Conceived and designed the experiments: JJB RWB FMU. Analyzed the data: RWB FMU JJB. Wrote the paper: RWB FMU JJB. Collated the data: JJB.
JJB is the data analyst for the MIKE programme. RWB was a resource specialist for MIKE since its inception and then a co-opted member of the MIKE Technical Advisory Group from 2003 to 2009. RWB and FMU were contracted by MIKE to carry out the analysis of the data presented in this publication for a report to the 15th meeting of the Conference of the Parties in Doha (Qatar) 2010. This does not alter the author's adherence to all the PLoS ONE policies on sharing data and materials.
Elephant poaching and the ivory trade remain high on the agenda at meetings of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Well-informed debates require robust estimates of trends, the spatial distribution of poaching, and drivers of poaching. We present an analysis of trends and drivers of an indicator of elephant poaching of all elephant species. The site-based monitoring system known as Monitoring the Illegal Killing of Elephants (MIKE), set up by the 10th Conference of the Parties of CITES in 1997, produces carcass encounter data reported mainly by anti-poaching patrols. Data analyzed were site by year totals of 6,337 carcasses from 66 sites in Africa and Asia from 2002–2009. Analysis of these observational data is a serious challenge to traditional statistical methods because of the opportunistic and non-random nature of patrols, and the heterogeneity across sites. Adopting a Bayesian hierarchical modeling approach, we used the proportion of carcasses that were illegally killed (PIKE) as a poaching index, to estimate the trend and the effects of site- and country-level factors associated with poaching. Important drivers of illegal killing that emerged at country level were poor governance and low levels of human development, and at site level, forest cover and area of the site in regions where human population density is low. After a drop from 2002, PIKE remained fairly constant from 2003 until 2006, after which it increased until 2008. The results for 2009 indicate a decline. Sites with PIKE ranging from the lowest to the highest were identified. The results of the analysis provide a sound information base for scientific evidence-based decision making in the CITES process.
In spite of the ban on trade in ivory since 1990 there is continuing widespread concern about the illicit ivory trade and the illegal killing of elephants, both of which, to judge from press reports, are evidently still with us. The ban was imposed by the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) in the 7th Conference of the Parties to CITES (CoP). In 1997 at the 10th CoP, three countries in Southern Africa (Botswana, Namibia and Zimbabwe) successfully argued that “some of their elephant populations were healthy and well-managed” and that “income from limited ivory sales would bring benefits to conservation and to local communities” (CITES Press Release:
The central issues upon which much of the current debate is focused are: (1) Is there a trend in elephant poaching and if so, how strong is it? (2) Have changes in CITES policy, and in particular the one-off ivory sales, had an impact on elephant poaching? Debates in successive CoPs have tended towards a polarization of views. One side contends that any relaxation of restrictions on trade in ivory amounts to a green light to poachers and that any perceived increase in poaching must be attributable to it. The opposing view argues that there are many factors that could potentially explain an increase, and that CITES listings cannot be assumed to be of any great interest to the poaching fraternity. To judge from the debates that have taken place, it appears that a sound evidence base, in support of either viewpoint, is lacking. What studies there have been have either been of limited geographical scope
The question of an association between CITES policy and trends in illegal ivory trade cannot be considered in isolation. There are many potential drivers of the illegal killing of elephants and it is necessary to situate the impact of CITES policy within a broader causal framework. Existing studies that have addressed the question (e.g.
CITES is a global treaty and assessing the impact of its decisions is best attempted at a global level. This has hitherto been difficult owing to the lack of data on illegal killing of elephants across the elephant's range, but data are now becoming available. A condition for the 1997 partial down-listing of the African elephant was the establishment of two global monitoring systems: Monitoring the Illegal Killing of Elephants (MIKE) and the Elephant Trade Information System (ETIS) (
Analysis of these data entails a number of limitations that need to be borne in mind. First, in spite of early efforts to achieve a representative selection of sites [
Despite these limitations, MIKE carcass encounter data provides a rich source of data on illegal killing of elephants from across the entire range of African and Asian elephants. We present the first analysis of carcass data from 66 MIKE sites over the period 2002–2009. Our aims were to
describe trends in the illegal killing of elephants over time;
identify site- and country-level factors associated with illegal killing of elephants;
describe and compare rates of illegal killing of elephants across sites and range states.
