The Best AI Tools in Ecology

I. Biodiversity Monitoring & Conservation

1. WildMe:

   • Summary: AI-powered platform for identifying individual animals from images/videos, aiding wildlife conservation.

   • Link: https://www.wildme.org/

2. iNaturalist:

   • Summary: Citizen science platform with AI for species identification from photos (though not solely an AI tool, it heavily relies on it).

   • Link: https://www.inaturalist.org/

3. Rainforest Connection:

   • Summary: Uses AI to analyze rainforest audio to detect illegal logging and poaching.

   • Link: https://rfcx.org/

II. Environmental Monitoring & Management

4. Google Earth Engine:

   • Summary: Cloud-based platform with AI for analyzing geospatial data (satellite imagery) for deforestation, land cover change, etc.

   • Link: https://earthengine.google.com/

5. Sentinel Hub:

   • Summary: Platform for accessing and processing satellite imagery, which can be used with AI for environmental monitoring.

   • Link: https://www.sentinel-hub.com/

III. AI Tools & Platforms (General)

6. Google Cloud Vertex AI:

   • Summary: While not ecology-specific, this platform provides the AI/ML tools to build custom ecological models.

   • Link: https://cloud.google.com/vertex-ai

7. Amazon SageMaker:

   • Summary: AWS platform for building and deploying machine learning models, useful for ecological data analysis.

   • Link: https://aws.amazon.com/sagemaker/

8. Microsoft Azure AI Platform:

   • Summary: Azure's AI services for building and deploying AI solutions in environmental science.

   • Link: https://azure.microsoft.com/en-us/solutions/ai/

IV. Specialized AI Applications in Ecology

• (AI-powered image recognition for species identification):

  • Summary: AI models identify species from camera trap photos, aerial imagery, or citizen science submissions.

  • Links:

    • (a) iNaturalist: (A key platform using AI for species ID)

      • https://www.inaturalist.org/

    • (b) Google Lens: (Can be used for some basic species identification)

      • (Within Google Search App or Google Photos)

    • (c) Search for research papers: (Use these databases for scientific studies)

      • Google Scholar: https://scholar.google.com/

      • Web of Science: (Often through a library subscription)

• (AI for acoustic monitoring of wildlife):

  • Summary: AI analyzes audio recordings (ecoacoustics) to detect animal calls, track populations, and monitor ecosystem health.

  • Links:

    • (a) Cornell Lab of Ornithology's Raven: (Software for sound analysis, often used in conjunction with AI)

      • https://ravensoundsoftware.com/

    • (b) Search for research papers: (Use these databases for scientific studies)

      • Google Scholar: https://scholar.google.com/

      • Bioacoustics journal: (Publisher's site or library access)

• (AI for predicting species distribution):

  • Summary: AI models predict where species are likely to be found based on environmental factors, aiding in conservation planning.

  • Links:

    • (a) Maxent: (Software commonly used for species distribution modeling; AI techniques are often integrated)

      • (Search for "Maxent species distribution modeling" to find download sites and documentation)

    • (b) R packages for species distribution modeling: (R is a statistical programming language used in ecology)

      • (Search for R packages like "sdm" or "biomod2" on the R project website)

    • (c) GBIF (Global Biodiversity Information Facility): (Data source for species occurrence)

      • https://www.gbif.org/

• (AI for wildfire prediction and management):

  • Summary: AI models predict wildfire risk, behavior, and spread, helping with prevention and firefighting efforts.

  • Links:

    • (a) US Forest Service publications:

      • https://www.fs.usda.gov/ (Search their site for "wildfire prediction AI")

    • (b) Google Earth Engine: (Can be used with AI for wildfire analysis)

      • https://earthengine.google.com/

• (AI for precision agriculture):

  • Summary: AI optimizes farming practices to reduce water and pesticide use, benefiting ecosystems.

  • Link:

    • (a) Farmers Edge: (Example of a platform)

      • https://www.farmersedge.ca/

• (AI for pollution monitoring):

  • Summary: AI analyzes data to track pollution sources and levels.

  • Links:

    • (a) Environmental Protection Agency (EPA) data: (Data that AI tools can analyze)

      • https://www.epa.gov/

    • (b) Remote sensing platforms: (Like Google Earth Engine)

• (AI for climate modeling):

  • Summary: AI improves the accuracy and efficiency of climate simulations.

  • Link:

    • (a) National Center for Atmospheric Research (NCAR): (A leading climate research center)

      • https://ncar.ucar.edu/

• (AI for conservation planning):

  • Summary: AI optimizes the design of protected areas and conservation strategies.

  • Links:

    • (a) World Wildlife Fund (WWF) resources: (For conservation strategies)

      • https://www.worldwildlife.org/

    • (b) Nature Conservancy resources: (For conservation approaches)

      • https://www.nature.org/en-us/

    • **(c) Search for research in conservation biology journals)

• (AI for ecological restoration):

  • Summary: AI monitors and optimizes restoration efforts.

  • Link:

    • (a) Society for Ecological Restoration (SER): (For restoration principles)

      • https://www.ser.org/

    • **(b) Search for research papers on "ecological restoration AI")

• (AI for invasive species management):

  • Summary: AI helps to identify and track invasive species.

  • Links:

    • (a) Global Invasive Species Database (GISD): (Data source)

      • http://www.iucngisd.org/gisd/

    • **(b) Remote sensing platforms (like Google Earth Engine) can be used with AI for this.)

• (AI for citizen science data analysis):

  • Summary: AI analyzes data collected by citizen scientists.

  • Link:

    • (a) iNaturalist: (As mentioned before)

      • https://www.inaturalist.org/