Sunday, December 9, 2007

Rapid Radiation, Borrowing and Dialect Continua in the Bantu Languages





Rapid Radiation, Borrowing and Dialect Continua in the Bantu Languages

Clare J. Holden & Russell D. Gray

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http://language.psy.auckland.ac.nz/publications/index.php?pub=Holden_and_Gray



Rapid Radiation, Borrowing and Dialect Continua
in the Bantu Languages


1. Introduction

Despite several decades of study, several fundamental questions about Bantu linguistic relationships remain unresolved, as well as numerous questions of detail (see Chapter 4 this volume). Phylogenetic analysis has shown that Bantu languages fit a branching-tree model of evolution surprisingly well, but a tree model does not explain all the variation in the Bantu linguistic data. Moreover, several different Bantu trees appear to fit the data almost equally well. Our difficulties in resolving the Bantu tree are o��en ascribed to a lack of data and research, and it is true that there are many more Bantu languages than linguists. However there are probably also more fundamental reasons why a single Bantu tree has proven elusive, arising from the historical processes under which these languages developed. In this chapter, we show how the networkbuilding method Neighbor-Net (Bryant & Moulton 2003) can be used to distinguish between different historical reasons why some linguistic relationships are not well resolved. We test three hypotheses for why some Bantu languages might not fit a tree model well: rapid radiation, linguistic borrowing and dialect chains, all thought to have been widespread within the Bantu family.
Bantu is a large family of over 450 languages that are spoken across sub-Equatorial Africa (Fig. 2.1). We define ‘Bantu’ in the sense of Ruhlen’s (1991) ‘Narrow Bantu’. Bantoid is the larger language group to which Bantu belongs. Bantoid belongs to the Niger-Kordofanian phylum, whose deepest branches are found in West Africa (Williamson & Blench 2000). Bantu languages are identified by codes originally assigned by Guthrie (1967–71), who classified Bantu languages into 15 zones (later expanded to 16), labelled A to S, based on geographical and linguistic criteria. (Many of these zones are probably not valid genetic groups.) Guthrie also divided the whole of Bantu into two large subdivisions, West Bantu and East Bantu (see Chapter 4 this volume). In this chapter, we use a modified version of Guthrie’s codes taken from Bastin et al. (1999) (for a correspondence between the codes of Bastin et al. and Guthrie see Maho 2002). Bantu is thought to have originated in Cameroon or Nigeria, where the non-Bantu Bantoid languages are spoken today. The spread of Bantu is associated with the spread of
farming; East Bantu in particular is associated with the Early Iron Age ‘Chifumbaze’ tradition in East and southeast Africa (Ehret 1998; Holden 2002; Phillipson 1993, 184–205; Vansina 1990).


Part 1: Tree approaches to Bantu language phylogeny

A number of Bantu trees have been published, constructed using different samples of languages and different tree-building methods. Distance-based lexicostatistical methods have been widely used as a heuristic device to infer Bantu relationships (Nurse 1996; see Chapter 4, this volume). Bastin et al. (1999) published the most comprehensive lexicostatistical Bantu trees, including 542 languages and dialects. Their linguistic data comprised coded information on cognates for 92 items of basic vocabulary, derived from the Swadesh 100-word list of basic vocabulary, with eight meanings (such as ‘snow’) that are not present in Bantu excluded (Bastin 1983). Subsequently, subsets of the data of Bastin et al. (1999) have been reanalyzed using phylogenetic tree-building methods, which use only innovations to define subgroups. In this respect, phylogenetic methods are comparable to the linguistic comparative method. Unlike the comparative method, however, phylogenetic methods use an explicit optimality criterion, such as maximum parsimony or likelihood, to choose among possible trees. Two further advantages of phylogenetic methods are that they let us test the fit of data on a tree using the consistency and retention indices, which measure the extent of homoplasy in the data set, and they allow us to evaluate the level of support in the data for each node, using tests such as bootstrap analysis, or, for Bayesian methods, by estimating the posterior probability of each node.
Holden (2002) reanalyzed 75 languages from Bastin et al. ’s (1999) data set using maximum parsimony tree-building methods, implemented by the computer program PAUP*4.0 (Swofford 1998; see Fig. 4.2 this volume). More recently, we have used Bayesian MCMC (Markov chain Monte Carlo) methods to infer phylogeny for 95 Bantu languages from the same dataset, using using the computer programs MrBayes (Huelsenbeck & Ronquist 2001) and BayesPhylogenies (Pagel & Meade 2004) (Fig. 2.2; Holden et al. 2005).
Instead of searching for the best tree(s) according to an optimality criterion, Bayesian methods sample trees in proportion to their likelihood, producing a sample of trees (usually several hundred) in which both moreand less-likely trees are included. One advantage of constructing a Bayesian tree sample is that it allows us to represent phylogenetic uncertainty in the sample, so that we do not have to treat the tree as if it were known without error, when, in fact, any tree remains simply a hypothesis about phylogeny. Our ability to reconstruct the true tree is inevitably limited by our data and our models of evolution; moreover, as we have noted, linguistic borrowing cannot be represented on a single tree.
Phylogenetic analysis suggests that Bantu basic vocabulary fits a tree model at least as well as typical biological data sets (Holden 2002; Sanderson & Donoghue 1989). For the 75-language Bantu tree constructed using equally weighted parsimony, Holden (2002) reported a consistency index (CI) of 0.65 and a retention index (RI) of 0.59; using weighted parsimony the CI was 0.72 and the RI was 0.68. These results indicate that Bantu linguistic evolution was substantially tree-like, at least for the basic vocabulary (other parts of the lexicon may be more prone to borrowing).
This was at first a surprising result, since borrowing among Bantu languages is thought to be widespread, whereas gene flow and hybridization are thought to be rare among many biological taxa. However, a branching tree model does not explain all the variation among the Bantu languages; there remains considerable conflicting signal in the Bantu data, which could be due either to borrowing or parallel evolution. In comparative perspective, Indo-European is even more tree-like than Bantu (Bryant et al. 2005; Rexova et al. 2003), but Austronesian may be less so (Gray & Jordan 2000).

Agreement and uncertainty among Bantu trees

Comparing Bantu trees, it is apparent that several major questions about Bantu linguistic relationships remain unresolved. Conflicts among Bantu trees are illustrated in Figures 2.2–2.4. Figure 2.2 shows a majority rule tree summarizing a Bayesian sample of 200 Bantu trees, sampled from 2 million trees, constructed using a reversible model of evolution with the computer program MrBayes (Huelsenbeck & Ronquist 2001). The majority rule tree in Figure 2.2 shows all nodes present on at least half the trees in the sample, plus all other compatible groupings. Alternative tree topologies (not shown) were also present in the sample. Node labels indicate the proportion of trees in the sample in which each node was found, which is equivalent to the posterior probability of that node. While many nodes were well supported in this analysis (being found in more than 95 per cent of trees in the sample) several nodes towards the root of the tree received much lower levels of support. An alternative summary of this tree sample, constructed using a consensus network (Holland & Moulton 2003), is shown in Figure 2.3. A consensus network allows us to represent the alternative tree topologies that were found in the sample.
This consensus network (Fig. 2.3) shows trees present in more than 18 per cent of the sample. Examining the consensus network reveals that most alternative tree topologies involve the East Bantu languages spoken in East Africa, particularly Yao P21, which has conflicting affiliations with both East and Southeast Bantu languages (among the la��er, especially with the N zone languages and Makwa P31). The 18 per cent threshold was chosen to illustrate the maximum amount of conflict among trees without becoming too visually complex; if we decrease this threshold, the main effect is to reveal even more complexity among the East African languages. Figure 2.4 illustrates a range of plausible alternative Bantu trees. Figure 2.4a summarizes the Bayesian tree sample shown in more detail in Figure 2.2. Figure 2.4b shows a number of alternative Bantu tree topologies, found in other analyses that used a variety of methods including maximum parsimony (Holden 2002; see Fig. 4.2), Bayesian methods with a nonreversible model of evolution (Holden et al. 2005) and Neighbor-Joining (Saitou & Nei 1987; unpublished work by Holden). Figures 2.2–2.4 illustrate that there is significant uncertainty regarding the shape of Bantu history that we cannot currently resolve. Summarizing across Bantu trees, we have divided Bantu into four major areas: West and East Bantu, and two groups of languages in Central and Southwest Africa that seem to be intermediate between West and East Bantu. These areas are shown on Figure 2.1. In phylogenetic analyses, languages in the intermediate zone usually cluster in two groups, labelled Central and Southwest Bantu on Figures 2.1 and 2.2 (Holden 2002; Holden et al. 2005). These categories broadly agree with much previous work in Bantu linguistics (Bastin et al. 1999; Heine 1973; Nurse 1996), although
under Guthrie’s traditional West and East Bantu division, our Southwest zone and parts of our Central zone would be grouped with West Bantu (see Chapter 4 this volume).


