Systematically designating conservation areas for protecting habitat quality and multiple ecosystem services

https://doi.org/10.1016/j.envsoft.2017.01.003Get rights and content

Highlights

  • We use the InVEST model to calculate habitat quality and five ecosystem services values.

  • We consider spatial autocorrelation and identify hotspots of ecosystem services.

  • We simulate reserve areas that protect ecosystem services by the Zonation software.

  • We developed the LISA-Zonation program for designating reserve areas.

Abstract

Habitat quality and ecosystem supply are important factors when identifying conservation areas. Traditionally, conservation planning approaches focus solely on habitat. In this study we calculated habitat quality and five other ecosystem service values through the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and also identified hotspots for each ecosystem service value through the Local Indicator of Spatial Association (LISA). Conservation areas that protect habitat quality and ecosystem services were simulated using the Zonation software with three scenarios of ecosystem service distribution. Four Boundary Length Penalties were also tested in terms of how well they produce suitable reserve sites. Finally, we developed the LISA-Zonation program which performs systematic conservation planning based on InVEST outputs. The ecosystem services hotspots represent spatial autocorrelations among neighboring cells and ecosystem service values, yielding conservation strategies which balance ecosystem service values with spatial connectivity. Our novel approach finds spatial autocorrelation of ecosystem services to identify conservation areas that provide potential benefits to people.

Introduction

Ecosystem services are central to modern conservation strategies. Traditionally, conservation planning has focused on the species and habitat component of natural capital, without due consideration to the many important ecosystem services supplied by that natural capital (Brooks et al., 2006). Thus, models designed to quantify supply of ecosystem services are developing rapidly. The mapping of ecosystem services is one of the most important methods for incorporating services into conservation policies. Mapping ecosystem services also contributes to our understanding of ecosystems requiring effective management (Burkhard et al., 2013, Crossman et al., 2013a, Crossman et al., 2013b). Although the concept of ecosystem services is mature to the point that it can inform policy-making, scientific understanding and implementation in real-world decision making remains a challenge (Ruckelshaus et al., 2015).

Recent efforts have been made to improve the evaluation and protection of ecosystem services in the conservation sciences (Daily et al., 2009). Numerous studies have used ecosystem services to generate conservation plans (Balvanera et al., 2014, Chan et al., 2006, Egoh et al., 2007, Egoh et al., 2010, Egoh et al., 2011, Chan et al., 2011, Moilanen et al., 2011). The inclusion of ecosystem services in conservation or land use planning offers an integrated multi-disciplinary approach to evaluating the benefits of various conservation objectives (Balvanera et al., 2014, Egoh et al., 2007) because of the potential to explicitly link conservation with human well-being (Chan et al., 2011). An integrated approach that focuses on the human benefits of conservation plans should lead to a better implementation of conservation actions within land use planning (Knight et al., 2006, Egoh et al., 2007).

Systematic conservation planning is a technique to identify optimal scenarios of habitat protection (Margules and Pressey, 2000, Lehtomäki and Moilanen, 2013) and restoration (Crossman and Bryan, 2006, Crossman and Bryan, 2009). Conservation planning models, such as Zonation, can be applied to ecosystem services (Moilanen et al., 2011, Thomas et al., 2013) to identify the best areas for preserving target habitat and the ecosystem services supplied by that habitat. Although much is known about individual ecosystem services, less attention is paid to the inter-relationship between multiple ecosystem services and the multiple ecosystem service benefits potentially available from systematic conservation planning approaches (Bennett et al., 2009, Chan et al., 2011). Typically the spatial relationships (Anderson et al., 2009), such as spatial autocorrelation between multiple ecosystem services is assumed (Troy and Wilson, 2006, Anderson et al., 2009) or is not considered (Chan et al., 2006). More recent studies have discussed the spatial correlations between biodiversity and ecosystem services (Bai et al., 2011, Maes et al., 2012, Bhagabati et al., 2014); some studies investigated the autocorrelation and heterogeneity of multiple ecosystem services (Wen et al., 2010, Maes et al., 2012, Plieninger et al., 2013, Su et al., 2014) and confirmed the existence of spatial autocorrelation for patterns of ecosystem service values and their changes (Wen et al., 2010, Plieninger et al., 2013, Su et al., 2014).

Here we use the spatial patterns and autocorrelations of habitat quality and multiple ecosystem services to identify efficient areas for conserving ecosystem services. Our novel approach takes into account the spatial connectivity of selected conservation areas and aims to select proportional representation of multiple ecosystem services rather than focus just on total amount of services. We first use the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tool to quantify six ecosystem services using land use and climate data. We then identify areas to conserve using three approaches, two of which combine Zonation with the autocorrelation of habitat quality and each ecosystem service. We introduce the Local Indicators of Spatial Association (LISA) statistic (Goovaerts, 2009) to analyse autocorrelation and identify ecosystem service hotspots. We develop a LISA-Zonation program in QGIS that performs autocorrelation analysis and implements Zonation for simulating potential conservation areas for habitat quality and multiple ecosystem services. Finally, we compare the potential conservation areas simulated under different scenarios of proportional selection.

Section snippets

Study area

The study area is the Wutu watershed located east of Taipei in Taiwan (Fig. 1). The total area of the Wutu watershed is 204 km2, and is covered mainly by forest (83% of the total area). Other land uses include built-up areas, agriculture, grassland, bare land, and water (Table 1). The elevation of this watershed ranges from 15 m to 873 m above sea level (Fig. S1). Slopes greater than 20° cover 16.3% of the total area (Fig. S1). Climate data were obtained from the Data Bank for Atmospheric

1Ecosystem service hotspots identified by LISA

The spatial distributions of carbon storage, habitat quality, and soil retention hotspots (Fig. 5a, b and e) have similar patterns. The hotspots are concentrated in the forested areas in the southern part of the study area where other land use types, especially built-up land, have less impact on ecosystem services. Most of the hotspots of nitrogen and phosphorous retention were located in the north-western part of the study area where multiple land-use types are present (Fig. 5c and d). Most of

Discussion

In our study, many of the spatial distributions of ecosystem service supply, as well as their hotspots, were related to forest areas, including carbon storage, habitat quality, soil retention, and water yield. Accordingly, Scenario 2 (LISA ecosystem service hotspots) and Scenario 3 (product of InVEST ecosystem service quantities and LISA ecosystem service hotspots) proposed more efficient methods for conservation areas by selecting more forested areas to conserve. Scenario 1 (InVEST ecosystem

Conclusions

The application of conservation planning tools that aim to map and prioritize ecosystem services data allow users to identify conservation areas which provide the greatest number of benefits. To achieve this, we bring together aspects of both ecosystem services and efficient reserve design into land management planning initiatives. We designed and compared reserve networks which consider autocorrelation with those which do not, we also considered reserved networks based on ecosystem services

Acknowledgements

The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contracts Nos. NSC 100-2410-H-002-196-MY3 and 101-2923-I-002-001-MY2, and the European Commission (EC) under the 7th Framework Programme for financially supporting this research under the SCALES projects (No. 226852). The authors also would like to thank Li-Chi Chiang and Johnathen Anthony for their constructive comments to this manuscript.

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