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A B S T R A C TThis study explored and applied the concepts of Fire Regulation Capacity (FRC) and Fire Protection Ecosystem Service (FPES) in the assessment of the effects of landscape change in a mountain fire-prone landscape in Portugal. We adopted a modeling and simulation approach using BFOLDS-FRM with landscape data for years 1990 and 2006 (observed) and with three landscape scenarios for 2020. Proxy indicators for FRC (burned area and fire intensity) and for economic damage by fire (loss of provisioning ES) were used to establish trends in the supply and value of FPES. We found decreased FRC to restrain simulated fires burning over 100 ha from 1990 on and to regulate Very High and Extreme fire intensity levels, particularly under our 2020 scenario of Forest expansion. FPES is also expected to decrease, as indicated by higher fire-related damages, particularly if fuel hazard increases in the landscape. However, there were differences among scenarios, suggesting potential tradeoffs between FPES and the supply of provisioning ES. Planning and management in this and similar areas experiencing farmland abandonment must consider fire trends and patterns, since landscape change is a major driver affecting FRC and FPES, which may further be decreased by future climatic conditions.

We assessed the effects of landscape change on the climate regulation ecosystem service in a mountain river basin of Portugal, through the quantification, valuation and mapping of carbon sequestration and storage. The analyses were based on land use and land cover (LULC) changes that took place between 1990 and 2006 and on expected changes defined by three LULC change scenarios for 2020. We used the Integrated Valuation of Ecosystem Services and Tradeoffs model for scenario building and carbon assessment and valuation, and several modelling tools to assess past, current and future carbon in four different pools. Soil organic carbon data was obtained through an extensive sampling scheme across the entire study area. Recent (1990Recent ( -2006 and expected landscape changes (2006-2020) affected considerably carbon sequestration and storage. Observed landscape changes generally promoted carbon sequestration and storage, and had a positive effect on the climate regulation ecosystem service, both biophysically and economically. Expected LULC changes further extend the capability of the landscape to increase carbon sequestration and storage in the near future. The carbon sequestered and stored in vegetation and soil contributes to avoid socio-economic damages from climate change, while increasing the economic value of particular LULC classes and the whole landscape. These results are essential to inform land planning, especially on how, where and when changes in landscapes may affect the provision of the climate regulation ecosystem service. ARTICLE HISTORY

1. Ecological 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. 2. 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. 3. 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. 4. 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. 5. 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.

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This article proposes and illustrates a practical methodological framework to evaluate the fitness for use of spatial data sets for environmental and ecological applications, focusing on user requirements for specified application contexts. The methodology is based on the use of metadata to analyze similarity between the data characteristics and the user's needs or expectations for several quality indicators. Additionally, the concept of 'critical factors' is introduced in this framework, allowing users to define which quality indicators have greater importance given their own requirements or expectations and the specified application contexts. The proposed methodology further allows integrating and interconnecting the spatial data quality (SDQ) evaluation methodology with metadata geoportals in WebGIS platforms, facilitating its operation by users from non-spatial disciplines and with often limited expertise on this subject. Examples of the evaluation of fitness for use for specific application contexts within the project BIO_SOS ('Biodiversity Multi-SOurce Monitoring System: From Space To Species' FP7 project) are presented. By providing a prompt and straightforward evaluation tool, the proposed methodology can encourage the implementation of SDQ evaluation routines in ecological assessment and monitoring programs, promoting a more adequate use of geospatial data and ultimately contributing to well-supported policy and management decisions.

Aim A major source of uncertainty in the application of species distribution models (SDMs) is related to input data quality. Citizen‐collected species occurrence data are often used for fitting SDMs when data from standardized and expert‐supported surveys are unavailable. Macroclimate variables are much more commonly used as predictors in SDMs than other sources coming from remote sensing data. Here, we assess the effects of using different data sources (in both response and predictor variables) on SDM performance across a wide range of species with contrasting distributional ranges. Location Iberian Peninsula. Taxon Birds. Methods A SDM ensemble‐forecasting approach was implemented using bird data from two different data sources: the eBird project and Atlases. We fitted SDMs with three predictor types: macroclimate, remotely sensed ecosystem functional attributes (EFAs) and their combination. Species were grouped in four range size classes. We assessed the uncertainty of model predictions by different evaluation metrics. Generalized linear mixed‐effects models tested the effect on model performance of input data source across distributional range sizes while accounting for different accuracy metrics. Pairwise comparisons between range projections were used to assess their spatial similarity. Results Data source, size class, predictor and accuracy metric showed significant effects on SDM performance. eBird‐based models outperformed those built with Atlas data for less widespread species. Climate predictors yielded models with the best performance, especially when combined with EFAs. However, the predictor contribution was consistent across bird datasets, being mostly driven by the species range. Main Conclusions Our models demonstrated the usefulness and complementarity of different input data sources when modelling species distribution across different distributional ranges. These findings highlight the need to integrate different data sources to improve the model predictions at regional scale. Our framework also underlines that model uncertainty should be examined more exhaustively at early stages of the modelling process.