Spatial distribution models

From the truth to the whole truth?

Senait D. Senay & Sue P. Worner

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

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

Species distribution models (SDMs)

Hyb

rid M

odel

s

Mec

hani

stic

Mod

els

Corr

elati

ve M

odel

sInfer 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 intensiveHigh 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

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.com ©Zotgeist 2007

Sources of uncertainty in SDMs

Environmental data (Predictors)

Data collinearity

Bias in species presence data

Lack of absence data

Climate change

Model uncertainty

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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.

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

NLPCA

PCA

Niche classification results

2. Pseudo-absence points selection

1. Presence-only models2. Presence –absence models

A. Random selectionB. Geographically weighted random

selectionC. Environmentally weighted random

selectionD. Spatial + Environmental + k-means

clustering

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

• 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)

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

SM1 SM2 SM3 SM40

102030405060

Pseudo-absence selection method

% p

redi

icte

d ar

ea

(New

Zea

land

)

Prevalence

Prevalence: the percentage of occurrence data divided by the total study area

SM1-lo

g

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rt

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n

SM1-nnet

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ree

SM1-sv

m

SM2-lo

g

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rt

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n

SM2-nnet

SM2-nb

SM2-ct

ree

SM2-sv

m

SM3-lo

g

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rt

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SM3-nnet

SM3-nb

SM3-ct

ree

SM3-sv

m

SM4-lo

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rt

SM4-kn

n

SM4-nnet

SM4-nb

SM4-ct

ree

SM4-sv

m0

0.2

0.4

0.6

0.8

1

1.2

sensitivityspecificity

3-step methodenvironmentalSpatialRandom

Specificity versus sensitivity

Prediction maps

0.35 % prevalence 15.29 % prevalence

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.

LOG NB CART CTREE KNN SVM NNET

0

10

20

30

40

50

60

70

80

Percentage of predicted area in New Zealand

A. a

D.v.v

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.

MAXENT- Presence-only modelling (Asian tiger mosquito)

Default settings Background data selection Feature function selection

0.856 0.955

MAXENT- Presence-only modelling (Maps)

Custom parametersDefault parameters

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.

Bio-Protection Research CentrePO Box 84Lincoln UniversityLincoln 7647, New ZealandP + 64 3 325 3696 F + 64 3 325 3864 www.bioprotection.org.nz

THANK YOU!