report on assessing impacts of future climate change on...

SERVIR Africa Biodiversity Project

Assessing the Vulnerability of Biodiversity to Climate Change

Report on Assessing Impacts of Future Climate Change on

VEGETATION in East Africa and Kenya-Tanzania

Borderlands

Expanding Datasets and Model Refinement

February 2012

A collaborative partnership between African Conservation Centre and National Museums of

Kenya, University of York, Missouri Botanical Gardens, 5East African Herbarium, Tanzania

Commission for Science and Technology

AFRICAN CONSERVATION CENTRE P O BOX 15289, 00509

Nairobi, KENYA

With kind support of:

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Project background

The African Conservation Centre (ACC), Missouri Botanical Garden, University of York, Yale University

and national institutions in Kenya and Tanzania have been studying the threats posed by climate

change and land fragmentation to biodiversity and rural livelihoods in East Africa. The initial project,

funded by the Liz Claiborne Art Ortenberg Foundation (LCAOF), has focused on the 60,000 square

kilometre region stretching across the Great Rift Valley from Serengeti and Maasai Mara in the west

to Tsavo and Mkomazi in the east: the Kenya-Tanzania Borderlands. The Borderlands account for 80%

of the large mammals, 50% of the vertebrates and 25% of the vascular plants found in Kenya and

Tanzania. The area also has many regionally endemic species and threatened animals and plants. The

diverse landscape spans 14 world-renowned parks, attracts over 1.5 million visitors a year and

generates a half-billion dollars in revenues for the two African nations. The LCAOF-funded pilot

project has brought together scientists and conservationists to map the distribution of animals,

plants and human livelihoods, and to model their vulnerability to climate change. The component of

the project focusing on the vegetation has aimed to model species distributions based on plant

collection data from across the region. The work is part of ongoing research to model the effects of

climate change on the vegetation of the East African region. We initially start with four major

research questions:

1) Can species distributions be predicted across East Africa using models developed for montane

forests in East Africa?

2) What are the implications of climate change for species distribution / prevalence?

3) How do these relate to the current protected area network, topography and land-use?

4) What are the implications for management / policy?

Summary

Plants are often overlooked in conservation planning, yet they are the foundation of all terrestrial

ecosystems. Species distribution modelling using herbarium specimen data provides a method for

predicting plant distributions, but data are often insufficient in spatial coverage and number of

records. We have continued to build up our herbarium collections and distribution of Acacia species

and ecoclimatic indicators to apply species distribution models to selected well-collected plants

across the East African landscape. Phases 1 and 2 of the Kenya-Tanzania Borderlands Project

compiled 9,055 records of plants, representing 171 plant indicator taxa. This has now increased

under the current phase to more than 30,000 records of plants, representing 370 plant indicator taxa

from 326 species. Our choice of indicator taxa was particularly focused on species within the Poaceae

as these cover a range of environments and also engender the future collaboration with the group

from Yale University modelling mammal distribution across East Africa. General Additive Models are

used to determine relationships between a selection of these plants and environmental variables.

Outputs include a probability surface of habitat suitability for each taxon. Analysing these predictions

in the context of the current protected area network shows that some of the richest areas of plant

biodiversity lie outside of protected areas. Therefore, many of Africa’s most famous National Parks

may not be preserving an important component of ecosystem diversity. We have assessed climate

change effects by running the General Additive Models with future climates derived from a regional

climate model and find that the limitations of protected areas in conserving biodiversity are

amplified. Areas with suitable climate for high-elevation, moisture-dependent taxa are predicted to

shrink towards mountain peaks, while areas suitable for low-elevation species are predicted to

undergo huge geographic shifts. We discuss the implications of our findings for plant and animal

ecological interactions and the need for a landscape- and regional-scale approach to conserving

biodiversity and managing natural resources. We discuss future development of the work and the

ways in which ground-truthing will be used to verify model predictions and provide more plant

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distribution data. Distribution Models (Box 1) are one of a range of tools used to predict suitable

conditions for a species (or infraspecific taxon) across a landscape based on limited information. East

African climatic and environmental conditions at locations of known species occurrence are used to

build up a climatic “niche” or “envelope” for each species that can be used to infer the suitability of

other geographic locations in a broader region. The application of DMs is increasingly far-reaching,

and includes use for managing resources, predicting the spread of invasive species/pathogens,

predicting the impacts of climate change, and planning the design of protected area networks. DMs

are particularly useful where logistical difficulties such as poor infrastructure or large geographic

scale preclude full inventories of areas. DMs also allow for the exploration of ‘what if’ scenarios, in

this case exploring the impact that climate change will have on current species distribution.

Potential plant indicators of the major ecosystems also exist for a large part of the East African

region. Pratt & Gwynne (1977) delineated six eco-climatic zones in Kenya, Tanzania and Uganda

(Appendix 1) based on moisture indices derived from monthly rainfall and evaporation. The eco-

climatic zones are well correlated with vegetation and land-use classes, and each eco-climatic zone is

represented by a number of characteristic species (Appendix 2). We therefore assume that by

modelling the distribution of these characteristic species, we can make a reasonable representation

of the biodiversity of the region and thus potentially provide a major contribution to reserve network

design.

Box 1. Distribution Models

Hundreds of kilometres separate some of the major

roads in East Africa. The logistical difficulties in

surveying remote areas can hinder ecological

surveys, with the result that there is little

information on plant and animal community

composition.

Distribution modelling can be used to predict the

occurrence of species or infraspecific taxa based on

their known climatic preferences in other areas. The

result is a probability surface indicating areas that

are most likely to contain suitable climatic

conditions for a given taxon.

NOTE: We prefer the term “Distribution Model” over

the more frequently used “Species Distribution

Model” to avoid taxonomic restriction.

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Work achieved and in development within Phase 3

Phase 3 work has focused on a number of areas with direct contributions from Mr Simon

Kang'ethe, Dr Aida Cuni Sanchez, Dr Phil Platts, Dr Marion Pfeifer and Dr Andrew Marshall. Work

within Phase 3 will be discussed under the following headings.

1) Plant data acquisition and rescue from Herbaria:

The Herbarium personnel are very supportive of the project, in particular Simon Kang'ethe at the

National Museums of Kenya and Maria Vorontsova, the Poaceae curator at Kew Gardens.

Although they have ongoing digitizing efforts, some extra assistance and supervision will enhance

this greatly and facilitate the updating of data on indicator species. Data acquisition, capture and

digitisation will continue to focus on our initial choice of indicator species together with additional

species that are representative of the broader East African ecosystems, a combination of indicator

taxa that will maintain the initial spatial focus on the Borderlands region while placing it in a larger

geographic and ecological context. This approach will provide opportunities to maximize synergies

with the vertebrate modelling and livelihood and land-use change aspects of the project. The

additional indicator species will include key food and habitat trees of birds and ungulates, ruderal

species indicative of particular land-use options, and orchids and other plants with restricted

ranges. We are also keen to test the broader applicability of the methods and models developed

for the Borderlands area on different ecosystems such as the Albertine Rift and the dry

ecosystems of north-eastern Kenya: both areas where predicted climate change impacts will be

assessed. Other institutions such as the University of Nairobi, DRSRS, Forest Department of

Uganda, Institute of Tropical Forest Conservation, and the Botanical Gardens of Makerere

University will also be engaged in this initiative. A total of 370 indicator taxa (species, subspecies

and varieties) from 326 species were selected to represent a cross-section of eco-climatic zones,

habitat specialisation, abundance and taxonomy (Appendix 2). Habitat specialists are included as

indicators of biodiversity, while generalists are included to represent the dominant habitat types.

For linkage with concurrent vertebrate modelling, the indicators also include taxa that are known

to be key dietary species for primates and birds. Plant collection data for the indicator taxa have

been collated from five herbaria: East African Herbarium, National Museums of Kenya (Nairobi);

Royal Botanic Gardens, Kew (UK); Missouri Botanical Garden (USA); National Herbarium of

Tanzania (Arusha); and University of Dar es Salaam (Tanzania). All captured data are in the process

of being entered into and standardised in the Missouri Botanical Garden’s TROPICOS database

(www.tropicos.org). After collation, collection data will be processed to remove errors and check

for suitability for modelling. Point collection data will be converted to raster grid cells with a

resolution of 1 arc-minute (1.85 km). Taxa with records in fewer than ten 1-arc-minute grid cells

were excluded from analyses due to the requirements of cross-validation procedures necessary

for model calibration. DM results will be modelled during 2012 under the support of additional

project funding – see section 9.

2) Collection of environmental data:

In addition to data on species distribution, we have continued to collate environmental data for

the region that will be used for developing the modelling-based assessment to be conducted in

2012. These data are stored at the University of York and are freely available to research

collaborators.

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Poverty Poverty has been defined by different measures. The human poverty index developed by

the United Nations measures deprivation in three dimensions: (1) the probability at birth of not

surviving until the age of 40 (times 100), (2) adult illiteracy rate, and (3) the un-weighted average

of population without sustainable access to an improved water source and percentage of children

underweight for their age (or in case of a second poverty index: percentage of population below

the income poverty line – 50% of median household disposable income). The Center for

International Earth Science Information Network (CIESIN: http://www.ciesin.columbia.edu/) at

Columbia University has been developing an online data resource on the distribution of poverty

around the world. Data available for Tanzania and Kenya have been downloaded from this Global

Poverty Mapping Project (http://sedac.ciesin.columbia.edu/povmap/).

