The Journal of Wildlife Management 79(2):325–337; 2015; DOI: 10.1002/jwmg.821
Research Article
Using Automated Recorders and Occupancy Models to Monitor Common Forest Birds Across a Large Geographic Region BRETT J. FURNAS,1 California Department of Fish and Wildlife, 601 Locust Street, Redding, CA 96001, USA RICHARD L. CALLAS, California Department of Fish and Wildlife, 1724 Ball Mountain Road, Montague, CA 96064, USA
ABSTRACT Automated recorders and occupancy models can be used together to monitor population trends
of multiple avian species across a large geographic region. Automated recorders are an attractive method for monitoring birds, because they leave a record that can be independently validated and multiple units can be programmed to repeatedly survey different locations at the same daily times. We assessed the use of automated recorders and single-species, single-season occupancy models to monitor common forest birds across a 5.4-million-ha region of northern California. Using a survey protocol of 5-minute recordings at 3 times of the morning repeated over 3 consecutive days at 453 sites, we detected 32 species at >10% of these sites. Five of these species (Steller’s jay [Cyanocitta stelleri], mountain chickadee [Poecile gambeli], redbreasted nuthatch [Sitta canadensis], dark-eyed junco [Junco hyemalis], and western tanager [Piranga ludoviciana]) were dominant with occupancies >0.5. We also modeled occupancy associations with elevation and canopy cover for brown creeper (Certhia americana), MacGillivray’s warbler (Geothlypis tolmiei), and western tanager and found the environmental conditions at which occupancy was maximized differed by up to 399 m in elevation and 17.9% canopy cover for these species. Given a sampling effort of 100 new sites per year, we demonstrated 80% power (a ¼ 0.1) to detect occupancy declines as small as 2.5% per year over 20 years for the 32 most common species. The effective radius of automated recorder surveys was approximately 50 m. In a field test, surveys conducted concurrently using automated recorders and point counts yielded similar occupancy estimates despite differences in detection probability. Our results suggest that automated recorders, used alone or in conjunction with point counts, can provide a practical means of monitoring common forest birds across a large geographic area. Ó 2014 The Wildlife Society. KEY WORDS automated recorder, California, common species, forest birds, multi-species monitoring, occupancy, point count, power analysis.
Long-term biodiversity monitoring across large geographic regions is needed to inform conservation planning efforts in the context of environmental changes affecting wildlife populations (Manley et al. 2005, Haughland et al. 2010, Schultz et al. 2013). Well-designed monitoring programs can identify population declines of common species early enough to facilitate adaptive planning (Holling 1978, Lancia et al. 1996, Koch et al. 2011, MacLeod et al. 2012). Potential outcomes include information leading to regulatory protection of a species or conservation actions averting the need for such action. Automated recorders are increasingly being used to survey birds, bats, and amphibians (Rempel et al. 2005, Acevedo and Villanueva-Rivera 2006, Brandes 2008, Gorresen et al. 2008, Celis-Murillo et al. 2009, Depraetere et al. 2012). They provide a record of species identification that can be independently reviewed and validated by multiple interReceived: 25 April 2013; Accepted: 16 October 2014 Published: 30 December 2014 1
E-mail:
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Furnas and Callas
Automated Recorders Monitoring Common Birds
preters (Rempel et al. 2005). For this reason and because of lower costs and the potential for greater standardization of methods, some researchers have advocated the use of automated recorders instead of expert personnel conducting point counts (Haselmayer and Quinn 2000, Hobson et al. 2002, Rempel et al. 2005, Brandes 2008). Since 2002, the California Department of Fish and Wildlife (CDFW) has used automated recorders to survey birds across a large portion of northern California in the United States. Skilled interpreters review these recordings to identify species based on the sounds captured by automated recorders. This project, named Ecoregion Biodiversity Monitoring project (EBM), is intended to track long-term population trends of numerous species for informing conservation planning. It is a component of the California Wildlife Action Plan (Bunn et al. 2007:55). Occupancy modeling has been recommended for monitoring programs because occurrence data are often easier and less expensive to collect than abundance data, especially across large spatial extents (MacKenzie and Nichols 2004). Data collected from automated recorders are particularly amenable to occupancy modeling, in part, because replicate 325
surveys can be synchronized to occur at comparable times of the day thereby reducing a temporal source of variability in detection probability (Brandes 2008, Gorresen et al. 2008). The audio record provided by these devices facilitates review of species occurrence by more than 1 person and these independent identifications can be used in occupancy models that address both omission and commission errors (Royle and Link 2006, Miller et al. 2011). We considered the efficacy of automated recorders to monitor common forest birds (occupancy >0.1) using singlespecies, single-season occupancy models. Our results provide a baseline of average occupancies of common forest birds across a large region over 5 years. We used additional occupancy covariate models to demonstrate the habitat associations of 3 avian species of conservation interest. We conducted a power analysis to determine the number of common species for which we could detect occupancy declines as small as 2.5% per year over 10 or 20 years. We evaluated the technical limitations of automated devices to record bird sounds from different distances and for discriminating these signals from background noise. We conducted a side-by-side comparison of automated recorders and point counts (Ralph et al. 1995, Bibby et al. 2000) to determine if these methods provided similar occupancy
estimates. Lastly, we considered the overall utility of automated recorders, used by themselves or in conjunction with point counts, for application in multi-species monitoring across a large geographic region.
