Regional Environmental Change

, Volume 17, Issue 2, pp 477–488 | Cite as

Whale watch or no watch: the Australian whale watching tourism industry and climate change

  • Jan-Olaf MeyneckeEmail author
  • Russell Richards
  • Oz Sahin
Original Article


Whale watching is a billion dollar industry worldwide. One of the most popular species for whale watching is the humpback whale (Megaptera novaeangliae). The migratory corridors, feeding, resting and calving sites which are used for whale watching may be influenced by changing ocean currents and water temperatures. Here, we used an innovative approach addressing the emerging issue of climate change on the whale watch industry. This involved participatory modelling using key stakeholders for the whale watching industry to develop a systems conceptualisation model for evaluating the potential effects of climate change based on a case study from the east coast of Australia. This participatory approach allowed us to identify the causal linkages (including feedback pathways) between different “Elements” of the system within which the whale watching industry operates. It also allowed us to integrate multiple drivers covering socio-economic and environmental aspects including climate change (e.g. temperature), policy (e.g. number of boats), ecology (e.g. number of whales) and socio-economics (e.g. number of tourists) to evaluate the changes in the overall system. We then developed a Bayesian belief network model from the systems conceptualisation on which stakeholders identified a priority issue (Profitability). Stakeholders provided the structure and the quantification of this model, and a sensitivity analysis was carried out to help identify important intervention points for the industry. Overall, our research illustrates how such a modelling process can assist local tourism operators and authorities in making rational management decisions within a holistic or systems-based framework and its approach is applicable to other regions.


Whale watching Climate change Adaptation Stakeholder Bayesian belief network 



We like to thank the workshop participants for their outstanding contribution to this research project and two anonymous reviewers for their valuable comments. The project was funded by the Griffith Climate Change Response Program (GCCRP) and undertaken under Griffith University Human ethics permit ENV/46/14/HREC.

Supplementary material

10113_2016_1034_MOESM1_ESM.docx (100 kb)
Supplementary material 1 (DOCX 99 kb)
10113_2016_1034_MOESM2_ESM.docx (826 kb)
Supplementary material 2 (DOCX 826 kb)
10113_2016_1034_MOESM3_ESM.docx (221 kb)
Supplementary material 3 (DOCX 221 kb)
10113_2016_1034_MOESM4_ESM.docx (41 kb)
Supplementary material 4 (DOCX 41 kb)
10113_2016_1034_MOESM5_ESM.docx (80 kb)
Supplementary material 5 (DOCX 79 kb)
10113_2016_1034_MOESM6_ESM.docx (41 kb)
Supplementary material 6 (DOCX 40 kb)
10113_2016_1034_MOESM7_ESM.docx (39 kb)
Supplementary material 7 (DOCX 39 kb)
10113_2016_1034_MOESM8_ESM.docx (40 kb)
Supplementary material 8 (DOCX 40 kb)
10113_2016_1034_MOESM9_ESM.docx (72 kb)
Supplementary material 9 (DOCX 71 kb)