PID Extent Data Type

POV_01 Global

Global_PrevalenceofChildMalnutrition: Global

subnational rates of child underweight status

database

Shapefile, Grid, Table

POV_02 Global GlobalInfantMortalityRates: Calculated from

births and deaths grids Shapefile, Grid, Table

POV_03 Kenya -

Kajiado Small-Area Estimates: Results from case studies Shapefile

Conservation The World Database on Protected Areas (http://www.wdpa.org/; by IUCN UNEP

WCMC, WCPA) is a global database that provides information on the location of protected areas,

including national parks, forest reserves, nature reserves, and many other categories. Also

included is information on the IUCN protection status. The map of biodiversity hotspots

(http://www.biodiversityhotspots.org/xp/hotspots/Documents/cihotspotmap.pdf) shows the names

and locations of 34 designated biodiversity hotspots in five broadly defined continental areas. The

background forest cover loss map was derived from MODIS land cover product MCD12Q1 by

processing hdf-rasters for 2001 and 2009, extracting and converting IGBP layers from these

rasters, extracting information on distribution of evergreen forest cover (LC02) in each year,

computing the forest change between 2001 and 2009 outside protected areas and computing the

relative forest change outside protected areas for the different countries. The Participatory Forest

Management layer was provided by Saiful Islam Khan.

PID Extent Data Type Source

CO_01 Global World Database On Protected Areas, version 2010 Shapefiles -

CO_02 Global Biodiversity Hotspots, revisited Shapefiles -

CO_03 East

Africa

National-level Background Forest Loss

(Unprotected areas) from MCD12Q1 2001 to 2009

using Evergreen Forest Cover Layer of IGBP

classification scheme

Raster of

percentage forest

change outside

protected areas

Marion

CO_04 Tanzani

a Participatory Forest Management Layer Shapefile Saiful Islam Khan

Earth Observation Derived Products Earth observation products available for ecological

applications have been described in detail by Pfeifer et al. (2011)1. The forest cover map was

provided by Phil Platts, who describes its creation: ‘Indigenous broadleaved forests in the Taita

1 Pfeifer, M. et al., 2011. Terrestrial ecosystems from space: a review of earth observation products for macroecology

applications. Global Ecology and Biogeography

5

bloc were identified from SPOT multi-spectral satellite images and subsequently ground-truthed

by P.K.E.P. (Clark & Pellikka). Estimates of forest cover in the Tanzanian blocs were based on those

updated with later imagery from 2000 onwards by the Remote Sensing and GIS Laboratory,

Sokoine University of Agriculture. We extracted all forests classified as sub-montane, montane or

upper-montane, and additionally considered any lowland forest contiguous with a submontane

patch, thus mitigating the elevational limits of montane vegetation classification (Pócs, 1976).

Forest extent was updated according to the baseline area report of Mbilinyi et al. (2006), a cover

and change map produced by Conservation International (2008), Google Earth images and local

knowledge (e.g. J. Fjeldså, University of Copenhagen, pers. comm.; see also

http://celp.org.uk/projects/tzforeco/). Forests in Udzungwa were updated according to Marshall

et al. (2010). The rainfall products are described in manuals or readme files attached with the

relevant products in their directories.

PID Dir Name Spatial Extent

Spatial

Resolution

Temporal

Resolution

EO_001 Fire

MODIS Burnt Area

Quarterly Burning

Probability

East Africa 500 m

Quarterly (derived

from 2001 and

2009)

Marion,

Phil

EO_002 Fire MODIS Burned Area

Product (MCD45)

East / Central

Africa 500 m 2001 to 2009

Marion

EO_003 Fire Minni_Africa_00to10 Africa 1 km 2001 to October

2010

Minni

EO_004 Fire AVHRR Global Seasonal

Fire Probability Global 8 km 1982 to 2000

Jose

EO_005 Fire MODIS Minni EA Grids F

C50 Africa 1 km

2001 to October

2010

Marion

EO_006 Land-

Cover

Globcover v22 Global

2006 Global 300 m 2006

-

EO_007

Land-

cover

Globcover v23 Global

2009 Global 300 m 2009

-

EO_008

Land-

cover

MODIS MCD12Q1 2001

to 2009 East Africa 500 m 2001 to 2009

Marion

EO_009

Land-

cover Africa v5 Grid GLC2000 Africa 1 km 2000

-

EO_010

Land-

cover

Forest Cover Eastern

Arc Mountains

Eastern Arc

Mountains NA -

Phil

EO_011

Topo-

graph

y DEM Aster EA Focal Sites 30 m 2011

Marion

EO_012

Topo-

graph

y SRTM 90m East Africa 90 m 2011

Marion

EO_013

Rain-

fall

Rainfall TRMM 1997 to

2006 Mean Annual East Africa 1 km

Mean Annual

rainfall

Phil

EO_014

Rain-

fall

Rainfall RFE 2001 to

2009 Africa 8 km 2001 to 2009

Marion

Africa Cover Data The Africover initiative of the Food and Agriculture Organization of the United

Nations (FAO) was established as a digital geo-referenced database. Africover datasets were

downloaded from http://www.africover.org/system/africover_data.php. The focus was on layers

of towns and roads. Further data could be downloaded.

PID Country Data Type

Africover Burundi Roads, Towns Shapefiles

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Africover Congo Roads, Towns Shapefiles

Africover Kenya Roads, Towns, Landcover, Landform Shapefiles

Africover Rwanda Roads, Towns Shapefiles

Africover Somalia Towns Shapefiles

Africover Tanzania Roads, Towns, Landcover, Landform Shapefiles

Africover Uganda Towns Shapefiles

Boundaries Country administration levels have been developed within the Bio-geomancer project

(http://www.gadm.org/country). Only country borders covered by Eco-Dynamic-Africa’s study

area have been acquired (http://www-users.york.ac.uk/~mp643/ecodynamic.htm). The algorithm

for the delineation of the Eastern Arc Mountain blocks was developed by Phil Platts and is

described in Platts et al. (2011)2.

PID Extent Data Type

BB_01 East Africa

Countries covered by study area: shapefiles showing

whole country borders and raster (500 m resolution)

containing cell based information on country within

Eco-Dynamic-Africa’s study area.

Shapefiles, Raster

BB_02 Eastern Arc Eastern Arc Mountains delineation created by Phil

Platts Shapefile

BB_03 East Africa

WDPA layer derived shapefiles of boundaries of

national parks, nature reserves, forest reserves and

game parks and their buffer zones (B01 – 0 to 1 km,

B12 – 1 to 2 km from park boundary, etc.) in East

Africa

Shapefiles

BB_04 Various Africa - boundaries Shapefile

Ground-truthing datasets Field data have been collected in January 2010 (Marion: hemispherical

images, Sunscan data and land cover for South Kenya and North Tanzania; hemispherical images

provided by Petri Pellikka for Kasigau), July 2010 (Marion: hemispherical images and land cover for

Tanzania), January 2007 (hemispherical images for Taita Hills provided by Alemu Gonsamo), and

June 2011. Field data collected by Seki in August 2011 (‘Iringa woodland plots’) have been

processed and analysed.

PID Country Source

Data

GT_01 Tanzania Marion Plots, Land cover, January and July 2010

GT_02 Tanzania, Kenya Marion, Petri, Alemu Plots, Environmental Traits, 2007 (Alemu) and 2010

GT_03 Tanzania, Kenya Marion, Petri, Alemu Plots, LAI, Reflectance, 2007 (Alemu) and 2010

GT_04 Kenya Marion Plots, LAI, Land cover, June2011

GT_05 Kenya Marion, Aida Plots, Olea population data

GT_06 Tanzania Seki, Marion Plots, LAI, Fcover, August 2011

2 Platts et al., 2011. Delimiting tropical mountain ecoregions for conservation. Environmental Conservation, 38, 312-

324.

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External datasets Species data have been collated and processed for the Eastern Arc Mountains

by Antje Ahrends and Phil Platts. These datasets have partly been produced in the context of the

Valuing the Arc (VtA) dataset, led by Andrew Balmford (University of Cambridge).

PID Country Source

Data

ED_01 Tanzania Phil, Antje Plant species data for the KITE project (University of York)

ED_02 Eastern Arc Mountains Simon Willcock Biome-specific carbon estimates derived from VTA dataset

Climate Climate data were compiled by extracting data from BIOCLIM

(http://www.worldclim.org/bioclim), from the CRES webpage of the Fenner School of

Environment and Society at Australian National University and from the Kenya Meteorological

Department. Further climate data are the rainfall grids derived from earth observation products.

PID Directory Name

Spatial

Extent

Spatial

Resolution

Temporal

Resolution

CL_01 BIOCLIM

dataset

Bio1 (Mean Annual Temperature),

Bio9 (Mean temperature of driest

quarter), Bio12 (Mean Annual

Rainfall), Bio13 (Precipitation of

wettest month), Bio14 (Precipitation

of driest month), Bio15 (Precipitation

Seasonality), Bio16 (Precipitation of

wettest quarter), Bio17 (Precipitation

of driest quarter)

Global 1 km

Mean values of

data derived

between ~ 1960

to 2000

CL_02 CRES dataset Climate Surfaces for Precipitation,

Tmax, Tmin and Tmean

Sub-

Saharan

Africa

3 min

Compiling data

between 1920

and 1980

CL_03 Rainfall

Monthly rainfall data for Marsabit

(Lat: 2.3284, Long: 37.9899) and

Wajir (Lat: 1.73331, Long: 40.0918)

Marsabit,

Wajir

(Kenya)

Station

data 1920 to 2010

Plot coordinates Plot coordinates of sites sampled and going to be sampled within ECO-DYNAMIC-

AFRICA, ICIPE CHIESA and WWF Tanzania REDD+ projects are stored as shape files, with or without

attribute data.

