Physical and ecological factors explain the distribution of Ross Sea Weddell seals during the breeding season

LaRue, Michelle A.; Salas, Leo; Nur, Nadav; Ainley, David G.; Stammerjohn, Sharon; Barrington, Luke; Stamatiou, Kostas; Pennycook, Jean; Dozier, Melissa; Saints, Jon; Nakamura, Hitomi

doi:10.3354/meps12877

Cited by 34 publications

(78 citation statements)

References 70 publications

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“…The confusion matrix excluded input from the Ross Sea, as seal presence there was informed by LaRue et al. () and manual inspection by authors given our experience in the region (i.e. we knew where to expect seal presence/absence, which is in contrast to the rest of the continent).…”

Section: Resultsmentioning

confidence: 99%

“…2 for definitions of region locations), number of 500 m 9 500 m maps searched, number of maps containing seals according to 'the crowd' of volunteers, and percentage of maps containing seals. Note that given authors' vast experience with the Ross Sea, seal presence on maps there was derived from LaRue et al (2019), which meant that virtually the whole region was inspected by the lead author. Therefore, the percentage of maps containing seals in the Ross Sea is a more accurate reflection of reality given authors' knowledge of the region and verification of results.…”

Section: Discussionmentioning

confidence: 99%

“…However, due to their dark coloration, large size and predictable location on the fast ice, remote sensing has more recently provided a much-needed method for setting a baseline and for ultimately determining population trends (e.g. LaRue et al 2011LaRue et al , 2019Ainley et al 2015).…”

Section: Introductionmentioning

confidence: 99%

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Engaging ‘the crowd’ in remote sensing to learn about habitat affinity of the Weddell seal in Antarctica

LaRue

Ainley

Pennycook

et al. 2019

Remote Sens Ecol Conserv

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Satellites Over Seals (SOS), a project initiated in late 2016, is a crowdsourced method to determine factors behind the presence/absence patterns and to ultimately determine the global population of the Weddell seal (Leptonychotes weddellii). An iconic species, the Weddell seal is proposed to be part of the Antarctic Research and Monitoring Program required in the newly designated Ross Sea Region Marine Protected Area. This species is easy to detect via satellite imagery, due to its large size (3–4 m long, 1 m wide) and its dark color contrasting with the Antarctic coastal fast ice, where it aggregates on during breeding season. Using very high‐resolution satellite imagery (VHR; 0.31–0.60 m resolution) and the online platform Tomnod, we used VHR images from November 2010 and 2011 to cover the entirety of available fast ice around Antarctica. Before correcting for time of day or date, we searched for the presence/absence to identify a subset of where abundance estimates should be concentrated. More than 325 000 citizen scientists searched 790 VHR images, covering 268 611 km2 of fast ice, to determine the locations of seals. Algorithms ranked searchers to the degree their votes corresponded with others, a measure of searcher relative quality that we used to filter out unreliable searchers. Seal presence was detected on only 0.55% of available maps (total n = 1 116 058) within fast ice, revealing a sparse, irregular distribution. The rate of false‐negative detections was 1.7%, though false positives were high (67%), highlighting the importance of training for image interpretation to ensure differentiation between seals and landscape features (such as large rocks, ice chunks or depressions/holes in the ice). This approach not only allowed us to assess image resolution and quality, but also training, outreach and the effectiveness of this platform for introducing citizen scientists to the ecology of the Southern Ocean.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Engaging ‘the crowd’ in remote sensing to learn about habitat affinity of the Weddell seal in Antarctica

LaRue

Ainley

Pennycook

et al. 2019

Remote Sens Ecol Conserv

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“…Monitoring populations elsewhere would necessitate costly logistical constraints if conducted via traditional survey methods [32][33][34][35]. We devised a method to engage citizen scientists in counting Weddell seals from high-resolution satellite images of Erebus Bay and ultimately from images for the entire continent [36]. We used an online platform to enable volunteers to inspect high-resolution images of fast ice [37,38] and to "tag" (i.e., mark in the images) all presumed seals.…”

Section: Introductionmentioning

confidence: 99%

Reducing error and increasing reliability of wildlife counts from citizen science surveys: counting Weddell Seals in the Ross Sea from satellite images

