Conserving bird populations requires knowledge of bird distribution and habitat use at all stages of their life cycle, including during breeding, migration, and non-breeding periods (Sherry and Holmes 1995). Monitoring birds’ habitat use during migration is a necessary component of conservation plans for migratory birds. Historically, researchers have primarily relied on in-person observations including mist-netting (Peach et al. 1996) and point counts (Ralph et al. 1995) for migration monitoring, but because birds are frequently detected by sound, audio recording technology offers opportunities to expand monitoring techniques. Here we present a method for using audio recorders to monitor the timing of migration in remote or seasonally inaccessible habitats.
Figuring out how to best monitor bird abundance and diversity in remote habitat is a current challenge. The climatic variation between winter and summer in high latitude continental regions increases the challenges associated with accessing remote areas during spring migration. Significant annual snow accumulation in the winter, followed by rapid melting as temperature increases in spring, makes unpaved roads impassable for a period of weeks in much of northern North America, typically overlapping the time period when migrant bird species begin to arrive in the region in spring. For example, in the State of Michigan, in the United States, many roads are closed to vehicles and unmaintained from November to April (Michigan Transportation Fund Act 1981). Developing survey monitoring protocols that can be implemented despite poor traveling conditions is a way to fill in gaps in knowledge of northern forest birds and birds in similarly remote habitats.
Autonomous recording units (ARUs) are programmable audio recorders that can be deployed in the field for long time periods to efficiently maximize the spatial and temporal extent of monitoring. Passive acoustic monitoring is widely used in ecology to monitor and study vocalizing organisms; ARUs have been deployed to study bats (Tuneu-Corral et al. 2020), whales (Baumgartner et al. 2019), invertebrates (Penone et al. 2013), amphibians (Dutilleux and Curé 2020), and birds (Shonfield and Bayne 2017). ARUs are also used to evaluate the success of conservation programs (Shonfield and Bayne 2017). Current challenges for implementing passive acoustic monitoring include the availability of reference sound libraries, minimizing errors in species identification, and determining the relationship between acoustic index values and their associated real-world underlying parameters (Gibb et al. 2019), as well as accounting for differences in the sampling detection space when deploying recorders at different sites and in different configurations (Darras et al. 2016). ARU deployments are frequently limited by both battery life and data storage capabilities, but rapid advances are currently being made in deploying fully autonomous systems that are solar powered and can automatically transmit data (Sethi et al. 2018).
Point-count surveys are the most commonly used bird monitoring protocol for long-term study sites (Ralph et al. 1995, Rosenstock et al. 2002), but ARUs are now viewed as a viable supplement to point-counts, especially during the breeding season when birds vocalize frequently (Furnas and Callas 2015, Klingbeil and Willig 2015, Shonfield and Bayne 2017, Darras et al. 2018, Darras et al. 2019). Many researchers have compared ARUs and point counts in terms of their estimates of species richness and relative abundance or occupancy (Haselmayer and Quinn 2000, Campbell and Francis 2011, Tegeler et al. 2012, La and Nudds 2016), including in temperate forest (Klingbeil and Willig 2015). However, none of these studies (including the 23 studies reviewed in Darras et al.’s  meta-analysis) compared point counts and ARUs during migration. Birds behave and vocalize differently during migration than during the breeding season (Rappole and Warner 1976, Morse 1991). Testing and refining migration-specific monitoring techniques for ARUs is therefore necessary to understand how data from ARUs compare to data from in-person observations.
ARUs are currently used during migration to record the flight calls of nocturnally migrating species. They are deployed to track the abundance of migrants as they move through an area and can provide helpful information about migratory flyway locations, migration phenology, and relative abundance of migrants (Evans and Rosenburg 2000, Farnsworth et al. 2004, Sanders and Mennill 2014). Understanding how migrating birds use remote habitats during the migratory period is a different challenge and requires different methods. Determining how birds are distributed, the relative abundance and species richness, and the timing of arrival and departure from remote areas during migration are all important research questions for applied conservation.
To take advantage of the large volume of data generated by continuously recording ARUs, researchers are actively developing methods for automated identification of vocalizing organisms (Salamon et al. 2016, Gibb et al. 2019, Cramer et al. 2020). Applying automated detection algorithms requires extensive calibration time and expertise in signal processing systems (Priyadarshani et al. 2018b). Even with extensive algorithm training, detection precision can still be low for some species (Ruff et al. 2020). We present a method that can be implemented by anyone with the skills to conduct point counts, that does not rely on machine learning for species identification and data processing. Because applications of ARUs surveying diurnal habitat use during the migratory period have been under-explored in the literature thus far, we demonstrated and assessed an immediately applicable monitoring technique.
We compared data from ARU surveys to in-person point count surveys during spring migration in the northern Great Lakes region of the United States. Our goal was to understand how ARUs could be applied to monitor diurnal habitat use during migration by examining whether ARUs could provide estimates of relative abundance and number of species that are comparable to estimates from in-person surveys. We asked the following questions. 1) What are the differences between the number of species detected using point counts and using ARUs? 2) Can ARUs give estimates of relative abundance for focal species that are correlated with estimates of relative abundance from point counts? 3) Can randomly sampling from extended duration audio recordings provide better estimates of focal species relative abundance or the number of species detected than consecutive minutes of audio recording?
