Building capacity for pro-poor responses to wildlife crime in Uganda: research findings
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This document summarizes research on interventions to reduce illegal wildlife hunting around protected areas in Uganda. It finds that addressing human-wildlife conflict and improving livelihood alternatives are the most effective approaches according to multiple methods: choice experiments of local communities, interviews with key informants, and preferences of Uganda Wildlife Authority staff. Households affected by problems like crop raiding are more likely to change behaviors with these interventions. While enforcement has a role, community engagement is important to make enforcement efforts more effective through increased intelligence. Overall the research found broad agreement that human-wildlife conflict compensation and wildlife-friendly enterprises would best promote conservation goals.
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Building capacity for pro-poor responses to wildlife crime in Uganda: research findings
- 6. The probability of a household hunting (for subsistence or commercial purposes) increases: • if they are better off • they report suffering from livestock predation • they do not feel they have benefited from revenue sharing No effect for households reporting losses from crop raiding
- 7. Wealthiest households are three times more likely to be hunters than the poorest households
- 8. On average hunters reported earning: • 100,000 sh per successful trip (50% success rate) • 450,000 sh per month during peak hunting season The average reported daily wage was 20-30,000 sh “if you worked for that money, you couldn’t get it easily” “I cannot stop hunting without another way of earning money”
- 9. Little deterrence effect found from law enforcement • Average encounter rate with rangers found to be 5% • Arrest rate only 0.14% • Average sentence 3 months and 400,000 sh fine “I am not afraid – I am too fast for the rangers to catch me” “you go with fear but you have to be alert” “even though there is fear, problems will force you to do what you are not supposed to do”
- 10. Enforcement: • Limited effectiveness in deterring hunting • Significant success in control of firearms • More likely to impact poorer households • Collusion an issue for professional and senior hunters Community conservation: • Perceptions of parks largely unfavourable • Key informants feel let down by failure to deliver on promises • Anger at perceived lack of response to crop raiding and livestock predation
- 12. Interventions Description HWC mitigation Designate 25% or 50% of revenue sharing funds specifically to fund human wildlife conflict mitigation Improve livelihoods Support wildlife friendly enterprise schemes to improve livelihood options available to offenders Eco-guards Employ village eco-guards/scouts to act as link between communities and UWA, respond to HWC Withdraw resource rights Withdraw all rights to harvest resources from within protected area boundaries Regulated hunting Allow a regulated trade in specific species, provided sustainability of offtake could be ensured Increase law enforcement Increase the probability of detection of wildlife crimes within protected area boundaries
- 13. • what interventions/policies have worked elsewhere? • how did we identify interventions/policies to be considered? • what are the interventions/policies we considered? • methods • choice experiments – how does the method work? – what are the results? • Scenario interviews – how does the method work? – what are the results? • Key informant interviews – people suggest what has been done in the past, what they think they are likely to get – the case of crop-raiding – reformed poacher associations • UWA staff • How do the results of the different approaches compare? Murchison Queen Preference Rank Preference Rank 50% revenue sharing for HWC 0.00 4 1.00 1 eco-guards 0.34 2 0.61 2 increase detection prob by order of 10 0.03 3 0.18 4 regulated hunting -0.18 5 -0.10 5 agri-environmental scheme 0.45 1 0.34 3
- 14. Households that suffer from human wildlife conflict were more likely to increase the time they allocate to legal livelihoods
- 15. Authorised resource users more likely to think options are fair
- 16. Improved livelihoods, ecoguards and human wildlife conflict mitigation all predicted to increase likelihood of households informing
- 17. We asked UWA staff which community interventions would have the greatest impact on reducing wildlife crime
- 18. Labour allocation Fairness Likelihood of informing Community preference UWA preference HWC mitigation + ++ ++ ++ ++ Improve livelihoods ++ ++ ++ ++ ++ Eco-guards ++ ++ ++ ++ + Withdraw resource rights - - + / - not assessed not assessed Regulated hunting - + / - + - - Increase law enforcement not assessed not assessed not assessed + ++
- 19. Summary: • Broad agreement found between different methods used • Overall HWC mitigation and wildlife friendly enterprise schemes most preferred options • Significant differences in responses found between sites • Households that suffer from crop raiding and livestock predation are more likely to change their behaviour • There is modest local support for increased enforcement • Community engagement approaches can improve the effectiveness of law enforcement through more intelligence
- 20. Thank you for listening
Notes de l'éditeur
- In this presentation and the presentation that follows, we will describe the results our research. As Dilys has said, the main aims of the research was to better understand the complex drivers behind why people get involved in wildlife crime and to identify ways in which efforts to tackle wildlife crime can be improved. For this presentation, I am going to focus today on our research towards the first part of that aim, which investigated who’s involved in wildlife crime, what the main drivers behind their involvement are and what the scale of the problem is.
