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      Using Africa's protected area network to estimate the global population of a threatened and declining species: a case study of the Critically Endangered White‐headed Vulture Trigonoceps occipitalis

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          Abstract

          The White‐headed Vulture Trigonoceps occipitalis (WhV) is uncommon and largely restricted to protected areas across its range in sub‐Saharan Africa. We used the World Database on Protected Areas to identify protected areas ( PAs) likely to contain White‐headed Vultures. Vulture occurrence on road transects in Southern, East, and West Africa was adjusted to nests per km 2 using data from areas with known numbers of nests and corresponding road transect data. Nest density was used to calculate the number of WhV nests within identified PAs and from there extrapolated to estimate the global population. Across a fragmented range, 400 PAs are estimated to contain 1893 WhV nests. Eastern Africa is estimated to contain 721 nests, Central Africa 548 nests, Southern Africa 468 nests, and West Africa 156 nests. Including immature and nonbreeding birds, and accounting for data deficient PAs, the estimated global population is 5475 ‐ 5493 birds. The identified distribution highlights are alarming: over 78% ( n = 313) of identified PAs contain fewer than five nests. A further 17% ( n = 68) of PAs contain 5 ‐ 20 nests and 4% ( n = 14) of identified PAs are estimated to contain >20 nests. Just 1% ( n = 5) of PAs are estimated to contain >40 nests; none is located in West Africa. Whilst ranging behavior of WhVs is currently unknown, 35% of PAs large enough to hold >20 nests are isolated by more than 100 km from other PAs. Spatially discrete and unpredictable mortality events such as poisoning pose major threats to small localized vulture populations and will accelerate ongoing local extinctions. Apart from reducing the threat of poisoning events, conservation actions promoting linkages between protected areas should be pursued. Identifying potential areas for assisted re‐establishment via translocation offers the potential to expand the range of this species and alleviate risk.

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          Diclofenac residues as the cause of vulture population decline in Pakistan.

          The Oriental white-backed vulture (OWBV; Gyps bengalensis) was once one of the most common raptors in the Indian subcontinent. A population decline of >95%, starting in the 1990s, was first noted at Keoladeo National Park, India. Since then, catastrophic declines, also involving Gyps indicus and Gyps tenuirostris, have continued to be reported across the subcontinent. Consequently these vultures are now listed as critically endangered by BirdLife International. In 2000, the Peregrine Fund initiated its Asian Vulture Crisis Project with the Ornithological Society of Pakistan, establishing study sites at 16 OWBV colonies in the Kasur, Khanewal and Muzaffargarh-Layyah Districts of Pakistan to measure mortality at over 2,400 active nest sites. Between 2000 and 2003, high annual adult and subadult mortality (5-86%) and resulting population declines (34-95%) (ref. 5 and M.G., manuscript in preparation) were associated with renal failure and visceral gout. Here, we provide results that directly correlate residues of the anti-inflammatory drug diclofenac with renal failure. Diclofenac residues and renal disease were reproduced experimentally in OWBVs by direct oral exposure and through feeding vultures diclofenac-treated livestock. We propose that residues of veterinary diclofenac are responsible for the OWBV decline.
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            Dropping dead: causes and consequences of vulture population declines worldwide.

            Vultures are nature's most successful scavengers, and they provide an array of ecological, economic, and cultural services. As the only known obligate scavengers, vultures are uniquely adapted to a scavenging lifestyle. Vultures' unique adaptations include soaring flight, keen eyesight, and extremely low pH levels in their stomachs. Presently, 14 of 23 (61%) vulture species worldwide are threatened with extinction, and the most rapid declines have occurred in the vulture-rich regions of Asia and Africa. The reasons for the population declines are varied, but poisoning or human persecution, or both, feature in the list of nearly every declining species. Deliberate poisoning of carnivores is likely the most widespread cause of vulture poisoning. In Asia, Gyps vultures have declined by >95% due to poisoning by the veterinary drug diclofenac, which was banned by regional governments in 2006. Human persecution of vultures has occurred for centuries, and shooting and deliberate poisoning are the most widely practiced activities. Ecological consequences of vulture declines include changes in community composition of scavengers at carcasses and an increased potential for disease transmission between mammalian scavengers at carcasses. There have been cultural and economic costs of vulture declines as well, particularly in Asia. In the wake of catastrophic vulture declines in Asia, regional governments, the international scientific and donor communities, and the media have given the crisis substantial attention. Even though the Asian vulture crisis focused attention on the plight of vultures worldwide, the situation for African vultures has received relatively little attention especially given the similar levels of population decline. While the Asian crisis has been largely linked to poisoning by diclofenac, vulture population declines in Africa have numerous causes, which have made conserving existing populations more difficult. And in Africa there has been little government support to conserve vultures despite mounting evidence of the major threats. In other regions with successful vulture conservation programs, a common theme is a huge investment of financial resources and highly skilled personnel, as well as political will and community support. © 2012 New York Academy of Sciences.
