Artificial Intelligence For Ecosystem Services Research Articles

Karst ecosystem services and land-covers dynamic: case study from Karst of Tuban and Tuban Regency, Jawa Timur, Indonesia

Karst area provides numerous ecosystem services from the interaction among its components of water, soil, and vegetation. However, a threat from unsustainable practice and resource overuses could diminish those services. Therefore, it is critical to steadily monitor the karst dynamics, especially in high-activity karst like the Karst of Tuban. This study aims to capture the karst dynamic, particularly the land-use change and how its impact on ecosystem services (ES). To achieve the research objective, an artificial intelligence tool of ARtificial Intelligence for Ecosystem Services (ARIES) is employed. ARIES examines a dataset for 6 years intervals beginning in 2012 to 2018 consisting of land-use and ecosystem change and ES using 150 m spatial resolution data. During that period, Tuban Regency undergoes a heavy change in artificial surface and a significant decrease in the water body and cropland. From the result, ARIES as the ES estimation tool can be used for robust calculation and preliminary study due to its limitation. The results also show the urge to address the issue of carbon storage and sediment regulation in the karst area and the land-cover changes in the Tuban Regency.

Economic Valuation of Auctioned Tourist Hunting Blocks in Tanzania

Economic valuation of ecosystems is increasingly being recognized as an important exercise to inform sustainable utilization and conservation of natural assets. It helps in planning and establishing fair profit margins that accrue either directly or indirectly from the consumptive and non-consumptive uses of ecosystem goods and services. This paper is based on a study which estimated the economic values of tourist hunting blocks (HBs) in Tanzania using the Analytic Multicriteria Valuation Method (AMUVAM). The study used a sample size of 12 out of 24 vacant hunting blocks which were to be auctioned to potential hunting companies in December 2022. The economic values of HBs were estimated using the time horizon of 10 years (the mean tenure for winning company). The results show that the economic values ranged from USD 6,215,588 to USD 653,470,695 per hunting block and the Existence Value (EV) constituted about 19% of the Total Economic Value (TEV). EV ranged from USD 632,210 to USD 125,147,285. The study underscores the need for decisions to allocate ecosystems, such as HBs, to both direct and indirect uses, to be guided by a though understanding of their values. We further recommend building the capacity of staff charged with the role of managing and allocating uses of these ecosystems to enable them undertake economic valuation of ecosystems using both simple and more robust analytical tools, such as the GIS, relational databases, and worldwide websites based tools, like InVEST (Integrated Valuation of Environmental Services and Tradeoffs), ARIES (Artificial Intelligence for Ecosystem Services), and Co$ting Nature.

Biophysical and economic assessment of four ecosystem services for natural capital accounting in Italy

We present methods and results of country-based natural capital assessments for four ecosystem services (ES) in Italy. The spatial mapping and the assessment have been carried out in both physical and monetary terms for (i) crop pollination, (ii) outdoor recreation, (iii) flood regulation (iv) and water provision, using the ARIES (Artificial Intelligence for Ecosystem Services) technology, which provides and integrates the necessary data and models. Extent, supply and use accounting tables have been developed for the same ecosystem services in line with the United Nations System of Environmental Economic Accounting (UN-SEEA) guidelines and Experimental Ecosystem Accounting (EEA) initiative. This work represents a first official and nationwide assessment of ecosystem services for the Italian Government in accomplishment of the Italian law n. 221/2015, applying a variety of different models and economic valuation methods to provide systematic and replicable information on natural capital through national accounting tables. We find that land management and maintenance of the countryside and forestland, which represent the typical Italian landscape, are fundamental. Our application also identifies several modelling challenges that need to be addressed before a methodological path for integrated ecosystem and economic accounting may be considered rigorous and reliable.

Landscape aesthetic modelling using Bayesian networks: Conceptual framework and participatory indicator weighting

Landscape aesthetics provides humans with health and social benefits contributing to overall well-being, thus representing a cultural ecosystem service. Landscape biophysical and social attributes create information that is interpreted as either beauty or blight by the mind of the beholder. The ARtificial Intelligence for Ecosystem Services (ARIES) modelling platform is quite suited to the landscape aesthetics paradigm, since it is characterized by both a strong focus on the spatial connectivity between ecosystems and beneficiaries and by the employment of Bayesian networks to quantify and communicate uncertainty. A conceptual framework based on landscape aesthetic abstraction levels was proposed to build these Bayesian networks, progressively linking tangible indicators to abstract dimensions and concepts. As input to ARIES, a simple and rapid participatory methodology was designed to weight indicators according to stakeholder preferences, from which values the probabilities were derived for use in canonical probabilistic models. The participatory indicator identification methodology generated both abstract and concrete terms, suggesting that the process should be supervised to obtain clear and tangible indicators. A sensitivity analysis revealed that individual visual blight indicators had more profound impacts on landscape aesthetic while the effect of beauty indicators was more subtle and balanced. Although the methodology may require a relatively large number of participants to derive probabilities, the procedure was not overly challenging for the participants. This methodology has the potential to be implemented widely, in various contexts and for different periods, accounting for alternative spatiotemporal variations and land cover contexts.