PID Country Source

Data

PL_01 Tanzania, Kenya Marion, Alemu, Petri

Alemu’s hemispherical images sites (January 2007), Petri’s

hemispherical images sites (January 2010), Marion’s

hemispherical images, land cover and Sunscan readings sites

(January and July 2010),

PL_03 WWF TZ REDD+ Seki Planned sites, Sites sampled in August 2011

Population Densities The Afripop project (http://www.afripop.org/), initiated in 2009, has been

developing population distribution maps (number of people in 100 m x 100 m cells) for Africa. Fine

resolution satellite imagery-derived settlement maps are combined with land cover maps to

reallocate contemporary census-based spatial population count data. Assessments have shown

that the resultant maps are more accurate than existing population map products, as well as the

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simple gridding of census data. Moreover, the 100 m spatial resolution represents a finer mapping

detail than has ever before been produced at national extents. Please contact Dr. Andy Tatem

(University of Florida, USA / Centre for Geographic Medicine, Kenya / Fogarty International Centre,

National Institutes of Health, USA ) for further information and on-going developments. Data are

provided as grids in Geographic, WGS84 coordinate reference system. Note that data have to be

imported into ArcGIS as float data.

PID Country Source

Data

POP_01 East Africa Afripop

East African country files: Kenya, Tanzania, Rwanda, Uganda, Burundi,

Democratic Republic of Congo, Somalia, Sudan, Malawi, Mozambique,

Zambia, Ethiopia

POP_02 East Africa Marion

500 m cells grids and 1 km cells grids with number of people (derived from

100m resolution grids by aggregation); derived for the study area of Eco-

Dynamic-Africa

Topography Digital Elevation Models provide information on terrain topography important in

many earth system processes and ecological applications, especially for predictive species

distribution models. The Shuttle Radar Topographic Mission (SRTM) provides elevation data from

raw radar echoes collected between 60° north and 54° south in 2000 (discussed in Pfeifer et al.,

2011). The ASTER GLOBAL DEM product is produced fully automated without ground-control

points using ephemeris and altitude data derived from positional measurements of the TERRA

platform instead, reaching vertical accuracies of < 25 m in many cases. Both products have been

downloaded from the WWW.

PID Location Source

Data

T_01 Study Sites Marion ASTER DEM v2: tiles with 30 m spatial resolution

T_02 East Africa SRTM 500 m spatial resolution, derived from SRTM15s

3) Collection of new botanical data and ground-truthing model predictions from Phase 2:

The high Acacia diversity areas in north-eastern Kenya, south-eastern Tanzania and central-west

Tanzania have not previously been highlighted as being of major importance for conservation of

the genus, largely due to lack of information. Botanists working in East Africa agree that these

areas are likely to be important for Acacia species. Two main foci of ground-truthing were

undertaken in 2011 with another phase planned for 2012. Andrew Marshall collected ten Acacia

species from the Iringa region (Iringa District) and Morogoro region (Mahenge district) of

Tanzania, from elevations ranging from 261-817 m. Provisional identifications were made for all

records at the National Herbarium of Tanzania.

Aida Cuni-Sanchez, Marion Pfeifer (York), Stephen Rucina Mathia (NMK) and Rob Marchant

undertook a 4-week botanical collection visit focusing on Acacia and indicator species in north

eastern Kenya and southern Ethiopia. Some 250 species were collected and identified and 40 plots

established within Marsabit National Park. This is a vital area of the research project as, although

the model records exceptionally high species diversity there is very little / no collection from this

relatively remote area. We will continue to develop this ground-truthing during 2012.

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4) Distribution Model development:

Empirical modelling methods used in this study were developed for the forests of the Eastern Arc

Mountains. Because the data consisted only of presence records for each species, we began by

generating background data (pseudo-absences) to constrain the models: a lack of absence data

had previously led to over-fitting of similar point-based models. For each species, pseudo-

absences were weighted 5:1 against the presences and distributed across the cells where other

Acacia species had already been collected. For each taxon, this procedure was repeated ten times

in order to assess the variability between runs.

Determine shape of species-environment relationship

Test for correlation

Calibrate model x 20

Elevation model

Topographicpredictors

Validate site locations

Collection summary

Speciespresence/absence

Climaticpredictors

Climate model

Mean habitat suitability

Guide fieldwork / ground-truth

Refine model

Iden

tify

poor

ly re

pres

ente

d re

spon

se c

urve

s

Cross-validation

Figure 1. The process of Distribution Modelling (adapted from Platts et al., 2008).

Distribution Models: (a) Taxa

The DM methods developed for the Eastern Arc Mountains were successful in producing models

for the wider East Africa region for lowland forest taxa. Models of predicted current distribution

will be produced for all indicator taxa when the upload has been finalised into TROPICOS and all

checks on the distribution finalised.

Distribution Models: (b) Biodiversity

Estimated regional Acacia biodiversity distribution is presented using the mean predicted climate

suitability of the 21 Acacia infraspecies that produced robust distribution models (AUCcv and

sensitivity > 0.7). North-eastern Kenya, south-eastern Tanzania and central-western Tanzania were

all revealed as potential hotspots for Acacia biodiversity. Of considerable management

importance is the fact that these biodiversity-rich areas (deep orange areas in Fig. 3a) were

outside of the current protected area network (Fig. 3b). Knowledge gaps are, however, highlighted

by the incomplete coverage of the herbarium record (Fig. 3a), and hence these findings, although

compelling, should be treated as preliminary. Northern and eastern Kenya, southern and north-

western Tanzania and most of Burundi have the fewest Acacia records, again a rationale for the

choice of ground-truthing areas as specified in section 3.

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(a) (b)

Figure 2. Predicted habitat suitability for (a) Acacia abyssinica subsp. calophylla and (b) Acacia

turnbulliana. Scalebar indicates mean climatic suitability from ten repeated model runs. Inset

shows known distribution of A. abyssinica subsp. calophylla (black tree icons) and A. turnbulliana

(red), adapted from Dharani (2006).

5) Climate Change products:

Climate predictions for 2020, 2055 and 2090 were derived from forecasts made by the Global

Circulation Model ECHAM5 that were subsequently downscaled via the Regional Climate Model

REMO (Potsdam Institute for Climate Impacts Research, Germany). For each climate model we

used two scenarios from the Fourth Assessment Report of the International Panel for Climate

Change (IPCC, 2007; IPCC-AR4). Under scenario A1B, global climate is predicted to increase by 1.7-

4.4ºC by 2099, and under scenario B1, by 1.1-2.9ºC. These represent the two most divergent

scenarios up to 2055 in terms of temperature and atmospheric CO2. This project is the first use of

the combination of ECHAM5 and REMO and shows that montane taxa such as Acacia abyssinica

subsp. calophylla and Prunus africana will reduce in range with increased temperature as they

would need to disperse up the elevation gradient to remain in temperatures and AMI equivalent

to those of the present day. As taxa track changing climatic conditions up the elevational gradient,

the available space decreases, competition for resources increases, and eventually some may

become locally extinct. The different ecological requirements of different species result in very

different responses to future climate change. Xerophytic species such as Acacia turnbulliana may

even benefit initially (Fig. 3b), whereas other taxa such as Khaya anthotheca and Bombax

rhodognaphalon var. rhodognaphalon may undergo major range shifts (Fig. 3d). Ecological

requirements for K. anthotheca and B. rhodognaphalon var. rhodognaphalon also highlight a

predicted warming of the Lake Victoria area, as identified by the REMO model forecasts. Within

the near future there are plans to indue an ensemble of climate model predictions made available

to the research program from CORDEX-Africa (http://start.org/cordex-africa/).

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(a)

(b)

(c)

Figure 3. Predicted impacts of climate change on habitat suitability for (a) Acacia abyssinica subsp. calophylla, (b) Acacia turnbulliana, (c) Prunus

africana and (d) Khaya anthotheca. IPCC scenario A1B predicts global temperature increase of 1.7-4.4ºC by 2099, and scenario B1, of 1.1-2.9ºC.

(d)

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6) Conservation applications:

There are powerful relationships between climate change and species diversity in East Africa,

particularly as a function of changes in rainfall amount and distribution. It is evident that the

protected area system is inadequate in conserving biodiversity, and climate change makes it more

urgent to identify what needs to be done to address the gaps. By combining the three strands of

the project, with plants providing the intersection between the vertebrates and the land-use

elements, we will be able to assess the ways in which habitats and associated biodiversity will

change in the future. By investigating this future change in the context of the current protected

area network and associated conservation initiatives that fall outside the national sphere, such as

community-based group conservation schemes, private game reserves and ranches and forest

reserves, we will map the locations in which the biodiversity changes will be most acute. Taking

this information forwards we can identify where the hotspots of potential conflict among

biodiversity conservation, climate change and land-use activity will be. Through participation of

stakeholders, appropriate policy and strategies can be devised to minimize the adverse impacts of

climate change. The results from distribution and climate modelling continue to reveal trends and

predictions of major relevance for the conservation of biodiversity in East Africa, expanding on the

more narrowly regional approach of Phase 1 of the project. The modelling methods and outputs

have progressed significantly since Phase 1 and into the future (section 9). Following the

presentation of these methods and results at the 19th

AETFAT Congress in Antananarivo,

Madagascar, in April 2010, a scientific paper on Acacia distribution in relation to protected areas is

accepted for publication in the journal Plant Ecology and Evolution.

7) Training of researchers:

The above three projects, partly seeded by the LCAOF support, will enable dedicated training on

ecosystem modelling and valuation of ecosystem services to be developed within a number of key

institutions across Eastern Africa. Specifically, within the current phase of the LCAOF project

William Kindeketa attended a training course on plant distribution modelling and statistics (using

the open-source stats package ‘R’) at the University of York.