We conducted in-person point counts alongside continuously recording ARUs on the southern shore of Lake Superior during two months at the start of spring migration. We compared both raw data and model-based estimates of the number of species detected and focal species relative abundance from point counts and ARUs. Our sampling scheme targeted diurnal land birds using the peninsular habitat during the migratory period. The sampled community consisted largely of passerine species that breed in forested habitat in North America, including Canada and Northern Michigan.
We conducted field work in a 2.7 km² area on the Point Abbaye peninsula in Baraga County, Michigan, USA (Fig. 1). Surveys took place from 2 April to 22 May 2019 and were conducted daily unless prevented by weather conditions. Field work was designed to coincide with the arrival and peak relative abundance of early-season migrating birds. Point Abbaye juts into the southern part of Lake Superior and comprises the western border of Keweenaw Bay. Habitat included forested wetland, upland hardwood, and hardwood forest disturbed by recent logging activity. We selected survey sites randomly across the study area using the R programming language and the rgdal, geosphere, rgeos, sp, maptools, and spatstat packages (Pebesma and Bivand 2005, Bivand et al. 2013, Baddeley et al. 2015, Bivand et al. 2018, Bivand and Rundel 2018, Bivand and Lewin-Koh 2019, Hijmans 2019, R Core Team 2020). We conducted a pilot study in 2018 to test our protocols and evaluate the accessibility of our randomly selected survey locations. See Appendix 1 for details about pilot year surveys, and survey site and date selection.
Birds were recorded using three SWIFT bioacoustic recorder rugged units (Cornell Lab of Ornithology, Ithaca, NY, USA) and one AudioMoth bioacoustic recorder that was housed in a thin plastic bag for light weather proofing (Hill et al. 2018, Open Acoustic Devices, Southampton, UK). SWIFT units used a built-in PUI Audio brand omni-directional microphone. The AudioMoth unit used an analog microelectro-mechanical systems (MEMS) microphone. We refer to both the SWIFT and AudioMoth units as “automated recording units” (ARUs). ARUs recorded at a sampling rate of 48 kHz and saved recordings as uncompressed .WAV files. The microphone gain was set to “mid-high” for the AudioMoth unit and 35 dB for the SWIFT units. The signal to noise ratio reported by device manufacturers is approximately 58 dB for the SWIFT units and approximately 44 dB for the AudioMoth unit.
ARUs recorded continuously for five hours each day, beginning within 10 minutes of local sunrise time (United States Naval Observatory 2016). The field technician manually re-programmed recorders approximately once per week to adjust for changing sunrise times. ARUs were attached to trees less than 0.6 m in diameter, and were placed 1.5–2 m above the ground (Darras et al. 2018). The SWIFT omni-directional microphones were always oriented downward to prevent precipitation landing directly on the microphone. After the five hour recording period ended each day, ARUs were moved to new locations for the next day’s samples, thereby rotating the ARU and point count samples through all 18 survey locations approximately every five days. The sampling order for the points was chosen randomly, and ARUs were deployed to the randomly selected point locations each day.
Point counts were conducted daily next to each ARU during the five hour recording period. Point counts involved recording all birds seen and heard at an unlimited distance during a stationary, 10-minute count. The technician noted wind speed, precipitation level, and non-bird noise level, which included both anthropogenic noise like boats and planes, as well as frogs and other taxa. We did not survey in high wind or heavy precipitation. See Appendix 1 for detailed point count protocols.
We conducted desk-based audio bird surveys by listening to ARU recordings played through headphones on a laptop computer in the lab after the end of the field season. We tested three types of desk-based audio surveys: 1) we listened to a recording of the 10 consecutive minutes during which the in-person point count was conducted; 2) we listened to 22 minutes from each recorder, selected randomly from the five hour recording duration; 3) we sampled a subset of 10 of the 22 random minutes (without listening to those minutes again). Our goal was to compare each of these desk-based ARU survey methods to in-person point count observations.
For each audio file, the desk-based survey technician noted the identity of each bird species that vocalized, the type of vocalization, and the 30-second time intervals in which each species vocalized. We discarded randomly sampled minutes that contained a human voice. While listening, we viewed spectrograms of the recording in Audacity (Audacity Team 2019). Detailed protocols for completing desk-based audio surveys can be found in Appendix 1, and a completed data sheet from a desk-based survey is shown in Fig. A1.6.
We summed the number of unique species detected (S) separately using each survey type: 10-minute in-person point counts (Sp), 10 consecutive-minute ARU surveys (S10C), 10 random-minute ARU surveys (S10R), and 22 random-minute ARU surveys (S22R) (Table 1). We calculated a value of S for each individual survey on each day, resulting in three or four values of S for each survey type on each day.
We created an index of daily relative abundance (A) for individual species using each survey type (Table 1). Our relative abundance indices were: the mean observed number of individuals per point count (Ap); the proportion of 30-second intervals with a vocalization calculated by surveying n minutes in sections of 10 consecutive minutes (AnC); the proportion of 30-second intervals with a vocalization calculated by surveying n minutes in sections of one minute chosen randomly from the five hour survey window (AnR). To reduce the number of zero relative abundance counts in our data, we calculated indices by grouping all surveys of each type for each day, so there was a single value for each relative abundance index on each day. Abundance index abbreviations (Table 1) indicate the number of minutes surveyed per day. For example, three samples of 10 consecutive minutes per day were aggregated for an index of A30C, indicating that 30 minutes total were sampled for each day.