STUDY AREA Avian surveys occurred across a 5.4-million-ha area of northern California in the United States (Fig. 1). Coniferdominated forests covering 64% of this region were primarily Klamath mixed conifer, Sierran mixed conifer, Douglas-fir (Pseudotsuga menziesii), white fir (Abies concolor), red fir (Abies magnifica), lodgepole pine (Pinus contorta), ponderosa pine (Pinus ponderosa), Jeffrey pine (Pinus jeffreyi), eastside pine, montane hardwood-conifer, and juniper (Juniperus spp.) forest types (Mayer and Laudenslayer 1988). Elevations ranged from 60 m to 4,270 m. The geology was a mix of steep mountains and volcanic plateaus, and average annual precipitation varied from 20 cm to 300 cm (Schoenherr 1992). Forest ownership was a mix of public (64%) and private (36%) lands including 9.4% designated as wilderness.
Figure 1. Northern California study area where we surveyed birds using automated recorders. We surveyed 453 sites in forested areas from 2006 to 2010. We surveyed each site for 5 minutes 3 times each morning repeated over 3 consecutive days during a single year. 326
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METHODS Avian Surveys We randomly selected survey sites without replacement each year from the portion of the U.S. Forest Service (2012a) Forest Inventory and Analysis (FIA) program hexagon grid overlapping the study area. The distance between adjacent hexagon centroids was 5.35 km. Survey sites occurred on private industrial and public forestland ownerships. They were usually located at hexagon centroids, but private property, steep terrain, and distance from roads often required relocating sites to more accessible locations within selected hexagons. In cases where relocation was infeasible, we did not sample the selected hexagon. For the reasons listed above the sampling design was best described as quasirandom. We reported summary statistics on biophysical and land management conditions (elevation, tree canopy cover, land ownership, wilderness status) at the sites we surveyed. Data analyzed in this study were limited to sites surveyed from 2006 through 2010 when survey methods were most consistent. We further limited our analysis to forested areas to illustrate the use of automated recorders across a widespread vegetation complex that was well sampled by our surveys. Using CALVEG land-use land-cover information derived from satellite imagery (U.S. Forest Service 2012b), we calculated forest land cover as the percentage of the 400-m-radius area surrounding each survey site in conifer or mixed hardwood-conifer life forms. We excluded from this study all sites surrounded by 0.1, because detection histories were too sparse to fit singlespecies models for less common species. To emphasize the utility of automated recorders, we restricted reporting of detailed species-level results to 8 focal species (olive-sided flycatcher [Contopus cooperi], red-breasted nuthatch [Sitta canadensis], brown creeper [Certhia americana], goldencrowned kinglet [Regulus satrapa], MacGillivray’s warbler [Geothlypis tolmiei], fox sparrow [Passerella iliaca], dark-eyed junco [Junco hyemalis], and western tanager [Piranga ludoviciana]) because these species have been prioritized for monitoring in California conifer forest habitats by California Partners in Flight (CalPIF 2002). However, we 327
also summarized community-level findings pertaining to the full set of birds we modeled. We mentioned these additional species by name only when we reported results with respect to the community of common species (see detailed results for all modeled species in supplemental material, available online at www.onlinelibrary.wiley.com). In addition to intercept-only occupancy models, for 3 of the CalPIF species (brown creeper, MacGillivray’s warbler, and western tanager), we evaluated additional models to demonstrate how survey data from automated recorders can be used to identify elevation (Rosenberg et al. 1999, Tingley et al. 2009, Jones et al. 2012) and tree