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DRAFT

Analysis of Ivory Demand Drivers

Daniel Stiles, Rowan Martin and Brendan Moyle

September 2015

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EXECUTIVE SUMMARY The China office of a major conservation organization commissioned this study to examine the possible demand drivers of the current elephant poaching crisis. It has been widely assumed that consumer demand for worked ivory, particularly in East Asia, has been the primary demand driver of increased elephant poaching. However, the sustained high level of elephant killing over the past decade suggests that the amount of raw ivory produced could greatly exceed the quantity that has been processed and put on sale to consumers. If this is indeed the case, explanation must be sought of why much more ivory is being imported than is needed to meet consumer demand. Suggestions have been made that speculators have been purchasing large quantities of poached ivory to store for future sale at expected great profit. Demand reduction strategies and tactics have been aimed at consumers. If speculators rather than consumers are driving demand and thus poaching, the demand reduction campaigns to date have been misdirected. The findings of this study could influence the formulation of future strategies to reduce demand drivers. This study aims to estimate the gross quantity of ivory that has been produced in Africa 2002-2014, both by legal and illegal means, using statistical modeling. Furthermore, the study approximates, based on certain assumptions, the weight of illegal raw ivory that moved through various stages of interchange from elephant death to end-users in East Asia. The quantity of ivory known to be ‘in the system’ in East Asia was estimated and compared to the quantity calculated to have been imported from Africa. The estimated quantity of ivory imported does indeed appear to greatly exceed the amount thought to have been processed and offered for sale to consumers, though because online and person-to-person illegal sales are concealed, there is a large gap in knowledge of the actual quantitative disparity. Statistical modeling was also undertaken to test a variety of hypotheses that posited drivers of the illegal killing trends from 2002 through to 2014, using variables that could be quantified directly or through proxies. The final conclusion of this study is that there has been stockpiling of raw ivory for speculative purposes. It is conceivable that more than 1,000 metric tons of illegal raw ivory remains stored in Chinese warehouses, and additional ivory is possibly stored in Africa and in other Asian countries (e.g. Malaysia, Vietnam). The study was carried out from 15 May to 30 June 2015, with updates to 30 September. The major findings of this study for the years 2002-2014 were:

1. The number of elephants killed illegally was estimated to be 362,940. The average killed annually 2002-2006 was well below 20,000, jumping to over 30,000 a year for 2007-2014.

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2. The total ivory production from illegal elephant killing was 2,747,977 kg, or 211,383 kg a year.

3. Average tusk weight for all age groups and both sexes combined was estimated to have fallen from 7.8 kg in 2007 to 3.5 kg in 2014. It therefore required more than double the number of elephants killed in 2014 to achieve the same total weight of ivory as in 2007, assuming the poachers made no hunting selection. Hunting trophy weight declines reported in recent years in southern Africa indicate that poaching selection for the larger tuskers has occurred.

4. The total legal ivory produced from elephant deaths was 1,138,749 kg.

5. The total ivory produced from all forms of elephant mortality was 3,886,726 kg, with

70% of it produced from illegal killing.

6. African government storerooms accumulated an estimated 500-543.1 metric tons (MT)

of ivory from all sources, after assumed field losses of 174.8 MT and leakages of 279.5 MT. The accumulated ivory was added to stocks existing from before 2002. End-2014 total stocks were estimated to amount to approximately 690 MT (759 U.S. tons) for all of sub-Saharan Africa, taking into consideration the legal 2008 ivory sales, other legal sales, and after stockpile destruction events.

7. Illegal ivory exports from Africa were estimated to total 2,402,236 kg, after deductions

made for law enforcement confiscations (local) and seizures (import/export) made in Africa. The figure assumes no stockpiling in Africa; therefore, it should be considered a maximum quantity.

8. The weight of ivory illegally imported to China, Hong Kong and Japan was estimated

to total 1,737,337 kg, after international seizures and China stockpile destruction events. The average illegal import was estimated to be 133,641 kg annually, with much less entering 2002 to 2006 and much more entering after 2007, reaching 200 MT a year. The great majority was assumed to enter China/Hong Kong based on seizure data.

9. An estimated 436,047 kg was available for illegal import to other countries, after

seizures, mostly located in Southeast Asia, but including Europe, the Middle East and North America. This equals an average illegal import of 33,542 kg annually, with much less available 2002 to 2006 and much more every year after 2007.

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10. The total inventory of raw and worked ivory ‘in the system’ held legally by China-HK and Japan, available for processing and sale respectively, was estimated to total 576.3 MT at the end of 2014.

11. The total of seized ivory ‘in the system’ held by China and Hong Kong at the end of

2014 totaled 53 MT. Japan claims to have no seized ivory government stockpile.

12. Raw and worked ivory is sold illegally online in China, but sellers are problematic to

find because they use up to twenty different code words for ivory and they tend to sell in members only forums. The quantities of ivory offered for sale and number of sellers are difficult to determine because the sellers stay online only for short periods and change their identities often. They are therefore difficult or impossible to monitor, unless a personal relationship is established with individual sellers.

13. From 15-30 May 2015 six online sites were searched and a total of 1,039 ivory items

were found for sale, after eliminating repeat postings.

14. Legal worked ivory in China is sold only in physical outlets, with an estimated 10 MT

on sale in 2014 in registered outlets. Very little illegal ivory is sold in physical outlets compared to legal ivory. Most illegal ivory is sold online and through personal networks, but because of the clandestine nature of sale no empirical estimate of quantity could be made, so statistical modelling was employed

15. Consumer demand for worked ivory sold in physical outlets in China-HK in 2010-2014

was much lower than earlier studies had asserted. Major Chinese cities have much less ivory displayed for sale than many smaller cities in other parts of the world.

16. Hong Kong private stockpiles dropped 40.7 MT through sales to consumers 2002-

2014, while 104 MT became ‘non-commercial’ by removal of the tusks from registration. Chinese consumers smuggled approximately 2.8 MT of ivory from Hong Kong on average per year to the mainland 2002-2014.

17. Statistical modeling provided estimates of consumer demand in China for worked ivory

of 48.4 MT from 2008 to 2014, the peak elephant poaching years. This is an average of about 7 MT per year, but with a spike in 2010 and 2011 of 11 to 12 MT per year, followed by a decline.

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18. The model estimated that from 2010 to 2014 consumer demand in China for legal ivory was an average of 3-4 MT per year and for illegal ivory was on the order of 6-8 MT per year.

19. No quantitative estimate could be made of ivory sold in the online and personal network illegal sector, thus demand in this segment of the black market could not be assessed from data. An estimated 200 MT a year of illegal ivory entered China-HK annually from the production model 2010-2014, and 6-8 MT a year was estimated of consumer demand in China for illegal ivory by the consumption model. With the Hong Kong smuggling to mainland China included, an average of approximately 8.8-10.8 MT of ivory was consumed annually in the illegal sector in China-HK.

20. The consumer demand for illegal worked ivory in China from 2008 to 2014 was

several orders of magnitude smaller than the estimated quantity of illegal raw ivory that was imported.

