6 Assessing Africa-wide pangolin exploitation by scaling local data
7.2 Implications for conservation 1 Conservation action
Many approaches have been suggested to monitor and manage exploitation at a local scale to reduce pressure on wildlife. One approach for reducing hunting activities, thus reducing pressure on wildlife, is to develop alternative protein or alternative livelihoods programmes (Foerster et al. 2012; Roe et al. 2015). Currently, however, the success of such projects in Central Africa is limited, because the pressure from hunting by commercial hunters outside of the livelihood project site often dwarfs that of the hunters based at the site (Wicander & Coad 2015). In Cameroon, Yasuoka (2006) calculated that subsistence hunting was sustainable, but commercial hunting was not. Commercial hunting supplies urban areas, where wild meat is rarely a necessity due to the availability of alternatives, but studies have shown that the consumption of wild meat is frequent (Wilkie et al. 2005; Mbete et al. 2011). Efforts to reduce urban demand for wild meat
may also reduce the need for commercial hunting. In areas where hunting is illegal, law enforcement may target particular areas (e.g. protected areas), or focus on regulating hunting methods (e.g. snares). Moreover, engagement with hunting communities may be the key to tracking exploitation more readily.
In rural areas, conservation actions could be sought that work with local communities to track their harvest of wildlife to ensure sustainable use of wildlife and the success of long- term community-based approaches. The indicators that I developed in Chapters 4 and 5 can be used to track exploitation over time at various scales to inform conservation decisions. For example, the indicators can be used to track exploitation over time at individual sites, or can be used to compare multiple sites. Recently, Avila et al. (2017) applied the mean body mass indicator that I developed in Chapter 4 to harvest data in three villages in Cameroon, and compared the MBMI for each village per month and by hunting method. Across time, the authors found no significant changes in the MBMI, but state that the area is likely to have relatively low hunting pressure and relatively high densities of wildlife. When comparing the MBMI for different hunting methods, the authors found that animals taken using guns had a higher mean body mass. Furthermore, Avila et al. (2017) suggested that the MBMI could be used for community-based monitoring approaches, which would allow communities to track their own harvests and understand the changing dynamics within their hunting system.
By comparing across many sites, patterns of harvests can be observed across large spatial scales, and can be used to inform global policy decisions (see pangolin example in the next section).
7.2.2 Policy
Given that the overexploitation of wildlife now represents one of the greatest pressures on biodiversity (Maxwell et al. 2016), the database and analyses presented here have important implications for future conservation policy. Consistent and accessible quantitative information is needed to inform decision-making in global policy arenas. For example, the outputs from this project could be considered as ‘Essential Biodiversity Variables’ (EBVs), defined as “a measurement required for study, reporting, and management of biodiversity change” (Pereira et al. 2013). EBVs on the exploitation of
wildlife can then contribute to initiatives such as the Biodiversity Observation Network of the Group on Earth Observations (GEO BON). GEO BON aims to coordinate and facilitate biodiversity observation to support policy decisions, and is one of the GEO’s Societal Benefit Areas that highlights the benefits of biodiversity to society. The database and indicators developed during this PhD may also contribute to reporting efforts on progress towards achieving the United Nations’ Sustainable Development Goals. In particular, goal 2 which states that by 2020 governments commit to “ensure sustainable food production”, and goal 15 which states that we must “promote implementation of sustainable management of all types of forest” (Target 15.2) and “take urgent action to end poaching and trafficking of protected species” (Target 15.7). For example, to ensure progress towards goal 2, research should focus on investigating the sustainability of the harvest of wildlife by analysing population, harvest, and life history trait data together, where available. Furthermore, the database could be used to assess how wild meat (and other wild-sourced foods if included) could contribute to achieving SDG2, which currently is assumed at zero. To do this, a consumption module could be added to the database, and analysed to quantify and map the contribution of wild-sourced foods to the diets of people. When combined with the aforementioned sustainability analysis, this would allow a sustainable contribution to be calculated and, if needed, a subsequent analysis of any nutritional ‘gaps’ that may remain to be estimated. Another component of goal 2 is ‘better access to food’ where, in areas where sustainable hunting is permitted, the size of the areas needed could be assessed using data on contemporary hunter territory sizes.
Specifically, the analyses on the exploitation of pangolins (earlier version of Chapter 6, Ingram et al. 2016) has been used to inform global policy change. The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) is an international intergovernmental agreement to ensure that the international trade in wild animals and plants (and their derivatives) does not threaten their existence. Both the African and the Asian pangolins were transferred from CITES Appendix II to Appendix I at the recent 17th meeting of the Conference of the Parties (Challender & Waterman 2017). Specifically, Chapter 6 informed the transfer of African pangolins to Appendix I (Challender & Waterman 2017), the most restrictive CITES Appendix, whereby the commercial and international trade in wild-caught pangolins is banned.