canopy cover (Beedy 1981, Verner and Larson 1989, Siegel and DeSante 2003) associations of forest birds. We fit curvilinear models that included elevation and canopy cover covariates and their quadratic terms: logit ðcj Þ ¼ aintercept þ ael ev el evj þ aqel ev ðel evj Þ2 ; or logit ðcj Þ ¼ aintercept þ acanopy200 canopy200j þ aqcanopy200 ðcanopy200j Þ2 : We extracted the elevation at each site surveyed from a 30-mraster digital elevation model, whereas we derived average tree canopy cover in the 200-m-radius area surrounding each site from the CALVEG land-use land-cover data (U.S. Forest Service 2012b). For each convex-shaped association curve, we identified the species optimum as the covariate value at which occupancy was greatest (Tingley et al. 2009). We solved all models by minimizing negative log likelihoods using the nlm function (see Section 3.1.1. in Royle and Dorazio (2008) for an example of this solution method) in the R programming language (Version 2.12, www.r-project.org). We used model averaging (Burnham and Anderson 2002) to make multi-model inferences about the role of survey date, elevation, and tree canopy cover covariates in explaining heterogeneity in detection probability. We included additional variables in all models to address non-independence among surveys; these were categorical variables representing the 3 different survey times and the 5 different survey years. We expected that detection probability would change over the course of the breeding season (Bibby et al. 2000, Conway et al. 2008, Tingley et al. 2009, McClure et al. 2011, Chambert et al. 2012), and used Julian day of survey to represent this covariate. We did not include a quadratic term to model a curvilinear relationship, because our surveys were already constrained to avoid the beginning (April) and end (July) of the breeding season. We also expected detectability to vary by elevation (Bailey et al. 2004; Betts et al. 2008; Ke´ry et al. 2010, 2013) and tree canopy cover (Bibby and Buckland 1987, Schieck 1997, Gonzalo-Turpin et al. 2008, Sillet et al. 2012). We averaged canopy cover in the 100-m-radius area surrounding each survey site using the CALVEG land-use land-cover data (U.S. Forest Service 2012b). We calculated model-averaged estimates of occupancy and detection probability parameters for all model subsets of survey date, elevation, and canopy cover covariates on detection probability, and always included time of day and year as categorical variables in the base and all other models. Because the ratio between the number of sites and number of 328
parameters was often 0.1; Table 1). They spanned 17 families and 5 orders with Passeriformes representing the majority (84%) of species. We excluded all detections for which interpreters indicated uncertainty about species identification in their notes. 329
Table 1. Common bird species detected using automated recorders at 453 forest sites in northern California, 2006–2010. Proportion of sites at which species was detecteda Common name Mountain quail Mourning dove Common nighthawk Hairy woodpecker Northern flicker Olive-sided flycatcher Western wood-pewee Hammond’s flycatcher Dusky flycatcher Pacific-slope flycatcher Cassin’s vireo Warbling vireo Steller’s jay Common raven Mountain chickadee Red-breasted nuthatch Brown creeper Golden-crowned kinglet Townsend’s solitaire Hermit thrush American robin Nashville warbler MacGillivray’s warbler Yellow-rumped warbler Hermit warbler Spotted towhee Fox sparrow Dark-eyed junco Western tanager Black-headed grosbeak Brown-headed cowbird Cassin’s finch Number of survey sites a
Scientific name
2006
2007
2008
2009
2010
Oreortyx pictus Zenaida macroura Chordeiles minor Picoides villosus Colaptes auratus Contopus cooperi Contopus sordidulus Empidonax hammondii Empidonax oberholseri Empidonax difficilis Vireo cassinii Vireo gilvus Cyanocitta stelleri Corvus corax Poecile gambeli Sitta canadensis Certhia americana Regulus satrapa Myadestes townsendi Catharus guttatus Turdus migratorius Oreothlypis ruficapilla Geothlypis tolmiei Setophaga coronata Setophaga occidentalis Pipilo maculatus Passerella iliaca Junco hyemalis Piranga ludoviciana Pheucticus melanocephalus Molothrus ater Haemorhous cassinii
