Determining marine mammal detection functions for a stationary land-based survey site
Eric M. Keen
A E , Janie Wray A , Benjamin Hendricks A , Éadin O’Mahony B , Chris R. Picard C and Hussein Alidina D
A North Coast Cetacean Society, 26 Cottonwood Rd. Alert Bay, British Columbia, V0N 1A0, Canada.
B University of St. Andrews, College Gate, St Andrews, KY16 9AJ, Scotland.
C Gitga’at Oceans and Lands Division, 445 Hayimiisaxaa Way, Hartley Bay, British Columbia, V0V 1A0, Canada.
D World Wildlife Fund – Canada, Nanaimo, British Columbia, Canada. 560 Johnson Street, Unit 259, Victoria, British Columbia, V8W 3C6, Canada.
E Corresponding author. Email: ericmkeen@gmail.com
Wildlife Research 48(2) 115-126 https://doi.org/10.1071/WR19232
Submitted: 24 November 2019 Accepted: 12 July 2020 Published: 8 September 2020
Abstract
Context: The shore-based survey is a common, non-invasive, and low-cost method in marine mammal science, but its scientific applications are currently limited. Such studies typically target populations whose distributions are not random with respect to nearshore sites and involve repeated scans of the same area from single, stationary platforms. These circumstances prohibit the use of classic distance sampling techniques for estimating animal densities or distributions, particularly the derivation of a detection function that describes the probability of detecting targets at various distances from the observer.
Aims: Here, we present a technique for estimating land-based detection functions, as well as quantifying uncertainty in their parameterisation, on the basis of the range-specific variability of observations from one scan to the next.
Methods: This Bayesian technique uses Monte Carlo simulation to determine the likelihood of thousands of candidate detection functions, then conducts weighted sampling to generate a posterior distribution estimate of the detection function parameterisation. We tested the approach with both archival and artificial datasets built from known detection functions that reflect whale and porpoise detectability.
Key results: When the base distribution of targets was random, the whale detection function was estimated without error (i.e. the difference of the median of the posterior and the true value was 0.00), and the porpoise detection function was estimated with an error equal to 4.23% of the true value. When the target base distribution was non-random, estimation error remained low (2.57% for targets concentrated offshore, 1.14% when associated with nearshore habitats). When applied to field observations of humpback whales and Dall’s porpoises from a land-based study in northern British Columbia, Canada, this technique yielded credible results for humpback whales, but appeared to underestimate the detectability of Dall’s porpoises.
Conclusion: The findings presented here indicate that this approach to detection function estimation is appropriate for long-running surveys in which scan regularity is high and the focus is on large, slow-moving, low herd-size, and easily detectable species.
Implications: The derivation of a detection function is a critical step in density estimation. The methodology presented here empowers land-based studies to contribute to quantitative monitoring and assessment of marine mammal populations in coastal habitats.
Additional keywords: abundance, population density, population distribution, statistical modelling.
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