57 resultados para Biodiversity monitoring
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Development of a Gulf community based natural resource monitoring program, with sawfish as an initial focus.
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Ground Cover Monitoring in the Fitzroy Basin.
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Management of insecticide resistance.
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This project will contribute to providing scientifically-based ecological assessments of fisheries stocks and habitats required under the Environmental Protection and Biodiversity Conservation (EPBC) Act 1999.
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Using the ABCD Framework as a aurrogate for biodiversity condition.
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Regional implementation of Integrated Pest Management (IPM) in Bundaberg production horticulture providing a unified approach to pest and disease control on an area-wide basis. This is aimed at reducing chemical dependency, increasing sustainability, profitability and enhancing biodiversity through detailed investigations, technology application, coordination, monitoring, communication and education.
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The aims of this project will provide capacity in virology expertise to help protect Australian cotton from virus diseases including both existing and those that pose significant biosecurity threats. This project will also provide continued capacity in virology to support the cotton industry.
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Estimating the environment impacts of land management practice change on the Great Barrier Reef water quality.
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National Monitoring for resistance to phosphine and grain protectants.
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Sustainable Farming Systems for Central Queensland.
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Enhanced On-farm Monitoring and Mitigation of Pesticide and Nutrient Transport.
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Many fisheries worldwide have adopted vessel monitoring systems (VMS) for compliance purposes. An added benefit of these systems is that they collect a large amount of data on vessel locations at very fine spatial and temporal scales. This data can provide a wealth of information for stock assessment, research, and management. However, since most VMS implementations record vessel location at set time intervals with no regard to vessel activity, some methodology is required to determine which data records correspond to fishing activity. This paper describes a probabilistic approach, based on hidden Markov models (HMMs), to determine vessel activity. A HMM provides a natural framework for the problem and, by definition, models the intrinsic temporal correlation of the data. The paper describes the general approach that was developed and presents an example of this approach applied to the Queensland trawl fishery off the coast of eastern Australia. Finally, a simulation experiment is presented that compares the misallocation rates of the HMM approach with other approaches.
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We used an established seagrass monitoring programme to examine the short and longer-term impacts of an oil spill event on intertidal seagrass meadows. Results for potentially impacted seagrass areas were compared with existing monitoring data and with control seagrass meadows located outside of the oil spill area. Seagrass meadows were not significantly affected by the oil spill. Declines in seagrass biomass and area 1 month post-spill were consistent between control and impact meadows. Eight months post-spill, seagrass density and area increased to be within historical ranges. The declines in seagrass meadows were likely attributable to natural seasonal variation and a combination of climatic and anthropogenic impacts. The lack of impact from the oil spill was due to several mitigating factors rather than a lack of toxic effects to seagrasses. The study demonstrates the value of long-term monitoring of critical habitats in high risk areas to effectively assess impacts.
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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)