21. Concurrent with the post-2007 increased rate of elephant poaching and ivory trafficking was a spike in imports of legal pre-Convention tusks to China. This spike was interpreted to reflect the general desire to accumulate raw ivory as an investment, not for immediate use in manufacturing.

22. The evidence in China of relatively low consumer demand growth for worked ivory

contemporaneous with massive increases in both legal and illegal raw ivory imports leads to a conclusion that speculative demand for raw ivory was occurring on a large scale from 2008 onwards. Investors were stockpiling raw ivory.

23. A number of different variables were statistically modeled in conjunction with

variables indicating the elephant poaching rates over points in time. Those showing significant correlations suggested that the increases in poaching were caused primarily by the Global Financial Crisis (GFC) beginning in late 2007. Investors in East Asia pulled assets out of falling stock and property markets, and shifted them to commodities, including ivory, which led to the commodity price surge of 2008-2012.

24. From 2008 to 2014 ivory, along with other luxury commodities, offered a much more

attractive investment prospect than bank interest, stocks, bonds and property. The legal ivory sale moratorium voted by CITES in 2007 guaranteed future scarcity, which provided an additional incentive to speculators to buy poached ivory.

25. The shift by traffickers beginning in about 2010 to ship large container loads of ivory

was correlated with a sharp drop in shipping rates caused by the GFC. The affordability

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of being able to transport cheaply large quantities of ivory may have led to traffickers putting out orders to buy larger quantities of ivory, contributing to the rise in poaching rates.

26. The online survey in China in May 2015 found that the average black market price of

99 whole tusks seen for sale on five websites and social media forums was USD 974/kg. The average weight of the tusks was 14.2 kg. Tusks weighing less than 5 kg averaged USD 2,100/kg in May 2014. The average price for >10 kg tusks in 2015 indicated a significant drop in price of well over 50% from a year earlier. No black market prices are available in China for tusks weighing over 10 kg in any year, but they can be assumed to have been considerably higher in 2014 than the USD 2,100/kg average found by investigators for <5 kg tusks.

27. Three indicators strongly suggest that in late May 2015 there was a move by ivory

hoarders to dispose of illegal stocks: (1) the steep drop in price, (2) the great rise in average size of tusks offered for sale from previous years, and (3) the eagerness expressed by vendors to sell. Data are not sufficient to determine whether more tusks were being offered for sale than earlier.

28. The GFC wrought major changes to the global economy and it appears simply

coincidental that the start of it was the same year (2008) the one-off sale was approved for China by CITES. Rather than poachers and traffickers in Africa responding to supply ‘signals’, speculators in China appear to be sensitive to economic demand drivers. The instigation of the CITES moratorium on future legal raw ivory sales in 2007 for at least a decade reinforced the perception that increased scarcity would drive prices higher.

29. Ivory seizure patterns suggest that China-HK is the main location of the speculators,

though other countries could also be involved (e.g. Malaysia, Singapore, Vietnam).

30. The one-time supply of 62 MT of legal raw ivory to China in 2009 and its subsequent

rationing did not provide the necessary quantity and conditions to prevent speculation from occurring.

31. The unstable economic situation in China-HK beginning in mid 2015 and continuing

volatility in stock markets, with falling commodity prices and a pledge by China to close its legal ivory market, first made on 29 May 2015 (after most price data were collected), present a combination of factors that makes it difficult to predict future trends in both speculator and consumer ivory demand and raw ivory prices.

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32. The evidence from this study suggests that ivory demand and prices are falling in China and Hong Kong. If this develops into a downward trend, it should translate into declines in raw ivory prices in Africa and decreased incentive to poach elephants.

33. This study recommends that a more sustained monitoring of illegal online sales be

made in China and other priority Asian ivory markets to gather data on ivory prices, quantities and types of ivory offered for sale.

Chinese consumer demand for worked ivory is not the major demand driver of elephant poaching – stockpiling raw ivory as an investment is. (Photo: D. Stiles)

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Analysis of Ivory Demand Drivers Background Poaching of elephants for their ivory has undergone a significant increase over the past 10 years. The driver for this increase appears to be growth in Chinese demand. Various wildlife conservation and animal welfare organizations have initiated ambitious demand reduction programmes in recent years in order to address this threat to elephant populations. Retail consumer demand for worked ivory has been the main focus of this effort to date. However, an additional driver might be speculative demand by investors and/or ivory factories concentrating on raw ivory, which is unlikely to respond to retail focused demand reduction approaches. To determine the main driver of ivory poaching in the Chinese market this study is aiming to gauge the extent of ivory ‘in the system’, i.e. total amount of ivory in stockpiles, seizures, warehouse and retail stocks, in comparison to the amount of ivory that has been poached since 2002. As illegal ivory continues to be openly retailed it can therefore be counted as being ‘in the system’. However, we hypothesize that the amount of ivory poached vastly exceeds the amount of ivory in the system and therefore there is a significant volume of hidden ivory being stockpiled in raw form and / or sold by speculative investors who may be ‘banking on extinction’. Speculative investment and stockpiling requires a different approach for demand reduction. The research period was 15 May to 30 June 2015, with revisions to 30 September 2015. Objectives The Terms of Reference call for the following information to be gathered and reported on:

1. Approximate weight of ivory poached from African and Asian elephant populations since 2002 and trend analysis in response to relevant factors (international ivory ban, stockpile sales, fluctuations in other investment commodities such as gold, art, property, oil price etc.).

2. Amount (weight) of ivory held globally in:

a. Government stockpiles of ivory b. retail warehouses c. processing warehouses d. large scale holdings (such as museums) e. shops f. online retailers g, auction houses

3. Statistical analysis between the estimated quantity of ivory poached (2002-2014) and the total ivory in short-term and long-term storage in China. The objective is to ascertain whether speculation is occurring and if there is evidence for groups / individuals who are stockpiling ivory for future sales, including auction houses, government agencies, private collectors, or factories.

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Methods

Objective 1a - Approximate amount (weight) of ivory poached from African and Asian elephant populations 2002-2014. There are several methodological problems in attempting to estimate the total weight of ivory poached 2002-2014. First, the total number of elephants illegally killed needs to be known, and second a model needs to be devised to estimate how much ivory would be produced from such a number. It should be stated from the outset that there are no systematic data for numbers of elephants illegally killed or ivory poached in Asia, as the Monitoring the Illegal Killing of Elephants (MIKE) program has only reported for Asia once (CITES 2013). The report concluded that, “No estimates of numbers killed have been produced for Asia, as the quality of information available on the status of elephant populations in that continent is limited, …” The Elephant Trade Information System (ETIS) seizure data cannot be used for Asia because the source of seized ivory is not always known. In most cases in which the source of ivory is known for a seizure made in Asia, it is in Africa (CITES 2010; Wasser et al. 2015). The ETIS report to the CITES 15th Conference of the Parties concluded, “the South Asia sub-region plays a relatively minor role in the illicit trade in ivory. India accounts for the lion’s share of the data, but overall there is little evidence to suggest that significant illicit ivory trade activity is taking place in this sub-region” (CITES 2010). Most of the poached ivory in South Asia appears to stay in the sub-region. There are anecdotal press reports of elephants killed in various Asian range State countries, primarily India and Indonesia, but they rarely report tusk weights (for obvious reasons, the tusks have been taken) and the cause of death is not always certain, as some are no doubt natural mortality or accidental (e.g. electrocution, train collisions, fights). If relatively reliable elephant demographic data and tusk weight distributions were known, it would be possible to model poached ivory offtake; but these are not known. For these reasons, no detailed estimates will be attempted for Asia. The first continent-wide assessment of illegal killing of African elephants was made by Wittemyer et al. (2014). The study used higher quality samples of the MIKE monitoring site data and resulting Proportion of Illegally Killed Elephants (PIKE) measure in two approaches, called respectively the ‘empirical approach’ and the ‘model approach’. Employing a combination of empirical, inferred and otherwise assumed quantitative variables involving elephant demographics (e.g. numbers, natality and natural mortality rates) and poaching rates as estimated by MIKE, the study concluded that an average of approximately 33,630 elephants were illegally killed annually between 2010 and 2012. The Wittemyer et al. (2014) study did not, however, attempt to estimate the quantity of ivory produced from the poached elephants.

