Understanding Spatial Variation in the Drivers of Nature-based Tourism and Their Influence on the Sustainability of Private Land Conservation

Ecological Economics 140 (2017) 225–234

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Ecological Economics journal homepage: www.elsevier.com/locate/ecolecon

Analysis

Understanding Spatial Variation in the Drivers of Nature-based Tourism and Their Influence on the Sustainability of Private Land Conservation Julia Baum a,⁎, Graeme S. Cumming a,b, Alta De Vos c a b c

Percy FitzPatrick Institute, DST-NRF Centre of Excellence, University of Cape Town, Rondebosch, Cape Town 7701, South Africa ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland 4811, Australia Department of Environmental Science, Rhodes University, P.O. Box 94, Grahamstown 6140, South Africa

a r t i c l e

i n f o

Article history: Received 1 November 2015 Received in revised form 25 April 2017 Accepted 2 May 2017 Available online xxxx Keywords: Ecotourism Nature-based tourism Social-ecological systems Private Land Conservation Private protected areas South Africa Cultural ecosystem services

a b s t r a c t Protected areas connect socio-economic and ecological systems through their provision of ecosystem goods and services. Analysis of ecosystem services allows the expression of ecological benefits in economic terms. However, cultural services, such as recreation opportunities, have proved difficult to quantify. An important challenge for the analysis of cultural services is to understand the geography of service provision in relation to both human and ecological system elements. We used data on visitation rates and measures of context, content, connectivity, and location for 64 private land conservation areas (PLCAs) to better understand geographic influences on cultural service provision. Visitation to PLCAs was influenced by a combination of ecological and socio-economic drivers. Variance partitioning analysis showed that ecology explained the largest proportion of overall variation in visitation rates (26%), followed by location (22%). In tests using generalized linear mixed models, individual factors that significantly explained visitation rates included the number of mammal species, the number of Big 5-species (ecological variables), the number of facilities provided (infrastructure) and average accommodation charges (affordability). Our analysis has important implications for the economic sustainability of PLCAs and more generally for understanding the relevance of spatial variation for analyses of cultural services. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Protected areas (PAs) are an important tool for the conservation of biodiversity (Chape et al., 2005; Mascia and Pailler, 2010). They are considered essential for maintaining ecological resilience and ecosystem functioning, both locally and for the broader landscapes in which they are embedded (Tilman and Downing, 1994). As recognized by global policy instruments like the Millennium Ecosystem Assessment (2005), the sustainable development goals (SDGs) and the Convention on Biological Diversity (CBD, particularly under Aichi target 11), ecosystems provide benefits which directly and indirectly support human wellbeing (CBD Secretariat, 2015; Daw et al., 2016; Millennium Ecosystem Assessment, 2005; United Nations, 2015). Thus, PAs are institutions that link social and ecological systems, through the provision of ecosystem services (Kettunen and ten Brink, 2013). The services provided by protected areas are not just related to biodiversity conservation, but encompass many other tangible (e.g., the generation of economic revenue) and intangible (e.g., providing visitors with a sense of place) benefits (Cumming et al., 2015; Infield, 2001; Maciejewski et al., 2015; Sundaresan and Riginos, 2010). In recent ⁎ Corresponding author. E-mail address: [email protected] (J. Baum).

http://dx.doi.org/10.1016/j.ecolecon.2017.05.005 0921-8009/© 2017 Elsevier B.V. All rights reserved.

years, the ecosystem services framework has become the de facto conceptual tool with which to understand these benefits and services, the links between ecological structures and processes, and their utilisation and valuation (Carpenter et al., 2009; Costanza et al., 1997; Daily, 1997; Guerry et al., 2015). Indeed, ecosystem services have become a conceptual bridge between conservation and developmental objectives (Daniel et al., 2012). Three major categories of ecosystem services are generally recognized: i) provisioning services, ii) regulating and supporting services, and iii) cultural services (Haines-Young and Potschin, 2013; Millennium Ecosystem Assessment, 2005). Of these, cultural services, the non-material benefits that people derive from nature, are generally the most poorly understood and the hardest to quantify (Hernández-Morcillo et al., 2013; van Jaarsveld et al., 2005). Cultural values have played an important role in motivating the protection of ecosystems and their integration into management can reduce resistance towards protected areas and help strengthen conservation efforts (Daniel et al., 2012; Infield, 2001). The asymmetries in knowledge between ecosystem service categories in assessments are problematic given the importance of cultural services for the sustainability of protected areas (Reyers et al., 2013). PAs exist because of decisions made by society to protect natural resources, based on the perceived benefits from PAs to society. Different approaches to categorizing the benefits provided to people by