Note that while April 16th and 17th data appear on plots and in results, ARU malfunctions on those dates made the number of sampled minutes, n, different for those two dates for some of our relative abundance indices. See Appendix 1 for detailed discussion of sample size on these dates. Because these dates coincided with an important arrival period of migrants into the study area, we did not exclude them from our analyses.
Observed number of species
To determine whether the survey type (Sp, S10C, S10R, S22R) significantly influenced the number of species detected, we modeled the number of species detected using a generalized linear mixed model (GLMM) with a Poisson error distribution and log link function, using the lme4 package in R (Bates et al. 2015, R Core Team 2020). Our fixed effects were survey type, first and second degree terms for day of year, wind, rain, noise, and interactions for the day of year terms and survey type, and for rain and survey type. We also used day of year as a random effect; we expected that the number of species detected by all surveys on each day would be strongly correlated, regardless of survey location or survey type. More information about our GLMM can be found in Appendix 1.
We compared relative abundance estimates from Ap to each of the three desk-based audio survey types (A30C, A30R, A66R) for all species that were detected at least twice using each survey type. To illustrate these analyses, we present detailed results for two example species, Regulus satrapa (Golden-crowned Kinglet) and Troglodytes hiemalis (Winter Wren). Winter Wrens were abundant in the survey area, and vocalized frequently and loudly during early spring, representing the “best case” scenario for detectability on ARU recordings. Golden-crowned Kinglets were abundant in the survey area, but vocalized quietly (though regularly) during early spring, and so represent a greater challenge for detection using ARUs.
For each relative abundance model, we fitted boosted regression trees (BRTs) (Friedman 2001, Elith et al. 2008) using 200 iterations of five-fold temporal block cross validation (Fig. A1.3, Roberts et al. 2017). This resulted in a total of 1000 BRT fits per relative abundance model. We generated predicted relative abundances by averaging predictions from the 1000 fits of each model. The predictor variables in our model were day of year (continuous) and wind speed (categorical with three levels representing Beaufort forces of 0-1, 2, or 3 or higher [Beaufort 1805]). We fit BRTs with the gbm package in R (Greenwell et al. 2019, R Core Team 2020). Details of BRTs, including model tuning and control of overfitting are in Appendix 1, and Figs. A1.4 and A1.5.
We assessed whether the observed values from the three desk-based audio survey indices (A30C, A30R, A66R) were correlated with the observed values from Ap, and whether the predicted values from the models trained with desk-based audio survey indices were correlated with the predicted values from models trained with Ap for all focal species using Spearman’s rank correlation coefficient. To assess whether the correlation coefficients for all species varied based on which abundance index pair was used and on whether coefficients were calculated from observed or predicted values, we fit a linear mixed model. The response variable was correlation coefficient, the fixed effects were index pair (Ap and A30C, Ap and A30R, or Ap and A66R), value type (observed or predicted), and their interaction (index pair x value type), and the random effect was species. We fit the model using the nlme package in R (Pinheiro et al. 2019, R Core Team 2020).
Between 2 April and 22 May 2019, we were able to survey on 37 days. During that time, we conducted 137 in-person point counts. We recorded 130 simultaneous 10-minute periods with ARUs (when a human observer was also present conducting a point count) and 124 periods of 22 randomly selected minutes from each morning (five hours of recording). All four audio recorders experienced occasional malfunctions; more details about these malfunctions can be found in Appendix 1. A complete list of the species detected by each survey method is in Table A1.1.
A Chi-square ANOVA comparing our full model to a null model with survey type removed showed that survey type (the S-index used) had a significant effect on the number of species detected (χ29, 21 =247, p < 0.0001). We detected a similar number of species using S10R as we did using Sp (Fig. 2; Table 2; change in the log of the number of species detected = -0.065, 95% CI [-0.2; 0.08], p = 0.3888). Using S22R, we detected significantly more species than by using Sp (Fig. 2; Table 2; change in the log of the number of species detected = 0.305, 95% CI [0.17; 0.44], p < 0.0001). We detected fewer species using S10C than using Sp (Fig. 2; Table 2; change in the log of the number of species detected = -0.614, 95% CI [-0.78; -0.44], p < 0.0001). Listening to randomly selected rather than consecutive minutes eliminated the gap in number of species detected between 10-minute point counts and 10-minute ARU surveys (Fig. 2). Day of year had a significant effect on the number of species detected (Table 2), with more species expected later in the migration season (Fig. 2).
Chi-square ANOVA showed that the overall effect of wind was not significant (χ219, 21 =1.44, p = 0.48) nor was the overall effect of precipitation (χ218, 21 = 3.45, p = 0.32). The overall effect of noise was significant (χ219, 21 = 10.3, p = 0.005). The interaction between survey type and day of year was not significant (Table 2), providing no evidence of a difference in the effect of survey method on the observed number of species over the course of the survey season.
BRT models of relative abundance over time differed in how well they showed the initial period of absence, and the increase in relative abundance corresponding with the arrival of migrant birds in our study area, depending on the survey method used (Fig. 3, Fig. A2.1). The general pattern of initial absence followed by arrival of migrants can be seen in both the raw data and the model predictions of relative abundance for Ap, A30R and A66R for Winter Wrens (Fig. 3 a, c, and d) and for Ap and A66R for Golden-crowned Kinglets (Fig. 3 e, and h). The same general pattern is visible for Ap and A66R for at least 12 additional species (Fig. A2.1). For most species, including our two example species, the observed relative abundance indices from ARU surveys were positively correlated with the observed relative abundance index from point counts (Fig. 4, Fig. 5, Table A2.1), indicating that the relative abundance proxies we calculated using ARUs are comparable to relative abundance estimates from in-person observations.