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The only study that has ever seriously attempted to estimate the number of poached elephants and the ivory that would be produced is Hunter et al. (2004)1. This study did not attempt to estimate total illegal ivory produced annually from poaching, but rather to estimate the total elephants needed to supply ivory markets found in Africa and Asia up to 2003. Because of data uncertainties, the estimate range was extremely broad and was thus not particularly useful (from 4,861 to 12,247 elephants a year for unregulated markets in Africa). A more serious fault was the average tusk weight that was assumed to make the estimate. Hunter et al. (2004) stated that the average was 3.68 kg per tusk and cited Milliken et al. (2002) as the source. The Milliken et al. (2002) report actually derived the average weight of confiscated raw ivory pieces as 3.65 kg, not the average weight of whole tusks. Tusks are commonly cut up into several pieces for easier concealment and shipping. This error has been repeated many times up to the present in estimates of elephants poached to produce a given quantity of seized ivory, most recently in Wasser et al. (2015), resulting in a large overestimate of elephant numbers killed. An examination of photographs taken of ivory confiscations is enough to show that the average tusk weight is up to three times 3.65 kg in most large seizures. Wasser et al. (2008) commented on the unusually large size of the tusks in the 6.5-ton seizure made in Singapore in 2002 and Wasser recently stated that the average tusk weight of a 2014 seizure in Mombasa was 20 kg (Sam Wasser in litt. to Daniel Stiles, 24 July 2015). Stiles (2011 and unpublished field research) found that larger tusks are exported while the smaller tusks remain in Africa for use by local carvers. The present report analysis (conducted by RM) for poached ivory quantity was based on the estimates for elephant populations given in the African Elephant Status Reports produced from the African Elephant Database (AED) of the IUCN/SSC African Elephant Specialist Group. There are three AED Status Reports (http://www.elephantdatabase.org) in the period 2002-2014 (2002, 2007 and 2012) used in this analysis2. This required population growth interpolation for the years between successive Status Reports. The ‘Definite’, ‘Probable’ and ‘Possible’ estimates given in the Status Reports were used, and the ‘Speculative’ numbers were excluded. It is realized that there are deficiencies in the data, thus the analysis results have a fairly large margin of error. The annual estimates for the numbers of elephants illegally killed were based on the differences between the expected numbers that would follow in the years after any given AED estimate (based on an assumed rate of 4% per annum for population increase) and the actual numbers derived from the observed rate of population increase/decrease between each pair of AED estimates. Losses due to natural mortality, problem animal control (PAC), trophy hunting and other legal offtake (culling, staff rations, etc.) must also be included in the model simulation yielding the expected number and their age and sex ratios. Subtracting the actual number from the expected number yields the illegally killed number. The weight of ivory for each sub-region of Africa is calculated based on the elephant population structure (age and sex) that is generated by the model simulation. 1Parker and Martin (1982) and Parker and Amin (1983) estimated how many elephants were needed per year in the early 1980s to supply raw ivory exports only, which numbered about 46,000. This included all sources.2 A summary report was presented in CITES (2014a), but it contained no tables or numbers, only a histogram.

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The influence of the high levels of illegal hunting from 2002-2014 on the population age structure are profound. Whereas a previously unexploited elephant population with a ‘normal’ age structure will increase at least 4.7% per annum, a heavily hunted population that has been subjected to a selective offtake over decades has a depleted age structure and is unable to achieve such a growth rate (Gobush et al. 2008). Using a population simulation model, the level of illegal hunting was adjusted to achieve the match with the AED population estimates. A dynamic selectivity function was used to select the age classes providing each year’s illegal harvest and the age structure was updated every year based on the previous year’s offtake. Ivory is obtained from illegal hunting, natural mortality, PAC, population management (culling), legal harvesting (staff rations, national celebrations) and trophy hunting. Craig (1989) estimated that for the main part of the life span of elephants (10-40 years of age) in Zimbabwe mortality was of the order of 0.5% per annum. In the simulation model used in this analysis, the average mortality over all age classes in an unexploited population is about 1.5%. This is a lower rate than that used in the Wittemyer et al. (2014) study because they were basing their rate on studies that reported all causes of mortality, which included illegal hunting. If the natural mortality 1.5% is added to the illegal hunting mortality used in this study, the total mortality rate 2002-2014 ranges from 3.8% to 8.2% per year. Population control culling can be disregarded as a source of ivory in the time frame for this study, as no African country has carried out a cull since the early 1990s. A few elephants are killed in some of the southern African countries to provide meat for staff rations and public holiday celebrations but the numbers are insignificant. The number of elephants taken as sport-hunting trophies is also very low in those countries which permit safari hunting. The quantity was estimated using the CITES annual trophy quotas3, and those reported as exported or imported in the CITES Trade Database. There is also a certain amount of unfound ivory from elephants that die in deep forest and other remote locations, which this analysis will treat as falling within the margins of statistical error (i.e. unmodelled). The various components of the analysis are described briefly below and some of them are elaborated in the subsections that follow: (1) A population simulation model (Appendix 1) was used to carry out the main analysis

and this was done by adjusting the level of illegal hunting after each AED population estimate, to give the value of the next population estimate (there are only three in the period 2002-2014). Since the last AED estimate was in 2012, the results for 2013 and 2014 have been estimated by retaining the same level of illegal hunting as applied in 2012.

(2) The starting population in the year 2002 used the age structure of a population subjected to illegal hunting at a level of 1% for some 30 years prior to 2002. This results in an age structure that is depleted in numbers in the older age classes ab initio. A

3 https://cites.org/eng/resources/quotas/index.php

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discussion of the reproductive parameters used in the model and a sensitivity analysis for variations in these parameters is given in Appendix 2.

(3) Ivory production in each year has been calculated from the losses in each male and female age class from 0-60 years. This requires a knowledge of the relationship between age and mean tusk weight (Appendix 3).