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ecosystems exist (e.g., Chan et al., 2012). A widely applied framework (Millennium Ecosystem Assessment, 2005) defines cultural services as including the categories spiritual and religious, aesthetic, inspirational, sense of place, cultural heritage, recreation and ecotourism, and educational. More recently, the Common International Classification of Ecosystem Services (CICES) has grouped cultural services into ‘physical and experiential interactions’, ‘intellectual and representational interactions’, ‘spiritual and/or emblematic services’ and ‘other cultural outputs’ (Haines-Young and Potschin, 2013). Examples of these categories as experienced in PAs are (among many others) the pleasure of watching and interacting with wildlife, outdoor activities (e.g., hiking and canoeing), and scenic beauty (Di Minin et al., 2013; Paloniemi and Tikka, 2008). Established facilities and the natural context and location of PAs provide visitors access to these non-material benefits, influencing how visitors value PAs. The value that society places on PAs has important implications for their sustainability (Mascia and Pailler, 2010). Ecotourism, in particular, has been looked to as a cultural service that can facilitate win-win scenarios for conservation and development, with the revenue derived from PA visitors being a potentially important source of income to aid PA sustainability (Lindsey et al., 2007). Ecotourism provides incentives for nature conservation and has the potential to contribute to poverty alleviation through job creation and by increasing demand for local products (Binns and Nel, 2002; Chape et al., 2005; Lindsey et al., 2007; Spenceley et al., 2002). In a global meta-analysis, Oldekop et al. (2015) found that PAs which are associated with positive socio-economic outcomes are more likely to report positive conservation outcomes. Such positive conservation and socio-economic outcomes, in turn, are more likely to occur when PAs adopt co-management regimes, empower local people, reduce economic inequalities and maintain cultural and livelihood benefits. In Southern Africa, ecotourism from different sectors in total generates roughly the same revenue as farming, forestry and fisheries combined, and its contribution to the economy has steadily increased over the past decades (Loon and Polakow, 2001; Scholes and Biggs, 2004). In South Africa, a country that is still impacted by its apartheid history and is seeking to develop socially just, economically viable and ecologically appropriate land-uses, ecotourism plays a vital role in conservation and development (Langholz and Kerley, 2006; Ramutsindela, 2004). Practitioners are only just beginning to realise the important opportunities that Private Land Conservation (PLC), and particularly naturebased tourism in privately owned reserves, can offer governments to meet global conservation and sustainability targets. As examples from Australia (Figgis, 2004; Fitzsimons and Wescott, 2008), Brazil (de Vasconcellos Pegas and Castley, 2016) and the USA (Langholz, 2010) show, private lands significantly contribute to biodiversity conservation, It is, however, also becoming evident that PLC does not necessarily obey the same rules as traditional, statutory PAs (Stolton et al., 2014). Maintaining and expanding a network of PAs is expensive and includes costs like salaries and fencing. Statutory PAs that are managed by mandated authorities often derive portions of their income from public funds and do not completely depend on internal income. For example, although South African National Parks (SANParks, the authority responsible for managing South Africa's national parks) derives large amounts of resources from ecotourism activities, the agency receives government funds for running priority programmes within national parks and procures substantial state subsidies in support of regional natural resource management programmes (SANParks, 2014). By contrast, Private Land Conservation Areas (PLCAs) depend almost entirely on income generated on the property or by the landowner. In the United States, for example, only a few external income sources exist for PLCAs, through incentives such as tax rebates, conservation easements (Rissman and Sayre, 2012), or non-governmental support such as funding from private donors (Paulich, 2010). This is also true in many other countries (see Stolton et al., 2014 for examples). The sustainability of private PAs is thus more directly dependent than that of most public lands on visitors paying for the provision of cultural services.

Regardless of PA type, ecotourism represents both an important economic influence to manage and understand cultural services, and one of the few avenues for readily quantifying them.Visitation rates, and income from visitors, are easily quantifiable metrics that can be used as “willingness to pay” measures (Alpízar, 2006; Chase et al., 1998; Ellingson and Seidl, 2007; Khan, 2004). Both internal and external factors are likely to influence visitation rates in private reserves. At larger scales, the context in which PLCAs are embedded will determine visitor's choices based on factors such as a country's stability, a region's safety and a destination's security. Mahony and Van Zyl (2002), for example, stated that tourism potential in parts of the Eastern Cape Province is restricted by negative perceptions about safety. Similarly, Kepe (2001) found that tourists tend to not visit surrounding locations of protected areas because safety is a concern. Landowners and managers of PLCAs, when asked to state negative future influences on their ventures, perceived political factors to be the most relevant (e.g., safety, legislation, land claims, mining and fracking) (Baum, 2016). For individual PLCAs, the ecological features of a reserve are important for attracting people, for example to enjoy game viewing or hiking in a natural environment. Visitors may have different attitudes, backgrounds and belief systems and people make decisions based on what they would like to see or experience (Martín-López et al., 2012; Neuvonen et al., 2010). However, features of convenience inside a reserve (e.g., accommodation), can further strongly influence visitor choices, as people do not only consider ecological features when deciding where to spend money and time (Puustinen et al., 2009; Seddighi and Theocharous, 2002). This means that, in addition to ecological “infrastructure”, elements of location, built infrastructure, discoverability, and affordability may be underlying drivers of the utilisation and valuation of cultural services in PLCAs. All of these potential drivers show heterogeneity in space. Furthermore, they do not occur in isolation from one another, meaning that a combination of factors may play an important role in determining spatial variation in nature-based tourism. Despite the importance of nature-based tourism for the social-ecological sustainability of PAs, remarkably little is known about its influence on PLCAs. Little information is available about the effects of internal or external drivers on visitation rates on private lands, particularly when taking spatial variation into consideration. South Africa offers a potentially insightful case for understanding the relevance of reserve location for cultural service use in PLCAs. Not only does it boast a diverse and growing nature-based tourism industry (particularly in the private sector), it also represents a diversity of private PA models that are managed under relatively well-developed policies and rules (Cumming and Daniels, 2014). In this study, we concentrated on the roles that elements of location, ecology, infrastructure and affordability play in the economic sustainability of PLCAs. We investigated two focal questions: (1) Can visitation rates be better explained by particular categories of drivers (i.e. “ecological features” vs. “infrastructure”); and (2) which specific factors (e.g., travel time, number of activities offered) best explain spatial variation in visitation rates in PLCAs? We expected that either predominantly socio-economic factors (e.g., infrastructure or marketing); predominantly ecological factors (e.g., presence of large mammal species in the reserve); or a combination of both socio-economic and ecological factors would be most relevant in determining nature-based tourism in private reserves. Socio-economic factors may enhance the demand for, and utilisation of cultural services. Ecological factors form the basis for the provision of cultural services. A combination of these factors may most strongly determine visitation rates, since visitors make choices based on both what they want to experience and how these experiences are facilitated. The key interest and novelty of the study lies in our quantification of the relative magnitudes of the different influences on PLCA nature-based tourism; understanding these has potentially important implications for the

