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Priority Areas for Conservation in the
Maiko, Tayna, Kahuzi Biega Landscape
A.J.Plumptre, S. Ayebare and D.Kujirakwinja.
October 2015
Priority areas for conservation in the MTKB Landscape
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Wildlife Conservation Society
The Wildlife Conservation Society (WCS) saves wildlife and wild places worldwide through science,
conservation action, education, and inspiring people to value nature. WCS envisions a world where
wildlife thrives in healthy lands and seas, valued by societies that embrace and benefit from the
diversity and integrity of life on earth. Our goal is to conserve the world's largest wild places in 15
priority regions, home to more than 50% of the world's biodiversity.
In the Albertine Rift region of Africa WCS has been supporting conservation since 1957 and is the
oldest International Conservation NGO working here. Our focus has been on building the capacity of
the protected area authorities in the region to be able to better manage their protected areas as
well as providing results of scientific research to better understand the importance of the Albertine
Rift and how best to conserve the incredibly rich biodiversity to be found here. Find more at
www.albertinerift.org; www.wcsuganda.org; and www.wcs.org
Citation: Plumptre, A.J., Ayebare, S. & Kujirakwinja, D. (2015). Priority Areas for Conservation in the
Maiko, Tayna, Kahuzi Biega Landscape. Unpublished Report for USAID and USFWS
Cover photo: View from Mount Kahuzi in the Kahuzi Biega National Park, one of the priority sites for
conservation in the MTKB landscape. ©A.J.Plumptre/WCS
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Executive Summary
The Albertine Rift is known to be one of the most biodiverse regions on the African continent,
having been designated an endemic bird area (Stattersfield et al.1998), a Global 200 Priority
Ecoregion (Olson and Dinerstein, 1998; Burgess et al., 2004) and part of the Eastern Afromontane
Hotspot (Brooks et al.,2004). The richness of vertebrate and plant taxa have been documented in the
past (Plumptre et al. 2003; 2007) and in 2003 it was estimated that there were about 5,793 plant
species and 1,757 terrestrial vertebrate species known for the region. More thorough surveys,
identification of new species and the finding of additional records for plants have increased these
numbers to 6,409 plants and 1,779 terrestrial vertebrates and more species are being discovered
and described every year, particularly from the amphibians and reptiles.
Six key landscapes for conservation were identified within the Albertine Rift of which one is
the Maiko-Itombwe Landscape as named by the Albertine Rift Strategic Framework plan of 2004 or
Maiko, Tayna, Kahuzi Biega Landscape (MTKB) as named by CARPE. A Landscape plan for MTKB was
developed by the Dian Fossey Gorilla Fund International together with Conservation International
under CARPE II and a Great Apes Conservation Plan was also developed for almost the same region
led by the Jane Goodall Institute although this plan encompasses the Maiko-Itombwe and Ngamikka
Landscapes of the Albertine Rift Plan. None of these plans though had much information on the
biodiversity and locations of species of conservation concern (apart from Grauer’s gorilla),
particularly the species endemic to the Albertine Rift and those that are globally threatened
according to the IUCN Redlist (IUCN 2015).
Under CARPE III (CAFEC) funding to the Wildlife Conservation Society (WCS) was given to
support conservation in the MTKB Landscape by USAID/CAFEC and part of this funding was to
support a landscape wide assessment of priority conservation areas in this landscape, one of the
larger landscapes under CARPE III. This report summarises the findings of this assessment.
The findings show that the north eastern parts of the MTKB Landscape are the most species
rich but that when it comes to endemic and threatened species then the highland areas in the east
and south east of the landscape are more important. In particular the Itombwe Massif, highland
areas of Kahuzi Biega National Park, Tayna, Usala and Kisimba-Ikobo Community Reserves and some
highland areas of Maiko National Park are very important for conserving these species. Large
mammal species of conservation concern include Grauer’s gorilla (Gorilla beringei graueri),
chimpanzee (Pan troglodytes), elephant (Loxodonta africana), Okapi (Okapia johnstonii) and the
monkeys mostly confined to this region; Owl-faced guenon (Cercopithecus hamlyni), L’Hoest’s
monkey (Cercopithecus lhoesti) and red colobus (Procolobus rufomitratus). These species tend to
occur in the lowland areas of Kahuzi Biega and into the Reserve des Gorilles de Punia and
northwards to Maiko National Park. Okapi are only found in Maiko and to the east of this park. The
red colobus (P.r.foai) are rarely recorded and most are found in the Ngamikka Landscape south of
MTKB.
Key areas for conservation in this landscape should be the Itombwe Massif, particularly the
highland areas and the escarpment above Lake Tanganyika southwards; the highland and lowland
areas of Kahuzi Biega Park extending to the Reserve des Gorilles de Punia, the Tayna, Kisimba-Ikobo
and Usala Community Reserves and the highland areas of Maiko Park.
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Table of Contents
Executive Summary ................................................................................................................................. 3
Table of Contents .................................................................................................................................... 4
The Maiko-Tayna-Kahuzi-Biega Landscape ............................................................................................. 5
Approach used to identify priority areas ................................................................................................ 7
Compiling and mapping biodiversity data .......................................................................................... 7
Modelling species distributions .......................................................................................................... 7
Predictor variables .......................................................................................................................... 9
Modeling methods ........................................................................................................................ 10
Results ................................................................................................................................................... 11
Results of IUCN mapping of all species across the Albertine Rift ..................................................... 11
Observations from SMART and survey data ..................................................................................... 13
Elephants ....................................................................................................................................... 13
Okapi ............................................................................................................................................. 13
Great Apes..................................................................................................................................... 13
Results of species distribution modelling ......................................................................................... 16
Priority Areas for Conservation in the MTKB Landscape ...................................................................... 20
Key areas identified from modelling and surveys ............................................................................. 20
Itombwe Reserve and Tanganyika escarpment ............................................................................ 20
Kahuzi Biega National Park and RGPU .......................................................................................... 20
Tayna, Kisimba-Ikobo and Usala ................................................................................................... 21
Maiko National Park ...................................................................................................................... 21
Conservation planning approach to priority conservation areas ..................................................... 21
References ............................................................................................................................................ 25
Acknowledgements ............................................................................................................................... 27
Appendix. Threatened and Albertine Rift endemic species found in the MTKB Landscape ................ 28
Mammals .......................................................................................................................................... 28
Birds .................................................................................................................................................. 29
Reptiles ............................................................................................................................................. 30
Amphibians ....................................................................................................................................... 30
Plants ................................................................................................................................................. 31
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The Maiko-Tayna-Kahuzi-Biega Landscape
The Maiko-Tayna-Kahuzi Biega (MTKB) Landscape or Landscape 10 of CARPE is one of the
larger landscapes identified as of conservation importance by USAID and its partners. Given its large
size and the limited resources from USAID to implement conservation activities, the Wildlife
Conservation Society (WCS) felt it was important to identify the critical areas for conservation within
this landscape. This report summarises the findings of an assessment made by WCS of the landscape
using a variety of approaches to determine these critical sites.
