Update digitalconservation_summary.md

final edits

digitalconservation_summary.md

@@ -7,15 +7,18 @@ title: Digital Conservation Practices and Perspectives - Summary of Findings
 
 
 !["Img 1"](assets/img/digconsummary/img1.png "Img 1"){: width="150%" height="150%" } \
-[Source](https://blog.nature.org/2013/05/27/boucher-bird-blog-apps-smart-birder/) \
+[Source](https://blog.nature.org/2013/05/27/boucher-bird-blog-apps-smart-birder/)
+
 Big data and related data collection tools such as acoustic sensors; machine learning and artificial intelligence (AI) algorithms; dashboards and platforms for sharing data – these all constitute “digital conservation.” Digital technologies have the potential to make conservation more holistic, responsive, and participatory. Already, platforms such as eBird enlist the public in monitoring projects and there is an effort to “democratize” AI tools so that more conservation organizations can use them. Digital technologies may also provide precise data and, as a result, the ability to prioritize conserving the places that will most effectively meet nature protection goals like the Kunming-Montréal Global Biodiversity Framework’s 30% by 2030 target.
 
-We hear a lot about the promises of digital conservation and less about the challenges. Is access to digital conservation democratic? Are outcomes lining up with expectations? In general, what do conservationists actually think about and do with digital tools? \
+We hear a lot about the promises of digital conservation and less about the challenges. Is access to digital conservation democratic? Are outcomes lining up with expectations? In general, what do conservationists actually think about and do with digital tools?
+
 !["Img 2"](assets/img/digconsummary/img2.png "Img 2") \
 [Source](https://cnr.ncsu.edu/news/2022/01/transforming-data-into-conservation/)
 
 ## Methods
-We conducted three surveys to understand practices and perspectives on digital conservation: 1) We counted and correlated keywords on conservation technology-related websites and the websites of 69 conservation organizations; 2) We conducted a more traditional survey of 45 organizations across Canada and the US on their use of conservation technologies, their goals in using them, and the outcomes they’ve seen; 3) We ran what’s known as a Q-method survey with 10 individuals in the sector to characterize distinct ways of thinking about digital conservation. Additionally, we explored a case study digital conservation project, interviewing five key informants and reviewing primary materials. \
+We conducted three surveys to understand practices and perspectives on digital conservation: 1) We counted and correlated keywords on conservation technology-related websites and the websites of 69 conservation organizations; 2) We conducted a more traditional survey of 45 organizations across Canada and the US on their use of conservation technologies, their goals in using them, and the outcomes they’ve seen; 3) We ran what’s known as a Q-method survey with 10 individuals in the sector to characterize distinct ways of thinking about digital conservation. Additionally, we explored a case study digital conservation project, interviewing five key informants and reviewing primary materials.
+
 !["Img 3"](assets/img/digconsummary/img3.png "Img 3"){: width="150%" height="150%" } \
 [Source](https://commons.wikimedia.org/wiki/File:Online_Survey_Icon_or_logo.svg)
 
@@ -31,7 +34,8 @@ Discussions around AI involve cost (44% of the time) more than surveillance (onl
 !["Img 4b"](assets/img/digconsummary/img4b.png "Img 4b"){: width="75%" height="75%" } \
 Note: “Average” refers to all mentions of the term, divided by the number of websites examined, and multiplied by 100. “Frequency” refers to the percentage of all websites examined that the term is mentioned on.
 