We avoid the difficulty of not having reliable patrol effort data by using the
The data were derived from 6,337 carcasses of elephants encountered by patrols in 66 MIKE sites in 36 range states in Africa and Asia between 2002 and 2009. This was the dataset remaining after removing three sites (Kahuzi Biega in the Democratic Republic of Congo, Bukit Barisan Selatan in Indonesia and Gua Musang in Malaysia) where no carcasses were recorded in any year. The distribution of the sites across the elephant range is shown separately for Africa and Asia in
For each carcass, cause of death was classified as illegal or not, and year of death was assigned according to standard carcass ageing criteria
We used the proportion of illegally killed elephants (PIKE) among the carcasses encountered by patrols as an indicator of poaching. The population parameter corresponding to this statistic is the probability that an elephant carcass was illegally killed. This is a relative measure and is not the proportion of elephants in the population that have been illegally killed – this cannot be estimated with the available data. The use of PIKE appears to sidestep the need for a measure of effort because we assumed that in the PIKE ratio, effort appears in both numerator and denominator and effectively “cancels out”. The simplification does not come free, however, and we critically examine the implicit underlying assumptions in the
We were guided in the choice of candidate covariates by the aims of the analysis, in particular to enable characterization of sites and countries with high levels of elephant poaching, and to contribute towards an understanding of its general causal background. Variables were selected on the basis of prior expectation of relevance to illegal killing. Site-level covariates included in the analysis are listed in
Name | Description | Source |
|
Area of site (km2) | AED |
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Estimated size of elephant population | AED |
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Estimated elephant density | Derived from |
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Net primary production (see text) | Imhoff et al, 2004 – CIESIN |
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Human population density | LandScan™, 2006 |
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= 1 if |
Derived from |
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Human footprint (see text) | WCS |
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Conservation Effort (see text) | AED |
AED: African Elephant Database.
CIESIN: Centre for International Earth Science Information Network.
WCS: Wildlife Conservation Society.
The variables
Country-level covariates were chosen to represent aspects of governance, demographic change, the economy and human development. The variables used are summarized in
Name | Description | Source |
|
Control of corruption | World Bank |
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Government effectiveness | World Bank |
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Political stability and absence of violence | World Bank |
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Rule of law | World Bank |
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Regulatory quality | World Bank |
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Voice and accountability | World Bank |
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Corruption perceptions index | Transparency International |
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Gross domestic product per capita | UNDP |
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Annual population growth rate | UNSD |
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Overseas development aid per capita | UNSD |
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Educational attainment | UNDP |
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Human life expectancy | UNDP |
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Human development index | UNDP |
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Index of domestic ivory markets | ETIS |
UNDP: United Nations Development Programme.
UNSD: United Nations Statistics Division.
ETIS: Elephant Trade Information System.
Data for both site- and country-level covariates were available over varying ranges of years, and in some cases only for one or two years. To overcome this, and to simplify the analysis somewhat, we took only the 2007 values of all variables. Preliminary analysis indicated that there was much more variability in the values of the covariates between countries and between sites than between the relatively short span of years covered by the data.
Before embarking on statistical modeling of PIKE, the covariates were subjected to preliminary exploratory analyses, separately at site and country levels. The aim was to understand the inter-relationships among covariates to aid in the selection of variables and the interpretation of the final models. For this we used principal components analysis (PCA), and obtained visualizations of the results using plots of the loadings of the first two principal components
The analysis of PIKE was based on fitting statistical models to the data. The basic statistical modeling tool used was hierarchical binomial logistic regression
Specifically, the fitted models were of the general form
The modeling strategy was as follows. First, a model with random intercepts for countries and sites within countries only was fitted, with no covariates. This was the minimal model, in the sense that it represented just the hierarchical structure of the data, without covariate effects. Next we added a polynomial function of year while at the same time determining the best fitting order of polynomial. An initial exploratory analysis of time trend, providing an idea of the polynomial order to expect, was accomplished by fitting a cubic spline smoother (using generalized additive models
Non-informative priors were used throughout. Specifically, these were as follows.
We took the view that there was no statistical basis for using the conventional null hypothesis testing approach to model selection: the data are purely observational, with no means of controlling for unwanted sources of variability as would be expected in a controlled experiment
Having fitted models to the data, we used the MCMC simulations to obtain predicted values of PIKE. The use of model predictions for inferences about PIKE is tantamount to using smoothed values rather than simply calculating the raw proportions directly from the data. The random “noise” in the raw data, not accounted for by the covariates, was summarized in the random effects, or residuals, at site and country levels.