Through CHIESA and WWF-REDD funding (section 9) we are in the process of developing a training

course to be run at Sokoine University of Agriculture (June 2012). Prof P. Munishi will arrange local

logistics (possibly the seminar room) that would have a generator to ensure constant electricity

supply. The course will be run by Andy Marshall, Phil Platts and Simon Willcock (possibly with

input from Marion Pfeifer) and will focus on four areas: quantitative methods using R, use of PGIS

and GIS for distribution modelling, land use cover change and processing of remotely sensed data.

The course will be focused around carbon assessment with a link to INVEST training that was

provided previously as part of Valuing the Arc. A manual and examples of practical applications

with associated PDF library will be provided to all attendees. Places will be limited to 20 with

participants coming from the WWF-REDD project, NAFORMA, and related projects such as CHIESA

and CCIAM. A list will be developed in the New Year to which we all can add names.

Accommodation will be in Morogoro, and SUA will provide a bus (project pays diesel) for transport

to and from hotel. We will try to have a practical day with field-based skills training.

8) Research outputs:

Research Output papers will be a focus in the coming years as the long and arduous job of

collecting and checking the data will be applied through the modelling techniques outlined in

section 4. The following papers and dissertation have in part or in whole been derived from the

LCAOF project.

13

Andrew R. Marshall, Philip J. Platts, Roy E. Gereau, William Kindeketa, Simon Kang'ethe & Rob

Marchant. The genus Acacia (Fabaceae) in East Africa: distribution, diversity and the

protected area network

Michelle Greve, Anne Mette Lykke, Christopher W. Fagg, Roy E. Gereau, Gwilym P. Lewis, Rob

Marchant, Andrew R. Marshall, Joël Ndayishimiye, Jan Bogaert, Jens-Christian Svenning

(Submitted). Realising the potential of herbarium records for conservation biology

Michelle Greve, Anne Mette Lykke, Christopher W. Fagg, Jan Bogaert, Ib Friis, Rob Marchant, Joël

Ndayishimiye, Brody S. Sandel, Christopher Sandom, Marco Schmidt, Jonathan R. Timberlake,

Jan J. Wieringa, Georg Zizka and Jens-Christian Svenning. (Submitted). Continental-scale

variability in browser diversity is a major driver of diversity patterns in Acacias across Africa

Enara Otaegi Veslin. Acacia taxa distribution modelling with MaxEnt: are Africa’s Acacia trees

sufficiently protected by National Parks?

Aida Cuni Sanchez Rob Marchant, Marion Pfeifer. (Submitted). Assessing regeneration of the

multipurpose African Olive in northern Kenya under climate change: implications for

conservation, hydrology and management.

9) Future development of the research:

During 2012 three new projects have been supported that will ensure the continued development

of the research surrounding impacts of climate change on plant-based ecosystems and the

management and livelihood implications of this.

9i Resilient pasts and sustainable futures? The social-ecological dynamics of East African

landscapes in temporal, spatial and social perspectives. A World Universities Network project

The Millennium Development Goals pledged to halve the number of people suffering from hunger

by 2015. Sub-Saharan Africa, the poorest region globally and arguably the area with highest

dependence on agriculture and ecosystem services (ES) to sustain livelihoods, also is characterized

by economic growth, rising populations, and upward development trajectories – all of which

threaten sustainability pledges. Although there is a growing emphasis on the role of ES for

livelihoods and national development, new challenges are rapidly emerging in the form of climate

change, land-use transformation, social regime shifts and population growth operating within an

increasingly complex global policy context. Predictive scenarios can be used to divine future

environmental, social and economic development targets. However, for scenarios to be useful and

socially significant and to fit within existing governance at local, national and international levels

there is much need for these to develop. Such a development can only be achieved by merging

views from the environmental, socio-political sciences and the NGO and Governmental sectors.

Members of the newly funded World Universities Network, including Petra Tschakert (Penn State

University), Ram Pandit (University of Western Australia), Dr Susannah Sallu (University of Leeds)

and Steve Cinderby (Stockholm Environment Institute), will meet within a workshop hosted by

colleagues at SEI-Africa and University of Dar es Salaam to develop a position/methods paper and

form a research grouping focused on the development of hybrid scenarios over the coming years,

both feeding into existing projects and developing opportunities in this research area.

9ii Climate Change Impacts on Ecosystem Services and Food Security in Eastern Africa (CHIESA).

Funded by the Finnish Ministry for Foreign Affairs (MFA), the € 7 Million project will run from

October 2011-Sept 2015. University of York are coordinating the Biodiversity Work package.

14

The objective of the CHIESA project is to fill critical gaps in knowledge related to climate and land

change impacts on ecosystem services and develop adaptation strategies towards it by building

the capacity of local research and administrative organisations by research, training and

dissemination. The project will build the capacity of research communities, extension officers and

decision-makers in environmental research in agriculture, hydrology, ecology and geoinformatics.

This will strengthen climate and land-use change monitoring and prediction systems and

adaptation strategies. There is a general lack of information on the impacts of climate change in

Africa on sensitive and unique ecosystems and on their services, especially with regard to

agriculture and food security. This knowledge gap reflects an overall deficit of on-the-ground

capacity in Africa to address climate change research and development. The geographical

coverage of CHIESA is Tanzania, Ethiopia and Kenya in eastern Africa, and especially project target

sites in the Jimma area, Pangani river basin and the Taita Hills – all situated in the Eastern

Afromontane Biodiversity Hotspot (EABH). The high human population density results in resource

competition between agriculture, forest and biodiversity conservation, water provision and

carbon sequestration. Due to climate and land use changes, exacerbated through high population

increase, EABH is at risk of extreme climatic changes, while the goods and services its ecosystems

provide are under significant threat.

The main policy beneficiaries of CHIESA are the government institutions that will be better

equipped for policy formulation through receiving early warnings for changes in ecosystem

services. We are working closely with national research organisations that are stakeholder

partners in the project in the three target countries of Kenya, Tanzania and Ethiopia and with

associated Agricultural ministries, with the Agricultural minister from each country on the project

steering committee. Importantly, the large focus of CHIESA is only training, and we have already

appointed 4 PhD researchers who will all contribute to the development of ecosystem modelling

and assessing impacts of climate change on ecosystems in East Africa. Specifically, these are

Mr Julius Dere (Jimma University, Ethiopia): Modelling Forest Biodiversity and Carbon Storage in

the Jimma Highlands of East Africa

Mr Peter Omeny (Kenya Meteorological Office, ICPAC, Nairobi): Modelling Regional Climate

Change in East African Mountains

Mr Mathew Mpunda (ICRAF, Tanzania): Modelling Forest Ecosystem Services in the East African

mountains

Mr Dickens Odeny (NMK, Nairobi): Modelling Forest Biodiversity and Carbon Storage in the East

African mountains

9iii Enhancing Tanzanian capacity to deliver short- and long-term data on Forest Carbon Stocks.

Coordinated by WWF-Tanzania, this 2 Million US $ project is running from Jan 2011-Jan 2014.

The University of York are part of an international team led by WWF-Tanzania to aid in the

creation of a carbon trading system for Tanzania which could help reduce deforestation and

mitigate the impacts of global climate change. Under a World Wildlife Fund Tanzania-coordinated

3-year project, researchers from Sokoine University of Agriculture (SUA) and the Environment

Department of the University of York are part of an international team carrying out vital

groundwork for the new system to generate payment for carbon storage. The team’s work

involves establishing methods for assessing, reporting and verifying the amount of carbon stored

in ecosystems and potentially lost through forest degradation and deforestation. The results will

be fed into the UN Reducing Emissions from Deforestation and Degradation (REDD) program,

which involves developing countries being compensated by developed ones for reducing

15

emissions from deforestation, enhancing the global carbon store. At the national level, the project

will contribute information coordinated by the Vice-President’s Office.

10) Acacia-specific database for East Africa

Plans have been developed for a new East Africa-wide database of Acacia records to be

established in 2012. The database will be managed by Andy Marshall, with the help of Liz Baker

(data collation and mapping) and Roy Gereau (taxonomic verification). This will serve not only as a

storehouse for Acacia data collected through the three LCAOF phases to date, but will also

incorporate data to be collected beginning in 2012.

Research Priorities

There are several areas for further research during Phase 4:

1) Expanding the indicator taxa to allow modelling of the broader East African landscape.

2) Model verification. Ground-truthing of the model outputs and generation of new data – for

example, from the south-eastern Tanzania and northeastern Kenya hotspot – which began during

2010 and will further continue. There will also be targeted assessments extending to northern

Kenya, southern Ethiopia and through Tanzania, the latter one via an extensive network of 3500

plots established as part of the REDDiness for the REDD program.

3) Refining future predictions. Climate change models will continue to be developed using

additional scenarios and different regional models to expand on the predictions made. These new

models will include access to the CORDEX-Africa ensemble of models and research by a Kenya

PhD.

4) Incorporation of humans and animals. With the development of a database of vertebrate

records across East Africa underway, we will soon have the opportunity to analyse plant-animal

interactions. Output from these interactions among plants, humans and animals will be used to

identify “hotspots of conflict” for biodiversity conservation.

5) Protected area updates. Improved geographic information will be sought on the

conservation initiatives that fall outside the national management sphere, such as community-

based group conservation schemes, private game reserves and ranches and forest reserves.

6) Extrapolation to the African continent. We are collaborating with Michelle Greve from

Aarhus University (Denmark), who is working on a project employing similar DM methods to

investigate Acacia distribution across the whole of Sub-Saharan Africa.

Policy

Following presentation of the above findings to the 2010 Kenya Biodiversity and Climate Change

Conference in Nairobi, it is apparent that there are several areas where the research can input

directly to the management of East Africa’s National Parks. A summary of the findings has already

been presented to conference participants, who included policy-makers and conservation

managers from across Kenya. The African Conservation Centre also continues to work closely with

the Kenya Wildlife Services (KWS) and other stakeholders including community conservation

groups to help implement the KWS long-term strategy for improving biodiversity conservation in

the region. Our conclusion, therefore, remains the same as in Phase 2: that new conservation

efforts do not necessarily have to follow the traditional format of protected areas and should work

closely with local people, but ultimately should be based on solid science that has to be driven by

verifiable and consistently collected data.

