Interpreting biogeographical boundaries among Afrotropical birds: spatial patterns in richness gradients and species replacement

Journal of Biogeography, 26, 459–474

Interpreting biogeographical boundaries amongAfrotropical birds: spatial patterns in richnessgradients and species replacementPaul H. Williams1, Helen M. de Klerk2,3 and Timothy M. Crowe2 1Biogeography and

Conservation Lab, The Natural History Museum, London SW7 5BD, U.K., 2Percy FitzPatrick

Institute, University of Cape Town, Rondebosch 7701, South Africa and 3Danish Centre for

Tropical Biodiversity, University of Copenhagen, DK-2100 Copenhagen Ø, Denmark

AbstractAim We analyse the geographical distribution of 1911 Afrotropical bird species using indicesof three simple biogeographic patterns. The first index, the frequency of species with rangeedges (Te), is formulated to map directly the density of species distribution limits, forcomparison with the results of traditional biogeographical classification and ordinationprocedures, in order to show variations in the strength and breadth of transition zones. Theother two indices are formulated to seek to distinguish as directly as possible between twocomponents within these transition-zone patterns: contributions from gradients in speciesrichness (Tg); and contributions from replacements among species (Tr). We test the ability ofthese indices to discover the same boundaries among Afrotropical bird faunas as one popularprocedure for classifying areas (TWINSPAN) and then use them to look for geographicaltrends in the different kinds of transition zones.

Location The analysis is restricted to the sub-Saharan or Afrotropical region, excluding theArabian Peninsula, Madagascar and all offshore islands.

Methods We record the presence of each species in 1961 1°×1° grid cells of the map. Toapply the three indices, each (core) grid cell in turn is compared with its neighbouring eightcells in the grid. The range edges index (Te) counts the number of species with range edgesbetween the core cell and the surrounding cells. The richness gradients index (Tg) counts thelargest difference in species richness measured diametrically across the core cell in any directionwhen there is a consistent trend in richness along this line of three cells. The speciesreplacements index (Tr) counts the number of species pairs recorded within a nine-cellneighbourhood that are not corecorded within any of the cells. Values for each of the 1961grid cells are calculated and used to produce colour-scale maps of transition zones.

Results Large-scale spatial patterns of variation in density of range edges (Te) are consistentwith classifications of the same data and with most previous biogeographical classificationsproposed for the region. Variation in richness gradients (Tg) and species replacements (Tr)explain different parts of this pattern, with transition zones around humid forests in theequatorial region being dominated by species replacement, and transition zones around deserts(most extensive in the north and south) being dominated by richness gradients.

Main conclusions The three indices distinguish the spatial arrangement and intensity ofdifferent kinds of transition zones, thereby providing a first step towards a more rigorousmechanistic understanding of the different processes by which they may have arisen and aremaintained. As an example of one such pattern shown by our analyses of Afrotropical birds,there is evidence for a broad latitudinal trend in the nature of transition zones in faunalcomposition (following the latitudinal distribution of the different kinds of habitat transitions),from being dominated by species replacements near the equator to being dominated by richnessgradients further from the equator.

KeywordsBeta-diversity, classification, ecotones, geographical range, latitude, ordination, turnover.

1999 Blackwell Science Ltd

460 Paul H. Williams, Helen M. de Klerk and Timothy M. Crowe

INTRODUCTION willingly admit that the change is far more gradual than a hard

black line would indicate. A broader band of colour, fadingHow may the processes governing differences in faunal

imperceptibly toward its edges, might carry a truer picture ofcomposition at large biogeographical scales be studied? A first

actual changes.’ Similar reservations about boundary lines forstep towards a more rigorous mechanistic understanding of

Afrotropical birds were expressed by Moreau (1966).the governing processes would be a clear description of the

Phytosociologists have developed the concepts andpatterns (Nelson & Platnick, 1981; Hengeveld, 1990; Blackburn

quantitative methods for describing variation in the& Gaston, 1998). Part of this endeavour is to ensure that

composition of species assemblages, mainly through their workdifferent kinds of pattern are appropriately discriminated,

at spatial scales within countries. They have embraced a rangebecause they may have different causes or may indicate different

of views of pattern in vegetation between two extremes,conditions. The purpose of this paper is to explore methods

corresponding to the ‘organismal’ and ‘individualistic’ ideas ofthat can be used to supplement the classificatory approach to

how species behave in species assemblages (e.g. Clements, 1916,biogeography, by helping in the visualization of different kinds

1949; Gleason, 1917, 1939; Shimwell, 1971). These two ideasof transition-zone patterns as an aid to interpreting the

in turn have contributed to the development of two classes ofboundary lines that result from area classifications, and for

descriptive methods (e.g. Gauch, 1982; van Tongeren, 1995):studying the factors that govern them.

first, classification or clustering, to discriminate major groupsClassification of geographical areas by biotic similarity has

of species or areas (separated by boundaries); and second,been a popular tool for academic studies. This popularity is

ordination, to discover the major axes of variation along whichperhaps due to the relative ease with which the concept of

these species or areas may be ordered. Popular examples includeclasses or groups may be communicated and comprehended,

TWINSPAN, a classification procedure, and DECORANA, anat least superficially. Such analyses have often been based on

ordination procedure (see Methods). Both have been appliedthe distribution of birds, both at the global scale (Sclater,

most often for studying variation in the composition of1858; Wallace, 1876), and at finer scales, such as within the

vegetation at spatial scales within countries (e.g. Ferry et al.,Afrotropical Region (Chapin, 1923, 1932; Diamond &

1989; Jarvis & Liu, 1993; Dickinson & Mark, 1994; Fensham,Hamilton, 1980; Crowe & Crowe, 1982; Guillet & Crowe,

1995; Bunce et al., 1996), although recently they have become1985). Classifications of areas by their biota have also become

popular for studying biogeographical patterns in a broadera focus for establishing a framework for more applied studies

range of organisms at the continental scale, notably in Europein conservation (e.g. within the Afrotropical Region: Udvardy,

(e.g. Dennis et al., 1991; Vaisanen et al., 1992; Myklestad &1975; Kruger, 1977; MacKinnon & MacKinnon, 1986; Stuart

Birks, 1993; Pekkarinen & Teras, 1993).& Adams, 1990; WCMC, 1992; Turpie & Crowe, 1994; WWF

Community ecologists have studied variation in species& IUCN, 1994; Itoua et al., 1997). Indeed, one interpretation

composition of faunas and floras among areas by using indicesis that classifications of biotas may be used as pragmatic (albeit

of ‘spatial turnover’ among species, though mostly at spatialremote) surrogates for currencies of biodiversity value at the

scales within landscapes (usage of the term spatial turnover,levels of species, characters or genes (Williams, 1996b).

as opposed to temporal turnover, follows, e.g. Whittaker, 1972;The general biological problem when seeking to classify

Wilson & Shmida, 1984; Delcourt & Delcourt, 1992; Harrisonbiotas into groups is that, across a broad range of spatial

et al., 1992). Spatial turnover is perhaps best known inscales, variation in the composition of species assemblages

connection with the idea of b-diversity, which applies to the‘hovers in a tantalizing manner between the continuous and

particular spatial scale of comparing samples from patches ofthe discontinuous’ (Webb, 1954 p. 364, describing plant

similar habitat (e.g. Whittaker, 1960, 1977; Magurran, 1988).communities). Problems of this kind have been found by

Depending on the questions being asked, some authors regardbiologists working in at least three disciplines: first, in

the underlying concept of compositional differentiation asbiogeography, from studies at global or continental scales;

including any differences in species richness among areas (C.second, in phytosociology, from studies of vegetation across a

Rahbek, personal com.), thereby viewing zones with steepbroad range of spatial scales; and third, in community ecology,

gradients in species richness as legitimate transition zones.from studies of the relationships between distributions of species

Other authors explicitly exclude any differences in speciesand environmental gradients, often at fine spatial scales.

richness from consideration (e.g. Wilson & Shmida, 1984;Biogeographers have used area classifications at the global

Harrison et al., 1992). The latter zones depend on spatialand regional scales for over a century, but have nonetheless

replacements between species, often with varying degrees ofbeen aware that, while the regional boundary lines drawn on

spatial overlap within the transition zone. This usage ofmaps from these classifications sometimes represent abrupt

‘overlap’ (e.g. Pielou, 1979; Rapoport, 1982; Shmida & Wilson,changes in faunal composition, they often represent features

1985) and ‘replacement’ (e.g. Whittaker, 1972; Rapoport, 1982;that are actually zones of more gradual transition in species

Wilson & Shmida, 1984) does not necessarily imply anycomposition. For example, in considering the grouping of

interaction between individual organisms, ecologicalAfrotropical bird faunas, Chapin (1923, p. 123) wrote: ‘I

equivalence, taxonomic equivalence, historical relationship, or

change with time.

