Assessing the effects of different land-use/land-cover input datasets on modelling and mapping terrestrial ecosystem services - Case study Terceira Island (Azores, Portugal)

Climate land use and other drivers' impacts on island ecosystem services: A global review

Islands are diversity hotspots and vulnerable to environmental degradation, climate variations, land use changes and societal crises. These factors can exhibit interactive impacts on ecosystem services. The study reviewed a large number of papers on the climate change-islands-ecosystem services topic worldwide. Potential inclusion of land use changes and other drivers of impacts on ecosystem services were sequentially also recorded. The study sought to investigate the impacts of climate change, land use change, and other non-climatic driver changes on island ecosystem services. Explanatory variables examined were divided into two categories: environmental variables and methodological ones. Environmental variables include sea zone geographic location, ecosystem, ecosystem services, climate, land use, other driver variables, Methodological variables include consideration of policy interventions, uncertainty assessment, cumulative effects of climate change, synergistic effects of climate change with land use change and other anthropogenic and environmental drivers, and the diversity of variables used in the analysis. Machine learning and statistical methods were used to analyze their effects on island ecosystem services. Negative climate change impacts on ecosystem services are better quantified by land use change or other non-climatic driver variables than by climate variables. The synergy of land use together with climate changes is modulating the impact outcome and critical for a better impact assessment. Analyzed together, there is little evidence of more pronounced effects for a specific sea zone, ecosystem, or ecosystem service. Climate change impacts may be underestimated due to the use of a single climate variable deployed in most studies. Policy interventions exhibit low classification accuracy in quantifying impacts indicating insufficient efficacy or integration in the studies.

Models of bee responses to land use and land cover changes in agricultural landscapes - a review and research agenda

Predictive modelling tools can be used to support the design of agricultural landscapes to promote pollinator biodiversity and pollination services. Despite the proliferation of such modelling tools in recent decades, there remains a gap in synthesising their main characteristics and representation capacities. Here, we reviewed 42 studies that developed non-correlative models to explore the impact of land use and land cover changes on bee populations, and synthesised information about the modelled systems, modelling approaches, and key model characteristics like spatiotemporal extent and resolution. Various modelling approaches are employed to predict the biodiversity of bees and the pollination services they provide, with a prevalence of models focusing on wild populations compared to managed ones. Of these models, landscape indicators and distance decay models are relatively simple, with few parameters. They allow mapping bee visitation probabilities using basic land cover data and considering bee foraging ranges. Conversely, mechanistic or agent-based models delineate, with varying degrees of complexity, a multitude of processes that characterise, among others, the foraging behaviour and population dynamics of bees. The reviewed models collectively encompass 38 ecological, agronomic, and economic processes, producing various outputs including bee abundance, habitat visitation rate, and crop yield. To advance the development of predictive modelling tools aimed at fostering pollinator biodiversity and pollination services in agricultural landscapes, we highlight future avenues for increasing biophysical realism in models predicting the impact of land use and land cover changes on bees. Additionally, we address the challenges associated with balancing model complexity and practical usability.

Mapping and assessing ecosystem services in Europe's Overseas: A comparative analysis of MOVE case studies

Mapping and Assessment of Ecosystems and their Services (MAES) has been widely applied on the European Union (EU) mainland, whereas the EU Overseas entities still bear potential for implementation. This paper presents novel applications of the MAES procedure in the EU Outermost Regions and Overseas Countries and Territories ("EU Overseas"). Eight case studies from different geographical areas were analysed through a comparative assessment by applying an established framework following key steps in the MAES process, in order to stipulate lessons learned and recommendations for MAES in the EU Overseas. These key steps include the identification of policy questions, stakeholder networks and involvement, application of MAES methods, dissemination and communication and implementation. The case studies were conducted and analysed under the umbrella of the EU MOVE pilot project, including the Azores, the Canary Islands, Saint Martin, French Guiana, Martinique, Reunion Island and the Falkland Islands. Each case study represented different governance, policy and decision-making frameworks towards biodiversity and environmental protection. Case studies predominantly addressed the policy domains of Nature and Biodiversity Conservation and Marine and Maritime Policy. Ecosystem Services (ES) were assessed across a wide range of themes, biomes and scales, focusing on terrestrial, coastal and marine ecosystems. Results show that the implementation of the case studies was accompanied by extensive communication and dissemination activities. First success stories were visible, where the MAES exercise led to meaningful uptake of the ES concept to policies and decision-making. Yet, there is still work to be done - major bottlenecks were identified related to the MAES implementation centring around financial resources, training and technical expertise. Addressing these aspects can contribute to an enhanced implementation of MAES in the EU Overseas in the future.