West Bantu

In our classification, West Bantu includes languages spoken in west-Central Africa, belonging to zones A, B, C, H and parts of D (Figs. 2.1–2.2). The most fundamental disputed question about the shape of Bantu history is whether or not West Bantu is monophyletic. In other words, do West Bantu languages share a unique common ancestor that is not also ancestral to East Bantu languages? Many published trees show the deepest splits on the Bantu tree to be within West Bantu, among the northwestern Bantu languages belonging to zones A and B (Bastin et al. 1999; Heine 1973; Holden 2002; Holden et al. 2005; Figs. 2.1 & 2.4b).
The alternative hypothesis is that the deepest split on the tree is between East and West Bantu. The Bayesian tree sample summarized in Figure 2.2 supports this alternative hypothesis. The status of West Bantu has profound consequences for reconstructing ancestral Bantu language and culture. If the earliest splits were within the northwestern Bantu languages of zones A and B, then those languages would become highly influential in reconstructing the earliest Bantu linguistic forms. Many historians, archaeologists and anthropologists also treat the Bantu language tree as a source of information about population history in this region (Ehret 1998; Holden & Mace 2003; Phillipson 1993; Schoenbrun 1998; Vansina 1984; 1990). Uncertainty in the tree has received li��le a��ention in such studies, although different researchers have used quite different trees. For example, in his classic study of political development in the Equatorial Bantu, Vansina (1990) assumed that there was a primary split between East and West Bantu; in contrast, Ehret (1998) subscribed to the alternative model that the deepest splits on the tree are within West Bantu languages. The topology of the tree chosen can have a significant effect on the conclusions of such studies; again, for example, in determining the influence of the northwestern Bantuspeaking societies for reconstructing of ancestral Bantu culture.

East Bantu

In our classification, East Bantu includes languages of zones E, F, G, J, N and S, plus some languages of zone M (Figs. 2.1–2.2). East Bantu is monophyletic on all published phylogenetic Bantu trees, but this clade is not well supported: it was not recovered in a bootstrap analysis (Holden 2002) and it has a very low posterior probability in Bayesian analyses (Holden et al. 2005; see also Fig. 2.2). Within East Bantu, the languages spoken in Southeast Africa, belonging to zones N and S, form a clade on previously-published phylogenetic trees. Languages in zone S o��en appear to be the most divergent within East Bantu when using ultrametric distance-based methods such as UPGMA; this is seen in some of the trees published by Bastin et al. (1999). However, maximum parsimony analysis (Holden 2002; see Fig. 4.2), which can display true branch lengths, suggests that this is because there was an increased rate of evolution among these languages. The languages spoken in East Africa sometimes form a clade within East Bantu, but not always (Fig. 2.4). In the present data set, the languages spoken in East Africa comprise zones E, F, G and J (Lakes Bantu), plus the individual languages Nyakyusa M31 and Yao P21 (Fig. 2.2). Again, alternative relationships among East Bantu languages imply different historical scenarios for the spread of these languages and their speakers. Within East Bantu, was there a primary division between the languages spoken in East and Southeast Africa, as Ehret (1998) suggests? Or are the deepest splits within the East African languages, suggesting that the East Bantu originated there, perhaps in association with the Urewe archaeological tradition around Lake Victoria (Holden 2002; Phillipson 1993)?

Southwest and Central Bantu

Bantu languages that seem to be intermediate between West and East Bantu belong to zones K and R Southwest Bantu), and to zones L and parts of M (Central Bantu). Central Bantu languages usually form a clade that is a sister-group to East Bantu (Figs. 2.2 & 2.4a). However, on some trees the Central languages split into an eastern and a western group, in which case the east-Central group usually forms a sister group to East Bantu, or occasionally to the southeastern (zones N-plus-S) clade within East Bantu, while the northwest-Central group clusters with Southwest Bantu (Fig. 2.4b). The position of the two languages Lega D25 and Binja D24 varies considerably across previously-published trees. They fall somewhere within, or as immediate outliers to, the East-plus-Central clade.
The position of Southwest Bantu is also somewhat variable among the trees that have been proposed. On some maximum parsimony trees, Southwest Bantu clusters with West Bantu languages of zones C and H (Holden 2002; Fig. 4.2). However, on the widely cited tree published by Heine (1973), and in Bayesian analyses (Holden et al. 2005; Fig. 2.2) Southwest Bantu forms a sister group to Central-plus-East Bantu. But in the Bayesian tree sample summarized in Figure 2.2, there is very strong support (posterior probability =1.0) for a clade which is a sister to Southwest Bantu and which comprises the languages of the Central Bantu and East Bantu regions of Figure 2.1 including Lega D25 and Binja D24 — regardless of how these languages are subgrouped among themselves.

Why is a single Bantu tree elusive?

It is unclear whether our difficulties in resolving the Bantu tree stem from a lack of data, or from a more fundamental mismatch between the actual process of Bantu evolution, o��en thought to be characterized by widespread borrowing and dialect continua, and a bifurcating tree model of language evolution. Regarding
the lack of data, the linguistic data published by Bastin et al. (1999) comprised only 92 meanings. A 200-word vocabulary list would probably be preferable. All phylogenetic analyses by Holden have also used this data set, so potentially suffer from the same problem. Counteracting this limitation, we should note that most of these 92 items have numerous distinct word forms (see Chapter 15 this volume), comprising over 1600 cognates in the 95-language sample. Regarding the tree model of linguistic evolution, trees are rather simplistic models of both biological and linguistic evolution. In biology, the importance of evolutionary processes such as hybridization, lateral gene transfer and recombination, especially in bacterial and viral evolution, is increasingly recognized
(Boucher et al. 2003; Stone 2000; Woese 1998). Linguistic borrowing and the formation of creole languages are analogous to lateral gene transfer and hybridization (see Ringe et al. 2002, for a discussion of these phenomena). Parallel evolution can also give rise to ambiguous relationships among taxa. Such complex relationships cannot be represented on a single tree. When placed on a tree, admixed languages are usually positioned near the root of the branch of the parent language that contributed most to the mixed language (Bryant et al. 2005; Cavalli-Sforza et al. 1994). Unlike trees, which only permit branching and divergence among taxa, networks can also have reticulations among branches, making it possible to show more than one evolutionary pathway on a single graph. For this reason, networks may be preferable for describing linguistic relationships involving creoles, or among languages with extensive borrowing, as they allow us to represent more than one ‘parent’ per language.

Part 2: Network approaches to Bantu language phylogeny

In this analysis, we used a new network-building method, Neighbor-Net, to investigate the affiliations of those Bantu languages whose position varies across different trees. A primary question concerns the earliest Bantu history — can we resolve the question of whether there was a primary split between East and West Bantu, or whether the deepest splits on the tree are within West Bantu? The position of Southwest Bantu is also unclear from previous studies — is it a sister-group to East Bantu, or does it cluster with West Bantu languages? Is Central Bantu a valid group, or should it be split into two? Within East Bantu, are the Lakes (J) languages outliers to other languages, or do all East African languages form a clade?

