducted an exploratory analysis that considered each habitat variable as an explanatory variable individually. We also fit a model including the quadratic term of each var- iable to allow for a non-linear relationship between the variable and the probability of nest occurrence. If this term was significant, we categorized the variable as ‘Quadratic’. Otherwise, we considered the variable as ‘Linear’ if the linear term was significant, or removed the variable from the final models if neither the quadratic nor linear terms were significant. We used a sig- nificance level of α = 0.1 to be conservative with our inclusion of variables, decrease the Type II error rate, and minimize the possibility of excluding potentially important habitat variables. PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 5/18 Williamson's Sapsucker Nesting Habitat Selection Table 1. Numbers of explanatory variables considered in generalized linear models for the probability of Williamson’s sapsucker nest occurrence in two regions of southern British Columbia. Variable type Okanagan-East Kootenay Region Western Region Field225 GIS225 GIS800 GIS Total Field225 GIS225 GIS800 GIS Total Quadratic 4(19) 5(22) 3(12) 0(0) 12(17) 3(17) 4(25) 2(11) 0(0) 9(16) Linear 6(29) 3(13) 3(12) 1(50) 13(18) 3(17) 4(25) 3(16) 0(0) 10(18) Non-sig 11(52) 15(65) 20(77) 1(50) 47(65) 12(67) 8(50) 14(74) 2(100) 36(65) Total 21 23 26 2 72 18 16 19 2 55 ‘Quadratic’ indicates habitat variables were significant in a model that included a quadratic term; ‘Linear’ indicates habitat variables were significant as a linear term in the model; ‘Non-sig’ indicates habitat variable was not significant. Subheadings under each Region indicate the type of habitat variables: field measurements (‘Field225’), geographic information system (GIS) data from a 225 m radius around the nest or plot center (‘GIS225’), and GIS data from an 800 m radius around the nest or plot center (‘GIS800’). The two ‘GIS’ variables separated from the 225 m and 800 m scales are included in the modelling process for both scales. Numbers in brackets indicate the percent relative to the column total. doi:10.1371/journal.pone.0130849.t001 We then tabulated the number of variables (per region) that were classified as Quadratic, Lin- ear, or not significant (Table 1). Following this initial evaluation, we assembled the significant variables in three separate models for each region: a ‘Field225’ model using field measurement data (10 variables in the Okanagan-East Kootenay Region, 6 variables in the Western Region), a ‘GIS225’ model using information within a 225 m radius of the nest or no nest plot center (9 variables in the Okana- gan-East Kootenay Region, 8 variables in the Western Region), and a ‘GIS800’ model using information within an 800 m radius of the nest or no nest plot center (7 variables in the Okana- gan-East Kootenay Region, 5 variables in the Western Region; Table 1). We removed variables that were not significant from these models to create the final six models from the remaining variables. We statistically compared the final models within regions using Akaike Information Crite- rion corrected for small sample size (AICc) [24]. Scale parameters indicated that overdisper- sion was not an issue in any of the final models. For comparisons both within and between regions, we calculated area under the curve (AUC) values, and Hosmer-Lemeshow test statis- tics. We also assessed the effect of specific habitat variables using a graphical approach for inference. Using the range of observed values for the habitat variables, we obtained predictions and associated standard errors from the best model in which that habitat variable was included. We then plotted these predictions and their 95% prediction intervals together with observed data to evaluate the probability of nest territory occurrence. We used statistical packages from the program R (version 3.0.2) for all analyses [25], [26]. Results We measured habitat attributes at a total of 138 Williamson’s sapsucker nest territories (97 in the Okanagan-East Kootenay, 41 in the Western Region) and 96 random points with no detec- tions of sapsucker presence or activity (68 in the Okanagan-East Kootenay, 28 in the Western Region). The initial exploratory analyses indicated that, when considered singly, both field- and GIS- derived habitat variables could potentially explain variation in the probability of occurrence of Williamson’s sapsucker nests. Of the habitat variables considered in preliminary analyses, 25 of 72 (35%) in the Okanagan-East Kootenay Region, and 19 of 55 (35%) in Western Region were categorized as ‘Quadratic’ or ‘Linear’ (Table 1). When comparing field- versus GIS- derived variables, more field-derived variables (47%) tended to be significant relative to GIS- PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 6/18 Williamson's Sapsucker Nesting Habitat Selection Table 2. Comparative statistics for models constructed of forest habitat variables in two regions of British Columbia. Region Variable type AICc AUC Concordance (%) Hosmer-Lemeshow test statistic(p-value) Okanagan-East Kootenay Region Field225 181.4 0.84 83.5 8.98(0.34) GIS225 209.5 0.73 72.3 9.11(0.33) GIS800 190.9 0.82 81.8 12.70(0.12) Western Region Field225 79.1 0.81 80.6 4.67(0.79) GIS225 80.1 0.80 79.8 10.63(0.22) GIS800 70.2 0.88 87.9 6.58(0.58) For the Field225, GIS225, and GIS800 models in each region, comparative statistics are given: Akaike’s Information Criterion corrected for small sample sizes (AICc), area under the curve (AUC) of receiver operating characteristic, percentage of concordant pairs (Concordance) and the Hosmer-Lemeshow test statistic with the associated p-value. doi:10.1371/journal.pone.0130849.t002 derived variables (25% at the 225 m and 36% at the 800 m scale) in the Okanagan-East Koote- nay Region. In the Western Region, both types of variables had similar proportions classified as significant (34% of field variables, and 44% or 24% of GIS variables). The remaining vari- ables were classified as not significant, and thus were not included in the secondary model construction. The secondary model selection using the reduced suites of variables yielded a set of three models per region that all adequately fit the data, as indicated by the Hosmer-Lemeshow tests that had p-values >0.1 (Table 2). Rankings of models composed of field versus GIS-derived variables varied between the two regions. In the Okanagan-East Kootenay Region, the Field225 model best explained nest territory selection by Williamson’s sapsucker, having the lowest AICc value and highest AUC value. In the Western Region, the GIS800 model best explained variation in nest territory selection, having the lowest AICc value and highest AUC value. The GIS225 model had the poorest fit in each region. All final models provided reliable discrimina- tion between nest territories and random points, with AUC values of 0.80 for all models in both regions, except the GIS225 model in the Okanagan-East Kootenay Region that had an AUC value of 0.72 (Table 2). Parameter values from each model indicated Williamson’s sapsucker nest territories were associated with habitat features related to suitable nest trees and foraging opportunities. In the Okanagan-East Kootenay Region, the Field225 model indicated Williamson’s sapsucker nest territories were positively associated with areas that had high densities of very large western larch trees (>8 trees/ha, DBH 57.5 cm), moderate densities of large hybrid spruce trees (50– 70 trees/ha, DBH 17.5–57.4 cm), very large snags (4–5 snags/ha, DBH 57.5 cm), and very large stumps (18–20 stumps/ha, 57.5 cm diameter; Fig 2). Additionally, probability of nest occurrence was negatively associated with areas that had higher densities of large Douglas-fir and lodgepole pine trees (>25 trees/ha, DBH 17.5–57.4 cm) and higher volumes of downed logs (>15 m /ha, LED 22.5 cm; Table 3, Fig 2). In the GIS225 model for the Okanagan-East Kootenay Region, probability of nest occur- rence was positively associated with areas that had a high percentage (>80%) of older age clas- ses of Douglas-fir trees (aged 80–119 yrs) in the canopy, and negatively associated with the percentage of canopy dominated by Douglas-fir trees in the younger age classes (< 80 yrs of age, Fig 3). In addition, this model indicated a non-linear relationship with the mean size of the openings around each nest or random point, where maximum probability of nest occurrence was in areas with openings averaging 6–7ha(Fig 3). The GIS800 model for the Okanagan-East Kootenay Region indicated Williamson’s sap- sucker selected nesting habitat relatively close to areas with a logging history in the last 40 yrs PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 7/18 Williamson's Sapsucker Nesting Habitat Selection Fig 2. Predicted relationships between probability of nest occurrence and six forest habitat variables included in the Field225 model (21 variables originally considered) for the Okanagan-East Kootenay Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g002 PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 8/18 Williamson's Sapsucker Nesting Habitat Selection Table 3. Parameter estimates for Field225, GIS225 and GIS800 models explaining Williamson’s sapsucker nest occurrence as a function of forest habitat variables in two regions of British Columbia. Region Variable Variable name Coefficient Standard z-score(p- type error value) Okanagan-East Kootenay Field225 Density of large live lodgepole pine trees (DBH 17.5– -0.018 0.009 -2.02(0.04) Region 57.4 cm) Field225 Density of large live hybrid spruce trees (DBH 17.5– 0.078 0.028 2.76(0.006) 57.4 cm) Field225 Quadratic term of above variable -0.001 0.0004 -1.90(0.06) Field225 Density of very large western larch (DBH 57.5 cm) 0.340 0.144 2.36(0.02) Field225 Density of very large snags (DBH 57.5) 0.896 0.355 2.53(0.01) Field225 Quadratic term of above variable -0.126 0.063 -2.00(0.05) Field225 Density of very large stumps (DBH 57.5 cm) 0.156 0.061 2.56(0.01) Field225 Quadratic term of above variable -0.004 0.002 -2.24(0.03) Field225 Volume of logs (LED 22.5 cm) -0.061 0.014 -4.24 −05 (2.23×10 ) GIS225 Percent Douglas-fir0–39 yrs (225 m) -0.042 0.019 -2.17(0.03) GIS225 Percent Douglas-fir40–79 yrs (225 m) -0.029 0.015 -1.99(0.05) GIS225 Percent Douglas-fir80–114 yrs (225 m) 0.018 0.008 2.27(0.02) GIS225 Mean area of canopy openings (225 m) 0.506 0.159 3.19(0.001) GIS225 Quadratic term of above variable -0.039 0.013 -3.09(0.002) GIS800 Distance to forest polygon with logging history, age -0.002 0.001 -2.21(0.03) <40 yrs GIS800 Percent Douglas-fir0–39 yrs (800 m) -0.163 0.052 -3.13(0.002) GIS800 Percent western larch 40–79 yrs (800 m) -0.088 0.044 -2.00(0.05) GIS800 Quadratic term of above variable 0.001 0.001 1.82(0.07) GIS800 Percent western larch 80–119 yrs (800 m) 0.156 0.074 2.11(0.04) GIS800 Quadratic term of above variable -0.007 0.003 -2.37(0.02) GIS800 Percent lodgepole pine 40–79 yrs (800 m) -0.046 0.023 -1.99(0.05) GIS800 Percent lodgepole pine 120 yrs (800 m) -0.965 0.314 -3.07(0.002) GIS800 Quadratic term of above variable 0.082 0.032 2.59(0.01) Western Region Field225 Density of large live trembling aspen trees (DBH 17.5– 0.062 0.023 2.67(0.008) 57.4 cm) Field225 Density of very large ponderosa pine trees (DBH 1.183 0.409 2.89(0.004) 57.5 cm) Field225 Quadratic term of above variable -0.134 0.064 -2.11(0.04) GIS225 Percent Douglas-fir 120 yrs (225 m) -0.044 0.015 -3.01(0.003) GIS225 Mean crown closure of trees >15m in height (225 m) 0.276 0.091 3.05(0.002) GIS225 Quadratic term of above variable -0.005 0.002 -3.07(0.002) GIS800 Percent non-forest vegetated area (800 m) 0.260 0.089 2.94(0.003) GIS800 Quadratic term of above variable -0.006 0.002 -2.91(0.004) GIS800 Percent ponderosa pine 80–119 yrs (800 m) 0.100 0.045 2.20(0.03) GIS800 Percent ponderosa pine 120 yrs (800 m) -0.237 0.123 -1.92(0.06) GIS800 Quadratic term of above variable 0.009 0.005 1.94(0.05) Coefficients, standard errors of the coefficients, and z-score values are provided for each of the variables in the final models. doi:10.1371/journal.pone.0130849.t003 (<200 m, Fig 4). Nest occurrence in this model was negatively associated with a high percent- age of Douglas-fir aged <40 yrs and lodgepole pine aged 40–79 yrs in the canopy (<2%, Fig 4). Nest occurrence was also negatively associated with areas having 5–7% canopy cover of PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 9/18 Williamson's Sapsucker Nesting Habitat Selection Fig 3. Predicted relationships between probability of nest occurrence and four forest habitat variables included in the GIS225 model (originally 25 variables considered) for the Okanagan-East Kootenay Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Variables were calculated from geographic information system (GIS) habitat variables within a 225 m radius of the nest or random point. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g003 lodgepole pine 120 yrs, or areas with 30% western larch aged 40–79 yrs, and was positively associated with areas having 10–15% western larch aged 80–119 yrs. In the Western Region, models suggested Williamson’s sapsucker selected moderately-open habitats with complex relationships with older age classes of ponderosa pine trees. In the Field225 model, the probability of Williamson’s sapsucker nest occurrence was positively related to higher densities of large trembling aspen (>50 trees/ha, DBH 17.5–57.4 cm) and moderate densities of very large ponderosa pine trees (4–5 trees/ha, DBH 57.5 cm; Table 3, Fig 5). The GIS225 model indicated nest occurrence was negatively associated with a high per- centage of Douglas-fir trees 120 yrs old (>10%), and positively associated with moderate amounts of crown closure (25–30%, >15 m tall; Fig 6). In the GIS800 model, nest occurrence was positively associated with 20–25% of an area covered by non-forest, positively associated with higher percentages of ponderosa pine aged 80–119 yrs in the canopy (>25%), and PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 10 / 18 Williamson's Sapsucker Nesting Habitat Selection Fig 4. Predicted relationships between probability of nest occurrence and six forest habitat variables included in the GIS800 model (originally 28 variables considered) for the Okanagan-East Kootenay Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Variables were calculated from geographic information system (GIS) data within an 800 m radius of the nest or random point. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 11 / 18 Williamson's Sapsucker Nesting Habitat Selection lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g004 Fig 5. Predicted relationships between probability of nest occurrence and two forest habitat variables included in the Field225 model (18 variables originally considered) for the Western Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g005 PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 12 / 18 Williamson's Sapsucker Nesting Habitat Selection Fig 6. Predicted relationships between probability of nest occurrence and two forest habitat variables included in the GIS225 model (originally 18 variables considered) for the Western Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Variables were calculated from geographic information system (GIS) data within a 225 m radius of the nest or random point. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g006 negatively associated with areas that had 10–15% of ponderosa pine aged 120 yrs in the can- opy, suggesting a threshold relationship with crown closure (Fig 7). PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 13 / 18 Williamson's Sapsucker Nesting Habitat Selection PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 14 / 18 Williamson's Sapsucker Nesting Habitat Selection Fig 7. Predicted relationships between probability of nest occurrence and three forest habitat variables included in the GIS800 model (originally 21 variables considered) for the Western Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Variables were calculated from geographic information system (GIS) data within an 800 m radius of the nest or random point. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g007 Discussion The habitat features associated with the presence of Williamson’s sapsucker nests varied between our two study regions with respect to the relative utility of field versus GIS-derived habitat variables for modelling probability of occurrence. In the Okanagan-East Kootenay Region, nest territories tended to occur in areas that contained open mature forests with very large western larch trees, with the model derived from field variables having the best predictive performance. Western larch typically occurs in mixed stands with other conifers rather than in pure stands, and therefore its occurrence may be better quantified by field surveys than by interpreted aerial photos. In contrast, nest territories in the Western Region occurred in forests with high densities of large trembling aspen trees and very large ponderosa pine trees, both of which can occur in pure stands, and thus may be well characterized from aerial photos, result- ing in the better predictive performance of GIS800 model in this region. The good predictive performance of this model in the Western Region suggests Williamson’s sapsuckers may have larger territories here than in other regions of British Columbia, as suggested by Gyug et al. [2]. However, the GIS-based models should be validated with an independent dataset before man- agement recommendations for the Western Region are made without consideration of field measurements, as other underlying processes may also differ between the two regions. Williamson’s sapsucker generally selects areas with relatively high densities of suitable nest- ing trees [6], [8], [11], [27]. In the Okanagan-East Kootenay Region, the sapsucker selected areas with high densities of very large (DBH >57.5 cm) dead trees and very large live western larch trees. In this region, distribution of western larch may be a limiting factor for nesting hab- itat selection [10]. Very large western larch trees are high quality nesting trees that provide an excellent substrate for excavation as their heartwood is often decayed and surrounded by decay-resistant sapwood, which may provide greater cavity stability and offer protection from nest predators such as squirrels, weasels, and bears [27], [28]. Very large western larch trees can stand for centuries [29], and may thus provide long-term breeding sites for Williamson’s sapsucker, a species known to reuse nest trees for multiple years. The western larch forests in British Columbia have the highest known breeding density of Williamson’s sapsucker (3.1 nests/km )[2]. Therefore, management of Williamson’s sapsucker in the Okanagan-East Koo- tenay Region should focus on retention and recruitment of very large western larch trees. Williamson’s sapsucker nests occurred in areas with relatively low volumes of coarse woody debris in the Okanagan-East Kootenay Region. Nielsen-Pincus and Garton [18] also found that nest sites of Williamson’s sapsucker had lower densities of downed logs than random points. Gyug et al. [14] suggested that dead and decaying wood should be managed to support ant colonies, which provide a major food source for Williamson’s sapsucker during the breed- ing season. This results in an apparent conundrum, as Williamson’s sapsucker selected