0.33 0.12 0.12 0.16 0.21 0.25 0.27 0.10 0.31 0.04 0.29 0.24 0.38 0.20 0.58 0.54 0.22 0.11 0.28 0.29 0.31 0.24 0.20 0.49 0.31 0.35 0.16 0.52 0.78 0.35 0.22 0.26 120
0.11 0.08 0.09 0.24 0.28 0.17 0.16 0.11 0.16 0.08 0.22 0.14 0.60 0.22 0.50 0.53 0.33 0.07 0.18 0.31 0.35 0.34 0.17 0.55 0.31 0.22 0.19 0.65 0.78 0.33 0.11 0.06 88
0.08 0.10 0.03 0.06 0.25 0.07 0.11 0.02 0.16 0.19 0.19 0.07 0.45 0.16 0.42 0.35 0.17 0.16 0.20 0.25 0.22 0.19 0.13 0.28 0.15 0.31 0.09 0.43 0.67 0.24 0.06 0.10 89
0.26 0.11 0.29 0.09 0.24 0.30 0.43 0.29 0.34 0.17 0.41 0.23 0.49 0.11 0.67 0.56 0.17 0.27 0.40 0.30 0.46 0.30 0.24 0.50 0.39 0.39 0.11 0.63 0.90 0.31 0.11 0.19 70
0.36 0.12 0.17 0.15 0.24 0.08 0.21 0.05 0.23 0.09 0.35 0.20 0.55 0.22 0.53 0.47 0.27 0.08 0.30 0.27 0.38 0.33 0.12 0.50 0.28 0.24 0.07 0.71 0.77 0.47 0.21 0.17 86
All species detected at >10% of all 453 sites are listed.
Uncertainty was most frequent for woodpeckers (Picidae, 13.0% of sites), Setophaga warblers (7.9% of sites), Empidonax flycatchers (7.3% of sites), and finches (Fringillidae, 1.3% of sites). The types of vocalizations used to make species identification were songs in 71% of instances, calls in 28% of instances, and wing noises or drumming in 0.8) for 34% of the 32 common avian species we modeled (see 330
supplemental material available online at www.onlinelibrary.wiley.com). The elevation of survey sites was associated with detection probability (relative importance >0.8) for 44% of species we modeled. Tree canopy cover in the vicinity of survey sites was associated with detection probability (relative importance >0.8) for 53% of species we modeled. Overall, at least 1 of the 3 potential detection covariates (survey date, elevation, canopy cover) was important (relative importance >0.8) for 78% of species we modeled. Detection probability varied considerably among species in terms of the time of morning a survey occurred (Fig. 2 and supplemental material available online at www.onlinelibrary. wiley.com). For example, common nighthawk, Pacificslope flycatcher (Empidonax difficilis), Townsend’s solitaire (Myadestes townsendi), American robin, and spotted towhee (Pipilo maculatus) were more detectable before sunrise than after. Hairy woodpecker (Picoides villosus), northern flicker (Colaptes auratus), western wood-peewee (Contopus sordidulus), Cassin’s vireo (Vireo cassinii), warbling vireo (Vireo gilvus), Steller’s jay (Cyanocitta stelleri), redbreasted nuthatch, brown creeper, Nashville warbler (Oreothlypis ruficapilla), hermit warbler, and brown-headed cowbird (Molothrus ater) showed the opposite pattern. For all species, the per survey detection probability was >0.2 for 72% of species-survey time combinations. Assuming 9 survey The Journal of Wildlife Management
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western tanager. Occupancy was maximized at a higher elevation and greater canopy cover for brown creeper than for MacGillivray’s warbler, whereas occupancy for western tanager was maximized at a lower elevation and lesser canopy cover than for the other 2 species (Figs. 3 and 4). Occupancy was maximized for brown creeper at an elevation 399 m higher and a canopy cover 17.9% greater (71.5% vs. 53.6%) than for western tanagers. Power to Detect a Trend Our analysis showed that all 32 species we modeled can be monitored to detect average annual declines as small as 2.5% over 20 years with statistical power >0.8 (see supplemental material available online at www.onlinelibrary.wiley.com). For a 10-year timeframe, only 6 species met the 2.5% per year standard (Steller’s jay, mountain chickadee, red-breasted nuthatch, yellow-rumped warbler [Setophaga coronata], darkeyed junco, and western tanager). However, 24 species met a relaxed 5.0% per year standard.