Constructing a Bantu network also lets us distinguish between the different linguistic processes that might underlie the weak or conflicting signals for some parts of the Bantu tree. Such processes include rapid radiation and borrowing, the la��er perhaps in the context of dialect continua. Rapid radiation may be inferred from a lack of phylogenetic signal, i.e. a rake- or star-shaped phylogeny, whereas reticulation would indicate possible borrowing. Reticulations can also pinpoint those languages which may have been each branch before the subsequent further spli��ing of that branch. This leaves a weak phylogenetic signal that can be difficult to detect, so that the language tree appears to be star- or rake-shaped (Bellwood 1996). Borrowing is the transfer of linguistic elements from one language to another, o��en between neighbouring languages. Extensive borrowing can lead to conflicting affiliations (where a language shows similarities to more than one divergent language groups) that cannot be represented on a single tree.
Unlike tree-building methods, constructing a network does not force the data into a bifurcating tree. If the data are truly tree-like, then the Neighbor-Net method will return an unrooted tree, but if there are conflicts within the data then it will construct a splits graph, in which conflicting relationships are represented by reticulations or joining among branches. From the shape of the Neighbor-Net network, we can infer whether either rapid radiation or borrowing occurred in different parts of the Bantu language family. On a network, we would expect rapid radiation to result in a star-shaped phylogeny, with poorly marked hierarchical structure but no evidence for conflict among language groupings. We would expect borrowing to result in reticulation among branches,
and we would expect dialect chains to be indicated by complex chains of reticulation involving numerous languages.

Methods

Data

The sample included 93 Bantu languages and two non-Bantu Bantoid (hence simply ‘Bantoid’) languages, Tiv and Ejagham. The la��er were used as outgroups to root the tree where appropriate (e.g. Fig. 2.2). Figure 2.1 shows the approximate geographical locations of the languages in the analysis. The data set included all languages for which linguistic data were published in Bastin et al. (1999), and for which ethnographic data from the corresponding cultural group were published in the Ethnographic Atlas (Murdock 1967). This data set was designed to let us use our knowledge of Bantu language relationships to study other aspects of cultural evolution in these populations in the future; this is possible insofar as linguistic relationships reflect population history (Barbujani 1991; Cavalli-Sforza et al. 1988). The data set is similar to the 75-language data set used by Holden (2002; see Fig. 4.2), except that in the 75-language data set, languages with more than 5 per cent missing data were excluded, whereas they have been included here. For this analysis, the data were coded in a multistate form, i.e. with each column representing a meaning, and most meanings having several different cognate forms. (We have also run this analysis with the data coded in binary form, i.e. each cognate having its own column; this made very li��le difference to the results.)

Analysis

Neighbor-Net (Bryant & Moulton 2003) is an agglomerative method for constructing networks that selects taxa on the basis of similarity and groups them together. The algorithms used in this method are analogous to the Neighbor-Joining method for building trees (Saitou & Nei 1987). In agglomerative tree-building methods, two taxa (or nodes) are chosen on the basis of similarity, then they are agglomerated (merged), the data matrix is reduced and we proceed to the next iteration. However, to construct a network, the Neighbor-Net method does not immediately agglomerate the selected taxa (or nodes). Instead, it waits until one of the chosen taxa (or nodes) has been grouped with a different node. Then the three nodes are reduced to two, and the process is repeated. The Neighbor-Net method represents similarities within the data set as a splits graph, constructed from a distance matrix. Splits are bipartitions of the data. An example of a splits graph for four languages, Ndebele S44, Swati S43, Ngoni S45 and Zulu S42, is shown in Figure 2.5. Sets of parallel lines indicate a single bipartition (split) in the data. The box shape at the centre of the graph is characteristic of conflicting data. Split A groups together Zulu and Ngoni on the one hand, and Ndebele and Swati on the other. Split B groups together Ndebele and Ngoni on the one hand, and Swati and Zulu on the other. Branch lengths (or edge weights) are proportional to the support for a split in the data: thus there is more evidence for split A than for split B. Distances between language pairs in the 95-language data set were calculated using PAUP v.4a (Swofford 1998), using mean character differences. Weighted splits were calculated and then represented as a splits
graph using the computer program SplitsTree v4beta.06 involved in borrowing. Complex chains of conflicting relationships involving numerous languages may indicate that borrowing occurred in the context of dialect chains. Under rapid radiation, a language diverges into several daughter languages very rapidly, so there is li��le time for linguistic innovations to accumulate in (Huson 1998; Huson & Bryant 2006). The complete Bantu network resulting from this analysis is shown in Figure 2.6. Separate splits graphs for East and West Bantu are shown in Figures 2.7a and 2.7b. There is some evidence that Neighbor-Net may overfit the data, meaning that it produces some false splits (Nakhleh et al. 2005). However, such false splits have very small edge weights. To guard against the possibility of including false splits in our Bantu network, we also constructed a network from which edges weighted less than 0.002 were excluded; this cut-off point was essentially arbitrary. Eighty-seven of 331 edges or 26 per cent had weights less than 0.002. The network of weights greater than 0.002 is shown
in Figure 2.8; it may be interpreted as a simplified and conservative estimate of the Bantu network.

Results

A splits graph of the complete sample of 95 Bantu and Bantoid languages is shown in Figure 2.6. The major groups including West, East, Southwest and Central Bantu are indicated. Figures 2.7a b show splits graphs for East and West Bantu, respectively, allowing us to focus on relationships within those groups in more detail. The simplified splits graph of edge weights greater than 0.002 is shown in Figure 2.8. Unless specified, the following discussion refers to splits that are present on both the complete network (Fig. 2.6) and on the reduced network (Fig. 2.8). On Figure 2.6, West Bantu languages cluster together on the top le�� of the graph, while East Bantu languages cluster to the bo��om right. Southwest and Central Bantu occupy a space intermediate between West and East Bantu, with Central Bantu being closer to East Bantu, and Southwest Bantu closer to West Bantu. This is in line with our expectations from Bantu trees (cf. Figs. 2.2 & 2.4). Although the absolute positions of the languages have been rotated on Figure 2.8, the relative positions of languages remain the same.

Central Bantu

Although Central Bantu is clearly defined by splits dividing this group from other Bantu languages, there are also conflicting splits dividing Central Bantu into (a) a northwestern group comprising Kaonde L42, Luba L33 and Songe D10S, and (b) an eastern group comprising M-zone languages. The northwestern group is more similar to West and Southwest Bantu, plus a number of East Bantu languages that border the West Bantu area (see Fig.
2.1), including subzones J5, J6, E5 and E6 plus Hima J13. The east-Central group is more similar to the other East Bantu languages. This suggests that there was an area of contact, leading to borrowing or convergence, among languages on at least one side of this Figure 2.7. a) Network of 48 East Bantu languages; relationships among East Bantu languages spoken in east Africa are particularly complex and conflicting. b) Network of 29 West Bantu languages, plus Tiv and Ejagham (Bantoid languages).

divide. Linguistically, Kaonde L42 falls within the northwestern group, but is also linked by conflicting splits to the eastern group. Geographically, Kaonde is closer to the eastern group (Fig. 2.1), so it seems likely that it originated as a northwest-Central language whose speakers later migrated south, and that there was subsequently borrowing between Kaonde and the east-Central languages.

Southwest Bantu

Southwest Bantu is very clearly separated from other languages, reflecting the robustness of this group in phylogenetic analysis (Fig. 2.2; Holden 2002). Figures 2.6 and 2.8 show a conflicting split linking Umbundu R11 to West Bantu (or alternatively, linking all the other Southwest Bantu languages to East-plus-Central
Bantu). This split aside, the splits graphs (Figs. 2.6 & 2.8) are consistent with a tree in which Southwest Bantu is an outlier to East-plus-Central Bantu, rather than clustering within West Bantu.

East Bantu

The following groups are supported by splits of varying lengths:
a) zone S;
b) zone N;
c) zone J (also known as Lakes Bantu);
d) zone F (including Sukuma F21, Nyamwezi F22 and Sumbwa F23).
Some of the most complex relationships in East Bantu appear among the East African languages of zones E (excluding E5 and E6), F and G. At the centre of the graph, there are no conflict-free major groupings among these languages, suggesting that these languages developed in a condition of dialect continua with borrowing across dialects (see Ringe et al. 2002 for discussion of an analogous situation among early Indo-European ‘Satem’ languages). The early evolution of these languages appears to be the least tree-like of all the Bantu languages in this analysis. Later, some clearer groups emerge such as Kaguru G12 + Gogo G11 + Luguru G35, and zone F, but these languages also continue to be involved in extensive conflicting relationships (Figs. 2.6, 2.7a & 2.8).
Within the S-zone languages, Venda shows conflicting relationships, being grouped on the one hand with Ndau S15 plus Shona S10, and on the other hand with the S30, S40 and S50 languages (Figs. 2.6, 2.7a & 2.8). Venda is geographically adjacent to the groups it shares similarities with, suggesting that borrowing
across neighbouring groups has occurred. However, it should be noted that adjacency alone does not always lead to linguistic convergence: historically specific factors must also have played a role.