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management of existing PLCAs, the development of effective business plans and the creation of new reserves. 1.1. Study Area We focused our study on South Africa's Western Cape province. The province comprises a broad diversity of private reserve types, offering a consistent legislative and socio-economic context that allows for comparisons across the landscape and a clear definition of the system to be assessed. South Africa boasts accessible wildlife, varied and impressive scenery and unspoiled wilderness areas. The Western Cape Province, our case study region, is an area of particularly high biodiversity. As the sole African winter rainfall region south of the Equator, it incorporates the Cape Floral Kingdom, which is one of the world's 25 biodiversity hotspots (Myers et al., 2000). The province contains six different biomes: coastal, lowland fynbos, midland-upland fynbos, renosterveld, succulent Karoo and thicket (SANBI, 2015). These biomes provide habitat for many endemic and endangered species (Turner, 2012). The province offers numerous attractions for nature-based tourism, such as the flowering of the west coast fynbos habitats, remote landscapes in the Karoo biome, mountainous habitats on the Swartberg ridge, unspoiled wetlands and beaches along the Garden Route, and more generally, many endemic and endangered animal and plant species (Turner, 2012). The PA network, which provides the opportunity for people to directly experience natural settings and benefit from cultural services, is important for nature-based tourism, and contributes substantially to the national economy (Cumming and Daniels, 2014; Government of South Africa, 2010; SANParks, 2014). The province contains seven national parks (SANParks, 2015) and 47 provincial reserves, of which 27 are accessible to the public (Western Cape Government, 2015); together protecting around 5400 km2 (Turner, 2012). Alongside these state-owned reserves, many private and co-managed conservation areas of different type and legal status

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exist. Based on their corporate models, two main PLCA types can be characterized in the Western Cape (Baum, 2016). Game reserves are, on average, larger in size, employ more staff members, keep large mammal species on the property, are relatively self-sufficient, offer guided drives, and make a higher marketing effort. Habitat reserves, by contrast, are usually associated with less active management and marketing effort, more indigenous flora and fauna, and are more often gazetted as formal protected areas (i.e., they form part of the country's “official” PA network). 2. Materials and Methods 2.1. Data and Data Collection We used data from interviews conducted at PLCAs distributed across the province (Fig. 1). A single interviewer (JB) conducted personal interviews in English with one representative (owner or manager) per PLCA, between September 2012 and June 2014, to obtain information about ecological and infrastructural features on site as well as details on tourism, economic conditions, and objectives. The sample used for analyses consisted of 64 PLCAs. The selection process was random and not biased by area size, ecosystems or legal status. Our only condition for selection of participants was that they obtained income from visitors through (for example) accommodation charges or activities on the property, such as guided drives or hiking. Property boundaries were either provided by the study participants or obtained and verified via other sources (Department of Environmental Affairs and Tourism, 2015; Google Earth; Google Maps) to ensure their accuracy. Additional data extraction and spatial analyses were conducted with the tools ArcGIS 10.0, Google Maps and GPS Visualizer (Schneider, 2015) using the datasets listed in Table 1, and as explained below. For data processing, categorisation and analyses, we largely follow the methodology of De Vos et al. (2016). In total, 24 variables were

Fig. 1. Distribution of participating private reserves in the Western Cape, indicated by red dots. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Table 1 Datasets used for analyses. Name of dataset

Author

Details

Protected areas in South Africa

(Department of Environmental Affairs, 2014)

Provincial Nature Reserves of the Western Cape

(Maciejewski, 2014)

NFEPA wetlands layer 2011. 1:50,000 River layer from South Africa's National Freshwater Ecosystem Priority Areas project. 1:500,000 Vegetation 2006

(BGIS, 2015)

Inventory of protected areas in South Africa (national, provincial and private reserves) Inventory of provincial nature reserves in the Western Cape Inventory of wetlands in South Africa Inventory of freshwater systems in South Africa

(BGIS, 2015)

(Mucina and Rutherford, 2006) The South African National Land (Van den Berg et Cover 2000 al., 2008)

