[MARMAM] Detection functions from shore

Eric Keen ericmkeen at gmail.com
Sat Sep 19 06:48:45 PDT 2020


Dear colleagues,

On behalf of my co-authors, I am pleased to announce the publication of the
following article in Wildlife Research.

Keen, E.M., J. Wray, B. Hendricks, É. O'Mahony, C.R. Picard, H. Alidina.
(2020) Determining marine mammal detection functions for a stationary
land-based survey site. Wildlife Research. https://doi.org/10.1071/WR19232

Pre-print PDF is available upon request.

Best wishes,
Eric Keen
ericmkeen at gmail.com

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.
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