For all species, correlations for predicted values of Ap and the three desk-based survey methods (A30C, A30R, A66R) were generally higher than correlations of the observed index values (Fig. 6, Table 3, Table A2.1). For correlations calculated with observed abundance index values, Ap appeared to be most strongly correlated with A30C; however, correlations calculated with predicted abundance index values were stronger between Ap and the two random-minute indices (A30R and A66R) (Fig. 6, Table 3). The strongest median correlation value was between predicted values of Ap and A30R, though predicted values of Ap and A66R were only slightly less correlated (Fig. 6, Table 3). The strong correlation between predicted values of the relative abundance indices indicates that our models found the same underlying signal regardless of whether training data were from ARUs or point counts.
The interaction between value type (observed or predicted) and index pair (Ap and A30C, Ap and A30R, or Ap and A66R) was significant according to a likelihood ratio test comparing linear mixed models with and without the interaction term (L = 7.85, df = 1, p = 0.01). This indicates that the correlation values depended on the value type and indices used.
Our results showed that ARUs recording for an extended duration can be as effective as in-person point counts for monitoring vocal migrating land birds in high latitude remote habitats during spring migration. The number of species detected by randomly sampling minutes from ARU recordings was similar to, or higher than, the number of species detected by point counts. Relative abundance models trained with ARU data showed the increase in relative abundance indicating the arrival of migrants at the study site, suggesting that ARUs can be used to track migration phenology in remote habitats for vocal species.
Data from randomly selected minutes of ARU recordings detected more species and produced modeled relative abundance estimates that better showed the expected seasonal pattern of migration timing than data from consecutive minutes of ARU recordings (Fig. 2, Fig. 3, Fig. A2.1). There are two likely explanations for this. First, randomly selected minutes are less temporally auto-correlated than consecutive minutes. For example, during a 10-minute in-person point count, little new information is gained during the seventh minute of the survey compared to what was collected during the sixth minute of the survey; a Winter Wren singing near the end of the sixth minute of a point count survey will likely still be singing in the beginning of the seventh minute. By selecting minutes randomly from across the five-hour survey window, the temporal correlation between each successive minute that is analyzed is minimized. Second, during the migration season, birds may move more within the study area than they would during the breeding season, when they have established a territory. The community of birds within the immediate detection radius of an observer (either a person or a recording ARU) may therefore change over the course of five hours. Wimmer et al. (2013) found that randomly selected minutes from extended duration recording provided better estimates of species richness than consecutive minute in-person surveys during the breeding season. Our findings lend support to the conclusion that using randomly selected minutes provides a more complete sample of the birds using a spatial location over the entire course of the survey window than consecutively sampled minutes.
For in-person point counts, the time taken to travel to a survey site takes up a major portion of the total time invested, so site visits are typically limited to once per day. With ARUs, no such constraints exist; it is possible to do multiple short-duration surveys from many locations over the course of one day without additional travel and field work logistics. We recommend that studies using ARUs on migration should randomly sample recordings of short periods of time (e.g., one-minute recordings) from a defined survey window relevant to the study question (e.g., the five hours following sunrise for passerines in temperate forest or twilight to dawn for crepuscular and nocturnal species). Our study focused on migration, but we recommend that studies using ARUs to monitor birds during wintering or breeding seasons (e.g., Wimmer et al. 2013) also consider using randomly selected minutes.
We did not detect an effect of either wind or rain in our model of the number of species detected. However, because we controlled for adverse weather conditions during our field surveys by not deploying ARUs on rainy or windy days, the number of high wind values in our data was low, as was the number of rainy survey days. We noted anecdotally that the wind values recorded in person for a survey day did not always correlate with the amount of wind heard while conducting our desk-based audio surveys; we speculate that wind direction in relation to the microphone may make a difference in how much wind is actually picked up by the ARU. Given that wind and rain have an effect on the detectability of birds in the study system (Ralph et al. 1995), they remain important predictors to include, despite not appearing significant in our model.
Interpreting the significance of the noise variable is challenging, because we used the variable to describe all non-avian noise in the environment, which could include waves, airplanes, and frogs. We suspect that the overall significance of the noise variable may be due to frogs. Future studies may want to consider distinguishing between other vocalizing taxa and surrounding environmental noise, as ARUs can be used to simultaneously sample multiple taxa (e.g., crickets and bats; Newson et al. 2017). Background noise in ARU recordings can impede the ability of human listeners or automatic identification algorithms to identify bird calls and songs (Priyadarshani et al. 2018b). We addressed background noise in ARU recordings both in the experimental design and in statistical analysis: we did not deploy ARUs during high winds or during heavy precipitation, and we included wind speed and noise as "nuisance" covariates in statistical models. However, most uses of ARUs will deploy ARUs for much longer periods of time (weeks or months), and will not have the option of avoiding recording during strong wind and precipitation. Most uses of ARUs will therefore require an audio cleaning step to identify and deal with sections of recordings with high amounts of background noise (Priyadarshani et al. 2016, Lostanlen et al. 2019).
