Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-10T07:42:10.272Z Has data issue: false hasContentIssue false

Comparison of local knowledge and researcher-led observations for wildlife exploitation assessment and management

Published online by Cambridge University Press:  20 August 2020

Andrew J Temple*
Affiliation:
School of Natural and Environmental Sciences, Newcastle University, NewcastleNE1 7RU, UK
Selina M Stead
Affiliation:
School of Natural and Environmental Sciences, Newcastle University, NewcastleNE1 7RU, UK Institute of Aquaculture, University of Stirling, StirlingFK9 4LA, UK
Edward Hind-Ozan
Affiliation:
Social Seas, YorkYO23 1ES, UK
Narriman Jiddawi
Affiliation:
Institute of Marine Sciences, Dar es Salaam University, PO Box 668, Zanzibar Town, Tanzania Institute of Fisheries Research Zanzibar, Ministry of Agriculture, Natural Resources, Livestock and Fisheries, PO Box 295, Zanzibar Town, Tanzania
Per Berggren
Affiliation:
School of Natural and Environmental Sciences, Newcastle University, NewcastleNE1 7RU, UK
*
Correspondence to: Dr Andrew J Temple, Email: andrew.j.l.temple@gmail.com

Summary

The use of local knowledge observations to generate empirical wildlife resource exploitation data in data-poor, capacity-limited settings is increasing. Yet, there are few studies quantitatively examining their relationship with those made by researchers or natural resource managers. We present a case study comparing intra-annual patterns in effort and mobulid ray (Mobula spp.) catches derived from local knowledge and fisheries landings data at identical spatiotemporal scales in Zanzibar (Tanzania). The Bland–Altman approach to method comparison was used to quantify agreement, bias and precision between methods. Observations from the local knowledge of fishers and those led by researchers showed significant evidence of agreement, demonstrating the potential for local knowledge to act as a proxy, or complement, for researcher-led methods in assessing intra-annual patterns of wildlife resource exploitation. However, there was evidence of bias and low precision between methods, undermining any assumptions of equivalency. Our results underline the importance of considering bias and precision between methods as opposed to simply assessing agreement, as is commonplace in the literature. This case study demonstrates the value of rigorous method comparison in informing the appropriate use of outputs from different knowledge sources, thus facilitating the sustainable management of wildlife resources and the livelihoods of those reliant upon them.