-Big data is one of the most common technologies conservation organizations report using. We surveyed local, regional, and international conservation organizations based in the US and Canada. Participation skewed towards local and regional organizations. Half – 22 out of 45 – reported using big data: data collected from sensors, drones, traps, or citizen science projects in relatively large quantities, at a rapid rate, and/or in a variety of formats. Only six reported using AI. Drawing on in-house capacity to utilize digital tools can be demanding in terms of expertise and time. Outsourcing might cost money. We found that organizations’ uses of big data and AI are mostly done by partners, though many also rely on in-house capacity. This suggests that access to digital conservation may be democratic inasmuch as less capable organizations can partner with groups that have the resources to utilize such tools.\
+Big data is one of the most common technologies conservation organizations report using. We surveyed local, regional, and international conservation organizations based in the US and Canada. Participation skewed towards local and regional organizations. Half – 22 out of 45 – reported using big data: data collected from sensors, drones, traps, or citizen science projects in relatively large quantities, at a rapid rate, and/or in a variety of formats. Only six reported using AI. Drawing on in-house capacity to utilize digital tools can be demanding in terms of expertise and time. Outsourcing might cost money. We found that organizations’ uses of big data and AI are mostly done by partners, though many also rely on in-house capacity. This suggests that access to digital conservation may be democratic inasmuch as less capable organizations can partner with groups that have the resources to utilize such tools.
+
 !["Img 5"](assets/img/digconsummary/img5.png "Img 5"){: width="150%" height="150%" }
 
 Real-time monitoring was the most common goal organizations reported using big data for. They want to be able to rapidly produce information on species and ecosystems. More precise data and a wider range of it were also common goals.
@@ -43,8 +47,7 @@ These weren’t just goals though – they were strongly seen as outcomes. For i
 
 Cost and capacity were seen as the biggest challenges that using big data presents, with surveillance not seen as an issue. Privacy concerns – such as the sensitivity of rare species data – were considered important by some respondents but not others.
 
-!["Img 7"](assets/img/digconsummary/img7.png "Img 7"){: width="150%" height="150%" } \
-
+!["Img 7"](assets/img/digconsummary/img7.png "Img 7"){: width="150%" height="150%" }
 
 Organizations using AI are interested in speeding up analysis, though accuracy and minimizing labour time and costs are also important. \
 The few organizations that reported using AI do not seem satisfied with it. For instance, only one strongly agreed that any of their goals were actually achieved (saving labour time and costs).
@@ -66,12 +69,12 @@ Two other perspectives are more optimistic - one emphasizes tools’ efficiency,
 !["Img 10"](assets/img/digconsummary/img10.png "Img 10"){: width="150%" height="150%" } \
 Note: “Statement” refers to each specific statement about digital conservation’s promises and challenges we ask participants to rank according to how strongly they agreed or disagreed with it. There are three colours of dots, each referring to a distinct perspective on digital conservation, defined by participants with similar sorts. “Z_Score” refers to the level of agreement or disagreement with the statement for each perspective. Statements are sorted such that at the top are statements the different perspectives had the least consensus about; statements at the bottom are those each perspective agrees with each other on.
 
+While big data and AI techniques may help “resource-constrained” conservation organizations achieve their missions “more cheaply and efficiently”, data for conservation applications often requires not only the right kind of expertise, but expensive storage and analytical infrastructures that have to be trusted and maintained for potentially indefinite periods. Digital technology might provide the infrastructure to scale conservation, but it actually requires infrastructuring itself. When conservationists turn to tech, they might find themselves working through new old problems, like how to manage biases in data from people, how to deal with model errors, and so on.
+
 !["Img 11"](assets/img/digconsummary/img11.png "Img 11"){: width="150%" height="150%" } \
 The Bird Returns project in California utilizes data from a variety of sources, including the eBird app, to inform a partnership with farmers to protect migratory waterbird habitat. \
 [Source](http://www.calricenews.org/wp-content/uploads/2017/06/BR-Workshop-Presentation-June20171.pdf)
 
-While big data and AI techniques may help “resource-constrained” conservation organizations achieve their missions “more cheaply and efficiently”, data for conservation applications often requires not only the right kind of expertise, but expensive storage and analytical infrastructures that have to be trusted and maintained for potentially indefinite periods. Digital technology might provide the infrastructure to scale conservation, but it actually requires infrastructuring itself. When conservationists turn to tech, they might find themselves working through new old problems, like how to manage biases in data from people, how to deal with model errors, and so on.
-
 ## Takeaways
 !["Img 12"](assets/img/digconsummary/img12.png "Img 12"){: width="150%" height="150%" } \
 [Source](https://www.iis-rio.org/en/projects/naturemap-priority-areas-for-conservation-and-restoration-of-natural-systems/)