Making maps, many maps! [What is GIS?]

1. Making maps, many maps![What is GIS?] Dr. Brian Klinkenberg Department of Geography, UBC For Zoology 502 March 9, 2008 Why do I want to know where they occur?

2. T o C • Why predict species ranges? • What is GIS? • Example: West Nile virus (based on species biology) • Example: Cryptococcus gattii (GARP: correlative model)

3. Why predict species distributions? • We need maps showing species distributions because land use activities, disease prevention actions, are often spatially explicit (e.g., SARA implications [e.g., spotted owl, tall bugbane, Pacific water shrew]) and occur at a range of scales. • We need multi-scale ‘scientific’ approaches because the impacts of land use and global change are multi-scaled. • We can’t sample everywhere so we can never really know the ‘truth.’ • Many different approaches, and each approach has its strengths and weaknesses. A good reference: Scott, M., P.J. Heglund, M.L. Morrison, M.G. Raphael, W.A. Wall & F.B. Samson (eds) 2002. Predicting Species Occurrences: Issues of accuracy and scale. Island Press, Covelo, CA. 847pp.

4. Distribution looks different at different scales Similar methods Different data Different utility

5. 2041-2070 2011-2040 2071-2100 Distributions will change http://www.glfc.cfs.nrcan.gc.ca/landscape/index_e.html

6. Range Distribution Habitat Observations What are we modeling? • Range • Total extent occupied by a given taxon; “limits within which a species can be found” (Morrison and Hall 2002). • Considers only geographic space.  • Distribution (fundamental niche) • Spatial pattern of environments suitable for occupation by a given taxon; “spread or scatter of a species within its range” (Morrison and Hall 2002). • Considers geographic space and environmental components. • Habitat (realized niche) • Combination of resources and conditions that promote occupancy, survival, and reproduction by individuals of a given taxon (Morrison et al. 1992). • Considers geographic space, environmental components and species responses.

7. Distributions: Level of detail Range Observations Distribution of Bidens amplissima Source: E-Flora BC Source: Conservation of Grizzly Bears in British Columbia. Min. of Environment, Lands and Parks, 1995

8. Level of detail continuum Range Map Dot Map • “Definitive” presences • Usually under-predicts occupied area • Usually over-predicts unoccupied area • Accuracy heavily dependent on sampling effort • Can be difficult to validate or test • “Definitive” Absences • Usually over-predicts occupied area • Usually under-predicts unoccupied area • Often subjective and difficult to replicate • Can be difficult to validate or test

9. Range Largely Deductive; using expert opinion based on coarse datasets. Distribution Deductive or Inductive; using statistical algorithms, GIS modeling on refined datasets. Habitat Deductive or inductive; using local knowledge based on specific research data. Observations Actual data from field sampling. Easy to generate. Limited local utility. More difficult to generate than range. Data intensive. Regionally useful. Based on research and/or local knowledge. Time-consuming and difficult to extrapolate. Locally useful. Raw data. Expensive. Limited utility without supplementary info. Tools and results along the continuum

10. 1 1 3 3 2 2 2 2 3 3 4 4 5 5 5 5 2 2 1 1 1 1 1 1 4 4 2 2 5 5 5 5 2 2 3 3 1 1 1 1 4 4 4 4 5 5 4 4 2 2 3 3 3 3 2 2 3 3 4 4 3 3 5 5 Two Broad Approaches • Deductive: conclusions are developed from combination of premises • spatial expressions of qualitative data • overlays of predictor variables • E.g., GIS-based multi-criteria evaluations • Inductive: conclusions are developed as an extrapolation from available data • quantitative and often statistical • what most folks consider “modeling” Quantitative Analysis

11. Model input: Occurrence data • Quality and Quantity  opportunistic vs. systematic  limited vs. abundant  presence-only vs. presence/absence • Correcting and Filtering  spelling, duplicates, misidentification  location, precision, spatial autocorrelation  seasonal, sinks, historical cut-off

12. Model input: Environmental data • Influence element distribution • Fewer variables better than more • Complete coverage of study area • Climatic influence on distribution

13. An Ecosystem Vegetation Climate Animals Terrain Micro -organisms Soil The BioticComponent An ECOSYSTEM Physical Parameters

14. DOMAIN Distribution model approaches • A variety of approaches: • similarity metrics (e.g., DOMAIN) • envelope models (e.g., BIOCLIM, ANUCLIM) • Maximum Entropy (e.g., MaxEnt) • rule-based (e.g., GARP) • splines (e.g., MARS) • classification trees (e.g., CART) • ordination (e.g., CCA, DA, Biomapper) • classical statistics (e.g., GLM, GAM, logistic regression) • neural networks • others … WhyWhere BIOMOD

15. Kappa 2. Convex Hull 1. HSI 5 7 1 6 4 2 3 4. DOMAIN Model (& threshold) Mann-Whitney Statistic 6. GAM 5. GLM 5 7 1 6 4 2 3 Model Actual Occurrences 7. GARP Comparison of distribution models (Elith and Burgman 2003) 3. ANUCLIM

16. Model evaluation • Commission vs. Omission Errors  insufficient sample size  measurement error  insufficient spatial resolution  critical environmental variables excluded • Validation Methods  expert review  classifying independent occurrence data  post-modeling field surveys

17. Model Selection • Depends on many factors…  data quality and quantity  study area size and history  element biology  intended use of predicted distribution • Use multiple models  overlapping predicted distributions  determine best model

