1. Theoretical Principles & Practical applications
Ecological Niche Modelling
Theoretical Principles & Practical applications

2. Lecture Structure Why model species ranges?
What is a niche? – fundamental and realized
Correlative range modelling – background and assumptions
Distribution datasets
Variables and their selection
Models and their selection
Model calibration and evaluation

3. Why model species ranges?
We need to know where species occur and why they occur where they do:
We want to predict where a particular species occurs.
We want to know more about organism-environment relationships.

4. Used in response to
Increase the rates of habitat, and face species loss;
Complete (spatial and temporal) distribution info for a large number of taxa;
Contrast the existing distribution data mostly collected in an ad hoc fashion.
Given the rate of species loss, it is unlikely that we will get the distribution data that we need in time if we rely on conventional survey techniques.
Atlases are an invaluable data source and cover very few taxa but they are very important for model development and calibration.

5. What we know about species distribution?
Despite many decades of investigation our knowledge is still incomplete and at broad scale!!!
USGS Forest Service
Atlas Florae Europeae - Jalas & Suominen ( )
EUFORGEN Network (2009)

6. Distribution models have been used to predict
species richness
centres of endemism
the occurrence of particular species assemblages
the occurrence of individual species
the location of unknown populations
the location of suitable breeding habitat
breeding success
species abundance
genetic variability of species
help target field surveys
aid in the design of reserves
inform wildlife management outside protected areas
guide mediatory actions in human–wildlife conflicts
monitor declining species
predict range expansions of recovering species
estimate the likelihood of species’ long-term persistence in areas considered for protection
identify locations suitable for introduction
identify locations suitable for reintroductions
identify sites vulnerable to local extinction
identify sites vulnerable to species invasion
explore the potential consequences of climate change

7. Modelling literature at a glance
2,215 scientific products
1,964 published papers
Source:

8. THEORETICAL PRINCIPLES: FUNDAMENTAL vs REALIZED NICHE
(G. Evelyn Hutchinson, 1957)
Definition: n-dimensional hypervolume described by N environmental and resource constraints within which a species can maintain a viable* population. In other terms, it is the combination of conditions and resources required by an individual species defines the area in which it is able to live.
Definition: the set of conditions actually used by given species/population after interactions with other species (predation and especially competition) have been taken into account.
* Viable = Survive + Grow + Reproduce

9. Principles: Range edges
WHAT DETERMINES THE EDGE OF GEOGRAPHIC RANGES?
Populations do not just cease to exist at the edge of their geographic distributions, but rather taper off gradually. The edges of geographic ranges are thus defined by a change in the local population dynamics, where net gains in population are reduced to levels lower than the net losses.
There are changes in local population dynamics at the edge of a distribution, and more net losses than net gains
THESE POPULATION LEVEL CHANGES ARE DUE TO:
Changes in abiotic factors (physical barriers, climate factors, absence of essential resources) and biotic factors (impact of competitors, predators or parasites)
Genetic mechanisms that prevent species from becoming more widespread.
Abiotic/biotic factors are only limiting because a species has not evolved the morphological + physiological + ecological means to overcome them.

10. SPECIES RANGES ON THE BOOKS
Biomes of the World

11. Fagus sylvatica
Quercus cerris
Corylus avellana
Castanea sativa
Quercus cerris

12. Specifics: HOW does NICHE-BASED MODELLIng works?

13. Niche-based modelling: assumptions
Environmental factors drive species distribution
Species are in equilibrium with their environment
Limiting variables – are they really limiting?
Evidence for species dying/not reproducing due to climate
Collinearity of variables
Static vs dynamic approaches: data snapshot or time series response?

14. Cautionary note on modelling in general
Risk of all models: the results of a model can never be better than the data used to construct and run it. If the basic input data is incorrect, this error can be multiplied many times over.
Need to understand assumptions, explicit and implicit.
Models are an abstraction of reality, the accuracy and assumptions depend on the scale of the model, and so although the model will improve our understanding of the processes involved, it does not necessarily mean that the outputs are 100% truth.

