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Phytodiversity assessment in the West African Sudan Zone

Authors:

Abstract

Introduction The main objective of the subproject W11 is to analyze and evaluate present and historical changes in phytodiversity along a climatic gradient under different utilization conditions. Here we present results of the study region in the North Sudanian Zone of Burkina Faso in the province Gourma. We propose an interdisciplinary approach with botany and remote sensing as participating disciplines to detect distribution patterns of phytodiversity. Methods The botanical data are stored in a relational vegetation database, which comprises 3300 relevés from BIOTA W11and preceding projects (SFB 268). The different databases are joint by species names and geographic coordinates. In this analysis 73 woody layer relevés are used to calculate phytodiversity indices, which are considered in analyses with satellite data. Structural vegetation parameters of 150 training areas were used to compute a vegetation map, which discriminates between several savanna types and land use forms. Consequently for each vegetation patch the mean of the Modified Soil Adjusted Vegetation Index (MSAVI) was computed. Results To link the botanical data with satellite data a correlative approach was chosen. The strong correlation between the mean of the MSAVI and the woody cover of 83 training areas can be used to link botanical diversity data with remote sensing derived parameters (Fig. 1). In the second analysis the correlation between the species richness (Fig. 2) respectively the Shannon diversity index (Fig. 3) of woody plants and Vegetation Index values is shown. Conclusion Biological survey information on phytodiversity and its distribution in space is always incomplete and the survey effort is mostly spread inconsistently across the land surface. To allow a spatially more complete and reliable view on the spatial distribution of the diversity of woody plants we used a combination of botanical and remote sensing data, which were linked by their geographic position. Due to the close correlation with woody Species Richness, the vegetation index MSAVI might serve as a rough but easy-to-obtain indicator of phytodiversity. The simple linear correlative approach is limited by several restrictions, but it is a good starting point for further analysis of biodiversity pattern. It is planned to use these maps in more sophisticated approaches of biodiversity modeling with genetic algorithms (GARP) to predict a wider range of species.
W e s t A f r i c a
Phytodiversity assessment in the West African Sudan Zone
König, K.3, Schmidt, M.2, Agbani, P.7, Agonyissa, D.6, Akoegninou, A.7, Bierschenk, T.4,
Dressler, S.2, Hahn-Hadjali, K.1, Langewiesche, K.4, Neumann, K.3, Ouédraogo, A.5, Runge, J.3,
Schareika, N.4, Sinsin, B.6, Thiombiano, A.5, Zizka, G.2
(1) Botanisches Institut, Univ. Frankfurt, (2) Botanik/Paläobotanik, Univ. Frankfurt und Forschungsinstitut Senckenberg, (3) Institut für Physische Geographie, Univ. Frankfurt ,
(4) Institut für Ethnologie und Afrikastudien, Univ. Mainz , (5) Unité de Formation et de Recherche en Sciences de la Vie et de la Terre, Univ. de Ouagadougou, Burkina
Faso, (6) Faculté des Sciences Agronomiques, Univ. Nationale du Bénin, (7) Faculté des Sciences et Techniques, Univ. Nationale du Bénin
W 11
W 11
J.G.-University
Mainz
J.W.G.-University
Frankfurt
Introduction
The main objective of the subproject W11 is to analyze and evaluate
present and historical changes in phytodiversity along a climatic
gradient under different utilization conditions. Here we present results
of the study region in the North Sudanian Zone of Burkina Faso in the
province Gourma. We propose an interdisciplinary approach with
botany and remote sensing as participating disciplines to detect
distribution patterns of phytodiversity.
Methods
The botanical data are stored in a relational vegetation database, which
comprises 3300 relevés from BIOTA W11and preceding projects (SFB 268).
The different databases are joint by species names and geographic
coordinates. In this analysis 73 woody layer relevés are used to calculate
phytodiversity indices, which are considered in analyses with satellite data.
Structural vegetation parameters of 150 training areas were used to compute
a vegetation map, which discriminates between several savanna types and
land use forms. Consequently for each vegetation patch the mean of the
Modified Soil Adjusted Vegetation Index (MSAVI) was computed.
Results
To link the botanical data with satellite data a correlative approach was chosen. The strong correlation
between the mean of the MSAVI and the woody cover of 83 training areas can be used to link botanical
diversity data with remote sensing derived parameters (Fig. 1). In the second analysis the correlation
between the species richness (Fig. 2) respectively the Shannon diversity index (Fig. 3) of woody plants and
Vegetation Index values is shown.
Relation between the Modified Soil Adjus-
ted Vegetation Index and Species Rich-
ness of woody plants (RPearson= 0.76,
p=0.01, n=21).
0
5
10
15
20
25
30
0.00 0.10 0.20 0.30 0.40
MSAVI
Species Richness
Species Richness of woody plants
0
1
2
3
0.00 0.10 0.20 0.30 0.40
MSAVI
Shannon Index
Relation between the Modified Soil Adjus-
ted Vegetation Index and the Shannon
Diversity Index of woody plants (RPearson=
0.6, p=0.05, n=21).
Shannon Index of woody plants
0.0
0.1
0.2
0.3
0.4
0.5
0 20406080100
woody cover (%)
MSAVI
Relation between the Modified Soil Adjus-
ted Vegetation Index and woody cover.
(RPearson= 0.87, p=0.001, n=83).
University of
Ouagadougou
Burkina Faso
University of
Abomey-Calavi,
Benin
Research Institute
Senckenberg
Frankfurt
Conclusion
Biological survey information on phytodiversity and its distribution in space is always incomplete and
the survey effort is mostly spread inconsistently across the land surface. To allow a spatially more
complete and reliable view on the spatial distribution of the diversity of woody plants we used a
combination of botanical and remote sensing data, which were linked by their geographic position. Due
to the close correlation with woody Species Richness, the vegetation index MSAVI might serve as a
rough but easy-to-obtain indicator of phytodiversity. The simple linear correlative approach is limited by
several restrictions, but it is a good starting point for further analysis of biodiversity pattern. It is planned
to use these maps in more sophisticated approaches of biodiversity modeling with genetic algorithms
(GARP) to predict a wider range of species.
Projection:UTM, WGS84,
Zone 31N
Vegetation & Land Cover MapMSAVI-Values
Legend
eau
Savane brûler
Fôret galerie
Savane arborée
Savana boisée
Savane herbeuse 1
Savana herbeuse 2
Jachère ancienne
Jachère jeune
Savane arbustive
Champ 1
Champ 2
Champ 3
Legend
High MSAVI value
Low MSAVI value
Projection:UTM, WGS84,
Zone 31N
Burkina Faso
Study area
Gorom-Gorom
Péhunco
Fada N‘Gourma
S - Sudanian Zone
N - Sudanian Zone
Sahel
BRAHMS
Herbarium
Database
VegDa
Vegetation
Database
GIS
-3,300 Relevés
-5 Observatories
-Ethnobotanical Data
-Ecological Data
-Taxonomic backbone
-12,000 Specimens
-Gazetteer
-Maps
-Thematic layer
-remote sensing data
Species names
Coordinates Coordinates
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