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Patterns of Distribution, Diversity and Endemism of Larger African Mammals

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Patterns of Distribution, Diversity and Endemism of Larger African Mammals S. Afr.]. Zool. 1994,29(1) 19 Patterns of distribution, diversity and endemism of larger African mammals J.K. Turpie' and T.M. Crowe FitzPatrick Institute, Universrtyof Cape Town, Rondebosch, 7700 Republic of South Africa Received 17 November 1992; accepted J June J993 Pallerns of distribution and diversity (= species richness) of larger African mammals, and three subsets thereof (ungulates, carnivores and primates), are identrtied and analysed quantitatively. Distributional patterns generally correspond well with those of presenf.<lay vegetation types' and of resident non-aquatic birds. However, the precise locations of zonal boundaries and the degree of zonation differ between groups, primarily reflecting their different ecological requirements. Large mammal diversrty increases towards the equator, but diversity palterns differ substantially between the groups. Diversity is positively correlated with vegetation type diversrty, and vegetation types analysed as separate unrts explain over 85% of the variance in the diversity of the mammal groups investigated. In addnion, both distributionat and diversity palterns appear to have been affected by past environmental factors. Diversrteits- en verspreidingspatrone van groot Afrika soogdiere, en die van drie groepe (hoefdiere, vie is­ vreters en primate), word g'-'dentifiseer en kwantitatief geanaliseer. Alhoewel die verspreidingspatrone goed qo_~~~nst~I'11_r:neU~.Le _v_an_l1u idige plantsoorte~9n>-plaasl ike_nie-akwatiese- yoels; verskil-verspreidi ngsgrense en die graad van verskeidenheid tussen groeps, wat verskillende ekologiese behoeftes weerspies!. Terwyl die verskeidenheid van groet soogdiere nader aan die ewenaar vermeerder I verskil die patrone van vefskeiden­ heid tot 'n groat mate tusssn groepe. Daar is 'n positiewe korrelasie tussen soogdierdiversiteit en plantegroei­ diversiteit, en plantegroei-soorte wat as afsonderlike eenhede g9-analiseer is, vorklaar meer as 85% van die variasie in die soogdiergroepe wat ondersoek is. Verder blyk dit asof beide verspreidings- en verskeiden­ heidspatrone beinvioed was deur omgewingsfaktore wat in die verlede geheers het. '" To whom correspondence should be addressed Any study of biotic distribution, diversity and endemism which occur outside of southern Africa. A 160-quadrat grid requires the identification of pallem before an understanding (Figure I), was used to extract the distributional information of the underlying causal processes can be achieved (Nelson for each species for each quadrat, using a scoring system of . ) 9 & Platnick 1981). In this study, we attempt to identify and 0--10 (0 = absen~ 10 = occurring throughout quadrat). The 0 0 interpret patterns of distribution, diversity and endemism for quadrat size was chosen on the basis of software limitations 2 larger African mammals, and focus also on three main d and was smaller in area than all but 4% of the species' e t subsets thereof: ungulates, carnivores and primates. These ranges. Four mammal data sets were prepared for analysis: a d patterns are then interpreted in the light of past and present all species studied; ungulates (89 species); camivores (60 ( r distribution and diversity of vegetation. The use of vegeta­ species) and primates (45 species). e h tion as the primary factor which influences distribution/ s Vegetational information was extracted from White's i l diversity patterns can be justified as being ' ... the most (1983) vegetation map of Africa. His 80 vegetation types b u meaningful ecological summary of the influences of soil, (including sub-categories) were grouped into 49 broader P climate, topography and other static and dynamic environ­ e categories, though still adhering to the 17 major groups h t mental factors' (Davis 1962). described by the author (Appendix 2). For each quadrat. the y b percentage cover of each vegetation type and the number of d Methods vegetation types present were recorded. e t n Raw data a r Numerical and statistical methods g Distributional information for larger mammals which occur e in southern Africa was extracted from Smithers (1983). Patterns of distribution and diversity were determined for c n e Supplementary information for the remainder of the each of the four data sets (all large mammals, ungulates, c i l cominent was obtained from Dorst & Dandelot (1970) and carnivores and primates) using multivariate quantitative r e Haltenorth & Diller (1977). This study is based on ideal analyses, following the approach employed by Crowe & d Crowe (1982) and discussed in detail by Field, Clarke & n broad distributions of mammals in Africa, i.e. not taking u into account changes brought about by human settlement