Artificial Intelligence and Conservation最新文献

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Law Enforcement for Wildlife Conservation 野生动物保护执法

Artificial Intelligence and Conservation Pub Date : 2019-02-28 DOI: 10.1017/9781108587792.002

引用次数: 3

Probabilistic Inference with Generating Functions for Animal Populations 基于生成函数的动物种群概率推理

Artificial Intelligence and Conservation Pub Date : 1900-01-01 DOI: 10.1017/9781108587792.010

D. Sheldon, Kevin Winner, Debora Sujono

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引用次数: 0

Engaging Citizen Scientists in Data Collection for Conservation 让公民科学家参与保护数据收集

Artificial Intelligence and Conservation Pub Date : 1900-01-01 DOI: 10.1017/9781108587792.011

Yexiang Xue, C. Gomes

{"title":"Engaging Citizen Scientists in Data Collection for Conservation","authors":"Yexiang Xue, C. Gomes","doi":"10.1017/9781108587792.011","DOIUrl":"https://doi.org/10.1017/9781108587792.011","url":null,"abstract":"Big data is becoming increasingly important for monitoring large-scale and complex spatial and temporal ecological processes to support informed decision-making in computational sustainability [Gomes 2009]. Historically, data collection methods for conservation typically involved many hours of field work performed by professional researchers, which could not scale up to meet today’s conservation goals. Citizen science programs, on the other hand, engage the general public in the data collection process. With the active participation of thousands of volunteers, citizen science programs have been very successful at collecting rich datasets for conservation, enabling the possibility of conducting ecological surveys across multiple years and at large continental scales. Over the last few years, there have been several successful citizen science programs. For example, in biology, citizen scientists help with bird and arthropod research using eBird [Sullivan et al. 2009] and BugGuide [www.bugguide.net.]. In environmental studies, citizen scientists contribute to monitoring coral bleaching conditions [Marshall et al. 2012]. To attract more people to contribute data, the success of citizen science programs relies on tapping into the intrinsic motivations of citizen scientists. The citizen scientists’ contribution cycle is shown in Figure 11.1. The self-fulfillment brought by participation while contributing to science keeps citizen scientists engaged, in return accelerating the whole contribution cycle. In this chapter, we illustrate effective ways to engage citizen scientists in data collection for conservation (Figure 11.2). Our approaches are motivated by our collaborations with two citizen science programs: the eBird and Nchioto projects. eBird is a citizen science program of the Cornell Lab of Ornithology which engages the general public in bird conservation. To understand the distribution and migration of birds, eBird enlists bird watchers to identify","PeriodicalId":260699,"journal":{"name":"Artificial Intelligence and Conservation","volume":"355 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121630423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

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