Estimates of abundance are more useful than estimates of occurrence for prioritizing conservation resources at dynamic temporal scales, such as during migration (Johnston et al. 2015). ARUs do not solve the problem of how to estimate true abundance during migration. We accounted for variation in detectability by controlling for observer effort and weather variables, but we recognize that imperfect detection, and the possibility of vocal behavior changing over time, means that the number of individuals detected is not necessarily a good estimate of the number of individuals present (MacKenzie and Kendall 2002). Hierarchical models that account for imperfect detection (MacKenzie et al. 2002, Kéry and Royle 2016) rely on assumptions about population closure that may be badly violated during migration, when birds are only present in stopover habitat for short periods of time. The period in which we can reasonably assume population closure for our study area during migration may be as short as several hours or as long as several days, depending on weather conditions. Therefore disentangling true occupancy or abundance from detectability is difficult, whether using traditional in-person survey methods or ARUs. The current standard in studies that examine abundance during the migration period is to account for detectability by controlling for effort and weather (Johnston et al. 2015).
It is possible that individual birds’ vocalizations may increase over the spring migration period, as birds prepare for the breeding season. Changes in vocalization behavior over the migration season could confound our estimates of relative abundance. One possible avenue for dealing with these issues is the method proposed by Metcalf et al. (2019) which used ARU data and dynamic occupancy models that allowed detectability and occupancy to vary over short timescales relevant to migration. We believe increases in relative abundance shown by our models reflect real increases in relative abundance associated with the arrival of these species in the study area, demonstrated by the abrupt arrival of focal species apparent in both raw data and model predictions (Fig. 3, Fig. A2.1) and by the moderate to strong correlations between results using Ap (relative abundance from point counts) and results using our ARU relative abundance indices for many focal species. ARUs can therefore provide valuable information about migration phenology, comparable to the information obtained by in-person surveys, even if estimating true abundance remains challenging.
The pattern of absence followed by arrival is visible for both Ap and A66R (and often A30R) for many species in addition to our example species (Fig. A2.1). This suggests that our methods are generalizable to many vocal birds in this region. The model predictions from random-minute ARU indices were more strongly correlated with Ap than were predictions from the consecutive minute ARU index (Fig. 6, Table 3). In contrast, raw observed values from A30C were more strongly correlated with observed values from Ap than were the random-minute indices (A30R and A66R). However, the goal of the abundance models was to produce similar summary conclusions from the data (i.e., similar out of sample predictions of relative abundance), not to reproduce the raw data values. Therefore, the random-minute ARU indices provided better estimates of relative abundance than did the consecutive minute ARU index.
Using correlation between point count and ARU relative abundance indices is an imperfect measure of ARU index performance. It works well when there is a strong directional trend in relative abundance, as we see with arriving migrant species. However, some vocal resident species (e.g., Common Raven, CORA, Fig. A2.1) showed no directional trend in relative abundance over time. Though the BRT models successfully showed the same overall trends for Ap, A30R, and A66R, the correlation coefficients were low (Table A2.1). Alternative measures for comparing models might more accurately describe how ARU surveys compare to point counts for all species, not only those that show strong directional trends in relative abundance.
Future studies might consider increasing ARU survey effort beyond our maximum of 66 randomly selected minutes per day. We were able to model relative abundance for more species with A66R (n = 30) than with A30R (n = 28). The rate at which we detected new species when sampling additional random minutes slowed notably with less than 22 minutes of sampling, suggesting that few new species would be detected by additional sampling, except in early April (Fig. A1.7). The optimum number of minutes to sample will likely depend on the study system and season.
We limited our survey window to the first five hours after sunrise in order to maintain similarity to common point count protocols. We limited our survey effort to 66 random minutes per day because we wanted to keep the technician work load for desk-based audio surveys similar to the time invested for point counts; when researchers do not have to travel between survey locations they may invest a higher percentage of their time listening to recordings. Future studies need not be bound to these constraints. ARUs can be used more flexibly when researchers are not concerned with direct comparison between in-person and ARU survey methods. Similarly, while we placed ARUs at about head-height because we were making direct comparisons to point counts, one could choose an optimal height to mount ARUs based on either behavioral characteristics of focal species or consideration of sound transmission and attenuation (Priyadarshani et al. 2018a). For example, researchers wishing to monitor canopy-dwelling forest birds may wish to place ARUs higher in order to better target those species.
Our study site and survey timing represent the northern end of the spring migratory journey, and therefore may represent a best-case scenario for ARU use during spring migration. Indeed, more southerly sites and the fall migratory period may present less favorable conditions for monitoring with ARUs, because many species may not vocalize as reliably when they are farther from their breeding grounds or moving away from their breeding grounds. We were unable to differentiate between individual birds using our study site as a stopover location before moving on to more northerly breeding grounds and those that would eventually establish a breeding territory locally. Future studies could evaluate the applicability of ARUs in migration stopover specifically by replicating this study farther south, where many of the species we detected will stopover but not breed. Further research is necessary to determine how far south these methods are applicable during spring, and whether they will work during fall migration.
Future studies using ARUs to monitor bird migration may wish to take advantage of ARUs’ unique ability to scale research in ways that may be infeasible or prohibitively expensive for in-person field work. ARUs can increase the amount of data collected without increasing the costs associated with technician-hours in the field (Williams et al. 2018). For example, ARUs could be deployed in dense, small-scale networks to examine micro-habitat use in stopover regions. Alternatively, they could be deployed on a latitudinal gradient covering hundreds or thousands of kilometers to examine how vocal behavior changes over the spring migration period as birds approach their breeding grounds.