(4) The selectivity function that determines which animals will be killed in each year of the simulation is critical to the functioning of the model. A central feature of this function is the dynamic adjustment to the age structure of the population under the regime of illegal hunting. The animals killed in each year influence the age structure of the population in the following year and this is catered for by updating the selectivity function each year with the numbers of animals killed in each age class in the previous year (Appendix 4).

After estimating the total ivory production in Africa, an even more daunting exercise must be carried out to estimate how much of this ivory reached China-Hong Kong. Figure 1 presents a flow chart indicating schematically the sources of both legal and illegal ivory and the points where quantitative data are needed. These will be explained further in the Results section. Objective 1b. Trend analysis of ivory poached in response to relevant factors (international ivory ban, stockpile sales, fluctuations in other investment commodities such as gold, art, property, oil price etc). This is included in Objective 3 and not treated separately here, as the methodology and variables of 1b and 3 are interrelated.

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Figure 1. Flow diagram of sources of ivory to China-HK and Japan.

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Objective 2. Amount (weight) of ivory held in:

a. Government stockpiles of ivory in China, Hong Kong and Africa The Chinese government ivory stockpile at the end of May 2015 was obtained from statistics released by the State Forestry Administration (SFA) for this report (collected by WJ). The Hong Kong government ivory stockpile figures were obtained from government statistics (AFCD 2014; Lo and Edwards 2015), press reports and a publication by Martin and Vigne (2015). The government stockpiles for a few African countries were obtained from recent press reports. Others are those reported by Milliken et al. (2010) in a report on a questionnaire carried out by TRAFFIC for the Convention on International Trade in Endangered Species (CITES) Secretariat pursuant to Decision 13.26 (Rev. CoP14) ‘Action plan for the control of trade in elephant ivory’. The CITES Secretariat has received the early 2015 ivory stockpile quantities of CITES Parties that have reported them under Res. Conf. 10.10 (Rev. CoP16), but these figures remain confidential and unavailable to the public or this study. The African stockpile figures presented in this report are therefore of low reliability overall.

b. Retail warehouses in China This category is being dropped because all ivory destined for retail sales known thus far in China resides either in category ‘c’ (processing warehouses) or category ‘d’ (shops). There are no independent retail warehouses, unless they are owned by illegal retailers, which are not possible to observe.

c. Processing warehouses in China and Japan (following the 2008 stockpile sales) The data for China was collected for 13 of the 34 registered factories by WJ. There was insufficient time to locate and arrange interviews with more factories, and some factories that were contacted declined to provide information. The data for Japan was reported by the Japanese government to CITES in document SC65 Doc. 42.1 Addendum, Annex 2 (CITES 2014b).

d. Large scale holdings There is extremely little ivory held in museums in China, Hong Kong and Japan, and all of it pre-dates 2002. All other large scale ivory holdings are located either in factory storerooms (2.c) or retail shops (2.e). The other large scale holdings category is, of course, stockpiles that are held by speculators. The quantity was estimated by comparing the amount of ivory estimated to have been illegally imported 2002-2014 with the amount estimated to have been processed and sold to consumers.

e. Shops in China and Hong Kong

There are 130 legal registered outlets in China scattered in 39 cities in eastern China. There are probably thousands of illegal shops selling illicit ivory in China. It was not possible to gather sufficient data to make a reliable estimate of the total weight of ivory held in these

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shops given the time and resources available for this largely desk study, but a rough estimate was made based on survey data reported by Vigne and Martin (2014). The legal ivory held by those with licenses in Hong Kong has been reported by the government up to the end of 2014, the most recent available figures (Lo and Edwards 2015). The figure is a combination of all raw and worked ivory held in private stockpiles and in shops. No disaggregated statistics are available.

f. Online retailers Data was collected by WJ over a period of two weeks 15-31 May 2015 from online websites and social chat rooms (WeChat, QQ, Weibo, Baidu Post Bar, Taobao, and Ali Baba-China). The search terms xiangya (elephant ivory), yadiao (ivory carving), baisuliao (white plastics) and bailiao (white raw materials), which are the most frequently used jargon for ivory pieces, were used for the online survey. WJ engaged in negotiations with sellers to verify that the ivory was genuine and obtained photographs. The sellers used false names and changed them often in order to avoid identification by government monitors, and they would go offline after a short period and return with a new name. They thus could not be tracked over time. In late June WJ also monitored the websites cited by the International Fund for Animal Welfare (IFAW 2012) as advertising large quantities of ivory. The following categories of ivory, with numbers and prices, were recorded:

a. whole raw tusks b. cut tusks (including tusk tips) c. semi-worked raw pieces (disks, plaques, etc.) d. worked ivory, broken down as:

i. jewelry (bangles, necklaces, earrings, rings, pendants, etc.) ii. name seal blanks

iii. trinkets (cigarette holders, chopsticks, <10 cm figurines (except netsukes) letter openers, combs, etc.)

iv. netsukes v. >10 cm carved objects (human, animal or mythical figures), boats, devil/magic balls, incense burners, musical instruments, containers (cups, boxes, vases), knives/swords, painted plaques, brush pots, etc. vi. carved tusks, including elephant bridges vii. polished tusks, including those engraved or painted

The data were tabulated. Data collected by IFAW (2012) and Gao and Clark (2014) are also included in this report for comparative analysis.

g. Auction houses in China The number of auction houses, number of ivory items and the value of ivory sold between 2006 and 2012 are reported from Gao and Clark (2014). WJ collected additional data from Artron for this study from 2012 up to March 2015 from 22 auction houses.

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Objective 3. Statistical analysis between Objective 1 (estimated quantity of ivory poached 2002-2014) and Objective 2 (total ivory in short-term and long-term storage) above. The aim is to ascertain whether speculation is occurring and if there is evidence for groups / individuals who are stockpiling ivory for future sales, including auction houses, government agencies, private collectors, and factories. The methodology for Objective 3 is included in the Results section reporting on this objective and that of Objective 1b above.

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RESULTS

Objective 1- Approximate amount (weight) of ivory poached from African elephant populations 2002-2014. Individual Regions The sub-regional elephant population estimates used in the analysis are the sum of the AED Definite, Probable and Possible numbers shown in the shaded columns in Table 1.

Table 1: Regional elephant population estimates

CENTRALAFRICA EASTERN AFRICA

Year Def Poss Prob D+P+P Spec Total Def Poss Prob D+P+P Spec Total

2012 16,486 65,104 26,310 107,900 45,738 153,638 130,859 12,966 16,700 160,525 7,566 168,091

2007 10,383 48,936 43,098 102,417 34,129 136,546 137,485 29,043 35,124 201,652 3,543 205,195

2002 16,450 32,263 64,477 113,190 82,563 195,753 117,716 17,702 22,511 157,929 5,738 163667

SOUTHERN AFRICA WEST AFRICA Year Def Poss Prob D+P+P Spec Total Def Poss Prob D+P+P Spec Total

2012 267,966 22,442 22,691 313,099 49,057 362,156 7,107 942 938 8,987 3,019 12,006

2007 297,718 23,186 24,734 345,638 9,753 355,391 7,487 735 1,129 9,351 2,939 12,290

2002 246,592 23,722 26,098 296,412 7,508 303,920 5,458 1,188 3,039 9,685 3,498 13,183

The regional illegal hunting analysis is shown in four tables (Tables 2-5) followed by the overall continental analysis in Table 6. A summary of key statistics for the population is then given in Table 7.