16

Appendices

Appendix 1. Eco-climatic zones of East African rangelands (Pratt & Gwynne, 1977).

Zone Climate Native Vegetation

I Afro-alpine

(climate governed by altitude, not

moisture)

Afro-alpine moorland and grassland, or

barren land, at high altitude above the

forest line.

II Tropical

(humid to dry sub-humid; moisture index

not less than -10)

Forests and derived grasslands and

bushlands, with or without natural glades.

III Dry sub-humid to semi-arid

(moisture index -10 to -30)

Land not of forest potential, carrying a

variable vegetation cover (moist

woodland, bushland or “savanna”), the

trees mostly Brachystegia or Combretum

(and their associates) and the larger

shrubs mostly evergreen.

IV Semi-arid


Dry forms of woodland and “savanna”,

often an Acacia-Themeda association,

including dry Brachystegia woodland and

equivalent deciduous or semi-evergreen

bushland.

V Arid


Mostly rangeland dominated by

Commiphora, Acacia and allied genera,

mostly of shrubby habit. Perennial grasses

can dominate, but succumb readily to

harsh management.

VI Very arid


Dwarf shrub grassland or shrub grassland.

Perennial grasses are localised within a

predominately annual grassland.

17

Appendix 2. Indicator plant taxa and database/model status at the completion of Phase 2. “Phase 3” indicates Phase 3 in process of being uploaded

to TROPICOS and then passed back for SDM and ground-truthing.

Models

Family Taxon name with authors Database DM Future

Acanthaceae Duosperma longicalyx (Deflers) Vollesen subsp. longicalyx phase 3 phase 3 phase 3

Amaranthaceae Cyathea polycephala Baker complete tested tested

Anacardiaceae Lannea alata (Engl.) Engl. phase 3 phase 3 phase 3

Anacardiaceae Sorindeia madagascariensis Thouars ex DC. complete phase 3 phase 3

Apocynaceae Saba comorensis (Bojer ex A.DC.) Pichon phase 3 phase 3 phase 3

Apocynaceae Tabernaemontana stapfiana Britten phase 3 phase 3 phase 3

Aquifoliaceae Ilex mitis (L.) Radlk. var. mitis phase 3 phase 3 phase 3

Araliaceae Cussonia holstii Harms ex Engl. complete tested tested

Araliaceae Polyscias fulva (Hiern) Harms phase 3 phase 3 phase 3

Araliaceae Polyscias kikuyuensis Summerh. complete phase 3 phase 3

Arecaceae Phoenix reclinata Jacq. partial phase 3 phase 3

Asteraceae Athroisma hastifolium Mattf. complete tested tested

Asteraceae Emilia somalensis (S. Moore) C. Jeffrey partial phase 3 phase 3

Asteraceae Senecio cyaneus O. Hoffm. phase 3 phase 3 phase 3

Asteraceae Senecio deltoideus Less. phase 3 phase 3 phase 3

Asteraceae Senecio hadiensis Forssk. phase 3 phase 3 phase 3

Asteraceae Senecio hochstetteri Sch. Bip. ex A. Rich. phase 3 phase 3 phase 3

Asteraceae Senecio schweinfurthii O. Hoffm. phase 3 phase 3 phase 3

Asteraceae Stoebe kilimandscharica O. Hoffm. phase 3 phase 3 phase 3

Asteraceae Tarchonanthus camphoratus L. phase 3 phase 3 phase 3

Balanitaceae Balanites aegyptiacus (L.) Delile phase 3 phase 3 phase 3

Balanitaceae Balanites glaber Mildbr. & Schltr. complete tested tested

Berberidaceae Berberis holstii Engl. complete tested tested

Bignoniaceae Kigelia africana (Lam.) Benth phase 3 phase 3 phase 3

Appendix 2. Continued.

Models


Bombacaceae Adansonia digitata L. phase 3 phase 3 phase 3

18

Bombacaceae Bombax rhodognaphalon K. Schum. var. rhodognaphalon complete tested tested

Burseraceae Commiphora africana (A. Rich.) Engl. phase 3 phase 3 phase 3

Burseraceae Commiphora africana (A. Rich.) Engl. var. africana phase 3 phase 3 phase 3

Burseraceae Commiphora africana (A. Rich.) Engl. var. glaucidula (Engl.) J.B. Gillett phase 3 phase 3 phase 3

Burseraceae Commiphora africana (A. Rich.) Engl. var. oblongifoliolata (Engl.) J.B. Gillett phase 3 phase 3 phase 3

Burseraceae Commiphora africana (A. Rich.) Engl. var. rubriflora (Engl.) Wild phase 3 phase 3 phase 3

Burseraceae Commiphora africana (A. Rich.) Engl. var. tubuk (Sprague) J.B. Gillett phase 3 phase 3 phase 3

Burseraceae Commiphora campestris Engl. subsp. magadiensis J.B. Gillett complete phase 3 phase 3

Burseraceae Commiphora habessinica (O. Berg) Engl. subsp. habessinica phase 3 phase 3 phase 3

Campanulaceae Lobelia giberroa Hemsl. phase 3 phase 3 phase 3

Chenopodiaceae Suaeda monoica Forssk. ex J.F. Gmel. complete tested tested

Chrysobalanaceae Parinari excelsa Sabine partial phase 3 phase 3

Combretaceae Combretum molle R. Br. ex G. Don complete tested tested

Combretaceae Terminalia kilimandscharica Engl. phase 3 phase 3 phase 3

Cornaceae Cornus volkensii Harms phase 3 phase 3 phase 3

Cupressaceae Juniperus procera Hochst. ex Endl. phase 3 phase 3 phase 3

Cyperaceae Cyperus ajax C.B. Clarke phase 3 phase 3 phase 3

Cyperaceae Cyperus ferrugineoviridis (C.B. Clarke) Kük. phase 3 phase 3 phase 3

Cyperaceae Cyperus grandbulbosus C.B. Clarke complete phase 3 phase 3

Dipsacaceae Pterocephalus frutescens Hochst. ex A. Rich. complete tested tested

Dracaenaceae Dracaena afromontana Mildbr. phase 3 phase 3 phase 3

Ericaceae Agarista salicifolia (Comm. ex Lam.) G. Don phase 3 phase 3 phase 3

Ericaceae Erica arborea L. phase 3 phase 3 phase 3


Models


Ericaceae Erica filago (Alm & T.C.E. Fr.) Beentje phase 3 phase 3 phase 3

Ericaceae Erica mannii (Hook. f.) Beentje subsp. usambarensis (Alm & T.C.E. Fr.) Beentje complete phase 3 phase 3

Euphorbiaceae Alchornea hirtella Benth. phase 3 phase 3 phase 3

Euphorbiaceae Croton macrostachyus Hochst. ex Delile phase 3 phase 3 phase 3

Euphorbiaceae Drypetes gerrardii Hutch. var. gerrardii phase 3 phase 3 phase 3

Euphorbiaceae Drypetes gerrardii Hutch. var. grandifolia Radcl.-Sm. phase 3 phase 3 phase 3

19

Euphorbiaceae Macaranga capensis (Baill.) Benth. ex Sim phase 3 phase 3 phase 3

Euphorbiaceae Macaranga capensis (Baill.) Benth. ex Sim var. capensis phase 3 phase 3 phase 3

Euphorbiaceae Macaranga capensis (Baill.) Benth. ex Sim var. kilimandscharica (Pax) Friis & M.G. Gilbert phase 3 phase 3 phase 3

Euphorbiaceae Ricinus communis L. phase 3 phase 3 phase 3

Euphorbiaceae Shirakiopsis elliptica (Hochst.) Esser phase 3 phase 3 phase 3

Fabaceae Acacia abyssinica Hochst. ex Benth. subsp. calophylla Brenan complete complete tested

Fabaceae Acacia adenocalyx Brenan & Exell complete complete tested

Fabaceae Acacia amythethophylla Steud. ex A. Rich. complete complete tested

Fabaceae Acacia ancistroclada Brenan complete complete tested

Fabaceae Acacia ataxacantha DC. complete complete tested

Fabaceae Acacia brevispica Harms subsp. brevispica complete complete tested

Fabaceae Acacia burttii Baker f. complete complete tested

Fabaceae Acacia bussei Harms ex Sjöstedt complete complete tested

Fabaceae Acacia dolichocephala Harms complete complete tested

Fabaceae Acacia drepanolobium Harms ex Sjöstedt complete complete tested

Fabaceae Acacia elatior Brenan complete complete tested

Fabaceae Acacia elatior Brenan subsp. elatior complete complete tested

Fabaceae Acacia elatior Brenan subsp. turkanae Brenan complete complete tested


Models


Fabaceae Acacia etbaica Schweinf. subsp. platycarpa Brenan complete complete tested

Fabaceae Acacia fischeri Harms complete complete tested

Fabaceae Acacia gerrardii Benth. complete complete tested

Fabaceae Acacia gerrardii Benth. var. calvescens Brenan complete complete tested

Fabaceae Acacia gerrardii Benth. var. gerrardii complete complete tested

Fabaceae Acacia gerrardii Benth. var. latisiliqua Brenan complete complete tested

Fabaceae Acacia goetzei Harms complete complete tested

Fabaceae Acacia goetzei Harms subsp. goetzei complete complete tested

Fabaceae Acacia goetzei Harms subsp. microphylla Brenan complete complete tested

Fabaceae Acacia hamulosa Benth. complete complete tested

Fabaceae Acacia hockii De Wild. complete complete tested

20

Fabaceae Acacia horrida (L.) Willd. subsp. benadirensis (Chiov.) Hillc. & Brenan complete complete tested

