A Methodology for Adaptable and Robust Ecosystem Services Assessment

Abstract

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.

Biodiversity and Resilience of Ecosystem Functions

“…97 Turnover in species communities might actually be the very thing that allows for resilient 98 functions. For example, in communities subjected to climatic warming, cold-adapted species 99 are expected to decline whilst warm-adapted species increase [30]. The decline of cold-100 adapted species can be limited through management [31], but in many cases their local loss 101 might be inevitable [32].…”

“…These proxy measures are currently used to inform on spatial and temporal trends in ecosystem function for the reporting and management of biodiversity change [4][5][6]. Such models use abiotic variables such as land cover, topography and climate data as explanatory variables in spatially-explicit statistical correlative models [96,97] or process models [98,99] in order to predict the provision of ecosystem functions and services. However, because models are parameterised and validated (where undertaken) on the current set of environmental conditions they are often only suitable for producing indicators of short-term ecosystem function flows rather than resilience under environmental perturbations (Figure 4).…”

Spatial patterns of agricultural expansion determine impacts on biodiversity and carbon storage

“…(Integrated Valuation of Ecosystem Services and Trade-Offs) (15), GLOBIO (16), ARIES (17), and others, are now being used to help inform a wide range of decisions related to land-use planning, infrastructure investment, urban water supplies, and disaster risk reduction (18)(19)(20)(21). In contrast to much of this sitebased work often occurring in governmental decision contexts, considerations of biodiversity and ES by corporations and multilateral organizations typically rely on approaches that assign an amount of benefit or impact on a per-area basis, without representation of spatial variability (22).…”

Livestock and Ecosystem Services: An Exploratory Approach to Assess Agri-Environment-Climate Payments of RDP in Trentino

“…Furthermore ARIES follows the open source software paradigm that relies on extensible, distributed data and model repositories made available as online services by the ARIES team and other independent research groups. Users commit to sharing data, thus contributing to the growth of the system over time [58]. Given the innovative nature of the tool, a relatively limited number of studies have so far been published [59] and, with just a few exceptions (e.g., the pilot assessment of ES in the Tecchio and Mergola watersheds, in Molise), none of them refer to Italian case studies.…”