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IMPROVING THE DESIGN AND COUNT METHODOLOGY OF THE MOURNING DOVE CALL-COUNT SURVEY IN THE EASTERN AND CENTRAL MANAGEMENT UNITS: PILOT STUDY, APRIL�JUNE 2011 AND 2012 FRANK F. RIVERA-MILÁN AND REBECCA RAU, U.S. Fish and Wildlife Service, Division of Migratory Bird Management, Branch of Population and Habitat Assessment, Patuxent Wildlife Research Center, Laurel, MD 20708 (frank_rivera@fws.gov) MARK SEAMANS, U.S. Fish and Wildlife Service, Division of Migratory Bird Management, Branch of Population and Habitat Assessment, 755 Parfet Street, Lakewood, CO 80215. Progress Report; Expected Completion: 2013 The goals of this project are (1) to augment the value of monitoring data for harvest management by improving the design and methodology of the Mourning Dove Call-Count Survey, and (2) to provide an independent measure of abundance that can be used in combination with band-recovery and part-collection survey data to guide regulatory decisions, estimate population trends, and make predictions about population response to management. To achieve these goals, we are surveying on-road and off-road points and analyzing survey data using a combination of count methods (e.g., conventional, multiple-covariate, and hierarchical distance sampling). In addition to point location (1 = on-road, 2 = off-road), we are exploring the effect of multiple covariates that may affect mourning dove detection probability and abundance along and away from roads (e.g., 2observer team, cluster size, detection time, detection form, time of day, sampling period, vegetation cover, and disturbance level among others). Table 1. Survey effort (k = 423 points) and sample size (n = 582 detections before data truncation at distance w = 180 m). Points were visited 3 times (April 16�30, May 1�14, May 15�June 5). We conducted training workshops at Patuxent Wildlife Research Center (Apr 2011) and Texas A&M, Kingsville (Apr 2012). In this report we provide 12 details of conventional and multiple-covariate distance sampling surveys conducted by 20 2-observer teams at 225 on-road points and 198 off-road points in 21 callcount routes in 7 states of the Eastern Management Unit (Table 1 and Fig. 1). Figure 1. Off-road point and habitats on Route 390 in Pennsylvania, off-road points were located 200�400 m from the nearest paved or unpaved road, including driveways. On-road and off-road points were sampled 3 times in April 16�30, May 1�14, and May 15�June 5 (i.e., survey effort/point, v = 3). Aural and visual detections were recorded during 6 1-min counts/point. Detection form was recorded as heard only (1 = no visual contact) or heard-seen or seen only (2 = visual contact). Two-observer teams surveyed all points, with one observer recording the data and the other measuring detection distances. Both observers remained side by side for 6-min, recording the time of first detection (6 1-min intervals) and measuring radial distances to calling and noncalling doves detected singly or the geometric center of clusters. A cluster was defined as 2 or more doves within 10 m of each
other, showing similar behavior (e.g., feeding on the ground). Rangefinders were used to measure exact detection distances. However, when this was not possible (e.g., dove heard only), detections were grouped into distance categories (0–15, 16–30, 31–45, 46–60, 61–90, 91–120, 121–180, 181–240, 241–340, and 341–440 m). The purpose of having two-observer teams was to increase the chance of meeting method assumptions (i.e., detecting all doves at point centers; determining their initial locations before movement; estimating cluster sizes accurately; and measuring distances exactly or at least allocating singles and clusters to correct distance categories). We truncated the distance data (w = 180 m) to reduce cluster size-bias effect, remove outliers, and improve the fit of detection models. After data truncation, we evaluated the fit of detection models with quantilequantile plots and goodness-of-fit tests. Model selection was based on minimization of Akaike Information Criterion (AIC). Models with differences in AIC < 2 were considered to be equally supported by the data. We used nonparametric bootstrapping for robust estimation of standard errors and 95% confidence intervals, and accounted for model selection uncertainty through model averaging. We made 582 mourning dove detections (n) at 423 surveyed points (k). Detection form was the only covariate that caused heterogeneity in the detection function of mourning doves (Table 2, Figs. 2 and 3). Overall, estimated density was 0.114 doves/ha (95% CI = 0.076, 0.174), encounter rate (n/K) was 0.308 (0.306, 0.309), detection probability was 0.371 (0.339, 0.406), and effective radius of detection was 110 m (105, 115; Tables 3 and 4). Factors affecting detection probability were the most important with respect to density variation; and the main source was detection form. Detection probability was 0.643 (0.502, 0.822) for doves heard only and 0.221 (0.165, 0.297) for doves heard/seen or seen only (Table 5). Density was 0.047/ha (0.033, 0.063) for doves heard only and 0.061//ha for doves heard-seen or seen only (Table 6). We tested a number of hypotheses, including a positive road bias on mourning dove detection and abundance. However, on-road detection was 0.339 (0.261, 0.440), off-road detection was 0.271 (0.142, 0.519), on-road density was 0.057/ha (0.034, 0.083), and