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![Potential natural vegetation maps
for western and central Kenya
Presently underutilized tools for the
selection of indigenous tree species and
their seed sources
Roeland Kindt (ICRAF / VVOB [Flanders, Belgium])
Jens-Peter B LillesĆø (ICRAF / FaL [Denmark])
Paulo van Breugel (ecologist, formerly IPGRI)](https://image.slidesharecdn.com/kindtseminar724thnovember-100617013118-phpapp02/85/Kindt-seminar-7-24th-november-1-320.jpg)
![Potential natural vegetation maps
for western and central Kenya
Presently underutilized tools for the
selection of indigenous tree species and
their seed sources
Roeland Kindt (ICRAF / VVOB [Flanders, Belgium])
Jens-Peter B LillesĆø (ICRAF / FaL [Denmark])
Paulo van Breugel (ecologist, formerly IPGRI)](https://image.slidesharecdn.com/kindtseminar724thnovember-100617013118-phpapp02/75/Kindt-seminar-7-24th-november-1-2048.jpg)
Uploaded byWorld Agroforestry (ICRAF)
Kindt seminar 7 24th november
Potential natural vegetation maps for western and central Kenya can be used to select indigenous tree species for planting by indicating the ecological suitability of areas. The maps were developed in the 1960s based on aerial photographs and fieldwork, delineating 17 potential natural vegetation types classified by structure and dominant species. While the maps provide a picture of climatic and soil variation, their vegetation boundaries and original species compositions may differ from current conditions. The maps could help diversify agroecosystems by matching species to mapped vegetation types.
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Kindt seminar 7 24th november
- 1. Potential natural vegetationmaps for western and central Kenya Presently underutilized tools for the selection of indigenous tree species and their seed sources Roeland Kindt (ICRAF / VVOB [Flanders, Belgium]) Jens-Peter B LillesĆø (ICRAF / FaL [Denmark]) Paulo van Breugel (ecologist, formerly IPGRI)
- 2. Overview ā¢ Which speciesto plant in a certain area for a certain purpose: use of potential natural vegetation maps to indicate ecological suitability and databases/books to select potential functions ā How were the maps and species lists developed? ā Do potential natural vegetation maps provide an adequate picture of climatic/edaphic variation in Kenyan highlands? ā Do potential natural vegetation maps delineate assemblages of indigenous tree species? ā What are the options for agroecosystem diversification?
- 3. Map of potentialnatural vegetation of south-western Kenya ā¢ High resolution maps available for south-western Kenya (Trapnell and co-workers) ā Four 1: 250,000 maps with vegetation boundaries for 1960 based on aerial photographs (1:30,000 and 1:50,000) and dense traverses (< 1 mile apart where accessible roads) ā Photographs mainly 1945-63 and fieldwork mainly 1945-1963 ā Vegetation classified in 18 groups, 23 subgroups, 55 classes and 217 subclasses ā Interpretation of climax vegetation types and eco-climatic conditions through remnants of climax types and pioneer (secondary) vegetation types ā Unfortunately limited documentation of criteria for vegetation types ā Publication of maps took long time (1966-1986) ā Maps have not been used in the realm of agroforestry
- 4. 1966 1976 1986 1969
- 6. 17 potential naturalvegetation types ā¢ Potential natural vegetation = the vegetation structure that would become established if all successional processes were completed under the present or future climatic and edaphic conditions ā¢ Determined from names of original vegetation types and eco-climatic maps ā¢ Main classification scheme is physiognomic (based on structure such as percentage aerial cover and height) (similar in other schemes) ā¢ Secondary classification scheme is floristic (based on dominant or typical species) ā¢ Other differences between types are interpretation of climatic conditions, but not determined from rainfall or altitude criteria (eg dry montane forest) ā¢ Four sheets produced (1:300,000; A3 format) ā¢ Excel sheet with uses and vegetation types compiled for 362 tree species that are indigenous to Kenya (types from legend map, other paper by Trapnell on forests, other literature, herbarium vouchers; uses from AgroforesTree Database + Useful trees book by RELMA) ā¢ Herbarium locations obtained for 110 species (but > 20 for only 2 species) ā¢ Detailed documentation of map interpretation and vegetation-specific lists almost finalized (want to avoid problem with original map!)
