Determination of Age and Geographical Origin of African Elephant Ivory

Transcription

1 Determination of Age and Geographical Origin of African Elephant Ivory Research project supported by the German Federal Agency for Nature Conservation and funded by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety Third progress report for the project part Geographical Origin Author: Stefan Ziegler, WWF Germany Translated from the original German version by Claudia Denkl, German Federal Agency for Nature Conservation

2 1. Project description In the 1980s the international trade in ivory led to a dramatic decrease of the population in many African countries. In 1989 the international community listed the African Elephant on Appendix I of CITES, and thus prohibited any commercial ivory trade. The strict trade prohibition and effective protective measures allowed the elephant populations in some African countries to recover, above all in Eastern and Southern Africa. While maintaining strict protection these countries were given the opportunity to deal with elephant products. Nevertheless, so far CITES has only allowed one-off sales and does not allow free trade of products made of elephant ivory. One of the main arguments for the quasi trade prohibition is the fact that it is very difficult to distinguish legal ivory from illegal ivory in the markets, so that the legal ivory trade would provide a perfect cover for smuggling. The isotope enrichment of certain chemical elements in the tusks of elephants is a good method to reliably identify the origin of elephant ivory. The geographical origin of ivory is determined by a combination of various geochemical routine analyses. Most common and most successful is the determination of the isotopic composition of the element strontium (Sr). But the composition of the stable isotopes carbon (C), nitrogen (N), oxygen (O), hydrogen (H) and sulphur (S) also allows a reliable assessment of the provenance, as elephants ingest these isotopes with the food they consume. For example, the isotopic composition of the element strontium in the food consists of the isotopes 87 Sr and 86 Sr that are combined in a distinct ratio that is related to the chemical composition of the geological substratum: young volcanic regions such as the East African Rift are characterized by a low 87 Sr/ 86 Sr ratio, whereas older parts of the earth's crust have a high 87 Sr/ 86 Sr ratio. Carbon and nitrogen isotopes can serve as indicator for the climate zone the elephant lived in. A very low 13 C ratio indicates densely forested habitats, a high ratio is indicative of savannah landscapes. In a similar way, a low 15 N ratio suggests humid conditions, whereas in drier elephant habitats a rather high ratio can be expected. Hence a relatively correct determination of origin is possible by defining the chemical composition of the tusks. The results of the analysis of about 500 geo-referenced ivory samples from museums, collections and big game hunters will allow the setup of a reference database. Isotope distribution maps of elephant ivory can be generated by using geo-statistic procedures. This database can be consulted for the determination of the geographical origin of illegal ivory and bring about a better coordination of protection measurements on international level. 2. Goals of the research project Until the end of the year 2012 the following goals shall be reached: Development of a reference database for ivory: A method will be established that can be applied for the determination of the geographical origin and the validity of the geographical indication of ivory. Application of the reference database for the enforcement: The reference database for elephant ivory will be suggested to national authorities and the international community of states as a support to enforcement.

3 2.1 Operating schedule Activities (quarter of the year) III 210 IV 2010 I 2011 II 2011 III 2011 IV 2011 I 2012 II 2012 III 2012 IV 2012 Literature review X Collection of samples X X X X X X Measurement isotopes X X X X X X X X Setup of the database X X Evaluation and analysis X X X X X X Press relations and awareness raising X X X X X X X X Publication and presentation of results X X X Preparation of a sideevent for CITES CoP 16 X Creation of a manual for the database (structure, application spectrum) 3. Activities during the report period X X The description of activities is based on the list of the planned activities as shown in paragraph 2.1 (columns coloured in grey). 3.1 Planned activities During the last six month the acquisition of samples can be regarded as successful. To the 300 existing samples collected during the first 1 ½ years of the project another 306 georeferenced samples could be added. An exact compilation of the samples with detailed indication of origin is given in table 1. The provision of 150 geo-referenced samples by the CITES management authority of Botswana was very gratifying. We also received several samples from Sudan, but without indication of the geographical origin, and therefore it is doubtful if these samples are taken from Sudanese elephants or from seizures. After several months of intensive preparatory work it was possible for the project participants to take 124 samples from the museum in Tervuren in Belgium. This museum has an extraordinary collection of elephant tusks from the former Belgian colony Congo, nowadays the Democratic Republic of the Congo. This part of Africa was defined as a white spot in the last progress reports as there were only very few samples available from this region. The georeferenced material from the museum in Tervuren closed this gap.

