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 1 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. 2 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