Region | |||||||
Year | Central Africa | Eastern Africa | SouthernAfrica | West Africa | Asia | Total | |
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0.00 (5) | 0.36 (165) | 0.19 (53) | 0.12 (17) | - (-) |
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0.70 (269) | 0.25 (336) | 0.11 (115) | 0.24 (21) | 0.08 (12) |
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0.79 (383) | 0.33 (259) | 0.21 (165) | 0.35 (34) | 0.05 (40) |
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0.54 (229) | 0.23 (243) | 0.06 (247) | 0.30 (10) | 0.12 (69) |
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0.63 (126) | 0.22 (239) | 0.19 (240) | 0.00 (4) | 0.18 (17) |
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0.87 (241) | 0.32 (288) | 0.16 (200) | 0.78 (18) | 0.03 (33) |
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0.86 (220) | 0.50 (495) | 0.22 (202) | 0.86 (22) | 0.09 (35) |
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0.64 (101) | 0.29 (952) | 0.31 (163) | 0.86 (35) | 0.50 (34) |
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There is considerable variability in the numbers of carcasses reported, both between sub-region and between sites within sub-regions and through time. Eastern Africa recorded twice as many carcasses as Southern and Central Africa and more than ten times as many carcasses as West Africa and Asia. Large numbers of carcasses were found at sites with large elephant populations and West African and Asian elephant populations are much smaller than in other sub-regions. Of the 2977 carcasses found in Eastern Africa about 50% (1529) were found at Samburu-Laikipia (SBR) in Kenya, collected using an informant network
Details of the results of the PCA analyses are provided in
The minimal model representing the data structure was a hierarchical logistic regression model with random effects for both sites and countries, and no covariates:
The trend is shown graphically in
Mean annual PIKE by year with 95% credible intervals.
The variables that were found to be important were
Model, |
Fixed effects |
|
|
1 | none | 1199.5 | 0.0000 |
2 | p( |
1062.2 | 0.0000 |
3 | p( |
1051.1 | 0.0000 |
4 | p( |
1044.2 | 0.0000 |
5 | p(year,5)+ |
1039.4 | 0.0002 |
6 | p( |
1033.7 | 0.0036 |
7 | p( |
1033.5 | 0.0040 |
8 | p( |
1033.4 | 0.0042 |
9 | p( |
1024.9 | 0.2958 |
10 | p( |
1023.2 | 0.6921 |
All models have random effects for countries and sites within countries. The wi column shows the AIC weights and p(year,5) is the polynomial of order 5 for the year effect.
The inference for country-level covariates is less clear. Both
Model term | Posterior mean | Lower limit | Upper limit | |
|
||||
p( |
linear | 3.95 | 2.75 | 5.17 |
quadratic | 2.47 | 1.20 | 3.75 | |
cubic | −3.24 | −4.48 | −1.99 | |
quartic | −3.31 | −4.51 | −2.12 | |
quintic | −2.83 | −4.04 | −1.61 | |
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0.64 | 0.25 | 1.06 |
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−0.75 | −2.09 | 0.60 | |
ln |
−0.68 | −1.14 | −0.23 | |
ln |
0.61 | −0.49 | 1.77 | |
Variance |
1.17 | 0.54 | 2.19 | |
|
|
−0.98 | −1.52 | −0.49 |
Variance |
0.64 | 0.01 | 1.86 | |
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||||
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|
0.89 | 0.52 | 1.28 |
|
−0.98 | −2.33 | 0.37 | |
ln |
−0.90 | −1.37 | −0.46 | |
ln |
0.53 | −0.59 | 1.73 | |
Variance |
1.27 | 0.62 | 2.28 | |
|
|
−1.10 | −1.63 | −0.60 |
Variance |
0.37 | 0.00 | 1.38 |
Further conclusions can be drawn from
Posterior mean of PIKE for varying (A)
For the purpose of comparisons among sites, the posterior predicted mean values of PIKE for each site (grouped by sub-region) in 2009 are shown in
Posterior mean value of PIKE with 95% credible intervals. Numbers are estimated elephant abundances at each site. The names of the sites corresponding to the site codes shown on the vertical axis are given in
The site- and country-level random effects (or residuals) are shown in
Referring to
Among site-level factors,
At country level, we conclude from the analysis that both governance and the level of human development are associated with PIKE, and that there is insufficient information in the data to reject one in favour of the other. It is not surprising to find these two aspects emerging jointly in our analysis – the relationship between governance and development has been researched extensively (see, for example, the “Governance and Development Review” published by the Institute of Development Studies at
Because of the covert nature of poaching, it is clearly virtually impossible ever to devise an absolute measure of the rate of poaching based on direct observation. For the purposes of comparison across sites, however, we suggest that the predicted PIKE means, as presented in
