DOI: 10.5061/dryad.s4mw6m96d
title: Combining point counts and autonomous recording units improves avian survey efficacy across elevational gradients on two continents

description: Accurate biodiversity and population monitoring is a requirement for
 effective conservation decision-making. Survey method bias is therefore a
 concern, particularly when research programs face logistical and cost
 limitations. We employed point counts (PCs) and autonomous recording units
 (ARUs) to survey avian biodiversity within comparable, high elevation,
 temperate mountain habitats at opposite ends of the Americas: 9 mountains
 in British Columbia (BC), Canada and 10 in southern Chile. We compared
 detected species richness against multi-year species inventories and
 examined method-specific detection probability by family. By incorporating
 time costs, we assessed the performance and efficiency of single vs.
 combined methods. Species accumulation curves indicate ARUs can capture
 ~93% of species present in BC but only ~58% in Chile, despite Chilean
 mountain communities being less diverse. The avian community, rather than
 landscape composition, appears to drive this dramatic difference. Chilean
 communities contain less-vocal species, which ARUs missed. Further, 6/13
 families in BC were better detected by ARUs while 11/11 families in Chile
 were better detected by PCs. Where survey conditions differentially
 impacted method performance, PCs mostly varied over the morning and with
 canopy cover in BC, while ARUs mostly varied seasonally in Chile. Within a
 single year of monitoring, neither method alone was predicted to capture
 the full avian community, with the exception of ARUs in the alpine and
 subalpine of BC. PCs contributed little to detected diversity in BC, but
 including this method resulted in negligible increases in total time
 costs. Combining PCs with ARUs in Chile significantly increased species
 detections, again, for little cost. Combined methods were among the most
 efficient and accurate approaches to capturing diversity. We recommend
 conducting point counts while ARUs are being deployed and retrieved in
 order to capture additional diversity with minimal additional effort and
 to flag methodological biases using a comparative framework.
description: <b>Survey Locations</b> In Canada
 (2019), we surveyed nine mountains in the D'ze Kant (Bulkley)-Nechako
 and Kitimat-Stikine regions of British Columbia (BC; 1000–1801 m
 elevation; Fig. 1). In Chile (2018), we surveyed 10 mountains in La
 Araucanía and Los Ríos regions (1000–1700 m elevation). These mountains
 fall within the traditional unceded lands of the Wet'suwet'en,
 Gitxsan, and Tsimshian First Nations in BC and the Mapuche people in
 Chile. The farthest latitudinal and longitudinal distance among survey
 locations was 117 km and 106 km, respectively, in BC, and 178 km and 60
 km, respectively, in Chile. Surveyed habitats across elevation gradients
 in both regions were classified as: montane habitat (≥50% tree cover, 1000
 – 1557m a.s.l.); subalpine (≥5 – 50% tree cover, 1169 – 1658m a.s.l.); and
 alpine (0 – 5% tree cover, 1319 – 1801m a.s.l; Boyle & Martin,
 2015). All point count locations are listed in the
 associated file: "PointCountLocations_BC_CH.xlsx"
 BC survey sites fall within five biogeoclimatic zones: Coastal
 Mountain Hemlock, Mountain Hemlock, Engelmann Spruce-Subalpine Fir, Boreal
 Altai Fescue Alpine, and Coastal Mountain-heather Alpine (British Columbia
 Ministry of Forests, Lands, Natural Resource Operations, and Rural
 Development, 2018). Montane habitat is primarily old growth conifer forest
 interspersed by avalanche chutes, producing age heterogeneity. The
 subalpine consists of woody shrubs, grasses, and perennial herbs with some
 tree cover, while the alpine is characterized by the presence of fescue
 grasses, heather, mosses, and lichens. In Chile,
 montane habitats are dominated by old growth mixed broadleaf-conifer
 forests, with about 10% mid-successional forest. Subalpine habitat is a
 mix of highland herbaceous meadows, shrubs, and sparse patches of trees
 and/or krummholz. Perennial herbaceous plants, shrubs, few or no trees,
 and bare rock/scree characterize alpine habitat. Vegetation structure
 varies within- and among-mountains based on natural disturbances (i.e.
 volcanic eruptions) and/or land-use history (Caviedes & Ibarra,
 2017). <b>Point Counts</b>
 Starting at sunrise, 95% of surveys were conducted within 5 hrs
 to encompass peak bird activity. The remaining 5% of surveys occurred 5–6
 hrs after sunrise within subalpine and alpine habitats due to logistical
 constraints (total range: 04:53–11:24 hrs in BC and 05:51–12:18 hrs in
 Chile). Each mountain was surveyed from bottom to top (upslope) along
 transects with five designated point counts, 200m apart, within each of
 the three habitat types for a total of 15 point counts per mountain. In
 BC, steep topography meant that the subalpine on Thornhill Mountain fit
 only four point count locations and that the alpine on Nadina Mountain was
 inaccessible until July. Thus, BC had 129 point count sites: one fewer
 subalpine site and five fewer alpine sites in total.
