1. Introduction to species distribution Models
Species Distribution Modeling (SDM)
Introduction to species distribution Models
Yacine Kouba (PhD)
Pyrenean Institute of Ecology

2. Species Distribution Modeling (SDM)
Outline of the presentation
BASIC CONCEPTS (Ecological niche)
SDM-OVERVIEW
APLICATIONS OF SDM
BASICS OF SDM
MAIN SHORTCOMINGS IN SDM
SDM-NEW CHALLANGES

3. Species Distribution Modeling (SDM)
Basic concepts
ECOLOGICAL NICHE
What drives species distributions?
All species have tolerance limits for environmental factors beyond which individuals cannot survive, grow, or reproduce (Von Liebig, 1840)

4. Environmental gradient
Species Distribution Modeling (SDM)
Basic concepts
TOLERANCE RANGE
Lower limit of tolerance
Upper limit of tolerance
Range of optimum
Environmental gradient
Low
Temperature
High

5. Species Distribution Modeling (SDM)
Basic concepts
LIMITING FACTORS
The geographical range of a species is not always limited by the presence of barrieres that prevent its spread.
It is often limited by a particular factor in the environmental that limits it ability to survive, grow or reproduce- These are Limiting Factors

6. Species Distribution Modeling (SDM)
Basic concepts
LIMITING FACTORS
Limiting factors include ABIOTIC (physical) factors and BIOTIC factors
Abiotic factors
. Light availability/ day lenght, seasonality
. Moisture/ water availability
. Temperature, diurnal temperature range
. Elevation or depth, pressure
. Salinity
. Wind
. pH, CO2, O2, availability of N, P, K, S, Mg, etc.

7. Species Distribution Modeling (SDM)
Basic concepts
LIMITING FACTORS
Limiting factors include ABIOTIC (physical) factors and BIOTIC factors
Biotic factors
. Competition
. Predation
. Parasitism
. Disease
. Pollinators, dispersal agents
. Suitable food availability
# Organism my have a wide range of tolerance to some factors and a narrrow range to other factors

8. Species Distribution Modeling (SDM)
Basic concepts
ECOLOGICAL NICHE
Miller& Spoolman 2009
Species A has a narrow niche breadth and is a speialist
Species B has a wide niche breadth and is a generalist

9. Species Distribution Modeling (SDM)
Basic concepts
ECOLOGICAL NICHE
What is it in detail?
(Peterson et al. 2011)

10. Species Distribution Modeling (SDM)
Basic concepts
ECOLOGICAL NICHE
What is it in detail?
Fundamental (theoretical) niche
- Is the full spectrum of environment al factors that can be potentially utilized by an organism
Realized (actual) niche
-represent a subset of a fundamental niche that the organism can actually utilize restricted by:
- dispersal limitations
- competitors, predators
- realized environment (existent
conditions)

11. Species Distribution Modeling (SDM)
Type of SDM
What are spatial predictions of species distributions?
# Interpolating biological survey data in space
#Quantitative predictive models of species-environment relationships
– Species abundance
– Probability a species is present
– Potential species distribution
– Habitat suitability
– Realized niche
In sum: SDMs are empirical models relating field observations to environmental predictor variables based on statistically or theoretically derived methods.
(Guisan & Zimmermann 2000)

12. Species Distribution Modeling (SDM)
SDM-overview
Species distribution= f(environmental data)
Species occurence
Environmental data
Prediction from model

13. Species Distribution Modeling (SDM)
SDM- overview
Rainfull zones suitability
Altitude zones suitability
Habitat model based on suitable rainfaull and altitude zones in comdination with species observation
Species observations

14. Model of niche in ecological dimensions
Species Distribution Modeling (SDM)
SDM-overview
Geographic Space
Environmental Space
ecological niche modeling
temperature
Model of niche in ecological dimensions
precipitation
Projection back onto geography
Native range prediction
Invaded range prediction