4 The cooperation with the International Council for Game and Wildlife Conservation (CIC) continued to be very successful. Big game hunters were addressed personally by members of the Executive Committee and agreed on providing samples from their trophies. 29 additional samples could be acquired by this, which increased the number of samples provided by big game hunters to 50 pieces. Presently there are 606 samples of verified origin from 23 African countries included in the database. Table 1 provides an overview of the samples. The assignment of the countries to the regions follows the classification of the African Elephant Database. Countries with sufficient material for a statistical analysis are marked in green. No material could be obtained yet from 15 African range states which are bordered in red and hatched. For several countries some samples are available but their number is not sufficient for statistical testing. What matters is not the absolute number of samples but the number of samples of high value (georeferenced samples). No material could be obtained yet from 14 African range states which are bordered in red and hatched. For several countries some samples are available but their number is not sufficient for statistical testing. No: Table 1: Origin and quantity of samples included in the reference database for African elephant ivory. Region in Africa Country Quantity of samples High quality museums states private persons zoos / others 1 East Ethiopia East Eritrea East Kenva East Rwanda East Somalia East Sudan East Tanzania East Uganda South Angola South Botswana South Namibia South Malawi South Mozambique South Zambia South South Africa South Swasiland South Zimbabwe West Benin West Burkina Faso West Ivory Coast West Ghana West Guinea West Guinea Bissau West Mali West Niger West Nigeria West Liberia West Senegal West Sierra Leone West Togo

5 No. Region in Africa Country Quantity of samples High quality museums States privates persons 31 Central Equatorial Guinea Central Dem. Rep. Congo Central Gabon Central Cameroon Central Congo Central Chad Central Central Afr. Rep The quantity of samples from Botswana, Burkina Faso, the Democratic Republic of the Congo, Malawi and South Africa (477 pcs./ 78% of the total quantity of samples) can be described as very good to draw statistically sound conclusions. It was possible to acquire at least five samples from Kenya, Tanzania, Angola, Namibia, Zimbabwe, Cameroon and Congo. This quantity is just about sufficient to allow statistical testing. However attempts should be made to gain more georeferenced material from these countries. In the 2nd progress report a method was applied that creates a dynamic priority list of countries on a scientific basis. This list should serve as a guide for the completion of the database in the future, even though the acquisition of samples is completed end of the year 2011 according to the task schedule. As the method for the development of the priority index was already explained in the second progress report only the results of the updated indices are presented here. Based on this method Zambia, Kenya, Tanzania, Mozambique and Chad reached index values higher than 0.1 and are therefore identified as countries of highest priority concerning the acquisition of samples. A priority index between 0.01 and 0.09 describes countries of subordinated but still high relevance for the acquisition of samples. 12 countries fall into this category (Ethiopia, Sudan, Uganda, Angola, Botswana, Namibia, Zimbabwe, Ivory Coast, Nigeria, Cameroon, Congo, Central African Republic). A priority lower than 0.01 indicates countries of subordinate priority, which are countries with few elephants or small elephant habitats, but also countries from where sufficient samples are already existing, e.g. Malawi, Burkina Faso and South Africa. Table 2: Priority index of the African elephant range states classified in highest, high and lower priority (colour-marked) used as guide for the acquisition of samples in the future No. Region in Africa Country Quantity of samples Köppen climate zones Elephant habitat (km 2 ) Subpopulations zoos / others Priority index 1 East Ethiopia ,072 2 East Eritrea < 0,01 3 East Kenya ,133 4 East Rwanda < 0,01 5 East Somalia < 0,01 6 East Sudan ,010 7 East Tanzania ,242 8 East Uganda ,015 9 South Angola , South Botswana < 0,01 11 South Namibia , South Malawi < 0,01 13 South Mozambique ,106