Fabaceae Acacia kirkii Oliv. subsp. kirkii complete complete tested

Fabaceae Acacia laeta R. Br. ex Benth. complete complete tested

Fabaceae Acacia lahai Steud. ex Hochst. & Benth. complete complete tested

Fabaceae Acacia mbuluensis Brenan complete complete tested

Fabaceae Acacia mellifera (Vahl) Benth. complete complete tested

Fabaceae Acacia mellifera (Vahl) Benth. subsp. detinens (Burch.) Brenan complete complete tested

Fabaceae Acacia mellifera (Vahl) Benth. subsp. mellifera complete complete tested

Fabaceae Acacia montigena Brenan & Exell complete complete tested

Fabaceae Acacia nigrescens Oliv. complete complete tested

Fabaceae Acacia nilotica (L.) Willd. ex Delile subsp. leiocarpa Brenan complete complete tested

21


Models


Fabaceae Acacia nilotica (L.) Willd. ex Delile subsp. subalata (Vatke) Brenan complete complete tested

Fabaceae Acacia oerfota Schweinf. complete complete tested

Fabaceae Acacia paolii Chiov. complete complete tested

Fabaceae Acacia paolii Chiov. subsp. paolii complete complete tested

Fabaceae Acacia paolii Chiov. subsp. paucijuga Brenan complete complete tested

Fabaceae Acacia persiciflora Pax complete complete tested

Fabaceae Acacia pilispina Pic. Serm. complete complete tested

Fabaceae Acacia polyacantha Willd. subsp. campylacantha (Hochst. ex A. Rich.) Brenan complete complete tested

Fabaceae Acacia pseudofistula Harms complete complete tested

Fabaceae Acacia reficiens Wawra subsp. misera (Vatke) Brenan complete complete tested

Fabaceae Acacia robusta Burch. subsp. usambarensis (Taub.) Brenan complete complete tested

Fabaceae Acacia rovumae Oliv. complete complete tested

Fabaceae Acacia schweinfurthii Brenan & Exell var. schweinfurthii complete complete tested

Fabaceae Acacia senegal (L.) Willd. var. leiorhachis Brenan complete complete tested

Fabaceae Acacia senegal (L.) Willd. var. senegal complete complete tested

Fabaceae Acacia seyal Delile complete complete tested

Fabaceae Acacia seyal Delile var. fistula (Schweinf.) Oliv. complete complete tested

Fabaceae Acacia seyal Delile var. seyal complete complete tested

Fabaceae Acacia sieberiana DC. var. sieberiana complete complete tested

Fabaceae Acacia sieberiana DC. var. woodii (Burtt Davy) Keay & Brenan complete complete tested

Fabaceae Acacia stuhlmannii Taub. complete complete tested

Fabaceae Acacia tanganyikensis Brenan complete complete tested

22


Models


Fabaceae Acacia thomasii Harms complete complete tested

Fabaceae Acacia tortilis (Forssk.) Hayne subsp. raddiana (Savi) Brenan complete complete tested

Fabaceae Acacia tortilis (Forssk.) Hayne subsp. spirocarpa (Hochst. ex A. Rich.) Brenan complete complete tested

Fabaceae Acacia turnbulliana Brenan complete complete tested

Fabaceae Acacia xanthophloea Benth. complete complete tested

Fabaceae Acacia zanzibarica (S. Moore) Taub. complete complete tested

Fabaceae Acacia zanzibarica (S. Moore) Taub. var. microphylla Brenan complete complete tested

Fabaceae Acacia zanzibarica (S. Moore) Taub. var. zanzibarica complete complete tested

Fabaceae Afzelia quanzensis Welw. phase 3 phase 3 phase 3

Fabaceae Albizia gummifera (J.F. Gmel.) C.A. Sm. var. gummifera partial phase 3 phase 3

Fabaceae Brachystegia microphylla Harms phase 3 phase 3 phase 3

Fabaceae Brachystegia spiciformis Benth. phase 3 phase 3 phase 3

Fabaceae Crotalaria agatiflora Schweinf. subsp. agatiflora complete phase 3 phase 3

Fabaceae Crotalaria agatiflora Schweinf. subsp. engleri (Harms ex Baker f.) Polhill complete tested tested

Fabaceae Crotalaria agatiflora Schweinf. subsp. imperialis (Taub.) Polhill complete phase 3 phase 3

Fabaceae Crotalaria arushae Milne-Redh. ex Polhill complete phase 3 phase 3

Fabaceae Crotalaria axillaris Aiton complete tested tested

Fabaceae Crotalaria balbi Chiov. complete phase 3 phase 3

Fabaceae Crotalaria barkae Schweinf. complete phase 3 phase 3

Fabaceae Crotalaria barkae Schweinf. subsp. barkae complete phase 3 phase 3

Fabaceae Crotalaria barkae Schweinf. subsp. cordisepala Polhill complete phase 3 phase 3

Fabaceae Crotalaria barkae Schweinf. subsp. teitensis (Sacleux) Polhill complete phase 3 phase 3

Fabaceae Crotalaria barkae Schweinf. subsp. zimmermannii (Baker f.) Polhill complete phase 3 phase 3

23


Models


Fabaceae Crotalaria bogdaniana Polhill complete phase 3 phase 3

Fabaceae Crotalaria brevidens Benth. var. intermedia (Kotschy) Polhill complete phase 3 phase 3

Fabaceae Crotalaria burttii Baker f. complete phase 3 phase 3

Fabaceae Crotalaria cephalotes Steud. ex A. Rich. complete phase 3 phase 3

Fabaceae Crotalaria comanestiana Volkens & Schweinf. complete phase 3 phase 3

Fabaceae Crotalaria cylindrica A. Rich. subsp. afrorientalis Polhill complete phase 3 phase 3

Fabaceae Crotalaria deflersii Schweinf. complete phase 3 phase 3

Fabaceae Crotalaria deserticola Taub. ex Baker f. subsp. deserticola complete phase 3 phase 3

Fabaceae Crotalaria dewildemaniana R. Wilczek subsp. oxyrhyncha Polhill complete phase 3 phase 3

Fabaceae Crotalaria distantiflora Baker f. complete phase 3 phase 3

Fabaceae Crotalaria glauca Willd. complete phase 3 phase 3

Fabaceae Crotalaria goodiiformis Vatke complete phase 3 phase 3

Fabaceae Crotalaria grandibracteata Taub. complete phase 3 phase 3

Fabaceae Crotalaria greenwayi Baker f. complete tested tested

Fabaceae Crotalaria incana L. subsp. incana complete phase 3 phase 3

Fabaceae Crotalaria incana L. subsp. purpurascens (Lam.) Milne-Redh. complete tested tested

Fabaceae Crotalaria keniensis Baker f. complete tested tested

Fabaceae Crotalaria laburnifolia L. subsp. eldomae (Baker f.) Polhill complete phase 3 phase 3

Fabaceae Crotalaria laburnifolia L. subsp. laburnifolia complete phase 3 phase 3

Fabaceae Crotalaria laburnifolia L. subsp. tenuicarpa Polhill complete phase 3 phase 3

Fabaceae Crotalaria lachnocarpoides Engl. complete tested tested

Fabaceae Crotalaria lotiformis Milne-Redh. complete phase 3 phase 3

Fabaceae Crotalaria lukwangulensis Harms complete phase 3 phase 3

Fabaceae Crotalaria mauensis Baker f. complete phase 3 phase 3


Models


Fabaceae Crotalaria massaiensis Taub. complete phase 3 phase 3

Fabaceae Crotalaria microcarpa Hochst. ex Benth. complete phase 3 phase 3

24

Fabaceae Crotalaria natalitia Meisn. complete phase 3 phase 3

Fabaceae Crotalaria natalitia Meisn. var. natalitia complete phase 3 phase 3

Fabaceae Crotalaria natalitia Meisn. var. rutshuruensis De Wild. complete phase 3 phase 3

Fabaceae Crotalaria oocarpa Baker subsp. microcarpa Milne-Redh. complete phase 3 phase 3

Fabaceae Crotalaria petitiana (A. Rich.) Walp. complete phase 3 phase 3

Fabaceae Crotalaria polysperma Kotschy complete phase 3 phase 3

Fabaceae Crotalaria pseudospartium Baker f. complete phase 3 phase 3

Fabaceae Crotalaria pycnostachya Benth. complete tested tested

Fabaceae Crotalaria recta Steud. ex A. Rich. complete phase 3 phase 3

Fabaceae Crotalaria rhizoclada Polhill complete phase 3 phase 3

Fabaceae Crotalaria scassellatii Chiov. complete phase 3 phase 3

Fabaceae Crotalaria serengetiana Polhill complete phase 3 phase 3

Fabaceae Crotalaria spinosa Hochst. ex Benth. complete phase 3 phase 3

Fabaceae Crotalaria tsavoana Polhill complete phase 3 phase 3

Fabaceae Crotalaria uguenensis Taub. complete phase 3 phase 3

Fabaceae Crotalaria ukambensis Vatke complete phase 3 phase 3

Fabaceae Crotalaria vallicola Baker f. complete phase 3 phase 3

Fabaceae Crotalaria vatkeana Engl. complete phase 3 phase 3

Fabaceae Dalbergia melanoxylon Guill. & Perr. phase 3 phase 3 phase 3

Fabaceae Indigofera masaiensis J.B. Gillett partial phase 3 phase 3

Fabaceae Newtonia buchananii (Baker f.) G.C.C. Gilbert & Boutique partial phase 3 phase 3

