The entire Australian marine jurisdictional area, including offshore and sub-Antarctic islands, is considered in this paper. Most records, however, come from the Exclusive Economic Zone (EEZ) around the continent of Australia itself. The counts of species have been obtained from four primary databases , but even these are an underestimate of described species. In addition, some partially completed databases for particular taxonomic groups, and specialized databases (for introduced and threatened species) have been used. Experts also provided estimates of the number of known species not yet in the major databases. For only some groups could we obtain an (expert opinion) estimate of undiscovered species. The databases provide patchy information about endemism, levels of threat, and introductions. We conclude that there are about 33,000 marine species  in the major databases, of which 130 are introduced, 58 listed as threatened and an unknown percentage endemic. An estimated 17,000 more named species are either known from the Australian EEZ but not in the present databases, or potentially occur there. It is crudely estimated that there may be as many as 250,000 species (known and yet to be discovered) in the Australian EEZ. For 17 higher taxa, there is sufficient detail for subdivision by Large Marine Domains, for comparison with other National and Regional Implementation Committees of the Census of Marine Life. Taxonomic expertise in Australia is unevenly distributed across taxa, and declining. Comments are given briefly on biodiversity management measures in Australia, including but not limited to marine protected areas. We consider here the Australian marine jurisdictional area from shore to the boundary of the Exclusive Economic Zone (EEZ). This comprises mainland Australia, Tasmania, and offshore islands including sub-Antarctic islands, but not the Australian Antarctic Territory, which is covered by the Antarctic regional synthesis 2 in area, with a further 2.04 million square km in Australia's Antarctic Territory, and an additional confirmed Extended Continental Shelf of 2.56 million km2. The mainland EEZ has a coastline of about 36,000 km, and spans more than 5,000 km from the tropics (9° S) to temperate latitudes (47° S). Even considering only shallow water, this extensive continuous coastline, together with about 12,000 islands from the tropics to the polar region, contains a wide range of tropical to sub-Antarctic shallow water conditions and habitats. Together with the deepwater areas of the continental shelf and the slope, the deeper abyssal regions, and the overlying water column, this constitutes a vast array of highly diverse habitats and ocean features; many have received limited, if any, exploration and we can only touch on a few aspects here.The Australian EEZ including the Territorial Sea  is aboutCondie and Harris A biogeographic analysis, or “bioregionalization,” of nearshore waters was undertaken in 1998 by the Interim Marine and Coastal Regionalisation for Australia (IMCRA) Technical Group IMCRA Version 3.3 and the NMB have been combined to create IMCRA Version 4.0 http://www.marinehub.org/). Thus, a refinement of the regionalization is now available, including depth-related biomes on the continental shelf. At about the same time, O'Hara IMCRA will continue to be refined. Recently, the range information on short-ranging demersal fish species on the continental shelf (which had not been included in the NMB) has been examined by Lyne et al. et al. For waters beyond the continental shelf, a subdivision of the Australian EEZ into 13 Large Marine Domains (LMDs) was developed by the Division of Marine Research of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Beagle in 1836 Challenger, 1872-76 included Australian samples in its global investigation of the deep sea, and its reports are a basis of many disciplines. After the establishment of the colony of New South Wales by the British in 1788, it was not long before the colonies  established various scientific societies and natural history museums, which were very active, and published the results of their scientific endeavors in a variety of journals as proceedings, transactions, and records  ds e.g., . DiscoveConsiderable effort was made on the taxonomy and descriptive ecology of organisms on accessible shores: some examples include Publications on phytoplankton have been available in Australia since the 1930s  provides data on Australian taxa to OBIS; at present some 30 percent of Australian taxa are represented on the OBIS database (more detail below). The Great Barrier Reef Seabed Biodiversity Project is a Census-affiliated project. These projects will report their findings separately from this article. The Census has links to many institutions and organizations at national and state levels, including museums, academia, and policy groups.The National Committee  comprises Australian representatives from Census of Marine Life projects – Census of Antarctic Marine Life (CAML), Census of Coral Reef Ecosystems (CReefs), REEFS, History of Marine Animal Populations (HMAP), Pacific Ocean Shelf Tracking Project (POST), Global Census of Marine Life on Seamounts (CenSeam), Continental Margin Ecosystems on a Worldwide Scale (COMARGE), and Tagging of Pacific Predators (TOPP) – and is also linked to the Barcode of Life initiative. OBIS Australia (http://www.marinehub.org/index.php/site/home/). The Hub, building on historic data collections, old and recent surveys conducted by Hub partners, state agencies, national and international fisheries groups, provides national leadership in describing, predicting, and managing Australia's marine biodiversity, and works closely with Census projects. Its work is particularly pertinent to the questions mentioned below in the The Commonwealth Environment Research Facilities (CERF) Program, a new Australian Government initiative supporting public good research, funds Australia's Marine Biodiversity Hub , an online distributed query network to faunal collections in Australian museums (http://www.ozcam.gov.au/search.php). Participants  are: Australian Biological Resources Study, Australian Museum, CSIRO, Museum and Art Gallery of the Northern Territory, Museum Victoria, Queensland Museum, Queen Victoria Museum and Art Gallery, South Australian Museum, Tasmanian Museum and Art Gallery, and Western Australian Museum. OZCAM aims to link the databases of the museums of Australia so that they can be queried simultaneously through a single portal. A subset of the current OZCAM is linked to OBIS, but by no means all museum records are currently digitized and therefore available through OZCAM. Here, we merely note that extensive marine collections  are held by Australia's museums and herbaria; the subset represented by OZCAM was analyzed for this study.1. Faunal groups interpretable over a limited area. For a small number of taxonomic groups some limited synthesis can be presented. These groups include algae, some mollusks, polychaetes, ascidians, vertebrates, and some crustaceans. They have had consistent sampling over much of the Australian EEZ and extensive taxonomic study by a currently active scientific group.2. Groups analyzed biogeographically on a national scale. There are a small number of groups for which the above is true and, in addition, a biogeographic analysis of the data has recently been attempted. These include fish, brittle stars, decapod crustaceans, and sponges.3. The National Marine Bioregionalisation of Australia The data on demersal fish The dataset on sponges http://www.environment.gov.au/coasts/mbp/imcra/nmb.html and data and products through http://www.environment.gov.au/erin/dig/index.html. These include the National Marine Bioregionalisation GIS, individual project reports and associated figures, fact sheets, and large-format plot files. The National Marine Bioregionalisation resulted in the production of a number of marine datasets, and these datasets are maintained and updated by the respective custodians. Datasets and custodians associated with the National Marine Bioregionalisation are as follows:Reports on the National Marine Bioregionalisation are accessible online through Australian Bathymetry Database National Sediments Database (MARS) Demersal Fish database (CSIRO Marine and Atmospheric Research)National Sponge database CSIRO Atlas of Regional Seas (CARS)/Oceanographic database (CSIRO Marine and Atmospheric Research)More recently, the 1,489 fish species with distributional records in the demersal fish database have been increased fivefold and the distributions of short-ranging demersal fish species on the shelf have been analyzed Detailed surveys. Within the Australian EEZ, certain localities have been sampled thoroughly using consistent methods for a wide range of taxa and depths, producing detailed datasets. These include the Great Barrier Reef World Heritage Area, and some nearshore, outer shelf and upper slope locations, such as Bass Strait, Northwest Shelf, and Dampier Archipelago. Considerable attention has been paid to refining methods of survey, making use of physical data to refine the design of biological sampling. Within the Census, this “surrogate-based” approach is being addressed in the Census synthesis project Predicting Seabed Species and Environments. Also many fine-scale surveys have been undertaken in shallow nearshore (diver-depth) and intertidal waters by numerous state agencies and university groups.4. http://www.cmar.csiro.au/marlin/) maintained by CSIRO Marine and Atmospheric Research (CMAR). The progress report of this Data Audit and Acquisition Project is available. It lists 56 metadata entries, which include references to cruise reports and publications. About eight of the datasets have unrestricted access, and 31 others would be available by consultation with the researchers or custodians. Some of these entries refer to massive survey efforts that recorded large numbers of species. To illustrate, here is part of one entry, concerning only one component of the data from the Great Barrier Reef Seabed Biodiversity Project (GBR SBBP):Australia's Marine Biodiversity Hub is interested in surrogacy and methods for prediction of biodiversity given limited data. To facilitate that work, it has audited available biological datasets from various sources that provide broad spatial scale, extend over a wide range of contrast in surrogates, and have broad coverage of taxa. These are generally presence/absence/abundance datasets from surveys with representative sampling. Metadata