The MTKB Landscape is part of the Albertine Rift, one of the most biodiverse regions of the
African continent. The Albertine Rift is an endemic bird area (Stattersfield et al.1998), a Global 200
Priority Ecoregion (Olson and Dinerstein, 1998; Burgess et al., 2004) and part of the Eastern
Afromontane Hotspot (Brooks et al.,2004). The richness of vertebrate and plant taxa have been
documented in the past (Plumptre et al. 2003; 2007) and in 2003 it was estimated that there were
about 5,793 plant species and 1,757 terrestrial vertebrate species known for the region. More
thorough surveys, identification of new species and the finding of additional records for plants have
increased these numbers to 6,409 plants and 1,779 terrestrial vertebrates and more species are
being discovered and described every year, particularly from the amphibians and reptiles. The
Albertine Rift is particularly important because it contains more globally threatened vertebrate
species (IUCN 2015) and more endemic vertebrates than any other region in Africa.
The MTKB landscape was identified as one of six key landscapes of the Albertine Rift in a
Strategic Framework Plan (ARCOS 2004) which identified conservation planning at the landscape
scale as a key first step. The CARPE Landscape Plan under phase II of CARPE was developed for the
landscape by CI and DFGFI (Conservation International 2010) but WCS and ICCN had some
reservations about some of the contents of the plan. Notable was the small number of species of
conservation concern identified in the landscape and the fact that of those that were listed at least
30% of the mammals were not found in the landscape and there were similar errors with other taxa.
The plan also tended to describe the landscape more than come up with concrete and logical
reasons to make interventions based on an assessment of the threats to conservation targets. A
separate planning process was supported by ARCUS Foundation through Jane Goodall Institute (JGI)
to plan for great ape conservation in a very similar region to the MTKB landscape but encompassing
forest to the west up to the Congo River and also further south to the proposed Ngamikka National
Park in the Misotshi-Kabobo region on Lake Tanganyika. This plan, revised in early 2015 (Maldonado
and Fourrier, 2015) focussed on conservation of chimpanzees (Pan troglodytes schweinfurthii) and
Grauer’s gorilla (Gorilla beringei graueri) and the habitats where they are found. This plan used the
The Nature Conservancy (TNC) planning approach to create a threats analysis on these conservation
targets and to identify interventions. While this plan used a threats-based approach it specifically
targeted apes and their habitats and did not address the needs of the many other species of
conservation concern in the MTKB landscape.
When WCS joined CARPE phase III for this landscape (WCS was not involved in the CARPE
program in this landscape prior to 2013, apart from some surveys contracted by CI in the Tayna and
Kisimba-Ikobo Reserves) it was felt that it would be useful to document the conservation targets for
the landscape more thoroughly as well as where in the landscape they occur. This was in part
because of the large size of the landscape and also because of the resources available to implement
conservation here. WCS had been mapping the distribution of species of conservation concern
throughout the Albertine Rift region with separate funding as part of the work of its Albertine Rift
Program and compiling this information together with data from surveys and other sources would
be useful in identifying priority areas in the landscape for conservation. This report summarises the
findings of this assessment.
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Figure 1. Map of the MTKB landscape showing elevation and the locations of protected areas
mentioned in this report. CR=Community Reserve; NR = Natural Reserve; NP=National Park
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Approach used to identify priority areas
Compiling and mapping biodiversity data
WCS has been compiling data from biodiversity surveys of the Albertine Rift region since
1997. To date much of the highland and medium altitude forest areas in this region have been
surveyed by WCS scientists with collaborating partners at Makerere University, Centre de Recherche
en Sciences Naturelles Lwiro, Chicago Field Museum, Fauna and Flora International, Trento Science
Museum, University of Texas at El Paso, and Missouri Botanical Gardens. In addition several lowland
forest sites have been surveyed in eastern Democratic Republic of Congo (DR Congo), providing data
for some of the lowland forest habitats. These data were used to model species distributions (see
below).
WCS has also been supporting the development and roll out of the Spatial Monitoring and
Reporting Tool (SMART), a software for collating and mapping data collected by rangers of village
scouts, across much of eastern DR Congo. Data on sightings of species of conservation concern
collected by ICCN rangers or community ecoguards using SMART were also collated for:
1. Large ungulates: elephant, Okapi, Buffalo, Bongo
2. Primates: gorilla, chimpanzee, L’Hoest monkey, Owl-faced monkey, Red colobus and grey-
cheeked mangabey
3. Birds: Congo peafowl (Afropavo congensis).
These data were mapped to show distributions of these species as they are much more
affected by man than the smaller endemic and threatened species that were modelled.
In addition an analysis was made by IUCN using the species range maps from the IUCN
Redlist site (IUCN 2015) for the Albertine Rift that mapped all species of mammal, bird and
amphibian to identify areas of species richness for the Albertine Rift (Carr et al. 2013) and these are
also presented in the results.
Modelling species distributions
We estimated the current and future distributions areas for 160 endemic species using field
data observations and species range maps. Species occurrence records for 117 species across 5 taxa:
Birds(25), Mammals(18), Plants(49), and Amphibians (14) were obtained from Wildlife Conservation
Society biodiversity survey data, Tanzania mammal data atlas , Global Biodiversity Information
Facility (GBIF 2012: http://www.gbif.org/), Chicago Field Museum and Texas Museum at El Paso. A
total of 32,854 presence records were used in the modeling process, Birds (8,765), large mammals
(17,345), small mammals (1,448), Plants (4,473), and amphibians (387) and these are mapped in
Figures 2a-2c. The species’ occurrence records sample sizes used for model parameterization varied
between 10 to 1,200 localities at 1km2 grid resolution.
Species range maps represent the extent of occurrence of a species and have been used for mapping
species richness (Graham & Hijmans, 2006). We used the altitudinal ranges to estimate area of
occupancy within the extent of occurrence by randomly selecting “ presence records” for species
that had fewer than 5 or no presence occurrence records. The distribution areas for 43 endemic
species; birds (13), amphibians (15) and small mammals (15) were estimated using the randomly
generated presence records from species’ range maps.
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Figure 2a. Species occurrence records for Mammals: Left – Large mammals; Right- small mammals
Figure 2b. Species occurrence records for amphibians (left) and reptiles (right)
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Figure 2c. Species occurrence records for birds (left) and plants (right)
Predictor variables
We selected 17 potential predictor variables relating to climate (9), topology (4), hydrology
(2), geology (1) and human activities(1) that are likely to influence the distribution of the birds,
mammals, plants, reptiles and amphibians in the Albertine rift (Table 1). All the predictor variables
were clipped to the area of interest and a pairwise pearson correlation between predictor variables
was obtained using ENMTOOLs (Warren et al. 2010; a toolbox for comparative studies of
environmental niche model; http://purl.oclc.org/enmtools). To minimize the effect of
multicolinearity and overfitting, only variables with less than (+/-0.75) correlation were retained.
Predictor variable were resampled to a 1km2 resolution using Arcgis 9.3 for model input.