In one relatively unexplored approach that draws onCorrespondence: Dr Paul H. Williams, Biogeography and Conservation

techniques from ecology, indices of spatial turnover amongLab, The Natural History Museum, Cromwell Road, London SW75BD, U.K. E-mail: [email protected]. species have been used to assess transition zones between

Blackwell Science Ltd 1999, Journal of Biogeography, 26, 459–474

Gradients and replacements among African bird faunas 461

biogeographical regions at the largest spatial scales (Williams, ideally they should be explored rather than excluded (even if

that were possible). However, the strength of most of these1996c). This earlier study was based on a variety of existing

indices of spatial turnover that had been formulated for work effects is not expected to be large in comparison with the effect

of uneven sampling effort (see below).using quadrats, which had usually been arranged in linear

transects (e.g. Whittaker, 1960; Greig-Smith, 1983; Wilson & We use data representing the distributions of all birds

occurring within the Afrotropical Region, including aquaticShmida, 1984; McCoy et al., 1986; Dale, 1988; Orloci & Orloci,

1990; Turner et al., 1991; Delcourt & Delcourt, 1992). The birds and all migrants at regular stop-over sites, but excluding

any records that are believed to represent occasional vagrantindices were then modified for use with neighbourhoods of

sample cells arranged in grids on maps, allowing variations in individuals (i.e. the total bird fauna as it affects the local

ecology and as perceived by a casual observer). Ideally, wethe strength and breadth of transition zones to be mapped in

two spatial dimensions. would compare this pattern with that for just the confirmed

breeding records of birds, although this information is notThe present paper changes the focus from what was available

to what is needed to describe three simple biogeographic available to us at present (the alternative of using both the

breeding and nonbreeding records, but for the breeding speciespatterns, by fine-tuning neighbourhood indices of turnover.

First, we plot an index of the density of species distribution alone, would confound the two kinds of information). We have

followed the species-level taxonomy of Sibley & Monroe (1990,limits (Te) for comparing variations in the strength and breadth

of transition zones in general with the boundaries found by 1993). The data have been newly compiled in a collaborative

project between the Percy FitzPatrick Institute (Cape Town)traditional classification and ordination methods. We then use

two further indices to distinguish two components within these and the Danish Centre for Tropical Biodiversity (Copenhagen),

and include information on restricted range species compiledgeneral transition-zone patterns: seeking to show as directly as

possible spatial variation in the contribution from gradients in by BirdLife International (for details, see Burgess et al., in

press). Like most large surveys, some unevenness in samplingspecies richness (Tg); and spatial variation in the contribution

from replacements among species (Tr). These indices are applied effort is unavoidable and, because not all species presences will

be detected, this unevenness increases the apparent patchinessto newly compiled data for birds, to describe faunal differences

at the intermediate spatial scale of biogeographical subregions of the original data (the relationship between apparent species

richness and sampling effort is described by species-and provinces within the Afrotropical Region (following the

hierarchical practice of, e.g. Wallace, 1876; Udvardy, 1975; accumulation curves, reviewed by, e.g. Colwell & Coddington,

1994). The effect is compounded because our data wereSmith, 1983). The resulting maps allow us to look for latitudinal

trends in the prevalence of the two kinds of transition zones, assembled from several survey sources. However, some of the

sampling effects should have been ameliorated by interpolation,as one step towards understanding the processes governing

them. which aims to discriminate between unrecorded presences of a

species and true absences. Data for some species were implicitly

interpolated, in that they have been taken from publishedMETHODS

range-filled maps, whereas data for other species have been

explicitly interpolated. In the latter cases, we assume aData

continuous distribution for the species between confirmed

records within reasonably uniform habitat, using knowledgeThe study area extends south from latitude 20° north, through

west, central and east Africa, to Cape Agulhas at nearly 35° of the species’ habitat associations, and taking into account

specialist opinion, especially concerning any known gaps insouth, but excluding the Arabian Peninsula, Madagascar and

all offshore islands. In order to study patterns at the scale of distribution, in order to estimate the expected distribution of

the species (but see the discussion of data interpolation). Afterbiogeographical subregions and provinces, we use data collected

at the scale of 1°×1° grid cells (each measuring the interpolation, cells without presence data for a species are

treated as absence data. The data then consist of 604,318≈100×100 km). One-degree grid cells are not strictly equal-

area because of the range of latitudes encompassed (the area different grid-cell presence records for 1911 species in 1961 grid

cells.per cell is reduced by 18% from the equator to the Cape).

Consequently, because of the general species-area relationship

alone (e.g. Connor & McCoy, 1979), some latitudinal bias willClassification

be expected in the number of species co-occupying one-degree

grid cells (a small effect, because it often takes about a ten- We use one of the most popular classification procedures,

TWINSPAN (two-way indicator species analysis) (Hill, 1979b,fold reduction in area to halve the number of species). Coastal

cells also have a reduced land area (a large effect, but for a 1994; see also Gauch & Whittaker, 1981; van Groenewoud,

1992; Tausch et al., 1995; Oksanen & Minchin, 1997; Podani,small proportion of all cells), and montane areas will have an

increased land area per grid cell (a small effect, for a small 1997). TWINSPAN partitions an ordination space for each

division into two groups, so these groups are determined moreproportion of all cells). Furthermore, the extent of habitat

suitable for each species would vary among grid cells even if by broader trends among species distributions rather than by

similarities between the most similar areas (a polythetic divisivethe cells were to have equal areas (a potentially large effect,

including the effect of large lakes, Williams, 1993). The effects technique). Our stopping rules for the divisions were chosen

in order to find a similar number of groups to previousof these factors and of their interactions may be complex and



biogeographic classifications (coincidentally they also divide Multiple-colour techniques have been used before in other

contexts (reviewed by Brewer, 1994), and Williams (1992)the occupied grid cells into two nearly equal groups for use in

randomization tests of the association of high index scores provides an example of its application to a three-dimensional

plot for comparing range-size rarity and different aspects ofwith boundary lines, see below). These rules are, first, to make

a maximum of five levels of division, and second, to divide diversity. First, scores on each axis are divided into colour-

scale classes of approximately equal intervals of score values,groups only where they consist of more than 100 grid cells (no

constraint is imposed that the grid cells in each group should so that the effect of re-scaling by DECORANA to present an

even turnover of species along each axis (Hill, 1994) should bebe geographically contiguous). Consequently this procedure is

unlikely to distinguish many small groups of grid cells, such preserved (equal-frequency classes for score values would not

preserve this even rate, but would have provided better colouras may be dominated by isolated montane faunas.

To examine spatial pattern in the TWINSPAN divisions, discrimination among areas for other purposes). Therefore

high rates of change of colour among neighbouring grid cellsand in the net contribution of either richness gradients or zones

of species replacement, the boundaries for the first five levels in some parts of the map should indicate high rates of species

turnover. To plot the three sets of scores on the same map,of division can be displayed using a two-colour graphical

technique. For each division of the grid-cell data, the two increasing scores on the first axis are shown by increasing

intensities of green, increasing scores on the second axis aredaughter groups of areas are each associated in the TWINSPAN

results with a set of preferential species (Hill, 1994). The ratio shown by increasing intensities of blue, and increasing scores

on the third axis are shown by increasing intensities of red (itbetween the sizes of these two groups gives information about

the symmetry of preferential species distributions between the must be borne in mind that extreme low scores on each axis

may be just as important as extreme high scores).two sides of the boundary: that is, whether the boundary

corresponds to a net steep gradient of species richness over its

entire length (ratios of preferentials far from 1.0) or to a netNeighbourhood turnover indices

zone of species replacement (ratios of preferentials close to

1.0). This pattern can be mapped by colouring the boundary To examine transition zones within restricted parts of the map

in more detail, comparisons of different patterns of speciesusing blue for low (gradient) ratios of less than 0.5 (an arbitrary

but convenient threshold), and green for high (replacement) distributions can be made using indices of the spatial turnover

among species within neighbourhoods of adjacent cells in theratios of more than 0.5. Unfortunately, using colour may cause

problems for a minority of colour-blind people. However, for grid. This is done by considering each grid cell in turn, along

with its eight nearest neighbours (i.e. its first-and second-ordermost people it is a very efficient way to communicate large

amounts of information quickly. neighbours: Smith, 1994), as though viewing just these groups

of cells through a window as it is moved across the grid

(Johnston et al., 1992; Fig. 1). No adjustment is made forOrdination

partially occupied neighbourhoods, because this would over-

value coastal cells with small amounts of turnover (Williams,Our interest in ordination is that it has the potential for

showing any evidence within the data for variation in the 1996c). The neighbourhood procedure has been implemented

using WORLDMAP software (Williams, 1996a,c).strength of group structure and boundaries. We use one popular

procedure, DECORANA (detrended correspondence analysis),

which is designed to re-scale the ordination axes for a near- Range edges of species

Biogeographical classifications using presence data have toconstant rate of species turnover along the axes (Hill, 1979a,

1994; see also Gauch & Whittaker, 1981; van Groenewoud, depend on what, in the data, appear to be edges of the areas

of occupancy for each species. The primary reason for studying1992; Tausch et al., 1995; Oksanen & Minchin, 1997; Podani,

1997). An axis with an even rate of turnover should be useful these edges is to measure variations in the strength and breadth

of all transition zones. However, some of the apparent speciesfor detecting and exploring geographical patterns of variation

in turnover rates (Wilson & Mohler, 1983). However, there are edges may in practice be artefacts of imperfect knowledge of

occupancy, or else edges of patchy areas of occupancy (forlikely to be problems in situations with multiple environmental

gradients because of dependency of the turnover rate on example, arising from habitat fragmentation), and therefore

internal to the full geographical extent of occurrence of thesubsidiary gradients in a nonlinear manner, a problem for

which there is no software solution available as yet (Oksanen species (usage of these terms follows Gaston, 1991, 1994). This

fine-scale patchy variation could be independent of broader& Tonteri, 1995). DECORANA applies detrending in an

attempt to reduce interdependency between axes. This trends (Gosz, 1993) and therefore could be misleading.