Vegetation of South Africa

Land cover of South Africa

considered as potential explanatory variables for variation in PLCA nature-based tourism. These were then grouped into response variables and predictor variables (Table 2). Two variables were used as measures of nature-based tourism and thus as response variables. They were derived from the interview dataset and represented overall annual visitation rates (gate.arr) and number of international guests (int.guests) for each PLCA. The predictor variables were grouped a priori into four categories, namely ‘location’, ‘ecology’, ‘infrastructure’ and ‘discoverability/affordability’. The category location included eight variables. The extent of each reserve (park.size) was obtained from the interview dataset and independently validated via datasets provided by different governmental authorities. The extents of reserves strongly influence their corporate models because of the relevance of extent for revenue generation: for example, available area can limit the carrying capacity of large mammal species on the property or the potential for longer hikes or drives. From Google Maps, applying the tool for route planning, we extracted travel distances on roads in minutes to the nearest airport (air.time), the

nearest town (town.time), the nearest national road (nroad.time) and the nearest coast (coast.time). Travel distances, measured in travel time, may have a strong influence on visitation rates because people may choose their destination with respect to how accessible a reserve is (Hearne and Salinas, 2002; Neuvonen et al., 2010). We calculated the number of provincial reserves (pr.no), national parks (np.no) and private reserves (ppa.no) within a 100 km buffer around each private reserve. The surrounding context may influence the attractiveness of a reserve if, for example, guests are travelling along a planned route and wish to visit several reserves on their trip which differ in terms of species or activities on offer. The category ecology comprised seven variables. Ecological attributes of a PA are key drivers for ecotourism (Dramstad et al., 2006; Neuvonen et al., 2010). In particular, charismatic wildlife is attractive to many international and local guests (Maciejewski and Kerley, 2014a). The numbers of mammal species (mammal.no) and Big5-species (Big5.no) in each private reserve were derived from interview data. Water has a strong influence on ecotourism as it attracts animals which can then be viewed more easily, provides recreational experiences, and is aesthetically pleasing to people (Nassauer et al., 2007). Similarly, vegetation and its diversity influence large mammal species carrying capacity, diversity and visibility (Dramstad et al., 2006), and are influenced by elevation. We extracted the elevation at the area's centroid (elevation), number of water bodies (waterbodies), the presence of rivers (rivers) and the number of different land cover classes (land.classes) for each reserve as well as whether or not a reserve is situated in- or outside the fynbos biome (fynbos). The category infrastructure consisted of two variables. An index for both the number of facilities (fac.no) and the number of activities (act.no) in each private reserve was extracted from interview data. Infrastructure, measured both in terms of facilities and activities provided by a reserve, represents aesthetic, recreational and experience-of-wilderness cultural ecosystem services. These three types of cultural ecosystem services are most commonly associated with ecotourism (Ode et al., 2008), and are thus important for this analysis in order to assess the variation in visitation rates to private reserves. The category discoverability/affordability included four variables. From interview data we derived the number of different entities

Table 2 Response and predictor variables used in RDA/variance partitioning to test variation in Private Land Conservation nature-based tourism. Nature-based tourism (response)

Location (predictor) Ecology (predictor)

Infrastructure (predictor)

gate.arr (number of overall visitors) int.guests (number of international visitors)

air.time (travel time waterbodies to nearest airport) (presence of waterbodies in PLCAs, y/n) nroad.time (travel elevation (centroid time to nearest elevation of PLCAs) national highway) coast.time (travel land.classes time to nearest (number of land coast) cover classes in PLCAs) town.time (travel mammal.no time to nearest (number of town) mammal species in PLCAs) park.size (PLCA big5.no (presence property size) of Big 5-species, y/n) np.no (number of river (presence of national parks in river in PLCAs, y/n) proximity) pr.no (number of fynbos (PLCAs provincial reserves located in fynbos in proximity) biome, y/n) ppa.no (number of PLCAs in proximity)

fac.no (index: provided types of facility per PLCA, min 0, max 6; e.g., restaurant, accommodation, conference venue)

Discoverability/affordability (predictor)

marketing (index: number of applied marketing mechanisms per PLCA, min 0, max 5; website, brochures, agents, newspaper adverts, other (such as word of mouth)) act.no (index: offered types of activity per PLCA, interact.ent (number of interactions to external entities min 1, max 10; e.g., guided drive, hiking, mountain per PLCA; such as companies, research institutions) biking, biltong hunting, birding) interact.pas (number of interactions to other protected areas per PLCA)

av.charge (average accommodation prices per night)

