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]

Title: Modelling invasive species-landscape interactions using high resolution

spatially explicit models.

Strong track record in biosecurity research

Pioneer in species distribution modelling.

PhD Research Project

Alien Invasive Species (AIS) cause Economical, Ecological and Health problems.

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 species-landscape interactions

What value am I adding by investigating this process that has been used to predict species distribution for almost a decade?

Species distribution modelling

In an ideal world, we will know everything about the invading species……..

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……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

Absence

points

Enviro.

Data

Model training

Model testing

M2

M3

M1

Test

data

Training

data

Prediction

Spps. Dist.

maps

Could be based on

an individual model

or ensemble

models

Confusion matrix

Info. theoretic

Bayesian stat.

Model Evaluation

Spatial

model

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]

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

Results: Absence data

New balanced

method

Environmentally

extreme points

Randomly

selected points Arbitrarily selected

geographical distance

Results: Multi-scenario modelling framework

- Model type was found to be the major factor

- Choice of environmental variables and data processing improved low performing models

Study to investigate sources of uncertainty in species distribution predictions

180 combinations

Multi-model and multi-scenario framework

Developed two new indices to evaluate modellers’ choice of factors

More results

Hybrid models

{Correlative SDM + Physiological data}

Selective landscape recording

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