1. The University of Copenhagen, Denmark & Makerere University, Kampala, Uganda Biodiversity: An Analysis of Taxa Congruence and the Question of Spatial Scale; and how this can contribute to Strategic Conservation Planning in Uganda Herbert Tushabe BSc., MSc. (MUK) Supervisor: Prof. Jon Fjeldså (Principal Supervisor) Zoological Museum, University of Copenhagen a PhD research project

2. Location in Africa Protected Areas Location in Africa

3. Objectives of the Study Main Objective  to develop and test methods for reserve selection and zonation based on taxa congruence and complementarity analyses. Specific objectives  To test whether selection of one or two taxa for conservation will effectively conserve other taxa - using data from Uganda’s Important Bird Areas (IBAs);

4. Criteria used, developed by BirdLife International:  Sites with globally threatened species  Sites with restricted-range species  Sites with biome-restricted assemblages  Sites with congregations of species, e.g. waterbirds. Congregations are considered for both global and sub-regional populations. Uganda’s Important Bird Areas IBAs range from <1 to 4,000 km2

5.  To test the usefulness of data collected by various levels of sampling effort in analysis of congruence;  To find the minimum set of Uganda’s IBAs one would need to effectively protect other taxa; and  To assess the usefulness of congruence and complementarity analysis in designing a system for protected areas; or for effective biodiversity conservation in existing ones. Specific Objectives Cont’d

6. Taxa Congruence: A Summary Conservation costs would be minimal, and efforts more effective, if the theory of congruence was true. This theory proposes that : conserving one or groups of several taxa in an ecosystem effectively conserves the rest and their species of conservation concern. In summary, areas that are species-rich for one or more taxa are rich for others; and rare species are nested within species-rich areas. Arguments (for and against): Identification of priority areas in light of huge gaps in data; and fragmented information Costs of inventories (resources, time), therefore use surrogates Criteria used in defining ‘hotspots’: absolute spp. richness, weighting, habitat loss Spatial scaling, taxa preferences, sampling effort differences Local, national, continental, global, biological commonalities

7. Study Approach  a practical test of congruence, complementarity and priority analysis using existing and field-collected data  This involves analysis of the importance of Uganda’s 30 important bird areas (IBAs) that were identified using internationally developed criteria.  the use of larger scale modelled data.  This involves use of prediction models already developed by the National Biodiversity Data Bank in Uganda, based on species distributions and environmental parameters associated with their habitats.  The Zoological Museum at the University of Copenhagen is currently employing the WORLDMAP software that uses interactive modelling to identify conservation priority areas, and some of Uganda’s IBAs have already been identified.  Results obtained by both models will be compared, as they are done at various spatial scales to determine levels of efficiency. Results obtained by modelling will be compared for efficiency with those obtained by use of extensive field work carried out in the IBAs, more especially as the field work will point out ‘negative’ records that may have been predicted. The study is involving two levels of analysis:

8. 1. Congruence and Complementarity Analyses  Data have been collected for the following taxa in 30 IBAs:  Vascular plants  Dragonflies  Butterflies  Birds  Analysis will evaluate the extent to which taxa overlap using various measures such as species richness; rarity and weighting (by developing a scoring system).  examine how areas complement each other in conservation of biodiversity – using the selected taxa as surrogates. Determine the minimum set required to conserve biodiversity in IBAs.

9. Sample Results  Three sites are considered here:  Bwindi Impenetrable NP (an IBA, forest ecosystem, 331km 2 )  Lutembe Bay (an IBA, wetland ecosystem, 8km 2 )  Sango Bay (outside IBA, savanna ecosystem, 6km 2 ) Area and species totals:

10. Species Accumulation

11. Accumulation Cont’d

12. Results Cont’d Correlation coefficients (Spearman’s) Correlation coefficients after correcting for area Here birds are good predictors for plants but poor for butterflies High Correlation Coefficients

13. Rainfall 2. Congruence using species prediction modelling Other Parameters that were considered: Human Population Density Ecological Zones Land Use/Land Cover Altitude Vegetation

14. Blue-spotted Wood Dove Northern Wheatear Prediction models examples Results of this model have been used to produce a bird atlas for Uganda that is soon to be published: CARSWELL, M., POMEROY, D., REYNOLDS, J. and TUSHABE, H. (in press). The Bird Atlas of Uganda. British Ornithologists’ Union/ British Ornithologists’ Club.

15. Prediction Modelling and Congruence  Analyses will be carried out to determine the extent of congruence of predicted species, and to determine whether the rare or other species of conservation concern (such as Red Data- listed species) are captured within the IBAs and other protected areas.  Also, in comparison with larger-scale modelled data, determine the extent to which congruence analyses are affected by spatial scale.

16. Analysis Tools Analysis Tools for Congruence: A computer programme, EstimateS (Colwell, 1994-99), will be used. This calculates the following estimators: Chao 1 (estimates true number of species in an assemblage based on number of rare spp in a sample Chao2 estimates the distribution of species among samples, using presence/absence data ACE (Abundance-based Coverage Estimator) developed by Chao & Lee (1992, 1994) estimates species richness based on abundance data (10 or fewer individuals in a sample) ICE (Incidence-based Coverage Estimator) developed by Chao & Lee (1992, 1994) estimates species richness based on incidence data (species in 10 or fewer sampling units) Other estimators calculated include: Jackknife 1; Jackknife 2; Bootstrap; Michaelis-Menten; as well as Alpha, Shannon and Simpson diversity indices These various estimators/indices have been tested by Colwell and Coddington (1994). In their analyses, the Chao2 and Jackknife 2 yielded the best results Analysis Tools for Complementarity: EstimateS calculates the Chao Estimator of Shared Species between sites, the Jaccard Index of Similarity as well as the Morisita-Horn Index. These can be used to evaluate the complementarity of the IBAs for species conservation.

17. Expected Results  Overall:  to help understand the extent of overlap of taxa in areas considered to be important for the conservation of species for one taxon.  to assess the extent to which conservationists can rely on results of the survey of one or few taxa that would act as surrogates for others, thereby saving resources and time in bd assessments for conservation planning.  show how smaller networks of reserves based on ideas of complementality can be more efficient in the conservation of biodiversity than larger areas that are difficult to manage.  Application to Conservation:  recommend conservation measures in areas selected as critical for biodiversity  scientific methods will be used for zonation of existing PAs to identify areas where conservation efforts can be intensified using hotspots identified  identification of most serious gaps based on complementarity analysis with pre-selection of areas which are already well protected