The definition of PIKE as the ratio of number of illegally killed carcasses to all carcasses encountered may sometimes be biased because of background variation in elephant mortality. PIKE could be biased downwards if the total carcass count is high because of adverse environmental conditions, such as drought. If these conditions cause high mortality while the true poaching rate remains constant, then PIKE will be lower. During CoP15 in 2010 it was pointed out that the Tsavo and Samburu-Laikipia sites in Kenya suffered from severe drought that could account for the drop in PIKE observed between 2008 and 2009. The analysis was re-run after eliminating all data from those two sites and the overall pattern in the trend remained largely unchanged (apart from 2002, when a very large proportion of the data came from Samburu-Laikipia). So, in this case at least, the analysis based on PIKE proved to be robust.
In principle, these variations in background mortality could be allowed for in the statistical analysis by a Bayesian hierarchical model in which the number of carcasses encountered by a patrol (the binomial “
Another source of bias inherent in the definition of PIKE is the implicit assumption that the probability of detection of a carcass is the same for all elephants, illegally killed or not. This assumption is questionable, especially in circumstances where patrols act on intelligence that directs them to illegally killed elephants. This is another source of variation that could be accommodated in the models mentioned above – by explicitly modeling the detection probability, with covariates of its own. On the other hand, if it could be assumed that the detection bias is more or less constant over time, then our estimated trend would still be reliable. However, between site comparisons remain questionable as detection bias is not expected to be the same at all sites. We note also that site year combinations where no carcasses were recorded may be due to low detection probabilities
A data quality issue arises from the conclusions from the present analysis that countries with high PIKE values tend to be those with poor governance and development indicators. The problem is that it is likely that these same factors cause MIKE data to be incomplete or otherwise deficient. It is not clear whether the result is a bias in PIKE, or an estimate with lower precision, or both. If there were under-reporting of illegal killing, then PIKE would be biased downwards, but if detection or reporting of all carcasses was generally deficient then we would expect lower precision in PIKE estimates.
MIKE is an ambitious project in that it aims to collect standardized data from sites across the entire elephant range, with all of its diversity in resources and capacity. It is perhaps not surprising that the flow of data through the MIKE process has been patchy and sometimes painfully slow. Although the available data has limitations, our analysis achieves the following:
estimation of the overall trend in illegal killing;
the identification of key drivers of illegal killing of elephants at site and national levels;
identification of sites of particular concern;
an analytical approach that (i) takes proper account of covariates at different levels in the data hierarchy, and (ii) enables predictions across all sites, including those with little data.
A full causal analysis of all potential drivers of illegal killing including the impact of CITES policy and demand for ivory requires more detailed data. One aspect of data from anti-poaching patrols that has been generally overlooked (here and elsewhere) is that the patrols are not passive observers of the process being monitored – they represent an intervention in that process by exerting a deterrent effect
In the meantime, our analysis represents the first attempt at a rigorous analysis of data on the illegal killing of elephants across the entire elephant range and identification of factors that contribute to a causal analysis. The results will be of relevance to the CITES process, not only with immediate consequences, but also as a foundation for further work.
Map of Africa sites with site codes.
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Map of (A) South Asia and (B) South-East Asia sites with site codes.
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The analysis presented in this paper was discussed at meetings of the MIKE-ETIS Technical Advisory Group (TAG) prior to its presentation at the 15th CITES Conference of the Parties in 2010, and was much improved as a results of contributions from several TAG members. Comments from Prof. Ken Burnham were particularly helpful. We also wish to thank the innumerable rangers and site managers who collected the data, and the elephant range States without whose collaboration this paper would not have been written. We are grateful to two reviewers whose comments on a first draft of the paper led to significant improvements.