Olivier Ferret
email: ferret@sphinx.cea.fr

Brigitte Grau

Michèle Jardino
email: jardino@limsi.fr

A cross-comparison of two clustering methods

Many Natural Language Processing applications require semantic knowledge about topics in order to be possible or to be efficient. So we developed a system, SEGAPSITH, that acquires it automatically from text segments by using an unsupervised and incremental clustering method. In such an approach, an important problem consists of the validation of the learned classes. To do that, we applied another clustering method, that only needs to know the number of classes to build, on the same subset of text segments and we reformulate our evaluation problem in comparing the two classifications. So, we established different criteria to compare them, based either on the words as class descriptors or on the thematic units. Our first results lead to show a great correlation between the two classifications.

Introduction

Among all the applications in Natural Language Processing (NLP), many require semantic knowledge about topics in order to be possible or to be efficient. These applications are, for example, topic segmentation and identification or text classification. As this kind of knowledge is not easy to build manually, we developed a system, SEGAPSITH (Ferret and Grau, 1998a), (Ferret and Grau, 1998b), to acquire it automatically. In this field, there are two classes of approaches. Supervised learning that requires to know a priori which topics have to be learned and to pos-sess a tagged corpus as a learning set. It is the approach generally adopted by the different systems, as those participating to TREC or TDT. However, we wanted to design a system allowing us to work in open domain, without any restriction about the subjects to be represented and, thus, to be recognized in texts. SEGAPSITH is grounded on an unsupervised and incremental learning based on a conceptual clustering method. After a thematic segmentation of the texts that divides a text in segments made of lemmatized words, i. e. thematic units, the system aggregates similar enough thematic units. Aggregation consists of regrouping all the words of the different similar units and associating to them a weight according to their occurrence number. This weight represents the importance of a word relative to the described topic. The incremental aspect allows us to augment topic knowledge by treating successive corpora without reconsidering the knowledge already existing.

In such an approach, an important problem consists of the validation of the learned classes. As we do not possess an existing classification that agrees with the granularity level of our classes, we decided to accomplish this evaluation by using a second classification method on the same data and by comparing their results. This second method is an entropy-based method, and requires to know the number of classes to form. So, if both results are similar enough, although the methods applied are different, we could conclude that the learned classes are quite relevant and that the two methods are efficient.

After applying the second method, we possess two sets composed by the same number of classes. Each class regroups thematic units and is described by a set of words. So, we established different criteria to compare them, based either on the words as class descriptors or on the thematic units they gather. After the presentation of the two methods, we shall present our tests and the first results we obtained.

Semantic domain learning

This description aims at showing the data used for learning, and the specificity of the learned classes.

The thematic segmentation: SEGCOHLEX

Studied texts are newspaper articles coming from two corpora: "Le Monde" and "AFP". Some of these texts have been used to build a lexical network where links between two words represent an evaluation of their mutual information to capture semantic and pragmatic relations between them, computed from their co-occurrence count. In order to build class of words linked to a same topic, we first realize a topic segmentation of the texts in thematic units (TU) whose words refer to the same topic, and learning is applied on these thematic units.

Text segmentation is based on the use of the collocation network. A topic is detected by computing a cohesion value for each word resulting from the relations found in the network between these words and their neighbors in a text. As in Kozima's work [START_REF] Kozima | Text segmentation based on similarity between words[END_REF], this computation operates on words belonging to a focus window that is moved all over the text.The cohesion values lead to build a graph and by successive transformations applied to it, texts are automatically divided in discourse segments. Such a method leads to delimit small segments, whose size is equivalent to a paragraph, i. e. capable of retrieving topic variations in short texts, as newswires for example. Table 1 shows an extract of the words belonging to a cohesive segment about a dedication of a book.

Semantic Domain learning in SEGAPSITH

Learning a semantic domain consists of aggregating all the most cohesive thematic units, TUs, that are related to a same subject, i. e. a same kind of situation. We only retain segments whose cohesion value is higher than a threshold, in order to ground our learning on the more reliable units. Similarity between a thematic unit and a semantic domain is evaluated from their common words. When the similarity value exceeds a threshold, the thematic unit is aggregated to the semantic domain, otherwise a new domain is created. Each aggregation of a new thematic unit increases the system's knowledge about one topic by reinforcing recurrent words and adding new ones. Weights on words represent their importance relative to the topic and are computed from the number of occurrences of these words in the TUs.

Units related to a same topic are found in different texts and often develop different points of view of a same type of subject. To ensure a better similarity between them, SEGAPSITH enriches a particular description given by a text segment by adding to these units those words of the collocation network that are particularly linked to the words found in the segment. Table 2 gives an extract of the words added to the segment of Table 1.

This method leads SEGAPSITH to learn specific topic representations (see Table 3) as opposed to [START_REF] Lin | Robust Automated Topic Identification[END_REF] for example, whose method builds general topic descriptions as for economy, sport, etc. Moreover, it does not depend on any a priori classification of the texts.

We applied the learning module of SEGAP-SITH on one month (May 1994) of AFP newswires, corresponding to 7823 TUs. In our experiments (Ferret and Grau, 1998a), (Ferret and Grau, 1998b), we showed that domains reach a stability at 15 to 20 aggregations, and that words having a weight below 0.1 are rarely related to the domain. Thus, we only selected domains resulting from at least 15 aggregations for our crosscomparison, i.e. 71 domains regrouping 4935 TUs and 4380 words having a weight upon 0.1. A lot of domains share common words, and are close enough to be considered as different representations of specific points of view of a general topic, as economy, sport, etc.

Entropy-based clustering

The second clustering method gives an optimal partition of the 4935 thematic units in 71 non- Each cluster is the merge of several thematic units and is represented by its centroid. We search for the partition which minimizes the Kullback-Leibleir divergence [START_REF] Cover | Elements of Information Theory[END_REF] between the word distributions of the thematic units and those of of their centroids. This entropybased measure is convex [START_REF] Jardino | Unsupervised non-hierarchical entropy-based clustering[END_REF], this propriety permits to get an optimal partition whatever the initial conditions.

Entropy

We assume that each thematic unit is represented by one quantitative vector whose components are the relative occurrences of a selection of words related to the unit. The advantages of this normalization is that the representation of the thematic units does not depend on the length of the units and can be modelized in the frame of the information theory [START_REF] Cover | Elements of Information Theory[END_REF].

Assuming that O w tu is the occurrence of the word labelled w in the thematic unit labelled tu and that O tu is the occurrence of all the words in the thematic unit tu, such that O tu = P w O w tu , each thematic unit vector component , p(w=tu), is :

p(w=tu) = O w tu O tu (1)
When the thematic units are unclassed, their entropy is given by [START_REF] Cover | Elements of Information Theory[END_REF] The cluster entropy is always higher than or equal to the unit entropy (log-sum rule [START_REF] Cover | Elements of Information Theory[END_REF]), so that the Kullback-Leibleir divergence defined as:

D K L = H K ; H T U (5)
is always higher than or equal to 0.

Clustering algorithm

Minimizing the Kullback-Leibler divergence amounts to minimize the entropy H K because H T U does not depend on the clusters.

The number of possible partitions is huge, roughly 4935 71 . We have observed that a random search is faster than a systematic one [START_REF] Jardino | Unsupervised non-hierarchical entropy-based clustering[END_REF], and we have used this paradigm to build the algorithm described below: 1-Define a priori, K, the cluster number, here K=71. 2-Initialize: put all the thematic units in one cluster, calculate the entropy H K (equation 3). The remaining K-1 clusters are empty. 3-Do the random selection of one thematic unit and of another cluster for this unit. 4-Move the unit from its former cluster to the new randomly selected one, calculate the new entropy. 5-If the new entropy is lower, leave the unit in its new cluster, otherwise move it back to its initial cluster. 6-Repeat 3 to 5 until there is no more change.

The optimal clustering of the 4935 thematic units in 71 clusters is performed on a workstation (SGI Indy) within twenty minutes.