Applying the methods described here can facilitate an increase in survey effort in difficult-to-access habitats in high latitude forests during migration. Temporal variation in accessibility in these habitats is dramatic, as unpaved roads typically turn from snow to slush to impassable mud before hardening into reliably dry surfaces in early summer. ARUs can eliminate many of the restrictive logistics and safety concerns for researchers interested in monitoring spring migration. Our method of using desk-based surveys of randomly selected minutes from ARUs can be used by any researcher with the skills to conduct point counts. Researchers can set up ARUs during winter conditions when access to study sites over snow is relatively easy (e.g., using snowmobiles, skis or snowshoes), and revisit to collect the audio data once conditions have stabilized in late spring. Our methods for using ARU data to model relative abundance of focal species and the number of species present during migration can be immediately applied to increase monitoring effort in logistically difficult regions.
We are deeply grateful to our local partners in the study region for their contribution to this research. Land access and logistical support was provided by the Keweenaw Land Trust and Pat Toczydlowski. Calvin and Steve Koski, Mark Summersett, and Bob and Nancy Korth provided invaluable support and assistance with transportation to the study site in difficult and unpredictable conditions. Our thanks also to Joseph Youngman, David Flaspohler, Drew Meyer and Dana Neufield for assistance with the pilot year surveys, to Alison Johnston for her insights about abundance modeling during migration, and to Jon Yearsley, Hannah White, and the Ecological Modeling lab at University College Dublin for consultation on modeling methods. This work was funded by grants to ER from the Wilson Ornithological Society and the Copper Country Audubon Society. WG was funded during this work by Science Foundation Ireland grant number 15/IA/2881.
Data and code to reproduce analyses can be found at https://doi.org/10.5281/zenodo.3964500 (ellieroark, 2020). Audio recording files used to produce this analysis are archived at https://doi.org/10.5281/zenodo.3964574 (Roark & Gaul, 2020).
Audacity Team. 2019. Audacity(R): Free Audio Editor and Recorder. [online] URL: https://audacityteam.org/
Baddeley, A., E. Rubak, and R. Turner. 2015. Spatial Point Patterns: Methodology and Applications with R. [online] URL: http://www.crcpress.com/Spatial-Point-Patterns-Methodology-and-Applications-with-R/Baddeley-Rubak-Turner/9781482210200/
Bates, D., M. Maechler, B. Bolker, and S. Walker. 2015. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67(1):1–48. https://doi.org/10.18637/jss.v067.i01
Baumgartner, M. F., J. Bonnell, S. M. Van Parijs, P. J. Corkeron, C. Hotchkin, K. Ball, … S. D. Kraus. 2019. Persistent near real-time passive acoustic monitoring for baleen whales from a moored buoy: System description and evaluation. Methods in Ecology and Evolution 10(9):1476–1489. https://doi.org/10.1111/2041-210X.13244
Beaufort, F. 1805. Beaufort Wind Scale. Retrieved July 28, 2020. [online] URL: https://www.spc.noaa.gov/faq/tornado/beaufort.html
Bivand, R., T. Keitt, and B. Rowlingson. 2018. rgdal: Bindings for the “Geospatial” Data Abstraction Library. [online] URL: https://cran.r-project.org/package=rgdal
Bivand, R., and N. Lewin-Koh. 2019. maptools: Tools for Handling Spatial Objects. [online] URL: https://cran.r-project.org/package=maptools
Bivand, R., and C. Rundel. 2018. rgeos: Interface to Geometry Engine- Open Source ('GEOS’). [online] URL: https://cran.r-project.org/package=rgeos
Bivand, R. S., E. J. Pebesma, and V. Gomez-Rubio. 2013. Applied spatial data analysis with R (2nd ed.). [online] URL: http://www.asdar-book.org/%0A
Campbell, M., and C. M. Francis. 2011. Using Stereo-Microphones to Evaluate Observer Variation In North American Breeding Bird Survey Point Counts. The Auk 128(2):303–312. https://doi.org/10.1525/auk.2011.10005
Cramer, J., V. Lostanlen, A. Farnsworth, J. Salamon, and J. P. Bello. 2020. Chirping up the Right Tree: Incorporating Biological Taxonomies into Deep Bioacoustic Classifiers. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Barcelona, Spain, May 2020, 901–905. https://doi.org/10.1109/ICASSP40776.2020.9052908
Darras, K., P. Pütz, Fahrurrozi, K. Rembold, and T. Tscharntke. 2016. Measuring sound detection spaces for acoustic animal sampling and monitoring. Biological Conservation 201:29–37. https://doi.org/10.1016/j.biocon.2016.06.021
Darras, K., P. Batáry, B. Furnas, A. Celis-Murillo, S. L. Van Wilgenburg, Y. A. Mulyani, and T. Tscharntke. 2018. Comparing the sampling performance of sound recorders versus point counts in bird surveys: A meta-analysis. Journal of Applied Ecology, 55(6):2575–2586. https://doi.org/10.1111/1365-2664.13229
Darras, K., P. Batáry, B. J. Furnas, I. Grass, Y. A. Mulyani, and T. Tscharntke. 2019. Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide. Ecological Applications 29(6). https://doi.org/10.1002/eap.1954
Dutilleux, G., and C. Curé. 2020. Automated acoustic monitoring of endangered common spadefoot toad populations reveals patterns of vocal activity. Freshwater Biology 65(1):20–36. https://doi.org/10.1111/fwb.13111
Elith, J., J. R. Leathwick, and T. Hastie. 2008. A working guide to boosted regression trees. Journal of Animal Ecology 77(4):802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x
ellieroark. 2020. ellieroark/StopoverHabitatMonitoring: First release of Stopover Habitat Monitoring project code (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.3964500
Evans, W. R., and K. V. Rosenberg. 2000. Acoustic Monitoring of Night-Migrating Birds : A Progress Report. In: Strategies for bird conservation: creating the Partners in Flight planning process. Proceedings of the 3rd Partners in Flight Workshop. US Department of Agriculture, Forest Service.
