programs such as the Conservation Reserve Program to capture the effects of incentives on landuse change and ecosystemservices ( Busch et al., 2012; Lewis et al., 2011; Lubowski et al., 2006; Nelson et al., 2008 ). The two great advantages of the revealed preference approach lie in its empirical basis and its inherent ability to capture the influence of multiple drivers in projecting landuse change. There are however, two outstanding questions about its application. Firstly, to what extent is the past able to predict the future? A complex systems view of landuse suggests that non-linear effects such as surprises, dependencies, and threshold effects may limit the predictability of past responses and demand new approaches to landuse change analyses ( Briassoulis, 2008; Dawson et al., 2010; Parker et al., 2008; Parrott and Meyer, 2012 ). Secondly, for most regions, the data required to build revealed preference models do not exist. To compensate for this, minimum data methods have been developed. Compared to revealed preference approaches, minimum data methods can provide landuse change models acceptable for use in policy analysis ( Antle and Valdivia, 2006 ). New methods combining the strengths of complex systems and econometric approaches are urgently required to support future assess- ments of market impacts on landuse and ecosystemservices. In modeling the impact of changes in landuse and management on ecosystemservices, substantial recent advances have been made and this is where the science underpinning the linkages in Fig. 1 is most developed. Several studies have measured, mapped, and modeled the spatial distribution of ecosystemservices produced from various land uses and these efforts have recently increased in sophistica- tion. For example, biophysical process models have been commonly used to map provisioning and regulating services such as food, bioenergy, water, and carbon ( Crossman and Bryan, 2009; Crossman et al., 2011b; Paterson and Bryan, 2012; Stoms et al., 2012 ). Recent calls have been made for advances in the mapping of cultural services ( Chan et al., 2012; Daniel et al., 2012 ). More sophisticated spatial properties such as supply, demand, flow, beneficiaries, and benefits transfer have been incorporated into the mapping of ecosystemservices and quantifying their benefits and costs ( Burkhard et al., 2012; Crossman et al., submitted for publication; Eigenbrod et al., 2010; Fisher et al., 2011; Syrbe and Walz, 2012 ). More focused progress in mapping the impact of landuse change on ecosystemservices is required for the accurate and meaning- ful assessment of the impact of market-based policy incen- tives.
The role played by Payments for ecosystemservices (PES) in promoting landuse interventions is increasingly being recognized as an important instrument for changing landuse management worldwide. Despite the increase, adoption of landuse interventions promoted by PES and factors influencing it are not well understood. This study was carried out to assess the adoption of landuse interventions promoted by PES scheme four years after its implementation in the Uluguru Mountains, Tanzania. The specific objectives of this study were to assess the adoption and factors that influenced it. The study employed questionnaire survey method to collect data from 219 households selected randomly. Focus group discussions and key informant interviews were also conducted to complement information obtained through questionnaire sur- veys. Descriptive and inferential statistical analyses were employed. Binary lo- gistic regression was used to analyse quantitative data obtained, while content analysis was applied to qualitative data. Results revealed that during the project implementation, 40% of the households did not adopt any of the pro- moted interventions. Unexpectedly, four years after the project ended, every household sampled had adopted the interventions. Households headed by younger heads and those with land ownership, households which received PES incentives and lived for a long time in the same area and those with more labour force and access to extension services were found to have adopted more interventions (p ≤ 0.05). Thus, the study concludes that socioeconomic characteristics, agricultural extension services and incentives initially provided to farmers are key factors influencing the adoption of landuse interventions. Therefore, it is recommended to the government that it should support far- How to cite this paper: Kagata, L., Mom-
As crop prices rise across the nation, and financial incentives for enrolling land in CRP stagnate, land-use conversion from CRP to crop production is increasing. Understanding the relationships between land choice factors in Cass County, ND is the intent of this study. As CRP contracts expire in Cass County, many of these acres will likely not be re-enrolled, but rather converted into cropland. By examining how decision factors influence farmers’ land-use choices, this study aims to predict how the acreage will be allocated and the potential repercussions it will have. While there have been similar studies done incorporating land-use change and CRP, the inclusion of satellite imagery and economic factors has been limited. This research converts the USDA NASS Cropland Data Layer (CDL) into field plots to limit the computational workload on performing a logistic regression on land-use choice parameters. The regression uses operating revenue and weather data as decision factors with the land-use of the parcel as the dependent variable. The relative effect of operating revenue and previous land-use was consistent across both CATMOD and MDC procedures in SAS. Previous years’ land-use was of far greater importance in determining subsequent land-use than operating revenue or weather variables.