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Table 3: Central Africa Ivory Production

AED POPULATION ILLEGALLY KILLED IVORY PRODUCTION

IH Set estimate Males Females Total RoI Males Females Total IH/Pop Males MTW Females MTW Total MTW

Year % No No No No % No No No % kg kg kg kg kg kg

2001 6.5067 113,190 51,521 61,669 113,190

2002 50,467 60,886 111,353 -1.62 4,134 3,736 7,870 7.07 81,971 10.55 28,165 4.01 110,136 7.44

2003 48,790 60,432 109,222 -1.91 4,537 3,176 7,713 7.06 83,171 9.75 24,816 4.16 107,987 7.45

2004 47,049 59,917 106,966 -2.07 4,466 3,090 7,556 7.06 80,851 9.63 23,881 4.11 104,732 7.37

2005 44,664 59,973 104,637 -2.18 5,001 2,392 7,393 7.07 80,723 8.59 18,872 4.20 99,595 7.17

2006 3.8945 102,417 42,205 60,212 102,417 -2.12 5,060 2,174 7,234 7.06 71,054 7.47 16,594 4.06 87,648 6.44

2007 41,519 61,661 103,180 0.74 3,293 951 4,244 4.11 40,035 6.47 7,505 4.20 47,540 5.96

2008 41,353 62,834 104,187 0.98 2,923 1,362 4,285 4.11 34,446 6.27 9,153 3.57 43,599 5.41

2009 41,540 63,802 105,342 1.11 2,674 1,659 4,333 4.11 30,534 6.07 10,112 3.24 40,646 4.99

2010 41,992 64,599 106,591 1.19 2,485 1,897 4,382 4.11 27,731 5.94 10,809 3.03 38,540 4.68

2011 3.8945 107,900 42,557 65,343 107,900 1.23 2,433 2,003 4,436 4.11 26,410 5.77 11,067 2.94 37,477 4.49

2012 43,489 65,752 109,241 1.24 2,113 2,383 4,496 4.12 22,667 5.71 11,848 2.64 34,515 4.08

2013 44,560 66,019 110,579 1.22 2,001 2,549 4,550 4.11 21,437 5.70 12,171 2.54 33,608 3.93

2014 45,464 66,436 111,900 1.19 2,188 2,414 4,602 4.11 23,544 5.72 12,419 2.74 35,963 4.16

Total ivory production 2002-2014 821,986 kg

IH = Illegal Hunting. In the table it is the percentage of the total population set after each AED estimate. RoI = Rate of Increase (or decrease) MTW = Mean tusk weight

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Table 4: East Africa Ivory Production

AED POPULATION ILLEGALLY KILLED IVORY PRODUCTION

IH Set estimate Males Females Total RoI Males Females Total IH/Pop Males MTW Females MTW Total MTW


2001 1.0750 157,929 71,885 86,044 157,929 2002 41 76,039 90,118 166,157 5.21 965 872 1,837 1.11 19,136 10.55 6,573 4.01 25,709 7.44 2003 80,257 94,349 174,606 5.08 1,057 868 1,925 1.10 20,957 10.55 6,861 4.20 27,818 7.69 2004 84,580 98,742 183,322 4.99 1,137 887 2,024 1.10 22,807 10.67 6,953 4.17 29,760 7.82 2005 89,047 103,287 192,334 4.92 1,192 930 2,122 1.10 24,022 10.72 7,393 4.23 31,415 7.87 2006 8.6390 201,652 93,654 108,000 201,654 4.85 1,260 966 2,226 1.10 25,541 10.78 7,692 4.24 33,233 7.94

2007 87,854 105,268 193,122 -4.23 10,913 7,632 18,545 9.60 222,975 10.87 63,555 4.43 286,530 8.22 2008 80,819 103,623 184,442 -4.49 11,676 6,033 17,709 9.60 220,306 10.04 51,192 4.51 271,498 8.15 2009 73,492 102,519 176,011 4.57 11,706 5,185 16,891 9.60 194,655 8.85 42,186 4.33 236,841 7.46 2010 66,175 101,822 167,997 -4.55 11,548 4,581 16,129 9.60 160,123 7.38 35,764 4.15 195,887 6.46 2011 8.6390 160,525 58,246 102,280 160,526 -4.45 12,100 3,307 15,407 9.60 113,687 5.00 17,215 2.77 130,902 4.52

2012 52,987 101,111 154,098 -4.00 9,664 5,134 14,798 9.60 71,982 3.96 15,292 1.58 87,274 3.14 2013 49,012 99,649 148,661 -3.53 8,635 5,655 14,290 9.61 53,057 3.27 16,169 1.52 69,226 2.58 2014 46,744 97,134 143,878 -3.22 7,034 6,803 13,837 9.62 42,923 3.25 19,928 1.56 62,851 2.42

Total ivory production 2002-2014 1,488,944 kg

Note: To enable the East Africa population to increase from 158,000 to 202,000 elephants between 2002 and 2006 and allow a minimum level of illegal hunting, the intercalving interval (ICI) was reduced to 41 months. Dunham (1988) recorded a calving interval of 41 months at Mana Pools NP.

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Table 5: Southern Africa Ivory Production

AED

estimate

POPULATION ILLEGALLY KILLED IVORY PRODUCTION

IH Set Males Females Total RoI Males Females Total IH/Pop Males MTW Females MTW Total MTW


2001 1.9565 296,412 134,918 161,494 296,412

2002 139,689 166,253 305,942 3.22 3,255 2,940 6,195 2.02 64,469 10.54 22,165 4.01 86,634 7.44

2003 144,358 171,227 315,585 3.15 3,514 2,875 6,389 2.02 69,774 10.56 22,733 4.21 92,507 7.70

2004 149,069 176,329 325,398 3.11 3,663 2,927 6,590 2.03 72,949 10.59 23,178 4.21 96,127 7.76

2005 153,833 181,573 335,406 3.08 3,814 2,979 6,793 2.03 76,235 10.63 23,595 4.21 99,830 7.82

2006 6.1560 345,638 158,670 186,969 345,639 3.05 3,962 3,035 6,997 2.02 79,228 10.64 24,010 4.21 103,238 7.85

2007 153,553 185,851 339,404 -1.80 14,206 9,805 24,011 7.07 284,772 10.66 81,129 4.40 365,901 8.11

2008 147,400 185,285 332,685 -1.98 14,785 8,750 23,535 7.07 272,242 9.79 70,766 4.30 343,008 7.75

2009 139,714 186,124 325,838 -2.06 16,045 7,008 23,053 7.08 266,582 8.84 58,482 4.44 325,064 7.50

2010 132,572 186,738 319,310 -2.00 15,461 7,131 22,592 7.08 249,514 8.58 57,702 4.30 307,216 7.23

2011 6.1560 313,099 125,050 188,047 313,097 -1.95 15,814 6,339 22,153 7.08 217,159 7.30 49,004 4.11 266,163 6.39