has been obtained for the most suitable available datasets, and the majority of them have been acquired for the Hub's use. Many are also in the metadatabase MarLIN MarLIN record number: 7036 Anzlic Identifier: ANZCW03060070362 area of the GBR shelf seabed was sampled by a 200 m tow of a 1.5 m epibenthic sled at 1191 sites, representing a full range of known physical environments, during six 1-month-long voyages on the AIMS [Australian Institute of Marine Science] vessel Lady Basten. More than 7,000 species/species-equivalent OTUs  were identified. The dataset comprises 79,173 site-by-species records. …etc.Abstract: The benthic invertebrate, plant and fish biodiversity of the 200,000 kmData from some other surveys  are in OZCAM. There is, in principle, much that could be done with these datasets from the viewpoint of the Census, but it cannot be prepared for this paper. There are, however, still gaps in many areas, and very little sampling beyond 1,000–2,000 m in depth.5. Other data exist in published records (literature), unpublished database records, and nondigital survey records, such as field data sheets.6. Regional checklists and summaries of flora and fauna: there are a number of published lists, and databases on restricted taxonomic groups; most are not used for this paper.Numbers of Living Species in Australia and the World  http://www.environment.gov.au/biodiversity/abrs/publications/other/species-numbers/index.html.7. An important source of information that is not an electronic database is the report Because of time constraints, data for analysis were restricted to the following electronic databases:www.obis.org.au. Data of some 10,000 records have been supplied by Australian institutions and organizations including CSIRO, OZCAM , other Australian museums with records not in OZCAM, the Australian Antarctic Division, the Australian Institute of Marine Science (AIMS) and the Bureau of Rural Sciences. Also Australian data of some 3,000 records were made available from international sources – surveys done by researchers from other nations, some collation exercises , and overseas museum holdings. OBIS can be searched by a latitude/longitude bounding box, 10 degrees square or smaller, by named EEZ, certain predefined Large Marine Ecosystems, or by species or higher groups . OBIS has comprehensive datasets for some faunal groups , but many groups have only partial datasets.OBIS. There are potentially about 50,000 recognized marine species in the Australian region see , of an ewww.cmar.csiro.au/caab) lists about 25,000 marine species, that is, about half of the estimated number of recognized Australian species, mainly with a single reference as an entry into the relevant literature (a citation that the species does occur in this region). Thus, CAAB is useful for a species-count in the Australian region, but not for finer-scale biogeographic questions. The main gaps in species listings are in phytoplankton, protists, and a few invertebrate groups. CAAB currently contains codes and taxonomic information for the following aquatic organisms in the Australian region:CAAB. The Codes for Australian Aquatic Biota database maintained by CSIRO Marine and Atmospheric Research , representing all currently recognized marine species in Australia;Around 20,000 codes for marine invertebrates in Australian waters, including sponges, stony corals, echinoderms, commercially important crustaceans and mollusks, tunicates, and other taxa;Codes for Australian seagrasses and mangroves, representing all currently recognized marine species;A preliminary selection of Australian seaweeds and microalgae.not represented in OBIS. AFD contains statistical summaries for taxa and is available at http://www.environment.gov.au/biodiversity/abrs/online-resources/fauna/afd/home.AFD. The Australian Faunal Directory, maintained by the Australian Biological Resources Study (ABRS), lists some 15,000 taxa recognized as marine in this study, that is, about 30% of anticipated named Australian marine species, a subset of which carry broad scale information about distribution (by IMCRA bioregion – see below). AFD is freely available on the Web, but extensive data requests can be obtained directly from to ABRS. Since it is a regional checklist and not a source of individual georeferenced data points, the Austraflian Faunal Directory is www.gbif.org, and are accessible by that route as well as through the OZCAM portal.OZCAM. As of August 2009, the Online Zoological Collections of Australian Museums database contains about 1.52 million data records for some 69,000 animal species, of which about 26,900 are considered to represent marine species in the Australian region . The OZCAM data are also exported to the Global Biodiversity Information Facility data portal, The database NIMPIS  The data used for this article are all freely available. The ABRS, CAAB, OBIS and OZCAM data are all freely available from the respective websites. The corrected and deduplicated combination used for the totals in www.obis.org.au/irmng/). Known synonyms  and variant spellings were also reconciled so far as was practicable to reduce multiple counting. The analysis considered described species only; unnamed species and taxa of uncertain attribution  were excluded.The AFD was queried for taxa listed in Available AFD data were essentially complete  for the following groups:Phylum PoriferaPhylum Platyhelminthes (marine species)Phylum BrachiopodaPhylum NematodaPhylum Arthropoda, Subphylum Crustacea:Subclass PhyllocaridaSubclass Eumalacostraca: Superorder Hoplocarida, Superorder Peracarida, Superorder EucaridaPhylum Mollusca: Class Aplacophora, Class Polyplacophora, Class Scaphopoda, Class Cephalopoda, Class Gastropoda (in part)Phylum SipunculaPhylum EchiuraPhylum KamptozoaPhylum Annelida: Class Polychaeta, Class Pogonophora, Superclass Clitellata (marine species)Phylum EchinodermataPhylum HemichordataPhylum Chordata:Subphylum TunicataSubphylum CephalochordataSubphylum Vertebrata: Higher Taxon Agnatha, Superclass Pisces, Classes Mammalia, Reptilia, Aves (marine species)For the remaining groups, data came mostly from a combination of CAAB, OZCAM, and OBIS records.The result was the best estimate of (1) the number of species for each higher taxon represented in AFD, CAAB, OZCAM, and OBIS combined, eliminating the majority of duplicates. From a range of sources , which is responsible for these listings under the Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act).Information about state of knowledge is based largely on expert opinion, but our starting criterion was the proportion, of those species estimated to occur in Australia, that are listed in the four major databases. For available taxonomic expertise, we used figures from the 2003/6 survey by the Australian Biological Resources Study (ABRS). ABRS will be conducting another survey in the near future. Data on “unknown” species were based on expert opinions. Data on introduced species are included in the AFD and were extracted by IMCRA 4.0 province, along with the general species counts, but that information is known to be incomplete. In Fauna of Australia and the Zoological Catalogue of Australia produced by ABRS, see Bibliography 2 in Australian Marine LifeIdentification guides should be authoritative, and reasonably comprehensive for their particular taxonomic scope, and they should include illustrations and, preferably, keys. They should not simply be species lists, however thorough, on the one hand, nor be beautifully illustrated but incomplete field guides to common and conspicuous species, on the other. Many guides to the flora and fauna of Australia are available, arising from the activities of scientific institutions and societies across the country, the leadership of inspired individuals, and the activities of the Australian Biological Resources Study. Some, although important reference works, do not fit the definition above 1. The number of species represented in AFD, CAAB, OZCAM, and OBIS combined, eliminating the majority of duplicates. )2. The total number of Australian species that are in some sense “anticipated known.” These are the above, plus estimates of numbers of species described but not yet included in AFD, CAAB, OZCAM, or OBIS, plus estimates of numbers of named species currently unrepresented in collections but considered likely to be detected in Australian waters given adequate sampling. )3. The likely number of “unknown” Australian species. These are either collected but not yet described as new taxa, or new (unnamed) taxa awaiting discovery. Estimates of these are based only on expert opinion and only for a few groups, and are very uncertain. . As a result, we have data for only 17 higher taxa . They shhttp://www.environment.gov.au/coasts/species/index.html.Australia recognizes a range of levels of threat, and in addition to listing species as formally threatened , it lists a number of others as protected. These various measures are explained at Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act). A number of marine species  are listed as critically endangered , endangered , vulnerable , and conservation dependent  (http://www.environment.gov.au/cgi-bin/sprat/public/publicthreatenedlist.pl?wanted=fauna). The Act also provides for the identification of threatened ecological communities. No marine communities have so far been listed, but two are being considered. In addition to identified threatened species and communities, a substantial number of others – all vertebrates – are “listed” under the Act (http://www.environment.gov.au/coasts/species/marine-species-list.html), which gives them protection against being killed, injured, or traded.The Australian Government lists threatened species under the The states have comparable legislation, though generally less powerful than the EPBC Act. The state listings of species differ slightly from those under the EPBC Act, but not so as to change the above picture significantly. A number of marine species, all vertebrates, are listed as threatened and more are listed as protected, and in some states protected species include certain plants .http://www.environment.gov.au/coasts/index.html).Threatened species are also typically included in the Marine Bioregional Profile for each regional marine plan prepared under the EPBC Act. Profiles are now on the Department of the Environment, Water, Heritage and the Arts for the South Eastern, South Western, North Western, North, and East regions  represents estimates of their number and shows that there are about 50,000 