breed- ing territories with a reduced volume of downed logs, but more logs should result in higher numbers of ant colonies, which should be correlated with higher preference, if preference was based on prey abundance alone. There may be a threshold value of downed logs above which higher densities of ants become superfluous to the breeding requirements of Williamson’s PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 15 / 18 Williamson's Sapsucker Nesting Habitat Selection sapsucker, or there may be reservoirs of ants in other types of dead and decaying wood. The Okanagan-East Kootenay region experiences a mixed-severity fire regime [30], so Williamson’s sapsucker may select for open stands of western larch historically maintained by frequent ground fires, which would have consumed most logs on the forest floor [29], [31]. Alterna- tively, the selection of very large trees may result in sapsuckers using older forest areas where log volumes have degraded over time. Nesting territories were positively associated with the presence of ponderosa pine trees in the Western Region, where measures of large diameter trees and older age classes of ponderosa pine were significant explanatory variables in both the GIS800 and Field225 models. William- son’s sapsucker excavate cavities in ponderosa pine trees [10], although it most commonly nests in trembling aspen where suitable western larch trees are unavailable [1], [11]. A rela- tively high density of large trembling aspen trees (>50 trees/ha, DBH 17.5–57.4 cm) was also positively associated with Williamson’s sapsucker nest territories in the Western Region. The probability of using aspen as a nesting tree differs throughout the Williamson’s sapsucker range; where present and abundant, aspen was often a significant nest tree species, but where absent, suitable conifers were used [10]. Therefore, Williamson’s sapsucker habitat selection models should be developed to accommodate the variable distribution of tree species across its range. Under the Species at Risk Act, government agencies in Canada have a legislated responsibil- ity to identify and protect critical habitat of species at risk, and implement a Recovery Strategy or Action Plan. Our results can be used to inform this process in two ways. First, models can be validated and then used in combination with previous habitat suitability models [15] to map locations of critical habitat for Williamson’s sapsucker. Mapping the relevant GIS-derived vari- ables may be complicated by the nature of the VRI data, which were derived for timber volume estimation based on the interpretation of aerial photographs. For example, habitat features that predict a high probability of Williamson’s sapsucker nest occurrence, such as densities of very large western larch, may not be mapped if patches are too small (i.e., <3 ha), or if these trees contribute little to merchantable stand volume. Therefore, delineation of critical habitat is likely best identified by spatial analyses that combine field- and GIS-derived variables, as in Sousa [6] and as currently proposed in the recovery strategy for Williamson’s sapsucker [15]. Second, important habitat features identified from field measurement data can guide specific management actions to ensure their retention and effective protection on the landscape. Retention of large diameter western larch within Williamson’s sapsucker habitat in the Oka- nagan-East Kootenay Region should be a key component of management plans. Long-term management should involve recruitment of large diameter western larch, perhaps by reducing stem density of small diameter trees (‘thinning-from-below’), given that western larch responds well to thinning operations [32]. No variables related to sap trees (i.e., small diameter Douglas- fir) were significant with nest territory selection by Williamson’s sapsucker, so these thinning operations would not appear to affect foraging requirements of the species. Prescribed burns would also reduce small diameter stem densities, clear the understory, and mimic the natural disturbance regime that likely created these open, mature stands. A combination of thinning and prescribed under-burns showed promise for successfully restoring an old-growth western larch forest in Montana [33]. Such an approach would be extremely valuable for restoration efforts aimed at increasing overall available habitat to increase populations of Williamson’s sapsuckers in regions where habitat is limited. Future research should seek to validate the habi- tat selection models presented here by manipulation of stem density or other experiments that may identify management practices useful in improving habitat quality and augmenting Wil- liamson’s sapsucker populations. PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 16 / 18 Williamson's Sapsucker Nesting Habitat Selection Similar considerations should be made for aspen stands within ponderosa pine forests. Our findings in the Western Region support the well-established relationship and biological impor- tance of large diameter trembling aspen to Williamson’s sapsucker and the broader cavity nester guild [1], [10], [11], [34]. Old-growth western larch and aspen stands with large diame- ter trees provide valuable habitat for many bird species in the area [35], [36]. Thus, conserva- tion efforts for Williamson’s sapsucker will likely have collateral benefits for avian biodiversity in mature conifer and mixed forests as a whole. Supporting Information S1 Table. Habitat variables (211 variables) considered in the construction of Williamson’s sapsucker nest territory selection models, prior to variable reduction. All GIS variables are repeated for the 225 m, 400 m and 800 m scales, with the exceptions of CROWNGT15_225, MaxCRNGT15225, MNCRN15225, DIST_TO_AGE_80 and DIST_to_OPEN_LT40yrs. (DOCX) Acknowledgments This paper was the result of a collaborative multiyear project that involved the assistance of many individuals and agencies, some of whom were involved in the Williamson’s Sapsucker Recovery Team (Canada). We thank the many biologists and technicians who assisted with field data collection. R. Davis of Forsite Consultants provided the Vegetation Resource Inven- tory GIS data for the variables we used for these analyses. R. Schuster provided R-code for cal- culating the percentage of concordant pairs. K. Cockle and K. Fort provided insightful comments on an earlier draft of the manuscript. Author Contributions Conceived and designed the experiments: MCD LWG AKSS KM. Performed the experiments: LWG IPO AKSS KM. Analyzed the data: MCD JN. Contributed reagents/materials/analysis tools: LWG IPO AKSS MCD JN KM. Wrote the paper: MCD LWG JN KM IPO AKSS. References 1. Gyug LW, Dobbs RC, Martin TE, Conway CJ. Williamson's sapsucker (Sphyrapicus thyroideus). The Birds of North America Online. 2012; 285. Available: http://bna.birds.cornell.edu/bna/species/285. 2. Gyug LW, Ohanjanian I, Steeger C, Manley IA, Davidson PW. Distribution and density of Williamson's sapsucker (Sphyrapicus thyroideus) in British Columbia, Canada. British Columbia Birds. 2007; 16: 2– 3. Gyug LW, Cooper JM, Steeger C. Distribution, relative abundance and population size of Williamson’s sapsucker in south-central British Columbia. British Columbia Birds. 2014; 24: 9–19. 4. Committee on the Status of Endangered Wildlife in Canada. COSEWIC assessment and status report on the Williamson's sapsucker, Sphyrapicus thyroideus, in Canada. Ottawa: COSEWIC; 2005. vii + 45 pp. 5. Species at Risk Act, SC 2002, c 29, s 2 (2002). 6. Sousa PJ. Habitat suitability index models: Williamson's sapsucker. Fort Collins (CO): U.S. Dept. of the Interior, Fish and Wildlife Service FWS/OBS-82/10.47; 1983. 13 p. 7. Conway CJ, Martin TE. Habitat suitability for Williamson’s sapsuckers in mixed-conifer forests. J Wildl Manage. 1993; 57: 322–328. 8. Bull EL, Peterson SR, Thomas JW. Resource partitioning among woodpeckers in northeastern Oregon. Portland (OR): U.S. Dept. of Agriculture, Forest Service Research Note PNW-RN-444; 1986. 20 p. 9. Thomas JW, Anderson RG, Maser C, Bull EL. Snags. In: Thomas JW, editor. Wildlife habitat in man- aged forests: the Blue Mountains of Oregon and Washington. U.S. Dept. of Agriculture, Forest Service Agriculture Handbook 553; 1979. pp. 60–77. PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 17 / 18 Williamson's Sapsucker Nesting Habitat Selection 10. Gyug LW, Steeger C, Ohanjanian I. Characteristics and densities of Williamson’s sapsucker nest trees in British Columbia. Can J For Res. 2009; 39: 2319–2331. 11. Crockett AB, Hadow HH. Nest site selection by Williamson and red-naped sapsuckers. Condor. 1975; 77: 365–368. 12. Crockett AB. Ecology and behavior of the Williamson’s sapsucker in Colorado. Ph.D. Dissertation, Uni- versity of Colorado. 1975. 13. Gyug LW, Steeger C, Ohanjanian IP. Williamson's sapsucker (Sphyrapicus thyroideus) sap trees in British Columbia. British Columbia Birds. 2009; 19: 6–12. 14. Gyug LW, Higgins RJ, Todd MA, Meggs JM, Lindgrem BS. Dietary dependence of Williamson’s sap- sucker nestlings on ants associated with dead and decaying wood in British Columbia. Can J For Res. 2014; 44(6): 628–637. 15. Environment Canada. Recovery Strategy for the Williamson’s Sapsucker (Sphyrapicus thyroideus)in Canada. Species at Risk Act Recovery Strategy Series. Ottawa: Environment Canada; 2014. vi + 32 pp. 16. Manly BFJ, McDonald LL, Thomas DL, McDonald TL, Erickson WP. Resource Selection by Animals: Statistical Design and Analysis for Field Studies. 2nd ed. Dordrecht (Netherlands): Kluwer Academic Publishers; 2002. 17. Birds and Burns Network. Instructions for vegetation measurements at nests and point counts [Inter- net]. 2005. Available: www.rmrs.nau.edu/lab/4251/birdsnburns/. 