Biodiversity: Difference between revisions
Content added Content deleted
mNo edit summary |
(Add descriptions for subareas) |
||
| (14 intermediate revisions by 4 users not shown) | |||
| Line 4: | Line 4: | ||
== Machine Learning Application Areas == |
== Machine Learning Application Areas == |
||
*[[Species Identification|Species identification]]: Machine learning tools are increasingly being used to help in the identification of organisms, both from photographic documentation and (in cases such as birds) audio recordings. These tools are deployed in personal apps and as part of citizen science projects, as well as within formal scientific monitoring efforts. |
|||
*[[Ecosystem Monitoring|Ecosystem monitoring]]: Understanding the overall state of an ecosystem can be valuable both in preserving biodiversity and in maintaining ecosystem services such as food, wood, pollination, and carbon sequestration. Machine learning has the potential to scale and democratize ecosystem monitoring. |
|||
*[[Biodiversity Data Analysis|Biodiversity data analysis]]: There is an increasing wealth of data available on biodiversity, species distributions, and ecosystem health (including some data gathered using ML approaches). Machine learning can be useful in working with this data and analyzing trends. |
|||
== Background Readings == |
== Background Readings == |
||
=== Primers === |
|||
*[https://www.cambridge.org/core/books/artificial-intelligence-and-conservation/17C33AF856648B208E47A10813CEC6DF Artificial Intelligence and Conservation]: A curated collection of case studies. |
|||
* |
*'''"AI empowers conservation biology" (2019)'''<ref>{{Cite journal|last=Kwok|first=Roberta|date=2019-03-04|title=AI empowers conservation biology|url=https://www.nature.com/articles/d41586-019-00746-1|journal=Nature|language=en|volume=567|pages=133–134|doi=10.1038/d41586-019-00746-1}}</ref>: A high-level overview of this problem space. Available [https://www.nature.com/articles/d41586-019-00746-1 here]. |
||
*'''"Climate, biodiversity, and land: using ML to protect and restore ecosystems" (2020)'''<ref>{{Cite web|url=https://slideslive.com/38926837/tackling-climate-change-with-ml?ref=og-meta-tags|title=Climate, Biodiversity, and land: Using ML to protect and restore ecosystems|last=Morris|first=Dan|date=2020-04-26|website=SlidesLive|url-status=live|archive-url=|archive-date=|access-date=2020-09-24}}</ref>: An overview talk, with pointers to practical starting points. Available [https://slideslive.com/38926837/tackling-climate-change-with-ml?time=40751s here]. |
|||
=== Textbooks === |
|||
*[https://coastalresilience.org/project/ai-conservation/ Natural Solutions Toolkit]: Example in-the-field implementations. |
|||
* '''"Artificial Intelligence and Conservation" (2019)'''<ref>{{Cite book|url=https://www.cambridge.org/core/product/identifier/9781108587792/type/book|title=Artificial Intelligence and Conservation|last=|first=|date=2019-02-28|publisher=Cambridge University Press|year=2019|isbn=978-1-108-58779-2|editor-last=Fang|editor-first=Fei|edition=1|location=|pages=|doi=10.1017/9781108587792|editor-last2=Tambe|editor-first2=Milind|editor-last3=Dilkina|editor-first3=Bistra|editor-last4=Plumptre|editor-first4=Andrew J.}}</ref>: A curated collection of case studies. Book description [https://www.cambridge.org/core/books/artificial-intelligence-and-conservation/17C33AF856648B208E47A10813CEC6DF here]. |
|||
*[https://slideslive.com/38926837/tackling-climate-change-with-ml?time=40751s Climate, biodiversity, and land: using ML to protect and restore ecosystems]: An overview talk, with pointers to practical starting points. |
|||
=== Other === |
|||
*'''Natural Solutions Toolkit'''<ref>{{Cite web|url=https://coastalresilience.org/project/ai-conservation/|title=AI for Conservation {{!}} Coastal Resilience|language=en-US|access-date=2020-09-24}}</ref>: Example in-the-field implementations. Available [https://coastalresilience.org/project/ai-conservation/ here]. |
|||
== Online Courses and Course Materials == |
== Online Courses and Course Materials == |
||
{{SectionStub}} |
|||
== Conferences, Journals, and Professional Organizations == |
|||
== Community == |
|||
=== Journals and conferences === |
=== Journals and conferences === |
||
* |
*'''PLOS Collections''': A collection featuring work on the ecological impacts of climate change, available [https://collections.plos.org/eco-climate-change here]. |
||
=== Societies and organizations === |
=== Societies and organizations === |
||
| Line 30: | Line 40: | ||
== Data == |
== Data == |
||
* [https://agentmorris.github.io/camera-trap-ml-survey/ Camera Trap ML Survey] |
|||
== References == |
== References == |
||
Latest revision as of 01:32, 10 March 2021
This page is about the intersection of ecosystems and machine learning in the context of climate change adaptation. For an overview of biodiversity monitoring as a whole, please see the Wikipedia page on this topic.
