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Crowd sourcing ecology: using the internet to develop hypotheses about pollinator preferences

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Christie Bahlai

Crowd-sourcing ecology: Predicting plant attractiveness to pollinators from internet image searches Bahlai and Landis presentation for Ecological Society of America Meeting 2014.

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Crowd-sourcing ecology: Predicting plant attractiveness to pollinators from internet image searches Christie Bahlai and Douglas Landis Michigan State University
Restoring landscapes to support pollinators Use plant communities that are: Attractive Diverse Locally adapted Native
Common garden experiments Soil types Local climate
Is there a better way? Goal: identify candidate plants for locally customized habitat restoration Can we use existing data to help narrow our search?
Hypothesis People like to take pictures of plants in bloom People like to post pictures on the internet Some of the pictures will capture insect visitations Plants that are highly attractive to pollinators will be photographed being visited by pollinators more frequently Ellen Booraem, http://ellenbooraem.blogspot.com/
Approach Determine search terms and engines to use Search for images of plants with experimentally known pollinator visitation rates See if relative visitation rate observed in searches predicts relative attractiveness of flower
Search terms and engines Plant species Search term Search engine Latin name Common name "Beneficial Insect" "Insect" "Bee" "Honey Bee" Bing Vicia faba Fava Bean 0 (7) 0 6 1 (17) Fagopyrum esculentum Buckwheat 0 (7) 5 8 3 Coriandrum sativum Coriander 0 (19) 0 3 0 (27) Lobularia maritima Sweet Alyssum 1 1 3 4 Anethum graveolens Dill 0 0 1 0 (21) Total 1 6 21 8 Google Vicia faba Fava Bean 2 3 11 8 Fagopyrum esculentum Buckwheat 1 3 7 4 Coriandrum sativum Coriander 1 4 2 2 Lobularia maritima Sweet Alyssum 1 1 2 2 Anethum graveolens Dill 1 3 0 0 Total 6 14 22 16
Testing the association Existing surveys: Tuell et al 2008 (Apis and non-Apis bees) Fieldler 2006 (Syrphid flies) Search Google images for [plant species] bee Record number of images with each taxa in them Photos: John Severns, Wojciech Ochwat, Kevin Hall
Results
Results Relationships between visitation rates in field for non-Apis bees, syrphids No relationship for Apis bees Model Slope (field/ images) Pseudo-R2 Apis bees - - Non-Apis bees* 0.10賊0.04 0.668 Syrphids 0.08賊0.72 0.003
Discussion Neat! It worked (for non-Apis bees)! Why were no relationships observed for Apis bees? Photos: John Severns, Wojciech Ochwat, Kevin Hall
Applications Passive crowdsourcing
More information See complete data+ analysis for this experiment on GitHub (cbahlai/Bee_images)
Thank you! Data collection: Julia Perrone Photos: Carolyn Malmstrom, Ashley Bennett, Rufus Isaacs

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Crowd sourcing ecology: using the internet to develop hypotheses about pollinator preferences

  • 1. Crowd-sourcing ecology: Predicting plant attractiveness to pollinators from internet image searches Christie Bahlai and Douglas Landis Michigan State University
  • 2. Restoring landscapes to support pollinators Use plant communities that are: Attractive Diverse Locally adapted Native
  • 3. Common garden experiments Soil types Local climate
  • 4. Is there a better way? Goal: identify candidate plants for locally customized habitat restoration Can we use existing data to help narrow our search?
  • 5. Hypothesis People like to take pictures of plants in bloom People like to post pictures on the internet Some of the pictures will capture insect visitations Plants that are highly attractive to pollinators will be photographed being visited by pollinators more frequently Ellen Booraem, http://ellenbooraem.blogspot.com/
  • 6. Approach Determine search terms and engines to use Search for images of plants with experimentally known pollinator visitation rates See if relative visitation rate observed in searches predicts relative attractiveness of flower
  • 7. Search terms and engines Plant species Search term Search engine Latin name Common name "Beneficial Insect" "Insect" "Bee" "Honey Bee" Bing Vicia faba Fava Bean 0 (7) 0 6 1 (17) Fagopyrum esculentum Buckwheat 0 (7) 5 8 3 Coriandrum sativum Coriander 0 (19) 0 3 0 (27) Lobularia maritima Sweet Alyssum 1 1 3 4 Anethum graveolens Dill 0 0 1 0 (21) Total 1 6 21 8 Google Vicia faba Fava Bean 2 3 11 8 Fagopyrum esculentum Buckwheat 1 3 7 4 Coriandrum sativum Coriander 1 4 2 2 Lobularia maritima Sweet Alyssum 1 1 2 2 Anethum graveolens Dill 1 3 0 0 Total 6 14 22 16
  • 8. Testing the association Existing surveys: Tuell et al 2008 (Apis and non-Apis bees) Fieldler 2006 (Syrphid flies) Search Google images for [plant species] bee Record number of images with each taxa in them Photos: John Severns, Wojciech Ochwat, Kevin Hall
  • 10. Results Relationships between visitation rates in field for non-Apis bees, syrphids No relationship for Apis bees Model Slope (field/ images) Pseudo-R2 Apis bees - - Non-Apis bees* 0.10賊0.04 0.668 Syrphids 0.08賊0.72 0.003
  • 11. Discussion Neat! It worked (for non-Apis bees)! Why were no relationships observed for Apis bees? Photos: John Severns, Wojciech Ochwat, Kevin Hall
  • 13. More information See complete data+ analysis for this experiment on GitHub (cbahlai/Bee_images)
  • 14. Thank you! Data collection: Julia Perrone Photos: Carolyn Malmstrom, Ashley Bennett, Rufus Isaacs

Editor's Notes

  • #8: Examine first 30 search results