et al., 2003) and support the livelihoods of at least 100 mil- lion people (Syampungani et al., 2009, Dewees et al., 2011) through a range of goods and services, leading them to be described as “ a pharmacy, a supermarket, a building supply store, and a grazing resource ” (Dewees et al., 2010: 61). They are also of global importance as a carbon store (Ribeiro et al., 2015). Rapid landuse change in these woodlands is occurring and is anticipated to continue (Ryan et al., 2016), leading to degradation with potentially devastating consequences for the livelihoods that they sup- port. Tobacco cultivation is a leading cause of landuse change and is common within miombo woodlands due to the suitability of the sandy soils and plentiful wood, which are needed to cure the leaves for storage (Geist, 1999). However, tobacco is nutrient hungry (Baris et al., 2000) and miombo soils are poor (Frost et al., 2003), therefore woodland is constantly cleared to continue to grow or expand cultivation (Sauer and Abdallah, 2007). This leads to deforestation, degradation (Lecours et al., 2012), and expansion of the agricultural frontier. Furthermore, eco- nomic incentives from tobacco cultivation drives in- migration and increases the demand for forest products and ES, yet access to miombo woodland is rarely regulated and the capacity to restrict overuse is weak (Luoga et al., 2005). Several studies on ES provision and use within miombo woodlands have found that the use of provisioning ES is extensive and that they are disproportionately used by the rural poor (Syampungani et al., 2009; Dewees et al., 2011; Njana et al., 2013). However, it is not known how environmental changes resulting from landuse will affect this relationship in the future (Ryan et al., 2016) particu- larly in remote areas, and what impact this loss will have on local communities. Consequently, this paper examines: (1) the types of provisioning ES used and by which house- holds to determine who will be vulnerable to future changes; (2) the perceived changes in the availability of these services in areas where landuse change is occurring; and (3) what this may mean for the future management of miombo woodlands.
To solve these problems and conserve the vast grasslands in its northern territory, the Chinese government has adopted several measures, including PES. This form of eco- compensation payment can be defined as ‗a type of institutional arrangement to protect and sustainably useecosystemservices, and to adjust the distribution of costs and benefits between different actors and stakeholders, mainly through economic measures‘ (CCICED 2007). PES programs internalize the benefits associated with enhancing or maintaining ecosystemservices to ensure that land managers and other providers of ecosystemservices have incentives that agree with the interests of the users of these ecosystemservices (Arrow et al. 2000; Pagiola et al. 2005; van Noordwijk and Leimona 2010). There has been an increasing number of publications that describe China‘s PES program, but most of them focus on the sloping land conversion program (SLCP)—the largest land retirement and reforestation program in the world (e.g. Bennett 2008; König et al. 2014a; Zhen et al. 2013) SLCP uses a public payment scheme that directly engages millions of rural households as core agents of the project‘s implementation. Although PES schemes assume that participation is voluntary, and that participants can negotiate a price that is acceptable to them, this is not how the program has been implemented in practice; participation is mandatory. Thus, as Bennett (2008) reported, some participants are likely to be undercompensated (i.e. paid less than they would request if they had freedom to negotiate the bid price). Any gap between the actual payments and what participants would bid if they were free to choose would reveal an important problem with the current approach, since voluntary participation requires what the participants consider to be a fair payment. Xu et al. (2010) revealed that the program could have significant implications for China‘s forests and remaining natural ecosystems, potentially representing a 10–20% increase in the current national forest area, a roughly 10% decrease in China‘s cultivated area, and a significantly positive impact on participant income due to the program‘s payments. However, it seems likely that the program‘s cost-effectiveness could be improved by targeting sites with the highest environmental benefits and allowing payments to reflect the heterogeneous opportunity costs faced by residents of the region, while also preventing farmers from reconverting their land to cultivation.