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              Detecting Regular Sound Changes in Linguistics as Events of Concerted Evolution

              Introduction Concerted evolutionary change is widespread in genetic systems, being implicated in the genome-wide control of repetitive elements [1–3], the evolution of gene families [2], and homogenization of Y chromosome sequences [4, 5] and as a means by which asexual organisms might escape the debilitating consequences of Muller’s ratchet [3]. It might arise from several mechanisms, including homologous recombination, that allow certain favorable elements to spread or damaging elements to be neutralized. Linguists have long recognized concerted change that affects copies of the same sound (or phoneme) appearing in different words as a central feature of linguistic evolution [6]. A well-known example is the ∗ p>f sound change in the Germanic languages wherein an older Indo-European p sound was replaced by an f sound, such as in ∗ pater>father, or ∗ pes, ∗pedis>foot (linguistic convention is to use the “>” symbol to indicate a transition from one sound to another, and here the ∗ symbol denotes a reconstructed ancestral form). These multiple instances of one phoneme changing to the same other phoneme yield regular sound correspondences between pairs or groups of languages. Linguists have proposed several explanations for the regularity of changes grounded in a number of basic processes, including speech production, perception, and cognition [7–9]. Can events of concerted change be detected statistically in sequence data, and do they improve the characterization of evolution and the inference of evolutionary histories? Although previous researchers working in a linguistic setting have used the concept of regular changes to build algorithms for automatically inferring cognacy, to our knowledge the model we report here is the first probabilistic description of concerted change. This places concerted evolution in a statistical setting that allows for formal hypothesis testing about the nature and rates of concerted changes. For example, the question of how many parallel changes are required to be recognized as an instance of concerted change is naturally dealt with in our model: the statistical signature of concerted or regular change is that the multiple parallel events are more probable if treated as a single coordinated change than as a collection of independent changes (Box 1). Usefully, the genetic and linguistic phenomena share fundamental properties relevant to their statistical characterization. Phonemes are the units of sound that make up words and distinguish one word from another, just as the four nucleotide bases (A, C, T, G) make up DNA gene sequences or the 20 amino acids make up protein sequences. The number of distinct sounds in a language varies greatly, but somewhere around 30–60 phonemes are commonly sufficient to describe the range of distinctive sounds in a language’s words [10]. Collections of words can therefore be thought of as providing phonemic “sequence information” that might be informative as to the history, rate, and patterns of concerted evolutionary change in language, and in a manner analogous to sequences of DNA. Statistical Modeling of Concerted Evolution We adopt a phylogenetic-statistical perspective that allows us to document events of concerted change that have occurred throughout the genealogical history of a linguistic or biological family, infer their historical patterning, and determine the rate and frequency with which they arise in nature [11, 12]. The statistical model we develop implements a fully probabilistic description of the sporadic or irregular and concerted or regular changes that characterize the temporal patterns of substitutions in strings of inherited information such as DNA or sound sequences as they evolve along the branches of the phylogenetic trees that record their evolutionary histories. In a linguistic context, sporadic changes refer to the replacement, over some arbitrary interval of time, of one phoneme in one place by another and are analogous to single nucleotide or amino acid substitutions in gene sequences. Concerted or regular changes describe the parallel change of one discrete element such as a nucleotide, phoneme, or amino acid to the same other discrete element at many different sites (Box 1). In contrast to genetic evolution, some historical linguists maintain that all sound changes are regular, with apparent irregularities arising from a number of processes working simultaneously, but others allow that sporadic effects also occur [13–15]. We will classify as irregular or sporadic all changes where there is not statistical evidence to support a concerted change. Some of these could be examples of rare regular changes, or of changes that occur in only a few phonetic contexts (Box 1). We implement the model in a Bayesian Markov chain Monte Carlo (MCMC) approach (Experimental