Consequently, it may be useful to check the distribution ofdetrending procedure has been criticised as overzealous, because

some useful information may be lost and artefacts may be all apparent range edges in comparison with any proposed

biogeographical boundaries.introduced (e.g. Tausch et al., 1995; ter Braak, 1995). We

assume that this effect is unlikely to be sufficiently strong to Our first index represents variation in occupancy by counting

the number of species with range edges among grid cells. It isalter our broader inferences.

To examine spatial pattern in the DECORANA ordination based on previous techniques that plot maps by overlaying

range edges for a set of species (e.g. Valentine, 1996; McGowan,scores, the grid-cell scores for the first three axes can be

displayed on a map using a three-colour graphical technique. 1974; McAllister et al., 1986; Roy et al., 1994; Jablenski &



if pi,n and pi,0 have different values for any of the values of n

(1–8) or is equal to 0 if they always have the same value, and

S is the total number of species in the database. This index

may also be expressed relative to the total number of species

in each neighbourhood of nine grid cells:

relative range edges Tes=(Ri:1 . . . S fn:1 . . . 8(pi,n, pi,0))/s (2)

where s is the number of species present in the neighbourhood

of nine cells.

High scores from these indices may reasonably be expected

to have at least two kinds of origin in the data: first, concerted

species gain or loss (depending on one’s viewpoint) along a

gradient of species richness; and second, replacement among

species.Figure 1 The neighbourhood approach to comparing cells within

a large grid. The left side of the diagram shows how three cells in Gradients in species richnessturn are considered as the core of a moving window of nine cells One contribution to patterns of species range edges comes(for the first and second positions of the core cell, the core cell is from gradients or trends in species richness. This has beenshown in mid and dark greys and nonoverlapped neighbouring recognized previously as a factor both to be distinguished fromcells are outlined in mid and dark greys; for the third position of

spatial turnover in the sense of replacement among species,the core cell, the core cell is shown in black and the neighbouring

and to be excluded from attempts to measure it (Wilson &eight cells are outlined in black). This moving window of cells

Shmida, 1984; Harrison et al., 1992; Williams, 1996c).scans successive rows of the grid, moving down a single row ofAn indirect index of gradients in species richness is thecells between each scan. The right side of the diagram shows how

heterogeneity among species-richness counts for grid cellsthe core cell (shown in solid black) is compared with thewithin a neighbourhood. Heterogeneity in species richnessneighbouring eight cells (outlined in black) for the index of species

replacement by complete segregation. This particular index could be measured using the sum-of-squares of deviations fromproceeds by comparing the distributions of pairs of species. For mean richness within neighbourhoods of nine grid cells (thespecies a and b, both occur within the neighbourhood of nine cells variance, which is adjusted for the number of occupied gridbut without co-occupying any one grid cell. This pairwise result cells in the neighbourhood, is not used because it would over-will contribute one replacement towards the total species

value any coastal, partially occupied neighbourhoods with lowreplacements score (see text and indices 5 and 6).

absolute heterogeneity, cf. Williams, 1996c; p. 583). However,

this heterogeneity index is sensitive to any differences in richness

scores among neighbouring cells, even if the richness patternValentine, 1994; Poynton & Boycott, 1996). It also showsis patchy at the scale of individual grid cells (e.g. if the coreresemblance in its use of presence/absence mismatches (0–1)cell were richer than all its neighbours) rather than showing abetween neighbouring cells to a form of ‘categorical wombling’broader and more consistent gradient.(Oden et al., 1993; Fortin & Drapeau, 1995). ‘Wombling’ is a

Another possibility is to use an index that is constrained toterm coined by Barbujani et al. (1989) for edge-detectionbe sensitive only to consistent trends in richness along a linemethods based on combined rates of change in multivariateof three or more grid cells (the index will remain sensitive todata (see also Womble, 1951; Bocquet-Appel & Bacro, 1994;patchiness at scales larger than the area spanned by the numberFortin, 1994). But in contrast to normal wombling procedure,of cells chosen). For each neighbourhood, grid cells arefor our index (1) the range-edge scores are attributed tocompared diametrically across the core cell to identify thoseparticular grid cells rather than to the grid-cell edges (althoughlines of cells with consistent trends. For these consistent trends,clearly the sum, mean, median or maximum wombling scoresthe difference between opposite cells is calculated, and thefor the edges of a grid cell could be assigned as the score formaximum value for the neighbourhood provides the score forthat cell). Our version of this index is a count of the numberthe core grid cell:of species with presence/absence differences between the core

cell and its neighbouring eight cells within the grid (a variantabsolute 3-cell gradients

of this index that also counted the numbers of mismatches for

each species gave similar results for these data, but would be Tg=max fn:1 . . . 8, m:5 . . . 8,1 . . . 4(an, a0, am) (3)more sensitive to isolated records in patchy data, see below):

and relative 3-cell gradientsabsolute range edges Te=Ri:1 . . . S fn:1 . . . 8(pi,n, pi,0) (1)

Tgs=(max fn:1 . . . 8, m:5 . . . 8,1 . . . 4(an, a0, am))/s (4)where pi,n is the presence/absence of species i in the nth of the

eight neighbouring cells (cells 1–8), pi,0 is the presence/absence where a0 is the species richness of the core cell in the nine-cell

neighbourhood, an is the species richness of the nth cell in theof species i in the core or central cell of the neighbourhood

(cell 0), the simple mismatch function f(pi,n, pi,0) is equal to 1 nine-cell neighbourhood (cells 1–8), am is the species richness



of the diametrically opposite mth cell in the nine-cell RESULTSneighbourhood (cells 5–8, 1–4), the gradient function f(an, a0,

The geographical distribution of species richness among one-am) is equal to (am – an) if an < a0 < am is true or is equal to 0degree grid cells in our data for birds of the Afrotropicalif it is not true, and s is the number of species present in theRegion is shown in Fig. 2(a).nine-cell neighbourhood.

Classification using TWINSPAN with our stopping rules

recognizes seventeen groups of grid cells within the overall

pattern of faunal variation for birds of the Afrotropical Region,Replacements among speciesas shown in Fig. 2(b). The hierarchical relationships amongAlongside gradients in species richness, an alternative patternthese groups and the sizes of the groups (group sizes rangeof species distributions contributing to patterns of range edgesfrom 33 to 265 one-degree grid cells) are presented in Fig. 3.is species replacement.The major groups are intuitively sensible. For example, theOne of the most straightforward indices of speciesfirst division is between the faunas of the edge of the Saharareplacement to interpret is neighbourhood segregation(with few species and a strong Palaearctic component) on the(Williams, 1996c), because it measures complete spatialone hand and the more southern areas on the other. The secondreplacement among species (at the particular scale of andivision within the southern group is between faunas of theinvestigation), without being influenced by any varying degreeshumid equatorial forest and of the more arid (and often moreof spatial overlap within a transition zone. Our index isopen) habitats. The area groups agree with those recognizedbased on Rapoport’s (1982) index of species segregation, whichby Crowe & Crowe (1982) at the level of subregions (Fig. 3),compares pairs of areas using counts of completelyexcept that these authors included the Cape within theirnonoverlapping species pairs. Neighbourhood segregation isSouthern Savanna Subregion (this difference may be the resultcalculated:of using a divisive rather than an agglomerative technique, or

could be due to differences in data). Also shown in Fig. 3

are the numbers of preferential species associated with eachabsolute segregation Tr=r+1 (5)subgroup at each division. The ratios between these

preferentials show that for the first four levels of division at

least, divisions between equatorial areas are dominated byandspecies replacements (Fig. 2b, in green), whereas at higher

latitudes around both the Sahara in the north and the Kalahari

in the south, they are dominated by species-richness gradientsrelative segregation Trs=(r+1)/((s2−s)/2) (6)(Fig. 2b, in blue).

