Monitoring tourists’ specialisation and implementing adaptive governance is necessary to avoid failure of the wildlife tourism commons
Introduction
Nature recreation is becoming increasingly popular globally (Balmford et al., 2009, 2015). Wildlife recreation is a type of nature recreation that involves interactions with wildlife. Wildlife watching activities were initially welcomed by conservation and environmental organisations as good sustainable alternative use of wildlife compared to other recreational activities such as fishing or hunting (Tisdell & Wilson, 2002). However, in the last two decades, many studies have shown that wildlife watching can have behavioural and physiological impacts on the animals (Amo, López, & Martín, 2006; Beale & Monaghan, 2004; Christiansen, Rasmussen, & Lusseau, 2013; Frid & Dill, 2002; Lusseau, 2003; McClung, Seddon, Massaro, & Setiawan, 2004; Velando & Munilla, 2011), which can affect the individuals’ survival and reproductive rates and result in population-level consequences (Bejder et al., 2006; Christiansen & Lusseau, 2015; McClung et al., 2004; Pirotta, New, Harwood, & Lusseau, 2014; Watson, Bolton, & Monaghan, 2014). The subject literature reports cases of both successful and unsuccessful governance of nature tourism systems. When managed successfully, sustainable nature tourism can alleviate poverty (Ferraro & Hanauer, 2014), stimulate development of infrastructure (Liu et al., 2012), create employment opportunities (Li, Jin, & Shi, 2018) and benefit wildlife conservation (R. C. Buckley, Castley, de Pegas, Mossaz, & Steven, 2012; Lindsey et al., 2014; Wilson, Hayward, & Wilson, 2017). However, when nature tourism systems fail it can lead to declines in wildlife population abundance (Lusseau & Bejder, 2007), reduced effectiveness of protected areas (Reed & Merenlender, 2008) and land-use conflicts with consequences for local populations (Sirima & Backman, 2013; Xi, Zhao, Ge, & Kong, 2014). This has led to a reconceptualization of wildlife tourism as a consumptive activity (James E.S. Higham, Bejder, Allen, Corkeron, & Lusseau, 2016; Meletis & Campbell, 2007) and the problem of how to manage it sustainably as a common pool resource issue (Briassoulis, 2002).
Hardin's (1968) paper introduced the concept of the “tragedy of the commons” to indicate the situation in which users of a common pool resource are trapped in a system of incentives that will encourage them to overexploit the resource and eventually collapse the socioecological system, unless the resource is managed by a central authority or under private property rights regimes. Since then, common pool resource research has documented cases of commons where users have been successful in self-organising and producing sustainable outcomes (Ostrom, 1990; Ostrom, Burger, Field, Norgaard, & Policansky, 1999; Ostrom, Janssen, & Anderies, 2007). We also have numerous examples of attempts to sustainably govern the commons that have failed (Acheson, 2006). Different governance structures have been proposed to manage common pool resources: private property (Lindsey et al., 2014; Muir-Leresche & Nelson, 2000; Wilson et al., 2017), government control (Lovejoy, 2006; Mayer et al., 2018), community-based management and co-management (Conley & Moote, 2003; Lamers, van der Duim, van Wijk, Nthiga, & Visseren-Hamakers, 2014; Sheppard, Moehrenschlager, Mcpherson, & Mason, 2010). Often, these governance solutions are advocated as panaceas, a single solution to every commons. But commons are complex systems and simple solutions are unlikely to be successful (Ostrom et al., 2007). Moving beyond panaceas requires us to navigate each single case study to find a sustainable solution.