Changes in climate are increasingly affecting the distribution and composition of ecosystems. This has profound implications for global biodiversity, as well as agriculture, disease, and natural resources such as wood and fish. Machine learning can help by supporting efforts to monitor ecosystems and biodiversity.
Machine Learning Application Areas[edit | edit source]
- Species identification: Machine learning tools are increasingly being used to help in the identification of organisms, both from photographic documentation and (in cases such as birds) audio recordings. These tools are deployed in personal apps and as part of citizen science projects, as well as within formal scientific monitoring efforts.
- Ecosystem monitoring: Understanding the overall state of an ecosystem can be valuable both in preserving biodiversity and in maintaining ecosystem services such as food, wood, pollination, and carbon sequestration. Machine learning has the potential to scale and democratize ecosystem monitoring.
- Biodiversity data analysis: There is an increasing wealth of data available on biodiversity, species distributions, and ecosystem health (including some data gathered using ML approaches). Machine learning can be useful in working with this data and analyzing trends.
Background Readings[edit | edit source]
Primers[edit | edit source]
- "AI empowers conservation biology" (2019)[1]: A high-level overview of this problem space. Available here.
- "Climate, biodiversity, and land: using ML to protect and restore ecosystems" (2020)[2]: An overview talk, with pointers to practical starting points. Available here.
Textbooks[edit | edit source]
- "Artificial Intelligence and Conservation" (2019)[3]: A curated collection of case studies. Book description here.
Other[edit | edit source]
Online Courses and Course Materials[edit | edit source]
🌎 This section is currently a stub. You can help by adding resources, as well as 1-2 sentences of context for each resource.
Conferences, Journals, and Professional Organizations[edit | edit source]
Journals and conferences[edit | edit source]
- PLOS Collections: A collection featuring work on the ecological impacts of climate change, available here.
Societies and organizations[edit | edit source]
Past and upcoming events[edit | edit source]
Libraries and Tools[edit | edit source]
Data[edit | edit source]
References[edit | edit source]
- ↑ Kwok, Roberta (2019-03-04). "AI empowers conservation biology". Nature. 567: 133–134. doi:10.1038/d41586-019-00746-1.
- ↑ Morris, Dan (2020-04-26). "Climate, Biodiversity, and land: Using ML to protect and restore ecosystems". SlidesLive. Retrieved 2020-09-24.
- ↑ Fang, Fei; Tambe, Milind; Dilkina, Bistra; Plumptre, Andrew J., eds. (2019-02-28). Artificial Intelligence and Conservation (1 ed.). Cambridge University Press. doi:10.1017/9781108587792. ISBN 978-1-108-58779-2.CS1 maint: date and year (link)
- ↑ "AI for Conservation | Coastal Resilience". Retrieved 2020-09-24.