Spatial modelling: a small step for science but a giant leap for biosecurity

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John Rogers
5 years ago
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1 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.

Stephen Hartley, Victoria University of Wellington Dr.

Collaborators Dr. Craig Phillips, Agresearch Crown Research Institute Dr.

2 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

Ethiopia o Junior researcher, Alemaya University,

In Geo- Information Science at Wagningen University in

Preparedness Agency as GIS specialist o United Nations

3 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

the basic step for efficient mitigation of effects

used to understand specieslandscape interactions

4 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. 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 Strong track record in biosecurity research 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?

5 Image credit: Species distribution modelling 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...

6 Workflow of SDMs Presence points Absence points Model training Training data Spatial model Enviro. Data M2 Prediction M3 M1 Test data Model Evaluation Confusion matrix Info. theoretic Bayesian stat. Model testing Spps. Dist. maps Could be based on an individual model or ensemble models

7 Methods: Target species Asian tiger mosquito [ Aedes albopictus] Western corn rootworm [Diabrotica v. virgifera] Common European wasp [ Vespula vulgaris] Yellow crazy ant [Anoplolepis gracilipes] Pine Processionary moth [Thaumetopoea pityocampa] Great white butterfly [ Pieris brassicae]

8 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 Arbitrarily selected geographical distance Environmentally extreme points 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

9 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

10 More results Hybrid Selective models landscape recording {Correlative SDM + Physiological data}

11 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.

12 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.

13 Questions

SENAIT D. SENAY INTEREST EDUCATION.

SENAIT D. SENAY INTEREST EDUCATION. SENAIT D. SENAY ssenay@umn.edu INTEREST I have a unique interest in working on climate change adaptation issues especially regarding the increased risk from alien invasive species on livelihoods, food