Transcript Species data Management System SMS version 0.1

Modeling species distribution using
species-environment relationships
Fabio Corsi
Istituto di Ecologia Applicata
Via L.Spallanzani, 32
00161 Rome ITALY
email: [email protected]
Conservation Needs
• Broad scale planning (eventually global)
– Metapopulation approach
– Identification of core areas and corridors
– ….
Which imply
– Detailed knowledge on actual species distribution
– Extensive data on species ecology and biology
– Spatially explicit predicting tools
The information “space”
• data are:
Quality
– fragmented
– localised
– on average, of modest
quality
Data availability
Extent
Can we use them for broad scale planning?
The answer is a set of new
questions
• Can we extrapolate existing knowledge to the
entire continent?
• Under which assumptions?
• For which use?
Can we extrapolate existing
knowledge to the entire
continent?
• Yes, using modeling techniques which
• enable to extrapolate from limited data new
information
• are cost effective
• produce updateable distributions
• define a repeatable approach
Spatial Modeling
Geographic space
E2
E1
Geographic space
Environmental space
En
En
E2
E1
E1
En
E2
Feedback
Under which assumptions?
• Species distribution is influenced by available
environmental data (e.g. test for randomness of point
data; Mantel test)
• Local variations of these relationships throughout the
study area can be neglected (e.g. stratification)
• Available data are sufficient to define speciesenvironment relationships (field validation, sensitivity
analysis, hope and fate )
Alternatives
Quantitative data
Distribution
Semivariogram structure
Feedback
For which use?
• Application of results include, but are not
limited to:
– Identify potential/critical corridors
– Predict areas of major conflicts
– Assessment of conservation scenarios and
management options on a cost/benefit basis
(zoning system)
– Include spatial elements in a PHVA
– …..
“Blotch” distribution
• The polygon defining the
distribution range of the
species as interpreted by the
specialist based on her/his
knowledge
• The environmental
requirements of the species
are synthesized directly into
the drawing itself
Deterministic overlay
• The analysis of the environmental
space is synthesized by the expert
knowledge (deductive approach based
on known ecological preferences)
• Simple overlay of environmental
variables layers
• The goal is to describe the distribution
within the “blotch” perimeter,
showing the areas of expected
occurrence.
• Mostly categorical models of
suitability
Selection
Avoidance
Statistical overlay
Observations
• Formal analysis of the
environmental space defined
by the available variables
• Result of previous analysis
control the overlay process.
• The goal is to describe the
variation of suitability within
the “blotch”
• Continuous suitability rank
surface
1
2
3
...
a b c ...
.5 12 60 ...
.7 31 20 ...
.2 7 50 ...
... ... ... ...
F2
F1
Habitat
Suitability
Examples
• Models developed at regional scale for
the large Italian carnivores and major
ungulate species
Available data
• Extent of Occurrence of each species
• known territories and point locations from previous
studies (e.g. radio tracking, direct investigations etc.)
• land cover maps, digital terrain model, population
densities, ungulates distributions, protected areas,
sheep and goats densities
The method (step 1)
• Environmental data pre-processed with
map algebra to account for individuals
awareness of the environment
The method (step 1)
• Surface of the circular
window is equal to the
average size of the
territories and/or home
range
•To each cell of the
study area is assigned a
value which is a
function of the
surrounding cells
x
x = f(x in blue cells)
Building the model (Step 2)
• Environmental characterisation of known
species locations based on available
environmental variables
L1
L3 Ln
Locations
L2
Environmental
variables
{
E1
E2
En
L1 L2 L3 ...Ln
E11 E12 E13 E1n
E21 E22 E23 E2n
En1 En2 En3 Enn
Building the model (Step 2)
• Calculating the species “ecological signature”
E1
E1
L1 L2 L3 … Ln
E11 E12 E13 E1n
E21 E22 E23 E2n
En1 En2 En3 Enn
S E1 / n = E1
S E2 / n = E2
...
S En / n = En
E2
En
En
E2
Building the model (Step 3)
• Calculating the distance of each portion of the
study area from the ecological signature in the
environmental variables space
E1
Px
Px
E1x
E2x
...
Enx
E1x
E2x
Ecological
Distance
Enx
En
E2
The method (Step 3)
• Species “ecological signature” calculated as
the vector of means and the variancecovariance matrix
L1 L2 L3…. Ln
E11 E12 E13 E1n
E21 E22 E23 E2n
En1 En2 En3 Enn
m
S
Vector of means
Variance-covariance
matrix
E1
E2
VE1 CE1E2 CE1En
CE2E1 VE2 CE2En
CEnE1 CEnE2 VEn
...
En
&
The method (Step 3)
• Using the above definition of “ecological
signature”, distances can be calculated using
the Mahalanobis Distance
D  x - m  S x - m 
2
x
-1
'
D = Mahalanobis distance (environmental distance)
at point x
x = vector of environmental variables measured in x
m = vector of the means
S = variance-covariance matrix
Mahalanobis distance
• takes into account not only the average value but also its
variance and the covariance of the variables measured
• accounts for ranges of acceptability (variance) of the
measured variables
• compensates for interactions (covariance) between
variables
• dimensionless
• if the variables are normally distributed, can be readily
converted to probabilities using the 2 density function
Map production
• The mean (m) and standard deviation (s of the
Ecological Distance is calculated for the territories
and locations
• The Ecological Distance surface is partitioned
according to the following threshold:
– m, m + 1s ,m + 2s, m + 4s, m + 8s, m + 16s
• First three classes account for more than 95% of
variability (assuming a normal distribution)
The Extent of Occurrence
• Accounts for variables that influence the
species distribution but cannot easily be
included in the analysis, such as:
– historical constraints
– behavioural patters
–…
• Mapped results are interpreted as expected
within the EO and potential outside the EO
Results
• Environmental
suitability
model for the
Wolf
Results
Cumulative frequency
• Cumulative frequency distributions
1.2
1
0.8
0.6
0.4
0.2
0
0
55
109
164
218
273
327
382
436
491
545
600
Mahalanobis distance
log-normal distribution of
dead wolves
environmental distance
classes in the study area
655
Results
• Environmental
suitability
model for the
Lynx
Results
• Environmental
suitability
model for the
Lynx
(boarder between
France, Switzerland
and Italy)
Results
• Environmental
suitability
model for the
Bear
Results
• Environmental
suitability
model for the
Deer
Towards a model for biodiversity
• Biodiversity distribution models may derive from:
– deterministic overlay of suitability models
– the analysis of the environmental suitability space
Species 1
Classification
Clustering
Species n
Species 2
…
Classification
• Map showing the result of the principal component analysis on the
suitability maps of the 3 species of large carnivores in the Alps
Alternatives
Quantitative data
Distribution
Semivariogram structure
Feedback