2023 Early Hearing Detection & Intervention Conference

March 5-7, 2023 • Cincinnati, OH

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9/26/2018 | 5:15 PM - 5:30 PM | Importance of Repeated Counts when Estimating Population Trends of Lekking Species with Dynamic N-mixture Models | Eccles Conference Center Auditorium

Importance of Repeated Counts when Estimating Population Trends of Lekking Species with Dynamic N-mixture Models

Population monitoring is important for informing conservation of lekking species such as greater sage-grouse (Centrocercus urophasianus), and N-mixture models may account for variation in detectability with repeated counts when estimating population size and trends. However, simulations to evaluate efficacy of N-mixture models and inform survey effort (i.e., number of repeated visits) typically do not consider scenarios with systematic trends in detectability. As a result, it is unclear how these models perform when trends in detectability confound inferences on population trends, and conclusions regarding survey effort may be overly optimistic. Here, we used GPS data from male sage-grouse to parameterize simulations of the detection process during lek counts, and then we compared estimates of population size (N) and trends (Lambda) from state-space models with either uncorrected peak counts or with N-mixture models that used repeated counts to account for detectability (p). When p varied randomly each year, we found that although peak count models consistently underestimated N by >40%, estimates of Lambda were accurate and similar to estimates from N-mixture models. When p systematically declined across years, N-mixture models estimated Lambda with little bias whereas peak count models strongly underestimated Lambda. However, as the number of sites with repeated counts decreased, absolute bias in estimates of Lambda from N-mixture models increased and resembled estimates from peak count models. We therefore recommend that researchers evaluate population trend models with systematic trends in p to better understand potential biases and inform survey design.

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Presenters/Authors

Adrian Monroe (), Colorado State University, adrian.monroe@colostate.edu;