- 7. Physiognomic vegetation types Forest touching and interlocking crowns ā„ 8 (10) m tall lianas Woodland ā„ (40) 50% cover, open ā„ 8 m tall Wooded grassland (savanna) 10 ā (40) 50% cover ā„ 6 m tall Bushland and thicket (impenetrable) ā„ (40) 50% cover 3 - 7 m tall Bushed grassland 10 ā (40) 50% cover < 6 m tall Special types: swamp, bamboo, afro-alpine, moorland
- 8. High mountain vegetationtypes (3) Afro-alpine Mountain scrubland and moorland Bamboo woodland and thicket
- 9. Mountain scrubland andmoorland Afro-alpine Bamboo woodland and thicket
- 10. Forest vegetation types(4) Moist montane forest Dry montane forest Dry intermediate forest Moist intermediate forest
- 11. Dry montane forest Moist montane forest Moist intermediate forest Dry intermediate forest
- 12. Woodland and savannavegetation types (4) Lowland Acacia woodland, bushland and thicket Upland Acacia woodland, savanna and bushland Moist Combretum-Terminalia savanna Dry Combretum savanna
- 13. Moist Combretum-Terminalia savanna Dry Combretum savanna Upland Acacia woodland, savanna and bushland Lowland Acacia woodland, bushland and thicket
- 14. Bushland and thicketvegetation types (3) Lowland Acacia woodland, bushland and thicket Evergreen and semi-evergreen bushland Semi-evergreen thickets
- 15. Evergreen and semi-evergreenbushland Semi-evergreen thickets Lowland Acacia woodland, bushland and thicket
- 16. Vegetation types onsoils with impeded drainage (3) Acacia and allied vegetation on soils with impeded drainage Swamp and Papyrus Open grassland areas on soils with impeded drainage
- 17. Open grassland areason soils with impeded drainage Swamp and Papyrus Acacia and allied vegetation on soils with impeded drainage
- 18. Distribution of vegetationtypes, climate and species ā¢ Both vegetation and species distribution can be explained by the same explanatory variables (biotic, abiotic, landscape configuration, evolutionary time) ā¢ Literature on vegetation types lists typical species for each type ā¢ Flora, databases, books and herbarium records list vegetation types (habitat) for each species ā¢ Present and historical climatic conditions can be determined from pollen composition ā¢ Vegetation map of Africa (White) turned out to be map of plant endemism (phytochoria) and was later used as biogeographical map for African terrestrial ecoregions ā¢ Sophisticated statistical models perform better when vegetation (landuse) is one of the explanatory variables
- 19. Biome 4 model(Kaplan et al. 2003) Equilibrium distribution of 28 major potential natural vegetation types (biomes) from latitude (photosynthetically active solar radiation), atmospheric CO2 concentration, mean monthly climate (mean monthly precipitation, temperature, and percent sunshine) and soil physical properties (water holding capacity and percolation rate)
- 20. Check of relevanceof maps ā¢ Vegetation boundaries vs. patterns in climatic/soil differences ā¢ Vegetation boundaries and original species composition vs. current species composition
- 21. Interpolated surface layers Data layer Resolution ā¢ Investigation Annual precipitation 5 km (grid) how well Annual potential evapotranspiration 5 km (grid) potential Mean minimum temperature of the coldest 5 km (grid) vegetation month Number of dry months 5 km (grid) types Rootable depth 1:1 000 000 (vector) correspond to Cation exchange capacity 1:1 000 000 (vector) climate, soil Soil water pH 1:1 000 000 (vector) and DEM Percentage of clay 1:1 000 000 (vector) information Percentage of sand 1:1 000 000 (vector) Altitude (DEM) 92 m (grid) Slope 92 m (grid) Topographic wetness index 92 m (grid)