2012 116,161 191,222 307,383 -1.82 17,276 4,482 21,758 7.08 200,713 6.18 35,823 4.25 236,536 5.78

2013 108,555 194,018 302,573 -1.56 16,317 5,104 21,421 7.08 158,463 5.17 31,070 3.24 189,533 4.71

2014 102,817 195,745 298,562 -1.33 14,742 6,403 21,145 7.08 120,464 4.35 32,428 2.69 152,892 3.85


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Table 6: West Africa Ivory Production

AED estimate




2001 5.4320 9,685 4,407 5,278 9,685

2002 4,360 5,279 9,639 -0.47 311 251 562 5.83 6,217 10.63 1,950 4.13 8,167 7.73

2003 4,299 5,276 9,575 -0.66 313 243 556 5.81 6,130 10.42 1,862 4.08 7,992 7.65

2004 4,196 5,304 9,500 -0.78 346 203 549 5.78 6,080 9.35 1,613 4.23 7,693 7.45

2005 4,072 5,350 9,422 -0.82 365 182 547 5.81 5,859 8.54 1,404 4.10 7,263 7.06

2006 5.9000 9,351 3,956 5,396 9,352 -0.74 358 182 540 5.77 5,540 8.23 1,375 4.02 6,915 6.81

2007 3,731 5,501 9,232 -1.28 468 122 590 6.39 7,132 8.11 988 4.31 8,120 7.32

2008 3,543 5,592 9,135 -1.05 440 142 582 6.37 5,905 7.14 1,156 4.33 7,061 6.45

2009 3,317 5,745 9,062 -0.80 485 85 570 6.29 5,183 5.68 690 4.32 5,873 5.48

2010 3,169 5,846 9,015 -0.52 422 150 572 6.35 3,862 4.87 751 2.66 4,613 4.29

2011 5.9000 8,987 3,121 5,867 8,988 -0.30 332 239 571 6.35 2,900 4.65 1,032 2.30 3,932 3.66

2012 2,990 5,983 8,973 -0.17 418 145 563 6.27 2,952 3.76 665 2.44 3,617 3.42

2013 3,018 5,956 8,974 0.01 268 297 565 6.30 1,871 3.71 1,085 1.94 2,956 2.78

2014 3,025 5,941 8,966 -0.09 284 280 564 6.29 1,905 3.57 1,003 1.91 2,908 2.74

Total ivory production 2002-2014 77,110 kg

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Table 7: All Africa Ivory Production

AED estimate




2001 2.3380 577,216 262,735 314,481 577,216

2002 270,799 322,638 593,437 2.81 7,572 6,841 14,413 2.43 149,968 10.53 51,572 4.01 201,540 7.44

2003 278,464 331,153 609,617 2.73 8,157 6,652 14,809 2.43 161,975 10.56 52,589 4.21 214,564 7.71

2004 286,090 339,815 625,905 2.67 8,457 6,746 15,203 2.43 168,159 10.58 53,372 4.21 221,531 7.75

2005 293,710 348,657 642,367 2.63 8,761 6,839 15,600 2.43 174,224 10.58 54,054 4.20 228,278 7.78

2006 6.6971 659,058 301,358 357,702 659,060 2.60 9,064 6,940 16,004 2.43 180,084 10.57 54,726 4.19 234,810 7.80

2007 289,156 356,425 645,581 -2.05 29,384 17,647 47,031 7.29 546,110 9.89 143,850 4.34 689,960 7.80

2008 276,897 354,701 631,598 -2.17 28,728 17,292 46,020 7.29 528,038 9.78 139,158 4.28 667,196 7.71

2009 263,060 354,346 617,406 -2.25 29,739 15,247 44,986 7.29 486,482 8.70 118,689 4.14 605,171 7.16

2010 248,498 355,081 603,579 -2.24 30,206 13,777 43,983 7.29 430,822 7.59 104,593 4.04 535,415 6.48

2011 6.1560 590,511 232,619 357,894 590,513 -2.16 31,495 11,533 43,028 7.29 433,168 7.32 90,591 4.18 523,759 6.47

2012 214,163 364,531 578,694 -2.00 34,364 7,820 42,184 7.29 390,858 6.05 63,436 4.31 454,294 5.73

2013 201,575 367,305 568,880 -1.70 29,213 12,271 41,484 7.29 273,849 4.99 60,791 2.64 334,640 4.29

2014 192,829 367,731 560,560 -1.46 25,871 15,020 40,891 7.29 201,630 4.15 64,184 2.27 265,814 3.46


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The contribution of each region to the overall ivory production in each year is shown in Figure 2 and the amounts from male and female elephants are shown separately. The apparent sharp rise in ivory production in 2007 is an artefact arising from applying a fixed percentage offtake for the period 2007-2012 higher (>6%) than for the 2002-2007 period (2.338%). It would have been possible to construct a profile over this period with different highs and lows in different years (see Fig.5). However, the area under the curve for any other profile would have to be similar in order to achieve the population reduction indicated by the AED estimates over the given period. The regional and continental ivory production in relation to the PIKE values is shown in Figure 3. It is noticeable that it is largely driven by the Southern Africa and East Africa components. There is little correspondence with the PIKE values. Central Africa was the only region to show a higher rate of illegal offtake in 2002-2006 than in 2007-2014. The higher offtake numbers in the earlier period were no doubt due to the fact that there were many more elephants to kill. In the Central African equatorial forests, as elephant numbers dropped, poachers found it harder to find them. There is an apparent discrepancy between the number of elephants lost in Central Africa indicated in Table 2 and the number implied by Maisels et al. (2013). Maisels et al. concluded that there was a 62% ‘loss’ in the elephant population from 2002 to 2011, but it is unclear what this percentage was based on. The Maisels et al. study does not actually state how many elephants were expected in 2011, or how many were lost since 2002. Using the 2002 AED report, there were an estimated 113,190 elephants (Definite+Probable +Possible) in the same five Central African countries included in the Maisels et al. study and in this study. In 2011 there were about 100,000 elephants in the five Central African countries, according to Maisels et al. (2013). This indicates that they expected, in the absence of poaching, approximately 270,000 elephants in 2011 in order to estimate a 62% loss. This figure apparently was derived from an expected density of elephants per square kilometer of range, not on a demographic projection from the elephant population estimate of 2002. This flawed methodology calls into question the validity of the 62% figure. Presumably the same 62% ‘loss’ would have existed in 2002 using their method. The method here estimates that 59,446 elephants were poached 2002-2011, not the 170,000 inferred by Maisels et al (2013).

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Figure 2: Regional contributions to illegal ivory production

YEAR2002 2007 20122003 2004 2005 2006 2008 2009 2010 2011 2013 2014

0

100

IVO

RY P

RO

DUC

TIO

N (t

onne

s)

200

300

400

500

600

700

CENTRAL AFRICA

EAST AFRICA

SOUTHERN AFRICA

WEST AFRICA&&

&&

&&

&&

REGIONS

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Figure 3: Regional ivory production and PIKE values

25

The modeled relationship between numbers of animals killed illegally, ivory production and mean tusk weight is shown in Figure 4 below. A number of observations can be made from this diagram.

