5 resultados para underwater
em eResearch Archive - Queensland Department of Agriculture
Resumo:
In 1999, the Department of Employment, Economic Development and Innovation (DEEDI), Fisheries Queensland undertook a new initiative to collect long term monitoring data of various important stocks including reef fish. This data and monitoring manual for the reef fish component of that program which was based on Underwater Visual Census methodology of 24 reefs on the Great Barrier Reef between 1999 and 2004. Data was collected using six 50m x 5m transects at 4 sites on 24 reefs. Benthic cover type was also recorded for 10m of each transect. The attached Access Database contains 5 tables being: SITE DETAILS TABLE Survey year Data entry complete REF survey site ID Site # (1-4) Location (reef name) Site Date (date surveyed) Observer 1 (3 initials to identify who estimated fish lengths and recorded benthic cover) TRANSECT DETAILS Survey ID Transect Number (1-6) Time (the transect was surveyed) Visibility (in metres) Minimum Depth surveyed (m) Maximum Depth surveyed (m) Percent of survey completed (%) Comments SUBSTRATE Survey ID Transect Number (1-6) then % cover of each of eth following categories of benthic cover types Dead Coral Live Coral Soft Coral Rubble Sand Sponge Algae Sea Grass Other COORDINATES (over survey sites) from -14 38.792 to -19 44.233 and from 145 21.507 to 149 55.515 SIGHTINGS ID Survey ID Transect Number (1-6) CAAB Code Scientific Name Reef Fish Length (estimated Fork Length of fish; -1 = unknown or not recorded) Outside Transect (if a fish was observed outside a transect -1 was recorded) Morph Code (F = footballer morph for Plectropomus laevis, S = Spawning colour morph displayed)
Resumo:
A comprehensive survey of the benthic assemblages of the Torres Strait was conducted in order to provide critical baseline information for regional marine planning, assessing the environmental sustainability of fisheries and understanding the ecosystems of the region. Over 150 sites throughout the region were sampled with a modified prawn trawl, towed underwater video, pipe dredge and epibenthic sled. This manuscript provides a broad overview of the activities undertaken and data collected. Two thousand three hundred and seventy-two different nominal species were sampled by the trawl and sled, only 728 by both gears. The towed video was not able to provide the same level of taxonomic resolution of epibenthic taxa, but was particularly useful in areas where the seabed was too rough to be sampled. Data from the trawl, sled and video were combined to characterise the epibenthic assemblages of the region. Data from the towed video was also used to provide a characterisation of the inter-reefal benthic habitats, which was then analysed in combination with physical covariate data to examine relationships between the two. Levels of mud and gravel in the sediments, trawling effort and seabed current stress were the covariates most significantly correlated with the nature of the seabed habitats.
Resumo:
Indo-Pacific mangrove swamps and seagrass beds are commonly located in close proximity to each other, often creating complex ecosystems linked by biological and physical processes. Although they are thought to provide important nursery habitats for fish, only limited information exists about their usage by fish outside of estuaries. The present study investigated fish assemblages in non-estuarine intertidal habitats where mangroves and seagrass overlap (the mangrove-seagrass continuum). Three habitats (mangrove, mangrove edge, seagrass) were sampled at 4 sites of the Wakatobi Marine National Park, Indonesia, using underwater visual census. Ninety-one species of fish were observed at a mean density of 130.1 +/- 37.2 ind. 1000 m(-2). Predatory fish (fish that feed on invertebrates and/or fish) were the most dominant feeding groups in the mangroves, whilst omnivores dominated on the mangrove edge and in the seagrass. Although the habitats along the mangrove-seagrass continuum were observed to be important for many fish, only 22 of the 942 coral reef species known within the area utilised mangroves as nursery habitat and only 15 utilised seagrass. Despite finding evidence that nursery grounds in mangroves and seagrass may not directly support high coral reef fish diversity, many of the coral reef nursery species found in this study are likely to be key herbivores or apex predators as adult fish on local coral reefs, and thus highly important to local fisheries. Although mangroves are not permanently inundated by the tide, this study highlights their importance as fish habitats, which at high tide support a greater abundance of fish than seagrass beds. In the light of the high rate of destruction of these habitats, their role in supporting fish assemblages requires consideration in marine resource management programs.
Resumo:
The Great Barrier Reef is a unique World Heritage Area of national and international significance. As a multiple use Marine Park, activities such as fishing and tourism occur along with conservation goals. Managers need information on habitats and biodiversity distribution and risks to ensure these activities are conducted sustainably. However, while the coral reefs have been relatively well studied, less was known about the deeper seabed in the region. From 2003 to 2006, the GBR Seabed Biodiversity Project has mapped habitats and their associated biodiversity across the length and breadth of the Marine Park to provide information that will help managers with conservation planning and to assess whether fisheries are ecologically sustainable, as required by environmental protection legislation (e.g. EPBC Act 1999). Holistic information on the biodiversity of the seabed was acquired by visiting almost 1,500 sites, representing a full range of known environments, during 10 month-long voyages on two vessels and deploying several types of devices such as: towed video and digital cameras, baited remote underwater video stations (BRUVS), a digital echo-sounder, an epibenthic sled and a research trawl to collect samples for more detailed data about plants, invertebrates and fishes on the seabed. Data were collected and processed from >600 km of towed video and almost 100,000 photos, 1150 BRUVS videos, ~140 GB of digital echograms, and from sorting and identification of ~14,000 benthic samples, ~4,000 seabed fish samples, and ~1,200 sediment samples.
