15 research outputs found
Marine Biodiversity in the Australian Region
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 (the Australian Faunal Directory, Codes for Australian Aquatic Biota, Online Zoological Collections of Australian Museums, and the Australian node of the Ocean Biogeographic Information System), 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 (mainly animals) 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
Science, biodiversity and Australian management of marine ecosystems
The United Nations Convention on Law of the Sea (UNCLOS) (United Nations 1982) came into effect in 1994. Signatory nations have substantial management obligations for conservation of marine natural resource and ecosystems. In this paper we discuss the challenges of defining and monitoring biodiversity at scales required for management of marine ecosystems. Australia\u27s area of immediate responsibility under UNCLOS covers an area of 11 million sq km with further linked responsibilities for an estimated area of 5.1 million sq km of continental shelf. This presents substantial data challenges for development and implementation of management. Acoustic seabed mapping is providing substantial information on seabed surface geology and topography and provides a surrogate basis for describing benthic habitat and seabed communities that have critical roles in marine food chains. The development of the Integrated Marine and Coastal Regionalisation of Australia (IMCRA 4.0, 2006) has provided a basis for planning marine biodiversity and resource management but the biological habitat interpretation of geological data is based very largely on demersal fish data. It is recognised in IMCRA 4.0 (2006) that revision and refinement of regionalisation requires further work in the areas of data coverage, ecosystem understanding and ecosystem surrogates and conceptual classification models. In this paper we discuss Australian experience highlighting problems and issues of relevance for scientifically based management of marine natural resource and ecosystems elsewhere in the world
A new extant family of primitive moths from Kangaroo Island, Australia, and its significance for understanding early Lepidoptera evolution
The Global Urban Tree Inventory: A database of the diverse tree flora that inhabits the world’s cities
Orchid diversity: Spatial and climatic patterns from herbarium records
Aim: We test for spatial and climatic patterns of diversification in the Orchidaceae, an angiosperm family characterized by high levels of species diversity and rarity. Globally, does orchid diversity correlate with land area? In Australia, does diversity correlate with herbarium collecting effort, range size, or climate niche breadth? Where are Australia’s orchids distributed spatially, in protected areas, and in climate space? Location: Global, then Australia. Methods: We compared orchid diversity with land area for continents and recognized orchid diversity hotspots. Then, we used cleaned herbarium records to compare collecting effort (for Australian Orchidaceae vs. all other plant families, and also among orchid genera). Spatial and climate distributions were mapped to determine orchids’ coverage in the protected area network, range sizes, and niche breadths. Results: Globally, orchid diversity does not correlate with land area (depauperate regions are the subantarctic: 10 species, and northern North America: 394 species). Australian herbarium records and collecting effort generally reflect orchid species diversity (1,583 spp.), range sizes, and niche breadths. Orchids are restricted to 13% of Australia’s landmass with 211 species absent from any protected areas. Species richness is the greatest in three biomes with high general biodiversity: Temperate (especially southwest and southeast Australia), Tropical, and Subtropical (coastal northern Queensland). Absence from the Desert is consistent with our realized climate niche—orchids avoid high temperature/low rainfall environments. Orchids have narrower range sizes than nonorchid species. Highly diverse orchid genera have narrower rainfall breadths than less diverse genera. Main conclusions: Herbarium data are adequate for testing hypotheses about Australian orchids. Distribution is likely driven by environmental factors. In contrast, diversification did not correlate with increases in range size, rainfall, or temperature breadths, suggesting speciation does not occur via invasion and local adaptation to new habitats. Instead, diversification may rely on access to extensive obligate symbioses with mycorrhizae and/or pollinators
Large‐scale eDNA metabarcoding survey reveals marine biogeographic break and transitions over tropical north‐western Australia
Aim: Environmental DNA (eDNA) metabarcoding has demonstrated its applicability as a highly sensitive biomonitoring tool across small spatial and temporal scales in marine ecosystems. However, it has rarely been tested across large spatial scales, or biogeographical barriers. Here, we scale up marine eDNA metabarcoding, test its ability to detect a major marine biogeographic break, and evaluate its use as a regional biomonitoring tool in Australia.
Location: North-western Australia (NWA)
Methods: We applied metabarcoding assays targeting the mitochondrial 16S rRNA and CO1 genes to 284 surface seawater eDNA samples collected from 71 mid-shelf, inshore, coastal and nearshore estuarine sites over 700 km of the NWA coastline.
Results: Metabarcoding detected a wide range of bony fish (404 taxa), elasmobranchs (44) and aquatic reptiles (5). We detected bioregional and depth differentiation within inshore bony fish communities. These findings support the presence of a marine biogeographic break, which is purported to occur in the vicinity of Cape Leveque, demarcating the border between the Kimberley and Canning bioregions. Inshore bony fish and elasmobranch communities, as well as coastal bony assemblages, were additionally found to differ between the South and North Kimberley regions suggesting previously unrecognised subregional differentiation among these taxa. The overall compositional data has been used to update distribution information for a number of endangered, elusive and data-deficient taxa, including sawfish (family: Pristidae), northern river shark (Glyphis garricki) and wedgefish (genus: Rhynchobatus).
Main conclusions: eDNA metabarcoding demonstrated a high level of sensitivity that was able to discern fine-scale patterns across the large-scale, remote and oceanographically complex region of North-western Australia. Importantly, this study highlights the potential of integrating broad-scale eDNA metabarcoding alongside other baseline surveys and long-term monitoring approaches, which are crucial for the sustainable management and conservation of marine biodiversity in this unique marine region.We have uploaded demultiplexed (unfiltered) data for public use. This is in a fastq format and corresponds to sample IDs (see Diversity and Distributions publication for more information). We have also uploaded a taxonomic (read abundance) matrix that has gone through our quality filtering (DADA2) pipeline and has been blasted against NCBI's GenBank (2019). This can be directly used for multivariate statistical analyses.
Funding provided by: Australian Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000923Award Number: LP160100839Four one-litre water replicates were sampled from 71 sites across the Canning/Kimberley bioregions in September 2017 and July/September 2018, totaling 284 samples over 700 km of coastline. Samples were taken on a transect line traversing a purported biogeographic break and more widely across the Kimberley region in mid-shelf, inshore, coastal and nearshore estuarine habitats. Samples were immediately stored on ice and were individually filtered across Pall 0.45mm Supor® polyethersulfone membranes using a Pall Sentino® Microbiology pump (Pall Corporation, Port Washington, USA) within five hours of collection.
DNA was extracted from half of the filter membranes using a DNeasy Blood and Tissue Kit (Qiagen; Venlo, Netherlands) with modifications. DNA was amplified using three previously published PCR assays (16S and COI) to target bony fish, elasmobranchs and aquatic reptiles from our mixed seawater samples (see Diversity and Distributions publication for more information).
Libraries were sequenced on 300 cycle (for unidirectional sequencing of the 16S & COI amplicons) MiSeq® V2 Standard Flow Cells on an Illumina MiSeq platform (Illumina, San Diego, USA), housed in the TrEnD Laboratory at Curtin University, Western Australia. Sequencing reads were demultiplexed and quality filtered in OBITools (v1.2.9; Boyer et al., 2014) and in R (v3.5.3; RStudio Team, 2015) using the DADA2 (v1.10.1) bioinformatics package (Callahan et al., 2016)