18. Nielsen-Pincus N, Garton EO. Responses of cavity-nesting birds to changes in available habitat reveal underlying determinants of nest selection. Northwest Nat (Olymp. Wash.). 2007; 88: 135–146. 19. British Columbia. Ministry of Forests. Silvicultural Systems Handbook for British Columbia. Victoria (BC): Forest Practices Branch; 2003. 208 p. 20. Husch B, Miller I, Beer TW. Forest Mensuration. 2nd ed. New York: Ronald; 1972. 21. Marshall PL, Davis G, LeMay VM. Using line intersect sampling for coarse woody debris. Vancouver (BC): British Columbia Forest Service, Forest Research Technical Report TR-003; 2000. 37 p. 22. British Columbia Ministry of Forests and Range. Vegetation Resources Inventory [Internet]. 2009. Available: www.for.gov.bc.ca/hts/vri/. 23. Venables WN, Ripley BD. Modern Applied Statistics with S. 4th ed. New York: Springer-Verlag; 2002. 24. Burnham KP, Anderson DR. Model selection and multi-model inference: A practical information-theo- retic approach. New York: Springer-Verlag; 2002. 25. Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006; 27: 861–874. 26. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioin- formatics. 2005; 21(20): 3940–3941. PMID: 16096348 27. McClelland BR. Relationships between hole-nesting birds, forest snags, and decay in western larch- Douglas-fir forests of the northern Rocky Mountains. Ph.D. Dissertation. University of Montana.1977. 28. McClelland BR, McClelland PT. Pileated woodpecker nest and roost trees in Montana: links with old- growth and forest "health". Wildl Soc Bull. 1999; 27: 846–857. 29. Arno SF, Smith HY, Krebs MA. Old growth ponderosa pine and western larch stand structures: influ- ences of pre-1900 fires and fire exclusion. Ogden (UT): U.S. Dept. of Agriculture, Forest Service Research Paper, INT-RP-495; 1997. 20 p. 30. Klenner WA, Walton R, Arsenault A, Kremsater L. Dry forests in the Southern Interior of British Colum- bia: Historic disturbances and implications for restoration and management. For Ecol Manage. 2008; 256: 1711–1722. 31. Hall FC. Fire and vegetation in the Blue Mountains: implications for land managers. Tall Timbers Fire Ecology Conference. 1976; 15: 155–170. 32. Seidel KW. Growth and yield of western larch in response to several density levels and two thinning methods: 15-year results. Portland (OR): U.S. Dept. of Agriculture, Forest Service Research Note PNW-RN-455; 1986. 18 p. 33. Fiedler CE, Harrington MG. Restoring vigor and reducing hazard in an old-growth western larch stand (Montana). Ecological Restoration. 2004; 22: 133–134. 34. Martin K, Aitken KEH, Wiebe KL. Nest sites and nest webs for cavity-nesting communities in interior British Columbia, Canada: nest characteristics and niche partitioning. Condor. 2004; 106: 5–19. 35. McClelland BR, Frissell SS, Fischer WL, Halvorson CH. Habitat management for hole-nesting birds in forests of western larch and Douglas-fir. Journal of Forestry. 1979; 77: 480–483. 36. Drever MC, Aitken KEH, Norris AR, Martin K. Woodpeckers as reliable indicators of bird richness, forest health and harvest. Biol Conserv. 2008; 141: 624–634. PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 18 / 18 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png PLoS ONE Pubmed Central

Using Field Data and GIS-Derived Variables to Model Occurrence of Williamson’s Sapsucker Nesting Habitat at Multiple Spatial Scales

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© 2015 Drever et al
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Abstract

Smith AK, Ohanjanian IP, Martin K (2015) Using Field Data and GIS-Derived Variables to Model Williamson's sapsucker (Sphyrapicus thyroideus) is a migratory woodpecker that breeds in Occurrence of Williamson’s Sapsucker Nesting Habitat at Multiple Spatial Scales. PLoS ONE 10(7): mixed coniferous forests in western North America. In Canada, the range of this wood- e0130849. doi:10.1371/journal.pone.0130849 pecker is restricted to three small populations in southern British Columbia, precipitating a Editor: Chao-Dong Zhu, Institute of Zoology, CHINA national listing as ‘Endangered’ in 2005, and the need to characterize critical habitat for its survival and recovery. We compared habitat attributes between Williamson’s sapsucker Received: January 29, 2015 nest territories and random points without nests or detections of this sapsucker as part of a Accepted: May 25, 2015 resource selection analysis to identify the habitat features that best explain the probability of Published: July 15, 2015 nest occurrence in two separate geographic regions in British Columbia. We compared the Copyright: © 2015 Drever et al. This is an open relative explanatory power of generalized linear models based on field-derived and Geo- access article distributed under the terms of the graphic Information System (GIS) data within both a 225 m and 800 m radius of a nest or Creative Commons Attribution License, which permits random point. The model based on field-derived variables explained the most variation in unrestricted use, distribution, and reproduction in any medium, provided the original author and source are nest occurrence in the Okanagan-East Kootenay Region, whereas nest occurrence was credited. best explained by GIS information at the 800 m scale in the Western Region. Probability of Data Availability Statement: The Williamson nest occurrence was strongly tied to densities of potential nest trees, which included open Sapsucker habitat plot data are available through the forests with very large (diameter at breast height, DBH, 57.5 cm) western larch (Larix occi- Government of Canada data portal(http://open. dentalis) trees in the Okanagan-East Kootenay Region, and very large ponderosa pine canada.ca/en/open-data) with directions on how to (Pinus ponderosa) and large (DBH 17.5–57.5 cm) trembling aspen (Populus tremuloides) access in the Methods section. trees in the Western Region. Our results have the potential to guide identification and pro- Funding: This research was funded by Environment tection of critical habitat as required by the Species at Risk Act in Canada, and to better Canada (support to MCD) and The Natural Sciences and Engineering Research Council of Canada (to manage Williamson’s sapsucker habitat overall in North America. In particular, manage- JN). Funding was provided for various phases of the ment should focus on the maintenance and recruitment of very large western larch and pon- field project to LWG by Canadian Wildlife Service, B. derosa pine trees. C. Ministry of Environment, Forest Investment Account (B.C. Ministry of Forests), B.C. Timber Sales, Weyerhaeuser Ltd., Pope and Talbot Ltd. (now PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 1/18 Williamson's Sapsucker Nesting Habitat Selection Interfor), Tembec Industries Ltd. (now Canadian Introduction Forest Products Ltd.), and Tolko Industries Ltd. The Williamson’s sapsucker (Sphyrapicus thyroideus) is a migratory woodpecker that breeds in the funders had a limited role in discussions of study montane mixed coniferous forests of western North America. Its breeding distribution extends design, and data collection, but no role in the analysis, decision to publish, or preparation of the from British Columbia to Baja California, Mexico [1]. In Canada, Williamson’s sapsucker is manuscript, and does not alter our adherence to restricted to the southern interior of British Columbia where it occurs primarily in mid-eleva- PLOS ONE policies on sharing data and materials. tion mixed coniferous forests containing either western larch (Larix occidentalis) or ponderosa Competing Interests: This work was supported by pine (Pinus ponderosa), along with Douglas-fir (Pseudotsuga menziesii) and often containing a Weyerhaeuser Ltd., Pope and Talbot Ltd. (now deciduous component of trembling aspen (Populus tremuloides)[2], [3]. In 2005, it was classi- Interfor), Tembec Industries Ltd. (now Canadian fied as ‘Endangered’ by the Committee on the Status of Endangered Wildlife in Canada, based Forest Products Ltd.) and Tolko Industries Ltd., and primarily on low population numbers and projected rates of habitat loss, particularly for forests coauthor A. Kari Stuart-Smith’s affiliation with with mature western larch [4]. Consequently, identification and protection of Williamson’s Canadian Forest Products Ltd. does not alter the authors' adherence to PLOS ONE policies on sharing sapsucker critical habitat is required for its conservation in Canada. data and materials. Habitat suitability models can help identify critical habitat, defined in the Species at Risk Act as ‘habitat that is necessary for the survival or recovery of a listed wildlife species’ [5]. These models can provide evidence for habitat attributes selected and used to a greater extent than suggested by their availability, and can thus be used to identify habitat features particularly important for management of species at risk. Sousa [6] developed a Habitat Suitability Index (HSI) for Williamson’s sapsuckers breeding in western North America, which involved four variables: percentage tree canopy closure, percentage of area dominated by aspen, average diameter at breast height (DBH) of overstory aspen, and number of suitable soft snags (DBH >30.5 cm, or DBH >45.7 cm for ponderosa pine) within a 4 hectare (ha) area around the nest [6]. When Sousa’s model was applied in Arizona [7], the HSI correctly indicated Williamson’s sapsucker selected nest territories in snow-melt drainages over areas on ridge tops, but incor- rectly classified 63.6% of plots not used for nesting as optimal breeding habitat. Redefining var- iables and making the model region-specific may improve the degree of this misclassification. Nesting habitat attributes used and