West Bantu

In previous phylogenetic analyses, it has proven difficult to resolve the relationships among West Bantu languages. The deepest splits involving West Bantu languages receive very low support on trees (Fig. 2.2; Holden 2002; Holden et al. 2005). Examining the splits graphs (Figs. 2.6, 2.7b & 2.8) suggests that there was a more or less simultaneous divergence of six groups of West Bantu languages.
These include:
a) the H languages plus Madzing B80 and Teke B73;
b) Duala A24, Puku A32, Bakoko A43, Fang A75, Kota B25 and Tsogo A43;
c) Sakata C34, Mongo C61, Nkundo Mongo C61, Kela C75, Tetela C71 and Lele C84;
d) Lingala C36, Doko C40 and Ngombe C41;
e) Kumu D37 and Bira D32;
f) Likile C57 and Mbesa C51.
There is li��le evidence for conflict (borrowing) among West Bantu languages. The shape of the network suggests that our difficulties in resolving relationships among West Bantu languages may result from rapid early radiation of these languages, leaving li��le phylogenetic signal or deep hierarchical structure in the data.
We also wished to address the question of whether some northwestern Bantu languages are the most divergent relative to other Bantu languages. In the tree sample shown in Figure 2.2 the most divergent West Bantu languages are Bubi A31 and a pair consisting of Likele C57 and Mbesa C51. On the 75- language maximum parsimony tree shown in Figure 4.2, Mpongwe B11 is also highly divergent. Investigating the affiliations of these languages on the complete splits graph (Fig. 2.6) yielded the following results. Bubi, a long isolated language spoken on Bioko Island, is linked by one split to the outgroups Tiv 802 and Ejagham 800. Presumably these similarities are primitive (i.e. they result from ancestral similarities, which other Bantu languages have lost). Another split links Tiv, Ejagham, Bubi, Kumu D37 and Bira D32. These similarities (among far-flung languages) are probably also primitive. Another split links Bubi to Bira, Kumu, Doko C40 and Ngombe C41; these are all geographically peripheral West Bantu languages (Fig. 2.1). One split links together all the West Bantu languages apart from Mpongwe B11. These links are consistent with the view that Bubi A31, and possibly Mpongwe B11, diverged or became isolated early among the West Bantu languages, and lack some innovations that most other West Bantu languages share. No splits linking Likile C57 and Mbesa C51 to any particular West Bantu (or other) languages were detected.
With the exception of Bubi A31, there was no evidence that languages of zones A and B are particularly divergent, either among themselves, or in relation to other Bantu languages. Zones A and B (apart from Bubi) appear to cluster with zone H (Figs. 2.2–2.3, 2.6, 2.7b & 2.8).


Discussion

The results of this analysis suggest why persistent ambiguities in the Bantu tree remain, despite several decades of study. From the shape of the network it appears that different historical processes caused the ambiguities in the East and West Bantu parts of the tree. West Bantu languages underwent a rapid early radiation — near simultaneous divergence of several major West Bantu branches — resulting in a star-shaped phylogeny with li��le internal structure. With the exception of the isolated language Bubi A31 (and possibly Mpongwe B11) we found no evidence that the greatest divergence within Bantu involved languages of zones A and B. There is li��le evidence for borrowing among West Bantu languages, possibly indicating that these speech communities were more isolated from one another than were early East Bantuspeaking populations. In contrast, both borrowing and dialect continua seem to have been important among East Bantu languages, especially in East Africa. There is extensive conflict at the centre of the graph, involving all East Bantu languages and also the eastern-Central Bantu languages. The fact that so many languages are involved suggests that the borrowing among these languages occurred early in their history, when their proto-languages were geographically closer together,
possibly in the context of dialect continua. There also seems to have been ongoing borrowing among the Bantu languages of zones E (excluding E5 and E6), F and G in East Africa. However, there is a fairly tree-like structure among the Southeast African languages of zones N and S, indicating that subsequent divergence among these languages was substantially tree-like. There is some evidence for more recent, localized borrowing, indicated by conflict involving fewer languages, for example that associated with Venda. Another case is the borrowing between Kaonde (a northwest-Central language) and the east-Central languages (Bemba, Lala and Lamba). Ethnographic evidence suggests that some of the languages that have experienced linguistic borrowing may have borrowed other cultural a��ributes along with vocabulary. For example, Kaonde-speakers are predominantly matrilineal, like the Bemba, Lala and Lamba, but unlike modern speakers of the other northwest-Central languages, Songe and Luba. A parsimonious interpretation is that a��er Kaonde speakers migrated south,
they borrowed both linguistic elements and matrilineal descent from their new neighbours.
This analysis shows how, by constructing a network, we can move beyond the question of whether, or how well, Bantu languages fit a tree model. Although previous phylogenetic analysis of Bantu languages showed that relationships among these languages are more tree-like than we might have expected, a tree model does not explain all the variation among them (Holden 2002). Moreover, several alternative trees appear to fit the Bantu linguistic data almost equally well, so that we cannot, at present, define a single best tree. A network model lets us test alternative hypotheses for why some Bantu relationships may not be tree-like, including rapid radiation, borrowing and past dialect continua, providing new insights into why parts of the Bantu language family have remained intractable to phylogenetic analysis.

Conclusion

Our results suggest that there was rapid early radiation among West Bantu languages; in contrast, there was extensive borrowing, partly within dialect continua, among East Bantu languages in East Africa. We propose that these are the underlying reasons why some parts of the Bantu language family have until now proven intractable to phylogenetic analysis.

Acknowledgements
C.J.H. was supported by a Research Fellowship from the AHRB Centre for the Evolutionary Analysis of Cultural Behaviour at University College London (UCL). This research received additional funding from the Marsden Fund, Royal Society of New Zealand and the UCL Graduate School Research Projects Fund.