anticipated known Australian marine species, of which almost 48,000 are animals. The difference between Australian known marine species (est.) and the Total species in AFD+CAAB+OZCAM+OBIS indicates the gap in our present electronic data holdings and survey effort to date, and is indicated in the “State of knowledge” column in Among the Australian fauna, vertebrates are a very well known group, although there is a continuing high rate of discovery of new fish and shark species. Invertebrates, in general, are less well known, but groups such as Porifera, Mollusca, Polychaeta, Crustacea , and Echinodermata, which are supported by current taxonomic expertise, are among the better known of the marine animals. Other invertebrate groups remain poorly known with little, if any, taxonomic support or expertise see .Some of the faunal data contain sufficient information about location that interpretations regarding regional fauna are possible. This has been prepared for Australia's LMD Clearly, kingdoms other than Animalia and Plantae are poorly represented in the databases used for this paper. Of them, some really are poorly known , but there have been extensive studies of others at least for some parts of the Australian marine jurisdictional area . It must also be recognized  and S1 tJason voyage to the Tasman Fracture Zone south of Tasmania) and there are few anywhere in Australia deeper than about 1,200 m. Early analysis of the recent Jason survey suggest distinct biomes or depth-delineated invertebrate communities deeper than 2,000 m, where the species and even the family may be new to science . Australia currently lacks the capability to explore the benthos in the deep parts of its EEZ .There has been only sparse sampling in most parts of Australia (exceptions are detailed above), even around the coastline. In principle this could be remedied, but in practice the resources required are very large. There have been few samples of any kind deeper than 2,000 m  in The number of species remaining to be discovered is difficult to estimate. The column headed Sponges –108+ (new –70%)Soft corals –141 (118 new –80%)Mollusks –462 (310 new –67%)Echinoderms –326 (82 new –38%)Crustaceans –529 (167 new –30%)Ascidians –50+ (40 new –80%) Similarly, a cruise in two newly declared marine reserves south of Tasmania revealed the following:Sponges –469 (100% unknown)Cnidaria –102 (92% unknown)Mollusks –77 (74% unknown)Echinoderms –128 (60% unknown)Crustaceans –106 (75% unknown)Ascidians –19 (100% unknown) For Bass Strait shelf macroinvertebrates, 51% of 803 species were undescribed It might thus be conservative to propose that only 20–30% of Australian species of these macrofaunal groups have been discovered, whereas the total for more cryptic groups, where we do not have current expertise, may be much higher. If we suppose as a starting figure for the whole biota that only 20% have been discovered, then the Australian marine biota is at least 250,000 species. This is undoubtedly an underestimate because surveys frequently sample only the larger species. In the above and other surveys, it is typical to catch half of the species only once (and this does not seem to change with the level of sampling effort employed to date). This implies that there are many more species out there that will never be sampled. However, it is not especially meaningful to speculate about the number of undiscovered species beyond noting that there are clearly many.2 in 2008, when its claim was recognized under the UN Commission on the Limits of the Continental Shelf. We will, in the near future, make some surrogate-based inferences about likely diversity outside surveyed areas, but it is unimaginable that we could survey it in many lifetimes.The Australian EEZ is enormous and Australia's seabed increased by 2.5 million kmAlthough it can be estimated for some species , the number of endemic species is almost impossible to estimate overall, and we have not offered any figures. Estimating endemism for Australia should only be attempted by first categorizing our waters into tropical and temperate. Put simply, endemism is low in the tropical north where many species are shared with the Indo-West Pacific, whereas in the temperate south, endemism is relatively high at the species level. Figures up to 90% have been mentioned. For the well-understood decapods, it is 40% The Australian Biological Resources Study recently surveyed the resources and holdings of Australia's natural science collections Survey of Australian Taxonomic Capacity (2003/2006)The ABRS http://www.ala.org.au/), an Australian Government-supported initiative. The critical need to digitize existing data , making it widely available for scientific research, was emphasized. Although development of tools to access existing collections may indirectly promote the need and support for fundamental taxonomic work in Australia, the scale of the decline in taxonomic expertise suggests that a more direct approach will be needed.A National Taxonomy Forum was called by ABRS to discuss these matters in 2007. The forum also discussed strategies to overcome impediments to taxonomic research, and exciting new approaches, including DNA techniques and the development of the Atlas of Living Australia  then the insects (c. 25 people). No researchers were recorded as working on the Cephalochordates, Hemichordates, Monotremes, or Other Minor Acoelomates.The results for animal groups are summarized in capacity is the availability of research vessels; Australia's research fleet is limited, but will shortly increase substantially. As noted above, Australia currently has no capacity to explore the seabed below about 2,000 m, and no deepwater autonomous underwater vehicles (AUVs) or remotely operated vehicles (ROVs). Australia has a number of small (less than 30 m) research vessels, mostly operated by state agencies and some by the Australian Institute of Marine Sciences, and it has a specialized Antarctic supply vessel, the Aurora Australis, which carries out oceanographic and biological research in the Southern Ocean, in addition to its supply role. At present, Australia has only one other oceangoing research vessel, the Southern Surveyor, operated by CSIRO as a Marine National Facility . The Southern Surveyor is near the end of its serviceable life and has had only a limited number of days at sea in recent years, but funding has now been allocated to increase its annual sea days and to build a replacement vessel that will operate for twice as many days per year. The planned replacement vessel will be a multipurpose research facility (not only for biodiversity research), but it will greatly increase Australia's capacity for research on the biota, especially offshore.Another aspect of http://www.abare.gov.au/). In outline, in 2006-7, Australia's commercial fisheries (including aquaculture) produced about 240,000 metric tons of seafood valued at about $2.18 billion Australia's marine biodiversity is the basis for many important commercial, tourism, and recreation activities. The overall value of ecosystem services from the world's oceans has been estimated to be about US$21 trillion per year http://www.daff.gov.au/fisheries) and in publications of the Bureau of Resource Sciences . There has been a disturbing trend toward increased overfishing in Commonwealth fisheries Information on the status of Australian Commonwealth fisheries is available from the Australian Government Department of Agriculture, Fisheries and Forestry (http://www.environment.gov.au/biodiversity/index.html.). The strategy lists a number of actions to be taken by all Australians, and identifies the main threats to biodiversity as follows:Australian and state governments have the conservation of biodiversity firmly embedded in their policies, the major instrument being the Australian Government's EPBC Act . At present, Australia's Biodiversity Conservation Strategy is undergoing revision, and a draft has been distributed for consultation Climate change (resulting in conditions such as prolonged drought)Invasive speciesLoss, fragmentation, and degradation of habitatUnsustainable use of natural resourcesChanges to the aquatic environment and water flowsInappropriate fire regimes.Naturally, its focus is largely terrestrial, but there is more emphasis on marine biodiversity than in the earlier edition of the strategy and there has been explicit attention given recently to marine biodiversity e.g., .Australians value their marine biodiversity for its aesthetic value, apart from the range of commercial values. Marine tourism is a major industry, valued at about $11.6 billion in 2006-7 Threats to biodiversity are widely discussed http://www.environment.gov.au/coasts/mpa/publications/pubs/heard-proposal.pdf), but also adjustments to the movements of fishing vessels in response to real-time data, so as to reduce bycatch of restricted species There has been substantial research in Australia into the effects of fishing – directly on target species, directly on nontarget species, and indirectly through impacts on habitats or through ecosystem connections There has also been research on invasive species There is increasing evidence of changes in the ranges of, and in the interactions between, native marine species under the influence of climate change e.g., . These phttp://www.environment.gov.au/coasts/mpa/nrsmpa/). Australia has made major achievements in the establishment of protection for marine biodiversity and a number of them are world renowned .Australian and state governments are committed to a policy of establishing a National Representative System of Marine Protected Areas (NRSMPA) (see http://www.environment.gov.au/coasts/mpa/nrsmpa/index.html), but there is uncertainty about whether this will be achieved in practice given the conflicting political and socioeconomic pressures.As of 2004, the NRSMPA covered approximately 64.8 million hectares, or 7% of Australia's marine jurisdiction, excluding the Australian Antarctic Territory. At that date, there were MPAs of some kind in 41 of the 60 IMCRA mesoscale bioregions on the shelf. This still falls short of the stated goal of establishing at least 10% of each marine bioregion within MPAs by 2012, but Australian and state governments remain committed to reaching that goal and planning is active around Australia. The NRSMPA system expanded to include nearly 9.5% of Australia's marine waters in 2008. However, much of this increased area of MPAs is neither on the continental shelf, nor within the nearshore  state waters, where