selected by Williamson’s sapsucker appear to vary geo- graphically. In Arizona, snag densities (i.e., number of dead trees) were the most important fac- tor influencing nest location in stands dominated by aspen [7]. In Oregon, the sapsucker preferred to nest in forest areas with <75% canopy cover and <34 m /ha basal area [8], with recommended estimates of 3.71 snags/ha with a DBH >30.5 cm to maximize a sapsucker pop- ulation [9]. In British Columbia, densities of 20 trees/ha with a DBH >57 cm and 60–150 trees/ha with a DBH >22 cm were recommended to maintain Williamson’s sapsucker breeding habitat [10]. This variation indicates the sapsucker has flexible nesting requirements and may be selecting (or avoiding) particular habitat features in different systems. Habitat selection by nesting Williamson’s sapsucker is likely influenced by both nesting and foraging requirements operating at multiple spatial scales from territories to the surrounding landscape. Crockett and Hadow [11] suggested nest trees in aspen were chosen based on prox- imity to foraging areas instead of nest tree features. Williamson’s sapsucker in Colorado had a minimum breeding territory size of 4 ha [12], while in British Columbia the minimum breed- ing territory was ~16 ha [10]. Territories must provide foraging opportunities for sapsuckers, including substantial quantities of sap and ants. Sap trees are typically coniferous trees (DBH 23–47 cm) within 100 m of the nest tree [13]. After eggs hatch, adults augment their diet with carpenter ants (Camponotus spp.) and other ants, which they also feed nestlings [14]. As car- penter ants depend on logs for nest substrate, the provision of downed logs within territories was considered an essential habitat attribute for nesting Williamson’s sapsucker [14]. While field measurements from the nest territory provide precise habitat use information at the terri- tory scale, models derived from landscape-level attributes can be used to extrapolate probabil- ity of occurrence outside sampled areas [15]. Therefore, a comparison of habitat selection PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 2/18 Williamson's Sapsucker Nesting Habitat Selection models using field- and GIS-derived variables may provide insights both into the scales at which Williamson’s sapsucker selects habitat attributes and into methods for characterizing habitat selection by the sapsucker. In this paper, our primary objective was to identify variables influencing Williamson’s sap- sucker nesting habitat selection at the spatial scale of breeding territories and, where possible, to identify associated threshold values. We performed a resource selection analysis [16], which compared habitat features at nest territories to areas where the sapsucker was confirmed to be absent (no detections of nests, sap feeding, or individuals), hereafter ‘random point’ or ‘no nest’. We used habitat measurements collected in the field and through a Geographic Informa- tion System (GIS) from the Vegetation Resource Inventory (VRI) databases maintained by the British Columbia Ministry of Forests, Lands and Natural Resource Operations. We compared the following sets of variables to assess which best explain nesting habitat selection: field mea- surements at 225 m radius and GIS information within a 225 m, 400 m, and 800 m radius of the nest. We then assessed the relative effectiveness of using available GIS databases to using field-derived data. Study Area This study encompassed the three disjunct geographic regions of the Williamson’s sapsucker breeding range in southern British Columbia [3]: the Western Region, west of the Okanagan valley principally near Merritt and Princeton; the Okanagan Region, east of Okanagan Lake and Okanagan Valley, south of Penticton and near the United States border; and the East Koo- tenay Region near Cranbrook (Fig 1). Forests cover much of the study area, dominated by Douglas-fir, western larch, ponderosa pine, lodgepole pine (Pinus contorta), trembling aspen, and hybrid spruce (Picea glauca x engelmannii). The forests of the Okanagan and East Koote- nay Regions have similar species compositions, dominated by Douglas-fir and western larch; the Western Region is dominated by ponderosa pine and aspen, and lacking western larch. We therefore analyzed the Okanagan and East Kootenay Regions as one group (the Okanagan-East Kootenay Region), separate from the Western Region. Fig 1. Map of the study area, indicating locations of Williamson’s sapsucker nests (black circles) and random points without detections (white circles) for three geographic regions where the species occurs in southern British Columbia, Canada. doi:10.1371/journal.pone.0130849.g001 PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 3/18 Williamson's Sapsucker Nesting Habitat Selection Methods Ethics statement The surveys were conducted primarily on public lands and private land with permission by landowner. No birds were handled and no samples collected. The Williamson’s Sapsucker Recovery Team (Canada) approved the data collection for avian nest and habitat monitoring. All surveys of nesting habitat were conducted to minimize disturbance on birds, usually after nesting activities were completed. All field activities were in agreement with federal and provin- cial legislation. Data used in this analysis can be freely accessed on the Government of Canada Open Data Portal (http://open.canada.ca/en/open-data) under the name ‘Williamson Sap- sucker habitat plot data’. Field methods We located Williamson's sapsucker nests during three field seasons (2006–2008) by using sys- tematic call- and drum-playback (CPB) surveys to detect the presence of sapsuckers and later searching these areas for nests, following the methods in Gyug et al. [2]. In 2008, we chose ran- dom points within the area of known occupancy, and conducted 25–60 minute CPB surveys. We included at least five well-spaced CPB points within a 225 m radius of each accessible point in May-June to confirm the presence or absence of Williamson’s sapsucker [3]. We characterized forest habitat around nests and random points with no detection of Wil- liamson’s sapsucker using circular vegetation plots in a 16 ha area (225 m radius). The 16 ha sampling area assumed a minimum territory size in which a breeding pair could meet its forag- ing requirements during the nesting period, based on mean nearest-neighbour nest distances of 450 m in the Okanagan-East Kootenay Region [2]. We used protocols from the Birds and Burns Network [17] for vegetation sampling of standing live and dead trees, with similar meth- ods used by Nielsen-Pincus and Garton [18]. At each nest tree or plot center, we sampled a 50 m variable-width transect in each of the four cardinal directions, with north and south tran- sects starting at the center and east and west transects starting 10 m from the center. In the remaining area around the nest or random point (60–225 m radius), we randomly sampled fif- teen additional 50 m transects, with the constraint that they lay within the boundaries of forest stands mapped by the provincial Vegetation Resource Inventory (VRI). A random point within the 60–225 m radius was selected as the centre point for each random transect, and the orienta- tion of each transect was determined by randomly selecting a bearing between 1 and 360 degrees. Widths of sampling swaths across the centre line of a 50 m transect were specific to the DBH classes of trees: 2 m width for small trees (DBH 7.5–22.4 cm), 6 m width for large live trees (DBH 22.5–57.4 cm), and 20 m width for both large dead standing trees (‘snags’, DBH 22.5 cm) that were 1.4 m in height and for very large live trees (DBH 57.5 cm). These widths resulted in plot sizes of 0.01, 0.03 and 0.1 ha respectively, which we used to convert counts to stem densities (number per ha). We recorded the following for each tree within a plot: species, DBH within size classes outlined in British Columbia silvicultural guidelines (17.5–22.4, 22.5–37.4, 37.5–52.4, 52.5–57.4, 57.5–67.4, 67.5 cm) [19], tree condition (alive or dead), the presence of nest cavities, and if the tree was known to contain a Williamson's sap- sucker cavity. We counted downed logs along all 50 m transects, and measured their large end diameter (LED), length within the plot, and diameter at the large and small end within the plot. We determined downed log volume (m /ha) using the plot method in 2006 and 2007 [20]. For most sites in 2007, we used the line intercept method [21] in addition to the plot method. PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 4/18 Williamson's Sapsucker Nesting Habitat Selection Estimates of downed log volume calculated using the plot method and line method were strongly correlated (Pearson’s r = 0.93, N = 30, P < 0.001), so we averaged the values in 2007 for plots where both methods were applied. In 2008, we used only the line intercept method. We counted stumps if their diameter at stump height (DSH) was 22.5 cm. Geographic Information System (GIS) data We extracted information from the Vegetation Resource Inventory (VRI) of the British Columbia Ministry of Forests and Range about vegetation cover within 225 m, 400 m and 800 m radius circles around the locations of nest trees and random points [22]. The VRI provided vegetation polygon characteristics derived from aerial photography calibrated with ground plots, from which we determined the total proportions of different habitat attributes. A prelim- inary evaluation revealed that the 400 m scale was strongly correlated with the 225 m scale (all Pearson’sr >0.85). Therefore, we only used the 225 m and 800 m radii in our analyses to cap- ture the nest territory (i.e., minimum territory size for the sapsucker