Comparing two classifications

We established different criteria for comparing the two classifications, based on the elements used to describe the classes. First, each class is a set of words with an occurrence number for each of them; second it is also a set of thematic units. So, the comparison can be done along these two points of view.

In order to evaluate the overlapping of the classes of words, we applied each classification method on the two classification results: the classes of words resulting from the second method are classified relative to the semantic domains. For comparing the classes of TUs, we applied the entropy measure on one hand to measure the overlapping of the classes, and and Mantel tests on the other hand to evaluate the differences in the repartition of all the TUs.

The word point of view

Classification by similarity

The classification of the clusters relative to the semantic domains exploits the same similarity measure than the one used for the learning phase. In a first step, some domains are selected according to the value of the activ function:

activ(d) = X i W d i W c i (6)
where W d i is the weight of the word i in the semantic domain d and W c i is the weight of the same word in the cluster c. This first step was used in the learning phase because the number of semantic domains was increasing rapidly and this measure leads to a first fast selection of interesting domains before evaluating an in-depth similarity. We kept this step, even if it was not necessary, in order to apply exactly the same method in the evaluation phase. Afterwards, each selected domain can be compared to the cluster by using the similarity measure given below. If one of these similarity values is greater than a given threshold, fixed to 0.25 in our tests, the cluster is linked to the domain that is the most similar to it. The similarity measure is: where the w index is used for indicating common words between the cluster c and the semantic domain d and the t index, for indicating all the words of the cluster or the domain. W is the weight of a word and O its occurrence number.

The similarity measure is only based on the common words. As learning is unsupervised and incremental, differences at time t might disappear at time t+1 . The comparison is based on the proportion of common words relative to the total of words of each entity to be compared. The evaluation of the common words in each entity is done according to their occurrence number and their weight. So, we avoid to obtain a high similarity value between two entities that only share very few words having a high weight. We combine these criteria in a geometrical mean for evaluating each entity and for computing the global similarity from the evaluation of the two entities in order to smooth the effect of few recurrent words when the domains are in their formation phase, words that would act as attractors otherwise.

Entropy-based classification

For each of the 71 clusters, we have searched for the nearest domain obtained with the same kind of entropy-based measure defined above. We assume that we have a probabilistic model which gives the predictions of the words according to the domains. In order to avoid the null value, nonlearned events are infered using the Witten-Bell interpolation [START_REF] Witten | The zero-frequency problem: estimating the probabilities of novel events in adaptative text compression[END_REF]. The interpolated value of the prediction of a word w, knowing the domain d is p 0 (wjd) such that:

p 0 (wjd) = O(w d) + n sw (d)=V O(d) + n sw (d) (8)
where n sw (d) is the number of words seen in each domain and V the size of the vocabulary. Each cluster is also defined by a set of words and we compare the distribution of the words in the cluster with the distributions of the words in the domains (equation 8) with the Kullback-Leibler divergence. Each cluster is associated with the nearest domain.

Comparison

The results of the two classifications described above are given in Table 4. For the similaritybased classification, only 3 clusters do not match with any domain and 47 different domains are selected for the 68 remaining clusters with 34 links that are one cluster-one domain. For the entropybased classification, 44 clusters have been associated to the 71 domains.

Several clusters are linked to the same domain. This can been explained by the closeness of some of the domains. This is shown when they are hierarchically classified; we obtain then 34 general domains that regroup 1 to 5 domains each. We also observe that most clusters are only linked to one domain. The two methods give almost the same results and show that the two classifications are similar. domain)cluster links links link (similarity) (entropy

) no link 3 1!1 34 29 1!2 8 8 1!3 3 4 1!4 1 2 1!5 1 1!6 1

Comparison with the coefficient

In order to compare more precisely our two classifications, we used the coefficient as it was done by Dietterich in [START_REF] Dietterich | An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization[END_REF] and as it is often done in the field of remote sensing for example. The coefficient measures the degree of agreement among several judgements and is expressed as follows:

k = 1 ; 2 1 ; 2 (9)
where 1 is the proportion of times that the judgments agree and 2 is the proportion of times that we could expect the judgments to agree by chance. As we are in a case of unsupervised classification whereas Dietterich's work was about supervised classification (building of decision trees), we have first set a one-to-one mapping between the semantic domains and the clusters.

This was done by a very simple procedure: we computed the size of the intersection between each cluster and each domain; then we iteratively mapped the cluster and the domain that had the largest intersection until each cluster was mapped with a domain. Of course, this is not an optimal procedure in order to ensure that the intersection of each couple of classes is the largest one but it can be considered as a baseline.

Then, the evaluation of the coefficient was done by building a matrix K K, with K, the number of classes (clusters or domains), such that each element k i j is equal to the number of TUs assigned to the class i by SEGAPSITH and to the class j by the entropy-based clustering. 1 , which estimates the probability that the two classifications agree, is defined by:

1 = P K i=1 k i i N ( 10
)
where N is the total number of TUs. It evaluates the proportion of TUs that were put in the same classes by the two clustering algorithms.

2 , which estimates the probability that the two algorithms agree by chance, is given by:

2 = K X i=1 ( k i+ N k +i N ) (11)
where k +i N and k i+ N are the marginal distributions.

The coefficient that results from the evaluation of 1 and 2 is equal to 0 when the two clustering algorithms agree only by chance and to 1 when they really agree for each TU. Negative values occur when there is a systematic disagreement.

For the 71 classes of our test set, we computed the coefficient in two cases. First, with a random mapping of the clusters and domains. We got K = -0.013, which is very close to the agreement by chance. Second, we applied the above mapping procedure and got K = 0.484, which indicates a significant correlation between the two classifications. We think that with a more complex mapping procedure, the would be higher.

Application of the Mantel Test

In this paradigm, each classified thematic unit, tu, is described according to its position in the classification in relation to all the classified elements. This position is characterized by a distance between tu and each other element. In the work we present here, we choose a simple distance: dist(tu 1 t u 2 ) = 0 if tu 1 and tu 2 are part of the same class; otherwise, dist(tu 1 t u 2 ) = 1 .

However more complex distances may be used when the classifications are hierarchical ones for example. After this first step, each tu i of the two classifications to compare is characterized by a vector, each element of which, d ij , is equal to the distance between tu i and tu j . Hence, each classification is characterized by a distance matrix, which is a square symmetric matrix of size N 2 = 4 9 3 5 2 . Comparing the two classifications amounts to compare their distance matrices.

In the ecology field, such kind of comparison is achieved by a statistical test, called the Mantel test [START_REF] Mantel | The detection of disease clustering and a generalized regression approach[END_REF]. In [START_REF] Legendre | Comparison of permutation methods for the partial correlation and partial correlation and partial mantel tests[END_REF], Legendre defines the Mantel test as " a procedure to test the hypothesis that the distances among objects in a matrix A are linearly independent of the distances among the same objects in another matrix B. The result of this test may be used as support for or against the hypothesis that the process that generated the first set of distances is independent of the process that generated the second set. The unique feature of the Mantel test is the use of a linear statistic to assess the relationship between two distance matrices". The basic statistic used in the Mantel test is the Z statistic:

ZS=

i=N X i=1 j=N X j =1
x ij y ij

As the elements of a distance matrix are not independent, the significance of ZS, the Z statistic that is computed for the two distance matrices to compare, is evaluated by comparing this value to the Z statistic that is computed for matrices whose rows and columns are randomly permuted. A distribution of random values is obtained by computing the statistic for many permuted matrices and if ZS is significantly above this distribution, the hypothesis that the two matrices are independent is rejected1 .