  1. Abbs DJ, McInnes KL (2004) The impact of climate change on extreme rainfall and coastal sea levels over south-east Queensland. Part 1: analysis of extreme rainfall and wind events in a GCM. CSIRO Atmospheric Research, Gold CoastGoogle Scholar
  2. Bindoff NL, Willebrand J, Artale V, Cazenave A, Gregory J, Gulev S, Hanawa K, Le Quéré C, Levitus S, Nojiri Y, Shum CK, Talley LD, Unnikrishnan A (2007) Observations: oceanic climate change and sea level. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  3. Bosch OJH, King CA, Herbohn JL, Russell IW, Smith CS (2007) Getting the big picture in natural resource management-systems thinking as ‘method’ for scientists, policy makers and other stakeholders systems research and other stakeholders. Behav Sci 24:217–232. doi: 10.1002/sres.818 Google Scholar
  4. Catenacci M, Giupponi C (2012) Integrated assessment of sea-level rise adaptation strategies using a Bayesian decision network approach. Environ Model Softw 44:1–14. doi: 10.1016/j.envsoft.2012.10.010 Google Scholar
  5. Cisneros-Montemayor AM, Sumaila UR, Kaschner K, Pauly D (2010) The global potential for whale watching. Marine Policy 34:1273–1278. doi: 10.1016/j.marpol.2010.05.005 CrossRefGoogle Scholar
  6. Corkeron PJ, Connor RC (1999) Why do baleen whales migrate? Marine Mamm Sci 15:1228–1245. doi: 10.1111/j.1748-7692.1999.tb00887.x CrossRefGoogle Scholar
  7. Cunningham PA, Huijbens EH, Wearing SL (2012) From whaling to whale watching: examining sustainability and cultural rhetoric. J Sustain Tour 20:143–161. doi: 10.1080/09669582.2011.632091 CrossRefGoogle Scholar
  8. Dalla Rosa L, Ford JKB, Trites AW (2012) Distribution and relative abundance of humpback whales in relation to environmental variables in coastal British Columbia and adjacent waters. Cont Shelf Res 36:89–104. doi: 10.1016/j.csr.2012.01.017 CrossRefGoogle Scholar
  9. Goodman LA (1961) Snowball sampling. Ann Math Stat 32(1):148–170. doi: 10.1214/aoms/1177705148 CrossRefGoogle Scholar
  10. Poloczanska ES, Babcock RC, Butler A, Hobday AJ, Hoegh-Guldberg O, Kunz TJ, Matear R, Milton DA, Okey TA, Richardson AJ (2007) Climate change and Australian marine life. Oceanogr Mar Bio 45:407–478. doi: 10.1201/9781420050943.ch8 Google Scholar
  11. Hoyt E (2001) Whale watching 2001: worldwide tourism numbers, expenditures and expanding socioeconomic benefits. International Fund for Animal Welfare, CrowboroughGoogle Scholar
  12. Jackson JA, Zerbini A, Clapham P, Garrigue C, Hauser N, Poole M, Baker CS (2006) A Bayesian assessment of humpback whales on breeding grounds of Eastern Australiaand Oceania (IWC Stocks E, E1, E2 and F). Paper SC/A06/HW52 presented to the IWC Scientific Committee. International Whaling Commission, CambridgeGoogle Scholar
  13. Johnson M (2009) Public participation and perceptions of watershed modeling. Soc Nat Resour 22:79–87. doi: 10.1080/08941920802220347 CrossRefGoogle Scholar
  14. Kauffman S (1993) The origins of order: self-organization and selection in evolution. Oxford University Press, OxfordGoogle Scholar
  15. Kjærulff U, Madsen A (2008) Bayesian networks and influence diagrams: a guide to construction and analysis. Springer, New YorkCrossRefGoogle Scholar
  16. Knowles T, Campbell R (2011) What’s a whale worth? Valuing whales for National Whale Day. International Fund for Animal Welfare (IFAW) & Economists at Large, Melbourne, AustraliaGoogle Scholar
  17. Korfmacher KS (2001) The politics of participation in watershed modeling. Environ Manage 27:161–176. doi: 10.1007/s002670010141 CrossRefGoogle Scholar
  18. Lambert E, Hunter CP, Pierce GJ, MacLeod CD (2010) Sustainable whale- watching tourism and climate change: towards a framework of resilience. J Sust Tour 18:409–427. doi: 10.1080/09669581003655497 CrossRefGoogle Scholar
  19. Loeb VJ, Santora JA (2015) Climate variability and spatiotemporal dynamics of five Southern Ocean krill species. Prog Oceanogr 134:93–122. doi: 10.1016/j.pocean.2015.01.002 CrossRefGoogle Scholar
  20. Loucks DP, van Beek E (2005) Water resources systems planning and management: an introduction to methods, models and applications. Unesco, ParisGoogle Scholar
