Download Spatial modelling - Better Border Biosecurity

Spatial modelling: a small step for science but a
giant leap for biosecurity
Senait Senay
Better Border Biosecurity (B3)
B3 Conference, May 2014
Acknowledgements
Main Supervisor
Assoc. Prof. Susan Worner, Bio-Protection Research Centre, Lincoln
University
Co-supervisors
Dr. Michael Rostas, Bio-Protection Research Centre, Lincoln University
Dr. Stephen Hartley, Victoria University of Wellington
Dr. Jeff Morisette, United States Geological Survey, Fort Collins,
Colorado
Collaborators
Dr. Craig Phillips, Agresearch Crown Research Institute
Dr. William Monahan, National Park Service, Fort Collins, Colorado
Funding source
Bio-Protection Research Centre , more recently B3
Data Courtesy
GBIF, MPI, DOC, Agresearch, BPRC, WORLDCLIM, CLIMOND
Background
o
BSc. in Forestry Science at the Southern
University of Ethiopia
o
Junior researcher, Alemaya University, Ethiopia
o
MSc. In Geo- Information Science at Wagningen
University in the Netherlands
o
Ethiopian Disaster Prevention and
Preparedness Agency as GIS specialist
o
United Nations agencies (UNDP & UNOCHA)
as Information Management Officer.
[Disaster Risk Reduction, Hazard Risk Assessment,
Integrated master plan development projects]
PhD Research Project
Title: Modelling invasive species-landscape interactions using high resolution
spatially explicit models.



Alien Invasive Species (AIS) cause
Economical, Ecological and Health
problems.
Strong track record in
biosecurity research
Studying invasive species-landscape
interaction is the basic step for
efficient mitigation of effects of AIS
Spatially explicit models have been
widely used to understand specieslandscape interactions
Pioneer in species
distribution modelling.
What value am I adding by investigating this
process that has been used to predict species
distribution for almost a decade?
Species distribution modelling
Image credit: http://www.bioacid.de/
In an ideal world, we will know everything about the invading
species……..
……However, we do not always have
biological information on what limits
an invading species
Correlative models
•
•
use geographical occurrences of the target species
• Instrumental in assessing cross border biosecurity risks
Continuous research have been undertaken to improve C-SDMs
• But…there is still room for more improvement....
Workflow of SDMs
Presence
points
Model training
Absence
points
Spatial
model
Training
data
Enviro.
Data
M2
M3
Model Evaluation
Test
data
Confusion matrix
Info. theoretic
Bayesian stat.
M1
Prediction
Model testing
Spps. Dist.
maps
Could be based on
an individual model
or ensemble
models
Methods: Target species
Asian tiger mosquito [ Aedes albopictus]
Yellow crazy ant [Anoplolepis gracilipes]
Western corn rootworm [Diabrotica v. virgifera]
Common European wasp [ Vespula vulgaris]
Pine Processionary moth [Thaumetopoea pityocampa]
Great white butterfly [ Pieris brassicae]
Results: Absence data
 The effect of absence data quality.
o Physical barriers
o Cryptic species
o Species did not reach location yet
 3 types of widely used pseudo-absence
selection methods were investigated
Randomly
selected points
Environmentally
extreme points
Arbitrarily selected
geographical distance
New balanced
method
Results: Multi-scenario modelling
framework
Study to investigate sources of uncertainty in species distribution predictions
- Model type was found to be the major
factor
- Choice of environmental variables and
data processing improved low
performing models
 Multi-model and multi-scenario
framework
 Developed two new indices to
evaluate modellers’ choice of
factors
180 combinations
More results
Hybrid
models
Selective
landscape recording
{Correlative SDM + Physiological data}
Potential outcome
 The newly developed methods can be used to improve
consensus among model results.
 The methods enable species distribution models to be
utilized in a climate change context.
 Accurate species distribution predictions are key to
optimize invasive species detection and surveillance
strategies.
Future intentions
 Continuing to improve reliability of species distribution
and dispersal models in light of more sophisticated
spatial data for biosecurity.
 Creating linkage between New Zealand researchers
and research institutions in East Africa.
Questions