Search

Seasonal forecasting of environmental conditions and marine species distribution has been used as a decision support tool in commercial and aquaculture fisheries. These tools may also be applicable to species targeted by the recreational fisheries sector, a sector that is increasing its use of marine resources, and making important economic and social contributions to coastal communities around the world. Here, a seasonal forecast of the habitat and density of dolphinfish ( Coryphaena hippurus ), based on sea surface temperatures, was developed for the east coast of New South Wales (NSW), Australia. Two prototype forecast products were created; geographic spatial forecasts of dolphinfish habitat and a latitudinal summary identifying the location of fish density peaks. The less detailed latitudinal summary was created to limit the resolution of habitat information to prevent potential resource over-exploitation by fishers in the absence of total catch controls. The forecast dolphinfish habitat model was accurate at the start of the annual dolphinfish migration in NSW (December) but other months (January - May) showed poor performance due to spatial and temporal variability in the catch data used in model validation. Habitat forecasts for December were useful up to five months ahead, with performance decreasing as forecast were made further into the future. The continued development and sound application of seasonal forecasts will help fishery industries cope with future uncertainty and promote dynamic and sustainable marine resource management. [ABSTRACT FROM AUTHOR]

Reduction of bycatch through spatial management is one possible approach in fisheries management. One requirement is description of the habitat of the species in question, and this is now possible for a range of species using data collected from electronic tags. Here we report on a spatial management approach used to limit unwanted capture of southern bluefin tuna (SBT, Thunnus maccoyii) in the eastern Australia longline fishery. The approach combines a habitat model conditioned with temperature preference data from 56 pop-up satellite archival tags deployed on SBT, and an ocean model to produce near real-time habitat predictions utilized by fishery managers during the fishing season. Three habitat types based on probability of SBT occurrence are identified in the fishing region, and spatial zoning by managers is used to regulate fisher access to these zones. This management approach has been used since 2003, and despite dynamic spatial management often being resisted by fishers or managers because of perceived implementation complexity, there has been an increase in complexity of the management actions over the 6-yr period. Managers now request more habitat predictions each season, update the spatial zoning more frequently, and apply more complicated management lines dividing the habitat types. This has resulted in an improved fit to the modeled habitat predictions, and as catch per unit effort (CPUE) of SBT is related to the habitat type, has contributed to the overall success of the management approach. This implemented management approach illustrates that managers and fishers can successfully implement complex dynamic spatial zoning. [ABSTRACT FROM AUTHOR]

Aggregation of reserve networks is generally considered desirable for biological and economic reasons: aggregation reduces negative edge effects and facilitates metapopulation dynamics, which plausibly leads to improved persistence of species. Economically, aggregated networks are less expensive to manage than fragmented ones. Therefore, many reserve-design methods use qualitative heuristics, such as distance-based criteria or boundary-length penalties to induce reserve aggregation. We devised a quantitative method that introduces aggregation into reserve networks. We call the method the boundary-quality penalty (BQP) because the biological value of a land unit (grid cell) is penalized when the unit occurs close enough to the edge of a reserve such that a fragmentation or edge effect would reduce population densities in the reserved cell. The BQP can be estimated for any habitat model that includes neighborhood (connectivity) effects, and it can be introduced into reserve selection software in a standardized manner. We used the BQP in a reserve-design case study of the Hunter Valley of southeastern Australia. The BQP resulted in a more highly aggregated reserve network structure. The degree of aggregation required was specified by observed (albeit modeled) biological responses to fragmentation. Estimating the effects of fragmentation on individual species and incorporating estimated effects in the objective function of reserve-selection algorithms is a coherent and defensible way to select aggregated reserves. We implemented the BQP in the context of the Zonation method, but it could as well be implemented into any other spatially explicit reserve-planning framework. [ABSTRACT FROM AUTHOR]

In recent years, predictive habitat distribution models, derived by combining multivariate statistical analyses with Geographic Information System (GIS) technology, have been recognised for their utility in conservation planning. The size and spatial arrangement of suitable habitat can influence the long-term persistence of some faunal species. In southwestern Victoria, Australia, populations of the rare swamp antechinus (Antechinus minimus maritimus) are threatened by further fragmentation of suitable habitat. In the current study, a spatially explicit habitat suitability model was developed for A. minimus that incorporated a measure of vegetation structure. Models were generated using logistic regression with species presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multi-spectral digital imagery, as the predictors. The most parsimonious model, based on the Akaike Information Criterion, was spatially extrapolated in the GIS. Probability of species presence was used as an index of habitat suitability. A negative association between A. minimus presence and both elevation and habitat complexity was evidenced, suggesting a preference for relatively low altitudes and a vegetation structure of low vertical complexity. The predictive performance of the selected model was shown to be high (91%), indicating a good fit of the model to the data. The proportion of the study area predicted as suitable habitat for A. minimus (Probability of occurrence ⩾0.5) was 11.7%. Habitat suitability maps not only provide baseline information about the spatial arrangement of potentially suitable habitat for a species, but they also help to refine the search for other populations, making them an important conservation tool. [Copyright &y& Elsevier]

Journal of Applied Ecology (2004) 41, 213 –223 [ABSTRACT FROM AUTHOR]