off-road density was 0.052/ha (0.032, 0.076; 13 Tables 7 and 8). Detection distance (m) 450 400 350 300 250 200 150 100 50 0 1 2 Detection form Figure 2. Box plot of mourning dove detection distance and detection form (1 = heard only, 2 = heard and seen or seen only). From these results, we concluded (1) that the value monitoring data can be augmented by improving survey design and count methods, and (2) that it is possible to provide an independent measure of density (number/unit area) and abundance (number in survey region) for mourning dove harvest management. In April�June 2012, we are planning to repeat surveys in the Eastern Management Unit and initiate surveys in the Central Management Unit (TX, AR, OK, KS, CO, LA, and NM). Figure 3. Detection functions of mourning doves heard only (solid line) and heard-seen or seen only (dashed line).
Page 1 and 2: U.S. Fish & Wildlife Service Webles
Page 3 and 4: CONTENTS Development and History of
Page 5 and 6: HISTORY AND ADMINISTRATION OF THE W
Page 7 and 8: Program Administration The USFWS Pr
Page 9 and 10: Appendix A - Priority Information N
Page 11 and 12: Table 1. Harvest and crippling of m
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Page 15: Figure 1. Estimated age ratios of m
Page 19 and 20: Table 5. Detection probability and
Page 21 and 22: mechanism for accurate aging of whi
Page 23 and 24: Currently laboratory measurements o
Page 25 and 26: Band-tailed Pigeons BAND-TAILED PIG
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Page 29 and 30: The mineral station design was adju
Page 31 and 32: unripe], cascara, and blue elderber
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Page 35 and 36: that Interior birds tended to retai
Page 37 and 38: Sandhill Cranes POPULATION GENETIC
Page 39 and 40: distinguishing between nearly ident
Page 41 and 42: Table 2. Pairwise Fst Values betwee
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Page 45 and 46: Alternatively, a different survey m
Page 47 and 48: (Bennett and Bennett 1990, Nesbitt
Page 49 and 50: Spatial Data Landscape-scale data
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Page 53 and 54: We found colt survival was lower on
Page 55 and 56: McWethy, D. B., and J. E. Austin. 2
Page 57 and 58: Recent advancements in technology a
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Page 63 and 64: American Woodcock HABITAT USE AND O
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Figure 4: Kriged 13th secondary fea
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Texas A&M University, College Stati
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ASSESSMENT OF TECHNIQUES FOR EVALUA
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adio-marked broods 5-7 days per wee
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adult female, considerably lower th
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FACTORS AFFECTING DETECTION OF AMER
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were neighbor = 1.0, observer = 1.0
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was included within the official su
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Beyer, H. L. 2004. Hawth's Analysis
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Marshbirds THE EFFECT OF WATERFOWL
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Table 2. Relative Importance of mod
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nests to account for repeated measu
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Literature Cited Bogner, H. E. and
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a common nest substrate. Tall forms
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despite rails’ apparent ability t
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EVALUATION OF AN EXPERT-BASED LANDS
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IMPLEMENTATION OF THE NATIONAL MARS
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truthed before the actual survey. H
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IMPLEMENTATION OF A NATIONAL MARSHB
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Table 1. Numbers of individuals of
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ESTIMATING POPULATION TRENDS, RELAT
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This abstract represents a final ab
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volunteers to cover new sites in la
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Plain of Louisiana (Figure 1). We s
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for concentrations of wintering sni
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Table 1. Model selection results an
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DEVELOPING OPTIMAL SURVEY TECHNIQUE
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Conway, C. J., and J. P. Gibbs. 201
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Figure 1. Red Slough Wildlife Manag
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estimate visual obstruction 10 m fr
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Table 3. Habitat measurements recor
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Reproductive Success and Survival i
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The goal of the study is to address
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When interacting with private lando
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