- 22. Interpolated surface layers Afro-alpine Crosses indicate 10%-25%-75%-90% quantiles 1800 and are centred on mean 1600 ALP Precipitation 1400 MIF MSM MCO MMF Precipitation Montane scrubland BAM 1200 SWADIF and moorland SET IAC 1000 Bamboo OGR DMF DCO MIX UAC EB Dry montane forest 800 LAC Evergreen bushland 600 Lowland Acacia-Commiphora Upland Acacia 1000 2000 3000 4000 Altitude Altitude
- 23. Forests moist intermediate moist montane 1600 Precipitation 1400 MIF MMF Precipitation Convex hulls delineate 1200 DIF all observations; line 1000 DMF types show concentric hulls after outer hull was 800 left out 1500 2000 2500 1800 Altitude Altitude 1600 1400 MIF dry intermediate MMF Precipitation 1200 dry montane DIF 1000 DMF 800 600 1000 1500 2000 2500 3000 Altitude
- 24. 1100 Woodlands lowland Acacia- Upland Acacia Commiphora 1000 Precipitation 900 Precipitation UAC 800 LAC 700 600 1400 800 1000 1200 1400 1600 1800 Altitude Altitude 1200 1000 Precipitation UAC 800 LAC 600 500 1000 1500 2000 Altitude
- 25. Moist Combretum-Terminalia 1400 Savannas Impeded Acacia MCO Precipitation 1200 Precipitation IAC 1000 Mixtures of evergreen DCO MIX UAC bushland and broad- 800 leaved savanna 600 1000 1200 1400 1600 1800 2000 2200 2400 1800 Altitude Altitude 1600 1400 MCO Precipitation 1200 Dry Combretum 1000 IAC Upland Acacia DCO UACMIX 800 600 500 1000 1500 2000 2500 3000 3500 Altitude
- 26. 1400 Bushland and thicket Semi-evergreen thickets 1200 Precipitation SET Evergreen bushland Precipitation 1000 MIX UAC EB 800 LAC 600 800 1000 1200 1400 1600 1800 2000 Altitude Altitude 1600 1400 1200 Precipitation lowland Acacia- SET 1000 Commiphora Upland Acacia MIXEB 800 UAC LAC 600 500 1000 1500 2000 Altitude
- 27. Linear discriminant analysis: predictions based on GIS layers All Impeded grassland Dry Intermediate Forest Swamp Dry Combretum Bamboo Moist Combretum Mixtures Impeded Acacia Upland Acacia Moist Intermediate Forest Evergreen bush Semi-evergreen thicket Moist Montane Forest Mountain moorland Alpine Lowland Acacia Dry Montane Forest 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Correct Same physiognomy Wrong
- 28. Linear discriminant analysis: predictions Impeded grassland Swamp Alpine Dry Intermediate Forest Mixtures Mountain moorland Dry Combretum Bamboo Upland Acacia Moist Combretum Semi-evergreen thicket Moist Intermediate Forest Lowland Acacia Moist Montane Forest Impeded Acacia Evergreen bush Dry Montane Forest 0 1000 2000 3000 4000 5000 6000 7000 correct physiognomic wrong
- 29. Ecotones and/or resolution? Dry montane forest Dry montane forest Moist montane Mount Kenya forest Moist montane forest
- 30. Linear discriminant analysis: commissions (false predictions) Impeded grassland Swamp Alpine Dry Intermediate Forest Mixtures Mountain moorland Bamboo Moist Intermediate Forest Moist Montane Forest Semi-evergreen thicket Upland Acacia Dry Combretum Moist Combretum Evergreen bush Lowland Acacia Dry Montane Forest Impeded Acacia 0 500 1000 1500 2000 2500 < 1 km 1 - 5 km > 5 km
- 31. Floristic differences MCO principal coordinates analysis based on Bray-Curtis distance 0.4 for presence-absence of 362 DCO species (Biodiversity.R) 0.2 MIF DIF Dim2 EB 0.0 SET MMF UAC IAC DMF LAC -0.2 BAM -0.4 -0.5 0.0 0.5 Dim1