Ordination using DECORANA results in the first four axes

of greatest variation in the composition of Afrotropical birdwhere r is the number of unique pairwise (subdiagonalfaunas that are described in Table 1. The interpretation of thetriangular half-matrix) species comparisons that show bothfirst three axes is straightforward, whereas the interpretationspecies to be present within a neighbourhood of nine cells butfor the fourth axis is not immediately apparent. Consequentlywithout co-occupancy of any one or more cells (Fig. 1), and sthe fourth axis is not considered further. The axes do notis the number of species present in the neighbourhood of nineappear to be truly orthogonal, because the grid-cell scores arecells. Neighbourhood segregation may be seen as belonging tosignificantly correlated (but only if inflation of significancea family of neighbour-similarity techniques (e.g. Dufrene &levels due to nonindependence of data points is ignored),Legendre, 1991; Williams, 1996c; Ruggiero et al., 1998).although for the first three axes the correlation coefficients areThe relative contributions of richness gradients and speciesbelow 0.5 (Table 2). Grid-cell scores for these first three axesreplacements may be compared by overlaying indices of theare plotted in Fig. 2(c) using the three-colour technique. Thetwo on maps in two different colours (for details and discussionspatial pattern in Fig. 2(c) is dominated by the four faunalof this technique, see Williams & Gaston, 1998). Scores ongroups that correspond to high values on each of the firsteach axis are divided into colour-scale classes of approximatelythree axes (humid tropics, southern Africa, and eastern ‘horn’)equal size by numbers of grid cells. Then, increasing scoretogether with the area of shared low scores on all three axesclasses for gradients are plotted in increasing intensities of blue,on the edge of the Sahara (the area with the highest scores onand increasing score classes for replacement are plotted inall three axes is weakly evidenced by yellow-brown cells to theincreasing intensities of green (a matching intensity of red isnorth and west of Lake Victoria). The larger areas of moreadded on the diagonal of the colour scale to provide neutraluniform colour in Fig. 2(c) correspond broadly to the majorgreys). Consequently, black grid cells correspond to low scoresregions of Fig. 2(b) and so corroborate the biogeographicalfor both, white cells correspond to high scores for both, andsubregions and provinces from TWINSPAN. Taking angrey cells show intermediate and positively, linearly covaryingalternative view of the same pattern, the more abrupt zones ofscores for both (after the equal-frequency transformation). In

colour transition among neighbouring grid cells in Fig. 2(c)contrast, areas with highly saturated green cells show a relative

provide support for the major divisions in Fig. 2(b) (e.g. aroundexcess of replacements over gradients, and areas with highly

the humid equatorial forests, through the Ethiopian highlands,saturated blue show a relative excess of gradients over

replacements. and around the Kalahari). However, in general these ordination





Figure 3 Classification of Afrotropical bird faunas in one-degree grid cells by TWINSPAN (stopping after five levels of division or when the

group size falls below 100 grid cells) (see Fig. 2b). The numbers of grid cells in each of the seventeen terminal groups are shown in parentheses

below the group names in italics. The figures shown at each division are the numbers of preferential species associated with the two subgroups

(Hill, 1994). Grey triangles indicate divisions with strong contributions from richness gradients as evidenced by ratios between numbers of

preferential species of less than 0.5 (the basal breadths of these triangles are scaled by 1—ratio). The names of the corresponding avifaunal

subregions from Crowe & Crowe (1982) are shown alongside the grey bars below.

scores show low spatial resolution of structure, with little in each neighbourhood) are shown in the right column (Fig. 4b,

d,f,h). Scores for the top row of cells in each map are biaseddetailed pattern.

Mapping turnover using the neighbourhood-based indices by the removal of cells to the north. Nonetheless, all of the

indices (and combinations of gradient and replacement indices)results in the maps shown in Fig. 4. The absolute indices are

shown in the left column of the figure (Fig. 4a,c,e,g), whereas succeed in recovering a similar pattern of transition zones to

TWINSPAN, as shown by the significantly higher scores fromthe same indices expressed relative to neighbourhood species

richness (in effect showing the proportion of species affected these indices for cells adjacent to the TWINSPAN boundary

Figure 2 Maps of geographical variation in Afrotropical bird faunas among one-degree grid cells. (a) Species richness as counts of the numbers

of species recorded as occupying each grid cell. Scores are divided into thirty-three colour-scale classes of approximately equal size by numbers

of grid cells, with maximum scores shown in red and minimum (nonzero) scores in blue (score-class boundaries: 36, 68, 111, 144, 181, 199, 221,

248, 266, 276, 283, 291, 299, 305, 312, 317, 323, 330, 337, 343, 350, 357, 366, 378, 388, 400, 414, 427, 443, 460, 479, 509, 687 species). The

horizontal scale bar represents 10° of longitude (≈1100 km at the equator). (b) Major variation in faunal composition summarized as

TWINSPAN boundaries between groups of similar grid cells (see Fig. 3 for the area classification). The successive divisions of the area

classification are shown by successively lighter (less saturated) boundary-line colours. Where the ratio between the numbers of preferential

species (Hill, 1994) associated with the two subgroups at each division is greater than 0.5 (tending towards symmetric species replacement), the

boundaries are shown in green. Alternatively, where the ratio between the numbers of preferential species associated with the two subgroups at

each division is less than 0.5 (tending towards asymmetric species richness gradients), the boundaries are shown in blue. (c) Major variation in

faunal composition summarized as DECORANA ordination scores for grid cells on the first three axes (see Table 1). Scores on each axis are

divided into sixteen colour-scale classes of equal score intervals (a total of 4096 classes, of which less than one quarter are used), with increasing

scores on the first axis shown in increasing intensities of green, increasing scores on the second axis shown in increasing intensities of blue, and

increasing scores on the third axis shown in increasing intensities of red (the colour scale is shown simplified with only six colour classes on each

axis, 216 classes in total). Consequently, black grid cells on the map show low scores on all three axes.



Table 1 Four axes of variation in the species composition of he could not map the variations. One important property ofAfrotropical bird faunas among one-degree grid cells by neighbourhood indices is that high scores are not necessarilyDECORANA. constrained to surround regions or to form linear features. An

example of this property may be seen in the way that many ofAxis Eigenvalue Interpretation

the grid cells with the highest scores for range edges form a

large block to the north and west of Lake Victoria, rather thanHigh-scoring grid Low-scoring grid

a line.cells cellsThe richness gradient indices (Fig. 4c,d) and species

replacement indices (Fig. 4e,f) differ from one another, but1 0.444 Humid equatorial Arid north, south

and east in combination correspond to the major range-edge features2 0.341 Arid south-west Arid north (Fig. 4a,b) and classification boundary features (Fig. 2b). (For3 0.173 Arid east (‘horn’ of Arid west (Sahara the richness gradient scores, very similar results were obtained

Africa) and Kalahari) using the neighbourhood heterogeneity index, and for the4 0.097 Western Sahara, Kalahari, south replacement scores, similar results were obtained using the

Cape and coast Ethiopian highlandsother dissimilarity and b-diversity-based indices described by

and interiorWilliams, 1996c.) Broadly, a comparison of these maps shows

that the richness-gradient index gives higher scores in the

southern Sahara, to the east of Lake Malawi, and aroundTable 2 Spearman rank correlation coefficients (rs) for comparisons the north-eastern Kalahari (Fig. 4c,d) than does the speciesof grid-cell scores for the four axes of variation in the species replacement index (Fig. 4e,f). In contrast, the speciescomposition of Afrotropical bird faunas among one-degree grid cells

replacement index gives higher scores around the edge of theby DECORANA. All correlation coefficients are formally significant

humid equatorial forests, and in western Angola (Fig. 4e,f).at P< 0.001 (the data points are not independent and the

Many finer details are also apparent that are not as clear inprobabilities of these associations are greatly inflated by spatialthe DECORANA results. These include strong zones of relativeautocorrelation).replacement between the Somali lowlands and the Ogaden,

and between the west coast of South Africa and the interiorAxis 1 2 3 4

(Fig. 4f cf. Figure 2c).4 –0.221 –0.549 –0.283 1 The two-colour overlay technique (Fig. 4g,h) allows a more3 0.438 0.396 1 direct comparison of these results. First, the degree of success2 0.318 1 in explaining major TWINSPAN boundaries (Fig. 2b) or the1 1

corresponding major range-edge features (Fig. 4a,b) can be

assessed by comparing how they match with the high-scoring

features of Fig. 4(g,h), which are shown in green (species

replacements), blue (richness gradients) or white (replacementslines, from the randomization tests (Manly, 1991) in Table 3.

High scores near the central Sahara for several of the relative and gradients). For example, in comparison with the ratios of

preferential species from the TWINSPAN results, the turnoverindices (Fig. 4b,d,f) show how, even when relatively few species

are present in an area, a high proportion of those species may indices agree well that the south Saharan boundary is dominated

by a richness gradient (blue), whereas the boundary surroundingcontribute to a pattern. Consequently, the relative indices

provide useful additional information. the humid equatorial forests is dominated by a zone of species

replacement (green). However, a potentially informativeThe range-edge index shows strong linear features of high

scores (Fig. 4a), which coincide with the major divisions found difference is found for the northern Kalahari boundary, which

although dominated by an overall richness gradient accordingby TWINSPAN (Fig. 2b). Particularly discrete features include:

(1) a south Saharan transition zone running east–west; (2) a to TWINSPAN, appears from the turnover indices to be

dominated by replacement zones in the north-west (green) andtransition zone surrounding the humid equatorial forests; (3)

a transition zone running eastwards from Lake Victoria to the by richness gradients in the north-east (blue).

coast; (4) a transition zone running north-eastwards through

the Ethiopian highlands (associated loosely with the northernDISCUSSION

part of the Rift Valley); (5) a subcoastal transition zone, running

along the eastern slope of the eastern plateau, from Kenya to Results from the neighbourhood approach show that turnover

indices can be used to map directly the variations in strengththe Cape; and (6) a transition zone running to the north and

east of the Kalahari. The most obvious difference between the and breadth of faunal transition zones along their lengths.