Figure 2. Detection probabilities of selected species for 5-minute automated recorder bird surveys conducted in northern California forests, 2006–2010. These species (olive-sided flycatcher [OSFL], red-breasted nuthatch [RBNU], brown creeper [BRCR], golden-crowned kinglet [GCKI], MacGillivray’s warbler [MGWA], fox sparrow [FOSP], darkeyed junco [DEJU], and western tanager [WETA]) were prioritized for monitoring in conifer forests by California Partners in Flight (CalPIF 2002).
replicates, site-level detection probability (p*) was >0.8 for 75% of species we modeled. Intercept-only occupancy models.—Our intercept-only occupancy models provided adjusted estimates of population status we presumed to be more accurate than naı¨ve frequencies of detection among sites (Table 2 and supplemental material available online at www.onlinelibrary.wiley.com). Five dominant species had occupancy estimates >0.5 (Steller’s jay, mountain chickadee [Poecile gambeli], red-breasted nuthatch, dark-eyed junco, and western tanager). Of the 32 species we modeled, the median coefficient of variation on occupancy was 0.084. The interquartile range was 0.070–0.106. Covariate occupancy models.—We identified habitat associations for brown creeper, MacGillivray’s warbler, and
Automated Recorder Signal Reception Degradation with Distance Our analyses of the playback field test revealed that the signal power of recorded bird sounds was reduced by approximately 10 decibels at 40 m for recorders and microphones placed in protective containers versus those that were not (Fig. 5). As expected, signal power was asymptotic at greater distances for recorders in protective containers. Indeed, we had great difficulty distinguishing bird sounds from background noise in spectrograms for distances of 80–100 m for recordings associated with protective containers. Our estimate of the background noise level was 41.7 dB, which was the median power value at 100 m for automated recorders in protective containers. Using this threshold, we found an effective distance of about 40 m for automated recorders placed in protective containers for the 3 bird sounds we tested. Comparison with Point Counts Survey-level detection probability was higher for point counts (median across species ¼ 0.53) versus automated recorders (median across species ¼ 0.40) given truncation of point counts at 50 m. Naı¨ve occupancy for point counts was always greater than for automated recorders at truncation thresholds >40 m; smaller truncation thresholds excluded
Table 2. Estimated occupancies of selected species from automated recorder bird surveys conducted in northern California forests, 2006-2010. Modeled occupancya Common name
Scientific name
C naı¨ve
C model
SE
CV
CILO
CIUP
Olive-sided flycatcher Red-breasted nuthatch Brown creeper Golden-crowned kinglet MacGillivray’s warbler Fox sparrow Dark-eyed junco Western tanager
Contopus cooperi Sitta canadensis Certhia americana Regulus satrapa Geothlypis tolmiei Passerella iliaca Junco hyemalis Piranga ludoviciana
0.174 0.490 0.234 0.130 0.172 0.128 0.581 0.777
0.180 0.538 0.286 0.199 0.178 0.132 0.622 0.782
0.018 0.026 0.024 0.024 0.018 0.016 0.025 0.020
0.102 0.048 0.085 0.120 0.103 0.123 0.039 0.025
0.151 0.495 0.248 0.163 0.149 0.108 0.581 0.748
0.212 0.580 0.328 0.241 0.210 0.161 0.662 0.813
b
a
We modeled occupancy using detection histories from 453 sites where we used automated recorders to survey birds over 3 5-minute periods each morning repeated over 3 consecutive days. cmodel ¼ estimated proportion of occupied sites, SE ¼ standard error, CV ¼ coefficient of variation, CI ¼ 90% confidence interval (lower [LO] and upper [UP] bounds). b Naı¨ve occupancy was the proportion of sites where a species was detected. Furnas and Callas
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Figure 3. Elevation–occupancy associations for brown creeper (BRCR), MacGillivray’s warbler (MGWA), and western tanager (WETA) based on occupancy models of survey data collected using automated recorders at 453 sites in northern California forests, 2006–2010. The optimum for each species was the elevation at which occupancy (proportion of occupied sites) was greatest.
Figure 5. Distance decay of signal power received by automated recorders. We conducted a field experiment in September 2013, whereby we recorded playbacks of bird sounds (brown creeper, American robin, and hermit warbler) from different distances at 8 montane conifer locations in northern California. The experiment compared automated recorders with and without protective containers. Variability in signal power decay is represented by the interquartile range (25–75 percentile values) of measurements. Results suggest an effective survey radius of about 40 m for automated recorders in protective containers, and that protective containers reduced audibility by 10 decibels at 40 m.
many point count detections but not automated recorder detections (Fig. 6). In contrast, modeled occupancies were in greater agreement than naı¨ve estimates especially at larger truncation thresholds. The greatest concordance between occupancy estimates from the 2 survey methods was for point count truncation thresholds of 50 m and 75 m.