to meet breeding and for- aging requirements) and an arbitrarily determined scale that was larger than this minimum territory scale. We defined habitat types by leading tree species, which included Douglas-fir, western larch, ponderosa pine, lodgepole pine, other coniferous species (total sum of hybrid spruce, subalpine fir, and other coniferous species observed), and broadleaf species, in each of four stand age classes based on available VRI information (0–39, 40–79, 80–119, and 120 yrs). In addition, we calculated the area consisting of cut blocks (<40 yrs old) in each polygon. We calculated two distance measures: the distance from a nest tree or random point to the nearest forest polygon aged 80 yrs (assigned a value of 0 if the point was in a forested polygon 80 yrs), and the distance from the nest or random point to the nearest edge of a forest cut block <40 yrs (assigned a value of 0 if the point was in a forest cut block <40 yrs). Data Analyses We compiled a total of 211 initial field and GIS variables representing habitat attributes that may influence Williamson’s sapsucker nest territory selection (S1 Table). To examine whether and how the probability of Williamson’s sapsucker nest occurrence varied with these forest habitat features, we constructed a series of Generalized Linear Models (GLMs) with ‘nest’ or ‘no nest’ as a binary response variable (where nest territories received a value of 1, and areas with no nests a value of 0). We used habitat variables as explanatory variables in each GLM, with a binomial error distribution and logit link function [23]. We divided the measured explanatory variables into the Okanagan-East Kootenay Region (n = 166) and Western Region (n = 69) datasets, and removed habitat attributes with no varia- tion. We screened these variables for correlations in a preliminary analysis, where we retained the more biologically relevant variable in a correlated pair (Pearson’sr >0.75) and excluded the other to avoid issues of multi-collinearity. This preliminary variable reduction resulted in 72 variables considered in model construction for the Okanagan-East Kootenay Region and 55 variables for the Western Region (Table 1). We first conducted an exploratory analysis that considered each habitat variable as an explanatory variable individually. We also fit a model including the quadratic term of each var- iable to allow for a non-linear relationship between the variable and the probability of nest occurrence. If this term was significant, we categorized the variable as ‘Quadratic’. Otherwise, we considered the variable as ‘Linear’ if the linear term was significant, or removed the variable from the final models if neither the quadratic nor linear terms were significant. We used a sig- nificance level of α = 0.1 to be conservative with our inclusion of variables, decrease the Type II error rate, and minimize the possibility of excluding potentially important habitat variables. PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 5/18 Williamson's Sapsucker Nesting Habitat Selection Table 1. Numbers of explanatory variables considered in generalized linear models for the probability of Williamson’s sapsucker nest occurrence in two regions of southern British Columbia. Variable type Okanagan-East Kootenay Region Western Region Field225 GIS225 GIS800 GIS Total Field225 GIS225 GIS800 GIS Total Quadratic 4(19) 5(22) 3(12) 0(0) 12(17) 3(17) 4(25) 2(11) 0(0) 9(16) Linear 6(29) 3(13) 3(12) 1(50) 13(18) 3(17) 4(25) 3(16) 0(0) 10(18) Non-sig 11(52) 15(65) 20(77) 1(50) 47(65) 12(67) 8(50) 14(74) 2(100) 36(65) Total 21 23 26 2 72 18 16 19 2 55 ‘Quadratic’ indicates habitat variables were significant in a model that included a quadratic term; ‘Linear’ indicates habitat variables were significant as a linear term in the model; ‘Non-sig’ indicates habitat variable was not significant. Subheadings under each Region indicate the type of habitat variables: field measurements (‘Field225’), geographic information system (GIS) data from a 225 m radius around the nest or plot center (‘GIS225’), and GIS data from an 800 m radius around the nest or plot center (‘GIS800’). The two ‘GIS’ variables separated from the 225 m and 800 m scales are included in the modelling process for both scales. Numbers in brackets indicate the percent relative to the column total. doi:10.1371/journal.pone.0130849.t001 We then tabulated the number of variables (per region) that were classified as Quadratic, Lin- ear, or not significant (Table 1). Following this initial evaluation, we assembled the significant variables in three separate models for each region: a ‘Field225’ model using field measurement data (10 variables in the Okanagan-East Kootenay Region, 6 variables in the Western Region), a ‘GIS225’ model using information within a 225 m radius of the nest or no nest plot center (9 variables in the Okana- gan-East Kootenay Region, 8 variables in the Western Region), and a ‘GIS800’ model using information within an 800 m radius of the nest or no nest plot center (7 variables in the Okana- gan-East Kootenay Region, 5 variables in the Western Region; Table 1). We removed variables that were not significant from these models to create the final six models from the remaining variables. We statistically compared the final models within regions using Akaike Information Crite- rion corrected for small sample size (AICc) [24]. Scale parameters indicated that overdisper- sion was not an issue in any of the final models. For comparisons both within and between regions, we calculated area under the curve (AUC) values, and Hosmer-Lemeshow test statis- tics. We also assessed the effect of specific habitat variables using a graphical approach for inference. Using the range of observed values for the habitat variables, we obtained predictions and associated standard errors from the best model in which that habitat variable was included. We then plotted these predictions and their 95% prediction intervals together with observed data to evaluate the probability of nest territory occurrence. We used statistical packages from the program R (version 3.0.2) for all analyses [25], [26]. Results We measured habitat attributes at a total of 138 Williamson’s sapsucker nest territories (97 in the Okanagan-East Kootenay, 41 in the Western Region) and 96 random points with no detec- tions of sapsucker presence or activity (68 in the Okanagan-East Kootenay, 28 in the Western Region). The initial exploratory analyses indicated that, when considered singly, both field- and GIS- derived habitat variables could potentially explain variation in the probability of occurrence of Williamson’s sapsucker nests. Of the habitat variables considered in preliminary analyses, 25 of 72 (35%) in the Okanagan-East Kootenay Region, and 19 of 55 (35%) in Western Region were categorized as ‘Quadratic’ or ‘Linear’ (Table 1). When comparing field- versus GIS- derived variables, more field-derived variables (47%) tended to be significant relative to GIS- PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 6/18 Williamson's Sapsucker Nesting Habitat Selection Table 2. Comparative statistics for models constructed of forest habitat variables in two regions of British Columbia. Region Variable type AICc AUC Concordance (%) Hosmer-Lemeshow test statistic(p-value) Okanagan-East Kootenay Region Field225 181.4 0.84 83.5 8.98(0.34) GIS225 209.5 0.73 72.3 9.11(0.33) GIS800 190.9 0.82 81.8 12.70(0.12) Western Region Field225 79.1 0.81 80.6 4.67(0.79) GIS225 80.1 0.80 79.8 10.63(0.22) GIS800 70.2 0.88 87.9 6.58(0.58) For the Field225, GIS225, and GIS800 models in each region, comparative statistics are given: Akaike’s Information Criterion corrected for small sample sizes (AICc), area under the curve (AUC) of receiver operating characteristic, percentage of concordant pairs (Concordance) and the Hosmer-Lemeshow test statistic with the associated p-value. doi:10.1371/journal.pone.0130849.t002 derived variables (25% at the 225 m and 36% at the 800 m scale) in the Okanagan-East Koote- nay Region. In the Western Region, both types of variables had similar proportions classified as significant (34% of field variables, and 44% or 24% of GIS variables). The remaining vari- ables were classified as not significant, and thus were not included in the secondary model construction. The secondary model selection using the reduced suites of variables yielded a set of three models per region that all adequately fit the data, as indicated by the Hosmer-Lemeshow tests that had p-values >0.1 (Table 2). Rankings of models composed of field versus GIS-derived variables varied between the two regions. In the Okanagan-East Kootenay Region, the Field225 model best explained nest territory selection by Williamson’s sapsucker, having the lowest AICc value and highest AUC value. In the Western Region, the GIS800 model best explained variation in nest territory selection, having the lowest AICc value and highest AUC value. The GIS225 model had the poorest fit in each region. All final models provided reliable discrimina- tion between nest territories and random points, with AUC values of 0.80 for all models in both regions, except the GIS225 model in the Okanagan-East Kootenay Region that had an AUC value of 0.72 (Table 2). Parameter values from each model indicated Williamson’s sapsucker nest territories were associated with habitat features related to suitable nest trees and foraging opportunities. In the Okanagan-East Kootenay Region, the Field225 model indicated Williamson’s sapsucker nest territories were positively associated with areas that had high densities of very large western larch trees (>8 trees/ha, DBH 57.5 cm), moderate densities of large hybrid spruce trees (50– 70 trees/ha, DBH 17.5–57.4 