The relative numbers on the Y-axis show a high production of ivory starting in 2007 with the increase in number of animal deaths. The ivory production declines over next 5 years in relation to the numbers killed and in the last two years (2013-2014) the numbers killed are relatively higher than the level of ivory production. The mean tusk weights over this period correlate closely with the ivory production. This decline in mean tusk weight should perhaps appear in the various ivory seizures made since 2007, but ETIS has not reported on this. Stiles (2011), however, found that the smaller tusks remain for use by craftsmen in Africa while the larger ones are exported, so export average weights will be above the overall average.

Noting the high peak in ivory production in 2007, the obvious question arises “would you get the same picture if you had used PIKE values to shape the illegal hunting profile over the

Figure 4: Numbers of elephants killed, ivory production and mean tusk weight

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same period?” This question is addressed below. The method used in Figure 4 to obtain the numbers of elephants killed and the weight of ivory produced relies on setting a level for illegal hunting after any AED population estimate that results in the simulation model generating the same number of elephants as that for the next AED estimate. In Figure 5 below, the set level of illegal hunting has been adjusted by the value of PIKE for each year between the AED estimates. The same selectivity function (Appendix 4) still operates. This results in higher values for illegal hunting being set initially but after multiplying by the PIKE ratio in each year the level is reduced.

Figure 5: Numbers of elephants killed, ivory production, mean tusk weight and PIKE The estimate of the numbers of elephants illegally killed by this method is 464,068 (versus 362,940 without PIKE) and the estimate of the total ivory produced is 5,430 MT (versus 2,747,977 MT without PIKE). Even with the multiple uncertainties in both the AED estimates and PIKE values, the differences between the results are significant, but not all types of elephant mortality have been modeled in Tables 2-5 and in Figures 2-5. Using the PIKE estimates does introduce a degree of variability to the rather rigid profile of Figure 3 and, in Figure 4.

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The scenario presented above does not take into consideration the effects that problem animal control (PAC) and trophy hunting (TH) have on illegal hunting (IH) numbers and illegal ivory production. The number of elephants illegally killed and the resultant illegal ivory produced are significantly decreased when PAC and TH are included in the model simulation.

With PAC and TH included in the model the offtakes from the population result in some changes to the total numbers of animals killed and the quantities of ivory produced from the population over the period 2002-2014, as shown in Table 8.

Table 8. Elephant numbers lost and ivory production in Africa 2002-2014

NUMBERS LOST IVORY PRODUCTION kg Percent Offtakes Cause Males Females Total Males Females Total 2002-2006 2007-2014

IH 172,690 190,250 362,940 1,740,813 1,007,131 2,747,977 1.47 6.06 PAC 31,020 6,630 37,650 469,100 61,039 530,139 0.5 0.5 TH 9,400 0 9,400 385,100 0 385,100 NM 51,950 66,680 118,630 29,104 194,406 223,510 Total 265,060 263,560 528,620 2,624,117 1,262,576 3,886,726 IH+PAC+TH 213,110 196,880 409,680 2,595,013 1,068,170 3,663,216 IH – illegal hunting PAC – Problem Animal Control TH – trophy hunting NM - natural mortality The total number of poached elephants 2002-2014 was estimated as 362,940, or an average of 27,918 per annum. The numbers through 2006 were well below 20,000 a year, jumping to 30,000 and more a year after 2007. The estimates in this study accord well with those produced by Wittemyer et al. (2014). The total illegal ivory produced from this poaching was estimated as 2,747,977 kg, or 211,383 kg a year. The number of elephants killed as problem animals can be highly variable from one country to the next. It has been set at 0.5% of the population for this analysis as a continental average. PAC is selective. Most of the elephants killed are males between the ages of 13-36 years old and females between the ages of 22-42 years old. More males are killed than females (5:1). An estimated 37,650 elephants were estimated as killed in PAC incidents, yielding 530,139 kg, all of which should in principle have ended up in government stores. Natural mortality resulted in the deaths of an estimated 118,630 elephants, producing approximately 223,510 kg of ivory. No recent studies have been conducted to estimate what proportion of natural mortality ivory might be found annually, usually called the ‘pick-up’ rate. Some carcasses could be found the year the animal dies, others might be found years later (tusks are durable), a certain number will not be found before decomposition renders the tusks unusable, and some will never be found because the animal died in deep forest. Estimates made more than 30 years ago were that about 20% of natural mortality tusks were recovered (Parker and Amin 1983; Douglas-Hamilton 1984 in Gobush 2013). With human population growth, infrastructure development and the rise in the value of ivory, the pick-up rate is probably closer to 80% now. Assuming that 50% of this ivory makes its way into the illegal trade chain and the other 50% finds it way to government storerooms, this calculation yields 89,404 kg of ivory becoming part of both illegal and legal production respectively.

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Formal trophy hunting takes place in 27 countries in Africa but the countries offering elephant hunting safaris over the period 2002-2014 were Botswana, Cameroon, Gabon, Mozambique, Namibia, South Africa, Tanzania, Zambia and Zimbabwe. Gabon and Zambia had a tusk quota for only 6 and 9 years respectively. Table 9 shows the total number of tusks allowed for export in each of the nine countries that allowed elephant trophy hunting over all or part of the period 2002-2013.

Table 9. CITES quotas for trophy tusk export and those actually exported, 2002-2013

Country Quota Number Actual Number Exported Percent

Botswana 7,480 5,155 68.9 Cameroon 1,920 471 24.5

Gabon 950 153 16.1 Mozambique 1,360 920 67.6

Namibia 2,070 1,026 49.6 South Africa 2,660 1,726 64.9

Tanzania 3,700 1,184 32.0 Zambia 600 203 33.8

Zimbabwe 10,800 6,936 64.2

TOTAL 31,540 17,774 56.4

Source: CITES Trade Database

It is difficult to assess how accurate the reporting is of the tusk export numbers (see CITES 2014a, p.25). Where the importing and exporting countries both reported on the same export, the numbers were often different. The larger number was always taken here. In later years the total weight of tusks was reported occasionally rather than the number of tusks, especially in Zimbabwe. A weight of 20 kg was used to calculate the number of tusks. In nine cases, it appeared that the exporting country reported total weight while the importing country declared tusk numbers, making it possible to calculate tusk average weight. The averages ranged from 11.2 kg to 30.5 kg for 553 tusks. The overall average is 20.1 kg. In correspondence with professional hunters for this study, the general opinion was that 20 kg would be a reasonable average to use, with weights higher in earlier years and lower in later years. Craig et al. (2011) found that the average weight of trophy tusks in Botswana was 25.4 kg 1996-2010, in an area with minimal poaching, so 20 kg is a generous average for all of the countries.

The total trophy tusk exports would therefore equal 355.5 metric tons (MT) 2002-2013. If we add the average annual weight as a proxy for 2014 the total is 385.1 MT. Not all trophy elephants carry two tusks and not all tusks from sport-hunted elephants are exported. The CITES Trade Database, in fact, reports a few cases of single trophy tusk exports. Debbie Peake of the Botswana Wildlife Management Association has complete records of sports-hunted elephants and the tusk sizes and weights in Botswana. From 2002 through 2013 some 2,833 elephants were legally hunted (Peake, in litt. to D. Stiles, July 2015), which should

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produce 5,666 tusks, yet only 5,155 tusks were reported as exported by CITES. Record-keeping and reporting errors probably account for some, but it does suggest that the CITES tusk numbers should be regarded as representing an absolute minimum number of elephants legally hunted.