Procedures) that, when applied to a set of related sequences, simultaneously estimates posterior distributions describing the phylogenetic trees or genealogies, and the matrices that record the instantaneous rates of change from one phoneme (gene, amino acid) to another either at a single site (sporadic changes) or simultaneously at multiple sites (regular changes). The model places no constraints on the nature, rate, or temporal patterning of either sporadic or regular changes, starting instead with a set of uniform prior beliefs and then estimating all rates and patterns of change from the historical traces or imprints these changes have left in the contemporary data. The sporadic change matrix is estimated as a single homogeneous process that applies throughout the tree. For protein sequence data, the model must estimate 380 distinct transition rates ([20 × 20] − 20) in the sporadic change matrix; for a phonetically transcribed data set of 62 distinct speech sounds, this number rises to 3,782 ([62 × 62] − 62). We therefore adopt a reversible-jump MCMC procedure that we have described elsewhere [16] to reduce the number of statistically distinct parameters. In comparison to the single sporadic matrix, the concerted or regular changes are discovered statistically on a branch-by-branch basis. The model proposes a separate sound change matrix and its position within the branch for each regular sound change that it identifies (Experimental Procedures). This general approach, when applied to linguistic data, allows us to trace the temporal patterns of phonemic change among a set of related languages. Here we fit the model to lexical data corresponding to 225 etymological classes in 26 Turkic languages that were phonetically coded following the North American Phonetic Alphabet for 62 phonetic symbols [17]. Ideally, the analysis would be carried out on phonemically coded data, but most available data sets only provide a standardized orthography that occasionally distinguishes allophones. In practice, this means that the results for a specific language could depend upon whether its transcription data were consistently subphonemic or phonemic relative to other languages in the data set. To the extent that such allophonic differences are regular, our analyses will not be affected. The phonetically coded data for each language were then multiply aligned by identifying cognate sites within each word (analogous to homologous gene-sequence alignment). This yielded a 26 languages × 1,120 sites matrix, where a site represents an aligned column of speech sounds. Results Fit of the Model to Transcribed Sound Data The sporadic-mutation-only model returns a mean log-likelihood in the Bayesian posterior distribution of phylogenetic trees of −32,303.9 ± 14.9 (mean ± SD), compared to −29,196.2 ± 15.1 for the model including regular and sporadic sound changes (hereafter the “regular model”), an improvement for the regular model of 3,108 log-units (Figure 1). The regular model’s improvement derives from its discovering an average of 74.27 ± 0.47 regular sound changes that have occurred in the phylogenetic history of the Turkic languages (mean ± SD in the posterior sample of trees; see Supplemental Experimental Procedures and Table S1 available online). A deviance information criterion test overwhelmingly favors the model of regular changes as a description of these data (ΔDIC = 3,739). Regular Changes and the Phylogeny of Turkic Languages Events of regular sound change can provide strong signals preferring some phylogenetic placements over others and can improve the estimation of divergence times over the sporadic-change-only model, which will routinely overestimate the amount of independent change by assuming that each phonemic substitution is independent (Box 1). Both effects can be seen in Figure 2, where the model including regular changes (shown along branches) produces a different and better-supported consensus dated tree than that derived from the sporadic model, and one that conforms more closely to linguistic scholarship [17, 20]. The regular-change tree largely replicates the proposed major and minor divisions of the Turkic languages [20], inferring a distinct Siberian branch, which also includes Yellow Uighur, now located in China. In contrast, the sporadic-sound-change model describes the Siberian languages as successively diverging from a Turkic trunk. The regular-sound-change tree estimates a mean divergence time between the outgroup Chuvash and other Turkic languages of 204 BCE, with a 95% credible interval of 605 BCE to 81 CE. This compares to proposals from glottochronological analyses that suggest dates of 30 BCE to 0 CE [21] and 500 BCE to 50 CE from historical data [18, 21, 22]. The sporadic-sound-change model estimates the mean age of the tree to be more than two millennia older (2408 BCE, 95% CI = 3994–1279 BCE), because it wrongly assumes that the many occurrences of regular sound change along the outgroup