Climate layers at a spatial resolution of ~1 km2 were obtained from the WorldClim database
(Hijmans et al. 2005; http:\\www.worldclim.org). Additional variables used in model prediction
included: cloud mean, cloud max, digital elevation model, aspect, slope, eastness, northness,
distance to rivers, drainage basin, lithology and distance to roads, . Cloud mean and cloud max were
computed from MOD09GA Surface Reflectance data which is provided in Hierarchical Data Format
(HDF) at daily temporal resolution and was calculated by G. Picton-Phillipps. A 90m digital elevation
model was obtained from the USGS ( http://srtm.usgs.gov/) and the slope, aspect, eastness and
northness were derived as well. Drainage basins were obtained from USGS Global data set of
2003.The distance to roads and rivers were derived by computing the euclidean distance from each
point in the study area to the nearest road or river. Rivers and roads data layers were obtained from
the African data sampler dataset (WRI 2010). Lithology reflects key geological parent materials
which are determinants in the distribution of vegetation (Source; U.S. Geological Survey/ The Nature
Conservancy).
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Table1 . Covariates used for modeling the distribution of endemic and threatened species in the
Albertine Rift
Covariate
Description of Variable
Bio2
Mean daily temperature range
Bio7
Temperature annual range
Bio6
Minimum temperature of coldest month
Bio5
Maximum temperature of warmest month
Bio12
Annual precipitation
Bio17
Precipitation of driest quarter
Bio16
Precipitation of wettest quarter
Cloud mean
Annual normal percent cloud cover
Cloud max
Maximum cloud cover for each pixel
DEM
Digital elevation model
Aspect
Direction a slope is facing
Slope
Rate of maximum change in elevation
Easteness
Orientation East - West
Northness
Orientation North- South
Drainage basins
Topographically delineated area drained by a stream system
Roads
Distance to nearest road
Lithology
Geologic parent material
Rivers
Distance to nearest river
Modeling methods
We used Maximum Entropy Species Distribution Modeling approach (hereafter ‘Maxent’,
Maxent version 3.3.3e; Phillips., et al, 2006), to estimate the current and future distribution areas for
endemic and threatened species in the Albertine rift. We selected Maxent, a machine learning
approach because it requires only species’ presence data and environmental variables (continuous
or categorical) , and has been shown to perform as well or better than other species distribution
modeling techniques (Phillips et al. 2006, Elith., et al 2006). Maxent makes inferences from
incomplete information and estimates species’ distributions by generating a probability distribution
of maximum entropy (ie:closest to uniform), subject to constraints imposed by the information
regarding presence records and the background information across the study area (Phillips et al.
2006; Elith et al. 2011). Maxent default parameters ( Auto features, convergence threshold of
0.00001, maximum number of background points =10,000, regularization multiplier=1) were used to
fit the models. However, about a third of the species were fitted using hinge features, which are
functions for piecewise linear splines and fit models closely related to Generalized Additive Models
(Elith et al. 2011).
Model accuracy was assessed by testing how well the model prediction differentiates
between suitable and unsuitable habitat at varying thresholds using the area under the receiver
operating characteristic curve (AUC) test statistic (Fielding & Bell, 1997; Freeman & Moisen, 2008).
AUC is a threshold independent metric that represents how likely a random selection from a
presence site is ranked compared to a random selection from an absence/pseudo absence site
(Fielding and Bell 1997; Phillips & Dudík, 2008). An AUC value of 0.5 indicates a model that performs
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no better than random while a model with perfect discrimination has a value of 1. Model outputs
with AUC values ≥ 0.8, were selected for the final analysis (Manel et al. 2001). 75% of the
occurrence records were used for training and 25% for testing . After assessing model accuracy, the
final models for all the species were fitted using all occurrence records. To convert the predicted
habitat suitability from a continuous logistic output format into a binary (presence/absence) output ,
the “maximum training sensitivity plus specificity” threshold rule was used(Liu et al 2005; Freeman
and Moisen, 2008).This threshold rule minimizes the mean error rate for positive observations and
the error rate for negative observations (Freeman and Moisen, 2008).
The predicted species’ distributional areas represent a range of environmental gradients
within which a species is viable, however some of the suitable habitat has been modified by human
activities. To remove areas that have been modified by human activities, we masked individual
species’ binary outputs, using the GlobCover 2009 land cover map that was re-classified into three
land cover classes; water, other, and natural vegetation (ESA, 2010). The “other” class was
characterized by agriculture areas, plantation forestry and human settlements. When exploring the
GlobCover 2009 land cover map in ArcGis 9.3 we found that there were vegetation misclassifications
in our study area. For example some of the high tropical forest reserves had been classified as
mosaic vegetation/cropland. We edited the landcover map by locking in protected areas, as natural
vegetation and using our previous landcover maps to produce the final layer used for masking
modified habitat.
Results
Results of IUCN mapping of all species across the Albertine Rift
Carr et al. (2013) combined the species range maps of all species known from the Albertine
rift for mammals, birds and amphibians as these three taxa had been mostly assessed for the IUCN
redlist (IUCN 2015) and so range maps were available for most species. These layers were re-
mapped to show the protected areas by WCS and are produced here to identify key areas of high
species richness in the MTKB landscape (figure 3).
The results show that for each of the three vertebrate taxa the richest area for species tends
to be in the east of the MTKB landscape, particularly towards the north east. The most species rich
areas of the Albertine Rift occur outside the MTKB landscape in the northern part of Virunga Park
and westwards in the lowland forest towards the northern part of Maiko National Park and the
Okapi Reserve.
These maps show the total species richness at a relatively coarse scale because it was
recognised that the maps of the extent of occurrence which are formed by drawing polygons around
all known sightings of a species (as mapped on the IUCN Redlist site) will include areas that are
probably unsuitable for the species. It is the area of occurrence which is important for a species and
this was why WCS went ahead to model the distribution of all endemic and threatened species
across the Albertine Rift.
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Figure 3. Map of relative species richness across the Albertine Rift region for mammals (top left),
birds (top right) and amphibians (bottom). Data are from IUCN (Carr et al. 2013).
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Observations from SMART and survey data
Data from WCS surveys and SMART data collected by ICCN and community reserves were
compiled to extract locations of sightings of key large mammal species. Large mammals are most
targeted by hunters in eastern DR Congo for bushmeat and in the case of elephants for ivory also. All
sites collect data on all large ungulates (elephant, okapi, buffalo and Bongo) and on the great apes
(chimpanzee and gorilla) but are more variable with which monkey and bird species they record on
patrols. We mapped the four large ungulates listed above, as well as red colobus,grey-cheeked
mangabey, l’Hoests and Owl-faced monkeys and the Congo Peafowl. L’Hoest’s monkey or mountain
monkey and the owl-faced monkey are mostly confined to the MTKB landscape although they do
occur in some forests in western Uganda and Rwanda and towards the Okapi reserve also. The
results show very patchy distributions of several of these species across the MTKB landscape (figure
4):
Elephants: Elephants have been heavily hunted in the landscape and only occur in the
western reaches of Kahuzi Biega National Park, the Maiko National Park, as well as the Reserve des
Goriles de Punia. Elsewhere very few sightings of elephant sign have been made. Congo peacock
was seen fairly frequently in the Reserve des Gorilles de Punia (RGPU) and in western Maiko
National Park but not further south than these sites. However this species may not be recorded at all
sites where SMART is being implemented.
