“Canonical” Ecological Niche Modelingcncdiversitas.org/sites/default/files/download/4.sdm... ·...

“Canonical” Ecological Niche Modeling

History

• Mechanistic approach

• Correlative approach Shabani, F., et al. (2016). "A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area." Ecology and Evolution 6(16): 5973-5986. Buschke, F. T., et al. (2015). "Simple mechanistic models can partially explain local but not range-wide co-occurrence of African mammals." Global Ecology And Biogeography: n/a-n/a. Kearney, M. and W. Porter (2009). "Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges." Ecology Letters 12(4): 334-350. Leibold, M. A. (1995). "The niche concept revisited: Mechanistic models and community context." Ecology 76(5): 1371-1382.

History • Mechanistic approach

– studies the relationships between distributions of a species, its growth patterns, and climate.

– limits application to higher-profile species.

Birch, L. C. (1953). "Experimental

background to the study of the distribution

and abundance of insects: II. the relation

between innate capacity for increase in

numbers and the abundance of three grain

beetles in experimental populations."

Ecology 34(4): 712-726.

Google “Species Distribution Model”

The Essence of the Method

• Use primary occurrence data

• Use raster environmental data sets

• Combine the two data streams in a model

• Use the model to make a map

• Map is interpreted as “probability of presence” of the species

• It’s simple, right?

It’s More Complicated… • Accumulate input data

• Data quality – occurrence data

• Data quality – environmental data

• Consistency between occurrence data and environmental data

• Check biogeographic/ecological scenario

• Choose area for model calibration

• Calibrate the model

• Model evaluation

• Check model extrapolation

• … NOW, you can interpret your model

Assess levels of spatial autocorrelation in environmental data, adjust input point data accordingly

Estimate ecological niche (various algorithms)

Evaluation reality of model transfer results, when possible

Transfer to other situations—time and space

Project niche model to geographic space

Model calibration, adjusting parameters to maximize quality

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 environmental data layers

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 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.

Assess extrapolation (MESS and MOP)

Owens, H. L., L. P. Campbell, L. 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.

Model evaluation

Peterson et al. 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modelling. Ecological Modelling, 213, 63-72.

Model thresholding Peterson et al. 2007. Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent. Ecography, 30, 550-560.

Assess spatial precision of occurrence data, adjust inclusion of data (obs and env) accordingly

General Methodological Summary: Peterson et al. (2011) Ecological Niches and Geographic Distributions, Princeton University Press, Princeton.

Refine estimate of current distribution via land use, etc.

Reduce dimensionality

Compare present and future to assess effects of change

Qiao, H., J. Soberón, and A. T. Peterson. 2015. No silver bullets in correlative ecological niche modeling: Insights from testing among many potential algorithms for niche estimation. Methods in Ecology and Evolution 6:1126-1136.

The BAM Diagram: A Useful Heuristic

GEOGRAPHY – THE DISTRIBUTION

The Area of Distribution

G Physiological requirements

(Abiotic)

A

Favorable biotic environment

(Biotic)

B

Accessible to dispersal (Movements)

M

The A and B Circles

• Physiological requirements

• Non-reactive variables. Uncoupled

• Roughly independent of the interactions

• Low “resolution”

• Biotic requirements and impacts. Resource consumption, interactions, competitors, predators...

• Variables interactive, dynamically coupled

• High resolution

A B

Ecological Niche Modeling

1. Accumulate Input Data

2. Integrate Occurrence and Environmental Data

3. Model Calibration

4. Model Evaluation

5. Summary and Interpretation

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 and environmental data

Assess spatial autocorrelation

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…

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

Data Cleaning

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

More dimensions - Curse of dimensionality

• The common theme of these problems is that when the dimensionality increases, the volume of the space increases so fast that the available data become sparse. This sparsity is problematic for any method that requires statistical significance. In order to obtain a statistically sound and reliable result, the amount of data needed to support the result often grows exponentially with the dimensionality. Also, organizing and searching data often relies on detecting areas where objects form groups with similar properties; in high dimensional data, however, all objects appear to be sparse and dissimilar in many ways, which prevents common data organization strategies from being efficient.

Curse of dimensionality

http://bit-player.org/extras/quasi/

Curse of dimensionality

http://www.visiondummy.com/2014/04/curse-dimensionality-affect-classification/

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.

Preliminary models

Reset data subsets based on evaluation results

Corroborated models ready for projection to

geographic times/regions of

interest

Elith, J., et al. (2006). "Novel methods

improve prediction of species distributions

from occurrence data." Ecography 29(2):

129-151.

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 …

Qiao, H., et al. (2015). "No silver bullets in correlative ecological niche

modeling: insights from testing among many potential algorithms for niche

estimation." Methods in Ecology and Evolution 6(10): 1126-1136.

Threshold-independent Approaches

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

Guisan, A., et al. (2014). "Unifying niche shift studies: insights from biological invasions." Trends in ecology & evolution (Personal edition).

Blonder, B., et al. (2014). "The n-dimensional hypervolume." Global Ecology And Biogeography 23(5): 595-609.

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

Ecological Niche Modeling

1. Accumulate Input Data

2. Integrate Occurrence and Environmental Data

3. Model Calibration

4. Model Evaluation

5. Summary and Interpretation

Assess levels of spatial autocorrelation in environmental data, adjust input point data accordingly

Estimate ecological niche (various algorithms)

Evaluation reality of model transfer results, when possible

Transfer to other situations—time and space

Project niche model to geographic space

Model calibration, adjusting parameters to maximize quality

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 environmental data layers

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 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.

Assess extrapolation (MESS and MOP)

Owens, H. L., L. P. Campbell, L. 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.

Model evaluation

Peterson et al. 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modelling. Ecological Modelling, 213, 63-72.

Model thresholding Peterson et al. 2007. Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent. Ecography, 30, 550-560.

Assess spatial precision of occurrence data, adjust inclusion of data (obs and env) accordingly

General Methodological Summary: Peterson et al. (2011) Ecological Niches and Geographic Distributions, Princeton University Press, Princeton.

Refine estimate of current distribution via land use, etc.

Reduce dimensionality

Compare present and future to assess effects of change

Qiao, H., J. Soberón, and A. T. Peterson. 2015. No silver bullets in correlative ecological niche modeling: Insights from testing among many potential algorithms for niche estimation. Methods in Ecology and Evolution 6:1126-1136.

ENM toolkits

• R – Biomod2 – Dismo – sdm – hypervolume – ecospat – ENMeval – MaxentVariableSelection – ModEco – LetsR – PresenceAbsence – Virtualspecies – MOP – SDMTools

• ENMTools in perl • NicheA in Java • openModeller in C/CPP • Desktop GARP in C • MAXENT in Java • MESS in Java

THANK YOU!

Any questions?