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/ece3.557901998nam a2200253 4500008004100000020002200041022001400063245006900077210006900146260004400215300001400259490001000273520123900283653001701522653001701539653002301556100002001579700001401599700001701613700001901630700001801649700002201667856005501689 2018 eng d a978-3-030-03990-5 a0302-974300aInvestigation of Unsupervised Models for Biodiversity Assessment0 aInvestigation of Unsupervised Models for Biodiversity Assessment aChambSpringer International Publishing a160 - 1710 v113203 a

Significant animal species loss has been observed in recent decades due to habitat destruction, which puts at risk environmental integrity and biodiversity. Traditional ways of assessing biodiversity are limited in terms of both time and space, and have high cost. Since the presence of animals can be indicated by sound, recently acoustic recordings have been used to estimate species richness. Bioacoustic sounds are typically recorded in habitats for several weeks, so contain a large collection of different sounds. Birds are of particular interest due to their distinctive calls and because they are useful ecological indicators. To assess biodiversity, the task of manually determining how many different types of birds are present in such a lengthy audio is really cumbersome. Towards providing an automated support to this issue, in this paper we investigate and propose a clustering based approach to assist in automated assessment of biodiversity. Our approach first estimates the number of different species and their volumes which are used for deriving a biodiversity index. Experimental results with real data indicates that our proposed approach estimates the biodiversity index value close to the ground truth.

10abioacoustics10abiodiversity10aUnsupervised model1 aMitrovic, Tanja1 aXue, Bing1 aLi, Xiaodong1 aRao, KVSN Rama1 aGarg, Saurabh1 aMontgomery, James uhttp://link.springer.com/10.1007/978-3-030-03991-2