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(external collaborators such as research institutions, authorities or companies) that each reserve interacts with (interact.ent) as well as the number of other PAs (national, provincial and private reserves) that each private reserve interacts with (interact.pa). These connections to external collaborators can support the publicity of a reserve, e.g. due to enhanced marketing which is important to create awareness of a reserve's existence (Lai and Shafer, 2005). An index for the individual marketing effort of each reserve was used (marketing), which was also obtained from the interviews and represents how many different marketing avenues are being used (e.g. website, advertising in magazines etc.). Average accommodation charges per person per night in South African Rand (av.charge) were either provided during interviews or obtained from reserve websites, for the year 2014. Pricing may influence visitor choices of destination depending on their budget and how much money they are willing to spend for an ecotourism experience (Seddighi and Theocharous, 2002). Tourism is dynamic and guest numbers were annual averages of overall visitation rates. Some reserves kept accurate booking systems whereas others kept rough records and thus provided estimates of visitation rates, in as much detail as possible. The stated information spanned a two-year timeframe during which interviews were undertaken. 2.2. Data Analysis We used redundancy analysis (RDA) and variance partitioning, which quantify broad patterns and interactions of variable categories in relation to the response variables (Borcard et al., 1992; Legendre and Legendre, 2012), to identify groups of variables that best explained spatial variation in nature-based tourism. This was followed by analysis using generalized linear mixed models (GLMMs) to further identify and validate the significance of specific predictor variable groups and of individual predictor variables. 2.2.1. Data Preparation and Reduction Prior to running RDA and GLMMs, all response as well as predictor variables were standardized to zero mean and unit deviation to remove the effects of scale. Standardization is of particular importance to the predictor variables in order to avoid single variables dominating the model and resulting in biases. We then reduced the number of variables used in the GLMMs to avoid overfitting. Prior to running full models, pairwise correlation tests were conducted to reduce the overall number of variables and avoid collinearity. Where a strong correlation occurred, generally the variable with the stronger relation to the response variables was chosen. For example, the variable ‘park.size’ dominated ‘elevation’. Further, some variables with weak relations to the response variables were dropped from the analyses, such as ‘coast.time’ or ‘interact.ent’. However, in a few instances we did not remove competing variables from the analyses in order to have the full set of factor types represented, according to our a priori hypotheses. For example, ‘park.size’ and ‘waterbodies’ indicated some correlation but were both thought to be relevant in the models. The remaining sub-set included 12 variables (Table 3) which represented the fixed effects used in the models: reserve size (park.size), travel time to nearest airport (air.time), number of national parks in 100 km buffer (np.no) and number of private reserves in 100 km buffer (ppa.no) representing factors of location; number of large mammal species (mammal.no), number of Big 5-species (Big5.no), whether or not reserves were situated in- or outside the fynbos biome (fynbos) and number of water bodies (waterbodies) representing factors of ecology; number of facilities provided (fac.no) and number of activities provided (act.no) representing factors of infrastructure; marketing effort (marketing) and average accommodation charges (av.charge) representing factors of discoverability/affordability. The use of site as a random effect allowed data from all sites to be combined in a single analysis.

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Table 3 Variable categories and corresponding variables used in generalized linear mixed models to test variation in Private Land Conservation nature-based tourism. Category

Corresponding variables

Location

PLCA size (park.size) travel time to nearest airport (air.time) number of national parks in 100 km buffer (np.no) number of private reserves in 100 km buffer (ppa.no) number of large mammals (mammal.no) number of Big 5-species (Big5.no) PLCA situated in fynbos biome or not (fynbos) number of waterbodies (waterbodies) number of facilities (fac.no) number of activities (act.no) marketing effort (marketing) average accommodation charges (av.charge)

Ecology

Infrastructure Discoverability Affordability

2.2.2. Redundancy Analysis and Variance Partitioning RDA was conducted in R (R Core Development Team, 2014), version 3.0.0 using the vegan package (Oksanen et al., 2013). RDA is related to regression in that an attempt is made to explain the variance in a dependent variable using a set of explanatory variables. What differentiates RDA is that there are multiple explanatory (independent) variables as well as multiple dependent variables, i.e. multiple X variables and multiple Y variables. The final aim of an RDA is usually to find the set of explanatory variables (represented as tables of predictor variables) that explains the greatest amount of variance in the set of dependent variables (represented by a table of response variables). Finding explanatory variables is achieved by a two-step process. First, a regression model is built of the multiple explanatory variables on the multiple dependent variables. In vegan this is executed by the function rda(). Second, the variance in the dependent variables is partitioned in order to find the set of explanatory variables which explains the greatest amount of variance observed in the dependent variables. This is carried out by the function varpart() which provides results in the form of pure explanatory fractions of variation for the response table as well as shared explanatory fractions, indicating interaction among variables from different groups. The response table comprised the variables representing measures for nature-based tourism, as described above, and the predictor tables comprised the four categories of variables: ecology, location, infrastructure and discoverability/affordability. Results are stated as adjusted R2 values which account for the inflation of R2 associated with the sample size and number of predictor variables. The significance of the overall model fit and the significances of variable effects were tested using an ANOVA.

2.2.3. Generalized Linear Mixed Models Our primary goal was to model visitation rates in private reserves as function of the variable categories we created earlier on (namely location, ecology, discoverability/affordability, and infrastructure). To identify the significance of variable groups and importance of individual driving factors, we ran two sets of models: one that assessed the variation in overall visitation rates (gate.arr) and one that assessed the variation in number of international visitors (int.guests). Firstly, for each set, we ran a ‘full model’ which included all 12 predictor variables. Then we tested the effects of variable groups by sequentially removing each of the categories (and all corresponding variables) from the full model and re-running it. In this step of the analysis, we separated the variables of the category ‘discoverability/affordability’ into two categories as we were particularly interested whether discoverability and affordability had differing effects on visitation rates. This left us with 5 categories. Finally, we also ran an intercept only model, for reference. In total, we thus ran 14 models where each of the two sets (for overall gate arrivals and international guests) comprised a sub-set of seven models (full model, intercept model and 5 category models) (Tables 4 and 6).