Taking this approach further, our results show how turnoverresults of the classification and neighbourhood approaches is

that the neighbourhood approach shows variations in strength indices can then be used to break down changes in species

composition across transition zones into components such asand breadth of the transition zones along their lengths, which

is absent from the classification results. For example, the feature gradients in species richness and zones of species replacement.

Therefore turnover indices should be useful tools forsurrounding the humid equatorial forests in Fig. 4(a) is generally

broader to the south of the Congo than to the north. This supplementing the analytical power of classification and

ordination approaches when seeking a mechanisticdifference in breadth was observed by Chapin (1932), although





Table 3 Test for association between high scores from transition- used to distinguish between boundaries dominated by richnesszone indices (Fig. 4) with TWINSPAN boundary lines (Fig. 2b), gradients and those dominated by species replacements, againusing the observed total score for the 960 grid cells situated as this technique cannot be used to discern any variations alongneighbours to the internal boundaries from Fig. 2(b), which is boundaries. Indeed, not even ordination approaches couldcompared with the distribution of total scores from 1000 random

measure or map directly the transition zones in faunaldraws of 960 cells without replacement from among the 1961

composition required for Chapin’s (1923) ‘broader band[s] ofoccupied cells. In order to combine scores from the gradients and

colour’, because transition zones will only be apparent fromreplacements measures in a single test (bottom row), the colour-classordinations indirectly, as obscure rates of change fromscores from Fig. 4(g, h) are used, because these scores have beencomparisons of neighbouring grid-cell scores or colours. Thustransformed to give comparable units, ranges of values, and

frequency distributions for the two components. As with most while ordination may summarize much of the variation in acomparisons for areas on maps, the probabilities of these reduced number of dimensions, mapping transition zonesassociations are greatly inflated by spatial autocorrelation. directly would still depend on applying a differential technique

such as wombling to the ordination scores (see Methods: RangeIndex Mean observed score/ Single-tailed p edges of species).

mean expected score by

random draws

Transition zones among Afrotropical bird faunasRange edges (Te) 1.17 < 0.001

Many of the major transition-zone features among AfrotropicalRichness gradients (Tg) 1.20 < 0.001bird faunas in our results are in broad agreement with theSpecies replacements (Tr) 1.30 < 0.001

Gradients (Tg)+ subregional and provincial boundaries recognized for birds byreplacements (Tr) 1.12 < 0.001 Chapin (1923) and by Crowe & Crowe (1982). Testing this

Range edges (Tes) 1.12 < 0.001 association formally (cf. Table 3) would give a false impressionRichness gradients (Tgs) 1.22 < 0.001 of precision because of the uncertainty concerning which gridSpecies replacements (Trs) 1.15 < 0.001 cells are truly adjacent to the boundary lines on the earlierGradients (Tgs)+ maps. Nonetheless, major transition-zone features in Fig. 4

replacements (Trs) 1.13 < 0.001appear to be broadly associated with the boundaries of Crowe

& Crowe’s (humid equatorial) Forest, Saharan, North-east

Arid, South-west Arid, Northern Savanna and Southern

Savanna Subregions. Similar broad agreement on the locationunderstanding of the governing processes. This kind of index

uses rigorous, quantitative procedures, and requires no of the major transition-zone features can also be seen with the

phytochoria from White (1983) (though there is less agreementassumptions about the greater importance of particular species

or about the number of regions to be distinguished (Williams, with Takhtajan’s (1986) floristic scheme, which includes a major

Uzambara-Zululand Region), with butterfly subregions and1996c; Ruggiero et al., 1998). They also have the potential to

show transition-zone features across a broad range of spatial divisions from Carcasson (1964), and with the general

biogeographical provinces from Udvardy (1975) and ecoregionsscales.

In contrast, the classification results serve to illustrate that from Itoua et al. (1997). Such broad similarity in spatial pattern

among taxa may point to shared or linked factors governingalthough area-classification can differentiate the relative

strengths of entire boundaries (to some extent) through the biological distributions, acting through present ecological

processes or resulting from processes in the more distant past.hierarchy of the classification, it still cannot discern any

variations in strength or breadth along boundaries. Our results are consistent with the belief that ecological

transition-zone features tend to follow latitudinal andFurthermore, although we show how TWINSPAN can be

Figure 4 Maps of geographical variation in Afrotropical bird faunas among one-degree grid cells using the neighbourhood approach. (a)

Absolute index of range edges (Te (index 1)=Ri:1 . . . S fn:1 . . . .8(pi,n, pi,0), see text). The horizontal scale bar represents 10° of longitude (≈1100 km at

the equator). (b) Relative index of range edges (Tes (index 2)=(Ri:1 . . . S fn:1 . . . .8(pi,n, pi,0))/s, see text). (c) Absolute index of richness gradients (Tg

(index 3)=max fn:1 . . . 8, m:5 . . . 8,1 . . . 4(an, a0, am), see text). (d) Relative index of richness gradients (Tgs (index 4)=(max fn:1 . . . 8, m:5 . . . 8,1 . . . 4(an, a0, am))/s,

see text). (e) Absolute index of replacements (Tr (index 5)=r+1, see text). (f) Relative index of replacements (Trs (index 6)=(r+1)/((s2 – s)/2),

see text). For each of these maps, scores are divided into thirty-three colour-scale classes of approximately equal size by numbers of grid cells,

with maximum scores shown in red and minimum (nonzero) scores in blue. Therefore, although the numerical values and dimensions differ, the

colour classes (with nearly equal areas) remain comparable among maps. (g) Absolute index of richness gradients (map c) overlaid on the

absolute index of replacements (map e). (h) Relative index of richness gradients (map d) overlaid on the relative index of replacements (map f).

For these two maps, scores on each of the two axes are divided into ten colour-scale classes of approximately equal size by numbers of grid

cells, and increasing intensities of blue are used to represent increasing scores for richness gradients and increasing intensities of green are used

for increasing scores for replacements (a matching intensity of red is added on the diagonal to provide neutral greys). Consequently, black grid

cells on the map show low scores for both, white shows high scores for both, and shades of grey show linearly covarying scores for both (after

the equal-frequency transformation). In contrast, areas of the map with highly saturated blue cells show an excess of gradients over

replacements, and areas with highly saturated green show an excess of replacements over gradients.



altitudinal bands. Ecological zones are strongly influenced by higher contribution from replacements, although steep

gradients from around Lake Victoria and the coast reduceclimate, and particularly by the amount of rainfall (e.g. Crowe

& Crowe, 1982; Turpie & Crowe, 1994), which in turn depends ratios on the equator), whereas medians outside this equatorial

region are nearly all below the line (a higher contribution fromin part on latitude and altitude. For example, particularly

influential factors might be the changes in vegetation when gradients). The strongest contribution to the pattern is the

increasingly dominant richness gradients from 5°-20° Northmoving from closed canopy forest to savanna, and from savanna

to grassland and desert. Any small regions for faunas associated (towards the Sahara). The latitudinal pattern might be

explained in part by the particular kinds of habitat in thesewith habitats at higher altitudes (like the central and east

African highland provinces or divisions of Chapin, 1923; regions, with species replacements between different kinds of

forest (humid and dry) around the equatorial region, and byCarcasson, 1964; Udvardy, 1975) were unlikely to be identified

in the TWINSPAN classification because the minimum-divisible richness gradients from forests to relatively species-poor deserts

when moving further north or south. If this switch fromgroup size was set to be large (as it was for Crowe & Crowe,

1982). However, the neighbourhood-based analyses have their replacements to gradients with increasing latitude were general,

it would be consistent with the frequently reported pattern ofresolution limited at the finer scale of one-degree grid cells and

are therefore able to respond to some of the smaller features latitudinal richness gradients (e.g. Pianka, 1966; Tramer, 1974;

Rabinovich & Rapoport, 1975; Schall & Pianka, 1978; Rohde,associated with mountains, which are more numerous in east

Africa (Fig. 4). This property may explain in part why the 1992). However, the pattern may not be general, it may conceal

much variation among subgroups of birds and among areasmountainous areas to the north and west of Lake Victoria

form a large block of cells with high turnover scores. But (shown even within Africa by the ranges of values for one-

degree bands in Fig. 5), and richness gradients in the reversebeyond this large block, both our TWINSPAN and turnover-

index results are indeed dominated at the largest scales by very direction would be expected at slightly higher latitudes (e.g. at

the northern edge of the Sahara). Even for the present results,long, latitudinally orientated features (Figs 2 and 4).

