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Jeffery Cavner, J.H. Beach, Aimee Stewart, CJ Grady [email protected], [email protected] ,[email protected], [email protected] Biodiversity Institute University of Kansas. Bridging Species Niche Modeling and Multispecies Ecological Modeling and Analysis. - PowerPoint PPT Presentation
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Bridging Species Niche Modeling and Multispecies Ecological Modeling and Analysis
Jeffery Cavner, J.H. Beach, Aimee Stewart, CJ [email protected], [email protected] ,[email protected], [email protected]
Biodiversity Institute University of Kansas
Species DiversityLmRAD (Lifemapper Range and Diversity)
Biodiversity - describe, visualize and analyze different aspects of the numbers and abundances of taxa in time and space.
Patterns of species richness - constituent species ranges sizes and spatial locations of those ranges.
Patterns related to species associations, co-occurrence, and species interactions requires testing against randomized distributions.
Species richness and species range can be summarized and linked by one basic analytical tool, the presence/absence matrix (PAM).
Lifemapper as an overarching architecture
• LmRAD is built on top of the existing Lifemapper architecture
• Lifemapper is an archival and species distribution modeling platform consisting of a computational pipeline, specimen data archive, predicted species distribution model archive
• Distribution models are built on-demand using openModeller.
• Inputs: climate scenario data and aggregated specimen occurrences from GBIF and user provided occurrence points.
The Presence Absence Matrix (PAM)
Data Matrix Grid
Most existingindices of biodiversityare simple combinations of :oVectors:
species richnesssizes of distributions“dispersion fields”“diversity fields”
oWhitaker’s beta diversityoThe dimensions of the PAM
Constraints
• Construction of PAMs can be an extremely time consuming data management task
• Current methods for working with these matrices can be computationally slow
Approach
• To overcome computational restraints we use a Python implementation of the Web Processing Service standard on a compute cluster, exposing spatial and statistical algorithms.
• Allows a variety of species inputs
• Extendable clients including Quantum GIS (QGIS) and VisTrails that share a common client library
Clients
Randomizing the PAM• To test the null hypothesis
• By producing the same richness and range patterns while ignoring realistic species combinations
• Two Types of Randomization: Swap and Dye Dispersion– Swap : keeps species richness and range size totals intact.
Additional Randomization methods
Dye Dispersion
– Geometric constraints model
– Assumes range continuity
– Reassembles ranges
– Keeps range size intact
QGIS is used as a WPS client
Using QGIS and WPS to construct a grid
The asynchronous nature of WPS combined with a computational pipeline and compute cluster allow a user to intersect hundreds of species layers at a time with the data grid to populate the PAM.
Terrestrial Mammals
Proportional Species Richness
Per-site Range Size
High YellowModerate RedLow Blue
Statistical services provide diversity indices and plots using WPS
By-species range-diversity plot
The plug-ins use a simple MVC pattern with QT threads for asynchronous WPS requests and a client library for the communication layer
Conclusion
Jeffery Cavner, J.H. Beach, Aimee Stewart, CJ Grady [email protected], [email protected], [email protected],
[email protected] Institute University of Kansas