Quantifying the role of saltmarsh as a vulnerable carbon sink: A case study from Northern Portugal.

Saltmarshes play a crucial role in carbon sequestration and storage, although they are increasingly threatened by climate change-induced sea level rise (SLR). This study assessed the potential variation in Blue Carbon stocks across regional and local scales, and estimated their economic value and potential habitat loss due to SLR based on the IPCC AR6 scenarios for 2050 and 2100 in three estuarine saltmarshes in northern Portugal, the saltmarshes of the Minho, Lima and Cávado estuaries. The combined carbon stock of these saltmarshes was 38,798 ± 2880 t of organic carbon, valued at 3.96 ± 0.38 M€. Local and regional differences in carbon stocks were observed between common species, with the cordgrass Spartina patens and the reed Phragmites australis consistently showing higher values in the Lima saltmarsh in some of the parameters. Overall, the Lima saltmarsh had the highest total carbon per species cover, with S. patens showing the highest values among common species. Bolboschoenus maritimus had the highest values in the Minho saltmarsh, while the other species presented a similar carbon storage capacity. Potential habitat loss due to SLR was most evident in the Cávado saltmarsh over shorter timescales, with a significant risk of inundation even for median values of SLR, while the Lima saltmarsh was shown to be more resistant and resilient. If habitat loss directly equates to carbon loss within these saltmarshes, projected CO2 emissions may range from 22,000 to 43,449 t by 2050 and 33,000 to 130,000 t by 2100 (under the IPCC SSP5-8.5 scenario). The study shows the importance of Blue Carbon site-specific estimates, acknowledging the potential future repercussions from habitat loss due to SLR. It emphasizes the need to consider local and regional variability in Blue Carbon stocks assessments and highlights the critical importance of preserving and rehabilitating these ecosystems to ensure their continued efficacy as vital carbon sinks, thereby contributing to climate change mitigation efforts.

Scientific modelling can be accessible, interoperable and user friendly: A case study for pasture and livestock modelling in Spain.

This article describes the adaptation of a non-spatial model of pastureland dynamics, including vegetation life cycle, livestock management and nitrogen cycle, for use in a spatially explicit and modular modelling platform (k.LAB) dedicated to make data and models more interoperable. The aim is to showcase to the social-ecological modelling community the delivery of an existing, monolithic model, into a more modular, transparent and accessible approach to potential end users, regional managers, farmers and other stakeholders. This also allows better usability and adaptability of the model beyond its originally intended geographical scope (the Cantabrian Region in the North of Spain). The original code base (written in R in 1,491 lines of code divided into 13 files) combines several algorithms drawn from the literature in an opaque fashion due to lack of modularity, non-semantic variable naming and implicit assumptions. The spatiotemporal rewrite is structured around a set of 10 namespaces called PaL (Pasture and Livestock), which includes 198 interoperable and independent models. The end user chooses the spatial and temporal context of the analysis through an intuitive web-based user interface called k.Explorer. Each model can be called individually or in conjunction with the others, by querying any PaL-related concepts in a search bar. A scientific dataflow and a provenance diagram are produced in conjunction with the model results for full transparency. We argue that this work demonstrates key steps needed to create more Findable, Accessible, Interoperable and Reusable (FAIR) models beyond the selected example. This is particularly essential in environments as complex as agricultural systems, where multidisciplinary knowledge needs to be integrated across diverse spatial and temporal scales in order to understand complex and changing problems.

Towards globally customizable ecosystem service models.

Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The ARtificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five "Tier 1" ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multi-criteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.

Identification of conservation and restoration priority areas in the Danube River based on the multi-functionality of river-floodplain systems.

Large river-floodplain systems are hotspots of biodiversity and ecosystem services but are also used for multiple human activities, making them one of the most threatened ecosystems worldwide. There is wide evidence that reconnecting river channels with their floodplains is an effective measure to increase their multi-functionality, i.e., ecological integrity, habitats for multiple species and the multiple functions and services of river-floodplain systems, although, the selection of promising sites for restoration projects can be a demanding task. In the case of the Danube River in Europe, planning and implementation of restoration projects is substantially hampered by the complexity and heterogeneity of the environmental problems, lack of data and strong differences in socio-economic conditions as well as inconsistencies in legislation related to river management. We take a quantitative approach based on best-available data to assess biodiversity using selected species and three ecosystem services (flood regulation, crop pollination, and recreation), focused on the navigable main stem of the Danube River and its floodplains. We spatially prioritize river-floodplain segments for conservation and restoration based on (1) multi-functionality related to biodiversity and ecosystem services, (2) availability of remaining semi-natural areas and (3) reversibility as it relates to multiple human activities (e.g. flood protection, hydropower and navigation). Our approach can thus serve as a strategic planning tool for the Danube and provide a method for similar analyses in other large river-floodplain systems.

Social equity shapes zone-selection: Balancing aquatic biodiversity conservation and ecosystem services delivery in the transboundary Danube River Basin.