As an exploratory step, we applied the Mantel test in order to compare the results of the two classification methods we presented in this article. We used the software developed by Adam Liedloff [START_REF] Liedloff | Mantel nonparametric test calculator[END_REF]. As the number of TUs is too large in comparison with the capabilities of this software, we experimented the Mantel test only on a subset of 1000 TUs. With the distance matrix computed from the results of the two classification methods, we got a Z statistic (ZS) equal to 940 894. The maximum value of ZS is 978 460 for the domains and 948 608 for the clusters. The random distribution was built from 99 permuted matrices and its ZS value is 937 708 232. As the proportion of the values from the random distribution that are above ZS is equal to zero, we can reject the hypothesis that the two matrices are independent and as a consequence, we can think that the two compared classifications are globally similar. However, as the results of the Mantel test are not easy to interpret, further tests must be performed to see what are the relations between these results and those of the other comparing methods and to determine if this test is actually suited for comparing such kind of classifications.

Conclusion

We presented in this paper an approach for evaluating the results of an unsupervised learning method, when no human evaluation is possible or when no classification exists as a reference. As a result, this method builds classes of weighted words that regroup thematic units. We defined in a previous work a stability threshold of these classes, thus we aim at evaluating this subset of classes. To do that, we applied another clustering method that only needs to know the number of classes to build on the same subset of TUs and we reformulate our evaluation problem in comparing the two classifications. Our first results lead to show a great correlation between the two results. We now have to develop other tests, for example on a different number of classes, to verify our first results. A second step will be to evaluate the methods on the same task, as a classification task for example, whose protocol has to be defined.

  tu) ln p(wjtu)] (2) with p(w tu) = Ow tu P w tu Ow tu When the thematic units are gathered in K clusters, labelled k, the cluster entropy is, H K : H K = ; X w k p(w k) ln p(wjk)] (3) where p(wjk) is defined as : p(wjtu 2 k) = p(wjk) = O w k O k (4) with O w k = P tu2k O w tu , O k = P tu2k O tu and p(w k) = O w

Table 2 :

 2 Extract of words selected in the collocation network for the segment of Table1

	words	weight	words	weight
	strider	0.683 entourer (to surround) 0.368
	toward	0.683	signature (signature)	0.366
	dédicacer (to dedicate)	0.522 exemplaire (exemplar) 0.357
	apposer (to append)	0.467		page (page)	0.332
	pointu (sharp-pointed)	0.454		train (train)	0.331
	relater (to relate)	0.445	centaine (hundred)	0.330
	boycottage (boycotting) 0.436	sentir (to feel)	0.328
	autobus (bus)	0.435		livre (book)	0.289
	enfoncer (to drive in)	0.410	personne (person)	0.267
	Table 1: Extract of a segment about a dedication
	inferred words	weight	inferred words	weight
	paraphe (paraph)		0.522	imprimerie (press)	0.418
	presse parisien (parisian-press)	0.480	éditer (to publish)	0.407
	best seller (best seller)		0.477 biographie (biography) 0.406
	maison d'édition (publishing house) 0.450	librairie (bookshop)	0.405
	libraire (bookseller)		0.447	poche (pocket)	0.389
	tome (tome)		0.445	éditeur (publisher)	0.363
	Grasset (a publisher)		0.440	lecteur (reader)	0.355
	rééditer (to republish)		0.428	israélien (Israeli)	0.337
	parution (appearance)		0.427	édition (publishing)	0.333
	words			occurrences weight
	juge d'instruction (examining judge)		58	0.501
	garde à vue (police custody)		50	0.442
	bien social (public property)		46	0.428
	inculpation (charging)			49	0.421
	écrouer (to imprison)			45	0.417
	chambre d'accusation (court of criminal appeal)	47	0.412
	recel (receiving stolen goods)		42	0.397
	présumer (to presume)		45	0.382
	police judiciaire (criminal investigation department)	42	0.381
	escroquerie (fraud)			42	0.381
	Table 3: The most representative words of a domain about justice
	overlapping clusters according to the word dis-
	tributions in the units. It is realized with an al-
	gorithm which looks like K-means (here K=71).

Table 4

 4 One domain and one cluster are associated to each thematic unit. It is then possible to calculate the number of domains and clusters which partially or fully overlap. Table5represents the intersection between the two partitions. For each domain we chose the cluster which has the highest intersection with the domain. Then we calculated the percentage of thematic units of this domain which are both in the domain and in this chosen cluster.

	: Number of links between one domain
	and the clusters

Table 5 :

 5 Coverage rates of UT which are common to each domain and those of the associated clusters which correspond to the highest intersection Height domains are identical to height clusters. The lowest coverage (18%) is obtained for one domain. The other seventy domains cover more than 20% of the clusters.

The Z statistic is maximal when the two distance matrices are identical: the xijyij term is not equal to zero only if xij and yij are equal to 1. Hence, each difference that could be introduced between the two matrices, decreases its value.


Mandë Holford
email: holford@biology.utah.edu

Nicolas Puillandre

Yves Terryn

Baldomero Olivera

Corinne Cruaud

Philippe Bouchet

Evolution of the Toxoglossa Venom Apparatus as Inferred by Molecular Phylogeny of the Terebridae

Toxoglossate gastropods (conids, terebrids, and turrids) are one of the most successful animal groups that use venom to capture their prey. These marine animals are generally characterized by a venom apparatus made up of a muscular venom bulb and venom gland. The toxoglossan radula, often compared to a hypodermeric needle for its use as a conduit to inject toxins into prey, is considered a major anatomical breakthrough that assisted in the successful initial radiation of these animals in the Cretaceous and early Tertiary. The pharmacological success of toxins from cone snails has made this group a star among biochemists and neuroscientists, but very little is known about toxins from terebrids and other Toxoglossa, and the phylogeny of these families is largely in doubt.

Here we report the very first molecular phylogeny for the Terebridae and use the results to infer the evolution of the venom apparatus for this group. Our findings indicate that most of the genera of terebrids are polyphyletic, and one species, ("Terebra" jungi) is the sister group to all other terebrids. Molecular analyses combined with mapping of the venom apparatus indicates the Terebridae have lost the venom apparatus at least twice during their evolution. Species in the genera Terebra and Hastula have the typical venom apparatus found in most toxoglossate gastropods, but all other terebrid species do not. For venomous organisms, the dual analysis of molecular phylogeny and toxin function is an instructive combination for unraveling the larger questions of phylogeny and speciation. The results presented here suggest a paradigm shift in the current understanding of terebrid evolution, while presenting a roadmap for discovering novel terebrid toxins, a largely unexplored resource for biomedical research and potential therapeutic drug development.

Introduction

Marine snails are not what initially come to mind when discussing venomous animals, but the toxoglossan gastropods that use venom to capture their prey are among the most highly successful groups of marine invertebrates. Toxoglossate ("poison tongue") gastropods include cone snails (Conus, within the family Conidae), auger snails or Terebridae, and "Turridae" (a complex of families) [START_REF] Taylor | Foregut anatomy, feeding mechanisms, relationships and classification of the Conoidea (=Toxoglossa) (Gastropoda)[END_REF] [START_REF] Puillandre | Starting to unravel the toxoglossan knot: molecular phylogeny of the "turrids[END_REF]. A venom apparatus made up of a muscular venom bulb and venom gland generally characterizes these marine mollusks. Toxins are injected into target animals via hollow disposable radular teeth, and act to immobilize prey or defend against predators [START_REF] Olivera | Conus venom peptides: reflections from the biology of clades and species[END_REF]. The toxoglossan radula, often compared to a hypodermeric needle, is considered a major anatomical breakthrough that assisted in the successful initial radiation of these animals in the Cretaceous and lower Tertiary [START_REF] Shimek | Functional morphology and evolution of the toxoglossan radula[END_REF]. There are believed to be >10,000 species of toxoglossate mollusks [START_REF] Bouchet | Turrid genera and mode of development: the use and abuse of protoconch morphology[END_REF]; Toxoglossa are the most abundant group of predatory snails in terms of species numbers.

The pharmacological success of toxins from cone snails has led to their extensive use in characterizing cellular communication in the nervous system, but very little is known about toxins from terebrids and other Toxoglossa, and the phylogeny of these families is largely in doubt.

