Testing Assumptions of Distance Sampling on a Pelagic Seabird

Lukacs, Paul M.; Kissling, Michelle L.; Reid, Mason; Gende, Scott M.; Lewis, Stephen B.

doi:10.1525/cond.2010.090204

Cited by 6 publications

(4 citation statements)

References 10 publications

(14 reference statements)

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“…Animals are undercounted because they are not available to be counted (e.g. underwater as in Lukacs, Kissling, Reid, Gende, & Lewis, 2010, termed availability bias), or observers miss animals that are available to be counted (termed perception bias ;Marsh & Sinclair, 1989). Aerial images improve perception bias, but not necessarily availability bias (Bayliss & Yeomans, 1990;Frederick, Hylton, Heath, & Ruane, 2003;Gibbs, Woodward, Hunter, & Hutchinson, 1988;Leonard & Fish, 1974).…”

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confidence: 99%

Estimating occupancy and abundance using aerial images with imperfect detection

et al. 2017

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Species distribution and abundance are critical population characteristics for efficient management, conservation, and ecological insight. Point process models are a powerful tool for modelling distribution and abundance, and can incorporate many data types, including count data, presence‐absence data, and presence‐only data. Aerial photographic images are a natural tool for collecting data to fit point process models, but aerial images do not always capture all animals that are present at a site. Methods for estimating detection probability for aerial surveys usually include collecting auxiliary data to estimate the proportion of time animals are available to be detected. We developed an approach for fitting point process models using an N‐mixture model framework to estimate detection probability for aerial occupancy and abundance surveys. Our method uses multiple aerial images taken of animals at the same spatial location to provide temporal replication of sample sites. The intersection of the images provide multiple counts of individuals at different times. We examined this approach using both simulated and real data of sea otters (Enhydra lutris kenyoni) in Glacier Bay National Park, southeastern Alaska. Using our proposed methods, we estimated detection probability of sea otters to be 0.76, the same as visual aerial surveys that have been used in the past. Further, simulations demonstrated that our approach is a promising tool for estimating occupancy, abundance, and detection probability from aerial photographic surveys. Our methods can be readily extended to data collected using unmanned aerial vehicles, as technology and regulations permit. The generality of our methods for other aerial surveys depends on how well surveys can be designed to meet the assumptions of N‐mixture models.

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confidence: 99%

Estimating occupancy and abundance using aerial images with imperfect detection

et al. 2017

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“…We conducted boat-based dive behavior trials to estimate pa, the probability that a murrelet was available for detection (i.e., not underwater) given presence. We determined that the probability of a murrelet being unavailable for detection was quite low (0.032 ± 0.007; see details in Lukacs et al 2010). Therefore, we assumed pa was close enough to 1 not to affect abundance estimates, and, like ps, did not include it in our integrated model.…”

Section: Discussionmentioning

confidence: 99%

Integrating multiple datasets to align biological and statistical populations for abundance estimation

Kissling,

Lukacs,

Nesvacil

et al. 2023

Preprint

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Ideally, a statistical population is the same as, or accurately represents its corresponding biological population. However, in practice, they rarely align in space and time, which can lead to variable exposure to sampling and biased inference. We often view a population mismatch as a temporary emigration process and resolve it with replicate and/or repeat sampling, though this approach is not feasible for all species and habitats. We developed a hierarchical Bayesian integrated model to estimate abundance of a biological population of the Kittlitz’s murrelet (Brachyramphus brevirostris), a highly mobile, non-territorial, ice-associated seabird of conservation concern in Alaska and eastern Russia. Our model combines datasets from boat and telemetry surveys to account for all components of detection probability, specifically using telemetry locations to estimate probability of presence (pp) and line transect distance sampling to estimate probability of detection (pd). By estimating pp directly, we were able to account for temporary emigration from the sampled area, which changed with shifting icefloes between sampling occasions. Between 2007 and 2012, annual pp was highly variable, ranging from 0.33 to 0.75 (median=0.50, SE=0.02), but was not predictable using five environmental covariates. In years when two boat surveys were conducted, our model reduced the coefficient of variation (CV) of abundance estimates by 13–35%, yet in the year with only one boat survey (2009), the CV skyrocketed about 10-fold, emphasizing the importance of a second survey if pp varies. Although we increased the precision of annual abundance estimates by accounting for pp, we did not see the same improvement in the estimate of mean r, or trend, indicating that while we reduced within-year variance, we failed to account for a source(s) of variation across years, which we suspect is related to the propensity for murrelets to skip breeding in some years. Our integrated model to resolve a population mismatch is simple, flexible, and scalable for generating unbiased and precise abundance estimates of highly mobile species that occupy dynamic habitats where other open population models are not feasible. Importantly, it improves inference of the biological population, which is the true population of interest. We urge ecologists to think critically about the population in which they want to draw inference, especially as tracking technology improves and model complexity increases.

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“…However, the flushing distances recorded in our study were much greater than previously documented for murrelets, and for most other species of waterbirds. For example, in Icy Bay, Alaska, murrelet responses (flying or diving) declined when experimental approach distances were > 150 m [ 51 ]. Likewise, in Pacific Rim National Park Reserve, British Columbia, the majority of marbled murrelets did not respond until approaching boats were within 40m [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Disturbance of a rare seabird by ship-based tourism in a marine protected area

et al. 2017

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Managers of marine protected areas (MPAs) must often seek ways to allow for visitation while minimizing impacts to the resources they are intended to protect. Using shipboard observers, we quantified the “zone of disturbance” for Kittlitz’s and marbled murrelets (Brachyramphus brevirostris and B. marmoratus) exposed to large cruise ships traveling through Glacier Bay National Park, one of the largest MPAs in North America. In the upper reaches of Glacier Bay, where Kittlitz’s murrelets predominated, binary logistic regression models predicted that 61% of all murrelets within 850 m perpendicular distance of a cruise ship were disturbed (defined as flushing or diving), whereas in the lower reaches, where marbled murrelets predominated, this percentage increased to 72%. Using survival analysis, murrelets in both reaches were found to react at greater distances when ships approached indirectly, presumably because of the ship’s larger profile, suggesting murrelets responded to visual rather than audio cues. No management-relevant covariates (e.g., ship velocity, route distance from shore) were found to be important predictors of disturbance, as distance from ship to murrelet accounted for > 90% of the explained variation in murrelet response. Utilizing previously published murrelet density estimates from Glacier Bay, and applying an average empirical disturbance probability (68%) out to 850 m from a cruise ship’s typical route, we estimated that a minimum of 9.8–19.6% of all murrelets in Glacier Bay are disturbed per ship entry. Whether these disturbance levels are inconsistent with Park management objectives, which include conserving wildlife as well as providing opportunities for visitation, depends in large part on whether disturbance events caused by cruise ships have impacts on murrelet fitness, which remains uncertain.

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Testing Assumptions of Distance Sampling on a Pelagic Seabird

Cited by 6 publications

References 10 publications

Estimating occupancy and abundance using aerial images with imperfect detection

Estimating occupancy and abundance using aerial images with imperfect detection

Integrating multiple datasets to align biological and statistical populations for abundance estimation

Disturbance of a rare seabird by ship-based tourism in a marine protected area

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