cm), very large snags (4–5 snags/ha, DBH 57.5 cm), and very large stumps (18–20 stumps/ha, 57.5 cm diameter; Fig 2). Additionally, probability of nest occurrence was negatively associated with areas that had higher densities of large Douglas-fir and lodgepole pine trees (>25 trees/ha, DBH 17.5–57.4 cm) and higher volumes of downed logs (>15 m /ha, LED 22.5 cm; Table 3, Fig 2). In the GIS225 model for the Okanagan-East Kootenay Region, probability of nest occur- rence was positively associated with areas that had a high percentage (>80%) of older age clas- ses of Douglas-fir trees (aged 80–119 yrs) in the canopy, and negatively associated with the percentage of canopy dominated by Douglas-fir trees in the younger age classes (< 80 yrs of age, Fig 3). In addition, this model indicated a non-linear relationship with the mean size of the openings around each nest or random point, where maximum probability of nest occurrence was in areas with openings averaging 6–7ha(Fig 3). The GIS800 model for the Okanagan-East Kootenay Region indicated Williamson’s sap- sucker selected nesting habitat relatively close to areas with a logging history in the last 40 yrs PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 7/18 Williamson's Sapsucker Nesting Habitat Selection Fig 2. Predicted relationships between probability of nest occurrence and six forest habitat variables included in the Field225 model (21 variables originally considered) for the Okanagan-East Kootenay Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g002 PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 8/18 Williamson's Sapsucker Nesting Habitat Selection Table 3. Parameter estimates for Field225, GIS225 and GIS800 models explaining Williamson’s sapsucker nest occurrence as a function of forest habitat variables in two regions of British Columbia. Region Variable Variable name Coefficient Standard z-score(p- type error value) Okanagan-East Kootenay Field225 Density of large live lodgepole pine trees (DBH 17.5– -0.018 0.009 -2.02(0.04) Region 57.4 cm) Field225 Density of large live hybrid spruce trees (DBH 17.5– 0.078 0.028 2.76(0.006) 57.4 cm) Field225 Quadratic term of above variable -0.001 0.0004 -1.90(0.06) Field225 Density of very large western larch (DBH 57.5 cm) 0.340 0.144 2.36(0.02) Field225 Density of very large snags (DBH 57.5) 0.896 0.355 2.53(0.01) Field225 Quadratic term of above variable -0.126 0.063 -2.00(0.05) Field225 Density of very large stumps (DBH 57.5 cm) 0.156 0.061 2.56(0.01) Field225 Quadratic term of above variable -0.004 0.002 -2.24(0.03) Field225 Volume of logs (LED 22.5 cm) -0.061 0.014 -4.24 −05 (2.23×10 ) GIS225 Percent Douglas-fir0–39 yrs (225 m) -0.042 0.019 -2.17(0.03) GIS225 Percent Douglas-fir40–79 yrs (225 m) -0.029 0.015 -1.99(0.05) GIS225 Percent Douglas-fir80–114 yrs (225 m) 0.018 0.008 2.27(0.02) GIS225 Mean area of canopy openings (225 m) 0.506 0.159 3.19(0.001) GIS225 Quadratic term of above variable -0.039 0.013 -3.09(0.002) GIS800 Distance to forest polygon with logging history, age -0.002 0.001 -2.21(0.03) <40 yrs GIS800 Percent Douglas-fir0–39 yrs (800 m) -0.163 0.052 -3.13(0.002) GIS800 Percent western larch 40–79 yrs (800 m) -0.088 0.044 -2.00(0.05) GIS800 Quadratic term of above variable 0.001 0.001 1.82(0.07) GIS800 Percent western larch 80–119 yrs (800 m) 0.156 0.074 2.11(0.04) GIS800 Quadratic term of above variable -0.007 0.003 -2.37(0.02) GIS800 Percent lodgepole pine 40–79 yrs (800 m) -0.046 0.023 -1.99(0.05) GIS800 Percent lodgepole pine 120 yrs (800 m) -0.965 0.314 -3.07(0.002) GIS800 Quadratic term of above variable 0.082 0.032 2.59(0.01) Western Region Field225 Density of large live trembling aspen trees (DBH 17.5– 0.062 0.023 2.67(0.008) 57.4 cm) Field225 Density of very large ponderosa pine trees (DBH 1.183 0.409 2.89(0.004) 57.5 cm) Field225 Quadratic term of above variable -0.134 0.064 -2.11(0.04) GIS225 Percent Douglas-fir 120 yrs (225 m) -0.044 0.015 -3.01(0.003) GIS225 Mean crown closure of trees >15m in height (225 m) 0.276 0.091 3.05(0.002) GIS225 Quadratic term of above variable -0.005 0.002 -3.07(0.002) GIS800 Percent non-forest vegetated area (800 m) 0.260 0.089 2.94(0.003) GIS800 Quadratic term of above variable -0.006 0.002 -2.91(0.004) GIS800 Percent ponderosa pine 80–119 yrs (800 m) 0.100 0.045 2.20(0.03) GIS800 Percent ponderosa pine 120 yrs (800 m) -0.237 0.123 -1.92(0.06) GIS800 Quadratic term of above variable 0.009 0.005 1.94(0.05) Coefficients, standard errors of the coefficients, and z-score values are provided for each of the variables in the final models. doi:10.1371/journal.pone.0130849.t003 (<200 m, Fig 4). Nest occurrence in this model was negatively associated with a high percent- age of Douglas-fir aged <40 yrs and lodgepole pine aged 40–79 yrs in the canopy (<2%, Fig 4). Nest occurrence was also negatively associated with areas having 5–7% canopy cover of PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 9/18 Williamson's Sapsucker Nesting Habitat Selection Fig 3. Predicted relationships between probability of nest occurrence and four forest habitat variables included in the GIS225 model (originally 25 variables considered) for the Okanagan-East Kootenay Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Variables were calculated from geographic information system (GIS) habitat variables within a 225 m radius of the nest or random point. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g003 lodgepole pine 120 yrs, or areas with 30% western larch aged 40–79 yrs, and was positively associated with areas having 10–15% western larch aged 80–119 yrs. In the Western Region, models suggested Williamson’s sapsucker selected moderately-open habitats with complex relationships with older age classes of ponderosa pine trees. In the Field225 model, the probability of Williamson’s sapsucker nest occurrence was positively related to higher densities of large trembling aspen (>50 trees/ha, DBH 17.5–57.4 cm) and moderate densities of very large ponderosa pine trees (4–5 trees/ha, DBH 57.5 cm; Table 3, Fig 5). The GIS225 model indicated nest occurrence was negatively associated with a high per- centage of Douglas-fir trees 120 yrs old (>10%), and positively associated with moderate amounts of crown closure (25–30%, >15 m tall; Fig 6). In the GIS800 model, nest occurrence was positively associated with 20–25% of an area covered by non-forest, positively associated with higher percentages of ponderosa pine aged 80–119 yrs in the canopy (>25%), and PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 10 / 18 Williamson's Sapsucker Nesting Habitat Selection Fig 4. Predicted relationships between probability of nest occurrence and six forest habitat variables included in the GIS800 model (originally 28 variables considered) for the Okanagan-East Kootenay Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Variables were calculated from geographic information system (GIS) data within an 800 m radius of the nest or random point. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 11 / 18 Williamson's Sapsucker Nesting Habitat Selection lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g004 Fig 5. Predicted relationships between probability of nest occurrence and two forest habitat variables included in the Field225 model (18 variables originally considered) for the Western Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g005 PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 12 / 18 Williamson's Sapsucker Nesting Habitat Selection Fig 6. Predicted relationships between probability of nest occurrence and two forest habitat variables included in the GIS225 model (originally 18 variables considered) for the Western Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Variables were calculated from geographic information system (GIS) data within a 225 m radius of the nest or random point. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g006 negatively associated with areas that had 10–15% of ponderosa pine aged 120 yrs in the can- opy, suggesting a threshold relationship with crown closure (Fig 7). PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 13 / 18 Williamson's Sapsucker Nesting Habitat Selection PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 14 / 18 Williamson's Sapsucker Nesting Habitat Selection Fig 7. Predicted relationships between probability of nest occurrence and three forest habitat variables included in the GIS800 model (originally 21 variables considered) for the Western Region of the Williamson’s sapsucker Area of Occupancy in British Columbia, 2006–2008. Variables were calculated from geographic information system (GIS) data within an 800 m radius of the nest or random point. Grey points depict nest occurrence (‘Nest’) or a random point (‘No nest’), and the title of each plot indicates the habitat variable. Solid lines indicate predicted values and thin dashed lines represent the 95% prediction interval. Horizontal line depicts proportion of points that were nests (i.e., the baseline probability of response variable having a value of 1). doi:10.1371/journal.pone.0130849.g007 Discussion The habitat features associated with the presence of Williamson’s sapsucker nests varied between our two study regions with respect to the relative utility of field versus GIS-derived habitat variables for modelling probability of occurrence. In the Okanagan-East Kootenay Region, nest territories tended to occur in areas that contained open mature forests with very large western larch trees, with the model derived from field variables having the best predictive performance. Western larch typically occurs in mixed stands with other conifers rather than in pure stands, and therefore its occurrence may be better quantified by field surveys than by