Best practices and software for the management and sharing of camera trap data for small and large scales studies. Issue 3 (1st August 2017)

• Record Type: Journal Article
• Authors: Scotson, Lorraine; Johnston, Lisa R.; Iannarilli, Fabiola; Wearn, Oliver R.; Mohd‐Azlan, Jayasilan; Wong, Wai Ming; Gray, Thomas N. E.; Dinata, Yoan; Suzuki, Ai; Willard, Clarie E.; Frechette, Jackson; Loken, Brent; Steinmetz, Robert; Moßbrucker, Alexander M.; Clements, Gopalasamy Reuben; Fieberg, John
• Editors: Rowcliffe, Marcus; De Angelo, Carlos
• Is Part Of: Remote sensing in ecology and conservation. Volume 3:Issue 3(2017)
• Page Start: 158
• Page End: 172
• Publication Date: 2017-08-01
• Subjects: Bycatch data -- data management -- macrosystem ecology -- metadata -- population trends -- species identification; Remote sensing -- Periodicals; Ecology -- Research -- Periodicals; Ecology -- Methodology -- Periodicals; Ecology -- Remote sensing -- Periodicals; Nature conservation -- Methodology -- Periodicals; 577.0723
• DOI: 10.1002/rse2.54
• Languages: English
• ISSNs: 2056-3485

Abstract

Abstract: Camera traps typically generate large amounts of bycatch data of non‐target species that are secondary to the study's objectives. Bycatch data pooled from multiple studies can answer secondary research questions; however, variation in field and data management techniques creates problems when pooling data from multiple sources. Multi‐collaborator projects that use standardized methods to answer broad‐scale research questions are rare and limited in geographical scope. Many small, fixed‐term independent camera trap studies operate in poorly represented regions, often using field and data management methods tailored to their own objectives. Inconsistent data management practices lead to loss of bycatch data, or an inability to share it easily. As a case study to illustrate common problems that limit use of bycatch data, we discuss our experiences processing bycatch data obtained by multiple research groups during a range‐wide assessment of sun bears Helarctos malayanus in Southeast Asia. We found that the most significant barrier to using bycatch data for secondary research was the time required, by the owners of the data and by the secondary researchers (us), to retrieve, interpret and process data into a form suitable for secondary analyses. Furthermore, large quantities of data were lost due to incompleteness and ambiguities in data entry. From our experiences, and from a review of the published literature and online resources, we generated nine recommendations on data management best practices for field site metadata, camera trap deployment metadata, image classification data and derived data products. We cover simple techniques that can be employed without training, special software and Internet access, as well as options for more advanced users, including a review of data management software and platforms. From the range of solutions provided here, researchers can employ those that best suit their needs and capacity. Doing so will enhance the usefulness of their camera trap bycatch data by improving the ease of data sharing, enabling collaborations and expanding the scope of research. Abstract : Camera trap studies world‐wide are accumulating massive volumes of bycatch images of non‐target species, which could be used to monitor under‐studied threatened species. A significant barrier to realizing the potential of bycatch data is inconsistencies in data management practises between research groups, leading to loss of data and difficulties in sharing and re‐use of data by secondary researchers. By following our nine data management recommendations, which were developed with input from leaders in data management and camera trap studies, researchers can reduce the loss of bycatch data, and maximize the ease of data sharing, thus enabling collaborations and expanding the scale and scope of research of under‐represented species. Photo credits: Oliver Wearn, and Tom Gray (WWF Cambodia & Wildlife Alliance).