DISCUSSION
Figure 4. Canopy cover–occupancy associations for brown creeper (BRCR), MacGillivray’s warbler (MGWA), and western tanager (WETA) based on occupancy models of survey data collected using automated recorders at 453 sites in northern California forests, 2006–2010. Average tree canopy cover for the 200-m-radius area surrounding each site was derived from land-use land-cover information (U.S. Forest Service 2012b). The optimum for each species was the canopy cover at which occupancy (proportion of occupied sites) was greatest. 332
Our results show the utility of automated recorders for monitoring common (c > 0.1) forest birds through the application of single-species, single-season occupancy models. This study represents the first large-scale example in western North America of multi-species, avian surveys using automated recorders. We estimated baseline occupancies of over 30 avian species including 8 focal species prioritized for monitoring by CalPIF. With a design including 3 daily 5-minute recordings repeated over 3 consecutive days, we demonstrated good statistical power (>0.8) for long-term (10–20 years) monitoring of average annual trends as small as 2.5% per year. Furthermore, because occupancy modeling can address differences in detection probabilities among survey methods, the automated recorders we used should provide comparable results to point count surveys truncated to about 50 m. Although not fully random, our survey locations were representative of middle elevation forest conditions across the study area, suggesting that our use of automated recorders provided accurate estimates of occupancy of common forest birds across a widespread habitat complex found in northern California. One approach for addressing The Journal of Wildlife Management
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Figure 6. Comparison of concurrent automated recorder and point count surveys. Surveys occurred twice on different mornings at 60 sites at 6 montane conifer locations in northern California in 2008. Occupancy (proportion of occupied sites) was calculated naı¨vely (A) and modeled (B) using data from automated recorders or point counts. Because of the small sample size, we reported occupancy as an average among species detected at >10% of sites for both survey methods. We truncated the point count data by distance such that the distance at which modeled occupancies were most similar represents an estimate of the effective survey radius of the automated recorder. The results suggest that occupancy modeling mitigated the problem of disparities in detections between the 2 survey methods and that the greatest concordance of modeled occupancy was between 50 m and 75 m.
site location biases would be to include occupancy covariates in each species’ model and calculate a weighted occupancy estimate using weights representing the mean values of these covariates across the study area (O’ Connell and Bailey 2011:198). Furnas and Callas
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Avian Surveys and Occupancy Modeling An advantage of automated recorders is that they leave a digital file that can be independently reviewed by more than 1 interpreter, thereby reducing observer bias. This is important because misclassification error can lead to substantial errors in occupancy estimates (Royle and Link 2006). Our interpreters reported sizeable (>5% of sites) identification uncertainties for Setophaga warblers (i.e., hermit warbler vs. black-throated gray warbler [Setophaga nigrescens]). We excluded all instances of uncertain species identification from our models, but this may have negatively biased some of our occupancy estimates. This is because when identification uncertainty concerns all surveys at a particular site, occupancy models lack information to estimate detection probability at that site. Much of the uncertainty about hermit warblers was due to geographical variation in its song form (Janes and Riker 2006) that we are now addressing by providing our interpreters with information on local dialects (B. J. Furnas, CDFW, unpublished data). We will rectify the problem for other species by updating our models to explicitly include information about interpretation uncertainty and differences between duplicate interpretation results into the model equations. Royle and Link (2006) provided a framework for this approach using a latent class mixture model, which could be expanded upon to include information about uncertainties and discrepancies (Miller et al. 2011). We relied on expert birders to interpret the automated recordings. Although some may consider our 70% test standard to determine eligibility too lenient, we used sample recordings from actual field deployments of automated recorders instead of reference examples of vocalizations available commercially or from sound archives. We found the often noisy field recordings in which multiple birds sang simultaneously better suited to assess biologists’ abilities to accurately interpret our recordings. In the near future, automated interpretation of automated recordings (Brandes 2008, Acevedo et al. 2009, Buxton and Jones 2012, Aide et al. 2013) may become a viable option that removes the need for full interpretation of recordings by humans. Automated recordings have additional value beyond their use in identifying species to estimate occupancies. Increasingly, researchers (Farina et al. 2011, Depraetere et al. 2012, Gasc et al. 2013, Wimmer et al. 2013) are analyzing the spectral complexity of soundscapes sampled by automated recorders for assessing biodiversity and evaluating ecological dynamics. Site-level detection probabilities using automated recorders were high (p* > 0.8) for most (75%) common species we evaluated, which illustrates their effectiveness for monitoring common forest birds. Our results showing how detection probability varied by species and time of day may assist others planning to survey birds using automated recorders to make decisions about study design and to better understand which species might be effectively monitored. Alone or in combination, survey date, elevation, and tree canopy cover explained detectability and improved model performance for most (78%) common species. The site-level factors, elevation and canopy cover, likely served as proxies for bird abundance (Ke´ry et al. 2010, 2013), which may in turn have been 333
