Human societies have been built on using biodiversity and thus value biodiversity for its intrinsic worth and its contribution toward the production of various ecosystem goods and services (ESs) that contribute to human well-being [1
]. Biodiversity also stabilizes the delivery of ecosystem services through time [3
]. However, many activities crucial for subsistence living have led to biodiversity loss [2
]. Among these activities is the human-induced land use and land cover (LULC) change that causes biodiversity decline through the loss, alteration, and fragmentation of habitats [6
]. Land use has been identified as the leading driver of global biodiversity change by the year 2100 [8
]. This is of concern not only for the ecosystems but also for human well-being, as loss of biodiversity also translates to loss of many important ecosystem goods and services. In the future, the world will face new interconnected land use challenges resulting from the anticipated increased in the demand for goods and services from limited land resources. [9
]. Land managers and policymakers are increasingly concerned about how to improve land management with a minimum impact on biodiversity and corresponding ecosystems. However, there is a significant gap in our understanding of the spatial and temporal ecology of biodiversity and ecosystem goods and services. This, in turn, impedes our ability to manage landscapes sustainably [10
]. Several global initiatives such as the Millenium Ecosystem Assessment (MA), the Intergovernmental Panel on Biodiversity and the Ecosystem Services (IPBES), and The Economics of Ecosystems and Biodiversity (TEEB) have emphasized the need to assess biodiversity and ecosystems to develop strategies for promoting human and ecological welfare [2
In this context, the notion of developing LULC scenarios with a subsequent assessment of changes in biodiversity and ecosystem services is gaining momentum. Scenarios provide insights into the future through visual representations based on assumptions or data trajectories. The information on the potential outcomes of alternative scenarios can be an important tool while making difficult policy decisions [13
]. These scenarios can communicate associated benefits and tradeoffs to policymakers and land planners and managers [10
]. They show that spatial and temporal changes in LULC can be used to assess biodiversity and ESs and the trade-offs that occur between different land uses. Advances in modeling software systems have made it possible to develop future scenarios and map biodiversity from different land use scenarios [17
]. A wide range of approaches are available to model LULC change scenarios ranging from machine learning approaches, cellular approaches, economic approaches, agent-based approaches, and hybrid approaches [20
]. While measuring and valuing biodiversity has progressed [21
], it remains a challenging task [8
]. One way to assess biodiversity is to spatially model habitat quality [22
]. However, this process requires incorporating many properties of the ecosystem, which are complex. Further, traditional terrestrial data collection methods have proved to be extremely time and resource-consuming [22
]. There are also accessibility difficulties in many valuable habitats [22
]. In addressing these challenges, models based on geographic information systems (GIS) offer varying strengths and weaknesses to the processing of LULC changes and biodiversity [20
]. The methods for habitat quality analysis have evolved into an active community with wide-scale operational applications [26
Many studies have modeled LULC changes and their effects on biodiversity. However, in developing countries like Indonesia, with usually limited data availability, modeling approaches are in their infancy. Moreover, no study has explicitly focused on LULC changes and biodiversity at both the local and landscape scales in Indonesia. This study is the first application of GIS-based integrated modeling tools in the assessment of biodiversity at a landscape scale in the district. In this paper, we use the Land Change Modeler (LCM) and InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Scenario Generator to develop alternative LULC scenarios. Further, we used the InVEST Habitat Quality model to assess habitat quality under each of the scenarios. The specific aim was to develop three alternative LULC scenarios and assess the spatial distribution of biodiversity under each of them. The Pulang Pisau district of Central Kalimantan province in Indonesia was used as a case study. Central Kalimantan hosts a wide diversity of flora and fauna [27
]. However, the rich biodiversity in the province of Kalimantan is declining at an unprecedented rate due to rapid LULC changes, mainly forest conversion caused by land-based economic development (mainly the establishment of oil palm plantations) [28
]. Oil palm expansion and deforestation in Central Kalimantan is one of the highest in Indonesia [29
] During the period of 1990–2005, at least 56% of oil palm expansion in Indonesia came from forest conversion [30
]. The comparison of the land cover map of Pulang Pisau between 2006 and 2015 shows that about 15% of the converted forests during this time came from new oil palm plantations. In the future, further economic development poses an additional threat to the biodiversity in the district. The information on the impact of possible LULC changes on biodiversity will help policymakers formulate appropriate land use policy.