  21. Meynecke J-O (2014) Whale trails—a smart phone application for whale tracking. In: Ames D, Quinn N (eds) 7th international congress on environmental modelling and software, San Diego, California, USA, 2014. International Environmental Modelling and Software Society (iEMSs) pp 1–7Google Scholar
  22. Nadkarni S, Shenoy PP (2004) A causal mapping approach to constructing Bayesian networks. Decis Support Syst 38:259–281. doi: 10.1016/s0167-9236(03)00095-2 CrossRefGoogle Scholar
  23. Noad MJ, Dunlop RA, Paton D, Cato DH (2011) Absolute and relative abundance estimates of Australian east coast humpback whales (Megaptera novaeangliae). J Cetacean Res Manag 3:243–252Google Scholar
  24. Patten BC, Jørgensen SE (1995) Complex ecology: the part-whole relation in ecosystems. Prentice Hall, Englewood CliffsGoogle Scholar
  25. Pörtner H-O, Karl DM, Boyd PW, Cheung WWL, Lluch-Cota SE, Nojiri Y, Schmidt DN, Zavialov PO (2014) Ocean systems. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp 411–484Google Scholar
  26. Ramp C, Delarue J, Palsbo PJ II, Sears R, Hammond PS (2015) Adapting to a Warmer ocean—seasonal shift of baleen whale movements over three decades. PLoS One 10:e0121374. doi: 10.1371/journal.pone.0121374 CrossRefGoogle Scholar
  27. Richards R, Sano M, Roiko A, Carter RW, Bussey M, Matthews J, Smith TF (2013) Bayesian belief modeling of climate change impacts for informing regional adaptation options. Environ Model Softw 44:113–121. doi: 10.1016/j.envsoft.2012.07.008 CrossRefGoogle Scholar
  28. Richards RG, Sahin O, Sano M, Meynecke J-O, Tiller R (2014) App2Adapt: using tablet technology to elicit conditional probabilities for Bayesian belief network. In: Ames DP, Quinn N (eds) 7th international congress on environmental modelling and software. San Diego, California, pp 18–23Google Scholar
  29. Roberts N (1983) Introduction to computer simulation: the system dynamics approach. Addison-Wesley, ReadingGoogle Scholar
  30. Smith JN, Grantham HS, Gales N, Double MC, Noad MJ, Paton D (2012) Identification of Humpback whale breeding and calving habitat in the Great Barrier Reef. Mar Ecol Prog Ser 447:259–272. doi: 10.3354/meps09462 CrossRefGoogle Scholar
  31. Tiller R, Gentry R, Richards R (2013) Stakeholder driven future scenarios as an element of interdisciplinary management tools; the case of future offshore aquaculture development and the potential effects on fishermen in Santa Barbara, California. Ocean Coast Manag 73:127–135. doi: 10.1016/j.ocecoaman.2012.12.011 CrossRefGoogle Scholar
  32. Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modelling. Ecol Model 203:312–318. doi: 10.1016/j.ecolmodel.2006.11.033 CrossRefGoogle Scholar
  33. van den Honert RC, McAneney J (2011) The 2011 Brisbane floods: causes, impacts and implications. Water 3:1149–1173. doi: 10.3390/w3041149 CrossRefGoogle Scholar
  34. Vang L (2002) Distribution, abundance and biology of Group V Humpback whales Megaptera novaeangliae: a review. The State of Queensland Environmental Protection Agency, BrisbaneGoogle Scholar
  35. Voinov A, Gaddis EJB (2008) Lessons for successful participatory watershed modeling: a perspective from modeling practitioners. Ecol Model 216:197–207. doi: 10.1016/j.ecolmodel.2008.03.010 CrossRefGoogle Scholar
  36. von Bertalanffy L (1950) An outline of general system theory. Br J Philos Sci 1:134–165. doi: 10.1093/bjps/I.2.134 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jan-Olaf Meynecke
    • 1
    • 2
    • 5
    Email author
  • Russell Richards
    • 1
    • 3
  • Oz Sahin
    • 2
    • 4
  1. 1.Griffith Centre for Coastal ManagementGriffith UniversityGold CoastAustralia
  2. 2.Griffith Climate Change Response ProgramGriffith UniversityGold CoastAustralia
  3. 3.School of Agriculture and Food SciencesUniversity of QueenslandSt LuciaAustralia
  4. 4.Griffith School of EngineeringGriffith UniversityGold CoastAustralia
  5. 5.Humpbacks and High-rises IncGold CoastAustralia

Personalised recommendations