associated with increased detection probability (Ke´ry 2002, Royle and Nichols 2003). Our results provide a good baseline for assessing relative abundances and habitat associations of common forest birds across a large geographic region. To do so, we assumed that occupancy is a good surrogate for abundance (MacKenzie and Nichols 2004) for forest birds surveyed within small quadrats (50 m effective survey radius) across a large geographic region (5.4 million ha). Our results quantify which birds were more common than others in northern California forests. An understanding of which of these species were dominant versus intermediately abundant can inform us about the degree of niche partitioning among species and how this may be related to habitat structure (Whittaker 1965, Beedy 1981). Furthermore, we provided an example of how optimal occupancy associations with elevation and tree canopy cover differed for 3 species; this analysis could be expanded to all common species surveyed by automated recorders. For most of the species we modeled, we believe our surveys met the closure assumption (MacKenzie et al. 2006:104) required of occupancy modeling. This is because survey replicates occurred over a short time period (3 days) during the breeding season when most songbirds maintained territories. Furthermore, our occupancy estimates were unlikely to have been affected much by immigration over the time period we surveyed, because our surveys began approximately 1 month after most migrants arrived from winter ranges based on our review each year of observations reported to the eBird database (Sullivan et al. 2009). Monitoring Trends Our power analysis demonstrated that, for a modest investment of 100 sites per year, automated recorder surveys should be able to monitor >30 avian species to detect declines as small as 2.5% per year over 20 years. This is the same standard recommended for the North American Breeding Bird Survey (Bart et al. 2004). Far fewer (6) species could be monitored for the same rate of annual decline over 10 years. Drawing conclusions about trends over less than a decade could be misleading because of the difficulty of distinguishing short-term population cycles arising from transient or random events (Elias et al. 2006, Ludwig et al. 2006) from long-term trends linked to stressors of conservation concern (e.g., climate change, habitat degradation). The value of long-term monitoring is exemplified by some notable programs including the North American Breeding Bird Survey (Sauer et al. 2003) and the Christmas Bird Count (Butcher et al. 2006) that have collected data for decades. Our power analysis results confirm that sustained commitment over many years will be required to monitor trends of conservation significance. In the short term, however, baseline results can help us better understand the relative abundances of species in a metacommunity and the habitat associations of individual species. In the long term, habitat-association models can help us identify range shifts associated with climate change or other environmental 334
stressors (Tingley and Beissinger 2009). Furthermore, monitoring trends of individual species need not be considered in isolation but can be compared to responses of other species based on whether or not they compete within the same niche. We focused on monitoring common species consistent with the Partners in Flight’s (Rich et al. 2004) goal of “keeping common birds common.” Common birds are important because they may disproportionately reflect ecological processes within an avian metacommunity (Lennon et al. 2004, Gaston and Fuller 2008, Koch et al. 2011, Inger et al. 2015). Nevertheless, rare species can also be addressed using multi-species occupancy models (MSOM) that pool data from species with similar traits or conservation issues (Dorazio and Royle 2005, Zipkin et al. 2009, Iknayan et al. 2014). The data we collected using automated recorders can be applied within either single- or multi-species modeling frameworks. We chose to use single-species occupancy models to monitor common species, because MSOMs tend to pull species-specific estimates towards the metacommunity mean, a phenomenon known as shrinkage (Link et al. 2002, Gelman and Hill 2007). Although the MSOM approach is useful to increase the precision of estimates of occupancy for rare species (Zipkin et al. 2009), it may do so at the expense of accuracy for other species. Automated Recorders versus Point Counts Our field test results suggest that surveys using automated recorders can provide modeled estimates of occupancy comparable to those using point counts, despite systematic differences in detection probabilities. However, because of a small sample size, we limited our comparison to average estimates of occupancy among a limited number of species. Additional tests are needed to evaluate