Dataset Information

Matching observations and reality: using simulation models to improve monitoring under uncertainty in the Serengeti.

ABSTRACT: 1. Planning for conservation success requires identifying effective and efficient monitoring strategies but multiple types of uncertainty affect the accuracy and precision of wildlife abundance estimates. Observation uncertainty, a consequence of sampling effort and design as well as the process of observation, is still understudied, with little attention given to the multiple potential sources of error involved. To establish error minimization priorities and maximize monitoring efficiency, the direction and magnitude of multiple sources of uncertainty must be considered. 2. Using monitoring of two contrasting ungulate species in the Serengeti ecosystem as a case study, we developed a 'virtual ecologist' framework within which we carried out simulated tests of different monitoring strategies for different types of species. We investigated which components of monitoring should be prioritized to increase survey accuracy and precision and explored the robustness of population estimates under different budgetary scenarios. 3. The relative importance of each process affecting precision and accuracy varied according to the survey technique and biological characteristics of the species. While survey precision was mainly affected by population characteristics and sampling effort, the accuracy of the survey was greatly affected by observer effects, such as juvenile and herd detectability. 4.Synthesis and applications. Monitoring efficiency is of the utmost importance for conservation, especially in the context of limited budgets and other priorities. We provide insights into the likely effect of different types of observation and process error on population estimates for savanna ungulates, and more generally present a framework for evaluating monitoring programmes in a virtual environment. In highly aggregated species, the main focus should be on survey precision; sampling effort should be defined according to wildlife spatial distribution. For random or slightly aggregated species, accuracy is the key factor; this is most sensitive to observer effects which should be minimized by training and calibration by observer.

SUBMITTER: Nuno A

PROVIDER: S-EPMC3644173 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Matching observations and reality: using simulation models to improve monitoring under uncertainty in the Serengeti.

Nuno Ana A Bunnefeld Nils N Milner-Gulland E J EJ

The Journal of applied ecology 20130204 2

<b>1.</b> Planning for conservation success requires identifying effective and efficient monitoring strategies but multiple types of uncertainty affect the accuracy and precision of wildlife abundance estimates. Observation uncertainty, a consequence of sampling effort and design as well as the process of observation, is still understudied, with little attention given to the multiple potential sources of error involved. To establish error minimization priorities and maximize monitoring efficienc ...[more]

PMID: 23658468

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Project description:PurposeLimited data exist on the actual transfer of skills learned using a virtual reality (VR) simulator for arthroscopy training because studies mainly focused on VR performance improvement and not on transfer to real word (transfer validity). The purpose of this single-blinded, controlled trial was to objectively investigate transfer validity in the context of initial knee arthroscopy training.MethodsFor this study, 36 junior resident orthopaedic surgeons (postgraduate year one and year two) without prior experience in arthroscopic surgery were enrolled to receive standard knee arthroscopy surgery training (NON-VR group) or standard training plus training on a hybrid virtual reality knee arthroscopy simulator (1 h/month) (VR group). At inclusion, all participants completed a questionnaire on their current arthroscopic technical skills. After 6 months of training, both groups performed three exercises that were evaluated independently by two blinded trainers: i) arthroscopic partial meniscectomy on a bench-top knee simulator; ii) supervised diagnostic knee arthroscopy on a cadaveric knee; and iii) supervised knee partial meniscectomy on a cadaveric knee. Training level was determined with the Arthroscopic Surgical Skill Evaluation Tool (ASSET) score.ResultsOverall, performance (ASSET scores) was better in the VR group than NON-VR group (difference in the global scores: p < 0.001, in bench-top meniscectomy scores: p = 0.03, in diagnostic knee arthroscopy on a cadaveric knee scores: p = 0.04, and in partial meniscectomy on a cadaveric knee scores: p = 0.02). Subgroup analysis by postgraduate year showed that the year-one NON-VR subgroup performed worse than the other subgroups, regardless of the exercise.ConclusionThis study showed the transferability of the technical skills acquired by novice residents on a hybrid virtual reality simulator to the bench-top and cadaveric models. Surgical skill acquired with a VR arthroscopy surgical simulator might safely improve arthroscopy competences in the operating room, also helping to standardise resident training and follow their progress.Level of evidence: 2

Dataset Information

Matching observations and reality: using simulation models to improve monitoring under uncertainty in the Serengeti.

Publications

Matching observations and reality: using simulation models to improve monitoring under uncertainty in the Serengeti.

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