much of the highly valued biodiversity and many of the pressures are located To return to the positive, however, the policy toward a NRSMPA continues to be pursued actively. It is an established component of Australian biodiversity management, having endured through a decade and numerous changes of Australian and state governments.Numbers of listed threatened species are indicated in the With only one oceangoing research vessel, Australia lags behind many other countries in its ability to survey its large EEZ. Robots (Argo floats and gliders) have become increasingly useful in collecting physical information — more data have been collected in the last two years from the Argo floats than previously existed — and there are plans to develop a biological sampling capacity on these robots, although possibly at the microbial level. Relatively new technologies, like swath bathymetry, are providing the ability to rapidly map and visualize the bottom in deeper waters, and this is essential for targeting subsequent biodiversity sampling. Biodiversity is increasingly being investigated at the taxonomic and genetic level to determine not only where (or which) species occur, but also how the populations are structured in relation to the physical environment.It is clear that Australia will never have the capacity to survey its entire EEZ and although high-priority areas will be targeted, many areas will remain unsurveyed or surveyed at low density. In response, Australia's Marine Biodiversity Hub is developing new statistical approaches to predicting biodiversity, using biological interpretations of physical data The object of modern biodiversity research is not, of course, merely to discover new species. There is increasing realization in Australia that for management of human activities, it is necessary to understand the ecosystem linkages all the way from the smallest organisms to the largest. Finding new species is of course an important component of that understanding, but the “voyages of discovery” of the future will also focus on processes.http://imos.org.au/auscpr_intro.html). More significantly, our emphasis on microbial systems has been minuscule in the past, but is now accelerating in several major scientific institutions  with the aid of modern genomic techniques. We predict that microbiological studies will be a major area of new discoveries in Australian marine biodiversity in the near future.This paper has stressed that we are particularly weak in our understanding of pelagic systems, especially the small organisms in those systems. Research efforts on phytoplankton have been small, but significant, for many years. Energetic early work on zooplankton was not followed up with substantial collection and monitoring efforts, but it is anticipated to increase with the recent establishment of new Continuous Plankton Recorder lines under Australia's Integrated Marine Observing System , are counts of species either described in litt. or anticipated to be present in Australian waters on the basis of projected percentages of global totals but not yet recorded. A smaller but more reliable number, formally described and listed in four major electronic databases are shown in the column of that heading. The databases are: The Australian Faunal Directory, AFD (ABRS); Codes for Australian Aquatic Biota, CAAB (CSIRO); OBIS ; and the Online Zoological Collections of Australian Museums, OZCAM. The column headed Australian unknown species (est.) gives expert opinions on species not yet recognised as new species, or undiscovered, i.e. never collected. Numbers of introduced species come from (0.07 MB XLS)Click here for additional data file.Table S2Method for combining provincial bioregions from IMCRA 4.0 to approximate the LMD . These are equivalent to LME (2002) and Spalding et al. 's (2007) Provinces for the Australian region. Records in the Australian Faunal Directory (AFD) are indexed by IMCRA 4.0 bioregions, not by the LMD which are more suitable for the present exercise. At the Provincial level, IMCRA 4.0 identifies Provinces, and Transition zones between them. For this article, records from those Provinces and Transition Zones were combined as follows to approximate the LMDs.(0.05 MB DOC)Click here for additional data file.Table S3Estimated numbers of described and undescribed species per taxon, by Large Marine Domain. Species richness estimates were based on AFD, but only those records with valid locations at LMD level were included. For many faunal groups represented in (0.08 MB DOC)Click here for additional data file.Text S1Identification guides and other guides to Australian biota. Bibliography 1 lists identification guides . Bibliography 2 lists items that are not identification guides by Census of Marine Life definition, but important reference works on marine fauna and flora of Australian Region.(0.11 MB DOC)Click here for additional data file.


Despite well-documented evidence that physical activity is beneficial to children, average fitness levels of US children have declined. Lack of physical activity has been associated with childhood obesity. We evaluated the effects of a physical activity program in the elementary school classroom on health outcomes.Three schools in the Independence School District in Independence, Missouri, were assigned to receive the ABC (Activity Bursts in the Classroom) for Fitness program, and 2 comparable schools served as controls. The program, led by classroom teachers, provides multiple, brief, structured physical activity breaks throughout the day. Baseline data for the study were collected in September 2007, and follow-up data were collected in April 2008.P <.001). Medication use for asthma (P = .03), attention-deficit hyperactivity disorder (P = .07), or either medication combined (P = .005) decreased.Physical fitness measures of upper-body strength, abdominal strength, and trunk extensor improved (The effects of the program on daily physical activity, fitness, and measures of health are beneficial. The US Department of Health and Human Services (HHS) recommends that children and adolescents engage in 60 minutes of physical activity per day . HHS indAlthough the health benefits of physical activity for children are well documented, the average fitness levels in children in the United States have been declining. This may be due in part to declining levels of daily physical activity in the school setting and outside of it -6. ObesiA nationwide survey found that parents seek ways to combat childhood obesity from schools more often than from health care providers and government agencies . No otheSome experts have argued that meeting the academic requirements of the No Child Left Behind Act has forced US school districts around the country to choose between physical activity and academic activity . ExistinThe Independence School District (ISD) in Independence, Missouri, collaborated with Yale University's Prevention Research Center to implement and study a nutrition and physical activity program for elementary-school children. The study protocol and consent form were approved by the Griffin Hospital  institutional review board and the Yale University  human subjects committee. The project aimed to promote a healthy lifestyle and reverse the trend of obesity among students and their families. The nutrition intervention has been described separately . The phyDeveloped with input from experts in education, the ABC for Fitness program encourages teaching during the activity bursts. ABC for Fitness aims to provide fun and creative activities that are noncompetitive, age-appropriate, and gender-neutral to promote an interest in physical activity among children and increase their behavioral capacity. It was designed to take the time that teachers spend getting restless students to settle down, or distracted students to concentrate, and convert this time into structured, productive bursts of physical activity spread throughout the day. The bursts were conducted during "down time" in the school day to help increase total daily teaching time. The program was flexible; activity intensity could be raised or lowered according to the athletic abilities and attention spans of the students. The program was intended as a supplement to physical education programs. Ideally, the activity bursts added at least 30 minutes of daily physical activity.Each activity burst had 3 components: 1) a warm-up that could include stretching or light aerobic activity , 2) a core activity consisting of strength activities or aerobic activities , and 3) a cool-down similar to warm-up activities, consisting of stretching or low-intensity activity. Although teachers were instructed to use all 3 components, they were not required to offer a particular ratio of strength activities to aerobic activities.The length and the daily number of activity bursts could vary. Several sample activities, class configurations, and educator resources were in the ABC for Fitness teacher's manual . TeacherFor this study, 1,216 students from grades 2 through 4 in ISD were asked to participate. Two students dropped out of the study because of lack of parental consent. The ISD student population is predominantly white and has few minority students. Overall, 62% of the student population is eligible for free and reduced-price meals. Eligibility rates approach 95% in some schools , and neaThe Missouri Department of Elementary and Secondary Education reported that 49% of girls and 37% of boys failed to meet aerobic capacity requirements of the Missouri Physical Fitness Assessment in 2004 , a 10% dThe implementation of the ABC for Fitness program began in August 2007 when training sessions were provided for classroom and physical education teachers before the start of the school year. Teachers completed a pretraining and post-training survey to assess changes in outcomes such as level of exercise literacy, general knowledge regarding physical activity, and self-efficacy for program implementation. The purpose was to establish the efficacy of the intervention in a randomized controlled trial. Five schools were randomly assigned to receive the intervention or to serve as the control group. The intervention also included a family/parental component in which fitness experts helped families learn how to be more active together. Schools assigned to the control group continued normal curricular activities during the intervention period and implemented the program in the following academic school year.  