1. Spatial distribution models From the truth to the whole truth? Senait D. Senay & Sue P. Worner

2. Outline  Alien invasive species (AIS)  Species distribution models (SDMs) and their use to detect and monitor AIS  Uncertainty in SDM results  Suggested technical improvements  Conclusion

3. Alien invasive species (AIS)  Cause Economical, Ecological and Health problems  Increased trade and tourism intensified AIS dispersal  Usually scarce or no complete bio-geographical information available  When the species is small or cryptic as in most insects are, it makes detection and control difficult

4. Species distribution models (SDMs) Hybrid Models Mechanistic Models Correlative Models Infer environmental requirements from known current geographical locations of species Excellent when we do not have much information about the species we are modelling. Use biological information to model species response to certain environmental conditions Process based model, minimizes error of prediction Data, & time intensive High cost Uses the combination of the previous 2 models. Not very common as often there are not enough frameworks that combine r these two modelling processes Requires expertise

5. SDMs as a prediction tool  How much do we know about the problem already?  how much of the truth?  To what level are we going to simplify our model?  Level of abstraction  What model are we going to use?  there are hundreds of models to choose from  Spatial or non spatial?  Do we use one or an ensemble of models?  If ensemble, How do we combine models? http://blog.potterzot.comhttp://blog.potterzot.com ©Zotgeist 2007

6. Sources of uncertainty in SDMs  Environmental data (Predictors)  Data collinearity  Bias in species presence data  Lack of absence data  Climate change  Model uncertainty ------1a ------1b ------2 ------3

7. 1. Predictors & Collinearity reduction Hypothesis: Multi-source, multi-scale, and Multi-temporal data coupled with appropriate dimension reduction methods yield better niche characterization than variables from limited spatiotemporal data sources. Methods: Compare conventional weather dataset used for species distribution modeling (dataset derived from temperature & rainfall variables) with dataset from multiple sources covering 9 environmental variables. (temperature, rainfall, humidity, radiation, elevation, slope, hillshade, vegetation index. - Analyze the effect of collinearity reduction methods (PCA, NLPCA) ( because multi-scale and multi-temporal data will be expected to have complex and non-linear relationship) Preliminary findings:  Multi-temporal and multi-scale data provided a detailed and better niche characterization  Non-linear PCA resulted in a better information extraction of multi- sourced datasets compared to regular PCA.

9. Principal components of the PCA and NLPCA transformed 9 variable multi-source dataset NLPCA PCA

10. Niche classification results

11. 2. Pseudo-absence points selection 1.Presence-only models 2.Presence –absence models A.Random selection B.Geographically weighted random selection C.Environmentally weighted random selection D.Spatial + Environmental + k-means clustering

12. Hypothesis: pseudo-absence method that considers both spatial and environmental space better characterizes species ranges leading to an improved model prediction. Methods: step 1: determine the distance at which the background data is bound with presence data for potential pseudo-absence selection. 2. Do environmental background profiling using one class support vector machine (OCSVM ) 3. Do K-means clustering to choose the final set of points to represent pseudo- absences. Models used: LOG, NB, CART, CTREE, KNN, SVM, NNET. Models that needed separate or detail parameterization were optimized before they are compared with the other models (these are, KNN, SVM, NNET) 3-step pseudo-absence selection method

13. The step 3 method optimized model predictions A Significant increase in model performance measures An interesting observations that models that had clear variation when used with other pseudo-absence methods, gave a more or less similar prediction under the 3-step modeling framework This makes this selection method specially desirable for model comparison, ensemble or consensus exercises. The K-means clustering rather than random selection in this method enables repeatable results which useful for model comparison exercises. Major results The effect of model type, pseudo-absence selection method and species on model performance (Tukey’s HSD test, P<0.05)

14. Kappa as a measure of prediction-reality agreement (Kappa >8.1 = excellent)

15. Prevalence Prevalence: the percentage of occurrence data divided by the total study area 3-step methodenvironmentalSpatialRandom Specificity versus sensitivity

16. Prediction maps 0.35 % prevalence15.29 % prevalence

17. 3. Model parameterization  Species Distribution Models get their information from our presence data.  It is essential to customize model parameters as per the species data structure.  Species relative occurrence area (ROA) can be a good indicator as to how species distribution data affects model prediction.

18. Contd... Model parametrization Hypothesis: Background sampling that takes species relative area of occurrence of presence data distribution gives better model predictions. Methods: Compare performance of model run based on an ROA customized parameters with model run with default parameter settings. Preliminary findings:  Species presence data distribution affects how our model is trained  Limited number of presence points does not necessarily mean inadequate data, both the presence data distribution should be assessed both in geographical and environmental space before deciding on model parameters.

19. MAXENT- Presence-only modelling (Asian tiger mosquito)  Default settings  Background data selection  Feature function selection 0.8560.955

20. MAXENT- Presence-only modelling (Maps) Custom parameters Default parameters

21. Concluding remarks…  Robust niche characterizing helps our models to predict better, using appropriate data is important.  Pseudo-absence data sampling methods greatly affect model result uncertainty and should consider both spatial and environmental space.  Model parameterization should be customized to species data structure and not be generally applied to all cases specially in presence-only models.

22. Bio-Protection Research Centre PO Box 84 Lincoln University Lincoln 7647, New Zealand P + 64 3 325 3696 F + 64 3 325 3864 www.bioprotection.org.nz THANK YOU!