Another point of interest is the absence from our species- both kinds of boundaries occur in most parts of the Afrotropical

Region among the lower levels of TWINSPAN division and atreplacement maps (Fig. 4e,f) of a single, strong longitudinal

feature running the entire length of the Great Rift system. a finer scale in the turnover-index maps. As yet, latitudinal

gradients in turnover remain generally poorly documentedAny vicariance between species (Nelson & Platnick, 1981;

Humphries & Parenti, 1986) resulting from the opening of the (Gaston & Williams, 1996).

In principle, neighbourhood indices of transition zones couldRift might have been expected to generate spatial replacement

among at least the less dispersive bird species. Our maps show be applied at any spatial scale, from primary regions of the

world to local ecotones (cf. Holland et al., 1991; Hansen & dithat this is not a dominant pattern, at least not among all birds

at the taxonomic rank of species. However, Guillet & Crowe Castri, 1992), just as area-classification and ordination

techniques have been applied at widely different spatial scales(1985) showed that different subgroups of Afrotropical birds

differ in the relative strengths of latitudinal and longitudinal (e.g. Holloway & Jardine, 1968; Ferry et al., 1989) and are

essentially scale-independent (Myklestad & Birks, 1993). Evencomponents of gradient patterns, so longitudinal patterns might

still be present in some subgroups. The maps for all species at the relatively large scale of Afrotropical subregions (e.g.

Leemans & Halpin, 1992), studying any changes in transitionwith high scores apparently most strongly associated with the

entire Rift system are (1) the plot of species richness in Fig. 2(a) zones could prove valuable for understanding the causes and

consequences of environmental change.(where high values are likely to be an effect of the mountains

and highlands associated with the Rift system in east Africa

on habitat patchiness and diversity), and (2) the plot showing

a gradient in species richness to the east, on the eastern slope Potential problems with neighbourhood indicesof the eastern plateau, in Fig. 4(c).

As with any technique, there are potential pitfalls andOne of the benefits to come from having separate indiceslimitations to the use of neighbourhood indices. Some of theof the three different turnover patterns is the possibility ofpotential problems foreseen below (scale, migration,using them to explore latitudinal trends in the nature ofabundances) are common to all approaches to studying spatialtransition zones. For birds in the Afrotropical region, the mapsvariation in faunal composition. Others (fragmentation, islands,overlaying the turnover indices (Fig. 4g) show that replacementinterpolation of data) are likely to be particularly severe forfeatures (green) appear to be rarer or weaker near the norththe neighbourhood approach.and south of the Afrotropical Region, where there is more

evidence of gradients (blue). This map is consistent with the

TWINSPAN results for preferential species (Figs 2b, 3), at least Spatial scale

In contrast to scale-independence in the formulation of thewhen considering the primary boundaries from the first four

levels of divisions for the Afrotropical bird data used here. The neighbourhood indices, scores from these indices are likely to

be strongly dependent on the spatial scale at which they arelatitudinal pattern is most easily and directly summarized in

Fig. 5, by plotting the median scores from the turnover indices used. This scale dependency applies to: (1) survey extent (world-

wide, continent-wide, etc.); (2) to grain size (grid-cell size); andfor one-degree belts of grid cells. This figure shows that medians

for latitudinal belts between 10° South and 10° North are (3) to neighbourhood size (Williams, 1996c). Indices of species

turnover are expected to be particularly sensitive to grain sizenearly all above the line of the overall Afrotropical median (a



Figure 5 Relative contributions of species replacements and richness gradients with latitude for Afrotropical bird faunas. This ratio is calculated

using the absolute turnover indices (gradients Tg (index 3)=max fn:1 . . . , m:5 . . . 8,1 . . . 4(an, a0, am), and replacements Tr (index 5)=r+1, see text) as

log10(Tr /(Tg+1)) for one-degree grid cells. The black squares show the median ratio for each one-degree latitudinal band, the long rectangles

span the lower quartile (25%) to upper quartile (75%) of ratios, and the grey lines span the minimum to maximum ratios. The horizontal line

shows the overall median among all Afrotropical ratios (4.55). Note that neighbourhood scores (and ratio scores) for grid cells are not

independent data points. Latitudes south of the equator are expressed as negative values.

(Rapoport, 1982; Gosz, 1993; Gaston, 1994) because of species- (2) effects of variation in island area; and (3) effects of variation

in isolation of islands from neighbours.area effects (see Stoms, 1994). The bird data were compiled at

a broad and coarse-grained spatial scale for the purpose of

recognizing subregional and provincial species pools. At this Migration

All of the approaches described here for studying spatialscale, it must be remembered that any apparent overlap of

records in a grid cell does not necessarily imply co-occurrence variation in faunal composition take a static view of distribution

patterns and are not formulated to take account of temporalamong species within a local habitat patch.

changes, whether they be seasonal migrations, metapopulation

effects, or longer term responses to environmental change.Fragmented distributions

Particularly for surveys using finer grain sizes, if species have Guillet & Crowe (1985) noted that the mobility of water birds,

combined with the seasonal and unpredictable nature of theirvery patchy, fragmented or mosaic distributions, then even if

transition zones are evident at coarser spatial scales, they are habitat in some parts of Africa, were not conducive to the

identification of avifaunal zones at some scales.likely to become obscured at a fine grain size by this more

local turnover (see Gosz, 1993: Fig. 1). Under these conditions,

neighbourhood indices may be unable to distinguish between Interpolation of data

Seeking ecological explanations for transition-zone featurespatchiness and smooth spatial trends.

may be fraught with a danger of circularity if models based

on habitat suitability (using ‘models’ in a broad sense) haveIslands

It may not be straightforward to apply neighbourhood indices been used directly or indirectly to interpolate the expected

distributions of species. For example, range-filling maps mayto data for areas if their geographical arrangement departs

strongly from a regular grid, such as may be the case within effectively be filling species ranges to the ranges of the better

known vegetation zones, so that techniques appliedarchipelagos of islands or habitat ‘islands’. The problem is one

of identifying comparable neighbourhoods of areas. In the case subsequently to the species data may merely recover the

vegetation pattern imposed on them by the interpolationof islands, comparability among neighbouring areas may be

compromised by: (1) effects of variation in numbers of islands; procedure. Similarly, Peters (1955) cautioned against



generalising species distributions to faunal boundaries and then REFERENCESclaiming the same species data as evidence in support of those

Barbujani, G., Oden, N. L. & Sokal, R. R. (1989) Detecting regionsboundaries. Another problem in distribution modelling is thatof abrupt changes in maps of biological variables. Syst. Zool. 38,

coarse-scale modelling of habitat suitability and its use to376–389.

predict potential distribution may ignore fine-scale habitatBlackburn, T. M. & Gaston, K. J. (1998) Some methodological issues

fragmentation (particularly by human activity), which for birdsin macroecology. Am. Nat. 151, 68–83.

at least could have a strong effect on observed distributions. Bocquet-Appel, J. P. & Bacro, J. N. (1994) Generalized wombling.

Syst. Biol. 43, 442–448.

ter Braak, C. J. F. (1995) Ordination. Data analysis in communitySpecies abundances ecology (ed. by R. H. G. Jongman, C. J. F. ter Braak and O. F. R.

A potentially serious constraint on our methods is that they van Tongeren), pp. 91–173. Cambridge University Press, Cambridge.

Brewer, C. A. (1994) Color use guidelines for mapping and visualisation.use only presence/absence data and are therefore insensitive toVisualisation in modern cartography (ed. by A. M. MacEachren andvariation in species abundances. For example, Lovett (1996)D. R. Fraser Taylor), pp. 123–147. Pergamon Press, Oxford.reports that for trees in the Eastern Arc mountains of Africa,

Bunce, R. G. H., Barr, C. J., Clarke, R. T., Howard, D. C. & Lane,there is a steady replacement of species with altitude in theA. M. J. (1996) ITE Merlewood land classification of Great Britain.presence/absence data, whereas weighting these data withJ. Biogeogr. 23, 625–634.

species abundances showed discontinuities in replacement. OurBurgess, N., de Klerk, H., Fjeldsa, J., Crowe, T. & Rahbek, C. (in

indices are therefore likely to miss some important aspects ofpress) A preliminary assessment of congruence of biodiversity pat-

variation, although representative abundance information is as terns between Afrotropical forest birds and mammals. Ostrich, inyet rarely available at large spatial scales (for application of press.wombling to species-abundance data, see Fortin, 1994). Carcasson, R. H. (1964) A preliminary survey of the zoogeography of

African butterflies. E. Afr. Wildlife J. 2, 122–157.

Chapin, J. P. (1923) Ecological aspects of bird distribution in tropical

Africa. Am. Nat. 72, 106–125.

Chapin, J. P. (1932) The birds of the Belgian Congo. Part I. Bull. Am.In conclusionMus. Nat. Hist. LXV, 83–98.