The inclusion criterion for the study was parental consent for students in both the intervention and control schools.Parents were sent an information sheet describing the study and were asked to contact the principal of their child's school if they chose to have their child opt out of participating in the study. Exclusion from the study was the result of either a lack of parental consent (in written form) or the inability of the child to participate in the physical activity program because of physical limitations. Baseline data were collected in September 2007 and follow-up data were collected in April 2008.Height and weight were measured for each child by the school nurse or wellness coordinator during a specified time set by school administrators. Children were measured fully clothed, except for shoes, and were not required to fast. A computerized body mass index (BMI) assessment tool, BMIforKIDz  was used to measure and record student BMI.2max) was measured as a proxy for general fitness by using the recommended calculation based on the number of laps completed and child's age  and 1 is the least desirable (needs improvement). The average score of these 14 items for the first (December 2007) and last quarter (June 2008) was used to assess behavior before and after the intervention for students in the second, third, and fourth grades.Student use of medication for attention-deficit hyperactivity disorder (ADHD) and asthma were collected by school nurses at each school at the beginning and end of the intervention period. These data were collected using a standardized medication log. No encouragement, suggestion, or guarantee of reduced use of medication for asthma or ADHD in children was given to parents at the outset of the study.Student attitudes toward physical activity were measured before and after the intervention by using a subset of the School Physical Activity and Nutrition (SPAN) Questionnaire. SPAN is a validated questionnaire . QuestioAcademic performance was assessed by comparing postintervention Missouri Academic Performance (MAP) scores of fourth-grade students with their preintervention MAP scores from third grade (2006-2007 school year). Classroom grades from the communication arts and mathematics components of the ISD Progress Report for the 2007-2008 school year for students in the second, third, and fourth grades for the first (December 2007) and last (June 2008) quarterly marking periods were also used to assess academic performance.t tests, Pearson's χ2 tests, and Mann-Whitney tests were used. Repeated-measures analysis of variance was used to assess the between-group differences for the anthropometric and physical activity measures. Pearson's χ2 was used to assess between-group differences for physical activity, medication use, and both academic performance measures. Wilcoxon rank sum test was used to assess between-group differences in anthropometric and physical fitness measures. The effect of school (site) on outcome measures was assessed by using repeated measures and logistic regression. All analyses were based on intent-to-treat principle with the exception of the analysis physical activity measures, which had a poor response rate at follow-up. The statistical software package SPSS version 15  was used to conduct all analyses. All tests were conducted with an α level set at .05.To assess groups' differences at baseline between the intervention and control groups, P = .49), grade level (P = .98), and age (P = .89), anthropometric measures: BMI (P = .06), BMI percentile (P = .21); use of medication for asthma (P = .44) or ADHD (P = .47) or both (P = .29); and academic performance: MAP mathematics (P = .14) and communication arts (P = .22), ISD progress report mathematics (P = .08) and communication arts (P = .48)  (P = .004), aerobic capacity (P <.001), abdominal strength (P <.001), upper-body strength (P <.001), trunk extensor (P <.001), left-side flexibility (P <.001), right-side flexibility (P <.001), classroom behavior (P = .02), and physical activity attitudes (P = .05) . The 2 study groups were similar in terms of demographic characteristics: sex ( = .48)  . The stuP = .05) .P = .01] and BMI [P  = .02]) (P <.001), upper-body strength (P <.001), and trunk extensor (P <.001). Greater improvement was observed in the control group compared with the intervention group in right-side flexibility (P = .03) (P <.001), abdominal strength (P  <.001), upper-body strength (P = .05), and trunk extensor (P <.001); however, it was not found to be a predictor of left-side (P = .20) or right-side flexibility (P = .47). School was also a predictor of anthropometric measures (P = .03), use of medication (P = .01), mathematics performance (P = .004), and communication arts performance (P = .02).Improvements in anthropometric measures at follow-up in the control group were found to be significant compared with those of the intervention group (weight [ = .02]) . The intP = .03) . In logiP = .03). Students in the intervention group also showed reduced use of ADHD medication compared with students in the control group (P = .07). The intervention group had a greater reduction in asthma medication use and ADHD medication use combined at follow-up compared with the control group (P = .005) (Students in the intervention group showed significantly reduced use of asthma medication compared with students in the control group ( = .005) .P = .86) (P > .05).No significant differences were seen between the intervention and control groups in behavior changes from baseline as measured by the ISD Work/Social Skills progress report (P = .86) . No signP = .35) and mathematics (P = .15). However, as measured by ISD progress reports, significant differences in academic performance were observed. In mathematics, the control group had a greater proportion of students whose academic performance improved compared to that of the intervention group . The proportion of students in the control group whose reading level improved was also greater than that of the intervention group .Academic performance measured by MAP achievement level scores showed no significant differences between the intervention and control groups in reading (This initial controlled study of ABC for Fitness suggests that the program can improve fitness, reduce medication use, and preserve teaching time and academic performance. The program can be used in elementary school classrooms with minimal interruptions in daily classroom management related to academics and classroom behavior. We found that students in the intervention group improved significantly in physical fitness and in reduced use of ADHD and asthma medication compared with the control group. Few differences were observed between the intervention and control schools in terms of physical activity attitudes, academic performance, and classroom behavior; however, measures such as these are less likely to show improvement in the short term. This study successfully influenced some of the "upstream" factors that over time can influence measures of weight and overall health.Several studies have shown how classroom-based physical activity programs can effectively improve behavior and increase in-school physical activity and overall physical activity ,21. Our Previous programs have attempted to add a new section into the curriculum, which forces the reduction or elimination of other educational time. The ABC for Fitness program can reduce downtime and increase teaching time. As several schools decrease the amount of time and money spent on physical education , this prThis intervention was not projected to change anthropometric measures in the short term, although that is a goal. The program may take years to influence health outcomes; meanwhile, other factors such as dietary patterns and social environments will need to be addressed. A more in-depth intervention, performed over a longer period, would allow these outcomes to be elucidated. As with ABC for Fitness, other health promotion programming can be targeted to the existing school calendar and meet these goals without interfering with the primary mission of schools .This study showed improvements in measures of fitness during 1 school year. Childhood physical activity has been shown to have a positive correlation with well-being in adulthood -26, in pOne concern of adding a new curriculum is the effect on behavior and academic performance, especially if the program involves physical activity. The program was designed with the intent to replace the time that teachers use to settle students down with structured activity bursts. By designing the program in this manner, it was believed that the time lost with the ABC program would not worsen academic performance or classroom behavior; this appears to be the case, because no significant change was seen in classroom behavior or MAP scores. Several studies have suggested the potential for physical activity to improve academics ,33,34.BMI decreased significantly in the control group compared with the intervention group. Most studies that have evaluated the effects of physical activity on BMI in elementary schools have found no beneficial effect . It is uOur study had several limitations. First, our population was not a true cross-section of all children; ISD is just 1 school district in the midwestern United States. The ISD population is predominantly white and has a high proportion of low-income students. The sample was small. Only 1 school district was used for the study, which made it difficult to assess academic performance and develop a large enough power to assess all outcome measures. The periods of intervention and follow-up were limited.We plan a larger-scale study based on the results of this study that will entail a larger cohort and multiple school districts for a more accurate cross-section of US children, better teacher training, and longer follow-up to allow ample time to see results. Additionally, we plan to combine ABC for Fitness with other intervention components, including nutritional education to children and parents, to influence downstream measures.This study demonstrates the feasibility of bursts of structured physical activity for elementary school students for 30 minutes or more of daily physical activity without reducing teaching time or requiring any special facilities. Beneficial effects on daily physical activity, fitness, and measures of health are suggested. The program can easily be incorporated into almost any school routine. Thus, we recommend concurrent replication and dissemination and more intensive study of the program.