Fabaceae Pterocarpus angolensis DC. phase 3 phase 3 phase 3


Models


Haloragaceae Gunnera perpensa L. phase 3 phase 3 phase 3

Lauraceae Ocotea usambarensis Engl. partial phase 3 phase 3

Loganiaceae Anthocleista grandiflora Gilg partial phase 3 phase 3

Loganiaceae Nuxia congesta R. Br. ex Fresen. phase 3 phase 3 phase 3

Loranthaceae Oncocalyx fischeri (Engl.) M.G. Gilbert partial phase 3 phase 3

Meliaceae Ekebergia capensis Sparrm. complete tested tested

Meliaceae Khaya anthotheca (Welw.) C.DC. complete tested tested

25

Monimiaceae Xymalos monospora (Harv.) Baill. ex Warb. complete tested tested

Moraceae Antiaris toxicaria Lesch. subsp. welwitschii (Engl.) C.C. Berg phase 3 phase 3 phase 3

Moraceae Ficus amadiensis De Wild. phase 3 phase 3 phase 3

Moraceae Ficus capreifolia Delile phase 3 phase 3 phase 3

Moraceae Ficus chirindensis C.C. Berg phase 3 phase 3 phase 3

Moraceae Ficus cordata Thunb. subsp. salicifolia (Vahl) C.C. Berg phase 3 phase 3 phase 3

Moraceae Ficus exasperata Vahl phase 3 phase 3 phase 3

Moraceae Ficus glumosa Delile phase 3 phase 3 phase 3

Moraceae Ficus ingens (Miq.) Miq. phase 3 phase 3 phase 3

Moraceae Ficus lutea Vahl phase 3 phase 3 phase 3

Moraceae Ficus natalensis Hochst. phase 3 phase 3 phase 3

Moraceae Ficus ottoniifolia (Miq.) Miq. subsp. ulugurensis (Warb. ex Mildbr. & Burret) C.C. Berg phase 3 phase 3 phase 3

Moraceae Ficus ovata Vahl phase 3 phase 3 phase 3

Moraceae Ficus populifolia Vahl phase 3 phase 3 phase 3

Moraceae Ficus scassellatii Pamp. subsp. scassellatii phase 3 phase 3 phase 3

Moraceae Ficus stuhlmannii Warb. phase 3 phase 3 phase 3

Moraceae Ficus sur Forssk. phase 3 phase 3 phase 3


Models


Moraceae Ficus sycomorus L. phase 3 phase 3 phase 3

Moraceae Ficus thonningii Blume partial phase 3 phase 3

Moraceae Ficus thunbergii Maxim. phase 3 phase 3 phase 3

Moraceae Ficus vallis-choudae Delile phase 3 phase 3 phase 3

Moraceae Ficus wakefieldii Hutch. phase 3 phase 3 phase 3

Moraceae Milicia excelsa (Welw.) C.C. Berg phase 3 phase 3 phase 3

Myricaceae

Morella salicifolia (Hochst. ex A.Rich.) Verdc. & Polhill subsp. kilimandscharica (Engl.) Verdc.

& Polhill phase 3 phase 3 phase 3

Myricaceae

Morella salicifolia (Hochst. ex A.Rich.) Verdc. & Polhill subsp. meyeri-johannis (Engl.) Verdc. &

Polhill phase 3 phase 3 phase 3

Myrtaceae Syzygium guineense (Willd.) DC. subsp. afromontanum F. White partial phase 3 phase 3

Myrtaceae Syzygium guineense (Willd.) DC. subsp. guineense partial phase 3 phase 3

26

Oleaceae Olea capensis L. subsp. macrocarpa (C.H. Wright) I. Verd. phase 3 phase 3 phase 3

Oleaceae Olea europaea L. subsp. cuspidata (Wall. ex G. Don) Cif. phase 3 phase 3 phase 3

Orchidaceae Aerangis thomsonii (Rolfe) Schltr. phase 3 phase 3 phase 3

Orchidaceae Aerangis brachycarpa (Rchb. f.) T. Durand & Schinz partial phase 3 phase 3

Orchidaceae Aerangis coriacea Summerh. phase 3 phase 3 phase 3

Orchidaceae Aerangis luteoalba (Kraenzl.) Schltr. var. rhodosticta (Kraenzl.) J.L. Stewart phase 3 phase 3 phase 3

Orchidaceae Aerangis somalensis (Schltr.) Schltr. phase 3 phase 3 phase 3

Orchidaceae Angraecopsis breviloba Summerh. phase 3 phase 3 phase 3

Orchidaceae Angraecopsis parviflora (Thouars) Schltr. phase 3 phase 3 phase 3

Orchidaceae Angraecopsis tenerrima Kraenzl. complete phase 3 phase 3

Orchidaceae Ansellia africana Lindl. phase 3 phase 3 phase 3

Orchidaceae Brachycorythis kalbreyeri Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Brachycorythis pleistophylla Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Brachycorythis pubescens Harv. phase 3 phase 3 phase 3


Models


Orchidaceae Bulbophyllum intertextum Lindl. phase 3 phase 3 phase 3

Orchidaceae Bulbophyllum stolzii Schltr. phase 3 phase 3 phase 3

Orchidaceae Cynorkis hanningtonii Rolfe phase 3 phase 3 phase 3

Orchidaceae Cynorkis kaessneriana Kraenzl. phase 3 phase 3 phase 3

Orchidaceae Cynorkis pleistadenia (Rchb. f.) Schltr. complete phase 3 phase 3

Orchidaceae Diaphananthe pulchella Summerh. var. pulchella phase 3 phase 3 phase 3

Orchidaceae Diaphananthe rohrii (Rchb. f.) Summerh. phase 3 phase 3 phase 3

Orchidaceae Diaphananthe rutila (Rchb. f.) Summerh phase 3 phase 3 phase 3

Orchidaceae Diaphananthe stolzii Schltr. phase 3 phase 3 phase 3

Orchidaceae Diaphananthe subsimplex Summerh. phase 3 phase 3 phase 3

Orchidaceae Disa deckenii Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Disa erubescens Rendle phase 3 phase 3 phase 3

Orchidaceae Disa stairsii Kraenzl. phase 3 phase 3 phase 3

Orchidaceae Disperis anthoceros Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Disperis johnstonii Rolfe phase 3 phase 3 phase 3

27

Orchidaceae Disperis kilimanjarica Rendle phase 3 phase 3 phase 3

Orchidaceae Disperis nemorosa Rendle phase 3 phase 3 phase 3

Orchidaceae Epipactis africana Rendle phase 3 phase 3 phase 3

Orchidaceae Eulophia galeoloides Kraenzl. phase 3 phase 3 phase 3

Orchidaceae Eulophia odontoglossa Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Eulophia orthoplectra (Rchb. f.) Summerh. phase 3 phase 3 phase 3

Orchidaceae Eulophia ovallis Lindl. subsp. bainesii (Rolfe) A.V. Hall phase 3 phase 3 phase 3

Orchidaceae Eulophia petersii Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Eulophia pyrophila (Rchb. f.) Summerh. phase 3 phase 3 phase 3


Models


Orchidaceae Eulophia schweinfurthii Kraenzl. phase 3 phase 3 phase 3

Orchidaceae Eulophia speciosa (R. Br. ex Lindl.) Bolus phase 3 phase 3 phase 3

Orchidaceae Eulophia streptopetala Lindl. phase 3 phase 3 phase 3

Orchidaceae Eulophia streptopetala Lindl. var. stenoophylla (Summerh.) P.J. Cribb phase 3 phase 3 phase 3

Orchidaceae Eulophia streptopetala Lindl. var. streptopetala phase 3 phase 3 phase 3

Orchidaceae Eulophia subulata Rendle phase 3 phase 3 phase 3

Orchidaceae Eulophia zeyheri Hook. f. phase 3 phase 3 phase 3

Orchidaceae Habenaria bracteosa Hochst. ex A. Rich. phase 3 phase 3 phase 3

Orchidaceae Habenaria chirensis Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Habenaria chlorotica Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Habenaria epipactidea Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Habenaria haareri Summerh. phase 3 phase 3 phase 3

Orchidaceae Habenaria helicoplectrum Summerh. phase 3 phase 3 phase 3

Orchidaceae Habenaria humilior Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Habenaria kilimanjari Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Habenaria malacophylla Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Habenaria petitiana (A. Rich.) T. Durand & Schinz phase 3 phase 3 phase 3

Orchidaceae Habenaria splendens Rendle phase 3 phase 3 phase 3

Orchidaceae Habenaria stylites Rchb. f. & S. Moore subsp. stylites complete phase 3 phase 3

Orchidaceae Habenaria vaginata A. Rich.l phase 3 phase 3 phase 3

28

Orchidaceae Liparis bowkeri Harv. phase 3 phase 3 phase 3

Orchidaceae Liparis caespitosa (Thouars) Lindl. phase 3 phase 3 phase 3

Orchidaceae Liparis deistelii Schltr. phase 3 phase 3 phase 3

Orchidaceae Polystachya bennettiana Rchb. f. phase 3 phase 3 phase 3


Models


Orchidaceae Polystachya caespitifica Kraenzl. subsp. latilabris (Summerh.) P.J. Cribb & Podz. complete phase 3 phase 3

Orchidaceae Polystachya confusa Rolfe phase 3 phase 3 phase 3

Orchidaceae Polystachya cultriformis (Thouars) Lindl. ex Spreng. phase 3 phase 3 phase 3

Orchidaceae Polystachya dendrobiiflora Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Polystachya fischeri Kraenzl. complete phase 3 phase 3

