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Título

Cost-effective monitoring of biological invasions under global change: a model-based framework

AutorVicente, Joana R.; Alagador, Diogo; Guerra, Carlos; Alonso, Joaquim M.; Kueffer, Christoph; Vaz, Ana S.; Fernandes, Rui F.; Cabral, Joao A.; Araújo, Miguel B. ; Honrado, Joao P.
Palabras claveAcacia dealbata
Climate change
Connectivity
Monitoring networks
Northern
Portugal
Optimization
Surveillance effort
Risk management
Scale dependence
Species distribution models
Fecha de publicación2016
EditorJohn Wiley & Sons
CitaciónJournal of Applied Ecology 53(5): 1317-1329 (2016)
ResumenEcological monitoring programmes are designed to detect and measure changes in biodiversity and ecosystems. In the case of biological invasions, they can contribute to anticipating risks and adaptively managing invaders. However, monitoring is often expensive because large amounts of data might be needed to draw inferences. Thus, careful planning is required to ensure that monitoring goals are realistically achieved. Species distribution models (SDMs) can provide estimates of suitable areas to invasion. Predictions from these models can be applied as inputs in optimization strategies seeking to identify the optimal extent of the networks of areas required for monitoring risk of invasion under current and future environmental conditions. A hierarchical framework is proposed herein that combines SDMs, scenario analysis and cost analyses to improve invasion assessments at regional and local scales. We illustrate the framework with Acacia dealbata Link. (Silver-wattle) in northern Portugal. The framework is general and applicable to any species. We defined two types of monitoring networks focusing either on the regional-scale management of an invasion, or management focus within and around protected areas. For each one of these two schemes, we designed a hierarchical framework of spatial prioritization using different information layers (e.g. SDMs, habitat connectivity, protected areas). We compared the performance of each monitoring scheme against 100 randomly generated models. In our case study, we found that protected areas will be increasingly exposed to invasion by A. dealbata due to climate change. Moreover, connectivity between suitable areas for A. dealbata is predicted to increase. Monitoring networks that we identify were more effective in detecting new invasions and less costly to management than randomly generated models. The most cost-efficient monitoring schemes require 18% less effort than the average networks across all of the 100 tested options. Synthesis and applications. The proposed framework achieves cost-effective monitoring networks, enabling the interactive exploration of different solutions and the combination of quantitative information on network performance with orientations that are rarely incorporated in a decision support system. The framework brings invasion monitoring closer to European legislation and management needs while ensuring adaptability under rapid climate and environmental change.
URIhttp://hdl.handle.net/10261/160066
Identificadoresdoi: 10.1111/1365-2664.12631
issn: 1365-2664
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