Transcript Species data Management System SMS version 0.1

Assessing conservation priorities: the
African Vertebrates Databank (AVD)
Luigi Boitani
Dept. Animal Biology, University of Rome
Istituto di Ecologia Applicata
Via L.Spallanzani, 32
00161 Rome ITALY
email: [email protected]
Participating Institutions
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IUCN/SSC
Conservation International - CABS
Natural History Museum – London
University of Cambridge
Natural History Museum – Copenhagen
Birdlife International
Istituto di Ecologia Applicata
University of Rome
Project goals
• Produce a continental scale conservation tool for African
vertebrates
• This major goal is achieved by:
– building a data bank on African vertebrates with the
aim of providing the raw data for future applications
and analyses on conservation options and priorities
– modeling actual and potential species distribution
Conservation Needs
• Broad scale planning (eventually global)
– Metapopulation approach
– Identification of core areas and corridors
– ….
which implies
– Detailed knowledge on actual species distribution
– Extensive data on species ecology and biology
– Spatially explicit predicting tools
GIS modeling
 Cost effective approach
 Maximizes the information obtainable from
the few data sets available
 Updateable distributions
 Repeatable approach
Three (four) pieces of
information
 Species Extent of Occurrence
 Environmental variables
 Species-environment relationship
+
 Validation data set and procedures
Other projects with similar
approach:
• African Mammal Databank (1999)
• Ecological Network for the Italian Vertebrates
(current)
• Asian Mammal Databank (submitted to the
EU)
Distribution modeling
• AVD integrates two (maybe three) levels:
– "Blotch" distribution
– Categorical-Discrete distributions obtained
through a deterministic approach based on GIS
overlay procedures
and maybe
– Probabilistic-Continuous distribution models
based on statistically supported GIS overlay
procedures.
“Blotch” distribution
Extent of
Occurence of the
Cheetah
Acinonyx jubatus
certain
possible
Categorical Discrete Model
Area of Occupancy
of the Cheetah
Acinonyx jubatus
Probabilistic Continuous Model
Suitability surface
for the Cheetah
Acinonyx jubatus
Categorical Discrete Model
Validation
• The AMD project was validated with field work carried
out in four selected countries in Africa
•Botswana, Cameroon, Morocco, Uganda
• 427 plots were allocated at random within the four
countries
• The presence/absence of each species at each of the
predetermined points was verified by:
•direct observation
•in loco collection of publications and scientific reports
•interviews with local experts/authorities/inhabitants
• In each country a team composed by a researcher from a
local Institution and one IEA staff member carried out
the field work
Categorical Discrete Model
Validation
Validation parameters
Valid plots = all plots falling inside the Extent of Occurrence + all
other plots in which the species was found during field work
Index of Accordance = Percentage of valid plots in accordance with
the Categorical Discrete model
% of Extent of
Occurrence in
sample areas
Number of valid
plots
Index of
Accordance (%)
6.94
128
51.56
Categorical Discrete Model
Validation
• The AVD limited budget resources will prevent direct
field work
• Similar scheme will be implemented using known species
locations from bibliography
Categorical Discrete Model
Products
Surfaces and percentage of each suitability class within the
Extent of Occurrence of the cheetah (Acinonyx jubatus)
suitable
OCCURRENCE
km
certain
2
moderately suitable
%
km
4 736 158
49
possible
46 130
Total
4 782 288
2
unsuitable
%
km
3 741 586
39
0
63 110
49
3 804 696
2
Total
2
%
km
%
1 041 932
11
9 519 676
98
1
61 065
1
170 305
2
39
1 102 997
11
9 689 981
100
Categorical Discrete Model
Products
Fragmentation indexes of the Area of Occupancy (all suitable
and moderately suitable areas) of the cheetah (Acinoyx jubatus)
Number Patches Mean Patch Size Patch Size SD
(NP)
(MPS)
(PSSD)
2
2
km
km
Largest Patch
Index (LPI)
%
Mean Shape
Index (MSI)
Area-Weighted
Mean Shape
Index (AWMSI)
suitable
3 569
1 348
40 385
18.73
1.26
24.17
moderately suitable
4 425
873
18 920
10.94
1.33
19.1
Total AO
614
14 127
309 897
88.53
1.25
20.87
Categorical Discrete Model
Products
Efficacy of protected areas for the species
Vulnerable (VU: A1d+2d,C1) as A. jubatus
Endangered (EN: C2a, D1) as A. j. hencki NW African cheetah
OCCURRENCE
SUITABILITY CLASS
inside
outside
Total
certain
suitable
4.99
43.88
48.88
moderately suitable
5.47
33.14
38.61
unsuitable
1.63
9.12
10.75
suitable
0.08
0.39
0.48
moderately suitable
0.06
0.60
0.65
unsuitable
0.06
0.57
0.63
12.28
87.72
100
possible
Total
Management tools
• The different types of distribution models produced can be
included in management tools of increasing information content:
– Blotch distribution
• hot spots identification
• effectiveness of protected areas
– Categorical Discrete Distribution Model
• population fragmentation
• management strategies for conservation
– Probabilistic Continuous Distribution Model
• metapopulation PVA
• corridors identification
Mammals biodiversity hotspots
281 species of large mammals
Mammals biodiversity hotspots
281 species of large mammals
AMD Products
• a printed volume containing for each of the 281 species:
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Taxonomic notes
IUCN threat category
Available ecological information
Bibliography
Extent of Occurrence (“Blotch”)
Categorical Discrete Model
Probabilistic Continuous Model
Comments and conservation issues
• set of 10 CD-ROM with the digital version of all the above
information
• web site with all data sets:
– www.gisbau.uniroma1.it/amd
AVD: where we are ?
• Mammals:
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Rodents
Bats
Insectivores
Others…
• Birds
• Amphibians
• Herps
– Snakes
– Lizards
– Others ?
• Fishes ??
Why modeling? And how
• Purpose of distribution maps
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–  conservation: maps must be current and at the appropriate
resolution/scale for each taxon
• Maps ARE models !!
• Increasing distribution information:
– Points
(if qualified: date, accuracy, species biology) (good
for transformation into blotches and/or for inductive
modeling and/or for model validation)
– Polygons (if qualified)
– Grids
(if all cells are qualified)
– Models (if validated)