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The GLMMs were fitted in R, using the lme4 package (Bates et al., 2015) and applying the glmer function, with Maximum Likelihood Estimation for Poisson counts. All GLMMs were tested for collinearity using the Variance Inflation Factor (VIF) (Zuur et al., 2010), which measures the inflation of variance in the estimated regression coefficients which is caused by multicollinearity. The presence of multicollinearity in the model affects the accuracy of the regression coefficients, distorting the impact of the independent variables on the dependent variables. The square root of the VIF shows how inflated the standard errors are in comparison to a model without collinearity problems. Variables with high VIF-values, N10 or higher, were removed. Model selection of the best fit models in each instance was based on Akaike's information criterion [AIC; (Akaike, 1974; Johnson and Omland, 2004)]. 3. Results 3.1. Redundancy Analysis and Variance Partitioning The RDA and variance partitioning showed that all four groups of predictor variables contributed to explaining substantial fractions of variation in PLCA nature-based tourism. The combined model explained 38% of the variation in the response table and left 62% of the variation unexplained (Fig. 2). The proportion of overall explained variation was significant (p = 0.001). Overall effects from global fractions (i.e., combined effects from several groups of variables) for all four groups of predictor variables were significant. Elements of ecology and location explained the largest proportion of overall variation in PLCA nature-based tourism with 26% (p = 0.002, F = 4.2) and 22% (p = 0.006, F = 3.2), respectively. Discoverability/affordability and ecology variables accounted for 12% (p = 0.029, F = 3.1) and 12% (p = 0.002, F = 5.3), respectively. Exclusive effects from individual fractions were only significant for one group of predictor variables, infrastructure, which explained 6% of overall variation in visitor numbers (p = 0.02, F = 3.1). All other groups of predictor variables did not show significant exclusive effects. 3.2. Generalized Linear Mixed Models The GLMMS identified similar general patterns to those identified by the RDA by showing that predictor variables of different groups explained some but not all of the variation in PLCA nature-based tourism. The best model (Table 4) for explaining overall visitation rates (gate.arr) represented the candidate model in which location variables were removed. This means that location variables (travel times, size of reserves or other protected areas occurring in proximity) were not strongly contributing to explaining overall visitation rates. Rather, variables of the categories infrastructure, discoverability, affordability and ecology played a stronger role in attracting visitors to PLCAs.

When examining model coefficients for individual variables in the best model for overall visitation rates (Table 5), the number of mammal species (mammal.no), number of Big 5-species (Big5.no) and average accommodation charges (av.charge) were significant (p b 0.05) in explaining overall visitation rates. Thus, factors of the groups ‘ecology’ and ‘affordability’ in combination best explained high visitation rates of overall guests to PLCAs when assessing individual main effects. As with the ‘overall visitor number’ models, location variables contributed least to the best model (Table 6) for explaining international visitation rates (int.guests). Two further candidate models (‘infrastructure removed’ and ‘discoverability removed’) showed AIC-values within ΔAIC = 2. When considering model coefficients for individual variables in the best model (Table 7), similar results to those found with the “overall visitation rates” model emerged. Here again, the number of mammal species (mammal.no), number of Big 5-species (Big5.no) and average accommodation charges (av.charge) significantly (p b 0.05) explained variation in international visitation rates. Additionally, the number of facilities provided (fac.no) significantly (p b 0.05) explained numbers of international visitors. Thus, factors of the groups ‘ecology’, ‘affordability’ and ‘infrastructure’ in combination best explained high visitation rates of international guests to PLCAs when assessing individual main effects. 4. Discussion We were able to explain a substantial proportion of the variation in visitation rates to PLCAs in the Western Cape Province of South Africa with a relatively small number of easily quantifiable and obtainable variables. Our results suggest that, as in other countries and protected area types (Chan and Baum, 2007; De Vos et al., 2016; Puustinen et al., 2009), a combination of ecological (species presence) and socio-economic (infrastructure, affordability) factors drive nature-based tourism decisions in private reserves in the Western Cape. De Vos et al. (2016) also identified accommodation costs and ecological variables as important variables in explaining variation of tourism rates in South African National Parks. Interestingly, a much higher proportion (63%) of variation in tourist numbers could be explained by contextual factors than the 38% we observed in our study. This can partially be explained by the tendency of national parks to have more homogenous management and dynamics than PLCAs (Baum, 2016). In national parks, facilities and activities provided and the marketing strategies show similar patterns and thus may explain why specific features and the financial settings have a higher influence. For PLCAs these conditions and dynamics are not uniform because every private reserve acts in an independent and individual manner. Key influences on visitation rates to PLCAs act together with strong covariance effects. Ecological features are the basis for PLCA existence and determine the corporate model that can be adopted by private landowners which in turn defines the type of nature-based tourism

Table 4 Candidate models and comparison statistics for the 7 generalized linear mixed models (labelled with IDs) predicting the variation in visitation rates of overall visitors to private reserves in the Western Cape. [AIC: a lower AIC indicates a better fit; ΔAIC indicates difference in AIC scores between each model and the best fit model; k indicates number of model parameters]. Candidate model

