Spatial Modelling
From Climate & Land Use Change to
Biodiversity Conservation
Loïc Pellissier (ETH, WSL) & Niklaus E. Zimmermann (WSL, ETH)
Introductory course in
Species Distribution Modelling
using R @ PKU, Beijing, China
Loïc Pellissier, Marc Grünig
Philipp Brun & Niklaus E. Zimmermann
|
6/13/2022
|
1
Part 1
Conceptual view on ecological variables
Introductory course in
Species Distribution Modelling
using R @ PKU, Beijing, China
Loïc Pellissier, Marc Grünig
Philipp Brun & Niklaus E. Zimmermann
|
6/13/2022
|
2
Quantifying niches: species distribution models
Recap from last slides: Assumptions important for SDMs
Spp= f(site)
Target Unit
Explanatory Unit
response variable
response variable
dependent variable
explanatory variable
predictor variable
independent variable
biotic {
biotic}
abiotic,
§ The statistical (SDM) modelling will calibrate the
distribution of species as a function of site factors.
§ We will do this in the R statistical environment
Introductory course in
Species Distribution Modelling
using R @ PKU, Beijing, China
Loïc Pellissier, Marc Grünig
Philipp Brun & Niklaus E. Zimmermann
|
6/13/2022
|
3
Conceptual considerations
Model classification
P) Precision:
Degree of exactness in measurement or
(model) prediction.
G) Generality: Applicability of a concept/model to a range
of instances [General rule/model w|out known
cause-and-effect].
R) Reality:
Collection of true causes and effects (in a
model), which underlie superficial appearances.
§ Only two out of these three model characteristics can be maximized
simultaneously. Consequently, the third attribute is sacrificed. This
leads ultimately to three theoretical model categories: empirical (PR),
mechanistic (RG), and analytical (GP). However, in practice there are
rather gradients than sharp limits between these model categories
Introductory course in
Species Distribution Modelling
using R @ PKU, Beijing, China
Loïc Pellissier, Marc Grünig
Philipp Brun & Niklaus E. Zimmermann
|
6/13/2022
|
4
Conceptual considerations
Model classification
R
P
G
After: Levins 1966, Sharpe 1990
§ Only two out of these three model characteristics can be maximized
simultaneously. Consequently, the third attribute is sacrificed. This
leads ultimately to three theoretical model categories: empirical (PR),
mechanistic (RG), and analytical (GP). However, in practice there are
rather gradients than sharp limits between these model categories
Introductory course in
Species Distribution Modelling
using R @ PKU, Beijing, China
Loïc Pellissier, Marc Grünig
Philipp Brun & Niklaus E. Zimmermann
|
6/13/2022
|
5
Conceptual considerations
Model classification
Reality
empirical
phenomenological
(ecological)
statistical
R
mechanistic
physiological
(fundamental)
process-based
P
Precision
G
Generality
Introductory course in
Species Distribution Modelling
using R @ PKU, Beijing, China
analytical
mathematical
theoretical
From: Guisan & Zimmermann 2000
Loïc Pellissier, Marc Grünig
Philipp Brun & Niklaus E. Zimmermann
|
6/13/2022
|
6
Conceptual considerations
Predictor classification
1
2
3
Resources: are used directly for physiological and
metabolic processes: light, water, nutrients, etc.
Direct: are directly impacting plants, but are not
resources: temperature, humidity, pH, etc.
Indirect: are not acting directly, but are often
correlated with 1 and 2: elevation, slope, aspect, etc.
After: Austin 1980, 1985; Austin et al. 1984; Austin & Smith 1989
Introductory course in
Species Distribution Modelling
using R @ PKU, Beijing, China
Loïc Pellissier, Marc Grünig
Philipp Brun & Niklaus E. Zimmermann
|
6/13/2022
|
7
Conceptual considerations
Predictor classification
Geology
Topography
Mesoclimate
Lat. / Lon.
Soils
ET
Precip.
Temperat.
Wind
Cloudiness
Radiation
Nutrients
Soil & Air Water
Heat Sum
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Introductory course in
Species Distribution Modelling
using R @ PKU, Beijing, China
Plant Growth,
Development, &
Potential Distribution
From: Guisan & Zimmermann 2000
Loïc Pellissier, Marc Grünig
Philipp Brun & Niklaus E. Zimmermann
|
6/13/2022
|
8