The Terebridae are a distinctive example of modular anatomical development within marine gastropods. All Terebridae are carnivorous and hunt prey, however, a significant number of terebrid species have lost the venom apparatus. Based on key structural differences in their foregut anatomy, Miller identified three distinct terebrid feeding types [START_REF] Miller | Studies on the biology of Indo-Pacific Terebra[END_REF][START_REF] Miller | Feeding mechanisms inf the family Terebridae[END_REF]: Type I terebrids, of which there are two groups, IA and IB, have salivary glands, a long eversible labial tube, and a short and slightly retractable buccal tube (Figure 3A). Type I terebrids lack a radular and venom apparatus and therefore do not use toxins to subdue and immobilize their prey. These terebrids feed by grasping the prey with the anterior part of the labial tube and engulfing the prey whole. On the other hand, Type II terebrids exhibit characteristic toxoglossan feeding in that they impale the prey with a hypodermic radular tooth through which they deliver immobilizing toxins expressed in the venom gland, then grasp and engulf their catch with the sphincter of the labial tube. In addition to the hypodermic radular teeth and venom apparatus, these terebrids have salivary glands, an eversible labial tube of moderate length, and a long retractile buccal tube (Figure 3B). The final terebrid type, Type III, is similar to Type I, but possess an unusual accessory feeding organ, accessory proboscis structure, that is mostly uncharacteristic in other terebrids (Figure 3C). Type III terebrids use the accessory feeding organ to grasp and ingest tentacles of cirratulid polychates. To understand how the three strategies evolved, it is imperative to establish a molecular phylogeny for the Terebridae.

Compared to cone snails, terebrids are not very well characterized; their taxonomy is primarily based on shell morphology [START_REF] Omaya | On some new facts of the taxonomy of Terebridae[END_REF] and relatively few studies describe their anatomy [START_REF] Taylor | Foregut anatomy, feeding mechanisms, relationships and classification of the Conoidea (=Toxoglossa) (Gastropoda)[END_REF][START_REF] Miller | Studies on the biology of Indo-Pacific Terebra[END_REF][START_REF] Miller | Feeding mechanisms inf the family Terebridae[END_REF][START_REF] Taylor | The anatomy of the foregut and relationships in the Terebridae[END_REF][START_REF] Bratcher | Living Terebras of the world[END_REF][START_REF] Terryn | A collectors guide to Recent Terebridae (Mollusca: Neogastropoda)[END_REF]. This is surprising, given the relative abundance of terebrids and their global tropical distribution and shallow water occurrence. The Terebridae comprise >300 known species [START_REF] Bratcher | Living Terebras of the world[END_REF][START_REF] Terryn | A collectors guide to Recent Terebridae (Mollusca: Neogastropoda)[END_REF], and there may be another 100 unnamed taxa, especially in deeper waters (100-500 meters).

We used a three gene data matrix consisting of mitochondrial 12S and 16S rRNA, and cytochrome oxidase subunit I (COI) sequences from 67 species to reconstruct the first molecular phylogeny of the Terebridae (Figure 1). We also assessed the evolution of terebrid feeding strategies by mapping the presence or absence of the venom apparatus on the phylogeny. The results presented here suggest a major shift in understanding terebrid evolution: our findings indicate that the Terebridae have independently lost the venom apparatus twice during their evolution.

The molecular phylogeny of the Terebridae additionally provides a roadmap for discovering novel terebrid toxins from those species with a venom apparatus. Given the similarities with cone snails, it is expected that the toxins from terebrids will be a rich, and at present, largely unexplored source of neuroactive peptides to investigate cellular communication in the nervous system. Reconstructing a reliable phylogeny for the Terebridae is the first step towards characterizing their toxins using the emerging biodiversity/exogenome combination strategy that has been developed for Conus venom peptides [START_REF] Olivera | Diversity of the neurotoxic Conus peptides: A model for concerted pharmacological discovery[END_REF].

Results

Phylogenetic analyses

One hundred and fifty six samples of terebrids were used to reconstruct the molecular phylogeny of the Terebridae. For the COI gene, 658 bp were sequenced. After alignments, we obtained a fragment of 534 and 455 bp for 12S and 16S genes respectively. Trees obtained independently with COI, 12S, and 16S genes were only partly resolved, but no contradiction were found (results not showed). Consequently, 12S, 16S and COI mitochondrial genes, including 131 ingroups, were used to produce a combined data set for phylogenetic analyses (Figure 1).

Phylogenetic analyses strongly indicate the Terebridae is monophyletic (Posterior

Probabilities (PP) = 1, Bootstraps (B) = 100), (Figure 1). "Terebra" jungi appears to be the sister group to all other terebrid (PP = 1, B = 92). Apart from "T." jungi (Clade A), there are four major clades within the tree, designated in Figure 1 Clade E is the largest clade and includes the genera Myurella, Clathroterebra, Terenolla, Hastulopsis, Strioterebrum, and the "Terebra" textilis-group [START_REF] Terryn | A collectors guide to Recent Terebridae (Mollusca: Neogastropoda)[END_REF]. Myurella itself is polyphyletic, with several species placed as sister groups of other genera. For example, Myurella affinis, the type-species, is the sister group of Terenolla, Hastulopsis, some Clathroterebra species and two other Myurella species (PP = 1, B = 94).

Clathroterebra is also polyphyletic, with the two representative species used in our analyses, C. poppei and C. fortunei, type species of Clathroterebra, appearing in separate well supported clades, (PP = 1, B = 100) for both. The distinctiveness of the monotypic genus Terenolla is confirmed in our analysis. Hastulopsis and Strioterebrum are paraphyletic. The "Terebra" textilis-group is dispersed within Clade E, and constitutes a group that includes a large amount of undescribed diversity, both at the genus and species levels, hence the various types (textilis III, IV, V, VII) listed in the tree.

Evolution of venom apparatus

Miller's type classifications are indicated in the tree in Figure 1 and Table 1.

"Terebra" jungi has a venom apparatus and is identified as a Type IIB feeder (Sysoev, personal comm.). Clade B corresponds to Miller's Type I feeders, which were further subdivided between Type IA and Type IB feeders. Type IA feeders are Acus crenulatus, and A. dimidiatus, [START_REF] Miller | Studies on the biology of Indo-Pacific Terebra[END_REF][START_REF] Miller | The biology of Terebra gouldi Deshayes, 1859, and a discussion of life history similarities among other terebrids of similar proboscis type[END_REF]. Acus areolatus, not hitherto recognized as a species of Acus, is however similar to the other species in Clade B in not having a venom apparatus and was identified as a Type IA feeder by Miller [START_REF] Miller | The biology of Terebra gouldi Deshayes, 1859, and a discussion of life history similarities among other terebrids of similar proboscis type[END_REF]. The Type IB feeders in Clade B are:

A. felinus, A. maculatus, and A. chloratus. The taxa that make up Clade C are all Type IIB feeders: Terebra guttata, T. subulata, T. babylonia, T. tricolor, Cinguloterebra fenestrata, C. anilis, and C. jenningsi (Taylor, personal comm.), [START_REF] Miller | Studies on the biology of Indo-Pacific Terebra[END_REF][START_REF] Taylor | The anatomy of the foregut and relationships in the Terebridae[END_REF]. Clade D, made up exclusively of members of the genera Hastula (H. strigilata, H. penicillata, H. solida, H. albula, H. cf. rufopunctata) and Impages (I. hectica) are Type IIA feeders [START_REF] Taylor | The anatomy of the foregut and relationships in the Terebridae[END_REF][START_REF] Miller | The Biology of Hastula inconstans (Hinds, 1844) and a discussion of life history similarities among other Hastulas of similar proboscis type[END_REF].

