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a PhD research project 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) The University of Copenhagen, Denmark & Makerere University, Kampala, Uganda Supervisor: Prof. Jon Fjeldså (Principal Supervisor) Zoological Museum, University of Copenhagen Location in Africa Location in Africa Location in Africa Protected Areas 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); Uganda’s Important Bird Areas 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. IBAs range from <1 to 4,000 km2 Specific Objectives Cont’d 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. 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 Study Approach The study is involving two levels of analysis: a practical test of congruence, complementarity and priority analysis using existing and field-collected data the This involves analysis of the importance of Uganda’s 30 important bird areas (IBAs) that were identified using internationally developed criteria. 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. 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. Sample Results Three sites are considered here: Bwindi Impenetrable NP (an IBA, forest ecosystem, 331km2) Lutembe Bay (an IBA, wetland ecosystem, 8km2) Sango Bay (outside IBA, savanna ecosystem, 6km2) Area and species totals: Bwindi Lutembe Sango Bay Area (sq. km.) Plants Butterflies Birds 331 324 310 348 8 34 89 223 6 38 17 135 Species Accumulation Lutembe: Species Accumulation and Rarefaction Curves for Dragonflies Lutembe: Species Accumulation and Rarefaction Curves for Plants 160 16 140 14 12 Number of Spp 100 80 60 10 8 6 40 4 20 2 Sampling Hours Sampling Points Rarefaction: Finite est. Rarefaction: Infinite est. Accumulation Accumulation Rarefaction: Finite est. Rarefaction: Infinite est. 27 25 23 21 19 17 15 13 11 9 7 5 3 235 222 209 196 183 170 157 144 131 118 105 92 79 66 53 40 27 14 1 0 0 1 Number of Spp 120 Accumulation Cont’d Lutembe: Species Accumulation and Randomised Curves for Birds Species Accumulation and Rarefaction Curves for Butterflies 80 180 70 160 140 60 Number of Spp 40 30 100 80 60 20 40 10 20 Rarefaction: Infinite est. Accumulation Observed Rarefaction: Finite est. Rarefaction: Infinite est. 27 25 23 21 19 17 15 13 11 TSC Hours Sampling Hours Rarefaction: Finite est. 9 7 5 3 29 27 25 23 21 19 17 15 13 11 9 7 5 3 1 0 0 1 Number of Spp 120 50 Results Cont’d Correlation coefficients (Spearman’s) Plants Plants Butterflies Birds Butterflies Birds 1 0.968900011 1 0.906587633 0.9828221 1 High Correlation Coefficients Correlation coefficients after correcting for area Plants Plants Butterflies Birds Butterflies 1 -0.098816773 1 0.896968382 -0.528566 Birds 1 Here birds are good predictors for plants but poor for butterflies 2. Congruence using species prediction modelling Rainfall Vegetation Other Parameters that were considered: Human Population Density Ecological Zones Land Use/Land Cover Altitude Prediction models examples Blue-spotted Wood Dove Northern Wheatear 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. 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 Datalisted 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. 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. 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