References

Barbujani, G., 1991. What do languages tell us about human microevolution? Trends in Ecology and Evolution 6, 151–6.
Bastin, Y., 1983. Classification lexicostatistique des langues bantoues (214 releves). Bull. Seanc. Acad. R. Sci Outre-Mer 27, 173–99.
Bastin, Y., A. Coupez & M. Mann, 1999. Continuity and divergence in the Bantu languages: perspectives from a lexicostatic study. Annales, Sciences humaines 162, 1–226.
Bellwood, P., 1996. The origins and spread of agriculture in the Indo-Pacific region: gradualism and diffusion or revolution and colonization?, in The Origins and Spread of Agriculture and Pastoralism in Eurasia, ed. D.R. Harris. London: UCL Press, 465–98.
Boucher, Y., C.J. Douady, R.T. Papke, D.A. Walsh, M.E.R.
Boudreau, C.L. Nesbo, R.J. Case & W.F. Dooli��le, 2003. Lateral gene transfer and the origins of prokaryotic groups. Annual Review of Genetics 37, 283–328.
Bryant, D. & V. Moulton, 2003. Neighbor-Net: an agglomerative method for the construction fo phylogenetic networks. Molecular Biology and Evolution 21, 255–65.
Bryant, D., F. Filimon & R.D. Gray, 2005. Untangling our past: languages, trees, splits and networks, in The Evolution of Cultural Diversity: a Phylogenetic Approach, eds. R. Mace, C. Holden & S. Shennan. London: UCL Press, 69–85.
Cavalli-Sforza, L.L., E. Minch & J. Mountain, 1988. Reconstruction of human evolution: bringing together the genetic, archaeological and linguistic data. Proceedings of the National Academy of Sciences of the USA 85, 6002–6.
Cavalli-Sforza, L.L., P. Menozzi & A. Piazza, 1994. The History and Geography of Human Genes. Princeton (NJ): Princeton University Press.
Ehret, C., 1998. An African Classical Age: Eastern and Southern Africa in World History, 1000 ���� to ���� 400. City?? (VA): University Press of Virginia.
Gray, R. & F. Jordan, 2000. Language trees support the express-train sequence of Austronesian expansion. Nature 405, 1052–5.
Guthrie, M., 1967–71. Comparative Bantu: an Introduction to the Comparative Linguistics and Prehistory of the Bantu Languages. Farnborough: Gregg International.
Heine, B., 1973. Zur genetischen Gliederung der Bantu-Sprachen. African Language Studies 14, 82–104.
Holden, C.J., 2002. Bantu language trees reflect the spread of farming across sub-Saharan Africa: a maximumparsimony analysis. Proceedings of the Royal Society of London Series B 269, 793–9.
Holden, C.J. & R. Mace, 2003. Spread of ca��le pastoralism led to the loss of matriliny in Africa: a co-evolutionary analysis. Proceeding of the Royal Society of London Series B 270, 2425–33.
Holden, C.J., A. Meade & M. Pagel, 2005. Comparison of maximum parsimony and Bayesian Bantu language trees, in The Evolution of Cultural Diversity: a Phylogenetic Approach, eds. R. Mace, C.J. Holden & S. Shennan. London: UCL Press, 53–65.
Holland, B. & V. Moulton, 2003. Consensus networks: a method for visualising incompatibilities in collections of trees, in Algorithms in Bioinformatics, eds. G. Benson & R. Page. Berlin: Springer-Verlag, 165–76.
Huelsenbeck, J.P. & F. Ronquist, 2001. MRBAYES: Bayesian inference of phylogeny. Bioinformatics 17, 754–5.
Huson, D.H., 1998 SplitsTree: a program for analyzing and visualizing evolutionary data. Bioinformatics 14, 68–73.
Huson, D.H. & D. Bryant, 2006. Application of phylogenetic methods in evolutionary studies. Molecular Biology and Evolution 32, 254–67.
Maho, J.F., 2002. Bantu Line-up: Comparative Overview of Three Bantu Classifications, pp. 1–59. Unpublished manuscript. Goteborg, Sweden: Goteborg University.
Available for download at www.african.gu.se/maho/jfmpubpapers.html.
Murdock, G.P., 1967. Ethnographic Atlas. Pi��sburgh (PA): University of Pi��sburgh Press.
Nakhleh, L., T. Warnow, C.R. Linder & K. St John, 2005. Reconstructing reticulate evolution in species — theory and practice. Journal of Computational Biology 12, 796–811.
Nurse, D., 1996. Historical classifications of the Bantu languages, in The Growth of Farming Communities in Africa from the Equator Southwards, vol. XXIX–XXX, ed. J.E.G. Su��on, Nairobi: British Institute in Eastern Africa, 65–75.
Pagel, M. & A. Meade, 2004. A phylogenetic mixture model for detecting pa��ern-heterogeneity in gene sequence or character-state data. Systematic Biology 53, 571–81.
Phillipson, D.W., 1993. African Archaeology. (Cambridge World Archaeology.) Cambridge: Cambridge University Press.
Rexova, K., D. Frynta & J. Zrzavy, 2003. Cladistic analysis of languages: Indo-European classification based on lexicostatistical data. Cladistics 19, 120–27.
Ringe, D., T. Warnow & A. Taylor, 2002. Indo-European and computational cladistics. Transactions of the Philological Society 100, 59–129.
Ruhlen, M., 1991. A Guide to the World’s Languages, vol. 1: Classification. London: Edward Arnold.
Saitou, N. & M. Nei, 1987. The Neighbor-Joining method: a new method for reconstruction of phylogenetic trees. Molecular Biology and Evolution 4, 406–25.
Sanderson, M.J. & M.J. Donoghue, 1989. Pa��erns of variation in levels of homoplasy. Evolution 43, 1781–95. Schoenbrun, D.L., 1998. A Green Place, a Good Place: Agrarian Change, Gender and Social Identity in the Great Lakes Region to the 15th Century. (Social History of Africa series.) Portsmouth: Heinemann.
Stone, G., 2000. Phylogeography, hypbridization and speciation. Trends in Ecology and Evolution 15, 354–5.
Swofford, D.L., 1998. PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Sunderland (MA): Sinauer Associates.
Vansina, J., 1984. West Bantu expansion. Journal of African history 25, 129–45.
Vansina, J., 1990. Paths in the Rainforests: Toward a History of the Political Tradition in Equatorial Africa. London: James Currey.
Williamson, K. & R.M. Blench, 2000. Niger-Congo, in African Languages: an Introduction, eds. B. Heine & D. Nurse. Cambridge: Cambridge University Press, 11–42.
Woese, C., 1998. The universal ancestor. Proceedings of the National Academy of Sciences of the USA 95, 6854–9.

Wednesday, October 3, 2007

10,000 Wildebeest Drown in Migration "Pileup"


Scores of wildebeest lay dead along the banks of the Mara River in Kenya, having washed downstream after a bizarre mass death that occurred early last week. An estimated 10,000 of the animals drowned while attempting to cross the river during an annual migration, wiping out one percent of the total species population. Photograph courtesy Terilyn Lemaire/Mara Conservancy/WildlifeDirect




10,000 Wildebeest Drown in Migration "Pileup"
Blake de Pastino
National Geographic News
October 1, 2007

In a bizarre mishap that conservationists describe as "heartbreaking," an estimated 10,000 wildebeest have drowned while attempting to cross Kenya's Mara River during an annual migration.
The deaths, which occurred over the course of several days last week, are said to account for about one percent of the total species population.
The drownings created a grotesque wildlife pileup, after part of the migrating herd tried to ford the Mara at "a particularly treacherous crossing point," according to Terilyn Lemaire, a conservation worker with the Mara Conservancy who witnessed the incident. (See a photo gallery of the mass drowning.)
The first animals into the river failed to cross and drowned, while others continued to stampede into the water behind them, Lemaire told National Geographic News by email.
"Once they jumped into the water, they were unable to climb up either embankment onto land and, as a result, got swept up by the current and drowned," she said.
Some 2,000 wildebeest drowned at the crossing in a single afternoon, Lemaire estimated.
"There was no unusual flooding at the time, and there seems to be no extraneous circumstances to these deaths," she said.
"The wildebeest merely chose a crossing point that was too steep."
Drowning deaths are not uncommon during the migration, Lemaire added, but her organization has never witnessed fatalities on this scale.
"It is customary every year for the wildebeest to pick a particularly treacherous crossing point and for there to be a significant die-off," she said, "but the number of deaths during these crossings almost never exceeds one thousand."
Fatal Migration
More than a million wildebeest undertake an epic migration every year in late summer, leaving their calving grounds in the Serengeti Plain of Tanzania to seek greener pastures in Kenya to the north.
The animals, also known as gnu, journey some 2,000 total miles (3,200 kilometers) each year, often joined by thousands of zebras and Thomson's gazelles.
The deaths occurred at Kenya's Maasai Mara National Reserve, as the herd was beginning its swing to the east on its way back to the Serengeti.
Since the drownings, the animals' bodies have washed downriver, beaching on the Mara's muddy banks and getting caught under a nearby bridge, Lemaire wrote on her blog for the nonprofit WildlifeDirect.
The remains formed what she described as "pungent islands of bloated carcasses."
"The crocodiles, storks, and vultures have not had to worry about where to find their next meal," she wrote.
"Those that aren't consumed will be left and will eventually decompose in the water. These thousands of carcasses will undoubtedly affect the health of the water, but to what extent, only time will tell."
Lemaire also declined to speculate, in her email to National Geographic News, on the impact the mass deaths might have on the wildebeests' overall population health.
"I would imagine that such a significant decrease in population would have an effect," she said, "but what that effect would be and to what extent, I cannot say."

