Aims of the
Global Survey and Inventory of Solifugae

HIGHER PHYLOGENY, CLASSIFICATION AND BIOGEOGRAPHY
 

Taxon Sampling:  Exemplars from the sister-group, Pseudoscorpiones, other Dromopoda, and more distantly related arachnid orders (Coddington, Harvey, Prendini and Walter 2004; Giribet, Edgecombe, Wheeler, and Babbitt 2002; Giribet and Ribera 2000; Shultz 1990; Weygoldt and Paulus 1979; Wheeler, Cartwright and Hayashi 1993; Wheeler and Hayashi 1998), available from the AToL: Spider Phylogeny and REVSYS: Vaejovidae projects, will be included as outgroups.  The ingroup sample will comprise exemplars from all solifuge families, subfamilies, and as many genera as it is possible to obtain fresh material for DNA isolation (ca. 450 samples representing 10 families and at least 26 genera are already available from prior collections by Prendini).  We will attempt to sequence DNA from museum material for crucial taxa of which no fresh samples are available.  Taxa of systematic and biogeographical significance will be targeted.  Sampling key genera in southern Africa and South America will facilitate assessment of relationships by providing rare material for morphological and molecular analyses. Sampling typical daesiids, as well as ammotrechids, ceromids, gylippids, melanoblossids, and mummuciids in both regions will be essential for placing them in a wider phylogenetic context, and illuminating putative Gondwana connections.  Sampling from the Palearctic region is crucial for fresh material of Galeodidae, Karschiidae and Rhagodidae, not found in southern Africa or the New World.  Collections from the Palearctic will establish whether the disjunct distribution of Gylippidae is real or artifactual.  It will be impossible to sample all enigmatic taxa.  Dinorhax, for example, known only from Vietnam and Indonesia, is placed in Melanoblossidae (Roewer 1934), but distribution and morphology suggest Karschiidae or Gylippidae.   Nevertheless, the sampling outlined will provide a foundation for family-level classification that facilitates eventual placement of such genera when more material is available.

Organismal Data Acquisition, Documentation and Storage:  All exemplar species from which DNA is sequenced will be scored in the organismal matrix (a compilation of data from the morphological, anatomical, cytogenetic and behavioral surveys) by Cushing’s team.  Available characters from the literature will be included, along with those newly discovered and documented through original observation.  To ensure consistent treatment and repeatability, characters will be critically examined in specimens, described using standard terminology agreed upon by senior personnel, and documented with illustrations.  Matrices will be compiled and edited using Nexus Data Editor and WinClada (Nixon 2002).  Digital images documenting character states and voucher specimen data will be databased.

 
DNA Sequencing:  The AMNH Molecular Systematics Lab, where sequencing will be conducted by a technician under Prendini’s direction, contains two ABI PrismTM 3730xl automated DNA sequencers, a Biomek NX sequencing robot for automated PCR and sequence purification, three Eppendorf Mastercyclers, two MJ Research Tetrad 4-head and two Dyad MJ Research Thermocyclers for PCR.  Standard protocols are used for DNA isolation, amplification and double-stranded sequencing (Prendini, Crowe and Wheeler 2003).  Sequence editing is conducted using SequencherTM 4.6 (Gene Codes Co., Ann Arbor, MI).  At least six gene loci (genome samples of 500–1000+ base-pairs that can be sequenced as single pieces in both directions), summing to ca. 5.6 kilobases, will be sequenced for all solifuge species for which fresh material is obtained: 18S rDNA, 28S rDNA, Histone 3 (nuclear genome); 12S rDNA, 16S rDNA, Cytochrome Oxidase I (mitochondrial genome).  These loci were chosen due to availability of primers that consistently amplify large, phylogenetically informative fragments in diverse arachnids and because they evolve at different rates, providing resolution at overlapping taxonomic levels (Prendini, Crowe and Wheeler 2003; Prendini, Weygoldt and Wheeler 2005).  Elongation Factor 1-α and Polymerase II (nuclear genome) and NADH dehydrogenase subunit I and Cytochrome Oxidase II (mitochondrial genome) may be added if containing sufficient variation and easy to amplify.

