Conservation areas generally consist of natural resources that must be sustained and preserved, such as habitats, species, ecosystems, and the services they provide. Accordingly, the natural environment of the conservation area is preserved and protected by policies that restrict certain human activities.
Many academic studies have focused on the provisioning capacity of natural resources, but the importance and provisioning capacity of ecosystem services have often been neglected. Along with the increasing awareness of how human activities exploit natural capital, conservationists and scientists have raised a call to establish, implement, and execute appropriate policies that incorporate ecosystem services into conservation strategies for real-word decision making since habitat quality and ecosystem supply are essential to identifying areas that require conservation measures. Although our scientific understanding of individual ecosystem services is becoming increasingly comprehensive, the inter-relationship and spatial patterns among multiple ecosystem services are scarcely discussed. However, appropriate policies that result in effective conservation and maintain ecosystem service benefits are necessary for human wellbeing.
Professor Yu-Pin Lin from the Department of Bioenvironmental Systems Engineering at National Taiwan University and his research team proposed a novel systematic approach to synchronously consider both the spatial connectivity of selected conservation areas and a dynamic system of multiple ecosystem services in balance. Habitat quality autocorrelations, multiple ecosystem services, and spatial patterns were considered when identifying ecosystem services conservation areas that are the most ‘efficient.’
The Wutu watershed located east of Taipei City was selected as the study area and is covered primarily by forest (83% of the total area). To estimate the habitat quality and ecosystem services, land use and climate data were used as inputs, and habitat quality and ecosystem services (HQ-ESs) were selected for quantification with the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) tool, including carbon storage, biodiversity (using habitat quality as a proxy), nitrogen retention, phosphorous retention, soil retention, and water yield. From this information, quantified ecosystem services were derived, and a sensitivity analysis of nutrient retention was conducted. The Local Indicators of Spatial Association (LISA)-Zonation R-software package was developed by the research team. R (https://www.r-project.org/), a free software source code form, and QGIS 2.2.0 (http://www.qgis.org/en/site/), an open Source Geographic Information System (GIS) licensed under the GNU General Public License, are adopted to integrate the LISA method into Zonation. This software developed by the team consisted of two main components: spatial autocorrelation analysis with LISA and conservation priority with Zonation. This integrated structure performs systematic conservation planning based on InVEST outputs and autocorrelation analyses, thus simulating potential conservation areas for habitat quality and multiple ecosystem services (Figure 1).
Figure 1. Study flow chart (modified from Lin et al., 2017)
Using three scenarios, the team then compared and analyzed the simulated conservation areas estimated by Zonation. In Scenario 1, the spatial distributions and quantities of HQ-ESs were derived from InVEST, resulting in the smallest proportions of conserved ecosystem services. In Scenario 2, hotspots of HQ-ESs were identified using LISA, resulting in the largest proportions of conserved ecosystem services. In Scenario 3, the InVEST ecosystem service values and LISA-derived HQ-ES hotspots were combined, resulting in proportions of conserved ecosystem services that fell between Scenarios 1 and 2 (Figure 2). The simulated results reveal that a more efficient method for selecting conservation areas is available when spatial autocorrelation applied in the conservation area selection process is incorporated with the balance of ecosystem services and spatial connectivity data of selected conservation areas. In all three scenarios, the same ecosystem services were identified, with target conservation areas of 10%, 20%, and 30% of the study area (Figure 3).
Figure 2. Potential conservation areas in Scenario 2 identified using Zonation with different representation targets: (a) 10%, (b) 20%, and (c) 30% (modified from Lin et al., 2017)
Figure 3. Proportions of ecosystem services in the reserve areas under Scenarios 1, 2 and 3 with different representation targets: (a) 10%, (b) 20%, and (c) 30% (modified from Lin et al., 2017)
Scenarios 2 and 3 identified more efficient conservation areas by selecting high proportions of forest reserve sites as the conservation area, while Scenario 1 identified conservation areas scattered over the entire study area and therefore comprised lower proportions of reserve sites. Although no single conservation strategy can be applied to all regions or all situations, the findings suggest that conservation area selection efficiency can be increased. Furthermore, we can better protect biodiversity and multiple ecosystem services when the total habitat area, habitat quality, connectivity, and spatial patterns are included in the conservation area evaluation process.
Reference
Yu-Pin Lin, Wei-Chih Lin, Yung-Chieh Wang, Wan-Yu Lien, Tao Huang, Chih-Chen Hsu, Dirk S. Schmeller, and Neville D. Crossman (2017). Systematically designating conservation areas for protecting habitat quality and multiple ecosystem services. Environmental Modelling & Software, 90, 126-146. DOI:10.1016/j.envsoft.2017.01.003.
Professor Yu-Pin Lin
Department of Bioenvironmental Systems Engineering
yplin@ntu.edu.tw