Chuvash branch are multiple instances of independent phonological change. The regular sound changes in Figure 2 include well-known linguistic processes affecting consonants, including voicing (e.g., q>G), devoicing (e.g., b>p), gliding (e.g., ž>j and ɣ>w), spirantization (e.g., q>χ, q>x), stopping (e.g., x>k), palatal fronting (e.g., š>s), debuccalization (e.g., s>h), deaffrication (e.g., č>š), and rhotacism (e.g., z>r). Regular changes affecting vowels include changes in height (e.g., ɨ>ə, i>e, u>o), backness (e.g., i>ə, i>ɨ, a>ɔ), and length (e.g., a>ā). Most of these regular changes make a substantial contribution to the log-likelihood: the geometric mean improvement is 89.1 ± 72.1 log-units per event, measured as the improvement in log-likelihood when the effect is added conditional upon all the other regular sound changes being present. The three largest effects are the a>ɔ, ž>j, and q>k transitions, each of which contributes at least 275 log-units to the overall likelihood. Because these sounds are common in our data set, they make a large contribution to the likelihood when they are part of a regular sound change (Box 1). The model also estimates the ordering of sound changes within a branch, in some cases allowing inferences to be made about “chaining” of sound changes. For instance, in the branch leading to Yakut, h>s appears before ž>h, indicating that h sounds at the beginning of the branch are more likely to be s by the end and that ž sounds later in the time period represented by that branch are more likely to be h by the end. Typically, around 29 of the 50 branches of the phylogenetic trees in the posterior sample record at least one event of regular change, with an average of 1.49 ± 2.49 such events per branch, although this distribution is skewed (mode = 0, range = 0 to 15). Of the roughly 74 regular sound changes, 43.03 ± 0.17 involve changes between pairs of consonants, 31.22 ± 0.44 involve pairs of vowels, and 0.02 ± 0.14 occur between a vowel and a consonant (all means ± SD refer to the distribution over the posterior sample). The same regular sound changes are frequently repeated in different parts of the tree such that 21 changes involve unique pairs of consonants, and 17 involve unique pairs of vowels (Table S1). Fewer than half (23 of 62) of all speech sounds produce a detectable regular sound change, and those that do tend to be more common (measured as a sound’s frequency of occurrence in the alignment, Spearman’s rs = 0.54, p  0.10), and vowels and consonants are equally likely to produce at least one event of regular change per sound (binomial test, p > 0.10). Comparison of Inferred Regular Sound Changes to Historical Linguistic Inferences Linguists have proposed regular sound changes affecting consonants and vowels in the Turkic language family based on historical linguistic studies of 23 of the 26 languages we report in Figure 2 (see also Table S2). A proposal takes the form of a putative proto- or ancestral sound changing to a different sound or set of sounds in a descendant language. For example, the ancestral u sound is proposed [17, 20] to have changed to o in Bashkir and Tatar, and to əʷ in Chuvash, but to have been retained as u in the other languages. In agreement with these proposals, the model of regular change finds a regular u>o sound change in the branch of the Turkic phylogeny that is ancestral to Bashkir and Tatar, and finds a regular o>əʷ event in the Chuvash branch (Figure 2). For each of 634 proposed sound changes in the 23 languages (Figure 3; Table S2), we calculated the probabilities that the regular and sporadic change models assigned to the descendant sound, conditional upon the ancestral sound. Where more than one sound change is proposed to have occurred from the same ancestral sound, we summed the probabilities over all of the proposed descendant sounds, along with, in some cases, proposed partially retained ancestral sounds. We then calculated the ratio of the probability derived from the regular model to the probability of the sporadic change model as a measure of relative performance. Red-tinted cells in Figure 3 denote instances where the regular change model improves on the sporadic model (ratio > 1 to 10) and generally correspond to cases in which the ancestral sound has been replaced by one or more different descendant sounds (Table S2). White cells correspond to ratios of approximately 1:1 and are typically cases in which ancestral sounds have been partially retained in the descendant languages. Blue-tinted cells record ratios < 1 where the regular model performs worse than the sporadic model. Overall, the model of regular change approximately doubles the probability of correctly predicting the descendant sounds, as estimated using a geometric mean of the ratios to account for positive skew (geometric mean ratio = 1.87 ± 2.98, range = 0.14 to 150.12, n = 371 language X ancestral sound combinations), performing somewhat better for vowels (mean ratio = 3.38 ± 5.38, range = 0.47 to 150.12, n = 97) than for consonants (mean ratio = 1.52 ± 2.46, range = 0.14 to 39.72, n = 274). This difference in performance might merely be because vowels change more readily (faster) than consonants and so are more likely to show a change from the ancestral state. These figures include instances in which the ancestral sound was partially retained, cases for which the regular model might not be expected to improve upon the sporadic model. For 179 of the proposals the ancestral sound is not retained, and for these, the model of regular change yields an approximately 4-fold geometric mean improvement (mean ratio 3.71 ± 5.14, range = 0.14 to 150.12) and is similar for vowels and consonants (vowels = 3.72 ± 5.40, consonants = 3.70 ± 4.90). A 4-fold improvement corresponds to the sporadic model assigning less than a 0.25 total probability to the proposed descendant sounds (mean = 0.16 ± 0.11). Regular Changes and Sound Transition Networks The transition rate matrices that characterize the sporadic and regular sound changes define a network of connected phonemic substitutions or transitions that arise over time as words evolve at the level of their sounds (Figure 4). The network identifies the two major recognized [23] divisions of highly interconnected sound changes among pairs of consonants (mean transition rate/103 years = 0.0061 ± 0.028) and among pairs of vowels (mean rate = 0.0091 ± 0.0373). Transitions between these two broad categories are rare, with a mean rate = 0.001 ± 0.003, corresponding to an approximately 0.2% chance of an ancestral consonant or vowel changing to the other category in 2,000 years. The network also finds the linguistically important bridge between consonantal and vowel changes through the high vowels (in particular through the semivowel or semiconsonant “w”). The regular sound changes (red lines in Figure 4) form a subset of the larger sound transition network, and sporadic and regular changes seem to obey the same rules. Consonantal changes group into subsets of articulation categories defined by the place and manner of vocal articulation. Sounds closer in speech production change to one another more readily than those further apart, highlighting a gradual or stepwise process of language change following “shortest routes,” similar to the phenomenon observed in protein evolution wherein amino acids are frequently replaced by amino acids with similar biochemical properties [24]. Thus, among the 43 regular consonant changes, 79% (n = 36) involved only a single change in one of the following: (1) voicing, (2) place of articulation (based on four categories: labial, dental/alveolar, postalveolar/palatal, and uvular/velar/glottal), or (3) manner of articulation (e.g., affricate to fricative), against a null expectation of 29% (χ2 = 50.9, p < 0.0001). Among the 30 vowel transitions, 70% (n = 21) involved only a single change in one of the following: (1) front-central-back, (2) open-mid-closed, or (3) rounding, against a null expectation of vowel pairs of 45% (χ2 = 7.5, p < 0.01) (Table S1). The Contribution of Regular Changes to Phonemic Evolution Regular sound changes emerge from our analyses as occupying a central role in sound evolution, consistent with the expectations of historical linguists [17, 20]. These regular sound changes accumulate approximately linearly in time, implying a constant rate of about 0.0026 regular sound changes per year (approximately one every 385 years) averaged over the tree (Figure 5A). The linear trend suggests that the model is not missing regular sound changes that occur deeper in the tree (i.e., older events) and supports a “uniformitarian” view—that this family of languages has been changing in the same ways throughout its history, an important assumption for statistical inference and ancestral reconstruction. The number of regular sound changes in a language’s history ranges from a low of 1 in Karaim and Balkar to a high of 15 in Chuvash (Figure 2B; the low count for Karaim might reflect phonetic transcription practices). The temptation is to interpret these as indicating different intrinsic rates, or perhaps different external pressures, for sound change, but large differences in the numbers of regular changes can arise among languages simply as a result of random fluctuations and shared phylogenetic histories. Thus, if events of regular change occur randomly at a constant rate (as in Figure 5A), then the number of such events per branch of the tree is expected to follow a Poisson distribution with mean rate given by 0.0026 × t, where t is the length of the branch in years. Following expectations, the cumulative density of the observed number of events per branch (including branches with no regular sound changes) shows a close fit to the Poisson expectation (Figure 5B). The 21 branches in which no regular sound change occurred, along with those in which multiple events are inferred, can all be considered as samples