Making maps, many maps!

[What is GIS?]

Dr. Brian KlinkenbergDepartment of Geography, UBC

For Zoology 502March 9, 2008

Why do I want to know where they occur?

T o C

• Why predict species ranges?• What is GIS?• Example: West Nile virus (based on

species biology)• Example: Cryptococcus gattii

(GARP: correlative model)

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.

Distribution looks different at different scales

Similar methods Different data Different utility

Distributions will change

2011-20402041-2070

2071-2100

http://www.glfc.cfs.nrcan.gc.ca/landscape/index_e.html

What are we modeling?

RangeDistributionHabitatObservations

• 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.

Source: Conservation of Grizzly Bears in British Columbia. Min. of Environment, Lands and Parks, 1995

Distributions: Level of detail

Distribution of Bidens amplissimaSource: E-Flora BC

Range Observations

a) “Definitive” Absences

b) Usually over-predicts occupied area

c) Usually under-predicts unoccupied area

d) Often subjective and difficult to replicate

e) Can be difficult to validate or test

a) “Definitive” presences

b) Usually under-predicts occupied area

c) Usually over-predicts unoccupied area

d) Accuracy heavily dependent on sampling effort

e) Can be difficult to validate or test

Dot Map Range Map

Level of detail continuum

Tools and results along the continuum• 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.

Two Broad Approaches1. Deductive: conclusions are developed

from combination of premises– spatial expressions of qualitative data– overlays of predictor variables– E.g., GIS-based multi-criteria evaluations

2. Inductive: conclusions are developed as an extrapolation from available data– quantitative and often statistical– what most folks consider “modeling”

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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

Model input: Environmental data

• Influence element distribution

• Fewer variables better than more

• Complete coverage of study area

• Climatic influence on distribution

Vegetation

Micro-organisms

The BioticComponent

An ECOSYSTEM Physical Parameters

Terrain

Climate

Soil

Animals

An Ecosystem

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 …

DOMAIN

BIOMODWhyWhere

Actual Occurrences

2. Convex Hull1. HSI

4. DOMAIN

5. GLM 6. GAM

7. GARP

Model (& threshold)

Model

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Comparison of distribution models(Elith and Burgman 2003)

3. ANUCLIM

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

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

T o C

• Why predict species ranges?• What is GIS?• Example: West Nile virus (based on

species biology)• Example: Cryptococcus gattii

(GARP: correlative model)

GIS?

GeographicInformation

System

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)

John Snow’s map

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

If geography matters, GIS can be used to study the

problem.

Definition of GIS

A system of hardware, software

data, peoplefor

collecting, sortinganalyzing and disseminating

information about areas of the earth

GIS integrates data.

GIS integrates technologies

GIS enables model development

T o C

• Why predict species ranges?• What is GIS?• Example: West Nile virus

(based on species biology)• Example: Cryptococcus gattii

(GARP: correlative model)

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.

West Nile virus presence in North America

Firstappearanceof positivebirds

Primary route of transmission

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

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)).

Mosquito abundance model

Flowchart illustrating the mosquito abundance model developed in our study.

Risk assessment

Flowchart illustrating the WNv risk assessment methodology used in the study

Model validation

A comparison of the model outputs and the observed mosquito numbers.

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)

Risk: Bird species abundances

Total abundance of high risk bird species in breeding season. The map shows the average number of individual birdsconsidered to be high risk species by the BCCDC.

Risk: Mosquito-bird cycle

Total risk of forming a mosquito-bird cycle.

Risk by Health Regions

• 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

Use of GIS in developing adulticiding scenarios

Use of GIS

Use of GIS

T o C

• Why predict species ranges?• What is GIS?• Example: West Nile virus (based on

species biology)• Example: Cryptococcus gattii

(GARP: correlative model)

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.

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

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.

Cryptococcus gattii identified

Image sources:

BCCDC, 2004

David Ellis, 2005

UBC, 2006

• Environmental sampling performed: Cryptococcus

gattii isolated from native vegetation, soil, air, water

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Human Cryptococcosis in British Columbia 1999-2007*

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*2007 data up to Nov 21/07

Study objective

To delineate the geographic areas where Cryptococcus

gattii is currently established and forecast areas that

could support Cryptococcus gattii in the future for

targeted public health messaging of Cryptococcus gattii

risk and prioritization of environmental sampling

Landscape epidemiology

Explores the relationship between the ecology and

epidemiology of infectious diseases to identify

geographical areas where disease transmission occurs

Ecological Niche Modeling

Realized Niche

FundamentalNiche

Ecological niche modelingEcological niche: the total range of environmental conditions that are suitable for a species existence and maintenance of populations (Grinnell, 1917).

Hutchinson (1959) provided the valuable distinction between the fundamental niche, which is the range of theoretical possibilities, and the realized niche (that part which is actually occupied, given interactions with other species such as competition). Although it can be argued that only the realized niche is observable in nature, by examining species across their entire geographic distributions, species’ distributional possibilities can be observed against varied community backgrounds, and thus a view of the fundamental ecological niche can be assembled (Peterson et al. 1999).

http://www.specifysoftware.org/Informatics/bios/biostownpeterson/PK_USDA_2005.pdf

Genetic Algorithm for Rule-set Prediction

• GARP is a species distribution or ecological niche

modeling algorithm

• GARP is used to predict whether an area of study is

suitable habitat for the species in question

• GARP works in an iterative process of rule selection,

evaluation, testing, and incorporation or rejection

GARP Animal

Human

Environmental

Elevation

Aspect

Slope

Biogeoclimatic

January Temp (x3)

July Temp (x3)

Precipitation (x3)

Soil (x2)

Determine significant variables

Methodology and Data

Resulting models

GARP20 ecological niche model outputs

produced for each set of casesGIS

Potential = 1-10 model agreement

Optimal = 11-20 model agreement

Model accuracy is based on:

# of correct predictions

# of correct predictions + commission and omission errors

Ecological niche modeling of C. gattii in BC

Ecological niche modeling of C. gattii in BC

Ecological niche modeling of C. gattii in BC

Observations

• Suitable ecological niche for Cryptococcus gattii is

available on the BC mainland

• Cryptococcus gattii distribution in BC associated with

areas having >1oC January average temperature and

<770m elevation (mean = 100m)

• Animal distribution of cryptococcosis corresponds

directly with human distribution

Observations

• Ecological niche modeling of Cryptococcus gattii

produced very accurate predictions (>98% accuracy)

• The ecological niche model based on environmental

sampling data produced the most conservative forecast

• Environmental sampling for Cryptococcus gattii in

geographic locations identified as “optimal” ecological

niche areas are currently underway

Conclusions

• Species distribution models can be used in a wide variety of applications (rare and invasive species management, infectious disease monitoring and prevention).

• Using several different approaches is considered the best option, since no one method works best in all situations.