15. Species Distribution Modeling (SDM)
The aplications of Species Distribution Models
1.-CONSERVATION & MANAGEMENT PLANNING:-MAPS OF RARE/ PROTECTED SPS DISTRIBUTION-MAPS OF HABITAT SUITABILITY ->PRIORIZE AREAS
2.-IMPACT ASSESMENT AND RESOURCE MANAGEMENT-ASSESMENT OF LAND USE CHANGE
3.-ECOLOGICAL RESTAURATION ->REINTRODUCTIONS
4.-IMPACT OF INVASIVE SPECIES -> (PATHOGENS)
5.-ASSESMENT OF EFFECT OF GLOBAL WARMING ON ECOSYSTEMS-PREDICTIVE MAPS OF SPECIES IN THE FUTURE

16. Species Distribution Modeling (SDM)
BASICS OF SPECIES DISTRIBUTION MODELING
EmpIrical Model (algorithm)
Data Model
Peterson et al. 2007

17. Species Distribution Modeling (SDM)
The data model
Species data
Sources
Personal collection: occurrence localities can be obtained during field surveys by an individual or small group of researchers
2) Large surveys: distribution information may be available from surveys undertaken by a large number of people.
3) Museum collections: occurrence localities can be obtained from collections in natural history museums.
4) Online resources: distributional data from a variety of sources are increasing being made available over the internet. For example, the Global Biodiversity Information Facility ( is collating available datasets from a diversity of sources and making the information available online via a searchable web portal.

18. Species Distribution Modeling (SDM)
The data model
Species data
#Biogeographical scale
Point observations, lots of them, opportunistic
Scale of analysis km
Many to one
#Ecological scale
Scale of data collection m2
Probability sample designs
Scale of analysis m2
One to one
• Does scale matter?

19. Species Distribution Modeling (SDM)
The data model
Species data
#Biogeographical scale

20. Species Distribution Modeling (SDM)
The data model
Species data
#Ecological scale

21. Species actual distribution
Species Distribution Modeling (SDM)
The data model
Environmental data
Species actual distribution
Direct vs indirect variables. Direct are those variables directly related with physiological aspects of the species studied.
•Biotic Interactions
•Disturbance
•Dispersal
Fundamental niche
From Franklin (2009)
Water
Light (PAR)
Heat Sum
Mineral nuntrients
Temperature regime
Soil
Precipitation
Evapo-transperation
Radiation regime
Geology
Topography
Climate

22. Species Distribution Modeling (SDM)
The data model
Environmental data
Percent slope
Mean Annual Precipitation
Mean July maximum
Mean January minimum
Topographic
Moisture Index
Soil Order
Summer Radiation
Winter Radiation

23. Species Distribution Modeling (SDM)
The data model
Environmental data

24. Species Distribution Modeling (SDM)
The data model
Environmental data
* Depending on the working scale environmental varible’s relevance change:

25. Species Distribution Modeling (SDM)
The emperical model 1 2 3 4
From Guisan and Zimmermann (2000)

26. 1 1 1 Species Distribution Modeling (SDM) “A complete step-evaluation”
The emperical model
“A complete step-evaluation” 1
Conceptual formulation
Theory 1 1
Sp data
Env. data
aborda
Does the model address the problem?
Does it describe the true relationship?
Is the model consistent with ecological theory?

27. 2 1 1 2 2 Species Distribution Modeling (SDM)
The emperical model
“A complete step-evaluation” 2
Statistical formulation
Theory 1 1
Sp data
Env. data 2 2
Model
1.-Do we have enough and/or representative data?
2.-Do we have the appropiate predictors?
3.-Is the model suitable for the sampling design used?
4.-Did we select a method correctly?

28. 3 1 1 3 2 2 Species Distribution Modeling (SDM)
The emperical model
“A complete step-evaluation” 3
Model calibration
Theory 1 1
Sp data
Env. data 3 2 2
Model
1. Cross-validation: - live-one-out
-k-fold Cross-validation
2. Bootstrapping
2. Model Averaging (Bayesian methods)

29. Prediction explanation
Species Distribution Modeling (SDM)
The emperical model
“A complete step-evaluation” 4
Model evaluation
Theory 1 1
Sp data
Env. data 3 2 4 2
Model
What are the measures of prediction accuracy? 4
Prediction explanation

30. Species Distribution Modeling (SDM)
The emperical model
“A complete step-evaluation”
### Measures of prediction accuracy
Threshold-dependent
Test statistics derived from the “Confusion matrix”
Theshold-independent
AUC