Type
Report
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Foundation for Environmental Conservation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alfaro-Shigueto, J, Mangel, JC, Darquea, J, Donoso, M, Baquero, A, Doherty, PD, Godley, BJ (2018) Untangling the impacts of nets in the southeastern Pacific: rapid assessment of marine turtle bycatch to set conservation priorities in small-scale fisheries. Fisheries Research 206: 185192.CrossRefGoogle Scholar
Anadón, JD, Giménez, A, Ballestar, R, Pérez, I (2009) Evaluation of local ecological knowledge as a method for collecting extensive data on animal abundance. Conservation Biology 23: 617625.CrossRefGoogle ScholarPubMed
Bland, JM, Altman, DG (1999) Measuring agreement in method comparison studies. Statistical Methods in Medical Research 8: 135160.CrossRefGoogle ScholarPubMed
Bland, JM, Altman, DG (2003) Applying the right statistics: analyses of measurement studies. Ultrasound in Obstetrics & Gynecology 22: 8593.CrossRefGoogle ScholarPubMed
Carkeet, A, Goh, YT (2018) Confidence and coverage for Bland–Altman limits of agreement and their approximate confidence intervals. Statistical Methods in Medical Research 27: 15591574.CrossRefGoogle ScholarPubMed
Croll, DA, Dewar, H, Dulvy, NK, Fernando, D, Francis, MP, Galván-Magaña, F et al. (2016) Vulnerabilities and fisheries impacts: the uncertain future of manta and devil rays. Aquatic Conservation: Marine and Freshwater Ecosystems 26: 562575.CrossRefGoogle Scholar
Daw, TM, Robinson, JAN, Graham, NAJ (2011) Perceptions of trends in Seychelles artisanal trap fisheries: comparing catch monitoring, underwater visual census and fishers’ knowledge. Environmental Conservation 38: 7588.CrossRefGoogle Scholar
Fischer, J (2000) Participatory research in ecological fieldwork: a Nicaraguan study. In: Finding Our Sea Legs: Linking Fishery People and Their Knowledge with Science and Management, eds Neis, B, Felt, L, pp. 4154. St. John’s, NL, Canada: ISER Books.Google Scholar
Foucault, M, Ewald, F (2003) ‘Society Must Be Defended’: Lectures at the Collège de France, 1975–1976. London, UK: Macmillan.Google Scholar
Griffin, L (2009) Scales of knowledge: North Sea fisheries governance, the local fisherman and the European scientist. Environmental Politics 18: 557575.CrossRefGoogle Scholar
Haas, PM (1989) Do regimes matter? Epistemic communities and Mediterranean pollution control. International Organization 43: 377403.CrossRefGoogle Scholar
Hind, EJ (2012) Last of the Hunters or the Next Scientists? Arguments For and Against the Inclusion of Fishers and Their Knowledge in Mainstream Fisheries Management. Doctoral thesis, National University of Ireland, Galway.Google Scholar
Hind, EJ (2015) A review of the past, the present, and the future of fishers’ knowledge research: a challenge to established fisheries science. ICES Journal of Marine Science 72: 341358.Google Scholar
Hirst, W, Phelps, EA, Buckner, RL, Budson, AE, Cuc, A, Gabrieli, JDE et al. (2009) Long-term memory for the terrorist attack of September 11: flashbulb memories, event memories, and the factors that influence their retention. Journal of Experimental Psychology: General 138: 161176.CrossRefGoogle ScholarPubMed
Jennings, S, Polunin, NVC (1995) Biased underwater visual census biomass estimates for target-species in tropical reef fisheries. Journal of Fish Biology 47: 733736.CrossRefGoogle Scholar
Jentoft, S (2005) Fisheries co-management as empowerment. Marine Policy 29: 17.CrossRefGoogle Scholar
Johannes, RE, Freeman, MMR, Hamilton, RJ (2000) Ignore fishers’ knowledge and miss the boat. Fish and Fisheries 1: 257271.CrossRefGoogle Scholar
Knapman, P (2005) Participatory governance in inshore fisheries co-management in England and Wales. In: Participation in Fisheries Governance, ed. Gray, TS, pp. 163178. Dordrecht, The Netherlands: Springer Netherlands.CrossRefGoogle Scholar
Matlin, MW (2004) Cognition. Hoboken, NJ, USA: Wiley.Google Scholar
Moore, JE, Cox, TM, Lewison, RL, Read, AJ, Bjorkland, R, McDonald, SL et al. (2010) An interview-based approach to assess marine mammal and sea turtle captures in artisanal fisheries. Biological Conservation 143: 795805.CrossRefGoogle Scholar
Murray, GD, Neis, B, Palmer, CT, Schneider, D (2008) Mapping cod: fisheries science, fish harvesters’ ecological knowledge and cod migrations in the Northern Gulf of St. Lawrence. Human Ecology 36: 581598.CrossRefGoogle Scholar
Neis, B, Schneider, DC, Felt, L, Haedrich, RL, Fischer, J, Hutchings, JA (1999) Fisheries assessment: what can be learned from interviewing resource users? Canadian Journal of Fisheries and Aquatic Sciences 56: 19491963.CrossRefGoogle Scholar
O’Donnell, KP, Molloy, PP, Vincent, ACJ (2012) Comparing fisher interviews, logbooks, and catch landings estimates of extraction rates in a small-scale fishery. Coastal Management 40: 594611.CrossRefGoogle Scholar
Peterson, AM, Stead, SM (2011) Rule breaking and livelihood options in marine protected areas. Environmental Conservation 38: 342352.CrossRefGoogle Scholar
Pilcher, NJ, Adulyanukosol, K, Das, H, Davis, P, Hines, E, Kwan, D et al. (2017) A low-cost solution for documenting distribution and abundance of endangered marine fauna and impacts from fisheries. PLoS ONE 12: e0190021.CrossRefGoogle ScholarPubMed
R Core Team (2019) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Rist, J, Milner-Gulland, EJ, Cowlishaw, GUY, Rowcliffe, M (2010) Hunter reporting of catch per unit effort as a monitoring tool in a bushmeat-harvesting system. Conservation Biology 24: 489499.CrossRefGoogle Scholar
Rozwadowski, HM (2002) The Sea Knows No Boundaries: A Century of Marine Science under ICES. Seattle, WA, USA: University of Washington Press and New York, NY, USA: University of Columbia Press.Google Scholar
Slater, MJ, Napigkit, FA, Stead, SM (2013) Resource perception, livelihood choices and fishery exit in a Coastal Resource Management area. Ocean & Coastal Management 71: 326333.CrossRefGoogle Scholar
Soto, CG (2006) Socio-cultural Barriers to Applying Fishers’ Knowledge in Fisheries Management: An Evaluation of Literature Cases. Doctoral thesis, School of Resource and Environmental Management, Simon Fraser University.Google Scholar
Stead, S, Daw, T, Gray, T (2006). Uses of fishers’ knowledge in fisheries management. Anthropology in Action 13: 7786.Google Scholar
Stephenson, RL, Paul, S, Pastoors, MA, Kraan, M, Holm, P, Wiber, M et al. (2016) Integrating fishers’ knowledge research in science and management. ICES Journal of Marine Science 73: 14591465.CrossRefGoogle Scholar
Temple, AJ, Wambiji, N, Poonian, CNS, Jiddawi, N, Stead, SM, Kiszka, JJ, Berggren, P (2019) Marine megafauna catch in southwestern Indian Ocean small-scale fisheries from landings data. Biological Conservation 230: 113121.CrossRefGoogle Scholar
Thurstan, RH, Roberts, CM (2010) Ecological meltdown in the Firth of Clyde, Scotland: two centuries of change in a coastal marine ecosystem. PLoS ONE 5: e11767.CrossRefGoogle Scholar
Wanyonyi, IN, Wamukota, A, Mesaki, S, Guissamulo, AT, Ochiewo, J (2016) Artisanal fisher migration patterns in coastal East Africa. Ocean & Coastal Management 119: 93108.CrossRefGoogle Scholar
Weale, A (1992) The New Politics of Pollution. Manchester, UK: Manchester University Press.Google Scholar