Ancient Pharaoh Temple Discovered Inside Egypt Mosque


Sections of columns and elaborately inscribed reliefs from an ancient Egyptian temple were recently discovered behind the walls of a mosque in Luxor. The oval relief, or cartouche, at center depicts the name of Ramses II, the pharaoh in whose name the temple was built around 1250 B.C. Photograph courtesy Zahi Hawass, Supreme Council of Antiquities



Ancient Pharaoh Temple Discovered Inside Egypt Mosque
Steven Stanek in Cairo, Egypt
for National Geographic News
September 27, 2007

Parts of a temple dating to the reign of pharaoh Ramses II have been discovered inside a mosque in Luxor, Egypt, officials report (see map).
Experts restoring the historic mosque uncovered sections of columns, capitals, and elaborately inscribed reliefs from one of the ancient temple's courtyards built around 1250 B.C.
The previously concealed architectural elements reveal well-preserved hieroglyphics and unique scenes depicting the powerful pharaoh.
The discovery is likely to touch a nerve among religious leaders, because the newly exposed reliefs contain representations of humans and animals, which are forbidden inside mosques, the experts said.
The mosque was erected as a shrine to Muslim saint Abul Haggag in the 13th century A.D. on the site of an earlier Christian church, which was itself built on top of the ancient temple, the archaeologists explained.
The discovery was made during repair work on the mosque after a fire damaged part of the structure in June.
"To do this project of restoration, [workers] had to reclean and reopen many things, and this is when the scenes were found, and they are really unique," said Zahi Hawass, secretary general of Egypt's Supreme Council of Antiquities.
(Hawass is also a National Geographic Society explorer-in-residence. National Geographic News is a division of the National Geographic Society.)
Encryptions and Glyphs
Christians, and later Muslims, frequently built their shrines on top of ancient Egyptian holy sites, said W. Raymond Johnson, an Egyptologist at the Oriental Institute at the University of Chicago who has seen the newly exposed temple sections.
Builders of both faiths usually erased or defaced ancient artwork in the temples, he said, but the newfound reliefs remain virtually untouched.
"We are very lucky that these have been so well preserved," Johnson said.
Rather than destroying the reliefs, the mosques builders carefully hid them away with a protective layer of straw-reinforced plaster, shielding them from the elements.
"We didn't know we would find the reliefs and the inscriptions in such good condition," said Mansour Boraik, general supervisor of the Supreme Council of Antiquities in Luxor.
"The people who built the mosque for Haggag … actually saved the inscriptions and reliefs."
More images and inscriptions will likely be discovered as the restoration continues, he said.
The reliefs are thought to depict the temple's dedication. (Read related story: "Giant Ancient Egyptian Sun Temple Discovered in Cairo" [March 1, 2006].)
Among the most important scenes are those that feature Ramses II offering the sun god Amun Re two obelisks to be installed at the temple's front facade. One of those obelisks still stands at the temple, and the other is now at the Place de la Concorde in Paris.

Another relief shows three statues of Ramses II wearing his traditional white crown.
Experts say the carved inscriptions provide some of best examples of cryptographic or enigmatic writing, an unusual form of hieroglyphic text in which each glyph could stand for an entire word, phrase, or concept.
"Moral Quandary"
Now that the depictions have been uncovered, archaeologists will likely have to negotiate with local religious leaders who see the exposed renderings in their mosque as a violation of Islamic law.
"There is no damage to the mosque whatsoever, but its a moral quandary because you have these two places of worship, one still alive and one from the past," said Johnson, of the Oriental Institute.
"It's a living sacred space."
Boraik said that his team is in talks with mosque leaders about how to proceed.
"I think all of them understand the importance of these things," he said.
The researchers expect to reach a compromise, they said, which might include retractable coverings or screens over the inscriptions. Removing the ancient features entirely would likely cause damage to the mosque.
"One has to be very sensitive about the restoration work and make sure the people know you are doing something good," said Salima Ikram, a professor of Egyptology at the American University of Cairo.
She added that such issues are common in a country with such a rich religious heritage.
"In a way the mosque is part of the history of the temple—both are significant monuments of antiquity."

Kenya Plans for Huge Sugar Factory Spark Bitter Dispute



Plans for a massive sugar factory in a Kenyan wetland could irreversibly ruin the ecosystem, conservationists say. The project, they warn, would wreak havoc on several indigenous groups, a vast array of bird and fish species, and two types of endangered primates—including the highly rare Tana River mangabey (above). But proponents note that the planned facility would provide up to 20,000 new jobs in an impoverished region of the country that has already seen several failed attempts at kick-starting the local economy. Photograph courtesy Julie Wieczkowski




Kenya Plans for Huge Sugar Factory Spark Bitter Dispute
Nick Wadhams in Nairobi, Kenya
for National Geographic News
September 24, 2007

Plans for a 50,000-acre (20,000-hectare) sugar production plant in Kenya's Tana River Delta have ignited a bitter dispute between conservation groups and economic-minded officials.
Supporters say studies show that sugar cane grows nearly three times as fast in the delta's rich wetlands as it does in western Kenya, where the bulk of the country's sugar is currently produced.
Such output would give Kenya's sugar industry a huge boost when it needs it most, proponents say, and would provide up to 20,000 new jobs in an impoverished region of the country.
But opponents argue that development could irreversibly ruin the delta, which is home to several indigenous groups, a vast array of bird and fish species, and two species of highly endangered primates.
On a single day in January of this year, the Mwamba Bird Observatory and Field Study Centre counted 15,000 water birds belonging to 69 species, including herons, terns, African skimmers, and stork.
Colin Jackson, who led the recent count, estimated that the census covered only 15 percent of the delta's acreage (see a map of Kenya showing the Tana River).
"I was stunned by the number of birds and the diversity, the volume—and we were only looking at a tiny percentage of the whole area," Jackson said.
He added that he was surprised at how little attention the sugar factory issue has received so far from other environmental groups.
"It appeared that there was actually very little concern being raised in the conservation world about this, even though the site is really, really important," Jackson said.
"Normally with a development of this size, which is huge and will have a major impact on the ecosystem, there's normally quite some noise being made."
Finding Balance
The situation in the Tana River Delta is a microcosm of the challenges facing Kenya and much of East Africa.
While the region boasts extraordinary biological diversity, it is also a place of deep poverty, leaving many people desperate for development.
Balancing the need to protect the ecosystem with locals' demands for jobs has not been easy.
The sugar project, to be run by Kenya's Mumias Sugar Company, is the latest in a string of proposed development ideas for the delta.
And other producers have been showing increased interest in the area in recent days.
Mat International Sugar announced this week that it has snapped up 223,000 acres (90,000 hectares) of land adjacent to Mumias's claim as part of a second, two-billion-dollar sugar project.
The firm hopes to produce 1.2 million tons of sugar each year.
"We intend to have seamless sugarcane farms where each village will have a plantation in addition to farms to take care of food security," Mat's project co-coordinator Moses Changwony told the East African Standard newspaper.
Under Pressure
One of Mumias's most important backers is the Tana and Athi Rivers Development Authority (TARDA), whose chair is a nephew of Kenyan President Mwai Kibaki.
President Kibaki has expressed his support for the project, which comes at a crucial time for the Kenyan economy.
Next February Kenya is expected to drop tariffs for sugar imported from other countries in the regional trading group known as Common Markets of Eastern and Southern Africa (COMESA).
Sugar from elsewhere in COMESA is about 40 percent cheaper than Kenyan sugar.
Government officials fear that without its own cheap source, Kenya's sugar industry could be wiped out—unless the country decides to extend the tariffs.
"In 2008, once COMESA starts bringing duty-free sugar [into the country] … it means we will lose out, so we have to produce sugar at low production cost so that we bridge the deficit gap," said TARDA spokesperson Damaris Kiarie.
An environmental impact study of the proposal is underway.
But even the team conducting the assessment acknowledges that it is under pressure to come up with a solution that doesn't just leave the delta in its current state.
"We have to be neutral and ensure that we come up with a solution which is all-inclusive so that the environmental crusaders will not say, Oh, they are biased on the others' side," said an environmental studies professor involved with the assessment.
The professor spoke on condition of anonymity because his contract bars him from talking publicly to the press.
"And we don't want the local people to say, Oh, we gave in to the international organizations and the local people, we leave them suffering," he added.
"In the delta, the poverty level is 73 percent—73 percent of the people live below one dollar per day. So if it is not a sugar project, we must recommend something which can be done which is viable."
Multiple Attempts
Over the years the delta has seen several failed attempts at bringing in money and development.
Among the most notorious was a World Bank project meant to protect the region's red colobus monkey and the crested mangabey—two species counted among the world's top 25 most endangered primates by the World Conservation Union.
The bank's solution was to relocate people, an idea that only fanned anger and resentment toward wildlife. Eventually the project was abandoned.
In a similar vein a reserve established in 1976 to protect the animals was abolished earlier this year.
Experts found that the reserve only increased locals' resentment toward the animals and put the primates under new threats of retaliation.
And in 2000 and 2001 deadly clashes erupted between two ethnic groups living in the delta: the Orma, who are traditionally herders, and the Pokomo, who are mostly farmers.
Many observers believe that the conflict over use of land and water was caused in part by numerous failed irrigation projects in the delta.
The newly proposed sugar projects would also require irrigation networks using water pumped in from the delta.
The Pokomo are largely in favor, while the Orma are opposed.
Tentative plans call for many farmers to be moved away from the delta, an idea that could lead to new animosity.
Critics of the sugar plans have suggested that developers focus instead on the resources that the delta already has: freshwater fisheries, mango plantations, rice, and tourism.
"The product is there already, why do they want to destroy that with sugar?" said Hadley Becha, head of the Conservation Program at the East African Wildlife Society.
Becha argues that the studies on sugar growth cited by proponents of the projects have not been submitted for peer review or public scrutiny.
"There are those who are saying that sugar is the only development," Becha said.
"But we are saying that sugar is not the only development, because it is not for and by the [local] people. It will displace them, it will make them lose their livelihoods."