from the same underlying stochastic process. A further characteristic of the Poisson process is that waiting times between successive events follow an exponential distribution. The distribution of waiting times between successive events of regular sound change on the phylogeny shows a striking fit to this expectation (Figure 5C). The observed range of 14 in the number of regular sound changes per language is, however, wide, being expected to occur in approximately 0.68% of outcomes (Figure 5D). The outgroup, Chuvash, with 15 regular sound changes, might be unusual in having four phonemes that are unique among this group of languages. These four phonemes account for five of the regular sound changes in the branch leading to Chuvash. Removing these five, Chuvash with ten events yields a range (10–1) that now falls well within the Poisson expectation. Discussion Our analysis has shown how a model of concerted evolution can discover the timings and phylogenetic placements of multiple events of regular sound change, and without prior knowledge of the forms those regular changes might take. The events we find conform closely to linguistic expectations, and the model produces a description of the sound transition networks among the 62 speech sounds that captures the well-known patterns of sound change. Including regular sound changes also improves the reconstruction of the phylogenetic tree describing the languages’ evolutionary histories and returns more plausible and less variable dates. This confirms the importance that historical linguists have long attached to including regular sound changes into attempts to reconstruct protolanguages, identify borrowings, and infer the genealogical history of a set of related languages, including their probable dates of origin and subsequent divergences. The close conformity of the timings of regular linguistic sound changes to a Poisson process model over the approximately 28,000 language-years of evolution represented by the branches of the Turkic tree is striking in revealing an underappreciated regularity in this otherwise complex process. It also provides a parsimonious explanation for why some languages experience so few and others so many regular sound events in their histories: these differences can in principal be explained as expected outcomes of a homogeneous random process, and hence there is no need to seek factors either internal or external to the languages in question to explain the variation among them, at least until the statistical expectation is violated. That such a complex phenomenon could conform so closely to a homogeneous random process over such long time periods is surprising but finds an interpretation in statistical theory: where the potential causes of a discrete phenomenon (such as a regular sound change) are many, independent, and rare, and each one is individually capable of causing a regular change, the waiting times between successive events can be shown [25, 26] to follow an exponential distribution (as in Figure 5C), and events per unit time will follow a Poisson distribution. This interpretation, then, draws researchers’ attention to the “catalog” or list of potential cognitive, linguistic, and social causes of regular sound changes to explain their timings and frequencies throughout history. The excellent fit of the Poisson distribution indicates that this catalog has stayed roughly stable for the at least two millennia over which the Turkic family diverged. Regular sound changes by their very nature make a disproportionate contribution to linguistic diversity. Regular sound changes might also help groups of language speakers create and then maintain a distinct identity [27, 28]. In this context, there are several reasons to believe that the 74 regular sound changes we have identified probably underestimate their true extent in these languages. For example, some regular changes might have decayed or been replaced by others over time, rare sound changes might not yet have been observed, and the relatively high rates of sporadic transition among vowels might also mean that some number of vowels affected by a regular change might have been masked by a later sporadic change. In addition to these factors, in the form used here, our model provides a general “context-free” statistical description of concerted change that can be applied to any evolving hierarchical system of discrete elements. As a result, we might have missed some forms of regular sound change that depend upon multiphoneme combinations (Box 1). Many Turkic languages, for example, can exhibit a form of correlation of sounds within words known as vowel harmony, whereby vowels (and some consonants) in a word are homogenized into classes. In some Turkic languages, words can be harmonized according to whether the vowels and the uvular/velar consonants have “front” or “back” articulation [20]. For example, the plural suffix in Turkish can depend on the class of the word, such that the