Modeling present and prospective distribution of Phyteuma genus in Carpathian region with machine learning techniques using open climatic and soil data
Species distribution modeling can be effectively carried out using open data and data analysis tools with machine learning techniques. Modeling of the distribution of Phyteuma genus in Carpathian region has been carried out with data from GBIF database, climatic data from Worldclim database, and soil properties data from Soilgrids soil information system. Spatial distribution modeling was accomplished with machine learning techniques that have marked advantages over more traditional statistical methods, like the ability to fit complex nonlinear relationships common in ecology.
Four methods have been examined: Maxent, Random Forest, Artificial Neural Networks (ANN), and Boosted Regression Trees. AUC and TSS criteria calculated for testing data with cross-validation have been applied for assessing the performance of the models and to tune their parameters. ANN with a reduced set of predictor variables (6 from initial 21) appeared to fare the best and was applied for predictive modeling. Prospective data based on future climate projections from Worldclim were input to the model to get the prospective distribution of the plant taxon considering expected climate changes under different RCPs
Similar to Modeling present and prospective distribution of Phyteuma genus in Carpathian region with machine learning techniques using open climatic and soil data
DATA MINING ATTRIBUTE SELECTION APPROACH FOR DROUGHT MODELLING: A CASE STUDY ...IJDKP
Similar to Modeling present and prospective distribution of Phyteuma genus in Carpathian region with machine learning techniques using open climatic and soil data (20)
Modeling present and prospective distribution of Phyteuma genus in Carpathian region with machine learning techniques using open climatic and soil data
1. Phyteuma vagneri
(
Phyteuma confusum
MODELING PRESENT AND PROSPECTIVE DISTRIBUTION OF PHYTEUMA GENUS IN
CARPATHIAN REGION WITH MACHINE LEARNING TECHNIQUES USING OPEN CLIMATIC AND
SOIL DATA
Assoc. Prof. Dr. Alexander Mkrtchian
2. Main problems in species distribution modeling
Problem Solution
Data of different formats, different
quality, duplicated data
Data preprocessing
Presence-only data availale Introducing randomly distributed
background data as a surrogate for
absence data
Data distribution violates normality
assumption
Using non-parametric methods (machine
learning)
Spatially autocorrelated data Explicit methods to account for spatial
autocorrelation; rigorous testing of
modeling results on test sets
3. • Mackey B. G., Nix H. A., Hutchinson M. F., Macmahon J. P., Fleming P. M. (1988). Assessing
representativeness of places for conservation reservation and heritage listing // Environmental
Management, 12 (4), pp. 501–514
• Franklin J. (1995). Predictive vegetation mapping: geographic modelling of biospatial patterns in
relation to environmental gradients // Progress in Physical Geography, 1995, 19, pp. 474–499
• Guisan A.; Zimmermann N. E. (2000). Predictive habitat distribution models in ecology // Ecological
Modelling, 135, pp. 147–186
• Elith J., Graham K. H., Anderson R. P. et al. (2006). Novel methods improve prediction of species’
distributions from occurrence data // Ecography, 29, pp. 129–151
• Elith J., Leathwick J. R., Hastie T. A. (2008). Working guide to boosted regression trees // Journal of
Animal Ecology, 2008, 77, 802–813
• Elith J., Phillips S. J., Hastie T., Dudı´k M., Chee Y. En, Yates C. J. (2011). A statistical explanation of
MaxEnt for ecologists // Diversity and Distributions, 17, pp. 43–57
• https://rspatial.org/raster/sdm/index.html
Main theoretical works
4. • BIOCLIM – based on «ecological envelope» principle, calculating the
probability of species in the attributes hyperspace
• Maxent – based on minimizing relative entropy of probability
densities in the attributes hyperspace
• General linear models (GLM)
• General additive models (GAM)
• Artificial neural networks (ANN)
• Random forest (RF)
• Bootstrap aggregation (bagging)
• Boosted regression trees (BRT)
• Support vectors machines (SWM)
Main types of models for species distribution modeling
From Elith J., Graham K. H., Anderson R. P. et al.
(2006). Novel methods improve prediction of
species’ distributions from occurrence data //
Ecography, 29, pp. 129–151
5. Model specifics and features selected have to be
meaningful from an ecological viewpoint
Predicted spatial pattern should look plausible from
an expert viewpoint
Residuals on test set has to be small and random
Quality, size and spatial extent of data, as well as
appropriate features selected are mostly more
important than the choice of the concrete model and
tuning of its parameters
6. Data obtained from GBIF for
the Carpathian region
contained 148 records of
Phyteuma genus in total.
After removing duplicates, 80
records have been kept. A
double of observation records
(160 points) were thus
designated as background, with
random coverage of geographic
space inside study area. Six
data points (3 from observed
data and another 3 from
simulated background data)
have subsequently been
removed due to omissions in
predictors data.
7. Data on climatic conditions were derived from WorldClim
database (1 km. sq. spatial resolution)
There are layers of 19 bioclimatic variables, derived from monthly temperature and
precipitation with a consideration to have biological significance.
17 out of 19 bioclimatic variables were taken out as predictive variables for SDM.
For data on soil conditions, SoilGrids digital maps of soil
properties were used (250 m spatial resolution)
From 11 available physical and chemical soil properties, 4 were chosen as the most
suitable predictors: soil acidity, organic carbon stock, cation exchange capacity, and
total nitrogen. Among six standard depth intervals available, 15–30 cm depth interval
was chosen as the most appropriate for the purpose.
8. • ML methods examined: Maxent, Random
Forest, Artificial Neural Networks (ANN),
Boosted Regression Trees.
• Assessing the performance of the models and
tuning their parameters: AUC and TSS criteria
calculated for testing data with 6-fold cross-
validation
• Tools used: R programming language and
software environment, SDMtune package
9. Method AUC
(training)
AUC
(testing)
TSS
(training)
TSS
(testing)
Maxent
(ME)
0.8162 0.796
(0.0138)
0.6114 0.5938
(0.0252)
Artificial neural networks
(ANN)
0.9437 0.9369
(0.005)
0.7508 0.7891
(0.018)
Random forest
(RF)
1 0.9321
(0.0076)
1 0.7851
(0.0261)
Boosted regression trees (BRT) 0.964 0.9304
(0.0062)
0.8145 0.775
(0.0162)
Performance metrics of different SDM methods calculated on training dataset and
with aforementioned testing procedure. For testing case, standard deviations are given in
parentheses.
Model AUC
(training)
AUC
(testing)
TSS
(training)
TSS
(testing)
Full set of 21 variables 0.9437 0.9369
(0.005)
0.7508 0.7891
(0.018)
Reduced set of 6 variables 0.94 0.9381
(0.0036)
0.7384 0.7893
(0.0132)
Performance metrics of ANN SDM method calculated with a full and reduced sets
of predictive variables
ANN method appears to work the best for the case
