Forecasts of urbanization scenarios reveal trade-offs between landscape change and ecosystem services

Abstract

Context Expansion of urban settlements has caused observed declines in ecosystem services (ES) globally, further stressing the need for informed urban development and policies. Incorporating ES concepts into the decision making process has been shown to support resilient and functional ecosystems. Coupling land change and ES models allows for insights into the impacts and anticipated trade-offs of specific policy decisions. The spatial configuration of urbanization likely influences the delivery and production of ES. Objective When considering multiple ES simultaneously, improving the production of one ecosystem service often results in the decrease in the provision of other ES, giving rise to trade-offs. We examine the impact of three urban growth scenarios on several ES to determine the degree to which spatial configuration of urbanization and the development of natural land cover impacts these services over 25 years. Methods We couple land change and ES models to examine impacts to carbon sequestration, surface water-run off, nitrogen and phosphorus export, organic farming and camping site suitability, to determine trade-offs among the six ES associated with each spatial configuration for western North Carolina. Results Consequences of urban configurations are dramatic, with degraded ES across all scenarios and substantial variation depending on urban pattern, revealing trade-offs. Counter-intuitive trade-offs between carbon sequestration and lands available for organic farming and camping were observed, suggesting that no configurations result in mutual benefits for all ES. Conclusions By understanding trade-offs associated with urban configurations, decision makers can identify ES critical to an area and promote configurations that enhance those.

Projecting Urbanization and Landscape Change at Large Scale Using the FUTURES Model

“…We furthermore evaluated pattern accuracy using a landscape similarity index (LSI) [14], for a combined measure indicating how well patches of simulated urban development mimic the spatial structure of observed urban growth. Using FRAGSTATS [47,48], we calculated: (1) number of urban patches (NP); (2) largest patch index (LPI); (3) mean Euclidean nearest-neighbor distance (ENN_MN); and (4) mean perimeter-area ratio (PARA_MN) for the reference year 2011 and the simulation runs (n = 10 years; 1992-2011).…”

“…A particularly beneficial feature of land change models, especially in the decision support context, is their ability to help explore different scenarios of land management and better understand trade-offs between alternative futures based on different planning interventions [11][12][13]. While land change models have enhanced our understanding of where and when land systems change, their ability to accurately project spatial patterns of change varies [14]. Challenges exist for representing the socio-ecological interactions and local, context-specific characteristics necessary for approximating complex, fine-scale patterns that are generalizable to larger extents [15].…”

Biodiversity constraint indicator establishment and its optimization for urban growth: framework and application

“…Some studies analyze the linear correction between the urban expansion result and the ecological indicators result [16,20], but they do not reflect the interaction between the urban expansion process and the ecological process. Even the urban expansion model hardly considers urban growth impacts on ecological processes [18,31], nor does it join the ecological process with the synthesis calculation in the urban expansion model; therefore, most current models cannot detect urban expansion from the perspective of ecological mechanisms and processes [18,20,35].…”

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

“…Landscape configuration has been demonstrated to be an important factor in many research foci including biodiversity [27,28,[65][66][67], nutrient cycles [29], pollination [30], water quality [31], climate change [68], and mapping ecosystem services [69,70]. To account for this, a landscape similarity index (LSI) [71] was calculated for each simulation run using FRAGSTATS 4.1 [72].…”