the comparability of these survey methods on a species-by-species basis that also addresses differences in habitat and other factors that may differentially affect detectability. Whether there was a difference in effective survey area was of greater concern than detection probability in our comparison of automated recorders with point counts. We needed to understand the effective area of our surveys to compare results from automated recorders with those using other survey methods, because, holding all other factors constant, occupancy is expected to be higher for a larger survey area (Gaston and He 2011). Our findings suggest that automated recorders in protective containers captured sounds well out to 40 m and occupancy estimates from automated recorders and point counts were most similar when we truncated the latter to 50 m or 75 m. Based on these 2 findings, our best guess is that the effective distance of the automated recorder we used was about 50 m. This suggests that automated recorders may be broadly comparable to point counts, because point counts are often truncated at 50 m because of inaccuracies in species identification beyond that distance (Ralph et al. 1995, Schieck 1997). Nevertheless, more investigation is warranted because the effective distance of automated recorders likely varies by species. Our study did not have sufficient sample size to test species separately. The The Journal of Wildlife Management
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effective survey area of automated recorders we used was also affected by the protective containers used to protect microphones. More expensive automated recorders with weatherproof microphones are available (e.g., Song Meter; Wildlife Acoustics, Inc., Maynard, MA); however, users must understand their effective recording distances to compare their occupancy estimates with point counts. Automated recorders offer an alternative to point counts. One advantage of recorders is that they are more amenable to scheduling surveys at comparable times of the day and for cost-effectively allowing multiple repeat surveys for use in occupancy modeling (Hobson et al. 2002, Rempel et al. 2005). By surveying at the same times each day, automated recorders are likely to reduce diurnal variability in modeled detection probability. Furthermore, the record provided by automated recorders allows more than 1 interpreter to independently review the data, an advantage that is usually not feasible for point counts. This feature allows us the flexibility to re-analyze all of our surveys to address falsepositives using an alternative model structure (Royle and Link 2006, Miller et al. 2011). We identified a number of issues for using automated recorders, many of which apply similarly to point counts (e.g., study design, detection probability, misclassification, closure assumption). Rather than choosing between automated recorders and point counts, we suggest that a combination of both methods may be the best approach. One option would be to conduct point counts during the deployment and retrieval of automated recorders. Both streams of data could be used in the same occupancy model to reduce bias and improve precision.
MANAGEMENT IMPLICATIONS Multi-species monitoring is an essential function of agencies charged with managing and conserving wildlife and the habitats on which they depend. Avian surveys are particularly useful because the taxonomic and behavioral diversity of birds make them good indicators of conditions in the larger ecological system that birds nest and forage within. Unfortunately, it may be expensive and logistically challenging to find enough skilled birders to sustain a program of annual point counts, especially for random sites across a large forested region surveyed during the short breeding season when migratory birds are best detected by their song. Our findings suggest that automated recorders, used by themselves or in conjunction with point counts, are well suited to help managers make this task less burdensome, in part, because automated recorders can make repeat surveys at synchronized times each morning to make occupancy estimates more precise and accurate. If a skilled birder is not required in the field to conduct every repeat survey, then effort can be redirected to sampling a greater number of independent sites over a large geographic area.
ACKNOWLEDGMENTS We thank D. Smith, D. Walker, T. Burton, J. Siperek, S. Torres, E. Loft, and D. Koch of the California Department Furnas and Callas
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of Fish and Wildlife for supporting the EBM project over the past decade. R. Barrett, S. Beissinger, and R. Bowie at the University of California at Berkeley provided valuable comments on methods and drafts of this article. Reviews by A. Kroll and three anonymous reviewers also improved the manuscript. D. Fix, R. Fowler, M. McGrann, M. Tingley, A. Engilis, and J. Trochet interpreted the automated recordings. R. Landers aided with mathematical analyses and field tests. None of this monitoring would have been possible without the hard work and dedication of our summer field crews and CDFW biologists. We thank the U.S. Forest Service, Bureau of Land Management, National Park Service, Sierra Pacific Industries, Roseburg Forest Products, Fruit Growers Supply Company, W. M. Beaty & Associates, Timber Products Company, and the Collins Almanor Forest for providing access to their lands. Essential funding was provided by the U.S. Fish and Wildlife Service through a series of State Wildlife Grants.
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