Monday, August 20, 2007

Why Desalination Doesn't Work (Yet)


Source: World Bank, 2007. The Most Arid Region in the World. With an average of only 1,383 cubic meters of renewable water resources per person per year in 2006, the MENA region falls far below the global average of 8,462. Environmental problems resulting from water issues cost MENA countries between 0.5 and 2.5 percent of GDP every year. People and economies also suffer from the consequences of droughts, floods and water-related public health issues. The region has responded to these water challenges with some of the best hydraulic engineers in the world, who have pioneered sophisticated irrigation and drainage systems as well as cutting edge desalinization technologies.



Why Desalination Doesn't Work (Yet)

By Michael Schirber, Special to LiveScience
livescience.com
posted: 25 June 2007 08:49 am ET


With water fast becoming a hot commodity, especially in drought-prone regions with burgeoning populations, an obvious solution is to take the salt out of seawater. Desalination technology has been around for thousands of years, after all. Even Aristotle worked on the problem.

Tantalizing as desalinated water might sound, the energy costs have made it rather unpalatable.

"Until recently, seawater desalination was a very expensive water source solution," said Gary Crisp, an engineer for the Water Corporation of Western Australia.

Drinking seawater straight is a bad idea because your body must expel the salt by urinating more water than it actually gains. Seawater contains roughly 130 grams of salt per gallon. Desalination can reduce salt levels to below 2 grams per gallon, which is the limit for safe human consumption.

Currently, between 10 and 13 billion gallons of water are desalinated worldwide per day. That's only about 0.2 percent of global water consumption, but the number is increasing.

"There is significant growth in desalination capacity throughout the world, and it is anticipated to continue for sometime," says Stephen Gray of Victoria University.

Gray has been chosen to lead a new research program in Australia—where many regions lack fresh water supplies—to improve the efficiency of desalination plants.

Aristotle's efforts

Back in the 4th century B.C., Aristotle imagined using successive filters to remove the salt from seawater.

But the first actual practice of desalination involved collecting the freshwater steam from boiling saltwater. Around 200 A.D., sailors began desalinating seawater with simple boilers on their ships.

The energy required for this distillation process today makes it prohibitively expensive on a large scale. A lot of the current market for so-called "thermal desalination" has therefore been in oil-rich, water-poor countries in the Middle East.

Since the 1950s, researchers have been developing membranes that could filter out salt, similar to what Aristotle originally envisioned. Presently, this membrane technique, sometimes called "reverse osmosis," requires one-fourth of the energy and costs half of the price of distilling saltwater.

"In the last ten years, seawater reverse-osmosis has matured into a viable alternative to thermal desalination," Crisp says.

Energy is key

But even with membranes, large amounts of energy are needed to generate the high pressure that forces the water through the filter. Current methods require about 14 kilowatt-hours of energy to produce 1,000 gallons of desalinated seawater.

A typical American uses 80 to 100 gallons of water a day, according to the U.S. Geological Survey. The entire country consumes about 323 billion gallons per day of surface water and another 84.5 billion gallons of ground water.

If half of this water came from desalination, the United States would need more than 100 extra electric power plants, each with a gigawatt of capacity.

Depending on local energy prices, 1,000 gallons of desalinated seawater can cost around $3 or $4. Although that might not seem like much, it is still cheaper in many places to pump water out of the ground or import it from somewhere else.

But the price difference will undoubtedly narrow, especially in regions that could experience more intense droughts owing to climate change.

Water use has been growing twice as fast as population growth, causing more and more communities to suffer water shortages. The demand for freshwater supplies will drive prices higher, making desalination increasingly attractive.

Brainstorming on membranes

The number of desalination plants worldwide has grown to more than 15,000, and efforts continue to make them more affordable.

Last month, Australia's largest scientific research agency joined with nine major universities in a membrane research program to reduce desalination energy costs, as well as maintenance costs associated with gunk sticking to membranes and fouling them up.

"Lowering the energy required for desalination and the fouling propensity of membranes are the two biggest challenges facing desalination," Gray says.

A team of diverse researchers will try to tackle these problems by developing new types of membrane materials. The goal is to cut in half the energy required for desalination.

"We would hope to have something available within the next 10 years," Gray said.

Global Warming: How Do Scientists Know They're Not Wrong?


image: undispatch.com

Global Warming: How Do Scientists Know They're Not Wrong?

By Andrea Thompson, LiveScience Staff Writer
posted: 16 July 2007 09:34 am ET

From catastrophic sea level rise to jarring changes in local weather, humanity faces a potentially dangerous threat from the changes our own pollution has wrought on Earth’s climate. But since nothing in science can ever be proven with 100 percent certainty, how is it that scientists can be so sure that we are the cause of global warming?

For years, there has been clear scientific consensus that Earth’s climate is heating up and that humans are the culprits behind the trend, says Naomi Oreskes, a historian of science at the University of California, San Diego.

A few years ago, she evaluated 928 scientific papers that dealt with global climate change and found that none disagreed about human-generated global warming. The results of her analysis were published in a 2004 essay in the journal Science.

And the Intergovernmental Panel on Climate Change (IPCC), the National Academy of Sciences and numerous other noted scientific organizations have issued statements that unequivocally endorse the idea of global warming and attribute it to human activities.

“We’re confident about what’s going on,” said climate scientist Gavin Schmidt of NASA’s Goddard Institute of Space Science in New York.

But even if there is a consensus, how can scientists be so confident about a trend playing out over dozens of years in the grand scheme of the Earth's existence? How do they know they didn’t miss something, or that there is not some other explanation for the world’s warming? After all, there was once a scientific consensus that the Earth was flat. How can scientists prove their position?


Best predictor wins

Contrary to popular parlance, science can never truly “prove” a theory. Science simply arrives at the best explanation of how the world works. Global warming can no more be “proven” than the theory of continental drift, the theory of evolution or the concept that germs carry diseases.

“All science is fallible,” Oreskes told LiveScience. “Climate science shouldn’t be expected to stand up to some fantasy standard that no science can live up to.”

Instead, a variety of methods and standards are used to evaluate the viability of different scientific explanations and theories. One such standard is how well a theory predicts the outcome of an event, and climate change theory has proven to be a strong predictor.

The effects of putting massive amounts of carbon dioxide in the air were predicted as long ago as the early 20th century by Swedish chemist Svante Arrhenius.

Noted oceanographer Roger Revelle’s 1957 predictions that carbon dioxide would build up in the atmosphere and cause noticeable changes by the year 2000 have been borne out by numerous studies, as has Princeton climatologist Suki Manabe’s 1980 prediction that the Earth’s poles would be first to see the effects of global warming.