plural of horse is [at-lar] (using a back vowel) whereas the plural of cat is [kedi-ler] (using a front vowel). A second and more general factor common in human languages is context, in which sound changes are influenced by where the sound occurs in a word, or by its proximity to other sounds [29]. Sounds can be lost within words in a manner equivalent to nucleotide deletions. Occasional metathesis, or reordering of sounds, is also observed. Finally, entire classes of phonemes often shift because of loss or gain of a phonemic feature like voicing, or when the change of one sound or phonemic distinction in a sound system may lead to cascades of other sound changes in the system, as has been postulated with the “Great Vowel Shift” in English [30]. These factors might prove valuable in understanding differences in the propensity of a given phonemic site to be affected by a regular change. There are methods for extending our theory to context-dependent regularities [29], and future work with our model will explore how they help to improve the statistical reconstruction of protowords. Molecular biologists might recognize genetic analogs to the linguistic processes of context and harmony in some features of gene conversion. Thus, a recent study [3] of the rotifer (Adineta vaga) genome identified “abundant” evidence of gene conversion manifested in greater-than-expected similarity among alleles—in a sense, the presence of one allele “harmonizes” the other by making a particular form of the other more likely. Equally, concerted evolutionary changes can sweep through genomes, deactivating transposable elements [31]. Here, the presence of a particular string of nucleotides in a wider context of a transposable element appears to invite a deactivating change. A model such as we describe here could identify these instances of gene conversion statistically and on a genome-wide basis and, if applied to a group of related organisms, could provide a description of their extent and taxonomic distribution in nature. Identification of such events might also prove valuable for inferring and dating molecular trees. We might expect concerted change to be a feature of evolving cultural systems where artifacts and institutions are hierarchically organized from a discrete set of repeatedly used building blocks (e.g., motifs, keystone technologies). Elements of style, dress, music, art, and technology might all be subject to forces that encourage a coordinated homogenization of these otherwise distinct building blocks, at least to some degree. Data sets here might not yet be as well developed as in genetics or linguistics, but the looming presence of “big data” [32] in the social sciences might allow a model such as we describe here to bring these phenomena to heel. Experimental Procedures Description of Transcribed Sound Data We used lexical data corresponding to 225 etymological classes in 26 Turkic languages [17, 20] that were phonetically coded with 62 symbols following the North American Phonetic Alphabet [17]. The phonetically coded data for each language were then multiply aligned by identifying cognate sites within each word (analogous to homologous gene sequence alignment). Choosing the pairing of sounds that maximized a likelihood function based on the following model aligned sounds in cognate words from the same etymological class. Observed forms in each language are assumed to have descended from an ancestor by a combination of (1) language-wide regular sound changes and (2) word-specific sporadic sound changes. For alignment, languages are assumed to be independent except through their shared descent from the ancestor. The algorithm recursively estimates the alignments, sound inventories, regular sound changes, and sporadic sound changes that maximize the likelihood function derived from this model. This yielded a 26 languages × 1,120 sites matrix. Statistical Model The sporadic sound changes are modeled as a continuous-time Markov process, widely used in models of DNA or protein sequence evolution, where in place of the usual 4 × 4 or 20 × 20 matrices of nucleotide or amino acid transitions, we erect a 62 × 62 sound transition rate matrix, denoted Q s  (Supplemental Experimental Procedures). We estimate the elements of Q s from the data employing a reversible-jump Markov chain Monte Carlo (RJ-MCMC) procedure described elsewhere [16] that allows the large number of potential parameters to be reduced to a potentially far smaller set of statistically distinct parameters, and without loss of statistical accuracy or prior knowledge on the part of investigators. We find that nine distinct rate classes, empirically estimated from the data, plus a category of rates estimated to be zero, are sufficient for the Turkic data. Regular sound changes of the general form denoting the i th sound changing to the j th are modeled in a stochastic matrix Q r that takes the form of an identity matrix with the i th diagonal element interchanged