4. Discussion and Conclusions
The authors modeled LULC changes and analyzed their effects on biodiversity under three different LULC scenarios. LCM and the InVEST Scenario Generator were used to model LULC, while the InVEST Habitat Quality tool was used to determine the effects on biodiversity. The study demonstrated that the trend of LULC change in the district will continue to be exacerbated in the BAU scenario, resulting in a higher loss of habitat quality for biodiversity. This loss is also well pronounced in the protected areas. Although agricultural expansion (development scenario) will result in lower habitat loss than the BAU scenario, there is a notable decrease in habitat quality due to external threats from surrounding anthropogenic land use practices. On the other hand, the LULC changes in the conservation scenario, which envision forest restoration, improve habitat quality for biodiversity. This shows that in terms of biodiversity, the regulated oil palm expansion in the DS is better off than BAU, where the oil palm expansion follows past trends. Also, if the government implements the restoration plan it will significantly improve habitat quality and biodiversity in the district. In summary, the continuing LULC changes will have a significant impact on the habitat quality and biodiversity in the district. This loss of biodiversity could be halted by implementing conservation measures such as the restoration of degraded lands.
The habitat quality modeling using scenarios in this study is comparable to other studies. [15
]. Three scenarios were developed for a forested landscape in Iran, with business-as-usual, protection-based zoning, and collaborative zoning to map biodiversity changes to inform policymakers in the region [15
]. The researchers held consultative meetings with conservation bodies in the region to develop scenarios that reflected changes in protected area boundaries in the landscape. Their results showed that excluding areas that are vulnerable to degradation from protection and establishing new protection in high quality forest cover resulted in an increase in high quality habitats in the landscape. In another study, the impact of land use change on biodiversity was assessed in five planned land reconfiguration futures, namely irrigated cropping, biodiversity and environmental planting, grazing, perennial horticulture, and agroforestry [41
]. The investigators used the InVEST Habitat Quality model and demonstrated spatial approaches to classifying landscapes for habitat quality based on the size, density, and distribution of native vegetation in the landscape. A study in China modeled land use change and its effect on biodiversity conservation using two land use management scenarios [42
]. The investigators developed scenarios based on the protection levels of the local government plans and projected biodiversity at local and regional scales.
We used an integrated modeling approach for scenario building, which is a distinguishable feature of this study. We modeled the LULC changes using statistical projection and vision-based scenario building processes. While the LCM used LULC from two points in time to project a future LULC based on the drivers of land cover change, the InVEST scenario generator simplified the process of modeling the future by using input from stakeholders and land suitability factors. However, there are several limitations to using the scenario-based approach for modeling biodiversity changes. First, although scenarios help to develop understanding of plausible future LULC, they are based on assumptions and do not represent an absolute future outcome. Second, the study considered LULC as a driving force of changes in biodiversity. However, many other important drivers influence biodiversity such as hunting, pollution and climate change, which are not captured in the model. In addition, other indirect factors, such as the market price of agricultural goods, which influence demand (and thus the need for an extension of plantations), were not incorporated in this study due to model limitations. As the models used in this study are not iterative and no validation was performed, there could be uncertainties regarding the results of this study. Therefore, where time and data are available, validation through field studies and more detailed finer resolution assessments would be suitable.
This study demonstrates a GIS-based integrated modeling approach to analyze the spatial pattern of LULC change under different scenarios and the resulting effects on habitat quality. Since the models can be used with readily available datasets, these methods could be used for rapid biodiversity assessments in data-poor regions like Indonesia. This study provides a real-world application of modeling to better inform land use planning and policies. The use of spatial tools to illustrate explicit LULC changes and their impact on habitat quality is useful in making land use management decisions. The conservation scenario in this study also provides a general guide to land managers and planners for the conservation of the pristine habitats in the district. Further, the approach of involving stakeholders contributes to sensitizing them to the impacts of LULC changes. During consultations, stakeholders were encouraged to develop appropriate land use management decisions for landscape-level conservation. The stakeholders also iterated that government persistence in biodiversity conservation and technical and financial support from the donor agencies is needed to implement the restoration plan. The findings of this study serve as a feasibility analysis based on a biophysical assessment of habitat quality. Further validation through review and in-depth field studies is recommended. Although biodiversity conservation results in improvements in many ESs there are several tradeoffs. Thus, it is essential to explore the synergies and tradeoffs between biodiversity and ecosystem services in landscape level planning. We recommend further studies on other important ecosystem services in the district to improve the findings of this study.