Salas

LaRue

Nur

et al. 2020

Preprint

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Citizen science programs can be effective at collecting information at large temporal and spatial scales. However, sampling bias is a concern in citizen science datasets and can lead to unreliable estimates. We address this issue with a novel approach in a first-of-its-kind citizen science survey of Weddell seals for the entire coast of Antarctica. Our citizen scientists inspected very high-resolution satellite images to tag any presumptive seals hauled out on the fast ice during the pupping period. To assess and reduce the error in counts in term of bias and other factors, we ranked surveyors on how well they agreed with each other in tagging a particular feature (presumptive seal), and then ranked these features based on the ranking of surveyors placing tags on them. We assumed that features with higher rankings, as determined by “the crowd wisdom,” were likely to be seals. By comparing citizen science feature ranks with an expert’s determination, we found that non-seal features were often highly ranked. Conversely, some seals were ranked low or not tagged at all. Ranking surveyors relative to their peers was not a reliable means to filter out erroneous or missed tags; therefore, we developed an effective correction factor for both sources of error by comparing surveyors’ tags to those by the expert. Furthermore, counts may underestimate true abundance due to seals not being present on the ice when the image was taken. Based on available on-the-ground haul-out location counts in Erebus Bay, the Ross Sea, we were able to correct for the proportion of seals not detected through satellite images after accounting for year, time-of-day, location (islet vs. mainland locations), and satellite sensor effects. We show that a prospective model performed well at estimating seal abundances at appropriate spatial scales, providing a suitable methodology for continent-wide Weddell Seal population estimates.

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“…CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted December 3, 2020. ; https://doi.org/10.1101/2020.12.03.409649 doi: bioRxiv preprint 4 volunteer skill, citizen science has been used to gather and process large datasets for over a decade [20,21], including for research on abundance and distribution of marine mammals [22][23][24][25]. Crowd-sourced science requires an up-front investment of time and labor to create a training system for new volunteers [26,27].…”

Section: Introductionmentioning

confidence: 99%

Accuracy and precision of citizen scientist animal counts from drone imagery

Wood

Robinson

Costa

et al. 2020

Preprint

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Repeated counts of animal abundance can reveal changes in local ecosystem health and inform conservation strategies. Unmanned aircraft systems (UAS) such as drones are commonly used to photograph animals in remote locations; however, counting animals in images is a laborious task. Crowd-sourcing can reduce the time required to conduct these censuses considerably, but must first be validated against expert counts to measure sources of error. Our objectives were to assess the accuracy and precision of citizen science counts and make recommendations for future citizen science projects. We uploaded drone imagery from Año Nuevo Island (California, USA) to a curated Zooniverse website that instructed citizen scientists to count seals and sea lions. Across 212 days, over 1,500 volunteers counted animals in 90,000 photographs. We quantified the error associated with several descriptive statistics to extract a single citizen science count per photograph from the 15 repeat counts and then compared the resulting citizen science counts to expert counts. Although proportional error was relatively low (9% for sea lions and 5% for seals during the breeding seasons) and improved with repeat sampling, the 12+ volunteers required to reduce error was prohibitively slow, taking on average 6 weeks to estimate animals from a single drone flight covering 25 acres, despite strong public outreach efforts. The single best algorithm was ‘Median without the lowest two values’, demonstrating that citizen scientists tended to under-estimate the number of animals present. Citizen scientists accurately counted adult seals, but accuracy was lower when sea lions were present during the summer and could be confused for seals. We underscore the importance of validation efforts and careful project design for researchers hoping to combine citizen science with drone imagery.

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Physical and ecological factors explain the distribution of Ross Sea Weddell seals during the breeding season

Cited by 34 publications

References 70 publications

Engaging ‘the crowd’ in remote sensing to learn about habitat affinity of the Weddell seal in Antarctica

Engaging ‘the crowd’ in remote sensing to learn about habitat affinity of the Weddell seal in Antarctica

Reducing error and increasing reliability of wildlife counts from citizen science surveys: counting Weddell Seals in the Ross Sea from satellite images

Accuracy and precision of citizen scientist animal counts from drone imagery

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