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Testing Niche vs. Neutrality Theories at a Continental Scale Using GBIF Data

This case study explores the factors that determine species distribution patterns at a continental scale, focusing on niche theory and neutrality theory. It highlights the value of data cleaning in analyzing ecological theories.

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Testing Niche vs. Neutrality Theories at a Continental Scale Using GBIF Data

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  1. Using GBIF data to test niche vs. neutrality theories at a continental scale, and the value of data cleaning Tomer Gueta, AviBar-Massada and Yohay Carmel Faculty of Civil and Environmental Engineering Technion– Israel Institute of Technology

  2. Testing ecological theories

  3. This case study What determine species’ distribution pattern at a continental scale?

  4. The Niche theory (Grinnell 1924) Environment! Niche theory Topography Land-use Niche Veg. cover

  5. The Neutral theory (Hubbell 2001) Stochastic processes and/or dispersal limitation predominant (Neutral theory)

  6. Niche vs. Neutrality The continuum hypothesis (Gravel et al 2006) Niche and neutral theories are located at the two ends of a continuum Niche Neutral

  7. The continuum hypothesis The continuum hypothesis (Gravel et al 2006) Niche and neutral theories are located at the two ends of a continuum Niche Neutral Modeling studies suggested that species richness is a main determinant • Species-rich communities are driven more strongly by neutral processes • Species-poor communities are driven more strongly by the niche Species-poor communities Species-rich communities species richness gradient

  8. Prediction Species richness gradient Species richness Low High

  9. Prediction- the missing link High effect Niche theory The effect of environmental factors on species distribution Neutral theory Low effect Species-poor communities Species-rich communities species richness gradient

  10. Prediction 1: Continuum hypothesis Modeling studies suggested that species richness is a main determinant • Species-rich communities are driven more strongly by neutral processes • Species-poor communities are driven more strongly by the niche (Niche) High effect A clear negative correlation= continuum (-) Environment effect (Neutral) Low effect Species-poor communities species richness gradient Species-rich communities

  11. Prediction 2: Niche Species-rich communities Species-poor communities Occurrence prob. Occurrence prob. Environmental gradient Environmental gradient (Niche) High effect Environment effect A clear positive correlation= niche (+) (Neutral) Low effect Species-poor communities species richness gradient Species-rich communities

  12. Prediction 3: Neutral

  13. Where? Australia

  14. What? (IUCN, 2008)

  15. Quantifying environmental effect High effect Niche theory The effect of environmental factors on species distribution Neutral theory Low effect

  16. Species Distribution Model (SDM) Niche characterization + Species “X” Occurrencedata Difference environmental factors Predicted species distribution map Extracting datafor validation Model validation SDM performance

  17. MaxEnt(Phillips et al. 2006) Niche characterization + Species “X” Occurrencedata Difference environmental factors Predicted species distribution map Extracting datafor validation Model validation MaxEnt ‘gain’

  18. Methods Species-richness gradient for each species

  19. Prediction 1: Continuum hypothesis Modeling studies suggested that species richness is a main determinant • Species-rich communities are driven more strongly by neutral processes • Species-poor communities are driven more strongly by the niche (Niche) High effect A clear negative correlation= continuum (-) MaxEnt ‘gain’ (Neutral) Low effect Species-poor communities species richness gradient Species-rich communities

  20. Prediction 2: Niche Occurrence prob. Occurrence prob. Environmental gradient Environmental gradient (Niche) High effect MaxEnt ‘gain’ A clear positive correlation= niche (+) (Neutral) Low effect Species-poor communities species richness gradient Species-rich communities

  21. Species richness • Organism perspective • Guilds • Biological characteristics: taxon, trophic level and body weight All Mammals Herbivores Carnivore Bats 1g-100g 100g-5000g

  22. The data Global Biodiversity Information Facility

  23. Study design Conclusions Conclusions Raw data Data cleaning

  24. Data cleaning Conventional data check and filtering Geospatial

  25. Data cleaning Taxonomic Temporal From 1,041,867 records to 515,479

  26. Results- 100km grid Spearman rank correlation test (rho)a non-parametric correlation test Total

  27. Results- 200km grid Total

  28. Results- 300km grid Total

  29. Conclusions • The effect of the Environment decreases with species richness • We exapmlefy the crucial role of data cleaning!

  30. Thank you for listening Citations (Data resource)

  31. Citations (Data resource)

  32. Citations (Data resource)

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