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      Depth and Medium-Scale Spatial Processes Influence Fish Assemblage Structure of Unconsolidated Habitats in a Subtropical Marine Park

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          Abstract

          Where biological datasets are spatially limited, abiotic surrogates have been advocated to inform objective planning for Marine Protected Areas. However, this approach assumes close correlation between abiotic and biotic patterns. The Solitary Islands Marine Park, northern NSW, Australia, currently uses a habitat classification system (HCS) to assist with planning, but this is based only on data for reefs. We used Baited Remote Underwater Videos (BRUVs) to survey fish assemblages of unconsolidated substrata at different depths, distances from shore, and across an along-shore spatial scale of 10 s of km (2 transects) to examine how well the HCS works for this dominant habitat. We used multivariate regression modelling to examine the importance of these, and other environmental factors (backscatter intensity, fine-scale bathymetric variation and rugosity), in structuring fish assemblages. There were significant differences in fish assemblages across depths, distance from shore, and over the medium spatial scale of the study: together, these factors generated the optimum model in multivariate regression. However, marginal tests suggested that backscatter intensity, which itself is a surrogate for sediment type and hardness, might also influence fish assemblages and needs further investigation. Species richness was significantly different across all factors: however, total MaxN only differed significantly between locations. This study demonstrates that the pre-existing abiotic HCS only partially represents the range of fish assemblages of unconsolidated habitats in the region.

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          Mapping spatial pattern in biodiversity for regional conservation planning: where to from here?

          Vast gaps in available information on the spatial distribution of biodiversity pose a major challenge for regional conservation planning in many parts of the world. This problem is often addressed by basing such planning on various biodiversity surrogates. In some situations, distributional data for selected taxa may be used as surrogates for biodiversity as a whole. However, this approach is less effective in data-poor regions, where there may be little choice but to base conservation planning on some form of remote environmental mapping, derived, for example, from interpretation of satellite imagery or from numerical classification of abiotic environmental layers. Although this alternative approach confers obvious benefits in terms of cost-effectiveness and rapidity of application, problems may arise if congruence is poor between mapped land-classes and actual biological distributions. I propose three strategies for making more effective use of available biological data and knowledge to alleviate such problems by (1) more closely integrating biological and environmental data through predictive modeling, with increased emphasis on modeling collective properties of biodiversity rather than individual entities; (2) making more rigorous use of remotely mapped surrogates in conservation planning by incorporating knowledge of heterogeneity within land-classes, and of varying levels of distinctiveness between classes, into measures of conservation priority and achievement; and (3) using relatively data-rich regions as test-beds for evaluating the performance of surrogates that can be readily applied across data-poor regions.
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            Habitat Specialization in Tropical Continental Shelf Demersal Fish Assemblages

            The implications of shallow water impacts such as fishing and climate change on fish assemblages are generally considered in isolation from the distribution and abundance of these fish assemblages in adjacent deeper waters. We investigate the abundance and length of demersal fish assemblages across a section of tropical continental shelf at Ningaloo Reef, Western Australia, to identify fish and fish habitat relationships across steep gradients in depth and in different benthic habitat types. The assemblage composition of demersal fish were assessed from baited remote underwater stereo-video samples (n = 304) collected from 16 depth and habitat combinations. Samples were collected across a depth range poorly represented in the literature from the fringing reef lagoon (1–10 m depth), down the fore reef slope to the reef base (10–30 m depth) then across the adjacent continental shelf (30–110 m depth). Multivariate analyses showed that there were distinctive fish assemblages and different sized fish were associated with each habitat/depth category. Species richness, MaxN and diversity declined with depth, while average length and trophic level increased. The assemblage structure, diversity, size and trophic structure of demersal fishes changes from shallow inshore habitats to deeper water habitats. More habitat specialists (unique species per habitat/depth category) were associated with the reef slope and reef base than other habitats, but offshore sponge-dominated habitats and inshore coral-dominated reef also supported unique species. This suggests that marine protected areas in shallow coral-dominated reef habitats may not adequately protect those species whose depth distribution extends beyond shallow habitats, or other significant elements of demersal fish biodiversity. The ontogenetic habitat partitioning which is characteristic of many species, suggests that to maintain entire species life histories it is necessary to protect corridors of connected habitats through which fish can migrate.
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              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.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                13 May 2014
                : 9
                : 5
                : e96798
                Affiliations
                [1 ]National Marine Science Centre, Southern Cross University, Charlesworth Bay, Coffs Harbour, New South Wales, Australia
                [2 ]Marine Ecosystem Research, New South Wales Department of Primary Industries, Coffs Harbour, New South Wales, Australia
                [3 ]Marine Ecology Research Centre, School of Environment, Science and Engineering, Southern Cross University, Lismore, New South Wales, Australia
                [4 ]Coastal and Marine Unit, Science Division, Office of Environment and Heritage, Woolongong, New South Wales, Australia
                University of Windsor, Canada
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: AS HM DB SS. Performed the experiments: AS. Analyzed the data: AS HM ML SS. Wrote the paper: AS HM ML SS.

                Article
                PONE-D-13-52302
                10.1371/journal.pone.0096798
                4019521
                24824998
                e4622a0d-6def-48ff-ab18-dabb4c178fb3
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 18 December 2013
                : 11 April 2014
                Page count
                Pages: 13
                Funding
                Funding was supplied by a postgraduate (PhD) research budget from Southern Cross University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Ecology
                Biodiversity
                Coastal Ecology
                Marine Ecology
                Spatial and Landscape Ecology
                Marine Biology
                Fisheries Science
                Marine Conservation
                Marine Monitoring
                Marine Technology
                Earth Sciences
                Marine and Aquatic Sciences
                Aquatic Environments
                Marine Environments
                Ecology and Environmental Sciences
                Environmental Protection

                Uncategorized
                Uncategorized

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