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To manage multiple ecosystem services (ES) effectively, it is essential to understand how the dynamics of ES maintain healthy ecosystems to avoid potential negative impacts on human well-being in the context of sustainable development. In particular, the Ionian Islands in the central Mediterranean are characterized by high natural, ecological, and recreational value; however, the intensification of human activities over time has resulted in the loss of natural ecosystems, which might have negatively impacted ES. Here, we aimed to assess and understand the spatiotemporal dynamics of ES supply and how these components interact across the Ionian Islands to optimize future ES provision and mitigate current trade-offs. We quantified multiple ecosystem services and analyzed their interactions at a temporal scale across the four prefectures of the Ionian Islands. Seven ES were quantified covering all three ES sections (provisioning, regulating and maintenance, and cultural) of the Common International Classification of Ecosystem Services (CICES). ES interactions were investigated by analyzing ES relationships, identifying ES bundles (sets of ES that repeatedly occur together across space and time), and specifying ES occurrence within bundles. The three ES groups exhibited similar patterns on some islands, but differed on islands with areas of high recreation in parallel to low provisioning and regulating ES. Temporal variations showed both stability and changes to the supply of ES, as well as in the interactions among them. Different patterns among the islands were caused by the degree of mixing between natural vegetation and olive orchards. This study identified seven ES bundles that had distinct compositions and magnitudes, with both unique and common bundles being found among the islands. The olive grove bundle delivered the most ES, while the non-vegetated bundle delivered negligible amounts of ES. Spatial and temporal variation in ES appear to be determined by agriculture, land abandonment, and increasing tourism, as well as the occurrence of fires. Knowledge about the spatial dynamics and interactions among ES could provide information for stakeholders and decision-making processes to develop appropriate sustainable management of the ecosystems on the Ionian Islands to secure ecological, social, and economic resilience.

The Mediterranean area is one of the most significantly altered biodiversity hotspots on the Earth’s surface; it has been intensively affected by anthropogenic activity for millennia, forming complex socioecological systems. In parallel, the long history of natural ecological processes and the deep interlinking with human populations led to landscape patterns, such as spatial heterogeneity, that facilitate the provision of essential ecosystem services (ESs). As such, a comprehensive understanding of the underlying factors that influence the supply of ESs is of paramount importance for effective forest management policies that ensure both ecological integrity and human welfare. This study aimed at identifying local specific interactions across three different spatial scales between landscape metrics and ESs using global and geographical random forest models. The findings showed that dense forest cover may have a positive effect on the supply of ESs, such as climate regulation and timber provision. Although landscape heterogeneity is considered among the main facilitators of ecosystem multifunctionality, this did not fully apply for the Marathasa region, as forest homogeneity seems to be linked with provision of multiple services. By assessing under which landscape conditions and characteristics forest ESs thrive, local stakeholders and managers can support effective forest management to ensure the co-occurrence of ESs and societal wellbeing.

This paper is part of a project that has received funding from the PRIMA Call 2020, Work program Topic 2.2.1, InnovaTive Resilient fArmiNg Systems in MedITerranean envIrONments (TRANSITION).

To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the \"sustainable intensification\" of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to that. At the same time, the global agricultural landscape is deeply heterogeneous, with differences in climate, soil, and land use inducing variations in how agricultural systems respond to farmer actions. The \"personalization\" of sustainable agriculture with the provision of locally adapted management advice is thus a necessary condition for the efficient uplift of green metrics, and an integral development in imminent policies. Here, we formulate personalized sustainable agriculture as a Conditional Average Treatment Effect estimation task and use Causal Machine Learning for tackling it. Leveraging climate data, land use information and employing Double Machine Learning, we estimate the heterogeneous effect of sustainable practices on the field-level Soil Organic Carbon content in Lithuania. We thus provide a data-driven perspective for targeting sustainable practices and effectively expanding the global carbon sink.

Understanding the suitability of agricultural land for applying specific management practices is of great importance for sustainable and resilient agriculture against climate change. Recent developments in the field of causal machine learning enable the estimation of intervention impacts on an outcome of interest, for samples described by a set of observed characteristics. We introduce an extensible data-driven framework that leverages earth observations and frames agricultural land suitability as a geospatial impact assessment problem, where the estimated effects of agricultural practices on agroecosystems serve as a land suitability score and guide decision making. We formulate this as a causal machine learning task and discuss how this approach can be used for agricultural planning in a changing climate. Specifically, we extract the agricultural management practices of \"crop rotation\" and \"landscape crop diversity\" from crop type maps, account for climate and land use data, and use double machine learning to estimate their heterogeneous effect on Net Primary Productivity (NPP), within the Flanders region of Belgium from 2010 to 2020. We find that the effect of crop rotation was insignificant, while landscape crop diversity had a small negative effect on NPP. Finally, we observe considerable effect heterogeneity in space for both practices and analyze it.