Wildbook: Crowdsourcing, computer vision, and data science for conservation

Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have built Wildbooks for whales (flukebook.org), sharks (whaleshark.org), two species of zebras (Grevy's and plains), and several others. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy's zebra) census using photographs taken by ordinary citizens in Kenya. The resulting numbers are now the official species census used by IUCN Red List: http://www.iucnredlist.org/details/7950/0. In 2016, Wildbook partnered up with WWF to build Wildbook for Sea Turtles, Internet of Turtles (IoT), as well as systems for seals and lynx. Most recently, we have demonstrated that we can now use publicly available social media images to count and track wild animals. In this paper we present and discuss both the impact and challenges that the use of crowdsourced images can have on wildlife conservation.Comment: Presented at the Data For Good Exchange 201

The National Early Warning Score and its subcomponents recorded within ±24 hours of emergency medical admission are poor predictors of hospital-acquired acute kidney injury

YesBackground: Hospital-acquired Acute Kidney Injury (H-AKI) is a common cause of avoidable morbidity and mortality. Aim: To determine if the patients’ vital signs data as defined by a National Early Warning Score (NEWS), can predict H-AKI following emergency admission to hospital. Methods: Analyses of emergency admissions to York hospital over 24-months with NEWS data. We report the area under the curve (AUC) for logistic regression models that used the index NEWS (model A0), plus age and sex (A1), plus subcomponents of NEWS (A2) and two-way interactions (A3). Likewise for maximum NEWS (models B0,B1,B2,B3). Results: 4.05% (1361/33608) of emergency admissions had H-AKI. Models using the index NEWS had the lower AUCs (0.59 to 0.68) than models using the maximum NEWS AUCs (0.75 to 0.77). The maximum NEWS model (B3) was more sensitivity than the index NEWS model (A0) (67.60% vs 19.84%) but identified twice as many cases as being at risk of H-AKI (9581 vs 4099) at a NEWS of 5. Conclusions: The index NEWS is a poor predictor of H-AKI. The maximum NEWS is a better predictor but seems unfeasible because it is only knowable in retrospect and is associated with a substantial increase in workload albeit with improved sensitivity.The Health Foundatio

Individual identification of the endangered Wyoming toad Anaxyrus baxteri and implications for monitoring species recovery

Monitoring the fates of individuals after release in the wild is essential for building effective species recovery programs. Current conservation efforts for the endangered Wyoming Toad (Anaxyrus baxteri) are limited by the size and number of toads that can be individually marked using invasive tagging techniques. We evaluated the use of natural patterns of wart-like glands on the dorsum of Wyoming Toads as a potential identification technique. We photographed 194 known-identity individuals (822 total images, representing 1,554 true matching-image pairs of the same individuals) from two captive-breeding facilities in 2011 and 2012. Spot patterns provided stable markings from metamorphosis through adult life stages, and naïve observers correctly matched 100% of a subset of photo pairs “by eye.” In contrast, computer-assisted identification performed relatively poorly: the two software platforms tested (Wild-ID and Hotspotter) failed to match 47% and 64% of true matching-image pairs, respectively. The use of higher-quality cameras with faster automatic focusing speeds yielded the largest improvement in matching success of any variable tested when using identification software. Simulated capture–recapture data demonstrated that using software to identify individuals would bias abundance estimates high by up to 920%

Non-natives: 141 scientists object [Letter]

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