Also in the 1980s, NASA climatologist James Hansen predicted with high accuracy what the global average temperature would be in 30 years time (now the present day).

Hansen's model predictions are “a shining example of a successful prediction in climate science,” said climatologist Michael Mann of Pennsylvania State University.

Schmidt says that predictions by those who doubted global warming have failed to come true.

“Why don’t you trust a psychic? Because their predictions are wrong,” he told LiveScience. “The credibility goes to the side that gets these predictions right.”

Mounting evidence

Besides their successful predictions, climate scientists have been assembling a “body of evidence that has been growing significantly with each year,” Mann said.

Data from tree rings, ice cores and coral reefs taken with instrumental observations of air and ocean temperatures, sea ice melt and greenhouse gas concentrations have all emerged in support of climate change theory.

“There are 20 different lines of evidence that the planet is warming,” and the same goes for evidence that greenhouse gases are increasing in the atmosphere, Schmidt said. “All of these things are very incontrovertible.”

But skeptics have often raised the question of whether these observations and effects attributed to global warming may in fact be explained by natural variation or changes in solar radiation hitting the Earth.

Hurricane expert William Gray, of Colorado State University, told Discover magazine in a 2005 interview, "I'm not disputing that there has been global warming. There was a lot of global warming in the 1930s and '40s, and then there was a slight global cooling from the middle '40s to the early '70s. And there has been warming since the middle '70s, especially in the last 10 years. But this is natural, due to ocean circulation changes and other factors. It is not human induced.”

Isaac Newton had something to say about all this: In his seminal “Principia Mathematica,” he noted that if separate data sets are best explained by one theory or idea, that explanation is most likely the true explanation.

And studies have overwhelmingly shown that climate change scenarios in which greenhouse gases emitted from human activities cause global warming best explain the observed changes in Earth’s climate, Mann said—models that use only natural variation can’t account for the significant warming that has occurred in the last few decades.

Mythic ice age

One argument commonly used to cast doubt on the idea of global warming is the supposed predictions of an impending ice age by scientists in the 1970s. One might say: First the Earth was supposed to be getting colder; now scientists say it’s getting hotter—how can we trust scientists if they’re predictions are so wishy-washy?

Because the first prediction was never actually made. Rather, it’s something of an urban climate myth.

Mann says that this myth started from a “tiny grain of truth around which so much distortion and misinformation has been placed.”

Scientists were well aware of the warming that could be caused by increasing greenhouse gases, both Mann and Schmidt explained, but in the decades preceding the 1970s, aerosols, or air pollution, had been steadily increasing. These tiny particles tended to have a cooling effect in the atmosphere, and at the time, scientists were unsure who would win the climate-changing battle, aerosols or greenhouse gases.

“It was unclear what direction the climate was going,” Mann said.

But several popular media, such as Newsweek, ran articles that exaggerated what scientists had said about the potential of aerosols to cool the Earth.

But the battle is now over, and greenhouse gases have won.

“Human society has made a clear decision as to which direction [the climate] is going to go,” Mann said.

Future predictions

One of the remaining skeptics, is MIT meteorologist Richard Lindzen. While he acknowledges the trends of rising temperatures and greenhouse gases, Lindzen expressed his doubt on man’s culpability in the case and casts doubt on the dire predictions made by some climate models, in an April 2006 editorial for The Wall Street Journal.

“What the public fails to grasp is that the claims neither constitute support for alarm nor establish man's responsibility for the small amount of warming that has occurred,” Lindzen wrote.

To be sure, there is a certain degree of uncertainty involved in modeling and predicting future changes in the climate, but “you don’t need to have a climate model to know that climate change is a problem,” Oreskes said.

Climate scientists have clearly met the burden of proof with the mounting evidence they’ve assembled and the strong predictive power of global warming theory, Oreskes said-- global warming is something to pay attention to.

Schmidt agrees. “All of these little things just reinforce the big picture,” he said. “And the big picture is very worrying.”

Water Discovered to Flow Like Molasses



Water Discovered to Flow Like Molasses

By Ben Mauk, Special to LiveScience
livescience.com
posted: 11 May 2007 08:58 am ET


The Taoist poet Lao Tse famously wrote that water exemplifies the highest good, benefiting all and flowing easily without effort. While this makes for a lovely metaphor, there's more to H20 than is dreamt of in Lao Tse's philosophies.

Researchers at Georgia Institute of Technology have found that, at the molecular level, water exhibits viscous, even solid-like properties.

When molecules of water are forced to move through a small gap between two solid surfaces, the substance's viscosity increases by a factor of 1,000 to 10,000, approaching that of molasses.

"In this small space between surfaces, the water, which is usually very fluid, organizes itself into a new state in which well-defined layers of molecules form," said Uzi Landmann, director of the Center for Computational Materials Science at Georgia Tech, in a phone interview with Live Science.

Layering refers to a structural phenomenon in which molecules form strata between which there is very little molecular exchange. Water molecules can move about fluidly within a single layer, but not between layers. This vertical structure resembles that found in solid substances.

Landmann directed the team of physicists that simulated the experiment and predicted the layering effect. Georgia Tech experimental physicist Elisa Riedo led the team that performed the actual experiments. Together they found that the simulation predictions matched the experimental results.

The experiment observed the properties visualized in the simulation by measuring the force required to push the solid walls together. Riedo found that the force oscillates predictably, becoming largest at the point when a layer of particles is squeezed out.

Riedo and Landmann's results stand at odds with long-held beliefs about water.

"The literature almost uniformly said that water doesn't layer," said Landmann. "Without direct evidence it was inferred that water would behave differently from those liquids that do."

Previously, experiments had not measured the force directly but rather had deduced it from other properties, since techniques at the time did not allow scientists to probe the one nanometer region required to observe the effect.

The layering phenomenon has been known for about 25 years. Hexadecanes (molecule chains of 16 carbon atoms) exhibit layering properties. These are featured in many common liquids, but not in water.

Applications for the findings can be found in fields ranging from pharmaceuticals to nanotechnology. The newfound viscosity of water suggests a cheap method for lubricating very narrow regions. Water was long thought too fluid to be useful for this purpose.

But it is not merely a matter of application, insists Landmann. "The question of the nature of materials on the small scale is itself fascinating."

On that point even Lao Tse agrees: "Magnify the small, increase the few."






Scientists Make Water Run Uphill

By Corey Binns, Special to LiveScience
livescience.com
posted: 29 March 2006 06:46 am ET

Toss water on a hot pan and it sizzles and evaporates. Toss water on a really hot pan, and the water beads up and starts roaming around.

Now, turn your hot pan into a hot small staircase and watch the water climb the stairs.

Researchers did just that, taking an everyday sighting in the kitchen to a new level in the lab.

How it works

If a pan's really hot, the water starts to evaporate before it even touches the surface. The evaporating water, in the airy form of a water-vapor cushion, holds the droplet above the pan. With moves as smooth as Fred Astaire, the droplet glides around on air.

When scientists heated a piece of brass with saw-tooth ridges-a thing that looks like a ratchet-water drops traveled quickly and in one direction: up.

[See the video. Credit: Heiner Linke, University of Oregon]

"The drop rides along on the vapor like a boat on a river," said physicist Heiner Linke from the University of Oregon. "The vapor is generated between the droplet and the ratchet's surface in a narrow gap, about the width of a human hair. The vapor needs a way to get out of there, and it's going to take the easiest way out. There's always going to be one direction in which it's easier to get out."

Video


Watch the Full Video

A liquid drop placed on a hot ratchet moves uphill. Credit: Heiner Linke, University of Oregon

The escaping vapor pulls the droplet along in the same direction.

The research is scheduled to be published in the April 14 issue of the journal Physical Review Letters.

Potential use

The traveling drops could prove helpful in cases where scientists need to cool something down with water or another liquid. Tiny air conditioners are used to cook microchips in laptop computers. But the cooling system itself requires extra energy, which creates more heat.

With the newfound trick, drops could potentially pump themselves, using heat that's already there. "Pumps that don't use moving parts are simpler to make, cheaper and live longer," Linke pointed out.

If the droplet pumps prove strong enough, Linke said they could be cooling computers in about six years.

In the meantime, schoolteachers have a new trick for the classroom.