Freshwater biodiversity is declining, despite national and international efforts to manage and protect freshwater ecosystems. Ecosystem-based management (EBM) has been proposed as an approach that could more efficiently and adaptively balance ecological and societal needs. However, this raises the question of how social and ecological objectives can be included in an integrated management plan. Here, we present a generic model-coupling framework tailored to address this question for freshwater ecosystems, using three components: biodiversity, ecosystem services (ESS), and a spatial prioritisation that aims to balance the spatial representation of biodiversity and ESS supply and demand. We illustrate this model-coupling approach within the Danube River Basin using the spatially explicit, potential distribution of (i) 85 fish species as a surrogate for biodiversity as modelled using hierarchical Bayesian models, and (ii) four estimated ESS layers produced by the Artificial Intelligence for Ecosystem Services (ARIES) platform (with ESS supply defined as carbon storage and flood regulation, and demand specified as recreation and water use). These are then used for (iii) a joint spatial prioritisation of biodiversity and ESS employing Marxan with Zones, laying out the spatial representation of multiple management zones. Given the transboundary setting of the Danube River Basin, we also run comparative analyses including the country-level purchasing power parity (PPP)-adjusted gross domestic product (GDP) and each country's percent cover of the total basin area as potential cost factors, illustrating a scheme for balancing the share of establishing specific zones among countries. We demonstrate how emphasizing various biodiversity or ESS targets in an EBM model-coupling framework can be used to cost-effectively test various spatially explicit management options across a multi-national case study. We further discuss possible limitations, future developments, and requirements for effectively managing a balance between biodiversity and ESS supply and demand in freshwater ecosystems.

Participatory coastal management through elicitation of ecosystem service preferences and modelling driven by "coastal squeeze".

The Baixo Vouga Lagunar (BVL) is part of Ria de Aveiro coastal lagoon in Portugal, which is classified as a Special Protection Area under the European Habitats and Birds Directives. This part of the system, corresponding to the confluence of the Vouga River with the lagoon, is very important culturally and socioeconomically for the local communities, taking place several human activities, especially agriculture. To prevent salt water intrusion from the Ria de Aveiro into agriculture fields, a floodbank was initiated in the 90's. In frame of ongoing changes in Ria de Aveiro hydrodynamics, the existing floodbank will be now extended, introducing further changes in the ecological dynamics of the BVL and its adjacent area. As a consequence, the water level in the floodbank downstream side is expected to rise, increasing the submersion period in tidal wetlands, and leading to coastal squeeze. The aim of this study is to apply an ecosystem based-management approach to mitigate the impacts on biodiversity resulting from the management plan. To do so, we have modelled the implications of the changes in several hydrological and environmental variables on four saltmarsh species and habitats distribution, as well as on their associated ecosystem services, both upstream and downstream of the floodbank. The ecosystem services of interest were prioritized by stakeholders' elicitation, which were then used as an input to a spatial multi-criteria analysis aimed to find the best management actions to compensate for the unintended loss of biodiversity and ecosystem services in the BVL. According to our results, the main areas to be preserved in the BVL were the traditional agricultural mosaic fields; the freshwater courses and the subtidal estuarine channels. By combining ecology with the analysis of social preferences, this study shows how co-developed solutions can support adaptive management and the conservation of coastal ecosystems.

Cost-effective restoration and conservation planning in Green and Blue Infrastructure designs. A case study on the Intercontinental Biosphere Reserve of the Mediterranean: Andalusia (Spain) - Morocco.

Green and Blue Infrastructure (GBI) is a network designed and planned to deliver a wide range of ecosystem services and to protect biodiversity. Existing GBI designs lacked a systematic method to allocate restoration zones. This study proposes a novel approach for systematically selecting cost-effective areas for restoration on the basis of biodiversity, ecosystem services, and ecosystem condition to give an optimal spatial design of GBI. The approach was tested at a regional scale, in a transboundary setting encompassing the Intercontinental Biosphere Reserve of the Mediterranean in Andalusia (Spain) - Morocco (IBRM), across three aquatic ecosystems: freshwater, coastal and marine. We applied Marxan with Zones to stakeholder-defined scenarios of GBI in the IBRM. Specifically, we aimed to identify management zones within the GBl that addressed different conservation, restoration and exploitation objectives. Although almost all conservation targets were achieved, our results highlighted that the proportion of conservation features (i.e., biodiversity, ecosystem services) that would be compromised in the GBl, and the proportion of provisioning services that would be lost due to conservation (i.e., incidental representation) are potentially large, indicating that the probability of conflicts between conservation and exploitation goals in the area is high. The implementation of restoration zones improved connectivity across the GBI, and also achieved European and global policy targets. Our approach may help guide future applications of GBI to implement the flexible conservation management that aquatic environments require, considering many areas at different spatial scales, across multiple ecosystems, and in transboundary contexts.