03022nas a2200217 4500008004100000022001400041245010500055210006900160260001600229520213400245653002202379653003402401653001702435653002602452653001402478653001702492653001302509100002302522700002102545856023802566 2019 eng d a2045-775800aTemporally adaptive acoustic sampling to maximize detection across a suite of focal wildlife species0 aTemporally adaptive acoustic sampling to maximize detection acro cOct-08-20203 a

1. Acoustic recordings of the environment can produce species presence–absence data for characterizing populations of sound‐producing wildlife over multiple spatial scales. If a species is present at a site but does not vocalize during a scheduled audio recording survey, researchers may incorrectly conclude that the species is absent (“false negative”). The risk of false negatives is compounded when audio devices have sampling constraints, do not record continuously, and must be manually scheduled to operate at pre‐selected times of day, particularly when research programs target multiple species with acoustic availability that varies across temporal conditions.

2. We developed a temporally adaptive acoustic sampling algorithm to maximize de‐ tection probabilities for a suite of focal species amid sampling constraints. The algorithm combines user‐supplied species vocalization models with site‐specific weather forecasts to set an optimized sampling schedule for the following day. To test our algorithm, we simulated hourly vocalization probabilities for a suite of focal species in a hypothetical monitoring area for the year 2016. We conducted a factorial experiment that sampled from the 2016 acoustic environment to com‐ pare the probability of acoustic detection by a fixed (stationary) schedule versus a temporally adaptive optimized schedule under several sampling efforts and moni‐ toring durations.

3. We found that over the course of a study season, the probability of acoustically capturing a focal species (given presence) at least once via automated acous‐ tic monitoring was greater (and acoustic capture occurred earlier in the season) when using the temporally adaptive optimized schedule as compared to a fixed schedule.

4. The advantages of a temporally adaptive optimized acoustic sampling schedule are magnified when a study duration is short, sampling effort is low, and/or spe‐ cies acoustic availability is minimal. This methodology presents the opportunity to maximize acoustic monitoring sampling efforts amid constraints.

10aadaptive sampling10aautomated acoustic monitoring10abioacoustics10adetection probability10aoccupancy10aoptimization10awildlife1 aBalantic, Cathleen1 aDonovan, Therese uhttps://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.5579https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.5579https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.5579https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ece3.557902193nas a2200133 4500008004100000245007900041210006900120260001600189300001100205520175700216100002301973700002101996856004202017 2019 eng d00aDynamic wildlife occupancy models using automated acoustic monitoring data0 aDynamic wildlife occupancy models using automated acoustic monit cSep-01-2020 ae018543 a

Automated acoustic monitoring of wildlife has been used to characterize populations of sound‐producing species across large spatial scales. However, false negatives and false positives produced by automated detection systems can compromise the utility of these data for researchers and land managers, particularly for research programs endeavoring to describe colonization and extinction dynamics that inform land use decision‐making. To investigate the suitability of automated acoustic monitoring for dynamic occurrence models, we simulated underlying occurrence dynamics, calling patterns, and the automated acoustic detection process for a hypothetical species under a range of scenarios. We investigated an automated species detection aggregation method that considered a suite of options for creating encounter histories. From these encounter histories, we generated parameter estimates and computed bias for occurrence, colonization, and extinction rates using a dynamic occupancy modeling framework that accounts for false positives via small amounts of manual confirmation. We were able to achieve relatively unbiased estimates for all three state parameters under all scenarios, even when the automated detection system was simulated to be poor, given particular encounter history aggregation choices. However, some encounter history aggregation choices resulted in unreliable estimates; we provide caveats for avoiding these scenarios. Given specific choices during the detection aggregation process, automated acoustic monitoring data may provide an effective means for tracking species occurrence, colonization, and extinction patterns through time, with the potential to inform adaptive management at multiple spatial scales.

1 aBalantic, Cathleen1 aDonovan, Therese uhttp://doi.wiley.com/10.1002/eap.1854