UNCERTAINTY IN PREDICTING SPECIES DISTRIBUTION · UNCERTAINTY IN PREDICTING SPECIES DISTRIBUTION A case study using four methods to map tiger occurrence in Central Sumatra Sunarto

UNCERTAINTY IN

PREDICTING SPECIES DISTRIBUTION

A case study using four methods to map tiger

occurrence in Central Sumatra

Sunarto & Marcella J. Kelly, Virginia Tech

Pre-assessment

How many of you have modeled species distribution?

For those who have done it, how many have:

accounted uncertainty/evaluated model accuracy?

Outline

Background

Why species distribution?

How species distribution is mapped

Uncertainty and other issues

Case study on predicting tiger distribution

Why map/model sp distribution?

Understanding the ecology

Fulfilling the need for (species) management

Why: Examples of ApplicationUnderstanding ecology:

Dispersal & barrier

Resources requirements

Interactions: wildlife & human

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w.tam

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Why: Examples of Application

Biodiversity conservation priorities

6 Bro

oks

et

al. 2

006

7

Conservation vs.

Development

High Conservation

Value Areas (HCVA)

8

9

MCP

Kernel Density

Home-range buffer 1

Home-range buffer 2

Grid

Predicting species distribution: typical steps

1. Determining scale

2. Selecting variables

3. Developing experimental design

4. Collecting data

5. Developing/choosing statistical procedures: range/envelope, distance/similarity, regressions,…

6. Building & selecting models

7. Translating mathematical model into distribution map

Uncertainty & other issues Imprecise data/measurement error: positional& attribute, applicable for both

species presence data (response variable) or environmental/habitat (predictor

variables)

Error/uncertainty in data transfer/treatments/manipulation

Uncertainty in selections of predictor variables

Uncertainty in species detection

Error/uncertainty in model parameter estimation

Uncertainty in modeling approach

Uncertain inferences

Fallacious/unrealistic assumptions

Natural variability/stochasticity of the system

Ambiguous or incorrect scientific questions

Aft

er

Morr

ison e

t a

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6

Other issues : scale

Adapte

d

from

: Kara

nth

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A case study: predicting tiger distribution

• Critically endangered

• Elusive: high association with uncertainty

• Urgent need for distribution maps

• Year of the tiger

Study Area

Study Area

17 km

17 km

Types of data

Species occurrence (response variables):

• Presence only

• Presence-’absence’

• Detection-Nondetection

• Count

Predictors: Macro-habitat (global/landscape level features)

Modeling approaches used:

Presence only data: Maximum Entropy (MaxEnt)

Presence-’absence’ data: Logistic regression (R)

Count data: Zero-inflated Negative Binomial regression (R)

Detection-non detection data: Occupancy (Program PRESENCE)

Relative importance of variablesMaxEnt

(% Contr.)

Logistic Regr.

(Logit link β)

Count

(Log link β)

Occupancy

(Logit link β)

Intercept NA -11.74 -2.38 (1.11) -8.48 (4.10)

Forest area in 2007 59.8 1.30 0.73 (0.22) 0.61 (0.50)

Altitude 0.7 113.09 20.94 (11.79) 94.08 (45.55)

Distance to core forest area 0.1 -0.77 NA -0.85 (0.66)

Road density 14.8 0.92 NA 0.05 (0.69)

Distance to road 12.5 NA NA NA

Deforestation from 2006 to 2007 1.7 NA NA NA

Distance to core of protected areas 8.8 -1.07 NA NA

Precipitation 1.7 NA NA NA

Summary of predictions

Maxent Logistic Count Occupancy

Minimum 0* 0 0 0

Maximum 0.9 1.0 128.3 1.0

Mean 0.237 0.329 2.036 0.423

CV 0.871 1.185 4.499 0.830

*) For Maxent outputs, no cell can be interpreted as complete absent even if prediction is (near) 0

(Phillips et al. 2006)

MaxEnt

Logistic

regression

Count

Occupancy

“All models are wrong, but

some are useful”(George Box, quoted in Kennedy 1992: 73).

Concordance (%) between modelsREFERENCES

Maxent Logistic Count OccupancyOverall

Accuracy

K-hat Overall

Accuracy

K-hat Overall

Accuracy

K-hat Overall

Accuracy

K-hat

Maxent 28 13 38 22 38 23

Logistic 28 13 47 34 52 40

Count 38 22 47 34 56 45

Occupancy 38 23 52 40 56 45

‘Correct predictions’ by different models ( based on presence-only tiger records in 20 grids collected from independent surveys)

34 1 = Very low, 2= low, 3= medium, 4= high

0%

25%

50%

75%

100%

1 2 3 4

Perc

enta

ge o

f co

rrect

pre

dic

tion

Treshholds on probability of occurrence

MaxEnt

Occupancy

Count

Logistic regression

Conclusions

Better to model than simply to map occurrence: accounting sampling efforts & environmental variables

Ignoring detection probability ~ underestimating the population parameters

Variation in results from different models

Model robustness for some variables & areas

Account for missing detections whenever possible: use occupancy

For some cases, presence-only model is still an acceptable choice.

Acknowledgement

Financial & programmatic supports

provided by: WWF Indonesia & Networks,

Virginia Tech, Hurvis Family, STF, PHKA, OFWIM

Field team: Zulfahmi, Harry Kurniawan,

Karmila Parakkasi, Eka Septayuda, Kusdianto,

Fendy Panjaitan, Agung Suprianto, E. Tugiyo, L.

Subali, H. Gebog, Herri Irawan, Roni Faslah,

Kokok Yulianto, Sunandar, Riza Sukriana,

Tarmison

Special thanks to: Drs. Sybille Klenzendorf,

Steve Prisley, Jim Nichols, Jim Hines, Dean F.

Stauffer, Mike R. Vaughan, WHAPA colleagues