Projecting Changes to Coastal and Estuarine Ecosystem Goods and Services—Models and Tools

Nathaniel S Lewis,Richard S Fulford,Chanda J Littles,Darryl E Marois

doi:10.1007/978-3-030-45843-0_12

Nathaniel S Lewis, Richard S Fulford + Show 2 more

Open Access

https://doi.org/10.1007/978-3-030-45843-0_12

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Abstract

Coasts and estuaries provide an abundance of ecosystem goods and services (EGS) to humans worldwide. Models that track the supply, demand, and change in EGS within these ecosystems provide valuable insights that have applications in the context of land-use planning, decision-making, and coastal community engagement. However, developing models for use in coastal and estuarine ecosystems is challenging given the multitude and variability of potential input variables, largely due to their dynamic nature and extensive use. Models that can incorporate scenarios of environmental change to forecast changes in EGS endpoints are highly valuable to decision-makers, but only a minor proportion of available EGS models offer this utility. In this chapter, we describe the domain of models most useful to coastal decision-makers, present models at multiple scales that can predict EGS changes, and examine specific examples that epitomize this utility. We also highlight common difficulties in modeling coastal and estuarine EGS and propose suggestions for integrating EGS models into the coastal management decision-making process during times of increasing environmental change.

Highlights

Coasts and estuaries provide an abundance of ecosystem goods and services (EGS) to humans worldwide
We present a suite of models that exemplify the approach of predicting EGS changes at different scales, outline the domain of models that may offer the most utility to coastal decision-makers, present examples epitomizing this utility, and highlight common difficulties across coastal and estuarine EGS models
The first model, HexSim, is a mechanistic model that describes living populations by tracking individuals; the second, XBeach, is a mechanistic model for estimating shore protection; the third, Atlantis, is a whole system model used in fishery management; the fourth, InVEST, contains many sub-models that can predict delivery of a suite of EGS; and the fifth, Artificial Intelligence for Ecosystem Services (ARIES), uses machine learning to trace ecosystem service flows to beneficiaries

Summary

Modeling Changes in Coastal and Estuarine EGS

Coastal and estuarine ecosystems host some of the most dynamic and productive habitats in the world (Costanza et al 1997; Costanza et al 2014), including: seagrass beds, mangroves, coral reefs, salt marshes, sandy beaches, and dunes (Barbier et al 2011). There are many EGS models (Bagstad et al 2013b; Turner et al 2016; Gret-Regamey et al 2017; Little et al 2017), this chapter focuses on models that provide predictions of how EGS may be affected as coastal ecosystems undergo change We further narrow this focus to models that demonstrate a high degree of utility to decision-makers based on relevant spatial scales and endpoints. We limit our focus to models with spatial scales ranging from local (e.g., estuary or bay) to regional (e.g., U.S Pacific West Coast), as they are the most likely to provide useful information when making resource management decisions (Turner et al 2016) These models vary greatly in their final output or endpoint. The first model, HexSim, is a mechanistic model that describes living populations by tracking individuals; the second, XBeach, is a mechanistic model for estimating shore protection; the third, Atlantis, is a whole system model used in fishery management; the fourth, InVEST, contains many sub-models that can predict delivery of a suite of EGS; and the fifth, ARIES, uses machine learning to trace ecosystem service flows to beneficiaries

HexSim Model

XBeach Model

Atlantis Model

InVEST Model Suite

ARIES Model Suite

Common Difficulties

Emerging Issues and Future Directions