Characteristic trajectories of ecosystem services in mountains

Abstract

Intensification of land use and management over recent decades has resulted in trade-offs between food or timber production and other ecosystem services (ES). Despite an increase in scholarly publications on ES, the temporal aspects of ES trade-offs have largely been neglected to date. Here we explore how past and future land-use trajectories (pathways of change) influence ES over time, using mountain landscapes as a model. Based on a synthesis of 51 cases of temporal changes in ES within mountain landscapes, we analyze how changes in land-use intensity influence the supply of ten key services and we describe six typical examples (archetypes) of ES change. Our analysis reveals that land-use intensity is an important factor shaping these archetypes. Land-use intensification often degrades ES (eg recreation and water regulation), with the exception of services targeted by intensification (food or timber) and with differences between forest and agricultural intensification. Service degradation following intensification is not always reversed by reductions in land-use intensity (termed "extensification"). A lthough human modifications of ecosystems affect the flow of ecosystem services (ES), policy and management interventions can be designed to at least partially restore ES and their contribution to human well-being (MA 2005). However, the management of multiple ES across landscapes can be challenging, given that tradeoffs between services often occur over space and time (Raudsepp-Hearne et al. 2010;Locatelli et al. 2014), for example, where increasing food production leads to decreases in regulating services. In contrast to the spatial dimension of ES, little attention has been paid to the temporal dimension of ES trade-offs (Holland et al. 2011;Bennett et al. 2015). Because critical land-use changes are anticipated in the future and competing demands on land imply trade-offs (Laurance et al. 2014), understanding the temporal changes in multiple ES caused by current and impending land-use is necessary to inform sound management decisions (Bennett et al. 2009), especially when these decisions are intended to re-establish complex services and restore biodiversity. Our aim is to contribute to this understanding by analyzing the temporal changes of multiple ES, using mountain landscapes as a model. The temporal trajectories (or pathways of changes) of ES are typically driven by human activities that modify land use and cover (Lautenbach et al. 2011;Renard et al. 2015). Changes in land use and land cover may lead to changes in ES, both qualitative (converting forests to agricultural lands replaces timber with food production) and quantitative (water-quality metrics are negatively affected by increased fertilizer use with agriculture) (Foley et al. 2005. Land intensification (ie increase in land-use intensity) has been a major motivation behind land-management changes in the past 50 years, and has resulted in rapid increases in agricultural yields but has also lead to biodiversity losses (Lambin et al. 2000). Conversely...

Forest ecosystem-service transitions: the ecological dimensions of the forest transition

“…3). This is consistent with other reviews of ecological service trade-offs following reforestation in montane areas (Locatelli et al 2017), and highlights the anthropogenic nature of emergent forest landscapes and their ecosystems (Lugo 2009). The case studies also illustrate that spatial patterns of new forests are important: in all our case studies, reforestation was concentrated in proximity to water sources or aquifers, which resulted in relatively high water provision services per unit area of forest post transition.…”

“…Forest cover may increase because people encourage or permit forests to return for recreation or spiritual reasons because they believe forests possess intrinsic value, or to conserve biodiversity. Cultural changes can be prompted by economic development (Stern 2004) combined with forest scarcity (Wilson 2015), as when, for example, a growing middle class desires forests for recreation and natural amenities (Locatelli et al 2017). But cultural changes are also shaped and motivated by social movements and processes that operate across these paradigms.…”

Catalyzing Transformations to Sustainability in the World's Mountains

“…Tourism tends to stand alone as an ES, sharing a weak, positive association with hazard protection. Notably, tourism exhibits a weak, negative association with ES such as food and forage, with implications for decision making and identifying trade‐offs in ES (e.g., Locatelli et al, ).…”

“…Alessa et al () present diverse examples of successful SES work in mountains but with a focus on the western United States. Data‐oriented global mountain syntheses have focused on the elevational distributions of protected areas in mountains using high‐resolution digital elevation models (Elsena et al, ), the mapping and classification of mountains (Körner et al, ; Price et al, ; Sayre et al, ), continental‐scale syntheses of observed alpine flora changes over time (Gottfried et al, ), estimates of future changes to biophysical aspects of the cryosphere (Huss et al, ), and trade‐offs in clusters of ES over time (Locatelli et al, ). No concerted efforts have synthesized the complex threats to MtSES worldwide or highlighted opportunities to address these challenges, partly due to the diverse sociocultural, political, and economic contexts in which mountains occur.…”

Relationships Between Ecosystem Services: Comparing Methods for Assessing Tradeoffs and Synergies

“…Static spatial correlation studies have been criticized because they omit landscape history, an important factor in understanding ES relationships (Tomscha and Gergel, 2016), and because they often present ES spatial correlations as interactions, even when they are not; they are simply evidence of nonrandom associations (Bennett et al, 2009;Cord et al, 2017). Less frequently applied are methods integrating ES temporal dynamics by analyzing historical datasets (Renard et al, 2015;Tomscha and Gergel, 2016;Zheng et al, 2014); as few studies have data on ES over both time and space Dittrich et al, 2017;Locatelli et al, 2017). Static spatial correlations can be calculated at different dates in order to detect changes in ES relationships overtime (Renard et al, 2015;Tomscha and Gergel, 2016).…”