with the off diagonal position (ji). Premultiplication of any stochastic matrix Q (e.g., that in P ( D | Q s , t ) ) by such a matrix is equivalent to adding all elements q i1 , q i2 ,…q ik to the corresponding values of q j1 , q j2 …q jk and then zeroing out the q i1 , q i2 ,…q ik . We then use a different RJ-MCMC procedure to propose possible Q r matrices in branches of the phylogenetic tree, thereby allowing regular changes to occur or not occur on a branch-specific basis. The model also estimates the position or timing of successive regular sound changes along a branch. Phylogenetic Inference We estimated time-dated phylogenetic trees by enforcing a variable-rates clock model that constrained all root-to-tip path lengths to have the same total time but allowed the average rates of sound evolution to vary throughout the tree. The variable-rate clock is modeled by applying a scalar multiplier to each branch of the tree that alters the rates in Q s by some fixed amount. We assume these scalars are drawn from a log-normal prior distribution with μ = 1 and unknown σ2 that we estimate from the data. We calibrated the trees against two points of reference: the current dates of dictionaries for each of the contemporary languages, and the Seljuk conquest of Baghdad (1055 CE), which is likely the latest date for divergence of Seljuk-derived languages (Turkish, Azeri, Gagauz) from other Oghuz languages (Turkmen), the earliest likely date being 985 CE [18, 19]. The parameters of the sound change model are estimated in a likelihood framework using Markov chain Monte Carlo methods [33] (Supplemental Experimental Procedures). Because the regular sound changes are directional, the likelihood depends upon the choice of a root in the tree. In practice, the likelihood is not able to determine the root with accuracy, and so most investigators root the tree using an outgroup. Here we use Chuvash. We ran many independent Markov chains to explore the model and then to infer the time-dated trees, allowing chains to run to stationarity following a burn-in of at least 10,000,000 iterations. Stationarity was assessed by enforcing a period of at least 10,000,000 iterations during which no average change in the likelihood occurred. Multiple independent runs were used to ensure convergence on a common consensus topology. The models were implemented in a modified version of BayesPhylogenies (http://www.evolution.reading.ac.uk/). In practice, to improve the rate of convergence of the Markov chains, we augmented the likelihood of the sound change model with that obtained from cognacy data for the same words, following methods described elsewhere [34, 35] (Supplemental Experimental Procedures). Author Contributions All authors contributed to modeling, computation, and analyses. M.P., T.B., and D.J.H. wrote the manuscript.
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                Author and article information

                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                22 January 2016
                February 2016
                : 6
                : 4 ( doiID: 10.1002/ece3.2016.6.issue-4 )
                : 1092-1103
                Affiliations
                [ 1 ]Hawk Conservancy Trust Andover Hampshire SP11 8DYUK
                [ 2 ] Centre for Wildlife Assessment and Conservation School of Biological SciencesUniversity of Reading Berkshire RG6 6ASUK
                [ 3 ] Department of Forest Resources and Wildlife ManagementNational University of Science and Technology BulawayoZimbabwe
                [ 4 ] Ornithology Section Department of ZoologyNational Museums of Kenya P.O Box 40658 Nairobi 00100Kenya
                [ 5 ]The Peregrine Fund 5688 West Flying Hawk Lane Boise Idaho 83709
                [ 6 ]BirdLife Botswana PO Box 26691 GaboroneBotswana
                [ 7 ]Rue de la Rivière 10220 Rouilly ‐ SaceyFrance
                Author notes
                [*] [* ] Correspondence

                Campbell Murn, Hawk Conservancy Trust, Andover, Hampshire, SP11 8DY, UK.

                Tel: +44 1264 773850;

                Fax: +44 1264 773772;

                E‐mail: campbell@ 123456hawkconservancy.org

                Article
                ECE31931
                10.1002/ece3.1931
                4761783
                26941945
                9c458bf3-abca-4e41-9714-bdd4cfe1f01f
                © 2016 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 September 2015
                : 04 December 2015
                : 09 December 2015
                Page count
                Pages: 12
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                ece31931
                February 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.7.6 mode:remove_FC converted:22.02.2016

                Evolutionary Biology
                decline,nest densities,protected areas,trigonoceps occipitalis,white‐headed vulture

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