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.

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.

Comparing strengths and weaknesses of three ecosystem services modelling tools in a diverse UK river catchment

Ecosystem services modelling tools can help land managers and policy makers evaluate the impacts of alternative management options or changes in land use on the delivery of ecosystem services. As the variety and complexity of these tools increases, there is a need for comparative studies across a range of settings, allowing users to make an informed choice. Using examples of provisioning and regulating services (water supply, carbon storage and nutrient retention), we compare three spatially explicit tools - LUCI (Land Utilisation and Capability Indicator), ARIES (Artificial Intelligence for Ecosystem Services) and InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs). Models were parameterised for the UK and applied to a temperate catchment with widely varying land use in North Wales. Although each tool provides quantitative mapped output, can be applied in different contexts, and can work at local or national scale, they differ in the approaches taken and underlying assumptions made. In this study, we focus on the wide range of outputs produced for each service and discuss the differences between each modelling tool. Model outputs were validated using empirical data for river flow, carbon and nutrient levels within the catchment. The sensitivity of the models to land-use change was tested using four scenarios of varying severity, evaluating the conversion of grassland habitat to woodland (0-30% of the landscape). We show that, while the modelling tools provide broadly comparable quantitative outputs, each has its own unique features and strengths. Therefore the choice of tool depends on the study question.

Modeling the effects of urban expansion on natural capital stocks and ecosystem service flows: A case study in the Puget Sound, Washington, USA

Urban expansion and its associated landscape modifications are important drivers of changes in ecosystem service (ES). This study examined the effects of two alternative land use-change development scenarios in the Puget Sound region of Washington State on natural capital stocks and ES flows. Land-use change model outputs served as inputs to five ES models developed using the Artificial Intelligence for Ecosystem Services (ARIES) platform. While natural capital stocks declined under managed (1.3–5.8%) and unmanaged (2.8–11.8%) development scenarios, ES flows increased by 18.5–56% and 23.2–55.7%, respectively. Human development of natural landscapes reduced their capacity for service provision, while simultaneously adding beneficiaries, particularly along the urban fringe. Using global and local Moran’s I, we identified three distinct patterns of change in ES due to projected landuse change. For services with location-dependent beneficiaries – open space proximity, viewsheds, and flood regulation – urbanization led to increased clustering and hot-spot intensities. ES flows were greatest in the managed land-use change scenario for open space proximity and flood regulation, and in the unmanaged land-use change scenario for viewsheds—a consequence of the differing ES flow mechanisms underpinning these services. We observed a third pattern – general declines in service provision – for carbon storage and sediment retention, where beneficiaries in our analysis were not location dependent. Contrary to past authors’ finding of ES declines under urbanization, a more nuanced analysis that maps and quantifies ES provision, beneficiaries, and flows better identifies gains and losses for specific ES beneficiaries as urban areas expand.

Coupling virtual watersheds with ecosystem services assessment: a 21st century platform to support river research and management

The demand for freshwater is projected to increase worldwide over the coming decades, resulting in severe water stress and threats to riverine biodiversity, ecosystem functioning, and services. A major societal challenge is to determine where environmental changes will have the greatest impacts on riverine ecosystem services and where resilience can be incorporated into adaptive resource planning. Both water managers and scientists need new integrative tools to guide them toward the best solutions that meet the demands of a growing human population but also ensure riverine biodiversity and ecosystem integrity. Resource planners and scientists could better address a growing set of riverine management and risk mitigation issues by (1) using a ‘virtual watersheds’ approach based on improved digital river networks and better connections to terrestrial systems, (2) integrating virtual watersheds with ecosystem services technology (ARtificial Intelligence for Ecosystem Services: ARIES), and (3) incorporating the role of riverine biotic interactions in shaping ecological responses. This integrative platform can support both interdisciplinary scientific analyses of pressing societal issues and effective dissemination of findings across river research and management communities. It should also provide new integrative tools to identify the best solutions and trade‐offs to ensure the conservation of riverine biodiversity and ecosystem services. WIREs Water 2015, 2:609–621. doi: 10.1002/wat2.1106This article is categorized under: Water and Life > Conservation, Management, and Awareness

A methodology for adaptable and robust ecosystem services assessment.

Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant “one model fits all” paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES - both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts.

From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments

Ecosystem services mapping and modeling has focused more on supply than demand, until recently. Whereas the potential provision of economic benefits from ecosystems to people is often quantified through ecological production functions, the use of and demand for ecosystem services has received less attention, as have the spatial flows of services from ecosystems to people. However, new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems' capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems. Our analysis includes five ecosystem services: carbon sequestration and storage, riverine flood regulation, sediment regulation for reservoirs, open space proximity, and scenic viewsheds. Each ecosystem service is characterized by different beneficiary groups and means of service flow. Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use. With the exception of the carbon sequestration service, regions that actually provided services to people, i.e., connected to beneficiaries via flow paths, amounted to 16-66% of those theoretically capable of supplying services, i.e., all ecosystems across the landscape. These results offer a more complete understanding of the spatial dynamics of ecosystem services and their effects, and may provide a sounder basis for economic valuation and policy applications than studies that consider only theoretical service provision and/or use.

Comparing approaches to spatially explicit ecosystem service modeling: A case study from the San Pedro River, Arizona

Although the number of ecosystem service modeling tools has grown in recent years, quantitative comparative studies of these tools have been lacking. In this study, we applied two leading open-source, spatially explicit ecosystem services modeling tools – Artificial Intelligence for Ecosystem Services (ARIES) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) – to the San Pedro River watershed in southeast Arizona, USA, and northern Sonora, Mexico. We modeled locally important services that both modeling systems could address – carbon, water, and scenic viewsheds. We then applied managerially relevant scenarios for urban growth and mesquite management to quantify ecosystem service changes. InVEST and ARIES use different modeling approaches and ecosystem services metrics; for carbon, metrics were more similar and results were more easily comparable than for viewsheds or water. However, findings demonstrate similar gains and losses of ecosystem services and conclusions when comparing effects across our scenarios. Results were more closely aligned for landscape-scale urban-growth scenarios and more divergent for a site-scale mesquite-management scenario. Follow-up studies, including testing in different geographic contexts, can improve our understanding of the strengths and weaknesses of these and other ecosystem services modeling tools as they move closer to readiness for supporting day-to-day resource management.

Spatial dynamics of ecosystem service flows: A comprehensive approach to quantifying actual services

Recent ecosystem services research has highlighted the importance of spatial connectivity between ecosystems and their beneficiaries. Despite this need, a systematic approach to ecosystem service flow quantification has not yet emerged. In this article, we present such an approach, which we formalize as a class of agent-based models termed “Service Path Attribution Networks” (SPANs). These models, developed as part of the Artificial Intelligence for Ecosystem Services (ARIES) project, expand on ecosystem services classification terminology introduced by other authors. Conceptual elements needed to support flow modeling include a service's rivalness, its flow routing type (e.g., through hydrologic or transportation networks, lines of sight, or other approaches), and whether the benefit is supplied by an ecosystem's provision of a beneficial flow to people or by absorption of a detrimental flow before it reaches them. We describe our implementation of the SPAN framework for five ecosystem services and discuss how to generalize the approach to additional services. SPAN model outputs include maps of ecosystem service provision, use, depletion, and flows under theoretical, possible, actual, inaccessible, and blocked conditions. We highlight how these different ecosystem service flow maps could be used to support various types of decision making for conservation and resource management planning.

Tools and methodologies to support more sustainable biofuel feedstock production

Increasingly, government regulations, voluntary standards, and company guidelines require that biofuel production complies with sustainability criteria. For some stakeholders, however, compliance with these criteria may seem complex, costly, or unfeasible. What existing tools, then, might facilitate compliance with a variety of biofuel-related sustainability criteria? This paper presents four existing tools and methodologies that can help stakeholders assess (and mitigate) potential risks associated with feedstock production, and can thus facilitate compliance with requirements under different requirement systems. These include the Integrated Biodiversity Assessment Tool (IBAT), the ARtificial Intelligence for Ecosystem Services (ARIES) tool, the Responsible Cultivation Areas (RCA) methodology, and the related Biofuels + Forest Carbon (Biofuel + FC) methodology.