accommodating 15.9 × 10 6 population (Dong, 2013). Es- pecially after the reform era of the late 1970s, the old collective production brigade farming system was aban- doned in favor of the household responsibility system to unleash farmers ’ incentives for higher productivity and more income (Liu et al. 2004). Large-scale state farms had been established as an important “grain base” through zealous reclamation of grassland, marginal woodland, and wetland. With the rapid rise in human population, human-induced changes in landuse/land cover form an important component of regional envir- onment and ecosystem service change. Whereas, at the
The taking and the critical habitat provisions of the Act have been particularly
controversial and, by many accounts, have led to some counterproductive outcomes. Together they provide strong incentives for landowners to avoid having their land designated as critical habitat, and evidence suggests that some landowners have engaged in a number of preemptive practices in advance of critical habitat designation (Adler 2008; Innes et al. 1998). In North Carolina, some forest landowners in the 1990s preemptively harvested timber in order to reduce red-cockaded woodpecker habitat on their land (Lueck and Michael 2003); the woodpecker was (and remains) an endangered species. Similar actions have been alleged in the Pacific Northwest with respect to the northern spotted owl, a threatened species. Other evidence of preemption has shown up in econometric analysis of data on land development: land in Arizona that was to be designated as critical habitat for the endangered cactus ferruginous pygmy owl was developed about a year earlier than similar land that was not so designated (List et al. 2006). And studies have documented that the mere listing of a species can discourage private landowners from participating in conservation efforts. Property owners within range of habitat of the endangered Preble‘s meadow jumping mouse, for example, revealed in surveys that they often would refuse to give biologists permission to conduct research on their land (Brook et al. 2003).
One variable for which age might be a proxy is the strength of habit a resource user has with their current landuse practices. Langpap (2004) find higher age to be negatively (and significantly) related to programme participation, but also found that years owning the land had a negative and significant effect, indicating that landowners who acquired their land more recently are more willing to participate. The author states, “One possible explanation is that landowners who have owned the property for a shorter time may be less likely to have developed a particular way of managing their forest, and thus could be more willing to accept alternate management plans” (pg. 383). Yu & Belcher (2011) support this reasoning as they find age to be insignificant, but that years of experience farming had a negative and significant relationship with programme participation. Mullan & Kontoleon (2012) also find age to be insignificant across the entire sample, but using a latent class model, find it to be insignificant for households with easy market access, but a negative predictor for households with constrained market access. This final example implies that even exogenous factors could relate to habit formation.
The deciduous miombo woodlands of sub-Saharan Africa extend for approximately 2.4 million km 2 (Frost et al., 2003) and support the livelihoods of at least 100 mil- lion people (Syampungani et al., 2009, Dewees et al., 2011) through a range of goods and services, leading them to be described as “ a pharmacy, a supermarket, a building supply store, and a grazing resource ” (Dewees et al., 2010: 61). They are also of global importance as a carbon store (Ribeiro et al., 2015). Rapid landuse change in these woodlands is occurring and is anticipated to continue (Ryan et al., 2016), leading to degradation with potentially devastating consequences for the livelihoods that they sup- port. Tobacco cultivation is a leading cause of landuse change and is common within miombo woodlands due to the suitability of the sandy soils and plentiful wood, which are needed to cure the leaves for storage (Geist, 1999). However, tobacco is nutrient hungry (Baris et al., 2000) and miombo soils are poor (Frost et al., 2003), therefore woodland is constantly cleared to continue to grow or expand cultivation (Sauer and Abdallah, 2007). This leads to deforestation, degradation (Lecours et al., 2012), and expansion of the agricultural frontier. Furthermore, eco- nomic incentives from tobacco cultivation drives in- migration and increases the demand for forest products and ES, yet access to miombo woodland is rarely regulated and the capacity to restrict overuse is weak (Luoga et al., 2005). Several studies on ES provision and use within miombo woodlands have found that the use of provisioning ES is extensive and that they are disproportionately used by the rural poor (Syampungani et al., 2009; Dewees et al., 2011; Njana et al., 2013). However, it is not known how environmental changes resulting from landuse will affect this relationship in the future (Ryan et al., 2016) particu- larly in remote areas, and what impact this loss will have on local communities. Consequently, this paper examines: (1) the types of provisioning ES used and by which house- holds to determine who will be vulnerable to future changes; (2) the perceived changes in the availability of these services in areas where landuse change is occurring; and (3) what this may mean for the future management of miombo woodlands.
