Using simulation to evaluate wildlife survey designs: polar bears and seals in the Chukchi Sea

Conn, Paul B.; Moreland, Erin E.; Regehr, Eric V.; Richmond, Erin L.; Cameron, Michael F.; Boveng, Peter L.

doi:10.1098/rsos.150561

Cited by 17 publications

(14 citation statements)

References 47 publications

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“…, Conn et al. ). However, for the purpose of this study where the focus was the certainty of detections, we did not consider autocorrelation to have any influence in our analysis.…”

Section: Discussionmentioning

confidence: 99%

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Monitoring marine mammals using unmanned aerial vehicles: quantifying detection certainty

et al. 2018

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Unmanned aerial vehicles (UAVs) are increasingly being recognized as potentially useful for detection of marine mammals in their natural habitats, but an important consideration is the associated uncertainties in animal detection. We present a study based on field trials using UAVs to carry out image‐based monitoring of cetaceans in two fjords in northern Norway. We conducted 12 missions to assess the effects of both environmental‐ and aircraft‐related variables on detection certainty. Images were inspected for animal presence and its associated detection certainty. Images were also assessed for potentially important covariates such as wave turbulence (sea state), luminance, and glare. Aircraft variables such as altitude, pitch, and roll were combined into a single variable—pixel size. We recorded a total of 50 humpback whales, 63 killer whales (KW), and 118 unidentified sightings. We also recorded 57 harbor porpoise sightings. None of the environmental conditions (sea state, glare, and luminance) affected the detection certainty of harbor porpoises. In contrast, increasing sea state and luminance had negative and positive effects, respectively, on the detection certainty of humpback and KW. The detection certainty was not significantly affected by pixel size for both harbor porpoises, and humpback and KW. Our results indicate that at lower altitudes, variations in aircraft position (pitch and roll) do not have a variable effect on detection certainty. Overall, this study shows the importance of measuring variability in both environmental and flight‐related variables, in order to attain unbiased estimates of detectability for UAV‐based marine mammal surveys, particularly in Arctic and sub‐Arctic regions.

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“…, Conn et al. ). However, for the purpose of this study where the focus was the certainty of detections, we did not consider autocorrelation to have any influence in our analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Few studies have shown how to integrate spatial autocorrelation in digital surveys. (Salberg et al 2009, Paiva et al 2015, Conn et al 2016. However, for the purpose of this study where the focus was the certainty of detections, we did not consider autocorrelation to have any influence in our analysis.…”

Section: Improving Uav Surveysmentioning

confidence: 94%

Monitoring marine mammals using unmanned aerial vehicles: quantifying detection certainty

et al. 2018

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“…This simulation approach has been used in many other studies. For example, simulations were used in an instrument-based survey to evaluate alternative survey designs for Arctic marine mammal populations (Conn et al, 2016) and to optimize animal detection given the breeding behaviour and logistical access for threatened species (Lanier et al, 2016). We found only a few simulation studies using regional-scale populations.…”

Section: Discussionmentioning

confidence: 99%

Optimization of a survey using spatially balanced sampling: a single-year application of clam monitoring in the Arcachon Bay (SW France)

Kermorvant

Caill-Milly

D’Amico

et al. 2017

Aquat. Living Resour.

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-Bivalves are important components of benthic marine and freshwater ecosystems throughout the world. One of the most exploited bivalves used for human consumption is manila clam (Venerupis philippinarum). In Arcachon Bay (SW France), commercial fishers and scientists have developed a monitoring survey to estimate clam stocks to assist in implementing a sustainable management strategy. The survey design that is currently used is based on standard stratified random sampling (StRS). The survey has been undertaken every 2 years since 2006. Each survey costs approximately €50 000, with funding provided by ∼20% of the commercial fishers. The survey is quite expensive, given that this resource is managed mostly at a regional level. In 2016 for instance, the survey was not done because of a shortfall in funds to support it. Recent studies on survey designs have focused on new developments that allow for higher statistical efficiency (lower sampling error) coupled with lower survey effort. Among these is the spatially balanced generalized random tessellation stratified (GRTS) design. The aim of this study is to compare the performance of the common StRS method with the GRTS design. To do this, we created a semi-virtual clam population by extrapolating the 2012 field survey results in the whole bay and simulated survey events with the two designs. We then assessed the two survey designs using three threshold precision levels (5%, 10% and 20% precision) for the two estimators of interest (biomass and abundance). We recommend the use of the GRTS design for clam surveys in Arcachon Bay. To achieve the same level of precision, GRTS requires less survey effort than StRS.

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“…Aerial surveys are an important tool for estimating abundance and distribution of vertebrate populations. Methods for design and data analysis of aerial surveys have been developed to accommodate visual observations where observers count animals from aircraft (Caughley, ; Caughley & Goddard, ; Certain & Bretagnolle, ; Goddard, ; Jolly, ; Pennycuick & Western, ; Siniff & Skoog, ; Watson, Parker, & Allan, ), and for photographic survey methods (Bechet, Reed, Plante, Giroux, & Gauthier, ; Boyd, ; Buckland et al., ; Conn et al., ; Leedy, ; Leonard & Fish, ; Ver Hoef & Jansen, ). Undercounting animals from aircraft presents a major estimation problem with both visual and photographic aerial surveys (Caughley, ; Graham & Bell, ).…”

Section: Introductionmentioning

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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Using simulation to evaluate wildlife survey designs: polar bears and seals in the Chukchi Sea

Cited by 17 publications

References 47 publications

Monitoring marine mammals using unmanned aerial vehicles: quantifying detection certainty

Monitoring marine mammals using unmanned aerial vehicles: quantifying detection certainty

Optimization of a survey using spatially balanced sampling: a single-year application of clam monitoring in the Arcachon Bay (SW France)

Estimating occupancy and abundance using aerial images with imperfect detection

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