Research output: Book/Report › Commissioned report
Density estimation implications of increasing ambient noise on beaked whale click detection and classification. / Marques, Tiago A.; Ward, Jessica; Jarvis, Susan; Moretti, David; Morrissey, Ronald; DiMarzio, Nancy; Thomas, Len.
University of St Andrews, 2010. 22 p. (CREEM Technical Report; No. 2010-1).Research output: Book/Report › Commissioned report
}
TY - BOOK
T1 - Density estimation implications of increasing ambient noise on beaked whale click detection and classification
AU - Marques, Tiago A.
AU - Ward, Jessica
AU - Jarvis, Susan
AU - Moretti, David
AU - Morrissey, Ronald
AU - DiMarzio, Nancy
AU - Thomas, Len
PY - 2010
Y1 - 2010
N2 - Acoustic based density estimates are being increasingly used. Usually density estimation methods require one to evaluate the effective survey area of the acoustic sensors, or equivalently estimate the mean detection probability of detecting the animals or cues of interest. This is often done based on an estimated detection function, the probability of detecting an object of interest as a function of covariates, usually distance and additional covariates. If the actual survey data and the data used to estimate a detection function are not collected simultaneously, as in Marques et al. (2009), the estimated detection function might not correspond to the detection process that generated the survey data. This would lead to biaseddensity estimates.Here we evaluate the influence of ambient noise in the detection and classificationof beaked whale clicks at the Atlantic Undersea Test and Evaluation Center(AUTEC) hydrophones, to assess if the density estimates reported in Marqueset al. (2009) might have been biased. To do so we contaminated a data set withincreasing levels of ambient noise, and then estimated the detection function accounting for the noise level as an additional covariate. The results obtained suggest that for the particular results obtained at AUTEC’s deep water hydrophones the influence of ambient noise on the beaked whale’s click detection probability might have been minor, and hence unlikely to have had an impact on density estimates. However, we do not exclude the possibility that the results could be different under other scenarios.
AB - Acoustic based density estimates are being increasingly used. Usually density estimation methods require one to evaluate the effective survey area of the acoustic sensors, or equivalently estimate the mean detection probability of detecting the animals or cues of interest. This is often done based on an estimated detection function, the probability of detecting an object of interest as a function of covariates, usually distance and additional covariates. If the actual survey data and the data used to estimate a detection function are not collected simultaneously, as in Marques et al. (2009), the estimated detection function might not correspond to the detection process that generated the survey data. This would lead to biaseddensity estimates.Here we evaluate the influence of ambient noise in the detection and classificationof beaked whale clicks at the Atlantic Undersea Test and Evaluation Center(AUTEC) hydrophones, to assess if the density estimates reported in Marqueset al. (2009) might have been biased. To do so we contaminated a data set withincreasing levels of ambient noise, and then estimated the detection function accounting for the noise level as an additional covariate. The results obtained suggest that for the particular results obtained at AUTEC’s deep water hydrophones the influence of ambient noise on the beaked whale’s click detection probability might have been minor, and hence unlikely to have had an impact on density estimates. However, we do not exclude the possibility that the results could be different under other scenarios.
UR - http://creem2.st-andrews.ac.uk/reports/
M3 - Commissioned report
T3 - CREEM Technical Report
BT - Density estimation implications of increasing ambient noise on beaked whale click detection and classification
PB - University of St Andrews
ER -
Research output: Contribution to journal › Article
Research output: Book/Report › Other report
Research output: Contribution to journal › Review article
Research output: Contribution to journal › Article
Research output: Contribution to journal › Article
ID: 5009510