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ECOLOGICAL NICHE MODELING METHODS UPDATETown Peterson, University of Kansas
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It Is A Bit Too Easy …
• Very easy access to lots of occurrence data
• Very easy access to rich geospatial data
• Easy-to-use modeling tools• Lots of literature setting out
the examples
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Ecological Niche Modeling
1. Accumulate Input Data2. Integrate Occurrence and Environmental Data3. Model Calibration4. Model Evaluation5. Summary and Interpretation
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Accumulate Input Data
Collate primary biodiversity data documenting occurrences
Process environmental layers to be maximally relevant to distributional ecology of species in question
Collate GIS database of relevant data layers
Assess spatial precision of occurrence data; adjust inclusion of data accordingly
Data subsetting for model evaluation
Occurrence andenvironmental data
Assess spatial autocorrelation
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Occurrence Data in Niche Modeling
• Goal is to represent the full diversity of situations under which a particular species maintains populations
• Spatial biases (i.e., non-random or non-uniform distribution within G) is not damning
• Biases within E are catastrophic, and will translate directly into biases in any niche estimate
• More is usually better, but not always…
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speciesLink Network
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Uncertainty in Direction and Distance
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Georeferencing should …
• Represent the place at which the species was found
• Represent the certainty and uncertainty with which that place is characterized
• Summarize the methods used to establish that place
• Preserve all of the original information for possible reinterpretation
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Internal Consistency Testing
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Data Cleaning• Attempt to detect meaningfully erroneous records, so
that they can be treated with caution in analysis• Use internal consistency to detect initial problems– Species names consistent?– Terrestrial species on land, marine species in the ocean?– Latlong matches country, state, district, etc.
• Use external consistency to go deeper– Occurrence data match known distribution spatially?– Occurrence data match known distribution environmentally?
• If precision data are available, filter to retain only records that are precise enough for the study
• Iterative process with important consequences
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Data Subsetting
• Must respond to the question at hand … why are you doing the study?
• Ideally completely independent data streams• Failing that, can be – Macrospatial– Microspatial (but see spatial autocorrelation)– Random
• Will return to this point later…
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Generalities: Environmental Data
• Raster format: i.e., information exists across entire region of interest
• Relevant information as regards the distributional potential of the species of interest
• More dimensions = better (generally), BUT – collinearity is bad– too many dimensions is bad
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Major Sources
• Climate data – long time span, but low temporal resolution
• Remote-sensing data – high temporal resolution, diverse products, short time span
• Topographic data – high temporal resolution, uncertain connection to species’ distributional ecology
• Soils data – uneven global coverage, categorical data
• Others
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Spatial Autocorrelation
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Two Major Implications• Non-independence in model evaluation– Available data are often split into data sets for calibration and
evaluation– Data points that are not independent of one another may end
up in different data sets, thereby compromising the robustness of the test
• Inflation of sample sizes– Because individual data points may be non-independent of one
another, sample sizes may appear larger than they actually are– This inflation may create opportunity for Type 1 errors in model
evaluation and model comparisons
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Process for Maximum Relevancy
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Integrate Occurrence and Environmental Data
Assess BAM scenario for species in question; avoid M-limited situations
Saupe et al. 2012. Variation in niche and distribution model performance: The need for a priori assessment of key causal factors. Ecological Modelling, 237–238, 11-22.
Estimate M and S as area of analysis in study
Barve et al. 2011. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling, 222, 1810-1819.
