uncertainty in predicting species distribution · uncertainty in predicting species distribution a...
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UNCERTAINTY IN
PREDICTING SPECIES DISTRIBUTION
A case study using four methods to map tiger
occurrence in Central Sumatra
Sunarto & Marcella J. Kelly, Virginia Tech
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Pre-assessment
How many of you have modeled species distribution?
For those who have done it, how many have:
accounted uncertainty/evaluated model accuracy?
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Outline
Background
Why species distribution?
How species distribution is mapped
Uncertainty and other issues
Case study on predicting tiger distribution
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Why map/model sp distribution?
Understanding the ecology
Fulfilling the need for (species) management
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Why: Examples of ApplicationUnderstanding ecology:
Dispersal & barrier
Resources requirements
Interactions: wildlife & human
ww
w.tam
rin.
pro
board
s.co
m/
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Why: Examples of Application
Biodiversity conservation priorities
6 Bro
oks
et
al. 2
006
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7
Conservation vs.
Development
High Conservation
Value Areas (HCVA)
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9
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MCP
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Kernel Density
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Home-range buffer 1
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Home-range buffer 2
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Grid
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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
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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
l. 2
00
6
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Other issues : scale
Adapte
d
from
: Kara
nth
& N
icho
ls 2
002
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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
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Study Area
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Study Area
17 km
17 km
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Types of data
Species occurrence (response variables):
• Presence only
• Presence-’absence’
• Detection-Nondetection
• Count
Predictors: Macro-habitat (global/landscape level features)
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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)
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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
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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)
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MaxEnt
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Logistic
regression
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Count
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Occupancy
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“All models are wrong, but
some are useful”(George Box, quoted in Kennedy 1992: 73).
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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
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‘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
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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.
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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
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