Okapi: Okapi were confined to Maiko National Park and Usala Community Reserve in the
MTKB landscape while bongo were more frequent in the RGPU and Kahuzi Biega Park and a few in
Luama Katanga and Luama Kivu reserves. Buffalo were relatively widespread but not common
anywhere. Of the monkeys the owl-faced monkey was mostly observed in Kahuzi Biega Park
together with the RGPU and western Maiko Park. Grey-cheeked mangabeys were most common in
Maiko Park while l’Hoests monkey was most frequently seen in RGPU. Red colobus were rarely
observed in the landscape and this is quite surprising. It is a monkey species that tends to be hunted
more effectively by people as it tends not to flee as far when threatened being larger and heavier.
Only in the proposed Ngamikka Park to the south of the landscape was it observed fairly frequently.
At this site it is the subspecies P.r.foai that occurs and it appears this place will be important for the
conservation of this subspecies.
Great Apes: For the two great ape species we mapped the locations of the species as well as
made predictive maps for the species using an occupancy analysis method correcting for spatial
autocorrelation (figure 5). The occupancy analysis was calculated using the R-package hSDM
(Vielledent et al. 2014). This package uses a Bayesian Hierarchical analysis that can incorporate
spatial dependency in the analysis. When trying to estimate occupancy across the MTKB Landscape
there are two issues that need to be considered: 1) imperfect detection and 2) spatial correlation.
When a team visits a site and walks through it they can either detect gorilla sign or they do not. If
they do not it may be because the gorillas were truly not there or because they were missed but
actually were there. Occupancy analysis enables an estimate of the detection probability to be
made, which is usually less than 1 because in most surveys some animals are missed. A hierarchical
or Mixture Model approach is used to estimate detectability and Bayesian Statistical methods are
very useful when these models become complex.
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Figure 4. Map of coverage of data collection (top left), elephant and Congo peacock (top right),
okapi, buffalo and bongo (bottom left) and monkey (bottom right) locations.
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Figure 5. Map of sighting of chimpanzees (top left) with predicted occupancy (top right) and map of
gorilla sightings (bottom left) with predicted occupancy (bottom right).
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Spatial correlation is important because most species show some form of geographical patchiness
and given the non-random nature of the sampling we made here it was important to factor in the
geographical patchiness in our analyses (Plumptre et al. in prep.). The results show that chimpanzees
are relatively widespread across the MTKB landscape and are predicted to be present across a large
area (blue colours in figure 5 top right). They seem to be faring better from hunting than the gorillas
which are primarily found in the RGPU and Kahuzi Biega Park (both the western part of the lowland
sector and the highland sector) as well as in Tayna and Kisimba-Ikobo Community Reserves and parts
of Itombwe Reserve.
Results of species distribution modelling
Individual species maps were generated for the four main taxa: mammals, birds, amphibians
and plants. We focused on the endemic and threatened species (listed as CR, EN or VU on the IUCN
redlist) in the Albertine Rift as these are the species of conservation concern. These maps were then
overlaid in ArcGIS 10.2 and the number of species summed for every 1 km2 cell for the four taxa.
Large and small mammals were separated in the mapping after reviewing the results as the small
mammals were much more restricted in range than the large ones. The predictions used climate
variables mainly and predict species distributions across the MTKB even in areas that are cultivated
as a result. Most of the cultivation is in the east and along the main roads through the forest (see
figure 1).
The results are mapped in figures 6a-c and show that for most taxa the highland areas to the
east of the landscape are most important for the conservation of the endemic and threatened
species. Large mammals are an exception because they tend to be wide ranging and more tolerant
of changes in climate. Of the few that occur in the landscape both endemic and threatened large
mammals are spread across the landscape but occurring in the central areas (Kahuzi Biega and
RGPU) and Itombwe Reserve to the south and the community reserves to the north up to Maiko
Park (figure 6a). Threatened plants are similarly widespread as they tend to be the timber species
harvested by people but can be relatively abundant where they are not harvested. (figure 6c).
For all birds, amphibians, small mammals and for endemic plants the highlands in the east of
the MTKB landscape are critical for their conservation. These highland areas of the Albertine Rift are
known to be especially rich in species particularly the restricted range and threatened species
(Plumptre et al. 2007). The highland areas of Tayna and Kisimba-Ikobo Community Reserves, Kahuzi
Biega Park and much of the Itombwe Reserve should be target areas for biodiversity conservation.
One area identified as currently unprotected and with no plans to protect as yet is the
highland escarpment running south from the eastern part of Itombwe Reserve down towards the
proposed Ngamikka Park (outside the MTKB landscape). This escarpment still has intact natural
habitat of montane forest and bamboo with a few gorillas and chimpanzees left. WCS made a survey
of the Balala forest in this area in February 2014 which showed that it was rich in species and worth
conserving still.
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Figure 6a. Maps of the number of endemic (or near endemic) large mammals (top left), threatened
large mammals (top right), endemic small mammals (bottom left) and threatened small mammals
(bottom right). Maps were generated by overlaying the species distribution maps and summing the
number of species present in 1 km cells.
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Figure 6b. Maps of the number of endemic birds (top left), threatened birds (top right), endemic
amphibians (bottom left) and threatened amphibians (bottom right). Maps were generated by
overlaying the species distribution maps and summing the number of species present in 1 km cells.
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Figure 6c. Maps of the number of endemic plants (top left) and threatened plants (top right). Maps
were generated by overlaying the species distribution maps and summing the number of species
present in 1 km cells.
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Priority Areas for Conservation in the MTKB Landscape
Key areas identified from modelling and surveys
The results of this assessment shows that the MTKB landscape is particularly rich in species
and important for the conservation of globally threatened and endemic species. The area of the
landscape is large, however and conservation practitioners cannot hope to conserve the whole
landscape with the current limited resources. There is therefore a need to concentrate efforts on
those sites that are important for biodiversity or still contain a reasonable number of some of the
large mammal species which are being hunted extensively across the landscape. From the analyses
presented above we present the key areas we have identified as priorities for conservation
intervention.
Itombwe Reserve and Tanganyika escarpment
This region is one of the richest areas for endemic species. Itombwe Reserve has several
species that are endemic to the massif, particularly some small mammals and amphibians but also
Schouteden’s swift (Schoutedenapus schoutedeni). Conservation of this massif has long been a goal
with reports made since the 1970s about the conservation importance of the area (Prigogine 1971-
1984; Wilson and Catsis, 1990; Doumenge, 1998; Ilambu et al. 1999; Greenbaum & Chifundera,
2012). It is known to also conserve the Congo Bay Owl (Phodilus prigoginei) which has only had one
observation outside Itombwe in the Kibira National Park in Burundi.
The results show that it is the highland areas of the Itombwe massif that are the most
important for the conservation of the threatened and endemic species. The proposed Itombwe
Reserve is shown in figures 6a-c and while it captures some of the highland escarpment there is
much of this region outside the proposed protected area. WCS made a survey of part of the
escarpment to the south of Itombwe in 2014, known as Balala Forest, and showed that this area still
contained many of the endemic and threatened species, with possibly some new amphibian species
for science. There is potential to conserve this area and potentially bring in some income from bird
tourism. It contained the Congo Bay Owl and Schouteden’s swift and had a road that reached the
edge of the forest which would make tourism a potential option. There were also signs of a few
remaining gorillas which had been found here in surveys by WCS in the 1990s (Ilambu et al. 1999).