18. T o C • Why predict species ranges? • What is GIS? • Example: West Nile virus (based on species biology) • Example: Cryptococcus gattii (GARP: correlative model)

19. GIS? Geographic Information System

20. Why geography matters • The examination of spatial patterns invites questions, raises concerns. • Theory of evolution (Darwin’s finches) • John Snow’s cholera mapping • He mapped deaths from cholera in 1854 • Map led him to question the quality of the water from the Broad Street pump • Removing the pump handle stopped the epidemic (over 500 people died)

21. John Snow’s map

22. Why Geography matters • Almost everything happens somewhere • Nothing is ‘atomic’, we must consider the whole (context is everything). (ecological fallacy, MAUP) • Knowing where some things happen is critically important • Position of country boundaries • Location of hospitals • Routing delivery vehicles • Management of forest stands • Locations of dead corvids • Streams suitable for Pacific Water Shrew

23. If geography matters, GIS can be used to study the problem.

24. Definition of GIS A system of hardware, software data, people for collecting, sorting analyzing and disseminating information about areas of the earth

25. GIS integrates data.

26. GIS integrates technologies

27. GIS enables model development

28. T o C • Why predict species ranges? • What is GIS? • Example: West Nile virus (based on species biology) • Example: Cryptococcus gattii (GARP: correlative model)

29. Modeling West Nile virus • West Nile virus (WNv) has recently emerged as a health threat to the North American population. After the initial disease outbreak in New York City in 1999, WNv has spread widely and quickly across North America to every contiguous American state and Canadian province, with the exceptions of British Columbia (BC), Prince Edward Island and Newfoundland. • In our study we developed models of mosquito population dynamics for Culex tarsalis and C. pipiens, and created a spatial risk assessment of WNv prior to its arrival in BC by creating a raster-based mosquito abundance model using basic geographic and temperature data. Among the parameters included in the model are spatial factors determined from the locations of BC Centre for Disease Control mosquito traps (e.g., distance of the trap from the closest wetland or lake), while other parameters related to the biology of the mosquitoes were obtained from the literature.

30. West Nile virus presence in North America First appearance of positive birds

31. Primary route of transmission

32. Integrated approach using GIS • Mosquito biology • Temperature • Precipitation • Vegetation • Water bodies • Mosquito habitat • Bird migration • Health regions • Population at risk • Landuse • Sensitive habitat • Disease surveillance • Monitor corvid populations • Mosquito trap data

33. Developing the model • Mosquitoes have four stages: egg, larva, pupa and adult. Generally mosquitoes grow more rapidly under higher temperatures. Previous studies concluded that the condition for proceeding into the next stage is determined by degree-days (i.e., a product of excess beyond the base temperature (in degrees) and its length (in days)).

34. Mosquito abundance model Flowchart illustrating the mosquito abundance model developed in our study.

35. Risk assessment Flowchart illustrating the WNv risk assessment methodology used in the study

36. Model validation A comparison of the model outputs and the observed mosquito numbers.

37. Risk: Mosquito presence Annual total of weighed daily mosquito numbers per gird cell (C. tarsalis only). Weight: 1 for daily mean temperature (T) below 16°C, 2 for 16°C ≤ T<20°C, 3 for 20°C ≤ T<24°C, 4 for 24°C ≤ T<28°C, 5 for T ≥ 28°C (Weight is determined for each day and for each grid cell)

38. Risk: Bird species abundances Total abundance of high risk bird species in breeding season. The map shows the average number of individual birds considered to be high risk species by the BCCDC.

39. Risk: Mosquito-bird cycle Total risk of forming a mosquito-bird cycle.

40. Risk by Health Regions

41. Use of GIS in developing adulticiding scenarios • First week of August: human cases have been reported; mosquito infection rates have been increasing; short-term weather forecast is continued hot and dry spell; MHO has given the order to spray • BCCDC will work with the regional health authority, local government, mosquito control contractor and the provincial emergency program to determine which areas can and should be sprayed to reduce the risk of human illness

42. Use of GIS

43. Use of GIS

44. T o C • Why predict species ranges? • What is GIS? • Example: West Nile virus (based on species biology) • Example: Cryptococcus gattii (GARP: correlative model)

45. Emerging infectious diseases • For some species (e.g,. Cryptococcus gattii) we have very little knowledge of its ecological requirements (what favours it, what is detrimental to it). • For species such as this we cannot develop distribution models based on species biology (it is unknown), so we let the software determine which environmental layers are more significant that others.

46. Cryptococcus gattii • Microscopic (1-2 µm) sized yeast-like fungus • Environmental reservoir is vegetation and soil • Traditionally associated with Eucalyptus trees in tropics and sub-tropics (e.g., Australia, California) • May cause illness in humans and animals: cryptococcal disease or cryptococcosis • Hosts are immunocompetent • Transmission by aerosolization and inhalation of spores

47. A cryptic story • An increase in the number of animal and human cryptococcosis noted in 2001. • Clinical symptoms: prolonged cough, sharp chest pain, unexplained shortness of breath, severe headache, fever, night sweats, weight loss; skin lesions (animals). • Profiles of human cases did not fit the traditional understanding of cryptococcosis. • All cases resided on or had visited Vancouver Island prior to the onset of illness.

48. Cryptococcus gattii identified • Environmental sampling performed: Cryptococcus gattii isolated from native vegetation, soil, air, water Image sources: BCCDC, 2004 David Ellis, 2005 UBC, 2006

49. 7

50. *2007 data up to Nov 21/07