The present study was designed to assess whether subjective sleep patterns differ between: (i) depressed patients and controls, and (ii) between subjects with different severity of depression. Based on available literature, it was hypothesized that sleep patterns must be different between the above mentioned groups.e.g., medical illness, environmental factors, other psychiatric illness etc, were not included in the study. Depression severity was assessed in the subjects with depression with the help of Beck Depression Inventory II. Subjective sleep complaints were noted in the presence of a reliable informant, preferably bed partner. All the information was recorded in a semistructured performa. Statistical analysis was done with the help of SPSS v 11.0. The Chi square and Fisher exact tests were used for categorical variables; independent t-test and one way ANOVA were applied for numerical variables. Ordinal variables were analyzed using Mann Whitney U and Kruskall-Wallis tests.This study included 60 subjects with major depressive disorder and 40 subjects in the control group. Subjects with sleep disturbance secondary to any other factor, P = 0.32) and gender (P = 0.14) distribution. Subjects in the depression group had lesser total sleep time (P = 0.001), longer sleep latency (P = 0.001), frequent awakenings (P = 0.04), greater wake-after-sleep onset and offset times (both P = 0.001), lesser sleep efficiency, and tended to wake up early . Subjects with severe depression were different from the mild and moderate groups with regards to total sleep time (P = 0.002), night-time sleep (P = 0.007), and sleep efficiency (P = 0.001) even when the three groups were comparable in age.Depression and control groups were similar in age (Depression is associated with sleep disturbances, not only qualitatively, but also quantitatively. Sleep disturbance arises only after a critical level of depression is reached, and depression of varying severity may selectively affect different sleep parameters. Sleep disruption is a common feature of depression, manifesting itself as reduced total sleep time (TST), increase in sleep latency, and frequent awakenings during night-time sleep. Moreover, the sleep schedule usually changes during depression and, when subjectively asked, sleep is reported to be nonrestorative.e.g., decrease in slow wave sleep (SWS) and REM latency, increase of REM density, and frequent arousals.[Changes that occur in sleep during depression, may be confirmed objectively with the help of polysomnography. These studies demonstrate changes in sleep architecture, arousals. One studarousals. Thus, eve.g., serum cortisol concentration, correlate with changes in sleep akin to those seen in depression.[Depression is a stressful situation and it leads to hyper-arousal, which is positively correlated with the severity of depression. The cognpression. Taken toAlthough a lot of research has been dedicated to the neurobiology of insomnia in depression,28–10 ver22et al,[The method of assessment of sleep is of paramount importance and it may affect the outcomes of the study. Various surveys conducted for the assessment of sleep patterns and disorders have used self-reported questionnaires with good sensitivity and specificity.–13 It haet al, conducteet al, The infoet al, Moreoveret al, In additet al, Hence, sIt must be remembered that subjective impairment in sleep is associated with cognitive arousal and it perpetuates the insomnia further. Clinically, subjective perception of sleep is more important as it can influence the response to treatment.–19 AnothHence, it is worthwhile to study whether subjective sleep disturbances vary with the severity of depression, as it may guide us regarding the pathophysiology of depression and help in better management of the patients.This study was conducted in the outpatient department of a medical college hospital. The objective of the study was explained to the subjects and their written informed consent was taken.e.g., recent death of a beloved, loss of job etc were excluded from the study. Patients taking sedating antidepressants for more than one week or who were prescribed antidepressants with hypnotic effects or benzodiazepine, those having history suggestive of sleep-related breath disorders or parasomnia were also excluded. Absence of a reliable informant to endorse sleep patterns of the study subjects led to the exclusion of the particular subject, but these informants were not necessarily the bed-partners of the patients included in the study.Subjects presenting with complaints of Major Depressive Disorder according to DSM-IV-TR criteria were incAge- and sex-matched people who were accompanying the patients were screened to be included in the control group. They were also screened for the presence of major depressive disorder and for the exclusion criteria mentioned above.Severity of depression was assessed with the help of Beck's Depression Inventory-II (BDI) and the depression group was divided into minimal, mild, moderate, and severe categories. Sleep asSubjects with depression were asked to provide information regarding their usual sleep habits prior to the and following illness, and we compared them to exclude any recall bias as far as possible. Subjects in the control group were encouraged to provide information about the past three months.Sleep history included total time spent in sleep during a day (twenty four hours), night-time spent on bed, time to bed, sleep latency, nocturnal awakenings, wake time after sleep onset, wake up time, wake time after sleep offset, refreshing sleep etc. They were asked if they frequently felt morning headaches or were sleepy during the day. As daytime napping is common in Asian cultures, data regarding daytime napping, its frequency, duration, time spent in naps, and dreaming during the naps were also gathered.Statistical analysis was done with the help of SPSS v 11.0 for Windows. Descriptive analysis was done for all the variables. Categorical variables were analyzed with the help of Chi-Square or Fisher's Exact tests. Independent sample t-test was used to compare numerical data between depressed and nondepressed groups and one-way ANOVA with post-hoc analysis was applied in the subgroup analysis of the depression group. For the comparison of rank-order data, Mann-Whitney U and Kruskall- Wallis tests were used.P = 0.14); age , frequency of not following the sleep schedule (P = 0.1), frequency of awakenings per night (P = 0.67), morning headache (P = 0.08), abnormal movements during sleep (P = 0.34), habit of daytime napping (P = 0.37), number of naps per day (P = 0.48), total time spent in naps (P = 0.07), and whether naps were planned or not (P = 0.59).Sixty subjects with major depressive disorder and 40 control subjects were included in the present study. Both groups were similar in gender distribution , nonrefreshing sleep (P = 0.001), and difficulty in leaving bed in the morning (P = 0.01) than the controls. On the other hand, daytime sleep (P = 0.001) was more frequent in the control group. Stressful periods changed the sleep pattern of the control group more commonly as compared to the depressed group (P = 0.01). Other variables are shown in P = 0.05) and it was seen that depressed subjects tended to wake up earlier.A significantly higher number of depressed subjects complained of nocturnal awakenings (P = 0.977). Severity of depression did not affect any of the categorical variables. Time to bed and wake up time also were not different among these groups . However, a few other parameters, e.g., total sleep time, night-time sleep, sleep efficiency, frequency of nocturnal awakenings (days/week), and time of first awakening after night-time sleep were different between the three groups [When the depression group was further categorized according to the severity of depression into ‘mild’, ‘moderate,’ and ‘severe’ categories, it was found that most of the subjects had mild to moderate depression and severe depression was seen in only 16% of the subjects. The age was not different among these three groups (F = 0.23; e groups .In this study, almost all sleep variables were affected by the presence of depression. Briefly, depressed subjects had lesser total as well as nocturnal sleep times, longer sleep latency, higher prevalence of nocturnal awakenings, early awakening after getting sleep, took more time to sleep again, had increased frequency of night awakenings, and took more time to leave their beds in the morning. Similarly, the majority of them complained of nonrefreshing sleep and had lesser sleep efficiency. Interestingly, probably for the first time, it has been shown that depression has not only the qualitative but also the quantitative impact on subjective sleep parameters, even when the three groups were comparable with regards to confounding variables including age.The differences between the objective sleep parameters of depressed subjects and the control group have been a matter of extensive research and this study corroborates previous results. Existing literature describes that total sleep time is reduced and sleep latency is increased in most psychiatric disorders, including depression. Another et al,[Although sleep latency and WASO were significantly different between the depressed cases and the controls, this effect did not persist when depression subjects were further grouped according to the severity of depression, contrary to the previous literature. However, the differences could be secondary to the differences in the diagnosis of depression, as we have included subjects with clinically significant depression and the severity was assessed with BDI. On the other hand, in the study by Taylor et al, all subje.g., the sample size was small due to stringent inclusion and exclusion criteria. This also prevents us from generalizing the results of the study, especially in subjects with co-morbid medical or psychiatric disorders. Secondly, we did not assess the effect of the number of depressive episodes, the duration of the current episode, or the effect of life stressors on the sleep variables. Although one recent study has shown that these parameters do not correlate with polysomnographic findings in depressed patients,[However, this study had a few methodological limitations, patients, we stillIn conclusion, this is probably the first study to demonstrate that depression has not only qualitative but also quantitative effects on subjective sleep disturbances. Further studies are required to find out whether objective evidence also exists for the same and to determine the neurobiological underpinnings of these findings.