Kahuzi Biega National Park and RGPU
The Kahuzi Biega National Park is important for both biodiversity conservation as well as
conservation of Grauer’s gorillas. In 1994 it was estimated to contain 70% of the Grauer’s gorilla
population in the World (Hall et al. 1998) although it is known that the number here have declined
drastically because of hunting by rebel groups (Plumptre et al. in prep.). The highland area of the
park in the east is particularly important for the conservation of endemic and threatened species
according to the modelling (figures 6a-c) but the lowland has been poorly surveyed for its
biodiversity. A recent WCS survey to the west of the park re-discovered a frog that has only been
found twice since its discovery 65 years ago; Cardioglossa cyaneospila in the lowland areas and
several specimens from the region may be new species.
The western part of the park seems to be the least impacted by rebel groups and mining
camps and it is here that gorilla sign is found more frequently together with elephant signs. These
also occur outside the park in the adjacent RGPU community reserve. The RGPU which was part of
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21
the Kasese region in Hall et al.’s (1998) survey was identified in those surveys as also being
important for gorillas. From the occupancy analysis ths is a critical region for the conservation of
remaining Grauer’s gorillas and conservation activities should be focused here to ensure the survival
of this species.
Tayna, Kisimba-Ikobo and Usala
This region is also important for biodiversity conservation and has also been poorly surveyed
for its biodiversity. Surveys of birds were made in this region by Prigogine (1953) which showed a
rhigh bird diversity but few other taxa have been surveyed here. Since that time though much of the
forest has been converted to agriculture in the area he surveyed, as deforestation has spread
westward from the east.
WCS made a short visit to Kisimba-Ikobo Community Reserve in 2013 but our teams were
attacked by people antagonistic to the reserve and we were only there for a few days. The bird team
noted that Grauer’s cuckoo-shrike (Coracina graueri) was particularly abundant. Although it has
been recorded in Kahuzi Biega National Park previously (Muhlenberg, Slowik and Steinhauer
undated) it is uncommon and has not been observed in surveys WCS has made there. Similarly our
botanical team noted several potentially new species of plant but unfortunately lost the specimens
when they were chased from the reserve. Therefore it appears the Tayna and Kisimba-Ikobo area
could be important for biodiversity conservation as predicted by the models but more surveys are
really needed.
Grauers gorillas still occur in Tayna, Kisimba-Ikobo and Usala. The encounter rates of signs of
this species per km walked by field teams was relatively high and indicate that this is one of the
areas (besides Itombwe) outside the Kahuzi-RGPU region for the conservation of this species. It also
has signs of okapis present in this area, particularly Usala and Tayna Community Reserves.
Maiko National Park
While Maiko National Park was not predicted to be important for biodiversity conservation it
does contain a reasonable number of signs of elephants, okapis and Grauer’s gorillas where field
teams have been able to access, Given the insecurity that has been present in the park since the
1960s it has been little surveyed and there is a need to get teams into the centre of the park as well
as the higher altitude areas to the east.
Conservation planning approach to priority conservation areas
The results given above show the areas important for different taxa. But is it necessary to
conserve all the sites of high biodiversity or would it be possible to capture most of the biodiversity
in one or two sites so that resources could be better invested? This is where conservation planning
methods become valuable.
Marxan (Possingham et al. 2000) is a software program that helps develop conservation plans
that ensures that all species are represented adequately in the final conservation plan. It uses an
algorithm called simulated annealing to try to find the optimum solution to this problem from
several billion possible options. We overlaid 5km2 hexagonal cells across the Albertine Rift as a
whole covering 993,044 km2. We assigned each species range to a cell if more than 50% of the cell
was predicted as containing the species. Targets are set in Marxan by the user to ensure that a viable
population for each species is conserved. For species that are relatively abundant where they occur
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we selected 30% of cells as a target but for species that require large areas of habitat to maintain
viable populations such as elephants, apes and large carnivores, or species that occur at very low
density we selected higher percentages of cells to ensure their needs would be ensured in the final
conservation plan. Marxan doesn’t necessarily find the optimal solution but usually gets close to it. It
therefore is useful to run Marxan many times and assess which cells are selected regularly and which
are not selected so often. We ran Marxan 100 times to obtain a percentage selection frequency for
each cell.
We used a cost layer that gave a cheaper cost to existing parks but did not reduce the costs
of the community reserves or the proposed Itombwe Reserve as they are not well protected at
present. The results show that all of the existing parks are selected because they were locked into
the analysis but there are areas selected outside the protected areas that are needed to conserve
the other biodiversity of the region (figure 7). These include the Tayna, Kisimba-Ikobo and Usala
region, the Itombwe Reserve (particularly the eastern part of it) and the escarpment south of
Itombwe towards the Misotshi-Kabogo region with the proposed Ngamikka National Park (figure 7).
This analysis therefore shows that these three areas are critical for species conservation and
do not conserve the same species but add to the biodiversity of the Albertine Rift. These are areas
identified in the section above and together with the Kahuzi Biega-RGPU region are the priority
areas for conservation to target in the MTKB landscape.
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Figure 7. The result of the Marxan analysis of the endemic and threatened species of five taxa in the
Albertine Rift. The result shows the percentage of selection after 100 runs of the analysis for each
5km2 cell
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Images of Priority sites in the MTKB Landscape: © A.J.Plumptre/WCS
Itombwe Reserve
North Balala – escarpment south of Itombwe
Kahuzi Biega National Park
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Acknowledgements
The support to making the analyses in this report comes from the US Fish and Wildlife Service and
USAID. Funding for the many surveys that WCS has made in the Albertine Rift region comes from
many sources including US Fish and Wildlife Service, IUCN SOS Program, Critical Ecosystems
Partnership Fund, Rainforest Trust, USAID, MacArthur Foundation and Daniel Thorne
Foundation.We are grateful to Dan Segan for the results of the Marxan analysis and help from
Ghislain Vieilledent for help with the spatial occupancy analysis.