Warwick (1982). Cluster analysis, non-metric multidimen­ y a and associated vegetation changes. Major contractions in sional scaling and information statistic lests were performed w e faunal ranges, however, such as the disappearance of many on the quadrat distributional data in order to identify major t a large mammal species from the south-western Cape (Skead mammalian zones. The Bray-Curtis measure of similarity G t 1980) are reflected in the distribution maps analysed (Bray & Curtis 1957) and the unweighted pair-group e n (Smithers 1983). method (Sneath & Sokal 1973) were used in cluster analyses i b In all, 211 species were studied (Appendix I). Insecti­ to identify any hierarchic similarity among groups of quad­ a S vores, rodents and hats were not included in this study, rats. This approach excludes negative matches as evidence y b owing to the lack of distributional information for species of similarity and takes variation in abundance into accounl, d e c u d o r p e R 20 S.-Afr. Tydskr. Dierk. 1994,29(1) , between mammal zones. /or-; • ~ A correlation analysis was used to determine the relation ,./. , • • 11 ;{ .. ,J.. between species diversity for the four data sets (number of " " species per quadrat) and the number of vegetation types ~ .. a a\ L" '" " " " " " " present. Stepwise multiple linear regression (Allen 1973) » ~ » M ~ ~ " " " " .. .. .,\ was used to relate species diversity to a combination of ~ ., w .. .. .. .. .. .. .. ,,'\ vegetation types weighted by their percentage cover of each Q " " .. .. .. a I\r, .. .. • .. -'\ quadrat. Residual plots (deviations from the value predicted " " ",. " ~ " n " n " n " " " .. .. .. .. ..7 by the regression) were used to recognize areas which devi­ L.!!, ate substantially from the general trends. In these analyses, .. .. ~ .. .. .. .. .. ~ ,00) ". " " " those quadrats which had unexpectedly high species diver­ ,~ ,~ ,~ ,.. 7 " ". '" '" ". sity were considered as possible palaeoecological 'refugia' '" '" '", '" ". '" II( 117/ (sensu Haffer 1969) and, where endemism was also high, ,,. \", '" '" ,1 on ". these refugia were considered to be centres of speciation ,a (i.e. Type" refugia, sensu Crowe & Crowe 1982). ~25 '" ... ". »t n,L 133 ,~ ,,,\ i'" '" '" '" Results ,~ ,., ,~ 'w \'" ,~' V The results for the cluster analyses and MDS for' al1 , ... '.1 • , '\" ,.. .. mammals studied, and each subset thereof, are summarized , p ~ 1~ '" .. ". in Figures 2 and 3 respectively, and the zones of mammal l!e[7 distribution supponed by cluster analysis, MDS and I-tests 'Fe::'"' ./ are depicted in Figure 4. These zones are listed in Tables 1-4, together with information on species diversity, Figure 1 The grid quadrat system used to extract data from the endemism and characteristic species. Patterns of African mammal distribution maps. Superimposed on the grid are the mammal diversity are depicted in Figure 5. Table 5 lists the positions of the major lakes and pans in Africa in the following correlation cocfficient~ between mammal diversity and the grid squares: 63. Lake Chad; 69. Lake Tana; 98. Lake Rudolf, number of vegetation types, and results of the multiple 106. Lake Albert; 112. Lake Leopold II; 116. Lake Victoria; 122. regression analyses between mammal diversity and the Lake Tan7"nia; 130. Lake Malawi; 139. Etosha Pan; 142. Lake different vegetation types are shown in Table 6, The residual Kariba; 148. Makgadikgadi Pan. plots from the correlation between mammal diversity and . vegetation diversity are sh?wn in Figures 6 and 7. ) 9 0 as opposed to the alternative Jaccard coefficient which is 0 DIscussion 2 sensitive to both presence or absence of species and does d Patterns of distribution e not consider variation in relative abundance (Field & Mac­ t a Farlane 1968). The validity of quadrat grouping recognized The subregional boundaries of large mammals correspond d ( wel1 with boundaries between major African vegetation by the cluster analyses was assessed by producing a two­ r e dimensional ordination for each of the four data sets, using types (i.e., forest, savanna and arid zones; Figure 4a). The h s provincial division of the Saharan Subregion represents the i non-metric multidimensional scaling (MDS). MDS is a less l b constraining approach than cluster analysis, since a nonhernmost Iimil for some species (e.g. Addax nasomacu­ u P lalis, Crocula crocUla). However, few species are endemic hierarchic pattern of quadrat similarity is not forced onto the e to this subregion and none are endemic to its Nonhern h data (Crowe & Crowe 1982; sec Shepard 1980). Once the t Province (Table I). The division of the Forest Subregion is y zones (groups of quadrats) were identified, the Information b Statistic test (I-test; Field 1969) was used a posleriori to in{luenced by carnivore and primate distributions, and its d e Western Province has the highest relative endemism
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