 During each 6-minute point count, birds were counted by sight and
 sound. Observers kept track of individual birds to minimize duplicate
 detections among point counts. Infinite radius detections were used to
 provide a fair comparison to ARU sampling; 95% of individuals in British
 Columbia and 99% of individuals in Chile were detected within 100m. Point
 counts that occurred near habitat transition zones did not record species
 that called  &gt;100m away if they were clearly within adjacent
 habitats or if they were in that direction and were unlikely to be in the
 focal habitat, based on their ecology. Counts were repeated three times
 within each respective breeding season: between May 30 – July 16 in BC,
 and between November 7 – December 21 in Chile, to assess detection
 probability and address seasonal variation in detection. Repeated site
 visits were separated by ~2 weeks. <b>Acoustic
 recordings and analysis</b> Song Meter SM4
 Autonomous Recording Units (ARUs; Wildlife Acoustics Inc. ©, Maynard, MA)
 with two omni-directional microphones were deployed at two point count
 sites within each habitat and away from habitat transition zones (6
 ARUs/mountain, &gt;400 m apart) in both BC and Chile. In BC, 36 units
 were deployed on all 9 mountains. As above, the alpine on Nadina Mountain
 was inaccessible resulting in ARUs being deployed at a total of 52 point
 count sites rather than 54. Units recorded for an average of 21 days
 within the BC breeding season: 10 – 20 days on six mountains, and 32 – 35
 days on the remaining 3 mountains, between June 3 and July 15.  In Chile,
 6 units were deployed on five mountains (30 point count sites in total)
 and recorded for an average of 6 days within the breeding season: 5 – 10
 days each, between November 13 – December 28. ARUs recorded at a sampling
 rate of 24000 Hz in stereo wav format using default acoustic gain settings
 for the microphones. Units were mounted on a tree within several meters of
 the point count site, or on a PVC pipe at ~1.5 m height in the alpine.
 Units were programmed to record 30-mins on and 30-mins off, starting at
 sunrise and ending 5 hours after sunrise (5 x 30-minute recordings/day).
 From the full deployment period, we randomly sampled two (BC) or three
 (Chile) different days per point count site. In BC, four days were
 selected for the three mountains where the ARUs were deployed for a longer
 period (early and late breeding season). To be comparable with point
 counts, we randomly chose one, 6-minute interval to analyze from each of
 the 5 x 30-minute recordings on these days (5 x 6-minute point
 counts/day). Thus, within each survey day, ARUs were sampled within hourly
 windows from between 0–1 hr after dawn (“hour 0”) to between 4–5 hrs after
 dawn (“hour 4”). If any given hour(s) within the selected day had
 unfavourable conditions (wind or rain) that interfered with the audio,
 another day was selected randomly to obtain the missing time period(s). A
 total of 700 site-surveys in BC and 450 site-surveys in Chile were
 analysed. Sound recordings were analyzed using
 Audacity® software (V2.3.0, Audacity Team, 2020). Three skilled observers
 reviewed all recordings: two in BC and one in Chile. All observers had
 experience conducting point counts in the same regions. In BC, both
 observers analyzed five of the same recordings to confirm detection
 consistency and conferred with each other on all recordings when species
 identification was uncertain. Spectrograms were scanned manually in stereo
 format as the observer listened to the recording. Species that were more
 difficult to identify were compared with recordings available on
 bioacoustics libraries such as the Xeno-Canto Foundation (2019) and/or
 sent to other skilled ornithologists. <b>Abiotic
 Variables</b> At each point count we recorded
 average temperature and wind speed using a Kestrel 3500 weather meter
 (Nielsen-Kellerman Company, PA, USA). We additionally scored wind as a
 categorical variable (Beaufort scale: 0-3) during point counts to allow
 for comparison with ARU wind scores that were assigned on the same scale
 based on interference with the audio recording. We also recorded percent
 canopy, understory (vegetation ~30cm in height), shrub, and ground cover
 (tundra vegetation, snow, rock, and dead trees) within a 50m radius of all
 point count sites. More canopy foliage in Chile was deciduous than in BC.
 Canopy cover value therefore increased with leaf-out during the season in
 Chile, while values in BC were static.
description: BC species are coded using four-letter alpha codes (for English
 common names);
 https://www.birdpop.org/pages/birdSpeciesCodes.php.
 Chilean species are coded using six-letter alpha codes (for
 scientific names). Species included in family-level
 groupings are listed in Table A1 of the associated manuscript.
 Habitat is coded as: AL (alpine), SA (subalpine) and UM (upper
 montane) Method is coded as: PC (human point count
 observation), ARU (autonomous recording unit) Locations
 (lat, long) of point count sites are available in the associated file:
 "PointCountLocations_BC_CH.xlsx"
 "spp_acc" files contain species precence/absence data
 formatted for species accumulation curves/Hill number calculations. Note:
 PC records were further subsetted to only those sites also surveyed by
 ARUs prior to analysis. "Unmarked" files
 contain family-level precence/absence data and site and survey-level
 condition variables for program "Unmarked".