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Appendix. Threatened and Albertine Rift endemic species found in the
MTKB Landscape
Mammals
FAMILY
SPECIES
ENGLISH
IUCN
category
IUCN criteria
Endemic
Rhinolophidae
Rhinolophus hillii
Hill's Horseshoe Bat
CR
B1ab(iii,v)+2ab(iii,v)
AR
Cricetidae
Dendromus kahuziensis
Kahuzi Climbing Mouse
CR
B1ab(iii)
AR
Cercopithecidae
Cercopithecus lhoesti
L'hoest's monkey
VU
A4cd
NE
Proboscidea
Loxodonta africana
African Elephant
VU
A2a
Hominidae
Pan troglodytes
Chimpanzee
EN
A4cd
Muridae
Lophuromys rahmi
Brush-furred Rat
EN
B1ab(iii)
AR
Hominidae
Gorilla beringei graueri
Grauer's Gorilla
EN
A1cd+2cd
NE
Cercopithecidae
Procolobus rufomitratus
Red colobus
EN
A1cd+2cd, B1+2abc
Proboscidea
Loxodonta cyclotis
Forest Elephant
EN
A2a
Soricidae
Crocidura lanosa
Kivu Long-haired Shrew
EN
B1ab(iii)
AR
Soricidae
Crocidura stenocephala
Musk Shrew
EN
B1ab(ii,iii)
AR
Soricidae
Crocidura kivuana
Kivu Shrew
VU
D2
AR
Soricidae
Sylvisorex lunaris
Forest Shrew
VU
B1ab(iii)
AR
Rhinolophidae
Rhinolophus ruwenzorii
Ruwenzori Horseshoe
Bat
VU
B1ab(ii,iii,iv,v)
AR
Muridae
Lophuromys medicaudatus
Brush-furred Rat
VU
B1ab(iii)
AR
Muridae
Thamnomys venustus
Charming Thicket Rat
VU
B1ab(iii)
AR
Cercopithecidae
Cercopithecus hamlyni
Owl-faced monkey
VU
A4cd
NE
Hippopotamidae
Hippopotamus amphibius
Hippopotamus
VU
A4cd
Soricidae
Myosorex schalleri
Schaller's Mouse Shrew
DD
AR
Soricidae
Paracrocidura graueri
Grauer's Montane Shrew
DD
AR
Muridae
Lophuromys cinereus
Brush-furred Rat
DD
AR
Soricidae
Paracrocidura maxima
East African Montane
Shrew
NT
AR
Soricidae
Crocidura niobe
Ruwenzori Musk Shrew
NT
AR
Soricidae
Myosorex babaulti
Mouse Shrew
NT
AR
Soricidae
Sylvisorex vulcanorum
Forest Shrew
NT
AR
Tenrecidae
Micropotamogale ruwenzorii
Ruwenzori Otter Shrew
NT
AR
Muridae
Grammomys dryas
Montane Thicket Rat
NT
AR
Sciuridae
Heliosciurus ruwenzorii
Montane Sun Squirrel
AR
Muridae
Praomys degraafi
AR
Muridae
Lophuromys woosnami
Woosnam's Brush-furred
rat
AR
Muridae
Mus bufo
Western Rift Pygmy
Mouse
AR
CR: Critically Endangered; EN: Endangered; VU: Vulnerable; DD: Data deficient; NT: Near Threatened
AR: Albertine Rift Endemic; NE: Near Endemic
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Birds
FAMILY
SPECIES
ENGLISH
IUCN
category
IUCN criteria
Endemic
Accipitridae
Gyps africanus
African White-backed
Vulture
CR
A2bcd+3bcd+4bc
d
Strigidae
Phodilus prigoginei
Congo Bay Owl
EN
B1ab(i,ii,iii,v)
AR
Caprimulgidae
Caprimulgus prigoginei
Itombwe Nightjar
EN
B1ab(i,ii,iii,v)
AR
Pycnonotidae
Chlorocichla prigoginei
Prigogine's greenbul
EN
B1ab(i,ii,iii,iv,v)
AR
Sylviidae
Bradypterus graueri
Grauer's Rush Warbler
EN
B2ab(ii,iii,iv,v)
AR
Ploceidae
Ploceus aureonucha
Golden-naped weaver
EN
C2a(ii)
AR
Ardeidae
Ardeola idae
Madagascar Squacco
Heron
EN
C2a(ii)
Gruidae
Balearica regulorum
Grey-crowned Crane
EN
A2acd+4acd
Strigidae
Glaucidium albertinum
Albertine Owlet
VU
C2a(i)
AR
Apodidae
Schoutedenapus schoutedeni
Schouteden's swift
VU
C2a(ii)
AR
Eurylaimidae
Pseudocalyptomena graueri
African Green Broadbill
VU
B1ab(i,ii,iii,v);C2a(
i)
AR
Prionopidae
Prionops alberti
Yellow-crested Helmet
Shrike
VU
C2a(i)
AR
Nectariniidae
Cinnyris rockefelleri
Rockefeller's Sunbird
VU
D1
AR
Estrildidae
Cryptospiza shelleyi
Shelley's Crimson-wing
VU
C2a(i)
AR
Accipitridae
Polemaetus bellicosus
Martial Eagle
VU
A2acde+3cde+4ac
de
Phasianidae
Afropavo congensis
Congo peacock
VU
C2a(i)
Psittacidae
Psittacus erithacus
Grey Parrot
VU
A2abcd+3bcd+4a
bcd
Bucerotidae
Bucorvus leadbeateri
Southern ground Hornbill
VU
A4bcd
Muscicapidae
Muscicapa lendu
Chapin's Flycatcher
VU
C2a(i)
Campephagidae
Lobotos oriolinus
Eastern Wattled Cuckoo
Shrike
DD
Phasianidae
Francolinus nobilis
Handsome Francolin
AR
Musophagidae
Tauraco johnstoni
Rwenzori Turaco
AR
Caprimulgidae
Caprimulgus ruwenzorii
Ruwenzori Nightjar
AR
Paridae
Parus fasciiventer
Stripe-breasted Tit
AR
Turdidae
Alethe poliophrys
Red-throated Alethe
AR
Turdidae
Cossypha archeri
Archer's Ground Robin
AR
Sylviidae
Apalis personata
Montane Masked Apalis
AR
Sylviidae
Oreolais ruwenzori
Collared Apalis
AR
Sylviidae
Graueria vittata
Grauer's Warbler
AR
Sylviidae
Hemitesia neumanni
Short-tailed/Neumann's
Warbler
AR
Sylviidae
Phylloscopus laetus
Red-faced Woodland
Warbler
AR
Muscicapidae
Melaenornis ardesiacus
Yellow-eyed Black
Flycatcher
AR
Muscicapidae
Batis diops
Rwenzori Batis
AR
Nectariniidae
Cyanomitra alinae
Blue-headed Sunbird
AR
Nectariniidae
Nectarinia purpureiventris
Purple-breasted Sunbird
AR
Nectariniidae
Cinnyris regia
Regal Sunbird
AR
Nectariniidae
Cinnyris stuhlmanni
Ruwenzori Double-
collared Sunbird
AR
Ploceidae
Ploceus alienus
Strange Weaver
AR
Estrildidae
Cryptospiza jacksoni
Dusky Crimson-wing
AR
CR: Critically Endangered; EN: Endangered; VU: Vulnerable; DD: Data deficient; NT: Near Threatened