The Kiskunság sand-ridge in the Danube-Tisza Interfluve repre- sents a biome transition zone (ecotone) between temperate deciduous for- ests and continental steppes. The characteristic feature of the landscape is the hierarchic mosaic pattern of ecosystems, which appears in different levels (Kovács-Láng et al. 1998). This can be principally related to geomor- phology and geological structure, to water-flow systems and to the his- tory of human land-use. Underground water forms a complex water-flow system, which covers the whole area of the landscape (Tóth 2001; Mádlné Szőnyi et al. 2009). Levels of this water flow system (regional, intermedi- ate and local) can be related to the levels of processes analyzed in this study. Precipitation recharges at higher elevations and starts gravitational water-flows towards the lower regions (Mádlné Szőnyi et al. 2009). Local and intermediate flow systems are based upon these ridge-level flows, and operate the subsystems between the dry and wet habitats. Landscape and habitat patterns formed this way show similar, fractal-like structure in different scales (Biró et al. 2007). Natural vegetation forms different com- munities, such as dry grasslands, sand forest-steppe mosaic to marshes, fens and alkali lakes. Human activity forms fine mosaics from the frag- mented patches of the natural habitats through afforestation, agricultural production, settlements, roads and canals.
Factors facilitating decisions to change land uses
The introduction of new land uses by local actors to improve livelihoods and reduce risks is facilitated by the states of or changes in social or ecological systems, which create “windows of opportunity” [ 62 , 63 ]. As shown by the response of the communities studied in West Kaliman- tan, floods, drought, or natural resource scarcity can trigger changes in forest use. It has been reported that extreme weather variability and restricted forest access due to logging conces- sions have triggered adjustments in land management in other areas in the region [ 64 , 65 ]. Other opportunities for new land uses can be triggered by changes in the social–institutional context at different scales; for example, when a new local leader introduces rules for use and management of community forests like in the case of West Kalimantan. In addition, external factors that trigger changes in land-use decisions include new forest and climate policies, demographic change, or economic development. Changes in government forest policies and in levels of control were common in the colonial and reformation period in Java and deter- mined the land-use decisions made by local people e.g. to plant or cut trees [ 66 , 67 ]. The analy- sis in the villages in Central Java showed that also a lack of labor due to migration and aging populations can lead to reforestation of abandoned agricultural land. In addition, increased commodity prices or construction of new roads or water systems also influenced peoples’ uses of ecosystems.
Results from this literature search were combined with other relevant information (see Supporting Information – Appendix S1, Tables S1 and S2 and Fig. S1) to develop a ‘threat matrix’ for ES impacts following transitions to SRC, Miscanthus or SRF. The threat matrix was assembled as a summary of all of the analysed literature and confidence assigned based on the amount of information available and agreement between stud- ies. For example the impacts of transitions from arable to Miscanthus on Hazard regulation was scored as positive and high confidence as: (i) of 11 studies that considered transitions from arable to second-generation energy grasses 10 report a positive effect; (ii) a number reviews (B€orjesson, 1999; Donnelly et al., 2011) and studies (Updegraff et al., 2004; Boardman & Poesen, 2006; Lattimore et al., 2009; Busch, 2012) explicitly con- sider how changes in agricultural practice under this transition promotes a reduction in surface runoff (Blanco-Canqui, 2010) and wind erosion (Busch, 2012; Holland et al., 2015). For the same service we found no studies that considered the implica- tion of landuse transitions from Forestry/Woodland to Miscan- thus. As across studies the length of the management cycle emerges as key to understanding the implications of transitions to 2G feedstock production (Lattimore et al., 2009; Donnelly et al., 2011; Schulze et al., 2012) it was considered that this tran- sition would have a negative impact on the provision of this service however, in the absence of specific reference state stud- ies, we assigned low confidence to this. Full discussion of the development of this matrix is provided by Holland et al. (2015). The scoring was designed to reflect the difference in confi- dence of effects, and it was weighted to reflect this and increase the differences between possible scores out of a potential score of 126. Fourteen key provisioning and regulating services affected by 2G crops were assessed to develop an ES score. Positive, neutral and negative impacts were scored alongside confidence in the available literature (Table 1).
Socio-economic information about human wellbeing and poverty levels was collected by reviewing available reports and other literature (Uganda Bureau of Statistics, 2010; International Institute for Sustainable Development, 2005; Bahiigwa and Muramira, 2001; Emwanu et al. 2003; Robinson and Pozzi, 2011; Rogers et al. 2006), as well as by field surveys. A total of 100 households were sampled randomly throughout the four wards of the town of Koboko and data on housing equipment, water and sanitation, and demographics were collected. The col- lection was carried out together with the town council staff in order to facilitate communication and clarify doubts about the questions asked. The main vulnerabil- ity factors were represented by food security, limited livelihood opportunities and firewood shortage. Other critical factors, particularly considering the high rate of population growth, were poor farming methods, land frag- mentation and degradation of forest land (International Institute for Sustainable Development, 2005; National En- vironmental Management Authority, 2006).