31. Species Distribution Modeling (SDM)
The emperical model
“A complete step-evaluation”
Threshold-dependent
Predictive map (probabilities of species occurance)

32. Species Distribution Modeling (SDM)
The emperical model
“A complete step-evaluation”
Threshold-dependent
The species records constitute the test data. Frequencies of each type of outcome are commonly entered into a confusion matrix

33. Species Distribution Modeling (SDM)
The emperical model
“A complete step-evaluation”
Confusion (errors) matrix
Sensitivity = TP / (TP + FN)
Specificity = TN / (TN + FP)
% Omission error = 1 - Sensitivity
% comission error = 1 - Specificity
Predicted
Observed
Presence
Absence
SUM TP FP FN TN
TR+FN
TN+FP n

34. Species Distribution Modeling (SDM)
The emperical model
“A complete step-evaluation”
Confusion (errors) matrix
% Correct classification = (TP+TN) / Total
Sensitivity = TP / (TP + FN)
Specificity = TN / (TN + FP)

35. Species Distribution Modeling (SDM)
The emperical model
“A complete step-evaluation”
AUC
# Area under the curve (AUC) of receiver operating characteristic (ROC) plot
––AUC compares a probabilistic prediction with a binary outcome
––Proportion of time a prediction of presence will be higher than prediction of absence for a true presence
––range 0.5 (no better than random) to 1.0 (perfect discrimination)

36. Species Distribution Modeling (SDM)
The emperical model
Types of algorithms
#1. Propfile methods: only consider 'presence' data
BIOCLIM: is a classic 'climate-envelope-model‘
Domain algorithm (Carpenter et al. 1993)
Mahalanobis (Mahalanobis, 1936)
Ecological-Niche Factor Analysis (ENFA; Hirzel et al, 2002)
Genetic Algorithm for Rule-set Production (GARP; Stockwell, 1999)
#2. Regression models: presence and absence (background) data
Generalized Linear Models (GLMs)
Generalized Additive Models (GAMs)
Bayesian model
#3. Machine learning methods: presence and absence (background) data
MaxEnt (Maximum Entropy; Phillips et al., 2004, 2006)
Boosted Regression Trees (Friedman, 2001)
Random Forest (Prasad et al. 2006)
Support Vector Machines (SVMs; Vapnik, 1998)
Artificial Neural Network (ANN; Older et al. 2008)

37. Species Distribution Modeling (SDM)
The emperical model
Types of algorithms

38. ## Spatial Autocorrelation (SAC)
Species Distribution Modeling (SDM)
Main shortcomings in SDM
## Multicollinearity
## Non-linearity
## Spatial Autocorrelation (SAC)

39. Species Distribution Modeling (SDM)
Main shortcomings in SDM
## Multicollinearity (between predictor variables)
Modest collinearity
Considerable collinearity

40. Species Distribution Modeling (SDM)
Main shortcomings in SDM
## Multicollinearity (between predictor variables)

41. Species Distribution Modeling (SDM)
Main shortcomings in SDM
## Non-linearity
• The relationship predictor-response variable is assumed to be linear and is not.

42. Species Distribution Modeling (SDM)
Main shortcomings in SDM
## Spatial Autocorrelation (SAC)
#Most statistics are based on the assumption that the values of observations in each sample are independent of one another.
# Ecology and Biogeography -> Spatial statistics
In spatial statistics the assumption of the indepence of the observations can be violated because of the presence of Spatial Autocorrelation.
So, what is Spatial Autocorrelation?
The simplest definition of spatial-autocorrelation states that pairs of subjects that are close to each other are more likely to have values that are more similar, and pairs of subjects far apart from each other are more likely to have values that are less similar.

43. Species Distribution Modeling (SDM)
Main shortcomings in SDM
## Spatial Autocorrelation (SAC)
Presence of species
Absence of species

44. Species Distribution Modeling (SDM)
SDM-NEW CHALLANGES
1. Improvement of methods for modeling presence-only data. 2. Improvement of methods for model selection and evaluation. 3. Accounting for biotic interactioons, and assessing model uncertainty.