Bridging Species Niche Modeling and Multispecies Ecological Modeling and Analysis Jeffery Cavner,...
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Transcript of Bridging Species Niche Modeling and Multispecies Ecological Modeling and Analysis Jeffery Cavner,...
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