Bridging Species Niche Modeling and Multispecies Ecological Modeling and Analysis

Jeffery Cavner, J.H. Beach, Aimee Stewart, CJ Gradyjcavner@ku.edu, beach@ku.edu ,astewart@ku.edu, cjgrady@ku.edu

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 jcavner@ku.edu, beach@ku.edu, astewart@ku.edu,

cjgrady@ku.eduBiodiversity Institute University of Kansas