- 32. Floristic differences MCO distance-based precipitation MIF redundancy 2 analysis based on Bray-Curtis distance (Biodiversity.R) 1 MMF DIF SET DCO CAP2 0 IAC LAC -1 DMF UAC EB -2 altitude BAM -4 -2 0 2 4 CAP1
- 33. Current patterns ofindigenous tree diversity around Mount Kenya Survey by Ogi et al. 250 quadrats of 50 Ć 100 m2 within map 279 indigenous tree species (174 species also in literature description)
- 34. Total and sharedspecies richness between literature and current species assemblages Potential Natural n Species (Based Species % Species total Kulczynski Vegetation Type total on total confirmed (survey) ecological (literature) shared) by survey distance Moist intermediate 57 105 51 31 61% 82 0.41 forest Dry Combretum 40 23 21 18 86% 108 0.45 Dry montane forest 37 91 58 31 53% 83 0.42 Moist montane 37 99 46 30 65% 85 MIF forest (dif 0.005) Lowland Acacia- 25 92 48 35 73% 102 0.36 Commiphora Evergreen bushland 16 44 38 18 47% 52 0.47 Dry intermediate 15 74 49 27 55% 63 0.43 forest Upland Acacia 7 22 20 6 30% 35 ST (dif 0.108) Acacia (impeded) 6 28 18 5 28% 18 UA (dif 0.042) Semi-evergreen 6 29 19 6 32% 52 DC thicket (dif 0.233)
- 35. Frequencies of species Potentialnatural Species Rank Frequency vegetation type (%) Moist intermediate forest Cordia africana 1 67 n = 57 Croton macrostachyus 2 60 Commiphora eminii 3 53 Bridelia micrantha 4 51 Markhamia lutea 5 37 Erythrina abyssinica 6 33 Croton megalocarpus 7 32 Catha edulis 8 23 Prunus africana 9 21 dry Combretum savanna Croton macrostachyus 1 45 n = 40 Combretum molle 2 42 Euphorbia tirucalli 3 40 Combretum collinum 4 35 Croton megalocarpus 5 32 Piliostigma thonningii 5 32 Azanza garckeana 7 30 Senna singueana 8 25 Dry montane forest Croton megalocarpus 1 62 n = 37 Euclea divinorum 2 51 Juniperus procera 3 49 Plectranthus barbatus 4 41 Rhus natalensis 5 38 Lippia javanica 6 35 Scutia myrtina 7 30 Psiadia punctulata 8 27 Solanum incanum 8 27 Olea europaea 10 22
- 36. Frequencies of species Potentialnatural Species Rank Frequency vegetation type (%) Moist montane forest Croton megalocarpus 1 70 n = 37 Croton macrostachyus 2 49 Species = 85 Commiphora eminii 3 41 S1 = 42 Prunus africana 3 41 Bridelia micrantha 5 27 Clerodendrum johnstonii 6 22 Cordia africana 7 19 Erythrina abyssinica 7 19 Vitex keniensis 7 19 Vangueria infausta 10 16 Lowland Acacia- Acacia tortilis 1 64 Commiphora n = 25 Terminalia brownii 2 60 Species = 102 Melia volkensii 3 52 S1 = 49 Albizia anthelmintica 4 40 Acacia senegal 5 36 Acacia ataxacantha 6 32 Berchemia discolor 6 32 Acacia mellifera 8 28 Combretum aculeatum 8 28 Senna singueana 8 28 Evergreen bushland Croton megalocarpus 1 63 n = 16 Euclea divinorum 2 44 Species = 52 Ipomoea kituiensis 2 44 S1 = 26 Acacia drepanolobium 4 38 Plectranthus barbatus 4 38 Acacia xanthophloea 6 31
- 37. Selection of speciesfor agroecosystem diversification ā¢ Select frequent species? Promote underutilized species? Balance with exotic species? ā¢ Timber, for example ā All confirmed species? ā Faster growing primary species with relatively high current frequencies? ā¢ Juniperus procera (49 and 27% of quadrats in dry forests) ā¢ Vitex keniensis (19 and 18% of quadrats in moist forests) ā Faster growing primary species with low current frequencies? ā¢ Hagenia abyssinica (1 quadrat), Zanthoxylum gillettii (none), Podocarpus latifolius (none) ā Slower growing primary species? ā¢ Olea europaea (22%), Ocotea usambarensis (1 quadrat), Cassipourea malosana (2 quadrats), Podocarpus falcatus (none)