Orchidaceae Polystachya isochiloides Summerh. complete phase 3 phase 3

Orchidaceae Polystachya leucosepala P.J. Cribb phase 3 phase 3 phase 3

Orchidaceae Polystachya lindblomii Schltr. phase 3 phase 3 phase 3

Orchidaceae Polystachya praecipitis Summerh. complete phase 3 phase 3

Orchidaceae Polystachya simplex Rendle phase 3 phase 3 phase 3

Orchidaceae Polystachya spatellata Kraenzl. phase 3 phase 3 phase 3

Orchidaceae Polystachya steudneri Rchb. f. phase 3 phase 3 phase 3

Orchidaceae Polystachya tenuissima Kraenzl. phase 3 phase 3 phase 3

Orchidaceae Polystachya transvaalensis Schltr. phase 3 phase 3 phase 3

Orchidaceae Polystachya vaginata Summerh. phase 3 phase 3 phase 3

Orchidaceae Rangaeris amaniensis (Kraenzl.) Summerh. phase 3 phase 3 phase 3

Orchidaceae Rangaeris muscicola (Rchb. f.) Summerh. phase 3 phase 3 phase 3

Orchidaceae Satyrium chlorocorys Rchb. f. ex Rolfe phase 3 phase 3 phase 3

Orchidaceae Satyrium crassicaule Rendle phase 3 phase 3 phase 3

Orchidaceae Satyrium ecalcaratum Schltr. phase 3 phase 3 phase 3

Orchidaceae Satyrium elongatum Rolfe phase 3 phase 3 phase 3

Orchidaceae Satyrium neglectum Schltr. var. neglectum complete phase 3 phase 3

Orchidaceae Satyrium robustum Schltr. phase 3 phase 3 phase 3

Orchidaceae Satyrium sceptrum Schltr. phase 3 phase 3 phase 3


29

Models


Orchidaceae Satyrium volkensii Schltr. phase 3 phase 3 phase 3

Orchidaceae Stolzia repens (Rolfe) Summerh. phase 3 phase 3 phase 3

Orchidaceae Tridactyle bicaudata (Lindl.) Schltr. phase 3 phase 3 phase 3

Orchidaceae Tridactyle furcistipes Summerh. phase 3 phase 3 phase 3

Orchidaceae Tridactyle inflata Summerh. phase 3 phase 3 phase 3

Orchidaceae Tridactyle tanneri P.J. Cribb complete phase 3 phase 3

Orchidaceae Tridactyle tricuspis (Bolus) Schltr. phase 3 phase 3 phase 3

Poaceae Aristida adscensionis L. phase 3 phase 3 phase 3

Poaceae Cenchrus ciliaris L. phase 3 phase 3 phase 3

Poaceae Chloris gayana Kunth phase 3 phase 3 phase 3

Poaceae Chloris roxburghiana Schult. phase 3 phase 3 phase 3

Poaceae Chrysopogon plumulosus Hochst. phase 3 phase 3 phase 3

Poaceae Cymbopogon nardus (L.) Rendle phase 3 phase 3 phase 3

Poaceae Digitaria abyssinica (Hochst. ex A. Rich.) Stapf phase 3 phase 3 phase 3

Poaceae Eleusine jaegeri Pilg. phase 3 phase 3 phase 3

Poaceae Enteropogon macrostachyus (Hochst. ex A. Rich.) Munro ex Benth. phase 3 phase 3 phase 3

Poaceae Enteropogon rupestris (J.A. Schmidt) A. Chev. phase 3 phase 3 phase 3

Poaceae Eragrostis braunii Schweinf. partial phase 3 phase 3

Poaceae Eriochloa fatmensis (Hochst. & Steud.) Clayton phase 3 phase 3 phase 3

Poaceae Hyparrhenia filipendula (Hochst.) Stapf phase 3 phase 3 phase 3

Poaceae Koeleria capensis Nees phase 3 phase 3 phase 3

Poaceae Panicum maximum Jacq. phase 3 phase 3 phase 3

Poaceae Pennisetum clandestinum Hochst. ex Chiov. phase 3 phase 3 phase 3

Poaceae Pennisetum mezianum Leeke phase 3 phase 3 phase 3


Models


Poaceae Pennisetum purpureum Schumach. phase 3 phase 3 phase 3

Poaceae Pennisetum sphacelatum (Schumach.) T. Durand & Schinz phase 3 phase 3 phase 3

Poaceae Setaria incrassata (Hochst.) Hack. phase 3 phase 3 phase 3

30

Poaceae Sorghum purpureosericeum (Hochst. ex A. Rich.) Asch. & Schweinf. phase 3 phase 3 phase 3

Poaceae Sporobolus ioclados (Nees ex Trin.) Nees phase 3 phase 3 phase 3

Poaceae Sporobolus spicatus (Vahl) Kunth phase 3 phase 3 phase 3

Poaceae Themeda triandra Forssk. phase 3 phase 3 phase 3

Poaceae Vossia cuspidata (Roxb.) Griff. phase 3 phase 3 phase 3

Podocarpaceae Afrocarpus falcatus (Thunb.) C.N. Page phase 3 phase 3 phase 3

Podocarpaceae Podocarpus latifolius (Thunb.) R. Br. ex Mirb. partial phase 3 phase 3

Polygonaceae Rumex abyssinicus Jacq. phase 3 phase 3 phase 3

Polygonaceae Rumex bequaertii De Wild. phase 3 phase 3 phase 3

Polygonaceae Rumex ruwenzoriensis Chiov. phase 3 phase 3 phase 3

Proteaceae Faurea saligna Harv. phase 3 phase 3 phase 3

Rhizophoraceae Cassipourea malosana (Baker) Alston phase 3 phase 3 phase 3

Rosaceae Hagenia abyssinica J.F. Gmel. phase 3 phase 3 phase 3

Rosaceae Prunus africana (Hook. f.) Kalkman complete tested tested

Rubiaceae Anthospermum usambarense K. Schum. partial phase 3 phase 3

Rubiaceae Mitragyna rubrostipulata (K. Schum.) Havil. phase 3 phase 3 phase 3

Rubiaceae Psychotria fractinervata E.M.A. Petit partial phase 3 phase 3

Rutaceae Vepris simplicifolia (Engl.) Mziray partial phase 3 phase 3

Salvadoraceae Azima tetracantha Lam. complete tested tested

Salvadoraceae Salvadora persica L. complete tested tested

Sapindaceae Allophylus ferrugineus Taub. var. ferrugineus complete tested tested


Models


Sapotaceae Chrysophyllum gorungosanum Engl. phase 3 phase 3 phase 3

Sapotaceae Pouteria adolfi-friedericii (Engl.) A. Meeuse subsp. keniensis (R.E. Fr.) L. Gaut. complete phase 3 phase 3

Sterculiaceae Dombeya burgessiae Gerrard ex Harv. phase 3 phase 3 phase 3

Sterculiaceae Dombeya kirkii Mast. phase 3 phase 3 phase 3

Sterculiaceae Dombeya rotundifolia (Hochst.) Planch. phase 3 phase 3 phase 3

Sterculiaceae Dombeya torrida (J.F. Gmel.) Bamps phase 3 phase 3 phase 3

Sterculiaceae Dombeya torrida (J.F. Gmel.) Bamps subsp. erythroleuca (K. Schum.) Seyani phase 3 phase 3 phase 3

Sterculiaceae Dombeya torrida (J.F. Gmel.) Bamps subsp. torrida phase 3 phase 3 phase 3

31

Sterculiaceae Leptonychia usambarensis K. Schum. phase 3 phase 3 phase 3

Sterculiaceae Sterculia africana (Lour.) Fiori phase 3 phase 3 phase 3

Theaceae Ficalhoa laurifolia Hiern phase 3 phase 3 phase 3

Typhaceae Typha latifolia L. phase 3 phase 3 phase 3

Ulmaceae Celtis africana Burm. f. partial phase 3 phase 3

Ulmaceae Celtis gomphophylla Baker phase 3 phase 3 phase 3

Verbenaceae Vitex doniana Sweet partial phase 3 phase 3

32

Appendix 3: University of York Budget Summary

Researcher Costs

Expense US$

Dr Aida Sanchez Salary and Employers NI 5,141.07 Dr Marion Pfeifer Salary and Employers NI 2,202.96 Tamara Dimova Salary and Employers NI 414.20 Total Salary and Employers NI 7,758.23

Associated Costs

Fieldwork - botanical assistant 2,000.00

Fieldwork cash advance 7,750.00

Fieldwork (claim against cash advance) 2,041.00

Travel/living expenses (Dr Aida Sanchez) 1,889.31

Total Associated Costs 5,930.31

Total UoY expenditures ($) 2011 13,688.54

Total UoY award ($) 39,242.80

Total remaining ($)2012 25,554.26

33

Appendix 4: Accounting for Project Expenditures in 2011

Researcher costs Budget Expenditure Balance

Project Researchers $21,570 $7,758 $13,812 Roy Gereau salary (MBG) $4,251 $4,087 $164 GIS Specialist salary (WK) $9,600 $6,400 $3,200 Total salary $35,421 $18,245 $17,176

Associated Costs Budget Expenditure Balance

Fieldwork (travel, permits, subsistence) $12,000 $7,527 $4,473

Data Collection (DRSRS, EA, herbaria) $10,000 $1,277 $8,723

TTravel Tz-UK (Kew) for WK $1,000 $1,000

Living expenses, UK @ $100/day $3,000 $1,889 $1,111

Computing $3,000 $3,000

Office expenses, Dar es Salaam $3,000 $393 $2,607

Associated Costs Grand Total $32,000 $11,086 $20,914

Total $67,421 $29,331 $38,090 Total expenditures in 2011 were $29,331, leaving a balance of $38,090. Additional fieldwork, data

collection, and computing to be conducted in the first six months of 2012 will complete current

analyses with remaining funds. William Kindeketa will return to Dar es Salaam from study leave

and will resume operation of office and other duties in support of project.