AR: Albertine Rift Endemic;
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Reptiles
FAMILY
SPECIES
ENGLISH
IUCN
category
IUCN
criteria
Endemic
Chamaeleonidae
Kinixys erosa
DD
Lacertidae
Adolfus vauereselli
AR
Viperidae
Atheris nitschei
AR
Chamaeleonidae
Bradypodion adolfifriderici
AR
Chamaeleonidae
Chamaeleo ituriensis
AR
Chamaeleonidae
Chamaeleo johnstoni
AR
Chamaeleonidae
Chamaeleo rudis
AR
Chamaeleonidae
Trioceros schoutedeni
AR
Gekkondiae
Cnemaspis quattuorseriata
AR
Viperidae
Leptosiaphos blochmanni
AR
Viperidae
Leptosiaphos graueri
AR
Viperidae
Leptosiaphos luberoensis
AR
Viperidae
Leptosiaphos meleagris
AR
Viperidae
Leptosiaphos rhodurus
AR
DD: Data deficient; AR: Albertine Rift Endemic;
Amphibians
FAMILY
SPECIES
ENGLISH
IUCN
category
IUCN
criteria
Endemic
Pipidae
Xenopus itombwensis
Itombwe massif clawed frog
CR
B1ab(iii)
AR
Hyperoliinae
Hyperolius leleupi
Luvubu Reed Frog
EN
B1ab(iii)
AR
Hyperoliinae
Hyperolius leucotaenius
White-striped Reed Frog
EN
B1ab(iii)
AR
Hyperolidae
Callixalus pictus
Painted African Frog
VU
AR
Hyperoliinae
Hyperolius castaneus
Ahl's Reed Frog
VU
B1ab(iii)
AR
Hyperoliinae
Hyperolius chrysogaster
Goldbelly Reed Frog
VU
B1ab(iii)
AR
Hyperoliinae
Hyperolius discodactylus
Disc-fingered Reed Frog
VU
B1ab(iii)
AR
Hyperoliinae
Hyperolius frontalis
Bushoho Reed Frog
VU
B1ab(iii)
AR
Kassinae
Afrixalus orophilus
Kivu Banana Frog
VU
B1ab(iii)
AR
Phrynobatrachidae
Phrynobatrachus
acutirostris
Rugegewald River Frog
VU
B1ab(iii)
AR
Phrynobatrachidae
Phrynobatrachus bequaerti
Visoke River Frog
VU
B1ab(iii)
AR
Phrynobatrachidae
Phrynobatrachus versicolor
Rwanda River Frog
VU
B1ab(iii)
AR
Arthroleptidae
Arthroleptis pyrrhoscelis
Kivu Screeching Frog
NT
AR
Arthroleptidae
Arthroleptis vercammeni
Mwana Screeching Frog
DD
AR
Arthroleptidae
Cardioglossa cyaneospila
Mukuzira Long-fingered Frog
DD
AR
Bufonidae
Laurentophryne parkeri
Parker's Tree Toad
DD
AR
Hyperolidae
Chrysobatrachus
cupreonitens
Itombwe Golden Frog
DD
AR
Hyperoliinae
Hyperolius diaphanus
DD
AR
Phrynobatrachidae
Phrynobatrachus asper
Itombwe River Frog
DD
AR
Hyperolidae
Leptopelis fiziensis
Mokanga Forest Treefrog
DD
Arthroleptidae
Arthroleptis aldolfifriderici
Rugegewald Screeching Frog
AR
Hyperolidae
Leptopelis karissimbiensis
AR
Pipidae
Xenopus vestitus
Kivu Clawed Frog
AR
Pipidae
Xenopus wittei
Witte's Clawed Frog
AR
CR: Critically Endangered; EN: Endangered; VU: Vulnerable; DD: Data deficient; NT: Near Threatened
AR: Albertine Rift Endemic;
Priority areas for conservation in the MTKB Landscape
Wildlife Conservation Society
31
Plants
FAMILY
SPECIES
IUCN
category
IUCN criteria
Endemic
Sapotaceae
Autranella
congolensis
Autranella congolensis
Autranella congolensis
CR
A1cd
Scytopetalaceae
Brazzeia longipedicellata
EN
B1+2c
Caesalpiniaceae
Dialium excelsum
EN
B1+2c
Huaceae
Afrostyrax lepidophyllus
VU
A1c, B1+2c
Lauraceae
Beilschmiedia ugandensis
VU
A2d
Acanthaceae
Brillantaisia lancifolia
VU
D2
Meliaceae
Entandrophragma cylindricum
VU
A1cd
Meliaceae
Entandrophragma utile
VU
A1cd
Clusiaceae
Garcinia kola
VU
A2cd
Meliaceae
Guarea cedrata
VU
A1c
Meliaceae
Guarea thompsonii
VU
A1c
Rubiaceae
Hallea stipulosa
VU
A1cd
Meliaceae
Khaya anthotheca
VU
A1cd
Meliaceae
Lovoa trichilioides
VU
A1cd
Melastomataceae
Memecylon bequaertii
VU
B1+2c
Lauraceae
Ocotea kenyensis
VU
A1cd
Rubiaceae
Pauridiantha divaricata
VU
B2ab(iii)
Rubiaceae
Pavetta intermedia
VU
B1+2c
Rosaceae
Prunus africana
VU
A1cd
Rubiaceae
Psychotria cyathicalyx
VU
B1+2b
Rubiaceae
Rytigynia nodulosa
VU
B1+2b
Asclepiadaceae
Secamone racemosa
VU
A2c
Apiaceae
Stenandrium gabonicum
VU
D2
Rubiaceae
Tarenna drummondii
VU
B1+2b
Rubiaceae
Tarenna quadrangularis
VU
B1+2b
Meliaceae
Turraeanthus africanus
VU
A1cd
Loranthaceae
Agelanthus myrsinifolius
AR
Rosaceae
Alchemilla hendrickxii
AR
Clusiaceae
Allanblackia kimbiliensis
AR
Orchidaceae
Ancistrorhynchus ovatus
AR
Myrsinaceae
Ardisia kivuensis
AR
Nephrolepidaceae
Arthropteris anniana
AR
Aspleniaceae
Asplenium bugoiense
AR
Aspleniaceae
Asplenium lambinonii
AR
Aspleniaceae
Asplenium mildbraedii
AR
Aspleniaceae
Asplenium rukararense
AR
Aspleniaceae
Asplenium tenuicaudatum
AR
Begoniaceae
Begonia pulcherrima
AR
Begoniaceae
Begonia schultzei
AR
Begoniaceae
Begonia zaïrensis
AR
Lauraceae
Beilschmiedia michelsonii
AR
Lauraceae
Beilschmiedia rwandensis
AR
Asteraceae
Bidens elliotii
AR
Dennstaedtiaceae
Blotiella bouxiniana
AR
Asteraceae
Bothriocline nyungwensis
AR
Asteraceae
Bothriocline ruwenzoriensis
AR
Orchidaceae
Bulbophyllum burttii
AR
Orchidaceae
Bulbophyllum vulcanicum
AR
Flacourtiaceae
Casearia runssorica
AR
Rubiaceae
Chassalia subochreata
AR
Melastomataceae
Cincinnobotrys speciosa
AR
Verbenaceae
Clerodendrum frutectorum
AR
Cucurbitaceae
Coccinia mildbraedii
AR
Sterculiaceae
Cola pierlotii
AR
Asteraceae
Crassocephalum ducis-aprutii
AR
Priority areas for conservation in the MTKB Landscape
