RESUMEN
We address the question of how to provide meaningful scientific information to support environmental decision making at the regional scale and at the temporal scale of several decades in a network of marine parks in the Kimberley region of Western Australia. Where environmental sustainability is affected by slow-dynamics climate change processes and one-off investments in large infrastructure which can affect a region for decades to come, both strategic and reactive planning is necessary and prediction becomes as urgent as standard adaptive management. At the interface between future studies, socio-economic modelling and environmental modelling, we define 18 scenarios of economic development and climate change impacts and five management strategies. We explore these potential futures using coupled models of terrestrial and marine ecosystem dynamics. We obtain a projection of the Kimberley marine system to the year 2050, conditional on the chosen scenarios and management strategies. Our results suggest that climate change, not economic development, is the largest factor affecting the future of marine ecosystems in the Kimberley region, with site-attached species such as reef fish at greatest risk. These same species also benefit most from more stringent management strategies, especially expansion of sanctuary zones and Marine Protected Areas.
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Reliable abundance estimates for species are fundamental in ecology, fisheries, and conservation. Consequently, predictive models able to provide reliable estimates for un- or poorly-surveyed locations would prove a valuable tool for management. Based on commonly used environmental and physical predictors, we developed predictive models of total fish abundance and of abundance by fish family for ten representative taxonomic families for the Great Barrier Reef (GBR) using multiple temporal scenarios. We then tested if models developed for the GBR (reference system) could predict fish abundances at Ningaloo Reef (NR; target system), i.e., if these GBR models could be successfully transferred to NR. Models of abundance by fish family resulted in improved performance (e.g., 44.1%
RESUMEN
Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.
Asunto(s)
Ecología/métodos , Modelos BiológicosRESUMEN
Low-trophic level species account for more than 30% of global fisheries production and contribute substantially to global food security. We used a range of ecosystem models to explore the effects of fishing low-trophic level species on marine ecosystems, including marine mammals and seabirds, and on other commercially important species. In five well-studied ecosystems, we found that fishing these species at conventional maximum sustainable yield (MSY) levels can have large impacts on other parts of the ecosystem, particularly when they constitute a high proportion of the biomass in the ecosystem or are highly connected in the food web. Halving exploitation rates would result in much lower impacts on marine ecosystems while still achieving 80% of MSY.