6 No. Region in Africa Country Quantity of samples Köppen climate zones Elephant habitat (km2) Subpopulations Priority index 14 South Zambia , South South Africa < 0,01 16 South Swasiland < 0,01 17 South Zimbabwe , West Benin < 0,01 19 West Burkina Faso < 0,01 20 West Ivory Coast , West Ghana < 0,01 22 West Guinea < 0,01 23 West Guinea Bissau < 0,01 24 West Mali < 0,01 25 West Niger < 0,01 26 West Nigeria , West Liberia < 0,01 28 West Senegal < 0,01 29 West Sierra Leone < 0,01 30 West Togo < 0,01 31 Central Equatorial Guinea < 0,01 32 Central Dem. Rep. Congo < 0,01 33 Central Gabon , Central Cameroon , Central Congo , Central Chad , Central Central Afr. Rep ,027 Measurement of the isotopes: During the report period 150 ivory samples were analyzed (51 samples from Burkina Faso, 59 from South Africa and 40 samples of unknown origin taken from ivory bracelets seized by customs at the DHL freight centre in Leipzig). The activity ratio of strontium 87 Sr / 86 Sr was not determined this time because this is the most expensive part of the analyses and it became apparent during the last six months that more than the 500 scheduled samples will be collected. As the activity ratio of strontium is only a decisive factor for the comparison of samples from old peneplains and volcanic regions the project participants decided to use the limited financial resources to enlarge the number of samples. Interpretation and analysis: In the 2 nd progress report it was outlined in detail that the correct allocation of samples to their country of origin is increased distinctly by the combination of two isotopes. The statistical analysis was now expanded to five isotopes (D/H, C, N, O, S). It applied the calculation of the so-called mahalanobis distance, which represents the distance between spots in a multidimensional vector-space and is used in multivariate statistics. The developed statistical method calculates the distances between the individual ivory samples in a five-dimensional space and shows the proximate neighbour of each sample and the correlation between them. The method is based on the assumption that samples with similar provenance also have similar isotopic patterns. The geographical origin of the proximate neighbour is therefore also decisive for the allocation of the analyzed sample. As their provenance is well-known the quality of differentiation of the reference-database can be evaluated.

7 The method was tested with 137 samples of well-known origin from Angola (5 samples), Burkina Faso (50), Cameroon (11), Namibia (5), South Africa (59) and Tanzania (7). As the origin of the reference samples is known the accuracy of the method can be assessed. Table 3 shows the differentiation quality that was 85% in this test run, which means that 17 of 137 samples were not allocated correctly. Table 3: Differentiation quality for 137 samples of known origin Burkina Faso Cameroon Tanzania Nami bia South Africa Total value Correct allocation /to Angola Burkina Faso % Cameroon % Tanzania % Angola % Namibia % South Africa % (Southern Africa) (2) (1) (3) (4) (59) (69) (96%) Total % All the samples from Burkina Faso were allocated correctly, but two samples from South Africa were wrongly assigned to Burkina Faso. One of these samples is from a legal estate, with the administrator indicating South Africa as country of origin. A verified proof of origin is not existent. A correct allocation to the countries of origin of over 80% was noticed for the samples from Cameroon and South Africa. The cluster Southern Africa with the countries South Africa, Namibia and Angola has a differentiation quality of 96%. If only a small number of georeferenced samples from an Elephant range state would be available the formation of geographical clusters could be useful to enlarge the quality of differentiation of the database. It is obvious that the differentiation quality rises by the increase of the number of samples from a country. The low number of samples from Namibia and Angola could be an explanation for the few correct allocations. But it can be expected that the isotope pattern of ivory from these two countries is similar to the elephant habitats in South Africa due to similar climate and geology. The samples from Tanzania deviate distinctively from them and could be assigned correctly although there were only few samples available. For the application of the reference database for ivory one question is very interesting: How does the isotope pattern of ivory from the countries of Southern Africa differ from ivory of other parts of Africa? For this reason we divided the samples of known origin into two groups. The group Southern Africa is formed by South Africa, Namibia and Angola and contains 69 samples; the group rest of Africa contains 68 samples from Burkina Faso, Cameroon and Tanzania. Another 40 samples of unknown origin were also analyzed and included into the statistics. At first the relative frequency distribution of the measured values of each isotope was illustrated for the three groups (fig. 1a-e). As mentioned in the second progress report the differentiation of the individual isotopes is rather low. The arrangement in groups of several countries increases this effect because of the interference. It is noticeable that the group unknown origin has a low standard deviation displayed by the narrow frequency curve. This suggests a quite small area of origin of the seized samples, but it is not possible to draw any well-founded conclusions about the country of origin of these unknown samples.