interpreted aerial photos. In contrast, nest territories in the Western Region occurred in forests with high densities of large trembling aspen trees and very large ponderosa pine trees, both of which can occur in pure stands, and thus may be well characterized from aerial photos, result- ing in the better predictive performance of GIS800 model in this region. The good predictive performance of this model in the Western Region suggests Williamson’s sapsuckers may have larger territories here than in other regions of British Columbia, as suggested by Gyug et al. [2]. However, the GIS-based models should be validated with an independent dataset before man- agement recommendations for the Western Region are made without consideration of field measurements, as other underlying processes may also differ between the two regions. Williamson’s sapsucker generally selects areas with relatively high densities of suitable nest- ing trees [6], [8], [11], [27]. In the Okanagan-East Kootenay Region, the sapsucker selected areas with high densities of very large (DBH >57.5 cm) dead trees and very large live western larch trees. In this region, distribution of western larch may be a limiting factor for nesting hab- itat selection [10]. Very large western larch trees are high quality nesting trees that provide an excellent substrate for excavation as their heartwood is often decayed and surrounded by decay-resistant sapwood, which may provide greater cavity stability and offer protection from nest predators such as squirrels, weasels, and bears [27], [28]. Very large western larch trees can stand for centuries [29], and may thus provide long-term breeding sites for Williamson’s sapsucker, a species known to reuse nest trees for multiple years. The western larch forests in British Columbia have the highest known breeding density of Williamson’s sapsucker (3.1 nests/km )[2]. Therefore, management of Williamson’s sapsucker in the Okanagan-East Koo- tenay Region should focus on retention and recruitment of very large western larch trees. Williamson’s sapsucker nests occurred in areas with relatively low volumes of coarse woody debris in the Okanagan-East Kootenay Region. Nielsen-Pincus and Garton [18] also found that nest sites of Williamson’s sapsucker had lower densities of downed logs than random points. Gyug et al. [14] suggested that dead and decaying wood should be managed to support ant colonies, which provide a major food source for Williamson’s sapsucker during the breed- ing season. This results in an apparent conundrum, as Williamson’s sapsucker selected breed- ing territories with a reduced volume of downed logs, but more logs should result in higher numbers of ant colonies, which should be correlated with higher preference, if preference was based on prey abundance alone. There may be a threshold value of downed logs above which higher densities of ants become superfluous to the breeding requirements of Williamson’s PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 15 / 18 Williamson's Sapsucker Nesting Habitat Selection sapsucker, or there may be reservoirs of ants in other types of dead and decaying wood. The Okanagan-East Kootenay region experiences a mixed-severity fire regime [30], so Williamson’s sapsucker may select for open stands of western larch historically maintained by frequent ground fires, which would have consumed most logs on the forest floor [29], [31]. Alterna- tively, the selection of very large trees may result in sapsuckers using older forest areas where log volumes have degraded over time. Nesting territories were positively associated with the presence of ponderosa pine trees in the Western Region, where measures of large diameter trees and older age classes of ponderosa pine were significant explanatory variables in both the GIS800 and Field225 models. William- son’s sapsucker excavate cavities in ponderosa pine trees [10], although it most commonly nests in trembling aspen where suitable western larch trees are unavailable [1], [11]. A rela- tively high density of large trembling aspen trees (>50 trees/ha, DBH 17.5–57.4 cm) was also positively associated with Williamson’s sapsucker nest territories in the Western Region. The probability of using aspen as a nesting tree differs throughout the Williamson’s sapsucker range; where present and abundant, aspen was often a significant nest tree species, but where absent, suitable conifers were used [10]. Therefore, Williamson’s sapsucker habitat selection models should be developed to accommodate the variable distribution of tree species across its range. Under the Species at Risk Act, government agencies in Canada have a legislated responsibil- ity to identify and protect critical habitat of species at risk, and implement a Recovery Strategy or Action Plan. Our results can be used to inform this process in two ways. First, models can be validated and then used in combination with previous habitat suitability models [15] to map locations of critical habitat for Williamson’s sapsucker. Mapping the relevant GIS-derived vari- ables may be complicated by the nature of the VRI data, which were derived for timber volume estimation based on the interpretation of aerial photographs. For example, habitat features that predict a high probability of Williamson’s sapsucker nest occurrence, such as densities of very large western larch, may not be mapped if patches are too small (i.e., <3 ha), or if these trees contribute little to merchantable stand volume. Therefore, delineation of critical habitat is likely best identified by spatial analyses that combine field- and GIS-derived variables, as in Sousa [6] and as currently proposed in the recovery strategy for Williamson’s sapsucker [15]. Second, important habitat features identified from field measurement data can guide specific management actions to ensure their retention and effective protection on the landscape. Retention of large diameter western larch within Williamson’s sapsucker habitat in the Oka- nagan-East Kootenay Region should be a key component of management plans. Long-term management should involve recruitment of large diameter western larch, perhaps by reducing stem density of small diameter trees (‘thinning-from-below’), given that western larch responds well to thinning operations [32]. No variables related to sap trees (i.e., small diameter Douglas- fir) were significant with nest territory selection by Williamson’s sapsucker, so these thinning operations would not appear to affect foraging requirements of the species. Prescribed burns would also reduce small diameter stem densities, clear the understory, and mimic the natural disturbance regime that likely created these open, mature stands. A combination of thinning and prescribed under-burns showed promise for successfully restoring an old-growth western larch forest in Montana [33]. Such an approach would be extremely valuable for restoration efforts aimed at increasing overall available habitat to increase populations of Williamson’s sapsuckers in regions where habitat is limited. Future research should seek to validate the habi- tat selection models presented here by manipulation of stem density or other experiments that may identify management practices useful in improving habitat quality and augmenting Wil- liamson’s sapsucker populations. PLOS ONE | DOI:10.1371/journal.pone.0130849 July 15, 2015 16 / 18 Williamson's Sapsucker Nesting Habitat Selection Similar considerations should be made for aspen stands within ponderosa pine forests. Our findings in the Western Region support the well-established relationship and biological impor- tance of large diameter trembling aspen to Williamson’s sapsucker and the broader cavity nester guild [1], [10], [11], [34]. Old-growth western larch and aspen stands with large diame- ter trees provide valuable habitat for many bird species in the area [35], [36]. Thus, conserva- tion efforts for Williamson’s sapsucker will likely have collateral benefits for avian biodiversity in mature conifer and mixed forests as a whole. Supporting Information S1 Table. Habitat variables (211 variables) considered in the construction of Williamson’s sapsucker nest territory selection models, prior to variable reduction. All GIS variables are repeated for the 225 m, 400 m and 800 m scales, with the exceptions of CROWNGT15_225, MaxCRNGT15225, MNCRN15225, DIST_TO_AGE_80 and DIST_to_OPEN_LT40yrs. (DOCX) Acknowledgments This paper was the result of a collaborative multiyear project that involved the assistance of many individuals and agencies, some of whom were involved in the Williamson’s Sapsucker Recovery Team (Canada). We thank the many biologists and technicians who assisted with field data collection. R. Davis of Forsite Consultants provided the Vegetation Resource Inven- tory GIS data for the variables we used for these analyses. R. Schuster provided R-code for cal- culating the percentage of concordant pairs. K. Cockle and K. Fort provided insightful comments on an earlier draft of the manuscript. Author Contributions Conceived and designed the experiments: MCD LWG AKSS KM. Performed the experiments: LWG IPO AKSS KM. Analyzed the data: MCD JN. Contributed reagents/materials/analysis tools: LWG IPO AKSS MCD JN KM. Wrote the paper: MCD LWG JN KM IPO AKSS. References 1. Gyug LW, Dobbs RC, Martin TE, Conway CJ. Williamson's sapsucker (Sphyrapicus thyroideus). The Birds of North America Online. 2012; 285. Available: http://bna.birds.cornell.edu/bna/species/285. 2. Gyug LW, Ohanjanian I, Steeger C, Manley IA, Davidson PW. Distribution and density of Williamson's sapsucker (Sphyrapicus thyroideus) in British Columbia, Canada. British Columbia Birds. 2007; 16: 2– 3. 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