Resumo:
Common coral trout Plectropomus leopardus is an iconic fish of the Great Barrier Reef (GBR) and is the most important fish for the commercial fishery there. Most of the catch is exported live to Asia. This stock assessment was undertaken in response to falls in catch sizes and catch rates in recent years, in order to gauge the status of the stock. It is the first stock assessment ever conducted of coral trout on the GBR, and brings together a multitude of different data sources for the first time. The GBR is very large and was divided into a regional structure based on the Bioregions defined by expert committees appointed by the Great Barrier Reef Marine Park Authority (GBRMPA) as part of the 2004 rezoning of the GBR. The regional structure consists of six Regions, from the Far Northern Region in the north to the Swains and Capricorn–Bunker Regions in the south. Regions also closely follow the boundaries between Bioregions. Two of the northern Regions are split into Subregions on the basis of potential changes in fishing intensity between the Subregions; there are nine Subregions altogether, which include four Regions that are not split. Bioregions are split into Subbioregions along the Subregion boundaries. Finally, each Subbioregion is split into a “blue” population which is open to fishing and a “green” population which is closed to fishing. The fishery is unusual in that catch rates as an indicator of abundance of coral trout are heavily influenced by tropical cyclones. After a major cyclone, catch rates fall for two to three years, and rebound after that. This effect is well correlated with the times of occurrence of cyclones, and usually occurs in the same month that the cyclone strikes. However, statistical analyses correlating catch rates with cyclone wind energy did not provide significantly different catch rate trends. Alternative indicators of cyclone strength may explain more of the catch rate decline, and future work should investigate this. Another feature of catch rates is the phenomenon of social learning in coral trout populations, whereby when a population of coral trout is fished, individuals quickly learn not to take bait. Then the catch rate falls sharply even when the population size is still high. The social learning may take place by fish directly observing their fellows being hooked, or perhaps heeding a chemo-sensory cue emitted by fish that are hooked. As part of the assessment, analysis of data from replenishment closures of Boult Reef in the Capricorn–Bunker Region (closed 1983–86) and Bramble Reef in the Townsville Subregion (closed 1992–95) estimated a strong social learning effect. A major data source for the stock assessment was the large collection of underwater visual survey (UVS) data collected by divers who counted the coral trout that they sighted. This allowed estimation of the density of coral trout in the different Bioregions (expressed as a number of fish per hectare). Combined with mapping data of all the 3000 or so reefs making up the GBR, the UVS results provided direct estimates of the population size in each Subbioregion. A regional population dynamic model was developed to account for the intricacies of coral trout population dynamics and catch rates. Because the statistical analysis of catch rates did not attribute much of the decline to tropical cyclones, (and thereby implied “real” declines in biomass), and because in contrast the UVS data indicate relatively stable population sizes, model outputs were unduly influenced by the unlikely hypothesis that falling catch rates are real. The alternative hypothesis that UVS data are closer to the mark and declining catch rates are an artefact of spurious (e.g., cyclone impact) effects is much more probable. Judging by the population size estimates provided by the UVS data, there is no biological problem with the status of coral trout stocks. The estimate of the total number of Plectropomus leopardus on blue zones on the GBR in the mid-1980s (the time of the major UVS series) was 5.34 million legal-sized fish, or about 8400 t exploitable biomass, with an 2 additional 3350 t in green zones (using the current zoning which was introduced on 1 July 2004). For the offshore regions favoured by commercial fishers, the figure was about 4.90 million legal-sized fish in blue zones, or about 7700 t exploitable biomass. There is, however, an economic problem, as indicated by relatively low catch rates and anecdotal information provided by commercial fishers. The costs of fishing the GBR by hook and line (the only method compatible with the GBR’s high conservation status) are high, and commercial fishers are unable to operate profitably when catch rates are depressed (e.g., from a tropical cyclone). The economic problem is compounded by the effect of social learning in coral trout, whereby catch rates fall rapidly if fishers keep returning to the same fishing locations. In response, commercial fishers tend to spread out over the GBR, including the Far Northern and Swains Regions which are far from port and incur higher travel costs. The economic problem provides some logic to a reduction in the TACC. Such a reduction during good times, such as when the fishery is rebounding after a major tropical cyclone, could provide a net benefit to the fishery, as it would provide a margin of stock safety and make the fishery more economically robust by providing higher catch rates during subsequent periods of depressed catches. During hard times when catch rates are low (e.g., shortly after a major tropical cyclone), a change to the TACC would have little effect as even a reduced TACC would not come close to being filled. Quota adjustments based on catch rates should take account of long-term trends in order to mitigate variability and cyclone effects in data.