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Modelling the abundance and spatial
distribution of mountain gorilla forage
species in the Virunga massif
Prepared by: Providence Akayezu
Thesis Assessment Board:
Dr. I.C. van Duren (1st Supervisor)
Dr. Ir. T.A. Groen (2nd Supervisor)
Dr. Y.A.Hussin (Chair)
Dr. Ir. R.A. (Rolf) de By (Eternal Examiner, ITC-GIP)
MSc. Thesis defense
01 March 2016
1. Research problem
Mountain gorillas (G. beringei beringei)
and their food species
Suitable habitat
Growing population
Herbivorous
Conservation Encroachment
Climate change
Study area
2. Datasets
Systematic sampling:
Braun-Blanquet scores for
food species abundance.
Random stratified
sampling for food
species biomass:
• Measure the stem length
• Harvest leaves and weigh
• Sundry
• Weigh until there is no
loss of weight.
1
2
1
94 plots 956 plots
3. Significant difference in gorilla food
species abundance or biomass between
vegetation types
Vegetation
types
Gorilla food
species
abundance
/biomass
• ANOVA test
• Games-Howell pairwise
comparison
• F statistics,
• p value,
• Significant
groups
Herbaceous Hagenia
Alpine
Hypericum Neoboutonia
Bamboo & herbaceous
H1: There is a significant difference in
individual gorilla food species abundance
or biomass between vegetation types
4. Relationship between gorilla food species
abundance/biomass and:
o Forest structure/topography variables
o Vegetation indices
Gorilla food
species
abundance
/biomass
Field variables
• Tree height
• Stem density
• Tree canopy
GIS and RS variables
• Vegetation indices
• Slope
• Northness
• Eastness
• Elevation
• Solar radiation
(Multiple) Linear Regression
R-square, F statistics,
p value
H1: Gorilla food species abundance/biomass increases in higher elevations,
west-facing slopes and small tree densities.
H1: The abundance/biomass of
gorilla food species is better
explained by NDVI than EVI2.
5. Mapping the forest canopy cover from
Aster imagery band reflectance
Nine Aster
bands
reflectance
values
Field
measured
forest canopy
Stepwise Multiple Linear
Regression
R-square, p
value
Forest
canopy
cover map
H1: There is a significant positive relationship between
the forest canopy cover and the Aster imagery band
reflectance values.
6. Modelling the distribution of five most
preferred gorilla food species
Species distribution modelling :
BRT
Five SDM
maps
Predictor variables
Species locations
H1: The BRT model can discriminate
gorilla food species presence/absence
with an accuracy greater than random
guess.
7. Results: Significant difference in gorilla food species
abundance/biomass between vegetation types
Results: Relating gorilla food species abundance/biomass
and forest structure/topography variables
Results: Relationship between gorilla food species
abundance/biomass and vegetation indices
Results: Forest canopy cover
Results: Five gorilla food species distribution maps
AUC=0.78
AUC=0.65
AUC=0.80
AUC=0.77
AUC=0.72
8.Conclusion
 The ANOVA and stepwise regression were mainly significant for
dataset 2.
 The Aster imagery poorly predicted the Virunga forest canopy.
 The EVI2 predicted gorilla species abundance than NDVI for dataset1;
but lower variance explained by the model.
 The BRT model performed with a reasonable accuracy (AUC> 0.70)
for four species and an AUC= 0.65 for one species.
 Most important predictors: elevation, eastness, slope, solar radiation,
vegetation types and forest canopy
 Galium spp. and Rubus spp. predicted to occur in elevations greater
than 3,500 m.
 P. linderi, L. alatipes and C. nyassanus lower probabilities of
occurrence in elevations greater than 3,500 m.
Providence_MSc defense University of Twente

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Providence_MSc defense University of Twente

  • 1. Modelling the abundance and spatial distribution of mountain gorilla forage species in the Virunga massif Prepared by: Providence Akayezu Thesis Assessment Board: Dr. I.C. van Duren (1st Supervisor) Dr. Ir. T.A. Groen (2nd Supervisor) Dr. Y.A.Hussin (Chair) Dr. Ir. R.A. (Rolf) de By (Eternal Examiner, ITC-GIP) MSc. Thesis defense 01 March 2016
  • 2. 1. Research problem Mountain gorillas (G. beringei beringei) and their food species Suitable habitat Growing population Herbivorous Conservation Encroachment Climate change Study area
  • 3. 2. Datasets Systematic sampling: Braun-Blanquet scores for food species abundance. Random stratified sampling for food species biomass: • Measure the stem length • Harvest leaves and weigh • Sundry • Weigh until there is no loss of weight. 1 2 1 94 plots 956 plots
  • 4. 3. Significant difference in gorilla food species abundance or biomass between vegetation types Vegetation types Gorilla food species abundance /biomass • ANOVA test • Games-Howell pairwise comparison • F statistics, • p value, • Significant groups Herbaceous Hagenia Alpine Hypericum Neoboutonia Bamboo & herbaceous H1: There is a significant difference in individual gorilla food species abundance or biomass between vegetation types
  • 5. 4. Relationship between gorilla food species abundance/biomass and: o Forest structure/topography variables o Vegetation indices Gorilla food species abundance /biomass Field variables • Tree height • Stem density • Tree canopy GIS and RS variables • Vegetation indices • Slope • Northness • Eastness • Elevation • Solar radiation (Multiple) Linear Regression R-square, F statistics, p value H1: Gorilla food species abundance/biomass increases in higher elevations, west-facing slopes and small tree densities. H1: The abundance/biomass of gorilla food species is better explained by NDVI than EVI2.
  • 6. 5. Mapping the forest canopy cover from Aster imagery band reflectance Nine Aster bands reflectance values Field measured forest canopy Stepwise Multiple Linear Regression R-square, p value Forest canopy cover map H1: There is a significant positive relationship between the forest canopy cover and the Aster imagery band reflectance values.
  • 7. 6. Modelling the distribution of five most preferred gorilla food species Species distribution modelling : BRT Five SDM maps Predictor variables Species locations H1: The BRT model can discriminate gorilla food species presence/absence with an accuracy greater than random guess.
  • 8. 7. Results: Significant difference in gorilla food species abundance/biomass between vegetation types
  • 9. Results: Relating gorilla food species abundance/biomass and forest structure/topography variables
  • 10. Results: Relationship between gorilla food species abundance/biomass and vegetation indices
  • 12. Results: Five gorilla food species distribution maps AUC=0.78 AUC=0.65 AUC=0.80 AUC=0.77 AUC=0.72
  • 13. 8.Conclusion  The ANOVA and stepwise regression were mainly significant for dataset 2.  The Aster imagery poorly predicted the Virunga forest canopy.  The EVI2 predicted gorilla species abundance than NDVI for dataset1; but lower variance explained by the model.  The BRT model performed with a reasonable accuracy (AUC> 0.70) for four species and an AUC= 0.65 for one species.  Most important predictors: elevation, eastness, slope, solar radiation, vegetation types and forest canopy  Galium spp. and Rubus spp. predicted to occur in elevations greater than 3,500 m.  P. linderi, L. alatipes and C. nyassanus lower probabilities of occurrence in elevations greater than 3,500 m.