15. Direct Indirect Specifics: variable selection Definition Example
Variables with biological relationship with study species
Variables that correlate with study species because of correlation with series of intermediate direct factors rather than direct relationship
Definition
Climate, nesting sites, soil nutrients (plants), interacting species, site isolation
Elevation, soil, topography, geology, soil nutrients (animals)
Example
Model structure easily interpreted in biological meaningful terms.
Direct biological relationship should generalize better to new areas, and be more effective for climate change modeling than indirect predictors.
Provides more info for conservation management
Data sets widely available in GIS
Low cost, ease of collection
Can be effective predictors, i.e. elevation in mountainous areas
Encompasses a range of correlated variables so should: result in parsimonious models if variable selection applied, recording fewer variables
Strength
Variables require greater effort to record
Data sets may need to be estimated for large spatial extents (using indirect variables reducing overall accuracy
Correlation with direct variables tend to be location specific
Limited interpretation – biological meaning inferred, resulting in increased uncertainty
Weakness

16. Variables and their selection
Species only select their habitats in the broadest sense (Heglund 2002), and distribution patterns are the cumulative result of a large number of fine scale decisions made to maximize resource acquisition.
The more accurately these fine-scale resources can be approximated and access quantified, the better the model should perform if all models were equal.
Predictions at broad scales can use broader environmental variables, often associated with the fundamental niche.
Finer scale predictions need to concern themselves more with those variables that determine the realized niche.
Source: Pearson & Dawson 2003

17. criticalities
Environmental variables are often direct variables, such as precipitation or temperature.
Soil conditions include pH, texture, organic carbon and fertility, but these measures are often difficult to obtain on an appropriate scale, since there can be considerable variation even within an area.
In general, it is a good idea to avoid indirect measures of a variable, which is obviously a challenge since much of a country is not monitored, and many such measures are not easily taken.
Features such as slope and altitude allow some degree of projection into the future, since the lapse rate (extent to which temperature changes with increasing altitude) may closely parallel changing climatic conditions.
Solar radiation and wind, which are essential for plant growth responses and dispersal are both particularly challenging to obtain accurate measures of.

18. Environmental Variables
What data are available and where?
Local scale (National/Regional)
Global scale (World/Continent)
Species
Museum/Herbarium data
Survey Atlas
Forest Inventories
Field data
Presence / Absence data
Taxonomic updates of older museum
Museum/Herbarium data
Survey Atlas
Expert Atlas
Presence / Absence data
Taxonomic updates of older museum
Environmental Variables
Hydrological Bulletin
Field data
National/Regional projects
Geoportals
WebGIS repositories
Hydrological Bulletin
Global Climatic repositories
International projects
Geoportals
WebGIS repositories

19. Examples: species Local scale (National/Regional)
Forest Inventory of Sicily (Italy)
National Forest Inventory of France
National Forest Inventory of Spain
Species information system of Spain
National Forest Inventory of Portugal
National Flora Database of Croatia

20. Examples: species Global scale (World/Continent) Forest Map of Europe
Forest Species distribution in Europe
Ecosystems map of America
Global species distribution
European forest genetic resources

21. Examples: environmental variables
Local scale (National/Regional)
Digital Elevation Model of Italy
National Geoportal of Italy
US Topographic maps
National Geoportal of Spain
National Climatic Registry of Spain
Andalusian Climatic Database

22. Examples: environmental variables
Global scale (World/Continent)
Global Digital Elevation Model
European Soil Map
World Environmental Data Explorer
World Soil Grid Database
World Climate Data
European Climate Dataset
Global Bioclimatic Data

23. How do we choose a model type?
BioClimatic envelope (Bioclim)
Ordination (ENFA)
Environmental Distance (DOMAIN, CSM)
Ordinary Regression (Arc-SDM)
Generalised additive models (GAM)
Generalised linear models (GLM)
Classification and regression trees (CART, RF)
Genetic Algorithm (GARP)
Artificial neural networks (SPECIES)
Boosted regression Trees (BRT)
Support Vector Machine (SVM)
Bayesian/Maximum Entropy (MaxEnt)
Profile Techniques
Regression-based Techniques
Machine Learning Techniques

24. Software available? http://openmodeller.sourceforge.net/
eHabitat Platform

25. Present potential distribution
What we can do with niche modelling?
Past reconstructions
Time
Present potential distribution
Future projections

26. Study of past to… Study of present to… Study of future to…
Species ecological behaviour
Inner plasticity and buffering attitude
Response to climatic variables
Range stability and oscillations
Putative glacial refugia
Demographic history
In situ conservation strategies
Study of past to…
Suitable surfaces for immediate reforestations
Amount of sites suitable for reforestation
Human effect on the current landscape
Ecological corridors
Present vulnerability
Study of present to…
Response of plants to changes
Bioclimatic niche reassessment
Extinction risk
Range shift and dynamics
Future conservation strategies
Areas to be selected as refugia
Study of future to…

27. Practical application: quercus suber L.

28. Current distribution
Vessella F., López-Tirado J., Simeone M.C., Schirone B., Hidalgo P.J. (2015)

29. Past reconstruction (120 kyr – present)

30. Future projections and climate change

32. Practical application: potential Forests in andalusia
Present