AIC

ΔAIC

K

Location removed (4): Marketing + Fac.No + Act.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Discoverability removed (2): Fac.No + Act.No + Air.Time + Park.Size + NP.No + PPA.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Infrastructure removed (3): Marketing + Air.Time + Park.Size + NP.No+ PPA.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Affordability removed (6): Marketing + Fac.No + Act.No + Air.Time + Park.Size + NP.No + PPA.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Ecology removed (5): Marketing + Fac.No + Act.No + Air.Time + Park.Size + NP.No + PPA.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Full Model (1): Marketing + Fac.No + Act.No + Air.Time + Park.Size + NP.No + PPA.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Intercept Only (7)

51.6468

0

10

57.0012

5.4

13

57.4329

5.8

12

59.7369

8.1

14

59.7369

8.1

14

59.7369

8.1

14

64.5160

12.9

2

J. Baum et al. / Ecological Economics 140 (2017) 225–234

231

Fig. 2. Venn diagram depicting the proportion of variation (Adj. R2) in Private Land Conservation nature-based tourism in the Western Cape; as explained by elements of location, ecology, infrastructure, and discoverability/affordability. For example, the underlined fractions combined represent the group of variables “location” with 22% (note: individual fraction values in this graph are rounded).

that can be offered to guests (e.g. by determining the carrying capacity for wildlife and the scenic landscapes for activities such as game viewing or mountain biking) (Clements et al., 2016). Our findings support the results of Clements et al., (2016) who identified four distinct PLCA business models with different financial productivity and owner objectives. Most profitable models represented reserves stocking charismatic species and offering other factors of convenience. In our own analysis, number of facilities and Big 5-species were positively related to the tourist numbers and income from tourism. This means that PLCAs with many facilities and Big 5-species receive more visitors than PLCAs which do not provide these features being representative for “game reserves”. Interestingly, visitors also seem to be attracted to reserves with lower average accommodation charges (as explained by the negative relation in the models) because pricing schemes in most Big 5-reserves are higher in comparison to “habitat reserves” (which focus on natural vegetation Table 5 Model coefficients for best-fit model (location variables removed) predicting overall visitor numbers to private reserves. Results include coefficient estimate (β), standard error SE(β), associated Wald's z-score (β/SE(β)) and significance level p for all predictors. Category

Fixed effect

β

SE(β)

Z

p

Discoverability Infrastructure

Marketing Fac.No Act.No Fynbos Mammal.No BIG5.No Waterbodies Av.Charge

0.3898 1.2805 0.3889 0.7228 −1.0301 1.2287 0.3288 −1.4868

0.7266 0.6854 0.6206 0.7917 0.4813 0.3663 0.2923 0.6900

0.536 1.868 0.627 0.913 −2.140 3.354 1.125 −2.155

0.59 0.06 0.53 0.36 0.032 0.0008 0.26 0.031

Ecology

Affordability

Bold indicates if significance level p b 0.05 to highlight the relevant variables in the table and which are discussed in the text.

settings, particularly the fynbos and succulent Karoo biomes, as well as outdoor activities). Overall, many individual elements of location, discoverability and ecology in our analysis did not appear to significantly contribute to explaining visitation in PLCAs. In other words, the adopted corporate model of game reserves appears to be more influential in attracting many tourists than other variables such as marketing or specific ecological features, like waterbodies. This has important implications for PLCAs as a strategy for biodiversity conservation, since habitat reserves (in our study more often located in the fynbos biome) may be protecting more biodiverse landscapes and have more eco-centric management practices than game reserves, which often stock charismatic species outside their natural ranges (Gallo and Pejchar, 2016; Maciejewski and Kerley, 2014b). This has important implications for PLC as a strategy for achieving biodiversity conservation targets in an increasingly stringent economic climate (Stolton et al., 2014), as optimising for nature-based tourism income may not equate to achieving optimal biodiversity outcomes. While PAs have intrinsic value as conservation instruments, and not only as mechanisms for generating ecotourism income (Chan et al., 2016), visitation to parks creates political support for the continued presence of conservation areas (Buckley, 2009a, 2009b; Lindsey et al., 2005). Both consumptive and non-consumptive uses of biodiversity in PAs can also contribute to conservation funding (Bovarnick et al., 2010; Maude and Reading, 2010). For private reserves and in general, nature-based tourism and biodiversity conservation are linked phenomena. On the one hand, nature-based tourism in private reserves contributes to biodiversity conservation (Cousins et al., 2008; Langholz and Lassoie, 2001). It can, for example, help to directly finance the conservation of threatened

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J. Baum et al. / Ecological Economics 140 (2017) 225–234

Table 6 Candidate models and comparison statistics for the 7 generalized linear mixed models (labelled with IDs) predicting the variation in visitation rates of international visitors to private reserves in the Western Cape. [AIC: a lower AIC indicates a better fit; ΔAIC indicates difference in AIC scores between each model and the best fit model; k indicates number of model parameters]. Candidate model

AIC

ΔAIC

k

Location removed (4): Marketing + Fac.No + Act.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Infrastructure removed (3): Marketing + Air.Time + Park.Size + NP.No + PPA.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Discoverability removed (2): Fac.No + Act.No + Air.Time + Park.Size + NP·No + PPA.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Affordability removed (6): Marketing + Fac.No + Act.No + Air.Time + Park.Size + NP.No + PPA.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Ecology removed (5): Marketing + Fac.No + Act.No + Air.Time + Park.Size + NP·No + PPA.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Full Model (1): Marketing + Fac.No + Act.No + Air.Time + Park.Size + NP.No + PPA.No + Mammal.No + BIG5.No + Waterbodies + Fynbos + Av.Charge Intercept Only (7)