Reduce dimensionality (PCA or correlation analysis)
Occurrence andenvironmental data
Occurrence andenvironmental data ready for analysis
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Assess BAM Scenario
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BAM I: Eltonian Noise Hypothesis
A
M
B A
M
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BAM II
ClassicBAM
Hutchinson’sDream
Wallace’sDream
All OK
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Project onto Geography
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Effect of BAM Scenarios
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BAM Conclusions
• Some situations are not amenable to fitting ecological niche models that will have predictive power
• Models tend much more to good fitting of the potential distribution, rather than the actual distribution
• Must ponder carefully the BAM configuration in a particular study situation to avoid configurations that will not yield usable models
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M and S as Study Area
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Test Arena: The Lawrence Species
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M and Model Training
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Model Evaluation
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M and Model Comparison
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Model Comparison
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M• When the species has no history in an area:– Use a radius related to dispersal distances
• When history is short (i.e., environment constant):– Use a radius representing compounding of
dispersal distances• When history is long (i.e., environmental
change is a factor) – Seek ways of assessing areas that the species’
distribution through time has covered…
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Icterus cucullatus
Sampling
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Reduce Dimensionality
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Model Calibration
Estimate ecological niche (various algorithms)
Model calibration, adjusting parameters to maximize quality
Model thresholding
Peterson et al. 2007. Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent. Ecography, 30, 550-560.
Occurrence andenvironmental data ready for analysis
“No Silver Bullet” paper to appear
Warren, D. L. and S. N. Seifert. 2011. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecological Applications 21:335-342.
Preliminary models
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Estimate Ecological Niche
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No Silver Bullets in ENM
• Single algorithms may perform ‘best’ on average• The best algorithm in any given situation, however,
may be other than the ‘best’• NSB thinking suggests that we should not use a
single approach• Use a suite of approaches (e.g., as implemented in
OM, BIOMOD, BIOENSEMBLES, etc.), challenge to predict, choose best for that situation
• Maxent is good, but it is not the only algorithm …
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Model Thresholding
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“Presence”
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Thresholding
• Use an approach that prioritizes omission error over commission error, in view of the greater reliability of presence data
• Minimum training presence thresholding seeks the highest suitability value that includes 100% of the calibration data
• Suggest (strongly) using a parallel approach that seeks that highest suitability value that includes (100-E)% of the calibration data
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Model Optimization and Parameter Choice
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Model Evaluation
Project niche model to geographic space
Model evaluation
Peterson et al. 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modelling. Ecological Modelling, 213, 63-72.
Preliminarymodels
Reset data subsets based on evaluation results
Corroborated models ready for projection to
geographic times/regions of
interest
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If predicted suitable area covers 15% of the testing area, then 15% of evaluation points are expected to fall in the predicted suitable area by chance.
• p = proportion of area predicted suitable
• s = number of successes• n = number of evaluation
points
Cumulative binomial distribution calculates the probability of obtaining s successes out of n trials in a situation in which p proportion of the testing area is predicted present. If this probability is below 0.05, we interpret the situation as indicating that the model’s predictions are significantly better than random.
Threshold-dependent Approach
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Threshold-independent Approaches
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http://shiny.conabio.gob.mx:3838/nichetoolb2/
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Significance vs Performance
• Predictions that are significantly better than random is important, and is a sine qua non for model interpretation
• BUT, it is also important to assure that the model performs sufficiently well for the intended uses of the output
• Performance measures include omission rate, correct classification rate, etc.
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Summary and Interpretation
Evaluation of model transfer results
Transfer to other situations (time and space)
Assess extrapolation (MESS and MOP)Owens, H. L., L. P. Campbell, L. Dornak, E. E. Saupe, N. Barve, J. Soberón, K. Ingenloff, A. Lira-Noriega, C. M. Hensz, C. E. Myers, and A. T. Peterson. 2013. Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecological Modelling 263:10-18.
Refine estimate of current distribution via land use, etc.
Compare present and “other” to assess effects of change
Models calibrated and evaluated, and transferred to present and “other” situations
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MESS and MOP• Both have the intention of detecting extrapolative
situations• MESS is implemented within Maxent• MESS compares the area in question to the
centroid of the calibration cloud• MOP compares the area in question to the nearest
part of the calibration cloud• Agree on ‘out of range’ conditions• MOP better characterizes similarities between
calibration and transfer regions, and thus is more optimistic as regards in-range extrapolation
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Ecological Niche Modeling
1. Accumulate Input Data2. Integrate Occurrence and Environmental Data3. Model Calibration4. Model Evaluation5. Summary and Interpretation