Vouchering and Archiving Data:  Specimens examined and illustrated for morphological analysis will be labeled as vouchers in the database, as will tissue samples stored in the Ambrose Monell Cryo Collection.  Morphological and genetic data will be centralized on the project website, allowing data exchange between project participants and dissemination of results.  Sequences will be submitted to GenBank, morphological data and images to MorphBank and MorphoBank, and trees to the Tree of Life and TreeBase.

Missing Data:  Although we will aim for complete organismal and molecular matrices, missing or inapplicable data are unavoidable.  We will use reductive coding (Strong and Lipsomb 1999) or construct chimeras or composite terminals when necessary.  Chimeras will be limited to taxa the monophyly of which is unlikely to affect results of the analysis, e.g., we might combine morphology, karyotype and DNA from three different Chelypus species to form a ‘Chelypus’ terminal.  The effects of missing data on analyses will be assessed by removing taxa with missing data, reanalyzing, and comparing to the original results.

Data Analysis:  Analyses will involve heuristic searches and explore varied optimality criteria, methods and programs.  Besides desktop computers, we will use the AMNH supercomputer cluster, allowing multiple analyses to address parameter sensitivity and explore the analytical space.  Searches for most parsimonious trees (Farris 1970, 1983; Kluge 1984) will use POY (Gladstein and Wheeler 1996-), TNT (Goloboff, Farris, and Nixon 2002), and PAUP* (Swofford 2002), each with parallel versions.   POY will be used for analysis of molecular data, as it is the only program implementing direct optimization (simultaneous alignment and tree-search), regarded as ideal in principle (Wheeler 1994, 1996, 1998, 1999, 2000, 2001, 2001a; Slowinski 1998; Giribet and Wheeler 1999; Giribet, Distel, Polz, Sterrer, and Wheeler 2000; Giribet, Edgecombe and Wheeler 2001; Wahlberg and Zimmerman 2000), but computationally demanding.  Strategies for rapid tree search (Goloboff 1999; Nixon 1999) will enhance searches throughout tree space.  Maximum likelihood (Cavalli-Sforza and Edwards 1967; Felsenstein 1979, 1981, 1981a, 1983[ Huelsenbeck and Crandall 1997) is also implemented in POY.  Likelihood is more computationally intensive than parsimony, so we will restrict these analyses to overlapping subsets of taxa, combining results with supertree methods (Gordon 1986; Baum 1992; Ragan 1992; Bininda-Emonds and Bryant 1998; Steel, Dress, and Bocker 2000; Semple and Steel 2000; Bininda-Emonds and Sanderson 2001).  We will use MrBayes (Huelsenbeck, Ronquist, Larget, Van der Mark, and Simon 2000-) to analyze the entire dataset with Bayesian methods (Rannala and Yang 1996; Mau and Newton 1997; Mau, Newton and Larget 1999). Analyses of data partitions (morphology, different loci) will be conducted separately and simultaneously.  Simultaneous analysis (and all analyses involving morphological data) will be restricted to parsimony because the likelihood assumption of uniform stochastic behavior is problematic for morphological data.  Partitioned Bremer support (Baker and DeSalle 1997; Baker, Yu and DeSalle 1998) will be used to address the relative contributions of different loci and morphological character systems to the simultaneous analysis.  Relative support for nodes will be assessed with branch support indices (Bremer 1988, 1994; Donoghue, Olmstead, Smith and Palmer 1992) and bootstraps (Felsenstein 1985; Sanderson 1989).  Adaptational and biogeographical hypotheses will be tested by optimization on the tree obtained by simultaneous analysis of all evidence, using WinClada (Nixon 2002) and MacClade (Maddison and Maddison 1992).  Ambiguous optimizations will be resolved with ACCTRAN, maximizing homology by favoring reversals (Swofford and Maddison 1987, 1992).

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