He) at SSRs and allozymes than selfing or annual species. However, some of these tree taxa have surprisingly low levels of nucleotide diversity at the DNA sequence level, which points to recombination as a potential generator of genetic diversity in these organisms. In this study, we examine how genome-wide and within-gene rates of recombination evolve across plant taxa, determine whether such rates are influenced by the life-form adopted by species, and evaluate if higher genome-wide rates of recombination translate into higher He values, especially in trees.Despite its role as a generator of haplotypic variation, little is known about how the rates of recombination evolve across taxa. Recombination is a very labile force, susceptible to evolutionary and life trait related processes, which have also been correlated with general levels of genetic diversity. For example, in plants, it has been shown that long-lived outcrossing taxa, such as trees, have higher heterozygosity (He (0.83 ± 0.29), and that trees have higher rates of genome-wide recombination than short-lived herbs and shrubs. A significant taxonomic component was further made evident by our models, as conifers exhibited lower recombination rates than angiosperms. This trend was also found at the within-gene level.Estimates of genome-wide (cM/Mb) recombination rates from 81 higher plants showed a significant phylogenetic signal. The use of different comparative phylogenetic models demonstrated that there is a positive correlation between recombination rate and Altogether, our results illustrate how both common ancestry and life-history traits have to be taken into account for understanding the evolution of genetic diversity and genomic rates of recombination across plant species, and highlight the relevance of species life forms to explain general levels of diversity and recombination. He) . These He. In the present study, we addressed these three key issues on higher plants by using a comparative phylogenetic approach on a large sample of average rates of recombination, estimated from total genetic map lengths and physical genome sizes, and mean values of He, calculated with SSR loci. We provide a first insight into the evolution of the genome-wide recombination rate across plant lineages, and show how this source of genetic variation is affected by different life traits once the phylogenetic signals of all parameters are accounted for. The control of such signals allowed us to discern whether species share similar levels of recombination due to common ancestry and/or to convergent life-history traits, such as growth habit, that have arisen independently in different lineages . Fin. FinHe. He at SSR loci were gathered for 81 higher plant species  that were classified according to their type of life-form . A preliminary standard correlation analysis (i.e. uncorrected for the phylogenetic relationships among species) revealed that these two traits were negatively correlated, and that trees had higher recombination rates than herbs but similar to shrubs , as the observed variance of the PICs for the recombination rate (0.0433) was much lower than expected by chance (0.2469).Estimates of genome-wide rate of recombination and bs Table . A set oHe , and marginally superior than in shrubs  between the genome-wide rate of recombination and He Fig. , and shoHe Fig. . The ind ± 0.29 bHe, with a very similar value to the one obtained with the previous model (0.84 ± 0.22). Furthermore, an important effect of life form, with conifer trees having significantly lower recombination rates than angiosperm trees and shrubs , a comparative analysis was performed on within-gene recombination rates recalculated from available nuclear gene DNA sequences retrieved from public databases  were lower in conifers than in angiosperms, while most of the non-conifer trees exhibited higher values than the non-domesticated herbs and shrubs, with the possible exception of Zea mays ssp. parviglumis. These results are obviously only exploratory, but they do open the door for extended comparisons once enough genomic data and physical genetic maps are available.The patterns obtained roughly point in the same direction than the trends observed at the genome-wide level  included, and the use of randomised datasets to determine significance, provided enough power to detect the presence (or absence) of a phylogenetic signal in our recombination rate data. The distribution of values was clearly non-random Figs.  &2, whic1 includeHe estimates at allozymes and SSRs . Furand SSRs ,33. This14 e.g. ,35], and, andHe e14 . Hown and He . On the n and He ,38,39]. Several studies have shown that the plant genome structure is highly heterogeneous and that recombination is not randomly distributed, occurring primarily within genes . SucThese similar levels of conservation in recombination rates inferred at different scales across plant species strongly differ from what has been reported for mammals. In these taxa, the rates of recombination at short scales appear to evolve faster than the rates at the genome-wide level , which sAmong the surveyed tree species, conifers seemed to be a remarkable exception. Most of the genome-wide recombination estimates for these taxa were far lower than those from angiosperms Figs. . Indeed,Arabidopsis), the genome regions where these elements occur have reduced levels of recombination , ,41, Arabon [e.g. ,41, but He along with low amounts of nucleotide diversity at candidate genes , we pooled all the data. Only those maps covering at least 60% of the genome were included in the database. Estimates of genetic map lengths (in cM) were corrected in order to account for variation in marker density across studies, and for undetected crossovers at distal terminal markers, as suggested elsewhere [He at SSR markers were also collected. Microsatellites were preferred to other codominant markers due to their increasing availability in the literature, to their putative neutrality and to their association with non-repetitive DNA in plant genomes, including trees [He values calculated from variation in at least five microsatellite repeats were included. Estimates based on population studies were favoured, but in some particular cases where such studies were not available , values determined from preliminary screen panels had to be used. The complete database and references to the primary literature are available in Additional file The average genome-wide recombination rate was calculated in cM/Mb for 81 plant species from 38 families including dicots, monocots and conifers. It was determined based on published estimates of total genetic map length and physical genome size as described elsewhere . Diploidlsewhere ,45,46. Elsewhere , or retrng trees ,49. Onlyet al. [Species were assembled in a phylogenetic tree with the program Phylomatic as implemented in Phylocom 3.41 . This pret al.  and adjuet al. .et al. [K statistic and its associated P-value were estimated from the variance of standardized contrasts, and compared with those obtained from a null model performed by reshuffling the trait values across the tips of the phylogeny. A significant phylogenetic signal was inferred at α = 0.05 when the mean observed variance of the contrasts was lower than 95% of the values produced by the null model. The phylogenetic independent contrasts were calculated for both the recombination rate and He by using the APE package for R [The presence of a phylogenetic signal for the recombination rate was determined following the procedure of Blomberg et al. ,54. Briege for R .He and life-form, three independent Generalized Linear Models were built. The first model was a non-phylogenetic (i.e. without taking the phylogenetic information of species into account) GLM with a Gaussian distribution of errors, which was made between the (log-transformed) recombination rate of species as dependent variable, and their respective He and life forms as dependent variables. In the second model, the phylogenetic relationships of species were incorporated as a correlating matrix, obtained from the phylogenetic tree above, into the GLM by using the generalized estimating equation (GEE) procedure. Such a procedure is generally used to fit the parameters of a GLM when the observations are correlated or non-independent. In our particular case, the common ancestry of species is a source of non-independence, which was taken into account with the inclusion of the above-mentioned matrix. The third model was similar to the second one, but on it, it was assumed that conifers and angiosperm trees were different "life-forms". The GEE procedure used in these last two models was the one implemented in the APE package [In order to determine the putative correlations between species ancestry,  package .Populus tremula or from Balsas for Zea mays ssp. parviglumis.Original DNA sequences from nuclear genes of non-domesticated species were downloaded from GenBank or obtained directly from the authors , and edited and aligned with Lasergen SeqMan vs. 7 . Domesticated taxa were deliberately excluded because most studies in these species focused on genes related to domestication, which typically show low levels of polymorphism and have followed artificial selection. Only those sequences spanning at least 800 base pairs (bp), having a minimum of 10 segregating sites, and sampled for more than 20 chromosomes were taken into account. Similarly, only DNA sequences from regions with low genetic differentiation or from single populations were used for those species with known population structure. For example, only sequences from Sweden were considered for θπ), the minimum number of recombination events (Rm) [ρ), and the recombination to mutation ratio (θ/ρ). The first two statistics were computed using DnaSP vs. 4.2 [ρ and θ/ρ were calculated with the composite-likelihood method of Hudson [rhothetapost software [Quercus crispula [Hordeum spontaneum [The aligned contigs were then used to estimate different diversity and recombination parameters such as the average number of nucleotide differences (nts (Rm) , the pop vs. 4.2 , while tf Hudson  implemenf Hudson , and witsoftware . Contrarcrispula  and Hordontaneum  and inclf: ratio of gene conversion to cross-over; He: mean expected heterozygosity; Mb: megabase; PIC: phylogenetically independent contrast; SSR: simple sequence repeats.cM: centimorgan; JPJ-C conceived the study, collected the data and wrote the manuscript, MV conceived the study, analysed the data, and edited the manuscript, SCGM conceived and coordinated the study, analysed the data, and edited the manuscript. All authors read and approved the final version of the manuscript.He) for 81 higher plant species classified according to their type of life-form. The rates of recombination were corrected following Hall & Willis (2005).Number of chromosomes and estimates of genetic map length, physical genome size, genome-wide rate of recombination and mean expected heterozygosity at SSR's (Click here for fileComparison of estimates of nucleotide diversity and recombination rates across different types of wild plant life-forms based on nuclear gene DNA sequences from population studies.Click here for file


The aim of the study was to evaluate factors influencing quality of life (QOL) in Moroccan postmenopausal women with osteoporotic vertebral fracture assessed by the Arabic version of ECOS 16 questionnaire.357 postmenopausal women were included in this study. The participants underwent bone mineral density (BMD) measurements by DXA of the lumbar spine and the total hip as well as X-ray examination of the thoraco-lumbar spine to identify subclinical vertebral fractures. Patients were asked to complete a questionnaire on clinical and sociodemographic parameters, and osteoporosis risk factors. The Arabic version of the ECOS16  was used to assess quality of life.2. One hundred and eight women (30.1%) were osteoporotic and 46.7% had vertebral fractures. Most were categorized as Grade1 (75%). Three independent factors were associated with a poor quality of life: low educational level , vertebral fracture , and history of peripheral fracture . Worse QOL was observed in the group with vertebral fracture in all domains except "pain": Physical functioning ; Fear of illness ; and Psychosocial functioning . The number of fractures was a determinant of a low QOL, as indicated by an increased score in physical functioning , fear of illness , and total score  after adjusting on age and educational level. Patients with higher Genant score had low QOL in these two domains too , and in the total score  after adjusting on age and educational level.The mean age was 58 ± 7.8 years, and the mean BMI was 28.3 ± 4.8 kg/mOur current data showed that the quality of life assessed by the Arabic version of the ECOS 16 questionnaire is decreased in post menopausal women with prevalent vertebral fractures, with the increasing number and the severity of vertebral fractures. Osteoporosis is a growing public health concern among the elderly population, particularly in postmenopausal women. It's a debilitating chronic disease that can reduce the quality of life (QOL) in a variety of ways, including diminished physical