The present results show that neighbourhood indices of spatial Clements, F. E. (1916) Plant succession: an analysis of the developmentturnover among species can contribute to describing the of vegetation. Publs Carnegie Instn. 242.

complexity of variation in transition-zone features at the Clements, F. E. (1949) Dynamics of vegetation. Hafner Press, New

York.intermediate spatial scale of biogeographical subregions andColwell, R. K. & Coddington, J. A. (1994) Estimating terrestrialprovinces, as a quantitative starting point for studies of

biodiversity through extrapolation. Phil. Trans. R. Soc. Lond. (B)governing processes. The major constraints on the use of these345, 101–118.indices are: first, the availability of representative data for

Connor, E. F. & McCoy, E. D. (1979) The statistics and biology ofspecies distributions on a regular grid of area cells; and second,the species-area relationship. Am. Nat. 113, 791–833.

that species distribution patterns must not be stronglyCrowe, T. M. & Crowe, A. A. (1982) Patterns in distribution, diversity

fragmented at the scale of the grid.and endemism in Afrotropical birds. J. Zool., Lond. 198, 417–442.

Dale, M. R. T. (1988) The spacing and intermingling of species

boundaries on an environmental gradient. Oikos, 53, 351–356.

Delcourt, P. A. & Delcourt, H. R. (1992) Ecotone dynamics in spaceACKNOWLEDGMENTS and time. Landscape boundaries, consequences for biotic diversity

and ecological flows (ed. by A. J. Hansen and F. di Castri), pp.We thank the many fieldworkers who contributed distribution

19–54. Springer-Verlag, London.records and we thank Louis Hansen and Thomas Lehmberg Dennis, R. L. H., Williams, W. R. & Shreeve T. G. (1991) A multivariateof the Danish Centre for Tropical Biodiversity (CTB), approach to the determination of faunal structures among EuropeanUniversity of Copenhagen, for checking data. The birds butterfly species (Lepidoptera. Rhopalocera). Zool. J. Lin. Soc.,database was developed jointly by the Percy FitzPatrick Institute Lond. 101, 1–49.

(South Africa) and the CTB, and we thank Colin Bibby and Diamond, A. W. & Hamilton, A. C. (1980) The distribution of forest

passerine birds and Quaternary climatic change in tropical Africa.Mike Crosby for permission to incorporate data on restrictedJ. Zool., Lond. 191, 379–402.range bird species compiled by BirdLife International. Thanks

Dickinson, K. J. M. & Mark, A. F. (1994) Forest-wetland vegetationto Carsten Rahbek, Miguel Araujo, Neil Burgess, Jon Fjeldsa,patterns associated with a Holocene dune-slack sequence, HaastKevin Gaston, Mark Hill, Chris Humphries, Clive Moncrieff,ecological district, south western New Zealand. J. Biogeogr. 21,Dick Vane-Wright, Mark Williamson and anonymous referees259–281.

for comments and discussion, and to Clive Moncrieff forDufrene, M. & Legendre, P. (1991) Geographic structure and potential

compiling the extended DOS versions of TWINSPAN andecological factors in Belgium. J. Biogeogr. 18, 257–266.

DECORANA. PHW thanks CTB for funding a visit to CTB. Fensham, R. J. (1995) Floristics and environmental relations of inlandHMdK thanks CTB, the Danish Research Academy, the Human dry rainforest in north Queensland, Australia. J. Biogeogr. 22,Sciences Research Council (SA), and the Foundation for 1047–1063.Research Development (SA) for funding a visit to CTB. TMC Ferry, B. W., Barlow, S. L. & Waters S. J. P. (1989) The shingle ridge

succession at Dungeness. Bot. J. Lin. Soc. 101, 19–30.thanks the Foundation for Research Development (SA).



Fortin, M.-J. (1994) Edge detection algorithms for two-dimensional Leemans, R. & Halpin, P. N. (1992) Biodiversity and global climate

change. Global biodiversity: status of the Earth’s living resourcesecological data. Ecology, 75, 956–965.

Fortin, M.-J. & Drapeau, P. (1995) Delineation of ecological boundaries: (ed. by B. Groombridge), pp. 254–255. Chapman & Hall, London.

Lovett, J. C. (1996) Elevational and latitudinal changes in tree as-comparison of approaches and significance tests. Oikos, 72, 323–332.

Gaston, K. J. (1991) How large is a species’ geographic range? Oikos, sociations and diversity in the Eastern Arc mountains of Tanzania.

J. trop. Ecol. 12, 629–650.61, 434–438.

Gaston, K. J. (1994) Rarity. Chapman & Hall, London. MacKinnon, J. & MacKinnon, K. (1986) Review of the protected areas

system in the Afrotropical Realm. IUCN, Gland.Gaston, K. J. & Williams, P. H. (1996) Spatial patterns in taxonomic

diversity. Biodiversity: a biology of numbers and difference (ed. by Magurran, A. E. (1988) Ecological diversity and its measurement.

Croom Helm, London.K. J. Gaston), pp. 202–229. Blackwell Science, Oxford.

Gauch, H. G. (1982) Multivariate analysis in community ecology. Manly, B. F. J. (1991) Randomization and Monte Carlo methods in

biology. Chapman & Hall, London.Cambridge University Press, Cambridge.

Gauch, H. G. & Whittaker, R. H. (1981) Hierarchical classification of McAllister, D. E., Platania, S. P., Schueler, F. W., Baldwin, M. E. &

Lee, D. S. (1986) Ichthyofaunal patterns on a geographic grid. Thecommunity data. J. Ecol. 69, 537–557.

Gleason, H. A. (1917) The structure and development of the plant zoogeography of North American freshwater fishes (ed. by C. H.

Hocutt and E. O. Wiley), pp. 17–51. John Wiley, New York.association. Bull. Torrey Bot. Club, 44, 463–481.

Gleason, H. A. (1939) The individualistic concept of the plant as- McCoy, E. D., Bell, S. S. & Walters K. (1986) Identifying biotic

boundaries along environmental gradients. Ecology, 67, 749–759.sociation. Am. Midl. Nat. 21, 92–110.

Gosz, J. R. (1993) Ecotone hierarchies. Ecol. Applic. 3, 369–376. McGowan, J. A. (1974) The nature of oceanic ecosystems. The biology

of the Pacific Ocean (ed. by C. B. Miller), pp. 9–28. Oregon StateGreig-Smith, P. (1983) Quantitative plant ecology, 3rd edn. Blackwell

Scientific Publications, Oxford. University Press, Corvallis.

Moreau, R. E. (1966) The bird faunas of Africa and its Islands.van Groenewoud, H. (1992) The robustness of correspondence, de-

trended correspondence, and TWINSPAN analysis. J. Veg. Sci. 3, Academic Press, London.

Myklestad, A. & Birks, H. J. B. (1993) A numerical analysis of the239–246.

Guillet, A. & Crowe, T. M. (1985) Patterns of distribution, species distribution patterns of Salix L. species in Europe. J. Biogeogr. 20,

1–32.richness, endemism and guild composition of water-birds in Africa.

Afr. J. Ecol. 23, 89–120. Nelson, G. & Platnick, N. I. (1981) Systematics and biogeography;

cladistics and vicariance. Columbia University Press, New York.Hansen, A. J. & di Castri, F. (eds) (1992). Landscape boundaries,

consequences for biotic diversity and ecological flows. Springer- Oden, N. L., Sokal, R. R., Fortin, M.-J. & Goebl, H. (1993) Categorical

wombling: detecting regions of significant change in spatially locatedVerlag, London.

Harrison, S., Ross, S. J. & Lawton J. H. (1992) Beta diversity on categorical variables. Geogr. Anal. 25, 315–336.

Oksanen, J. & Minchin, P. R. (1997) Instability of ordination resultsgeographic gradients in Britain. J. anim. Ecol. 61, 151–158.

Hengeveld, R. (1990) Dynamic biogeography. Cambridge University under changes in input data order: explanations and remedies. J.

Veg. Sci. 8, 447–454.Press, Cambridge.

Hill, M. O. (1979a) DECORANA—a FORTRAN program for de- Oksanen, J. & Tonteri, T. (1995) Rate of compositional turnover along

gradients and total gradient length. J. Veg. Sci. 6, 815–824.trended correspondence analysis and reciprocal averaging. Cornell

University, Ithaca. Orloci, L. & Orloci, M. (1990) Edge detection in vegetation: Jornada

revisited. J. Veg. Sci. 1, 311–324.Hill, M. O. (1979b) TWINSPAN—a FORTRAN program for arranging

multivariate data in an ordered two-way table by classification of Pekkarinen, A. & Teras, I. (1993) Zoogeography of Bombus and

Psithyrus in northwestern Europe (Hymenoptera, Apidae). Ann.the individuals and attributes. Cornell University, Ithaca.

Hill, M. O. (1994) DECORANA and TWINSPAN, for ordination and Zool. Fennici. 30, 187–208.

Peters, J. A. (1955) Use and misuse of the biotic province concept. Am.classification of multivariate species data. A new edition together

with supporting programs, in FORTRAN 77. ITE, Huntingdon. Nat. 89, 21–28.

Pianka, E. R. (1966) Latitudinal gradients in species diversity: a reviewHolland, M. M., Risser, P. G. & Naiman R. J. (eds) (1991) Ecotones,

the role of landscape boundaries in the management and restoration of concepts. Am. Nat. 100, 33–46.