48.1

0

10

48.7

0.5

12

50.0

1.9

13

52.3

4.2

14

52.3

4.2

14

52.3

4.2

14

64.4

16.2

2

species and habitats such as the wild dog in South Africa or oak forests in the USA (Knoot et al., 2010; Lindsey et al., 2005). Furthermore, naturebased tourism can provide financial viability for PLCAs (Buckley, 2009a). On the other hand, nature-based tourism may be increasingly dependent on private reserves because statutory reserves may be underdeveloped and insufficiently funded, such as in Nicaragua (Barany et al., 2001). Taken in context, our results identified several interesting patterns in nature-based tourism use that have important international implications for the economic viability and sustainability of private reserves. The insights can be used to develop new approaches to pricing in PLCA nature-based tourism (Alpízar, 2006), although starting conditions and management choices can constrain future options for natural resource use and the PLCA's ability to persist in a dynamic environment (Clements et al., 2016). In terms of a broader sustainability agenda, there seems to be scope for PLCAs with highly differing objectives and approaches. Tourists are attracted by certain pull factors (Chan and Baum, 2007) but these may differ between different demographics and types of visitors, and there may be a trade-off between them. For instance, in a survey of private protected areas in South Africa, Lindsey et al. (2007) showed that more experienced and revisiting tourists and local guests tended to distance themselves from charismatic species and focus more on local birds and vegetation. This clientele is probably very important for habitat reserves, which provide non-safari type of activities and tend to be less business-oriented (Hausmann et al., 2017). In Brazil, de Vasconcellos Pegas and Castley (2016) found that most private reserves which engage in ecotourism are of small size and focus on outdoor and educational activities. There is large potential to increase ecotourism in such small reserves which also enhance conservation outcomes. Different kinds of PAs appear to provide a diversity of important cultural services to society at large. Implicit in this observation is that they Table 7 Model coefficients for the best-fit model (location variables removed) predicting international visitor numbers to private reserves. Results include coefficient estimate (β), standard error SE(β), associated Wald's z-score (β/SE(β)) and significance level p for all predictors. Category

Fixed effect

β

SE(β)

Z

p

Discoverability Infrastructure

Marketing Fac.No Act.No Fynbos Mammal.No BIG5.No Waterbodies Av.Charge

0.6059 1.6994 0.1596 0.8130 −1.3217 1.5158 0.3895 −1.7529

0.8736 0.7885 0.7010 0.9553 0.5482 0.4204 0.3211 0.7652

0.694 2.155 0.228 0.851 −2.411 3.606 1.213 −2.291

0.49 0.03 0.82 0.39 0.016 0.00031 0.23 0.022

Ecology

Affordability

Bold indicates if significance level p b 0.05 to highlight the relevant variables in the table and which are discussed in the text.

also cater to different socio-economic classes and demographics, and consequently require different corporate management models. Thus, there may be a far greater diversity of PLCAs than statutory areas. This diversity of corporate models (we identified two large groupings in “game” and “habitat” reserves) probably explains the large proportion of unexplained variation in our study, and suggests that different factors need to be considered in designing optimal private conservation management plans compared to statutory models and strategies. Encouragingly, PLC may be able to provide national PA networks with a greater diversity of protected areas, increasing redundancy and resilience of the larger PA network. It should, however, be noted that a diversity of protected areas does not necessarily point to complementarity. Furthermore, it is essential to engage more landowners in private conservation through communicating the conservation message to appeal to a broader audience (Kusmanoff et al., 2016). In conclusion, it appears that the economic success of PLCAs depends strongly on management objectives as well as on what people want to utilize and experience or how they perceive and value an area and their time spent at a destination. The economic success of PLCAs is, as a result, driven by the spatial heterogeneity of factors which influence both the potential corporate models and visitor choices. Since visitation rates are strongly influenced by ecological factors and their socio-economic context, individual reserves can improve their nature-based tourism success by focusing on the development, enhancement and increased utilisation of these factors according to their specific opportunities and purposes.

Acknowledgements We are grateful to the many PLCA owners and managers who gave generously of their time and expertise to provide the data that we used in this analysis. This research was funded by a James S. McDonnell Foundation Complexity Scholar award to GSC and the DST-NRF Centre of Excellence at the Percy FitzPatrick Institute. References Akaike, H., 1974. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723. Alpízar, F., 2006. The pricing of protected areas in nature-based tourism: a local perspective. Ecol. Econ. 56:294–307. http://dx.doi.org/10.1016/j.ecolecon.2005.02.005. Barany, M.E., Hammett, A.L., Shillington, L.J., Murphy, B.R., 2001. The role of private wildlife reserves in Nicaragua's emerging ecotourism industry. J. Sustain. Tour 9 (2), 95–110. Bates, D., Mächler, M., Bolker, B., Walker, S., Christensen, R., Singmann, H., Dai, B., 2015. Package “lme4.”. Baum, J., 2016. The Influence of Location on the Structure and Functioning of Networks of Private Protected Areas in the Western Cape of South Africa. University of Cape Town. BGIS, 2015. Biodiversity GIS. [WWW Document]. URL. http://bgis.sanbi.org/ (accessed 3.1.15).

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