Type III feeders are represented in the phylogenetic tree by Clade E, which includes species of Myurella (M. columellaris, M. undulata, M. paucistriata, M. affinis, M. flavofasciata, and M. parkinsoni), Hastulopsis (H. conspersa and H. pertusa), and Terenolla (T. pygmaea) (Taylor, personal comm.), [START_REF] Miller | Studies on the biology of Indo-Pacific Terebra[END_REF][START_REF] Taylor | The anatomy of the foregut and relationships in the Terebridae[END_REF].

In order to efficiently characterize toxins from various terebrid species, it is essential to first identify those species that have a venom apparatus. Shown in Figure 2 is the mapping of the presence or absence of a venom apparatus in the terebrid species used to construct the molecular phylogeny in Figure 1. The map clearly indicates that terebrids have lost the venom apparatus twice during their evolution. It is inferred that the ancestor of all terebrids had a venom duct, and the groups corresponding to Clade B and Clade E have lost it. In addition to "Terebra" jungi, there are two distinct groups of terebrids, those in Clades C and D, which use the typical toxoglossate venom apparatus to hunt prey.

Discussion

The first molecular phylogeny of the Terebridae, presented in this report, significantly updates current hypotheses about the evolution of this group, and simultaneously facilitates efforts to characterize terebrid toxins. The results highlight several problematic propositions about the Terebridae, while confirming a few established hypotheses. One such hypothesis is that the Terebridae family is monophyletic. Our findings confirm that shell shape was intuitively a good character to group all terebrids in one taxon. Another substantiated hypothesis is that the monotypic genus Terenolla belongs in the Terebridae and is not a columbellid as suggested earlier [START_REF] Bratcher | Living Terebras of the world[END_REF]. A distinctive discovery revealed in Figure 1, is that "Terebra" jungi is a sister group to all other terebrid species. This is an original finding unappreciated until the present molecular analysis.

The prevailing implications of this phylogeny are that there is little congruence between the former genus-level classification based on shell morphology and clades recognized by molecular characters. The molecular phylogeny indicates that most of the genera proposed in the family Terebridae will have to be revised. This is evidenced by, e.g., species earlier assigned to Terebra now appearing throughout the tree in Clades B ("Terebra" areolata, here classified in Acus), C (T. subulata, type species of Terebra, and several others), E ("Terebra" textilis group), in addition to "Terebra" jungi, which will require the establishment of a new genus. Despite this non-monophyly of shell-defined genera, but based on the inclusion of their type species (with the exception of the type species of Acus) in the analysis, we tentatively ascribe existing generic names to the four Evolutionary trends in the Toxoglossa have been reconstructed primarily through studies involving radular formation and anatomy of the digestive system [START_REF] Shimek | Functional morphology and evolution of the toxoglossan radula[END_REF][START_REF] Taylor | The anatomy of the foregut and relationships in the Terebridae[END_REF][START_REF] Kantor | The morphology of toxoglossan gastropods lacking a radula, with a description of new species and genus of Turridae[END_REF][START_REF] Kantor | Formation of marginal radular teeth in Conoidea (Neogastropoda) and the evolution of the hypodermic envenomation mechanism[END_REF][START_REF] Mills | Radular tooth structure of three species of Terebridae (Mollusca: Toxoglossa)[END_REF][START_REF] Simone | Comparative morphology and systematics of Brazilian Terebridae (Mollusca, Gastropoda, Conoidea), with descriptions of three new species[END_REF].

The molecular phylogeny presented here paints a plausible picture of how terebrids evolved such a diversity of feeding strategies. Our findings suggest that all terebrids appear to be derived from a common ancestor with a venom apparatus of the Miller Type IIB (Figure 2). Furthermore, mapping of the venom apparatus indicates that two lineages of terebrids independently lost their ability to hunt prey using toxins, Clades B and E.

There is considerable correlation between our molecular phylogeny, Miller's anatomical groupings, and the ecological distribution of the terebrid species used in this study. Miller separated terebrids with a venom apparatus, Type II feeders, into two distinct groups, IIA and IIB. Type IIA and IIB terebrids differ in the shape of the buccal tube and shell morphology [START_REF] Miller | Studies on the biology of Indo-Pacific Terebra[END_REF]. Type IIA terebrids have a long and slender buccal tube, small shining shells, with 7-10 whorls and a flared aperture. Terebrids of Type IIB have a short, thick buccal tube, the shells are large, long and slender, with 15 or more whorls and a constricted aperture. Our phylogenetic analyses supports this separation, as the shell shape of the species in Clade C, Terebra, are slender and multiwhorled, whereas those of Clade D, Hastula, are shiny with fewer whorls. The separation of the two clades is further supported by the ecological differences in their habitats. Terebra species of Clade C live buried in sandy or muddy subtidal flats, whereas Hastula species of Clade D live predominantly on surf beaches or in sand in reef pockets [START_REF] Miller | Studies on the biology of Indo-Pacific Terebra[END_REF][START_REF] Miller | The Biology of Hastula inconstans (Hinds, 1844) and a discussion of life history similarities among other Hastulas of similar proboscis type[END_REF]. Similarly, terebrids that feed without the use of a venom apparatus, Types I and III feeders, are represented by two different clades in the molecular phylogeny, Clades B and E, respectively. Our analyses goes further to identify that the non-venom apparatus feeders, Clades B and E, are not sister groups.

The molecular phylogeny and venom apparatus map constructed for the Terebridae indirectly provides an instructive field guide for characterizing novel toxins from this group using the biodiversity-first, exogenomic strategy recently applied to cone snails [START_REF] Olivera | Diversity of the neurotoxic Conus peptides: A model for concerted pharmacological discovery[END_REF][START_REF] Olivera | Conus peptides: biodiversity-based discovery and exogenomics[END_REF]. The exogenomic strategy was used to characterize cone snail toxins that target nicotinic receptors. In this strategy, phylogeny and molecular biology techniques are used to identify "exogenes," which are genes of the toxins expressed in the venom duct. Exogenes are expressed to be used exogenously, outside of the organism; hence, they rapidly evolve to respond to cues in their biotic environment, and are thus a powerful marker for differentiating ecological or evolutionarily distinct organisms.

Clades C and D are two major terebrid groups from which it would be productive to investigate toxins for biochemical characterization (Figure 5). This detailed distinction of terebrids with and without toxins greatly facilitates efforts to characterize terebrid toxins. Furthermore, as Clades C and D are not sister clades, they may produce highly divergent toxins that could result in varied functional properties upon further characterization. Initial reports on the toxins from terebrids [START_REF] Imperial | The augertoxins: biochemical characterization of venom components from the toxoglossate gastropod Terebra subulata[END_REF][START_REF] Imperial | Venomous auger snail Hastula (Impages) hectica (Linnaeus, 1758): Molecular phylogeny, foregut anatomy, and comparative toxinology[END_REF] indicate they have a comparable organization of precursor structures characteristic of conotoxin gene superfamilies, namely: a highly conserved signal sequence, followed by a propeptide region, then the mature toxin region. As has been demonstrated with cone snails, the conserved signal sequence and propeptide region in terebrids is an exploitable feature that can be used to facilitate their characterization using molecular biology techniques, e.g. as the basis for designing PCR primers. The Terebridae phylogeny in Figure 1 sets the stage for efficient characterization of terebrid toxins and identification of the gene superfamilies that encode the toxins. Preliminary results from the toxins characterized from Terebra subulata [START_REF] Imperial | The augertoxins: biochemical characterization of venom components from the toxoglossate gastropod Terebra subulata[END_REF] and Hastula hectica [START_REF] Imperial | Venomous auger snail Hastula (Impages) hectica (Linnaeus, 1758): Molecular phylogeny, foregut anatomy, and comparative toxinology[END_REF] indicate terebrid toxins are not posttranslationally modified, facilitating the ability to chemically synthesize these peptides for testing of their function. The biochemical and genetic characterization of terebrid toxins, while identifying novel compounds useful for investigating cell communication in the nervous system, will also provide additional characters to further clarify the phylogeny and evolutionary biology of these organisms. For toxoglossa gastropods, the dual analysis of molecular phylogeny and venom function is an instructive combination for unraveling the bigger question of speciation. This work is a first attempt to address these issues for the Terebridae.