8 rel. frequency Unbekannt delta13c Figure 1a: Frequency distribution for the variable 13C in the three groups. rel. frequency Unbekannt delta15n Figure 1b: Frequency distribution for the variable 15N in the three groups. rel. frequency Unbekannt delta18o Figure 1c: Frequency distribution for the variable 18O in the three groups.

9 rel. frequency Unbekannt D/H Figure 1d: Frequency distribution for the variable D/H in the three groups. rel. frequency Unbekannt delta34s Figure 1e: Frequency distribution for the variable 34S in the three groups. To identify the possible geographical origin of the unknown samples we applied the method of the proximate neighbour but increased the number of analyzed neighbours to five. This means - in mathematical terminology - that the Mahalanobis distance of the reference samples to the five proximate neighbours is calculated. The results are shown in table 4. Table 4: Proximate neighbours of the 40 samples from seized ivory. Proximate neighbours Unknown Burkina Faso Cameroon 2 1 Ghana 1

10 Most of the proximate neighbours of the samples of unknown origin can be found in the group unknown. The results correlate with the frequency distributions in fig. 1a-e as it is quite homogenous material coming from a regionally limited area. The consideration of the proximate neighbours in table 4 indicates the region from where the unknown samples could possibly originate and includes all the isotope reference values of the database. As proximate neighbours also reference samples from Burkina Faso, Cameroon and Ghana were identified. But as there is not a very high allocation to these countries and there are only few samples from most of Western Africa is can be assumed that the samples are from a country with few referenced material. Nigeria meets this condition and is also the geographical link between Cameroon, Ghana and Burkina Faso. Additionally, the seizure was posted in Lagos and came to Leipzig from there by air freight. Assumed orgin of the seized ivory bangels Figure 2: Possible origin of the ivory seized in Leipzig. The statistical method of the discriminant analysis allows the calculation of a so called discriminat variable that can be used to distinguish between the groups Southern Africa and Rest of Africa. The discriminant variable is the factorial product of the five measured isotopes and is shown in fig. 3 as frequency distribution. Unlike the individual isotopes (fig. 1a-e) the discriminant variable defines the groups quite well. The overlapping of the two curves is caused by the wrong allocation of four samples (see table 3), but for at least two samples the origin is not free of doubt: (I) Sample from a legal estate (see above) and (II) sample from a museum that was probably classified incorrectly (NHMS London, registration number ZD ; origin: Cape Province (Addo), year 1920). The isotope pattern is similar to the samples from Cameroon.

11 rel. frequency discriminant variable Figure 3: Frequency distribution for the discriminat variable in two country groups. Figure 4: Proximate neighbours of the sample ZD from the NHMS London. Summary and future p rospects: The developed statistic is a sensitive method with a determined differentiation quality on country level of 85%. It can be expected that the accuracy of the method can be increased by including additional samples. The reason is that the more georeferenced samples from an elephant range state are available the more accurate will the differentiation quality of the statistical method be, using the technique of the proximate neighbour. The discriminant analysis makes it possible to distinguish between the samples from southern Africa and the rest of Africa. Only 4 of 137 samples were classified incorrectly (differentiation quality: 97%). The next step will be to identify characteristic isotope patterns for climate classes and make fundamental investigations for predicting the isotope pattern in elephant habitats from where no reference ivory is available yet.