The services provided by a given community of mobile agents are highly contextual. They may be influenced by either the composition of the mobile agent community and/ or the recipient community (at least for services produced by the interaction between mobile agent and recipient, such as parasitoid – pest), through alterations in the effectiveness of individual species in differing community contexts (Kremen 2005). For example, a disease host that transmits its disease infrequently to a vector can dilute the disease if more competent hosts co-occur, but may become the main source of the infection if it is the most competent host in the community (Ostfeld & LoGiudice 2003). Competitive or facilitative interactions between species can alter functional outcomes (e.g. pest control, Cardinale et al. 2003; decom- position, Jonsson & Malmqvist 2003). Context (community composition) is altered in turn by changes in landscape structure that affect non-random community assembly/ disassembly processes. For example, small fragments of forest in the northeastern USA have lost many of the least competent vertebrate hosts for the vector of Lyme disease, resulting in less disease dilution and higher Lyme disease infection risks in humans (Allan et al. 2003; Ostfeld & LoGiudice 2003). Non-MABES ecosystemservices can also be influenced by context; for example, trees in the Australian landscape provide water filtration services, while in a South African landscape, they reduce groundwater discharge, a negative environmental service (van Wilgen et al. 1998; Eldridge & Freudenberger 2005).
Data for this study includes sale transaction information, such as land price, appraisal value, sales time, contract type and land class, which came from the County Equalization Office in each of the four counties. The associated GIS parcel maps were obtained from county GIS offices. Other variables describing the social and natural status of farmlands were constructed with ArcGIS software using several GIS databases, including the United States Department of Agriculture (USDA) Soil Survey Geographic Database (SSURGO), the Conservation and Recreation Lands (CARL) dataset, the National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-CAP) land cover database, and other Michigan GIS data on rivers, lakes, wetlands, cities and major roads. Information covering 337 parcel transactions was collected for the years 2003-2007. Of these 220 observations are used for sales price regression model and 283 for the appraisal value regression model. According to Michigan agricultural statistics, average per-acre farm real estate value increased annually from 2003 to 2008, but a 3.8% decline to $3,370 per acre occurred in 2009. The annual Michigan Land Value survey also showed that 2009 survey reported land values declined around 0.8% statewide compared with 2008 (Wittenberg and Harsh, 2009). Thus, the land prices here are unlikely to have been influenced by the U.S. economic crisis during our study period (2003-2007).
In the context of landuse, it is proposed to consider impacts generally as reversible in the broad sense of revers- ibility. This means that abandoned land spontaneously de- velops towards a site-dependent potential natural vegetation (PNV) if the absence of human action continues during a sufficient length of time (regeneration time, also called relaxation time). At the end, an abandoned area can be considered as roughly equivalent, although not identical, to its pre-impact state. However, there are situations where the regeneration time, according to current knowledge, will exceed the modelling horizons of usual LCA studies, or even will exceed any finite number of years: a high salinity area in very dry climate could be barren for an indefinite time period. Such impacts are called permanent impacts. Permanent impacts can be expressed by multiplying the
Modeling of ESs and landuse legacy
Mixed-effect models were built (package lme4 and function lmer in R, www.R-project.org ) to estimate (1)
the current level of ES provision across landuse types (hereafter LU-models), and (2) the inﬂuence of landuse trajectories on the level of ES provision of current landuse (hereafter LUxT-models). We modeled three key ES: water regulation and soil protection, gamma distributed with log as link function, and habitat quality, logit transformed and normally distributed with identity as link function (see supplementary material SM 2 ). We were interested in making inferences about the mean of current landuse, compared to the whole of the study area in terms of ES provision rather than in testing differences between particular landuse types. For that, LU-models included varying-intercept and landuse as random effect. Similarly, LUxT-models included varying- intercept, but they also incorporated the statistical interaction between landuse and landuse trajectory as random effect (see LUxT-Models below). In addition, we tested the signiﬁcance of landuse trajectory effect on ES provision across current landuse by comparing