METHODS

Material

All specimens used were collected during several expeditions to the West Pacific since 2004, and specifically fixed for molecular analysis. Living specimens were anesthetized using MgCl 2 ; a piece of tissue (usually foot) was cut and fixed in 95% ethanol. Table 1 lists all specimens used in this study and the expedition sources. Vouchers are kept in the collection of the Muséum National d'Histoire Naturelle (MNHN). The species included in taxon sampling represent 12 of the 15 identified genera as defined in a recent shell morphology based revision of the Terebridae [START_REF] Terryn | A collectors guide to Recent Terebridae (Mollusca: Neogastropoda)[END_REF]. Outgroups were chosen to form a non-monophlyetic group as recommended by [START_REF] Darlu | La reconstruction phylogénétique[END_REF]. Representative species of Conidae and "Turridae" (s.l.) were chosen as closely related outgroups, and one species from the non-neogastropod family Harpidae was chosen as distant outgroup.

Sequencing

DNA was extracted from foot or other tissue using Qiagen QIAamp Dneasy Tissue kit.

Fragments of mitochondrial genes 12S, 16S and COI were amplified using universal primers 12S1/12S3 [START_REF] Simon | The Polymerase Chain Reaction: DNA extraction and amplification[END_REF], 16Sar/16Sbr [START_REF] Palumbi | Nucleic Acids II: The Polymerase Chain Reaction[END_REF] and LCO1490/HCO2198 [START_REF] Folmer | DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates[END_REF] respectively.

All PCR reactions were performed in 25 μl, containing 3 ng of DNA, 1X reaction buffer, 2.5 mM MgCl 2 , 0.26 mM dNTP, 0.3 mM each primer, 5% DMSO, and 1.5 units of Qbiogene Q-Bio Taq. Amplification consisted of an initial denaturation step at 94 o C for 4 min, followed by 37 cycles of denaturation at 94°C for 30 sec, annealing at 54 o C for 12S gene, and 52 o C for 16S, followed by extension at 72 o C for 1 min. The final extension was at 72 o C for 5 min. The amplification of COI genes were performed similarly, except there were two annealing cycles: the first repeated 5 times at an annealing temperature of 45 o C, and the second repeated 30 times at 50 o C as described by [START_REF] Herbert | Biological identifications through DNA barcodes[END_REF]. PCR products were purified using exonucleaseI and phosphatase and sequenced using the Applied Biosystem BigDyeTerminator V3.1 kit and the ABI3730XL sequencer.

All genes were sequenced in both directions for increased accuracy of each sequence.

Sequences were deposited in GenBank (Genbank accession numbers submitted but not yet assigned). COI sequences were also deposited in BOLD (Conoidea Barcode and Taxonomy project).

Phylogenetic analyses:

COI sequences were manually aligned and 12S and 16S were automatically aligned using ClustalW multiple alignment implemented in BioEdit version 7.0.5.3 [START_REF] Hall | BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT[END_REF]. The accuracy of automatic alignments was confirmed by visual inspection. Hyper-variable regions of 12S and 16S genes were excluded from further analyses due to ambiguities in the alignments. Nucleotide substitution models were selected for each gene separately using the program Modeltest [START_REF] Posada | Selecting Models of Nucleotide Substitution: An application to Human Immunodeficiency Virus (HIV-1)[END_REF], in conjunction with PAUP 4.0b10 [START_REF] Swofford | PAUP,* Phylogenetic Analysis Using Parsimony* and other methods[END_REF]. Best model of evolution and parameters estimated for each gene are TrN + I (0.537) + G (0.885) for COI gene, TVM + I (0.310) + G (0.581) for 12S gene and GTR + I (0.402) + G (0.483) for 16S gene. Phylogenetic analyses were based on reconstructions using Maximum Likelihood (ML) and Bayesian Analysis (BA). ML heuristic search was conducted with 100 replicates with TBR branch-swapping using PhyML 2.4.4 [START_REF] Guindon | A simple, fast, and accurate algorithm to estimate large phylogenies by Maximum Likelihood[END_REF], and robustness of the nodes was assessed using nonparametric bootstrapping [START_REF] Felsenstein | Inferring Phylogenies[END_REF] with 1000 bootstraps replicates for ML analysis.

BA consisted of six Markov chains (10,000,000 generations each with a sampling frequency of one tree each hundred generations) run in two parallel analyses using Mr.

Bayes [START_REF] Huelsenbeck | MrBayes: Bayesian inference of phylogeny[END_REF]. The number of swaps that are tried each time the chain stops for swapping was 4, and the chain temperature was set at 0.08. When the log-likelihood scores were found to stabilize, a consensus tree was calculated after omitting the first 25% trees as burn-in.

For the combined analyses of the three genes, the same parameters were used for the ML analysis. For the BA, 1 different model was applied for each gene, each with 6 substitution categories. For the COI gene, as saturation was found on the third base of the codon, different models were applied for the 2 partitions (bases 1 and 2 vs base 3).

Finally, we have four unlinked partitions (COI bases 1 and 2, COI base 3, 12S, and 16S).

Venom apparatus mapping

Data from cited literature and personal communications from A. Sysoev, and J. Taylor helped to determine the presence or absence of a venom apparatus in the species used for the phylogenetic analysis. The absence or presence of the venom apparatus was then mapped on the tree using Mesquite V. 2.01 [START_REF] Maddison | Mesquite: A modular system for evolutionary analysis Version 2[END_REF], using the option "tracing character history." The parsimony ancestral reconstruction method was used. The three types of foregut anatomy Miller used to define feeding strategies within the Terebridae are depicted (Types I, II, and III). Anatomical features are labeled and highlighted in color: yellow = salivary glands (sg), red = buccal tube (bt), green = venom bulb (vb) and radular sac (rs), blue = accessory feeding organ (afo). Also labeled are the proboscis (p), found only in Type II terebrids, and the labial tube (lt). (After Miller, 1970).

  as Clades B-E. Clade B is comprised primarily of the genus Acus plus one species currently placed in Terebra, Terebra aerolata, which we define as Acus aerolatus (PP = 1, B = 100). Clade C includes Cinguloterebra and additional species currently placed in Terebra, including the type species of Terebra, T. subulata (PP = 1, B = 76). Clade D includes species currently placed in the genera Hastula and Impages (PP = 1, B =100).

  major clades: Clade B is thus Acus (H. & A. Adams, 1853), Clade C is Terebra (Bruguière, 1789), Clade D is Hastula (H. & A. Adams, 1853), and Clade E is Myurella (Hinds, 1844).

Figure 1 .

 1 Figure 1. Combined Phylogenetic Tree. Consensus tree of ML and BA analyses using

Figure 2 .

 2 Figure 2. Possession of Venom Apparatus Mapped onto Terebrid Phylogeny. The

Figure 3 .

 3 Figure 3. Schematic Representation of Miller's Foregut Anatomy of the Terebridae.

Figure 4 .

 4 Figure 4. Terebrids with Venom Apparatus: Representative shell images of the

  Figure 3.

Table 1 . List of Terebrid Samples Used in Molecular Phylogeny.