and emotional functioning. Vertebral fractures, the hallmark of osteoporosis, are commonly associated with back pain, kyphosis, and height loss. Therefore, they can lead to a reduced mobility and may be very painful, which can limit everyday activities ,2. ReducSeveral specific questionnaires have been developed to measure QOL in osteoporosis. The most widely used are the Osteoporosis Quality of Life Questionnaire (OQLQ) ,7, the OThere is no Arabic version of ECOS-16 to evaluate QOL in Moroccan osteoporotic women. QOL depends on the cultural background of each nation. Therefore, the QOL of Moroccan osteoporotic women should be evaluated using questionnaires developed for the Moroccan population.The aim of this study was to assess QOL in Moroccan post-menopausal women with osteoporotic vertebral fractures using a standard Arabic version of ECOS-16.In this cross-sectional study, 357 ambulatory post-menopausal women living in urban areas of Morocco were sent to our outpatient Bone Densitometry Center. Recruitment was based on voluntary enrolment. All subjects were referred to this center for osteoporosis risk factors, including menopause. Informed consent was obtained from all subjects and the study was approved by ethics committee of our university hospital. We excluded from the study all patients with a history of: (1) taking drugs known to influence bone metabolism in the past 2 years, such as vitamin D, calcium, corticosteroids, bisphosphonates and hormone replacement therapy; (2) musculo skeletal, thyroid, parathyroid, adrenal, hepatic, or renal disease; (3) malignancy; and (4) hysterectomy. No adjunction or modification in treatment has been authorized.Each patient completed a questionnaire on clinical and sociodemographic parameters, and osteoporosis risk factors. The age of menopause, the time since menopause, educational level, personal history of peripheral fracture, back pain, and comorbid conditions were recorded.Weight and height were measured without clothes or shoes at the time of bone densitometry measurements. The Body mass index (BMI) was calculated as body weight/height2 (kg/m2).Lateral radiographs of the thoracic and lumbar spine were made by standard methods. Morphometry was done from T4 to L4. Vertebral fractures were diagnosed by the Genant semiquantitative method , a visuaLumbar spine, trochanter, femoral neck and total hip BMD were measured by dual-energy Xray absorptiometry with a Lunar prodigy densitometer. Daily quality control was carried out by measurement of a Lunar phantom. At the time of the study, phantom measurements showed stable results. The phantom precision expressed as the CV(%) was 0.08. Both T and Z scores were obtained. In the T-score calculations, the manufacturer's ranges for European reference population were used because of the absence of a Moroccan database. Osteoporosis was defined as a T-score lower than -2.5, according to the World Health Organisation study group definition .The specific QOL questionnaire: ECOS-16 was used to measure QOL. The 16 items are divided qualitatively into four dimensions: Pain; Physical functioning; Fear of illness; and Psychological functioning. ECOS-16 generates a single summary score obtained from the arithmetic mean of the answered items, so the total score ranges from 1 (best HRQOL) to 5 (worst HRQOL). The two summary scores PCS  and MCS  were also calculated.The ECOS-16 questionnaire was adapted and translated into Arabic to be used in Moroccan patients with vertebral fractures. The translation followed proposed guidelines by Guillemin and et al ,27. In tStatistical analysis was performed with the Windows 13.0 version of SPSS software . Values are expressed as mean ± S.D or percentages.For the validation of the Arabic ECOS-16 questionnaire, internal consistency reliability was evaluated using Cronbach's alpha, and the test-retest reliability was evaluated by intra-class correlation coefficients (ICC) for the global score. Cronbach's alpha was calculated in each dimension of the instrument to assess the internal consistency reliability. A high alpha coefficient  suggests that the items within a dimension measures the same construct and supports the construct validity . The ICCItem internal convergency represents the correlation between different domains. The domain which measures similar dimensions produces high correlations. Values above 0.60 correspond to a high correlation, moderate between 0.30 and 0.60, and low correlation below 0.30.For the comparison between fractured and non-fractured patients, we used Student's t-test for quantitative variables and Chi-square test for qualitative variables. A logistic regression analysis was used to discriminate between the fractured and non fractured groups and to assess risk factors of vertebral fractures. Odds ratios (OR) and 95% confidence intervals (CI95%) were calculated.In order to quantify the impact of the number and the severity of vertebral fractures on QOL, multiple linear regression was performed to assess independent factors associated with a poor QOL after adjusting on potential confounding variables.A statistical significance level of p < 0.05 was used in all statistical tests performed.Table The questionnaire had been generally well accepted by all patients. The mean duration of administration of the Arabic version of ECOS-16 was 5.8 ± 3.6 minutes. It has been correctly completed by 97% of patients with no missing or confusing items.The internal consistency was very high with a Cronbach's alpha coefficient of 0.92 among the 16 items. Test-retest reliability was analysed with an Intraclass Correlation Coefficient of 0.92. When the different dimensions of ECOS-16 were analyzed, the internal consistency by parameter was good with a Cronbach's alpha coefficient comprised between 0.73 and 0.89 (Table All domains of ECOS 16 are correlated between themselves. The Spearman correlation coefficients are comprised between 0.328 and 0.756. The two dimensions of the ECOS 16 correlated significantly with each other (rho = 0.675) , with longer duration of menopause (p < 0.001), with higher parity (p = 0.01), with lower educational level (p = 0.002), with a history of peripheral fracture (p = 0.001), and with lower BMD at all the sites (p ≤ 0.001). Logistic regression showed that older age , and lower lumbar BMD  were independent factors of vertebral fracture after adjusting on age, educational level, history of peripheral fracture, and lumbar BMD.Univariate analysis showed that worse HRQoL was associated to older age , higher BMI , lower educational level , higher parity , concomitant disease , history of peripheral fracture , and vertebral fracture .A multivariate analysis was carried out to identify patients' characteristics that were related to the ECOS-16 score. It shows that three independent factors were associated with a poor quality of life: low educational level , vertebral fracture , and a history of peripheral fracture  Table .Patients with at least one vertebral fracture had higher ECOS-16 scores in three domains Table : PhysicaTotal score and all domains, except "pain", increased with increasing number of vertebral fractures in univariate analysis: Physical functioning ; Fear of illness , psychosocial functioning , and total ECOS-16 score .Linear regression shows that patients with higher number of fractures had worse QOL in two domains after adjusting on age and educational level: Physical functioning ; Fear of illness , and total ECOS-16 score  ; Fear of illness ; Psychosocial functioning ; total score , and in multivariate analysis after adjusting on age and educational level: Physical functioning , Fear of illness , and total score  Table .This study shows that vertebral fractures, their number and the severity of deformities have a negative impact on QOL. Indeed, ECOS-16 scores progressively increased in patients with vertebral fractures in all dimensions, except "Pain", and in both component summary scores (PCS and MCS). QOL was impaired in patients with greater number of vertebral fractures and higher Genant score except in the domains of "pain" and "psychosocial functioning". These findings underline the validity of the Arabic version of the ECOS 16 questionnaire.We chose and used the ECOS-16 questionnaire because it is self-administered, short, simple and easy to score. Our study showed that cross-cultural adaptation of this questionnaire maintains the psychometric properties found in the original version. This was demonstrated through the short time needed to complete the questionnaire, the low percentage of incomplete questionnaires, the high alpha coefficients for internal consistency and the good reproducibility when using the test-retest. Cronbach's alpha was > 0.70 for the total score, the four dimensions, and the two summary scores of ECOS-16, which is in the range for internal consistency. Our results are similar to those reported in the original and Italian versions which has been validated recently for use in Italian patients ,30.All domains and the two summary scores correlated significantly between them (rho>0.3). The Italian study showed the same results .Studies showing impaired QOL in patients with vertebral fractures have been published in other countries. Adachi and al , represeIn our study, the domain of "pain" did not show differences either between patients with and without vertebral fractures or within patients according to the number and severity of vertebral fractures. Other studies found that the pain domain was discriminant in osteoporotic women, but patients were recruited on the basis of symptoms related to clinically apparent fractures and compared with patients without back pain ,35. ThisAnother finding in the present study is that patient's educational level is also a determinant factor in the QOL impairment. In our study, a high level of education seems to be a protective factor against worse QOL. This finding has been reported in previous studies in patients with musculoskeletal problems ,37, and Our study has strengths and some limitations. The recruitment was not based on symptoms related to vertebral fractures. It had permit to evaluate the impact of old and recent, symptomatic and asymptomatic vertebral fractures on QOL. We also took into account comorbidities in the evaluation of the factors influencing the ECOS-16 score. Indeed, beside vertebral fractures, these comorbid conditions may influence QOL, especially in this elderly population. However, cross-sectional methodology did not allow us to compare the changes of QOL between patients with and without fractures. Moreover, the subjects were not recruited from the community at large, but rather, were selected from patients who underwent bone density determinations. This selection bias likely explains the relatively high prevalence of osteoporosis in the subjects studied.This study has revealed that QOL in Moroccan postmenopausal women is impaired by the presence of vertebral fracture, by the increasing number and by the severity of vertebral fractures. Currently, the endpoint in the treatment of osteoporosis is considered to be the prevention of fracture, with an increase of the BMD as the surrogate endpoint. Our data indicates that the measurement of QOL is mandatory for the evaluation of osteoporotic patients. This finding will not only provide an added parameter to evaluate the effectiveness of a given program, but will also focus care providers to be more attentive to the nonmedication aspects of osteoporosis management.The authors declare that they have no competing interests.FA, NHH and RA conceived the study and supervised its design, execution, and analysis and participated in the drafting and critical review of the manuscript. FA and RA did data management and statistical analyses. All other authors enrolled patients, participated in data acquisition and critical revision of the manuscript. FEA wrote the paper with input from all investigators.