Outstanding Challenges in the Transferability of Ecological Models.

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.

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes.

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability.

Ocean zoning for conservation, fisheries and marine renewable energy: assessing trade-offs and co-location opportunities.

Oceans, particularly coastal areas, are getting busier and within this increasingly human-dominated seascape, marine biodiversity continues to decline. Attempts to maintain and restore marine biodiversity are becoming more spatial, principally through the designation of marine protected areas (MPAs). MPAs compete for space with other uses, and the emergence of new industries, such as marine renewable energy generation, will increase competition for space. Decision makers require guidance on how to zone the ocean to conserve biodiversity, mitigate conflict and accommodate multiple uses. Here we used empirical data and freely available planning software to identified priority areas for multiple ocean zones, which incorporate goals for biodiversity conservation, two types of renewable energy, and three types of fishing. We developed an approached to evaluate trade-offs between industries and we investigated the impacts of co-locating some fishing activities within renewable energy sites. We observed non-linear trade-offs between industries. We also found that different subsectors within those industries experienced very different trade-off curves. Incorporating co-location resulted in significant reductions in cost to the fishing industry, including fisheries that were not co-located. Co-location also altered the optimal location of renewable energy zones with planning solutions. Our findings have broad implications for ocean zoning and marine spatial planning. In particular, they highlight the need to include industry subsectors when assessing trade-offs and they stress the importance of considering co-location opportunities from the outset. Our research reinforces the need for multi-industry ocean-zoning and demonstrates how it can be undertaken within the framework of strategic conservation planning.

Spatial access priority mapping (SAPM) with fishers: a quantitative GIS method for participatory planning.

Spatial management tools, such as marine spatial planning and marine protected areas, are playing an increasingly important role in attempts to improve marine management and accommodate conflicting needs. Robust data are needed to inform decisions among different planning options, and early inclusion of stakeholder involvement is widely regarded as vital for success. One of the biggest stakeholder groups, and the most likely to be adversely impacted by spatial restrictions, is the fishing community. In order to take their priorities into account, planners need to understand spatial variation in their perceived value of the sea. Here a readily accessible, novel method for quantitatively mapping fishers' spatial access priorities is presented. Spatial access priority mapping, or SAPM, uses only basic functions of standard spreadsheet and GIS software. Unlike the use of remote-sensing data, SAPM actively engages fishers in participatory mapping, documenting rather than inferring their priorities. By so doing, SAPM also facilitates the gathering of other useful data, such as local ecological knowledge. The method was tested and validated in Northern Ireland, where over 100 fishers participated in a semi-structured questionnaire and mapping exercise. The response rate was excellent, 97%, demonstrating fishers' willingness to be involved. The resultant maps are easily accessible and instantly informative, providing a very clear visual indication of which areas are most important for the fishers. The maps also provide quantitative data, which can be used to analyse the relative impact of different management options on the fishing industry and can be incorporated into planning software, such as MARXAN, to ensure that conservation goals can be met at minimum negative impact to the industry. This research shows how spatial access priority mapping can facilitate the early engagement of fishers and the ready incorporation of their priorities into the decision-making process in a transparent, quantitative way.