Wildlife tourism and recreation systems can be described as made of four main components: the wildlife and the habitat, the tourists, the businesses that make up the tourism offer and the institutions and rules regulating the system. These subsystems are relatively separable, but interact in complex ways to produce outcomes at the system level, which then produce feedback that influences the individual subsystems (Ostrom, 2009). A number of variables have been identified as important to determine the outcomes of socioecological systems (Ostrom, 2009), such as the size and location of the resource system (the destination), the number and growth rate of the resource units (the wildlife) and the socioeconomic attributes of the users (tourists and tour operators). Measuring these variables in real systems provides insights into the social, economic and environmental outcomes of socioecological systems. Wildlife recreation destinations are dynamic, and, as the destination develops, it experiences substantial changes in these system properties that are important in determining if sustainability will be achieved (Ostrom, 2009). As a consequence, governance structures that used to be successful might eventually become inappropriate (Partelow & Nelson, 2018). Nearly 30 years ago, Duffus and Dearden published their conceptual model of non-consumptive wildlife-oriented recreation (Duffus & Dearden, 1990), a framework that brought together Butler's tourism area lifecycle (Butler, 1980), Bryan's tourist specialisation continuum (Bryan, 1977) and the concept of Limits of Acceptable Change – LAC - (Stankey, McCool, & Stokes, 1984). The temporal dynamics of a wildlife tourism destination (Fig. 1) can be described by following the change in the number of tourists visiting it through time. First, the destination goes through an exploration phase, where mostly specialist tourists start discovering the area. This phase is followed by a development phase, characterised by an exponential growth in the number of tourists, infrastructure development at the destination and a shift in tourist typology from specialists to a mixture of specialists and generalists. The last phase of the wildlife tourism destination lifecycle is the consolidation phase, when the number of tourists (mostly generalists) plateaus. Together with these changes in the social and economic dynamics of the wildlife tourism destination, effects on the environment also occur as the number of tourists increases and specialist wildlife watchers are displaced by more generalist tourists, who require more infrastructure and place greater pressure on the environment. After the consolidation phase there are three possible trajectories for the wildlife recreation destination: i) the industry can collapse because of a decline in attractiveness due to overcrowding and environmental degradation; ii) a stagnation phase, where numbers of visitors remain the same; iii) a period of rejuvenation, where the industry changes dramatically allowing a second period of growth (Catlin, Jones, & Jones, 2011; Duffus & Dearden, 1990). Since the publication of this conceptual framework, empirical research has attempted to identify the three stages of development in real tourism case studies and understand how to best manage the destination to minimise permanent effects on the environment and avoid the collapse of the tourism area (Catlin et al., 2011). However, management usually lags behind development and it is likely to intervene only after these effects have started to become obvious (Higham, 2007), and at that point some irreversible consequences might have already started to appear. An ultimate goal is to develop a mechanism to set up institutions and governance structures during the destination's initial phase that can ensure tourism remains sustainable by either avoiding collapse, or finding a stable state in which the destination can remain economically viable without damaging its social and environmental capitals.
Here we aim to investigate the institutions and governance structures that can result in socioeconomically and ecologically sustainable wildlife recreation operations at different stages of the tourism destination life cycle. We define a destination as an area where a number of wildlife watching operations exploit the same wildlife population (Center for Responsible Travel, 2014; Hughes, 2001; Semeniuk, Haider, Cooper, & Rothley, 2010). We build an individual based model (DeAngelis & Mooij, 2005) to simulate a generic wildlife watching destination (Fig. 2), with tourists, tour operators and wildlife agents (Pirotta & Lusseau, 2015) with the aim to determine how changes in the characteristics of tourists, their phenotype thereafter, can influence the sustainability of the destination. Individual-based models are a useful framework to study complex systems as they can show how system level properties emerge from the adaptive behaviour of individuals as well as how the system affects individuals (Railsback, 2001). As we saw, tourist phenotype changes drastically during the life cycle of a destination, yet the effect of these changes in customer phenotype on socioecological sustainability is largely unknown. We test how different tourists’ characteristics and tourism volume trends influence the economic and ecological dynamics of a wildlife watching socioecological system and which governance structure is more likely to achieve a viable industry and the persistence of the targeted wildlife population.
Section snippets
Overview
The model has three main entities: the tourists, the tour operators and the wildlife (Fig. 2). We tested four different governance scenarios: voluntary code of conduct, licensing, user group governance and co-management. For each of these scenarios we simulated socioeconomic and ecological dynamics of the wildlife tourism destination, varying two parameters: type of tourists (3 values: mostly specialists, mostly generalists and mixed) and trend in demand (3 values: increasing, decreasing and
Results
We found that tourist phenotype influenced the socioeconomic and ecological dynamics of the simulated wildlife tourism destination (Fig. 3, Fig. 4). Under any governance scenario and with any trend in demand, a destination visited mainly by generalist tourists will have the highest number of tour operators still in business after 40 years (between 6 and 17 – Fig. S3), while a destination dominated by specialist tourists will only have between 1 and 5 tour operators still in business after 40
Discussion
Looking at the three dimensions of sustainability (social, economic and ecological), we can see that there is no clear winner among the governance strategies tested in these simulations (Fig. 7). Both code of conduct and licensing governance structures can lead to overexploitation when the number of generalist tourists is increasing and the other scenarios can lead to low profits and a very small number of tour operators remaining in the industry. The main driver of sustainability was the type
Limitations
As any model, the simulations presented in this study represent a simplified version of a real wildlife tourism system. From this simplification a number of limitations arise that need to be acknowledged and discussed.