Pielou, E. C. (1979) Biogeography. John Wiley, New York.of changing environments. Chapman & Hall, London.

Holloway, J. D. & Jardine, N. (1968) Two approaches to zoogeography: Podani, J. (1997) On the sensitivity of ordination and classification

methods to variation in the input order of data. J. Veg. Sci. 8,a study based on the distributions of butterflies, birds and bats in

the Indo-Australian area. Proc. Lin. Soc. Lond. 179, 153–188. 153–156.

Poynton, J. C. & Boycott, R. C. (1996) Species turnover betweenHumphries, C. J. & Parenti, L. R. (1986) Cladistic biogeography.

Oxford University Press, Oxford. Afromontane and eastern African lowland faunas: patterns shown

by amphibians. J. Biogeogr. 23, 669–680.Itoua, I., Underwood, E., Olson, D., Dinerstein, E., Loucks, C. &

Wettengel, W. (1997) Terrestrial ecoregions of Africa [map]. WWF, Rabinovich, J. E. & Rapoport, E. H. (1975) Geographical variation of

diversity in Argentine birds. J. Biogeogr. 2, 141–157.Washington D.C.

Jarvis, D. I. & Liu, H. (1993) Vegetation patterns in the Pinus yun- Rapoport, E. H. (1982) Areography, geographical strategies of species.

Pergamon Press, Oxford.nanensis-sclerophyllous broadleaved forests, Mianning County, Si-

chuan Province, China. J. Biogeogr. 20, 505–524. Rohde, K. (1992) Latitudinal gradients in species diversity: the search

for the primary cause. Oikos, 65, 514–527.Johnston, C. A., Pastor, J. & Pinay G. (1992) Quantitative methods for

studying landscape boundaries. Landscape boundaries, consequences Roy, K., Jablonski, D. & Valentine J. W. (1994) Eastern Pacific

molluscan provinces and latitudinal diversity gradient: no evidencefor biotic diversity and ecological flows (ed. by A. J. Hansen and F.

di Castri), pp. 107–125. Springer-Verlag, London. for ‘Rapoport’s rule’. Proc. Natl. Acad. Sci. USA, 91, 8871–8874.

Ruggiero, A., Lawton, J. H. & Blackburn T. M. (1998) The geographicKruger, F. J. (1977) Ecological reserves in the Cape Fynbos: towards

a strategy for conservation. S. Afr. J. Sci. 73, 81–85. ranges of mammalian species in South America: spatial patterns in



environmental resistance and anisotropy. J. Biogeogr. 26, 1093–1103. Webb, D. A. (1954) Is the classification of plant communities either

possible or desirable? Bot. Tidsskr, 51, 362–370.Schall, J. J. & Pianka, E. R. (1978) Geographical trends in numbers

of species. Science, 201, 679–686. White, F. (1983) The vegetation of Africa: a descriptive memoir to

accompany the Unesco/AETFAT/UNSO vegetation map of Africa.Sclater, P. L. (1858) On the general geographical distribution of the

members of the class Aves. J. Proc. Lin. Soc. (London) Zool. 2, Natural Resources Research XX. UNESCO, Paris.

Whittaker, R. H. (1960) Vegetation of the Siskiyou Mountains, Oregon130–145.

Shimwell, D. W. (1971) Description and classification of vegetation. and California. Ecol. Monogr. 30, 279–338.

Whittaker, R. H. (1972) Evolution and measurement of species diversity.Sidgwick and Jackson, London.

Shmida, A. & Wilson, M. V. (1985) Biological determinants of species Taxon, 21, 213–251.

diversity. J. Biogeogr. 12, 1–20. Whittaker, R. H. (1977) Evolution of species diversity in land com-

Sibley, C. G. & Monroe, B. L. Jr (1990) Distribution and taxonomy munities. Evol. Biol. 10, 1–67.

of birds of the World. Yale University Press, New Haven. Williams, P. H. (1992) Salvaging more from the sinking Ark. Annual

Sibley, C. G. & Monroe, B. L. Jr (1993) A supplement to ‘Distribution Report for Science 1992 (ed. by Anonymous), pp. 30–31. The Natural

and taxonomy of birds of the World’. Yale University Press, New History Museum, London.

Haven. Williams, P. H. (1993) Measuring more of biodiversity for choosing

Smith, C. H. (1983) A system of world mammal faunal regions. I. conservation areas: using taxonomic relatedness. International Sym-

Logical and statistical derivation of the regions. J. Biogeogr. 10, posium on Biodiversity and Conservation (ed. by T.-Y. Moon), pp.

455–466. 194–227. Korean Entomological Institute, Seoul.Smith, P. A. (1994) Autocorrelation in logistic regression modelling of Williams, P. H. (1996a) WORLDMAP 4 WINDOWS: Software and

species’ distributions. Global Ecol. Biogeogr. Letts. 4, 47–61. Help Document 4.1. Privately distributed (http: //www.nhm.ac.uk/Stoms, D. M. (1994) Scale dependence of species richness maps. Prof. science/projects/worldmap/), London.

Geogr. 46, 346–358. Williams, P. H. (1996b) Measuring biodiversity value. Wld. Conserv.Stuart, S. N. & Adams, R. J. (1990) Biodiversity in Sub-Saharan Africa 1, 12–14.

and its islands: conservation, management, and sustainable use. Williams, P. H. (1996c) Mapping variations in the strength and breadthIUCN SSC occasional paper no. 6. IUCN, Gland. of biogeographic transition zones using species turnover. Proc. R.

Takhtajan, A. L. (1986) Floristic regions of the World. University of Soc. Lond. (B) 263, 579–588.California Press, Berkeley. Williams, P. H. & Gaston, K. J. (1998) Biodiversity indicators: graphical

Tausch, R. J., Charlet, D. A., Weixelman, D. A. & Zamudio, D. C. techniques, smoothing and searching for what makes relationships(1995) Patterns of ordination and classification instability resulting work. Ecography, 21, 551–560.from changes in input data order. J. Veg. Sci. 6, 897–902. Wilson, M. V. & Mohler, C. L. (1983) Measuring compositional change

van Tongeren, O. F. R. (1995) Cluster analysis. Data analysis in along gradients. Vegetatio, 54, 129–141.community ecology (ed. by R. H. G. Jongman, C. J. F. ter Braak Wilson, M. V. & Shmida, A. (1984) Measuring beta diversity withand O. F. R. van Tongeren), pp. 174–212. Cambridge University presence-absence data. J. Ecol. 72, 1055–1064.Press, Cambridge. Womble, W. H. (1951) Differential systematics. Science, 114, 315–322.

Tramer, E. J. (1974) On latitudinal gradients in avian diversity. Condor, WWF & IUCN. (1994) Centres of plant diversity. A guide and strategy76, 123–130. for their conservation. Volume 1: Europe, Africa, South West Asia

Turner, S. R., O’Neill, R. V., Conley, W., Conley, M. R. & Humphries, and the Middle East. IUCN, Cambridge.H. C. (1991) Pattern and scale: statistics for landscape ecology.

Quantitative methods in landscape ecology: the analysis and in-

terpretation of landscape heterogeneity (ed. by M. G. Turner and

BIOSKETCHESR. H. Gardner), pp. 17–49. Springer-Verlag, New York.

Turpie, J. K. & Crowe, T. M. (1994) Patterns of distribution, diversity

and endemism in larger African mammals. S. Afr. J. Zool. 29, 19–32.Paul Williams is a member of the Biogeography &Udvardy, M. D. F. (1975) A classification of the biogeographicalConservation Group at The Natural History Museum,provinces of the World. IUCN occasional paper no. 18. IUCN,London, working on quantitative approaches to measuringMorges.

biodiversity value and assessing priorities for conservationVaisanen, R., Heliovaara, K. & Immonen A. (1992) Biogeography of

northern European insects: province records in multivariate analyses (see http://www.nhm.ac.uk/science/projects/worldmap/).(Saltatoria; Lepidoptera: Sesiidae; Coleoptera: Bupestridae, Cer- Other research interests include the phylogeny, systematics,ambycidae). Ann. Zool. Fennici. 28, 57–81. distribution and ecology of bumble bees. Helen de Klerk is

Valentine, J. W. (1966) Numerical analysis of marine molluscan ranges completing a Ph.D. on the biogeography and conservationon the extratropical northeastern Pacific shelf. Limnol. Oceanogr. of sub-Saharan birds at the Percy FitzPatrick Institute,11, 198–211. University of Cape Town, with Tim Crowe, whose interests

Wallace, A. R. (1876) The geographical distribution of animals, with include systematics and biogeography of African birds,a study of the relations of living and extinct faunas as elucidating

particularly the francolins, quails and pheasants (http://the past changes of the Earth’s surface, (2 Vols). Macmillan, London.

www.uct.ac.za/depts/fitzpatrick).WCMC (1992) Global biodiversity: status of the Earth’s living resources.

Chapman & Hall, London.


Documents

Interpreting biogeographical boundaries among Afrotropical birds: spatial patterns in richness gradients and species replacement