- Dilys has already summarised the findings of the evidence review that we presented last year. These findings fed into the design of the following stages of the research: the investigation of the drivers of wildlife crime and the evaluation of potential policy changes and interventions to combat wildlife crime. The results of the both strands of the research then feed in to our recommendations for UWA which we will present in the final presentation later on. So why does understanding the motivations of those engaged in wildlife crime help? Well, firstly, it is doubtful that those engaged in wildlife crime are a single homogenous group who are driven by the same motivations. By understanding why people become involved
- Once we had that definition, my colleague Mariel Harrison undertook an extensive review of the literature from Uganda, but also incorporating findings from elsewhere, to identify the current level of understanding regarding the main drivers of wildlife crime in the country. Mariel identified four main reasons that typically drive involvement in wildlife crime: to basic subsistence needs, to generate financial income, to retaliate against perceived injustices and to satisfy traditional cultural practices. One of the aims of our research was then to try to better understand the relative importance of each of these possible drivers, with the ultimate aim of helping us to identify approaches to combat them.
- So this graph shows the findings of our unmatched count technique survey, with the y axis showing the proportion of households in which at least one person has been involved in each of the activities in the year leading up to being interviewed. What we can see is that the most common activity of all is hunting in order to sell the catch, with 42% of households in our survey sample estimated to have been involved in this activity, which is a huge number of households if we think back to the populations sizes of our sample areas. These numbers broadly correspond to what people told me in private.
- We were also able to see how the proportion of people involved in each of the 5 activities varied spatially. These results are at quite a coarse scale because indirect approaches tend not to be highly precise and give you large confidence intervals, but we can see clear spatial trends and areas where illegal activities are more common. In general we found a lower proportion of households around Queen Elizabeth were involved in the different activities but this is offset by the fact that household population density is much higher around Queen than it is for Murchison.
- now, in addition to giving us estimates of the proportion of households involved in each of the 5 wildlife crimes, we can also use the results of the unmatched count technique to develop profiles of the households that have a greater probability of being involved. What we find is that the probability of being involved in hunting increases if a household is better off, if they report suffering from livestock predation and if they feel they have not benefitted from revenue sharing. The result for poverty score is particularly interesting. Rather than poorer households being more likely to poach, we have the opposite with better off, middle-class households being the ones who are more likely to poach. This doesn’t mean that richer households are going to be the only hunters but if you randomly select a group of hunters, you would expect them to be better off on average than the wider population.
- now, in addition to giving us estimates of the proportion of households involved in each of the 5 wildlife crimes, we can also use the results of the unmatched count technique to develop profiles of the households that have a greater probability of being involved. What we find is that the probability of being involved in hunting increases if a household is better off, if they report suffering from livestock predation and if they feel they have not benefitted from revenue sharing. The result for poverty score is particularly interesting. Rather than poorer households being more likely to poach, we have the opposite with better off, middle-class households being the ones who are more likely to poach. This doesn’t mean that richer households are going to be the only hunters but if you randomly select a group of hunters, you would expect them to be better off on average than the wider population.
- The results of the key informant interviews suggests that on average hunters can expect to earn about $135 a month during peak hunting periods. The maximum figure was about $450 a month, which is more than our university educated research assistants were earning. Again, the numbers are only approximate but they give a sense of the contribution that hunting makes to local income, particularly at times when there are few alternatives. Many people in our sample villages are wet season farmers who have little other ways of earning money during the dry season. This comes through in two commonly heard sentiments: it’s very difficult to earn the type of money you can earn through hunting and a lot of the time there isn’t any alternative anyway
- Given the potential income that can be gained from hunting, we can see that there’s a strong financial pull towards hunting but how much do we know about the deterrence effect from the current conservation regime? One of the questions we heard repeatedly from UWA staff, was “why do people continue to risk their lives and risk going to jail by going into the park to poach?” Well, the answer to that is probably that the perceived risk is very low.
- In this talk, I am going to focus on the second phase of the research – evaluating the potential effectiveness of interventions and policies designed to reduce wildlife crime
- Preferences have been standardised ARUs and Non-ARUs essentially the same except for regulated hunting
- Top plot is for households that have (right two panels) and haven’t (left two panels) suffered from crop raiding. Bottom plot is the same but for livestock predation instead. Why are these three options the most likely to encourage people to spend more time on their farms? Scenario interviews allow us to explore the reasons behind people’s predicted behaviour changes. Wildlife friendly enterprise – increase profitability of farm-based activities Human wildlife conflict mitigation – reduce costs of wildlife to farm-based livelihood activities ecoguards – provide employment opportunities, reduce HWC