Farnsworth, A., S. A. Gauthreaux, and D. Van Blaricom. 2004. A comparison of nocturnal call counts of migrating birds and reflectivity measurements on Doppler radar. Journal of Avian Biology 35(4):365–369. https://doi.org/10.1111/j.0908-8857.2004.03180.x
Friedman, J. 2001. Greedy Function Approximation : A Gradient Boosting Machine. The Annals of Statistics, 29(5):1189–1232. https://doi.org/10.1214/009053606000000795
Furnas, B. J., and R. L. Callas. 2015. Using automated recorders and occupancy models to monitor common forest birds across a large geographic region. Journal of Wildlife Management 79(2):325–337. https://doi.org/10.1002/jwmg.821
Gibb, R., E. Browning, P. Glover-Kapfer, and K. E. Jones. 2019. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods in Ecology and Evolution 10:169–185. https://doi.org/10.1111/2041-210X.13101
Greenwell, B., B. Boehmke, J. Cunningham, and GBM Developers. 2019. gbm: Generalized Boosted Regression Models. R package version 2.1.5. R package version 2.1.5.
Haselmayer, J., and J. S. Quinn. 2000. A Comparison of Point Counts and Sound Recording as Bird Survey Methods in Amazonian Southeast Peru. The Condor 102(4):887–893. [online] URL: http://www.jstor.org/stable/1370317
Hijmans, R. J. 2019. geosphere: Spherical Trigonometry. [online] URL: https://cran.r-project.org/package=geosphere
Hill, A. P., P. Prince, E. Piña Covarrubias, C. P. Doncaster, J. L. Snaddon, and A. Rogers. 2018. AudioMoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment. Methods in Ecology and Evolution 9(5). https://doi.org/10.1111/2041-210X.12955
Johnston, A., D. Fink, M. D. Reynolds, W. M. Hochachka, B. L. Sullivan, N. E. Bruns, … S. Kelling. 2015. Abundance models improve spatial and temporal prioritization of conservation resources. Ecological Applications 25(7):1749–1756. https://doi.org/10.1890/07-1650.1
Kéry, M., and J. A. Royle. 2016. Applied hierarchical modeling in ecology: analysis of distribution, abundance and species richness in R and BUGS (volume 1 – prelude and static models). London: Academic Press.
Klingbeil, B. T., and M. R. Willig. 2015. Bird biodiversity assessments in temperate forest: the value of point count versus acoustic monitoring protocols. PeerJ 3:e973. https://doi.org/10.7717/peerj.973
La, V. T., and T. D. Nudds. 2016. Estimation of avian species richness: biases in morning surveys and efficient sampling from acoustic recordings. Ecosphere 7(4):e01294. https://doi.org/10.1002/ecs2.1294
Lostanlen, V., J. Salamon, A. Farnsworth, S. Kelling, and J. P. Bello. 2019. Robust sound event detection in bioacoustic sensor networks. PloS One 14(10):e0214168. https://doi.org/10.1371/journal.pone.0214168
MacKenzie, D. I., and W. C. Kendall. 2002. How should detection probability be incorporated into estimates of relative abundance? Ecology 83(9):2387–2393. https://doi.org/10.1890/0012-9658(2002)083[2387:HSDPBI]2.0.CO;2
MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, A. A. Royle, and C. A. Langtimm. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83(8):2248–2255. https://doi.org/10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2
Metcalf, O. C., J. G. Ewen, M. McCready, E. M. Williams, and J. M. Rowcliffe. 2019. A novel method for using ecoacoustics to monitor post-translocation behaviour in an endangered passerine. Methods in Ecology and Evolution 10(5):626–636. https://doi.org/10.1111/2041-210X.13147
Michigan Transportation Fund Act, MCL Act 51, Section 247.655a. 1981. [online] URL: http://tinyurl.com/seasonal-road-law
Morse, D. H. 1991. Song Types of Black-Throated Green Warblers on Migration. The Wilson Bulletin 103(1):93–96.
