The biological importance of the Eastern Arc Mountains of Tanzania and Kenya

Burgess, Neil D.; Butynski, Thomas M.; Cordeiro, Norbert J.; Doggart, Nike; Fjeldså, Jon; Howell, Kim M.; Kilahama, Felician Bakamaza.; Loader, Simon P.; Lovett, Jonathan Cranidge; Mbilinyi, Boniface; Menegon, Michele; Moyer, David; Nashanda, Evarist; Perkin, Andrew; Rovero, Francesco; Stanley, William T.; Stuart, Simon N.

doi:10.1016/j.biocon.2006.08.015

Cited by 478 publications

(489 citation statements)

References 105 publications

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“…Our estimation of current forest cover at over 4300 km 2 is more than some previous studies, but less than the 5700 km 2 of natural forest proposed by Newmark (2002). Other sources put the figure closer to 3500 km 2 (Mbilinyi et al 2006;Burgess et al 2007 and references therein), but are similar to Newmark's estimate if woodlands are included. The land cover data used here have been iteratively improved during a series of workshops in Tanzania through the Valuing the Arc Programme (http://valuingthearc.org/), and were further corrected for the current application by reference to forest change estimates, government reports and our own field notes.…”

Section: Discussionsupporting

confidence: 68%

Delimiting tropical mountain ecoregions for conservation

Platts

Burgess²,

Gereau

et al. 2011

Envir. Conserv.

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SUMMARYEcological regions aggregate habitats with similar biophysical characteristics within well-defined boundaries, providing spatially consistent platforms for monitoring, managing and forecasting the health of interrelated ecosystems. A major obstacle to the implementation of this approach is imprecise and inconsistent boundary placement. For globally important mountain regions such as the Eastern Arc (Tanzania and Kenya), where qualitative definitions of biophysical affinity are well established, rulebased methods for landform classification provide a straightforward solution to ambiguities in region extent. The method presented in this paper encompasses the majority of both contemporary and estimated preclearance forest cover within strict topographical limits. Many of the species here tentatively considered 'near-endemic' could be reclassified as strictly endemic according to the derived boundaries. LandScan and census data show population density inside the ecoregion to be higher than in rural lowlands, and lowland settlement to be most probable within 30 km. This definition should help to align landscape scale conservation strategies in the Eastern Arc and promote new research in areas of predicted, but as yet undocumented, biological importance. Similar methods could work well in other regions where mountain extent is poorly resolved. Spatial data accompany the online version of this article.

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Section: Discussionsupporting

confidence: 68%

Delimiting tropical mountain ecoregions for conservation

Platts

Burgess²,

Gereau

et al. 2011

Envir. Conserv.

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“…The Eastern Arc Mountains of East Africa comprise a chain of 13 mountain blocks stretching some 900 km from Taita Hills in Kenya through Tanzania's North and South Pare, West and East Usambara, Nguu, Nguru, Ukaguru, Uluguru, Malundwe, Rubeho, Udzungwa, and Mahenge (CEPF 2005;Burgess et al 2007). The Eastern Arc Mountains, declared as one of the 34 globally important biodiversity hotspots (Myers et al 2000;URT 2005;Okayasu 2008) and one of the top eco-regions for biodiversity importance in Africa (Burgess et al 2006), represent some of the oldest geological formations on the continent (Burgess et al 2007;Okayasu 2008).…”

Section: Study Area Eastern Arc Mountainsmentioning

confidence: 99%

Availability, Preference, and Consumption of Indigenous Forest Foods in the Eastern Arc Mountains, Tanzania

Msuya

Kideghesho

Mosha

2010

Ecology of Food and Nutrition

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“…The mountains are a chain of 13 disjoint blocs, isolated from the surrounding lowlands since the Miocene about 30 million years ago (Schlüter, 1997). Today they support 3300-5700 km 2 of moist tropical forest, though it has been estimated that this may be less than 30% of the original forest cover (Burgess et al, 2007b). Much of the remaining area is protected by forest and nature reserves, national parks and community-based management, many covering critical water catchments; the EAMs are a source of drinking water and hydroelectric power for over half of Tanzania's urban population.…”

Section: Study Regionmentioning

confidence: 99%

“…The archipelago-like distribution of mountain blocs promotes significantly higher range-size rarity than is found in some other high biodiversity tropical ecosystems (Taplin and Lovett, 2003;Burgess et al, 2007a), rendering EAM flora particularly sensitive to further fragmentation. Species richness scores are high and the concentrations of endemism are exceptional (Burgess et al, 2007b), though many hundreds of endemic plants and animals are threatened by extinction.…”

Section: Study Regionmentioning

confidence: 99%

Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

Platts

McClean

Lovett

et al. 2008

Ecological Modelling

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Article:Platts, Philip J. orcid.org/0000-0002-0153-0121, McClean, Colin J. orcid.org/0000-0002-5457-4355, Lovett, Jon C. et al. (1 more AbstractThe Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical forest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree taxa (38 species), using generalised additive models, plot data and environmental predictor maps at 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating autocovariate terms to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for taxa with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, whilst beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision. Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the 60 | Platts PhD Thesis intentions of the analyst (explanation or prediction) and on the quality of distribution data.Generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce.

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The biological importance of the Eastern Arc Mountains of Tanzania and Kenya

Cited by 478 publications

References 105 publications

Delimiting tropical mountain ecoregions for conservation

Delimiting tropical mountain ecoregions for conservation

Availability, Preference, and Consumption of Indigenous Forest Foods in the Eastern Arc Mountains, Tanzania

Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

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