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Climate Matching for Pest Distribution: A Comprehensive Overview

Climate matching represents an innovative approach in ecological modeling, utilizing the observed distribution of pests rather than relying on biological or experimental data. While effective for weed species, data insufficiencies exist for arthropods and pathogens. This method involves a simple matching procedure that defines habitat spaces and uses extensive weather data from the NCEP grid. The climate match is quantified as a percentage based on multiple climate variables, ensuring rigorous analysis and accuracy. This overview includes essential steps, observation sources, and variables for effective climate matching.

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Climate Matching for Pest Distribution: A Comprehensive Overview

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  1. Part 4 Climate Matching

  2. Climate Matching • Unlike other NAPPFAST models, climate matching creates models from the observed distribution of the pest not from biological or experimental data. • Climate matching works well for weeds but for arthropods or pathogens there is insufficient observations. • Observations can be obtained from taxonomic portals such as the Global Biodiversity Information Facility.

  3. Climate Matching • Simple climate matching procedure similar to BIOCLIM. • Climate envelope defines habitat space within 1.28 standard deviations of average for each variable. • Weather data is based on a 32 K NCEP grid includes 12+ climate variables • The climate match is expressed as a percentage of the total years x total variables for each pixel. Beaumont et al 2005 Ecological modeling

  4. Climate Matching Variables (1) annual values of extreme minimum temperature, (2) extreme maximum temperature, (3) annual number of frost free days, (4) annual degree days, (5) annual precipitation, (6) annual Potential Evapotraspiration (PET), (7) average temperature of the three warmest months in the year, (8) PET for the 3-month period found in 7, and (9) precipitation for the period found in 7.

  5. Climate Matching Procedure • Search for species observations in GBIF • Format the observations data • Compile a configuration file to select variables • Submit to ZedX (offline) • Wait for products

  6. GBIF Search

  7. Configuration File

  8. Products • Map of observed locations • Map of predicted zones • Quality control statistics • Percent accuracy (all, withheld) • Number of observations/ Number of continental regions • Average date of observations • Area projections for US

  9. Acacia decurrens home.vicnet.net.au

  10. Acacia decurrens

  11. Acacia decurrens

  12. Acacia decurrens

  13. Acacia decurrens Australian herbarium

  14. Kikuyu grass Australian Herbaria Calflora www.tropicalgrasslands.asn.au

  15. Kikuyu grass Australian Herbaria Calflora

  16. Kikuyu grass

  17. Kikuyu grass

  18. Kikuyu grass

  19. Kikuyu grass

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