32
Wildlife Conservation Society
FAMILY
SPECIES
IUCN
category
IUCN criteria
Endemic
Orchidaceae
Cynorkis kassneriana
AR
Orchidaceae
Cynorkis symoensii
AR
Orchidaceae
Diaphananthe ugandensis
AR
Melastomataceae
Dinophora spenneroides
AR
Lycopodiaceae
Diphasiastrum carolinum
AR
Athyriaceae
Diplazium humbertii
AR
Orchidaceae
Disa eminii
AR
Melastomataceae
Dissotis ruandensis
AR
Orchidaceae
Eggelingia ligulifolia
AR
Lomariopsidaceae
Elaphoglossum kivuense
AR
Myrsinaceae
Embelia libeniana
AR
Loranthaceae
Englerina schubotziana
AR
Ericaceae
Erica johnstoniana
AR
Ericaceae
Erica kingaensis
AR
Loranthaceae
Globimetula kivuensis
AR
Tiliaceae
Grewia mildbraedii
AR
Orchidaceae
Habenaria coeloglossoides
AR
Hypericaceae
Harungana montana
AR
Asteraceae
Helichrysum helvolum
AR
Lycopodiaceae
Huperzia mildbraedii
AR
Hypericaceae
Hypericum humbertii
AR
Balsaminaceae
Impatiens erecticornis
AR
Balsaminaceae
Impatiens gesneroidea
AR
Balsaminaceae
Impatiens irangiensis
AR
Balsaminaceae
Impatiens iteberoensis
AR
Balsaminaceae
Impatiens keilii
AR
Balsaminaceae
Impatiens mildbraedii
AR
Balsaminaceae
Impatiens purpureo-violacea
AR
Balsaminaceae
Impatiens warburgiana
AR
Acanthaceae
Isoglossa laxiflora
AR
Acanthaceae
Isoglossa vulcanicola
AR
Rubiaceae
Ixora burundiensis
AR
Rubiaceae
Keetia angustifolia
AR
Asphodelaceae
Kniphofia princiae
AR
Asteraceae
Lactuca stipulata
AR
Melastomataceae
Lijndenia bequaertii
AR
Lobeliaceae
Lobelia mildbraedii
AR
Lobeliaceae
Lobelia petiolata
AR
Lobeliaceae
Lobelia stuhlmannii
AR
Orchidaceae
Margelliantha burtii
AR
Lamiaceae
Margelliantha lebelii
AR
Theaceae
Melchiora schliebenii
AR
Orchidaceae
Microcoelia bulbocalcarata
AR
Orchidaceae
Microcoelia nyungwensis
AR
Acanthaceae
Mimulopsis excellens
AR
Acanthaceae
Mimulopsis runssorica
AR
Annonaceae
Monanthotaxis orophila
AR
Moraceae
Musanga leo-errerae
AR
Lauraceae
Ocotea michelsonii
AR
Lamiaceae
Octomeron montanum
AR
Dennstaedtiaceae
Odontosoria africana
AR
Apiaceae
Oenanthe mildbraedii
AR
Rubiaceae
Otiophora pauciflora
AR
Rubiaceae
Oxyanthus troupinii
AR
Rubiaceae
Oxyanthus ugandensis
AR
Rubiaceae
Pavetta bagshawei
AR
Rubiaceae
Pavetta pierlotii
AR
Rubiaceae
Pavetta troupinii
AR
Priority areas for conservation in the MTKB Landscape
Wildlife Conservation Society
33
FAMILY
SPECIES
IUCN
category
IUCN criteria
Endemic
Thymeleaceae
Peddiea orophila
AR
Thymeleaceae
Peddiea rapaneoides
AR
Apiaceae
Peucedanum runssoricum
AR
Loranthaceae
Phragmanthera edouardii
AR
Urticaceae
Pilea bambuseti
AR
Lamiaceae
Plectranthus serrulatus
AR
Araliaceae
Polyscias kivuensis
AR
Orchidaceae
Polystachya aconitiflora
AR
Orchidaceae
Polystachya cribbiana
AR
Orchidaceae
Polystachya dewanckeliana
AR
Orchidaceae
Polystachya doggetti
AR
Orchidaceae
Polystachya eurygnatha
AR
Orchidaceae
Polystachya fabriana
AR
Orchidaceae
Polystachya fallax
AR
Orchidaceae
Polystachya kermesina
AR
Orchidaceae
Polystachya mildbraedii
AR
Orchidaceae
Polystachya pachychila
AR
Orchidaceae
Polystachya poikilantha
AR
Orchidaceae
Polystachya troupiniana
AR
Orchidaceae
Polystachya virginea
AR
Orchidaceae
Polystachya vulcanica
AR
Orchidaceae
Polystachya woosnamii
AR
Hippocrateceae
Pristimera polyantha
AR
Rubiaceae
Pseudosabicea arborea
AR
Rubiaceae
Psychotria bugoyensis
AR
Rubiaceae
Psychotria kahuziensis
AR
Lamiaceae
Pycnostachys goetzenii
AR
Ranunculaceae
Ranunculus rugegensis
AR
Zingiberaceae
Renealmia montana
AR
Zingiberaceae
Renealmia orophila
AR
Orchidaceae
Rhipidoglossum ovale
AR
Rosaceae
Rubus kirungensis
AR
Rosaceae
Rubus runssorensis
AR
Rubiaceae
Rytigynia bagshawei
AR
Rubiaceae
Rytigynia bridsoniae
AR
Rubiaceae
Rytigynia bugoyensis
AR
Rubiaceae
Rytigynia kigeziensis
AR
Rubiaceae
Rytigynia ruwenzoriensis
AR
Acanthaceae
Saintpauliopsis lebrunii
AR
Araliaceae
Schefflera urostachya
AR
Asteraceae
Senecio johnstonii
AR
Asteraceae
Senecio mariettae
AR
Asteraceae
Senecio rugegensis
AR
Asteraceae
Senecio transmarinus
AR
Asteraceae
Solanecio kanzibiensis
AR
Turneraceae
Stapfiella claoxyloides
AR
Turneraceae
Stapfiella lucida
AR
Orchidaceae
Stolzia cupuligera
AR
Orchidaceae
Stolzia williamsonii
AR
Apocynaceae
Strophanthus bequaertii
AR
Gentianaceae
Swertia adolfi-friderici
AR
Gentianaceae
Swertia macrosepala
AR
Apocynaceae
Tabernaemontana odoratissima
AR
Acanthaceae
Thunbergia mildbraediana
AR
Orchidaceae
Tridactyle eggelingii
AR
Orchidaceae
Tridactyle virgula
AR
Fabaceae
Trifolium purseglovei
AR
Caryophyllaceae
Uebelinia kiwuensis
AR
Priority areas for conservation in the MTKB Landscape
34
Wildlife Conservation Society
FAMILY
SPECIES
IUCN
category
IUCN criteria
Endemic
Ericaceae
Vaccinium stanleyi
AR
Asteraceae
Vernonia kirungae
AR
Asteraceae
Vernonia scaettae
AR
Melastomataceae
Warneckea walikalense
AR
Xyridaceae
Xyris valida
AR
CR: Critically Endangered; EN: Endangered; VU: Vulnerable; AR: Albertine Rift Endemic;