Newson, S. E., Y. Bas, A. Murray, and S. Gillings. 2017. Potential for coupling the monitoring of bush-crickets with established large-scale acoustic monitoring of bats. Methods in Ecology and Evolution 8(9):1051–1062. https://doi.org/10.1111/2041-210X.12720
Peach, W. J., S. T. Buckland, and S. R. Baillie. 1996. The use of constant effort mist-netting to measure between-year changes in the abundance and productivity of common passerines. Bird Study 43(2):142–156. https://doi.org/10.1080/00063659609461007
Pebesma, E. J., and R. Bivand. 2005. Classes and methods for spatial data in R. Retrieved from R News 5 (2) [online] URL: https://cran.r-project.org/doc/Rnews/
Penone, C., I. Le Viol, V. Pellissier, J. F. Julien, Y. Bas, and C. Kerbiriou. 2013. Use of large-scale acoustic monitoring to assess anthropogenic pressures on orthoptera communities. Conservation Biology 27(5):979–987. https://doi.org/10.1111/cobi.12083
Pinheiro, J., D. Bates, S. DebRoy, D. Sarkar, and R Core Team. 2019. nlme: Linear and Nonlinear Mixed Effects Models. [online] URL: https://cran.r-project.org/package=nlme
Priyadarshani, N., I. Castro, and S. Marsland. 2018a. The impact of environmental factors in birdsong acquisition using automated recorders. Ecology and Evolution 8(10):5016–5033. https://doi.org/10.1002/ece3.3889
Priyadarshani, N., S. Marsland, and I. Castro. 2018b. Automated birdsong recognition in complex acoustic environments: a review. Journal of Avian Biology 49(5):1–27. https://doi.org/10.1111/jav.01447
Priyadarshani, N., S. Marsland, I. Castro, and A. Punchihewa. 2016. Birdsong denoising using wavelets. PLoS ONE 11(1):1–26. https://doi.org/10.1371/journal.pone.0146790
R Core Team. 2020. R: A Language and Environment for Statistical Computing. Retrieved from https://www.r-project.org/
Ralph, C. J., J. R. Sauer, and S. Droege, technical editors. 1995. Monitoring bird populations by point counts. Gen. Tech. Rep. PSW-GTR-149. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station: p. 161-168. [online] URL: https://www.fs.usda.gov/treesearch/pubs/31755
Rappole, J. H., and D. W. Warner. 1976. Relationships between Behavior, Physiology and Weather in Avian Transients at a Migration Stopover Site. Oecologia 26:193–212. https://doi.org/10.1007/BF00345289
Roark, E., and W. Gaul. 2020. Point Abbaye Stopover Habitat Monitoring Audio Files [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3964574
Roberts, D. R., V. Bahn, S. Ciuti, M. S. Boyce, J. Elith, G. Guillera-Arroita, … C. F. Dormann. 2017. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40:913–929. https://doi.org/10.1111/ecog.02881
Rosenstock, S. S., D. R. Anderson, K. M. Giesen, T. Leukering, and M.F. Carter. 2002. Landbird Counting Techniques : Current Practices and an Alternative Published. The Auk 119(1):46–53. [online] URL: https://www.jstor.org/stable/4090011
Ruff, Z. J., D. B. Lesmeister, L. S. Duchac, B. K. Padmaraju, and C. M. Sullivan, C. M. 2020. Automated identification of avian vocalizations with deep convolutional neural networks. Remote Sensing in Ecology and Conservation 6(1):79–92. https://doi.org/10.1002/rse2.125
Salamon, J., J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. 2016. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PloS ONE 11(11). https://doi.org/10.1371/journal.pone.0166866
Sanders, C. E., and D. J. Mennill. 2014. Acoustic monitoring of nocturnally migrating birds accurately assesses the timing and magnitude of migration through the Great Lakes. The Condor 116(3):371–383. https://doi.org/10.1650/condor-13-098.1
Sethi, S. S., R. M. Ewers, N. S. Jones, C. D. L. Orme, and L. Picinali. 2018. Robust, real-time and autonomous monitoring of ecosystems with an open, low-cost, networked device. Methods in Ecology and Evolution 9(12):2383–2387. https://doi.org/10.1111/2041-210X.13089
Sherry, T. W., and R. T. Holmes. 1995. Summer versus winter limitation of populations: What are the issues and what is the evidence? In T. E. Martin and D. M. Finch, editors, Ecology and management of Neotropical migratory birds: A synthesis and review of critical issues (pp. 85–120).
Shonfield, J., and E. M. Bayne. 2017. Autonomous recording units in avian ecological research: current use and future applications. Avian Conservation and Ecology 12(1):14. https://doi.org/10.5751/ace-00974-120114
Tegeler, A. K., M. L. Morrison, and J. M. Szewczak. 2012. Using extended-duration audio recordings to survey avian species. Wildlife Society Bulletin 36(1):21–29. https://doi.org/10.1002/wsb.112
Tuneu-Corral, C., X. Puig-Montserrat, C. Flaquer, M. Mas, I. Budinski, and A. López-Baucells. 2020. Ecological indices in long-term acoustic bat surveys for assessing and monitoring bats’ responses to climatic and land-cover changes. Ecological Indicators 110 (October 2019) 105849. https://doi.org/10.1016/j.ecolind.2019.105849
United States Naval Observatory. 2016. Sun or Moon Rise/Set Table for One Year. Retrieved August 30, 2019, [online] URL: https://aa.usno.navy.mil/data/docs/RS_OneYear.php
Williams, E. M., C. F. J. O’Donnell, and D. P. Armstrong. 2018. Cost-benefit analysis of acoustic recorders as a solution to sampling challenges experienced monitoring cryptic species. Ecology and Evolution 8(13):6839–6848. https://doi.org/10.1002/ece3.4199
Wimmer, J., M. Towsey, P. Roe, and I. Williamson. 2013. Sampling environmental acoustic recordings to determine bird species richness. Ecological Applications 23(6):1419–1428. https://doi.org/10.1890/12-2088.1