Towards globally customizable ecosystem service models

Javier Martínez-López,Kenneth J Bagstad,Stefano Balbi,Ainhoa Magrach,Brian Voigt,Ioannis Athanasiadis,Marta Pascual,Simon Willcock,Ferdinando Villa

doi:10.1016/j.scitotenv.2018.09.371

Abstract

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.

Highlights

Over a decade after the publication of the Millennium Ecosystem Assessment (MEA, 2005), ecosystem service (ES) modeling is slowly becoming a more mature field
As land cover in both nations is increasingly split between natural ecosystems and cropland outside of protected areas with demand for pollination (Fig. 3B), these nations may face increasing spatial segregation between areas of pollinator supply and agricultural demand (Fig. 3C–D)
High topographic and land cover heterogeneity and an abundance of small farms may enable the persistence of some pollinator habitat at finer scales than our model could detect

Summary

Introduction

Over a decade after the publication of the Millennium Ecosystem Assessment (MEA, 2005), ecosystem service (ES) modeling is slowly becoming a more mature field. The generation of ES models will be accessible and rapid, yet customizable, efficiently reusing place-specific data and knowledge. Model and data customization are important for capturing local knowledge, improving credibility, and reducing the inherent inaccuracies of global and other large-scale data (Cerretelli et al, 2018; Zulian et al, 2018). Customization would extend beyond input data to include model structure, accounting for key differences in how ES are generated (Smith et al, 2017) and used by people (Wolff et al, 2017). Customizable ES models capable of synthesizing and reusing dispersed knowledge could help break from the long-standing dichotomy of using one-size-fits-all versus place-based approaches for ES assessments (Carmen et al, 2018; Rieb et al, 2017; Ruckelshaus et al, 2015)

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