Many components of real wildlife tourism systems have not been addressed in this study. We did not consider the impact of differences in some of the variables identified by Ostrom (2009) that can affect sustainability of socioecological systems, for example the focal species. We
Conclusions
The expectations and preferences of tourists have a strong influence on the sustainability of a wildlife tourism destination. The dominating tourist phenotype in a destination can influence both the exploitation of the environment and the socio-economic success of the industry. We did not find a strong effect of governance type on the outcomes of the destination, with no governance structure appearing more successful than the others in every situation. What we find instead is that the best
Author contribution
All authors contributed to conceiving the idea and designing the model. FM and DL planned and carried out the simulations. All authors contributed to the interpretation of the results. FM led the writing of the manuscript. All authors provided critical feedback and helped shape the research, analysis and manuscript.
Data statement
All the scripts necessary to reproduce the results presented in this paper are available at Mancini, F. (2018) WildlifeWatchingIBM. https://doi.org/10.5281/zenodo.1443307. https://zenodo.org/account/settings/github/repository/FrancescaMancini/WildlifeWatchingIBM.
Declaration of competing interest
None.
Acknowledgments
This work was funded by the University of Aberdeen, through MASTS (the Marine Alliance for Science and Technology for Scotland), and Scottish Natural Heritage (SNH), through a Dominic Counsell studentship, and their support is gratefully acknowledged. The authors would also like to thank Alex Douglas for allowing us to run the simulations on the Catling computer cluster.
Francesca Mancini: Francesca is an early-career researcher and an ecological modeller at the Centre for Ecology & Hydrology. Francesca completed her bachelor's degree in Biological Sciences at La Sapienza University of Rome. She obtained an MRes degree in Applied Marine and Fisheries Ecology at the University of Aberdeen, where she also completed a PhD on sustainable management of wildlife tourism.
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Francesca Mancini: Francesca is an early-career researcher and an ecological modeller at the Centre for Ecology & Hydrology. Francesca completed her bachelor's degree in Biological Sciences at La Sapienza University of Rome. She obtained an MRes degree in Applied Marine and Fisheries Ecology at the University of Aberdeen, where she also completed a PhD on sustainable management of wildlife tourism.
Ben Leyshon: Ben has a BSc in Marine and Freshwater Biology, University of London and an MSc in Rural Resource Management University College of North Wales. For 27 years Ben has worked for Scottish Natural Heritage and he is Operations Manager for the Scottish Highlands. Ben has a lead on marine issues in the north of Scotland and in particular the Moray Firth. This has involved working with port and harbour authorities, the energy sector, fisheries, recreation and tourism groups. Ben is actively involved in marine planning and policy and has represented SNH on multiple marine partnerships and management groups.
Fiona Manson: Dr Fiona Manson is a marine adviser at Scottish Natural Heritage (SNH). Fiona provides advice on the conservation of marine wildlife in Scotland, covering a range of sectors including marine tourism. She leads on the development and promotion of the Scottish Marine Wildlife Watching Code. Prior to working at SNH, Fiona has worked in fisheries research and aquaculture in Scotland, Australia and Iceland.
George M. Coghill: George is SICSA Chair in System Modelling and Professor of Computing Science at the University of Aberdeen. His main research interests are in Model-based Systems & Qualitative Reasoning, Bio-inspired Computing, and Philosophy of Information & Modelling. His research is very interdisciplinary and he has applied it to areas across a spectrum from biology and medicine, to music and sociology. He is a Fellow of the Institution of Engineering and Technology.
David Lusseau: David is Professor of Behavioural Biology at the University of Aberdeen. He works at the intersection of life, formal, and social sciences to understand how individuals make decisions when uncertain and what the consequences of those decisions are for their lives and their contributions to others. He obtained his PhD at the University of Otago in 2003. He was elected Fellow of the Royal Statistical Society in 2009, the Royal Society of Biology in 2016, and member of the Young Academy of Scotland in 2011. He currently serves on IUCN Species Survival Commission and IUCN Sustainable Use and Livelihoods Specialist Group.
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Present address: UK Centre for Ecology & Hydrology, Maclean Building, Crowmarsh Gifford, Wallingford, OX10 8BB, UK.