 1

Table 1 . List of Terebrid Samples used in Molecular Phylogeny (cont d)

 1

	MNHN N°Cruise	Coordinates, Depth	Genus	Species (Author)	COI 16S 12S	Miller Type	Clade
	30482	BOA1	15°41.6'S, 167°02.1'E, 268-445 m	Cinguloterebra	fujitai (Kuroda & Habe, 1952)	x	x			C
	30530	EBISCO	20°29'S, 158°42'E, 197-230 m		n. sp. Terryn III	x	x	x		E
	30515	EBISCO	20°06'S, 160°23'E, 280-304 m		n. sp. Terryn III	x	x	x		E
	30535	Panglao 04	9°33.0'N, 123°46.5'E, 8-14 m	Hastula	lanceata (Linnaeus, 1767)	x	x			D
	30370	Panglao 04	9°37.4'N, 123°46.9'E, 3-20 m	Acus	maculatus (Linnaeus, 1758)	x	x	x	I B	B
	30404	Panglao 04	9°35.3'N, 123°52.2'E, 84-87 m	Cinguloterebra	triseriata (JE Gray, 1824)	x	x	x		C
	30464	Panglao 04	9°35.3'N, 123°52.2'E, 84-87 m	"Terebra" textilis-group	textilis type IV	x	x	x		E
	30442	Panglao 04	9°33.5'N, 123°48.6'E, 80-120 m	Hastulopsis	sp.	x	x	x		E
	30443	Panglao 04	9°37.4'N, 123°54.5E, 6-8 m	Acus	felinus (Dillwyn, 1817)	x	x	x	I B	B
	30444	Panglao 04	9°37.4'N, 123°54.5E, 6-8 m	Terebra	subulata (Linnaeus, 1767)	x	x	x	IIB	C
	30445	Panglao 04	9°37.4'N, 123°54.5E, 6-8 m	Acus	felinus (Dillwyn, 1817)	x	x	x	I B	B
	30446	Panglao 04	9°37.4'N, 123°54.5'E, 4-5 m	Terenolla	pygmaea (Hinds, 1844)	x	x	x	III	E
	30448	Panglao 04	9°37.4'N, 123°54.5'E, 4-5 m	Terenolla	pygmaea (Hinds, 1844)	x	x	x	III	E
	30449	Panglao 04	9°37.4'N, 123°54.5'E, 4-5 m	Terenolla	pygmaea (Hinds, 1844)	x	x	x	III	E
	30430	Panglao 04	9°37.4'N, 123°54.5E, 6-8 m	Myurella	affinis (JE Gray, 1834)	x	x	x	III	E
	30452	Panglao 04	9°37.4'N, 123°54.5E, 6-8 m	Myurella	affinis (JE Gray, 1834)	x	x	x	III	E
	30439	Panglao 04	9°33.4'N, 123°48.4'E, 3 m	Myurella	affinis (JE Gray, 1834)	x	x	x	III	E
	30511	Panglao 04	9°35.7'N, 123°44.4'E, 0-2 m	Terenolla	pygmaea (Hinds, 1844)	x	x	x	III	E
	30510	Panglao 04	9°35.7'N, 123°44.4'E, 0-2 m	Myurella	columellaris (Hinds, 1844)	x	x		III	E
	30459	Panglao 04	9°35.7'N, 123°44.4'E, 0-2 m	Myurella	kilburni (RD Burch, 1965)	x	x	x		E
	30460	Panglao 04	9°35.7'N, 123°44.4'E, 0-2 m	Myurella	affinis (JE Gray, 1834)	x	x	x	III	E
	30587	Panglao 04	9°37.4'N, 123°46.9'E, 3-20 m	Acus	areolatus (Link, 1807)	x	x	x	I A	B
	30472	Panglao 04	9°41.8'N, 123°51.1'E, 10 m	Cinguloterebra	fenestrata type I		x	x	IIB	C
	30534	Panglao 04	9°42.1'N, 123°51.4'E, 3-4 m		n. sp. Terryn I	x	x	x		E
	30465	Panglao 04	9°29.4'N, 123°56.0'E, 15-20 m	Myurella	flavofasciata (Pilsbry, 1921)	x	x	x	III	E
	30408	Panglao 04	9°29.4'N, 123°56.0'E, 15-20 m	Myurella	nebulosa (GB Sowerby I, 1825)	x	x	x		E
	30450	Panglao 04	9°43.3'N, 123°48.8'E, 123-135 m	Hastulopsis	sp.	x	x	x		E
	30407	Panglao 04	9°32.8'N, 123°45.9'E, 0-2 m	Impages	hectica (Linnaeus, 1758)	x	x	x	IIA	D
	30451	Panglao 04	9°36.8'N, 123°52.2'E, intertidal	"Terebra" textilis-group	textilis type IV	x	x	x		E
	30431	Panglao 04	9°36.8'N, 123°52.2'E, intertidal	Terebra	laevigata (JE Gray, 1834)	x	x	x	IIB	C
	30454	Panglao 04	9°36.4'N, 123°53.8'E, 60-62 m	Hastulopsis	sp.	x	x	x		E
	30513	Panglao 04	9°36.4'N, 123°53.8'E, 60-62 m	cf. Myurella	sp.	x	x	x		E
	30455	Panglao 04	9°36.4'N, 123°53.8'E, 60-62 m	Clathroterebra	poppei (Terryn, 2003)	x	x	x		E
	30483	Panglao 04	9°32.8'N, 123°42.1'E, 3-35 m	Terebra	subulata (Linnaeus, 1767)	x	x	x	IIB	E
	30481	Panglao 04	9°35.7'N, 123°44.4'E, 0-2 m	Myurella	affinis (JE Gray, 1834)	x	x	x	III	E
	30401	Panglao 05	9°39.2'N, 123°47.5'E, 255-268 m	Clathroterebra	fortunei type II	x	x	x		E
	30582	Panglao 05	9°39.2'N, 123°47.5'E, 255-268 m	"Terebra" textilis-group	textilis (Hinds, 1844)	x		x		E
	30418	Panglao 05	9°39.2'N, 123°47.5'E, 255-268 m	Cinguloterebra	fenestrata type II	x	x	x	IIB	C
	30410	Panglao 05	9°39.2'N, 123°47.5'E, 255-268 m	Cinguloterebra	fenestrata type I	x	x	x	IIB	C
	30395	Panglao 05	9°37.5'N, 123°40.2'E, 606-631 m	"Terebra" bathyrhaphe-group	jungi (Lai, 2001)	x	x	x	IIB	A
	30390	Panglao 05	9°29.4'N, 123°44.4'E, 271-318 m	Cinguloterebra	cf. fenestrata (Hinds, 1844)	x	x	x	IIB	C
	15724	Panglao 05	9°27.4'N, 123°49.4'E, 273-356 m	Cinguloterebra	cf. fujitai (Kuroda & Habe, 1952)	x	x	x		C
	16731	Panglao 05	9°30.1'N, 123°41.6'E, 356-396 m	"Terebra"	sp.	x	x	x		E
	16735	Panglao 05	9°36.2'N, 123°43.8'E, 382-434 m	Cinguloterebra	cf. fenestrata (Hinds, 1844)	x	x	x	IIB	C
	30584	Panglao 05	9°34.3'N, 123°37.8'E, 729-733 m	"Terebra" bathyrhaphe-group	jungi (Lai, 2001)	x	x	x	IIB	A
	30406	Panglao 05	6°24.1' S, 156°20.4' E, 1045-1207 m	Cinguloterebra	sp.	x				C
	30479	Salomon 2	7°13.8' S, 158°29.4' E, 286-423 m	"Terebra" textilis-group	textilis type VII	x	x	x		E
	30528	Salomon 2	7°43.5' S, 158°29.7' E, 336-341 m	"Terebra" textilis-group	textilis type VII		x	x		E
	30424	Salomon 2	8°36.7' S, 157°21.0' E, 150-160 m	"Terebra" textilis-group	textilis type VI		x	x		E
	30391	Salomon 2	7°59.3' S, 157°33.3' E, 260 m	Clathroterebra	fortunei type II	x	x	x		E
	30501	Salomon 2	8°25.5' S, 159°26.4' E, 543-593 m	"Terebra" bathyrhaphe-group	jungi (Lai, 2001)		x	x	IIB	A
	30487	Salomon 2	8°39.5' S, 157°23.0' E, 214-243 m	Cinguloterebra	lima (Deshayes, 1857)	x	x	x		C
	30579	Salomon 2	8°39.5' S, 157°23.0' E, 214-243 m	"Terebra" textilis-group	trismacaria (Melvill, 1917)	x	x	x		E
	30492	Salomon 2	8°39.5' S, 157°23.0' E, 214-243 m	"Terebra" textilis-group	trismacaria (Melvill, 1917)	x	x	x		E
	30499	Salomon 2	8°39.5' S, 157°23.0' E, 214-243 m		Unidentified juvenile	x	x	x		E
	30409	Santo 06	15°33.1'S, 167°17.8'E, 15-25 m	Terebra	tricolor(GB Sowerby I, 1825)	x	x	x	IIB	C