Ecosystems provide numerous benefits to people. These benefits are called ecosystemservices and they include, among others, food, fresh water, fertile soils, timber, medicines and recreation opportunities. In order to meet increasing human needs, natural ecosystems have been converted into heavily managed ecosystems, such as cropland and pasture, and their ecosystemservices are used exhaustively (De Fries et al., 2004; Foley et al., 2005; Rodríguez et al., 2006). Land conversion and landuse intensification are major drivers of ecosystem degradation, biodiversity loss and ecosystem service depletion (Foley et al., 2005; Pereira et al., 2012). More sustainable landuse and land management practices could prevent further ecosystem degradation and ensure the continued provision of ecosystemservices. To guide sustainable land management strategies, in-depth information about the current and potential impacts of land management on ecosystemservices is needed urgently. Substantial efforts to improve the quantification of ecosystems services and to understand ecosystems’ contribution to human well-being have been made (Crossman et al., 2013a). Nevertheless, there are still many knowledge gaps about how ecosystems generate services, how to consistently identify and quantify ecosystemservices, how these services interact, and how changes in land management affect these services (Carpenter et al., 2009; De Fries et al., 2004; De Groot et al., 2010b; Villamagna et al., 2013). The empirical information about the capacity of ecosystems to provide a number of ecosystemservices simultaneously is fragmented, and a solid scientific basis for integrating ecosystemservices into landuse decisions is still missing (Ehrlich et al., 2012; Nelson and Daily, 2010; Turner and Daily, 2008). This calls for better understanding and quantification of ecosystemservices under alternative land management states or systems (Balmford et al., 2008; De Groot et al., 2010a; ICSU et al., 2008) and for further development of mapping and modelling tools that synthesize information to support decision-making with regard to land management (Nelson and Daily, 2010; Vigerstol and Aukema, 2011).
to management actions such that managers can make effective decisions (Rodríguez et al. 2005, 2006, Tallis and Polasky 2009, Cabell and Oelofse 2012). Such assessments are paramount to maximizing human well-being, enabling adaptive management, and improving resilience in the social-ecological system (Carpenter et al. 2005). The spatial patterns of social-ecological systems, e.g., the number, location, and relative proportion of different land-use types, can vary at differing spatial scales, which can then influence ecological functions (Pringle et al. 2010). Repercussions of outcomes at a particular spatial scale may affect biodiversity and ecosystem conservation, as well as stakeholder interests and institutional responsibilities (Hein et al. 2006). Thus, to make effective land-management decisions, baseline data about the biophysical and social settings are required at the spatial scales of the decisions being made (DeFries et al. 2004, Nicholson et al. 2009). Effects of management actions may have different results across spatial scales (Concepción et al. 2012), e.g., at the individual patch level compared to a municipality, or entire landscape. Therefore high quality local data and multiscale analyses are fundamental to design adequate management plans, understand the trade-offs they encompass, and facilitate decision making (Carpenter et al. 2009).
explained by the first principal component. With a loading of 0.90, the importance of hills and valleys to human wellbeing in the study area can be estimated by the first principal component. The principal component analysis also revealed that museums and monuments tend to group together in indicating wellbeing. This is probably due to their strong ability in representing cultural identity of the people (Mazumdar & Mazumdar 2004; Foxall 2013). Considering the highest weights for principal component one and two, components one seem to identify strongly with landscape aesthetics and naturalness, and component two seem to identify strongly with social, spiritual and mental tranquillity. The importance of an indicator to measure human wellbeing showed to be dependent on the wellbeing constituent it was correlated to. This notwithstanding, it was interesting to note that ‘worship places’ were perceived to be more important for physical health than ‘sport grounds’. This finding supports Margry’s (2008) assertion that one purpose of undertaking a religious activity is to have physical healing. And because over 90% of Kenyans subscribe to a religious faith (www.africa. upenn.edu/NEH/kreligion.htm, 24.02.2017), it was a crucial variable in the study area and its role in promoting wellbeing was by chance expected to be high. However, it was not established whether the religious activities and physical health were directly or indirectly related to each other. Although religion and spirituality have been theoretically connected to wellbeing in literature (Biedenweg et al. 2014), their linkages to ES and wellbeing indicators have been demonstrated for the first time using this study. The double-headed arrows in the framework for the inter-linkages between CES and human wellbeing within the DPSIR model, demonstrate the fact that at times the ecosystem could resist pressures originating from the drivers and hence its state remains uninterrupted. This demonstration invokes the theory of resistance, resilience and stability of ecosystems (Müller et al. 2010; Müller et al. 2016) and elevates a more compelling debate of the inter- linkages between CES, wellbeing and the DPSIR model (a tripartite framework). Similarly, Nassl & Löffler (2015) postulate that some changes caused