- 38. Limitations of vegetationmaps to map species distribution ā¢ Changes in climatic or soil conditions from those associated with the map ā Need for successional processes (pioneer, climax) ā Ecosystem restoration: first abiotic, then biotic filters? ā Changes in ecological/dispersal/habitat pools? (landuse, climate change, invasive species) ā¢ Mapped vegetation types are often mosaics with some small vegetation types that differ from the main type ā¢ Vegetation types have ecotones where each species reaches another environmental limit (fuzzy boundaries between vegetation types), whereas maps show hard boundaries (indicate ecotone width by LDA?) ā¢ Species consist of different populations (provenances) that differ in adaptation to local conditions (precautionary principle!)
- 39. Seed sources ofCalliandra calothyrsus identified during seed source survey (2004) Good seed sources 57 56 55 54 53 52 51 50 49 1 47 48 46 2 3 44 4 45 31 32 5 30 33 34 35 6 29 7 11 28 27 36 43 37 89 9 22 23 25 41 12 26 39 13 21 24 38 15 14 17 16 40 42 Poor seed sources 18 20 19
- 40. Correspondence to other vegetation classification schemes White. 1983. 1:5,000,000 42 54 19a 45 11a 4 65 4 42 19a Good correspondence for high mountain 45 19a vegetation(65), montane forest (19a), 11a 45 evergreen and semi-evergreen bushland (45), 45 moist Combretum savanna (11a), lowland 42 42 Acacia bushland (42) and semi-evergreen thickets (45)
- 41. Correspondence to other vegetationclassification schemes Olson et al. 42 2001. 54 19a 45 1:5,000,000 11a 4 65 From White 4 42 19a AT0711 45 19a AT1313 11a 45 AT0108 AT1005 45 AT0721 42 42 AT0711 AT0108 What happened with evergreen and AT0108 semi-evergreen bushland, semi-evergreen thickets and in the western part of the map? AT0711 AT0716 AT0108 Boundary between lowland Acacia types?
- 42. Conclusions ā¢ Bad news:good models and detailed maps for species distribution or suitability require good presence-only or presence-absence data, and detailed input maps for large range of explanatory variables, whereas neither are commonly available for most components of biodiversity (including tree populations!) ā¢ Good news: potential natural vegetation maps can provide a reasonable summary of climate and the potential distribution of indigenous tree species, they are available for most places on earth and information is available on their species assemblages ā¢ Best news: we already compiled information for a couple of hundred species for a detailed map for central and western Kenya + confirmed some of climatic/floristic information + have information for their uses for many ā¢ Way forward: combine existing potential natural vegetation maps with more extensive set of presence- data and GIS layers to build better species suitability maps
- 43. A (some) wordof thanks ā¢ Meshack ā¢ Sammy, Jonathan, Sally-Anne, Walter ā¢ Trees and markets (Tony) ā¢ Our donors ā¢ Everybody in the audience today
- 44. Topics for discussion? ā¢How confident should users be when using the maps or we when we advise? ā¢ Further testing of maps ā¢ Expansion of maps to White/WWF ecoregions, Eastern Africa, ā¦ ā¢ Sharing of information (printed maps, website, documentation) ā¢ How to deal with biotic and abiotic changes