	30389	Santo 06	15°28.7'S, 167°15.2'E, 19 m	Acus	maculatus (Linnaeus, 1758)	x	x	x	I B	B
	30378	Santo 06	15°33.1'S, 167°12.2'E, 3-40 m	Myurella	nebulosa (GB Sowerby I, 1825)		x	x		E
	30629	Santo 06	15°33.1'S, 167°12.2'E, 3-40 m	Myurella	undulata (JE Gray, 1834)	x	x	x	III	E
	30628	Santo 06	15°33.1'S, 167°12.2'E, 3-40 m	Myurella	undulata (JE Gray, 1834)	x	x	x	III	E
	30626	Santo 06	15°31.7'S, 167°09.7'E, 18-21 m	"Terebra" textilis-group	textilis type V	x	x	x		E
	30440	Santo 06	15°31.7'S, 167°09.7'E, 18-21 m	"Terebra" textilis-group	textilis type V	x	x	x		E
	30456	Santo 06	15°31.7'S, 167°09.7'E, 18-21 m	"Terebra" textilis-group	textilis type V	x	x	x		E
	30458	Santo 06	15°31.7'S, 167°09.7'E, 18-21 m	"Terebra" textilis-group	textilis type V	x	x	x		E
	30634	Santo 06	15°31.7'S, 167°09.7'E, 18-21 m	"Terebra" textilis-group	textilis type V	x	x	x		E
	30617	Santo 06	15°31.7'S, 167°09.7'E, 18-21 m	"Terebra" textilis-group	textilis type V	x	x	x		E
	30618	Santo 06	15°31.7'S, 167°09.7'E, 18-21 m	"Terebra" textilis-group	textilis type V	x	x	x		E
	30386	Santo 06	15°36.6'S, 167°10.1'E, 8-20 m	Terebra	subulata (Linnaeus, 1767)	x	x	x	IIB	C
	30619	Santo 06	15°33.1'S, 167°12.2'E, 3-40 m	Hastulopsis	conspersa (Hinds, 1844)	x	x	x	III	E
	30387	Santo 06	15°33.1'S, 167°12.2'E, 3-40 m	Terebra	guttata (Röding, 1798)	x	x	x	IIB	C
	30376	Santo 06	15°33.1'S, 167°12.2'E, 3-40 m	Terebra	guttata (Röding, 1798)	x	x	x	IIB	C
	30620	Santo 06	15°31.3'S, 167°10.4'E, 3-18 m	Myurella	undulata (JE Gray, 1834)	x	x	x	III	E
	30494	Santo 06	15°34.4'S, 167°13.1'E, 9 m	Acus	crenulatus (Linnaeus, 1758)	x	x	x	I A	B
	30377	Santo 06	15°34.4'S, 167°13.1'E, 9 m	Acus	crenulatus (Linnaeus, 1758)	x	x	x	I A	B
	30623	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	Hastulopsis	conspersa (Hinds, 1844)	x	x	x	III	E
	30480	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	Hastulopsis	amoena (Deshayes, 1859)	x	x	x		E
	30463	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	Hastulopsis	conspersa (Hinds, 1844)	x	x	x	III	E
	30478	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	Hastulopsis	conspersa (Hinds, 1844)	x	x	x	III	E
	30388	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	Hastulopsis	pertusa (Born, 1778)	x	x	x	III	E
	30392	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	Hastulopsis	pertusa (Born, 1778)	x	x	x	III	E
	30373	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	Acus	dimidiatus (Linnaeus, 1758)	x	x	x	I A	B
	30394	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	Terebra	laevigata (JE Gray, 1834)	x	x	x	IIB	C
	30475	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	"Terebra" textilis-group	textilis type V	x	x	x		E
	30476	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	"Terebra" textilis-group	textilis type III	x	x	x		E
	30621	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	"Terebra" textilis-group	textilis type IV	x	x	x		E
	30616	Santo 06	15°36.9'S, 167°10.5'E, 6-33 m	"Terebra" textilis-group	textilis type IV	x	x	x		E
	30558	Santo 06	15°31'S, 167°09'E, intertidal	Strioterebrum	plumbeum (Quoy & Gaimard, 1833)	x	x	x		E
	30469	Santo 06	15°31'S, 167°09'E, intertidal	Strioterebrum	plumbeum (Quoy & Gaimard, 1833)	x	x	x		E
	30632	Santo 06	15°31'S, 167°09'E, intertidal	Terebra	laevigata (JE Gray, 1834)	x	x	x	IIB	C
	30614	Santo 06	15°31'S, 167°09'E, intertidal	Strioterebrum	cf. arabella (Thiele, 1925)		x	x		E
	30613	Santo 06	15°31'S, 167°09'E, intertidal	Terebra	laevigata (JE Gray, 1834)	x	x	x	IIB	C
	30567	Santo 06	15°34.7'S, 167°13.8'E, 14-25 m	Myurella	nebulosa (GB Sowerby I, 1825)	x	x	x		E
	30374	Santo 06	15°31.4'S, 167°09.7'E, intertidal	Terebra	punctatostriata (JE Gray, 1834)		x	x		C
	30612	Santo 06	15°29'S, 167°14.94'E, 2-4 m	Myurella	parkinsoni (Bratcher & Cernohorsky, 1976)	x	x	x	III	E
	30471	Santo 06	15°29'S, 167°14.94'E, 2-4 m	Myurella	parkinsoni (Bratcher & Cernohorsky, 1976)	x	x	x	III	E
	30611	Santo 06	15°35.2'S, 167°59.4'E, intertidal	Strioterebrum	plumbeum (Quoy & Gaimard, 1833)	x	x	x		E
	30610	Santo 06	15°35.2'S, 167°59.4'E, intertidal	Strioterebrum	cf. Plumbeum (Quoy & Gaimard, 1833)	x	x	x		E
	30609	Santo 06	15°35.2'S, 167°59.4'E, intertidal	Strioterebrum	sp.	x	x	x		E
	30608	Santo 06	15°35.2'S, 167°59.4'E, intertidal	Hastula	strigilata (Linnaeus, 1758)	x	x		IIA	D
	30607	Santo 06	15°35.2'S, 167°59.4'E, intertidal	Hastula	strigilata (Linnaeus, 1758)		x	x	IIA	D
	30420	Santo 06	15°35.2'S, 167°59.4'E,intertidal	Hastula	strigilata (Linnaeus, 1758)	x	x	x	IIA	D
	30416	Santo 06	15°35.2'S, 167°59.4'E, intertidal	Hastula	strigilata (Linnaeus, 1758)		x	x	IIA	D
	30414	Santo 06	15°26.6'S, 167°15.2'E, intertidal	Myurella	affinis (JE Gray, 1834)	x	x	x	III	E

Acknowledgements:

The authors thank Yuri Kantor for processing many of the terebrid specimens in the field; Virginie Heros and Philippe Maestrati for assistance throughout, from the field to curating samples at MHNH; the staff of MNHN's "Service de Systématique Moléculaire" for technical facilities; and John Taylor and Alexander Sysoev for providing unpublished anatomical information on terebrid taxa used in this study.

Funding:

The Panglao 2004 and Santo 2006 expeditions, which were the source of many specimens, were supported among others by the Total Foundation. Joint funding from National Science Foundation (NSF) CHE and OISE postdoctoral fellowship (0610202) for MH also supported this work.