Therefore, the number of elephants killed in sport-hunting should not simply be calculated as 8,887 (half of 17,774) 2002-2014, but rather as falling within a range of 8,800-10,000, and for heuristic purposes we use 9,400.

The number of elephants killed in legal harvesting (offtake for staff rations or approved ceremonies) is not indicated in Table 8, but is included in the PAC estimate.

Discussion The question to be asked is whether it is possible to estimate losses of elephants from a sequence of projections of population numbers – particularly when there are long intervals between successive estimates such as is the case with the AED Status Reports.

In this study a population simulation model was used and:

(1) set illegal hunting levels as a percentage of the total population for the period from one AED population estimate to the next and iterated the hunting level until it gives the correct value for the next AED estimate;

(2) used a dynamic selectivity function to determine the numbers of male and female elephants killed in each age class (the selectivity function is adjusted in each year of the simulation run by the numbers of animals removed in the previous year);

(3) recorded the losses in numbers of elephants resulting from the illegal hunting as determined by the simulation model; and

(4) recorded the weight of ivory generated from males and females in each age class.

Both the AED Status Reports and the MIKE data have limitations – which the authors of both sources of data have pointed out. In the MIKE reports presented to CITES there are no estimates given of the total number of elephants killed illegally in Africa over the period 2002-2014 and the emphasis has been on detecting trends in the population using the PIKE index.

This study estimates that the total weight of ivory produced between 2002 and 2014 is of the order of 3,900 MT from all sources, derived from the deaths of some 529,000 elephants, almost 70% of them killed illegally (362,940). Ivory production peaked at about 700 MT per year sometime between 2007 and 2010, but with the decline in mean tusk weight, less than 300 MT per year is now being produced.

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Objective 2. Amount (weight) of ivory held in:

a. Government stockpiles of ivory in China, Hong Kong and Africa China

The Chinese SFA reported that they held 34 MT of legal raw ivory and 3,657 pieces of legal worked ivory at the end of May, 2015. In addition, China Customs reported for this study that it held 20.867 MT of illegal confiscated ivory up to June 2014.

There are four enterprises that technically owned the 62 MT of raw ivory that were purchased at auction from southern Africa in 2008 – the Chinese Arts and Crafts (Group) Company (CAC), the Beijing Ivory Carving Factory (BICF), the Beijing Mammoth Art Co. (BMA) and the Guangzhou Daxin Ivory Craft Factory (Daxin). The BICF and BMA declined to report their stocks for this study, but CAC reported that they held 23.5 MT of raw ivory and Daxin reported 15.4 MT. These two alone total 38.9 MT. It is thought that at least some, if not all, of the excess ivory from that reported by the SFA was obtained from purchases of legal pre-Convention ivory, mainly from Europe (Mundy 2014). Figure 6 shows the number of records of tusks imported from the European Union’s 28 Member States from 2003 to the end of 2012. Each record reported one or more tusks (see Table 17 below). A further 147 tusks from all sources were imported by China in 2013, according to the CITES Trade Database. Pre-Convention raw ivory weighing thousands of kilograms entered China-Hong Kong 2002-2013, with a marked increase beginning in 2010. Most raw ivory entering Hong Kong is re-sold to China (Dentex, pers. comm. to D. Stiles, May 2015).

The pre-Convention ivory that is ‘in the system’ in China should be deducted from the total that is estimated in this study, as none of it derives from elephants poached 2002-2014. The post-2002 total is therefore approximately 37 MT of legal ivory and 20.9 MT of illegal ivory.

Figure 6. Tusk export records by the EU 2003-2012

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Hong Kong

The total amount of ivory in the government stockpile consists entirely of illegal confiscated pieces. As of mid-2014, Hong Kong Customs reported seizing about 33 MT of ivory (TRAFFIC 2014). By the end of 2016, all but perhaps 3 MT, to be used for educational and scientific purposes, should have been destroyed.

Africa

African government stockpiles are comprised of a mixture of ivory that is potentially legal to sell internationally by CITES regulations, derived from natural mortality, PAC deaths and pre-Convention stocks, and illegal ivory that cannot be sold derived from poaching or trafficking arrests and import-export trade seizures. The most comprehensive single set of data on stockpiles was prepared for CoP10 in Harare, Zimbabwe in 1997 by TRAFFIC East/Southern Africa (CITES 1997a). They estimated there were more than 272 MT of government stockpiled ivory in all African range (and some non-range) States that reported. Of this total, 87% was accounted for by the seven largest ivory stockholders: South Africa, Burundi, Tanzania, Namibia, Sudan, Botswana and Zimbabwe.

Current stockpile weights were found for only nine countries in Africa from press reports. Four of these are now negligible after recent stockpile destruction events (Chad, Congo, Ethiopia and Gabon). The five others are shown in Table 10. Different quantities were reported for Tanzania by various sources.

Table 10. African ivory stockpiles as reported in the press

Country Weight (MT) Date Source

Kenya 137.7 16.9.2015 1,

Malawi 4 4.2015 2

Tanzania 112-137 3-11.2015 3,4,5,6,7

Togo 5.2 8.2013 8,9

Zimbabwe 70 12.2014 10,11,12

TOTAL 328.9-353.9 Sources:

1 – http://www.the-star.co.ke/news/kenya-holding-138-tonnes-ivory-1519kg-rhino-horns#sthash.UE8agfK4.dpuf 2 - http://voices.nationalgeographic.com/2015/04/20/whats-behind-malawis-decision-not-to-burn-its-ivory-stockpile/ 3 - http://allafrica.com/stories/201402270049.html?viewall=1/ 4 - http://www.eturbonews.com/43466/zambia-reaffirms-its-commitment-against-illegal-wildlife-trade/ 5 - http://www.dailymail.co.uk/news/article-2586894/WORLD-PICTURE-EXCLUSIVE-Haul-shame-This-shocking-photo-shows-time-biggest-stockpile-illegal-ivory-earth.html / 6 - http://www.eturbonews.com/45621/good-hope-save-elephants-tanzania-comes-light/ 8 - http://www.ippmedia.com/frontend/index.php?l=69824 / 7 - http://www.economist.com/news/middle-east-and-africa/21631202-claims-links-between-politicians-and-poachers-merit-further-investigation-big 8 - http://gulfnews.com/news/world/other-world/togo-goes-high-tech-in-crackdown-on-ivory-smuggling-1.1345359

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9 - http://www.google.com/hostednews/afp/article/ALeqM5iFMrNTF7YZaZbT-E_PTakcmCX3eg?docId=6368efec-bf98-478d-88f5-2c03afe07276 10 - http://ewn.co.za/2014/12/24/27-Zim-elephants-to-be-exported-to-China 11 - http://allafrica.com/stories/201412230125.html 12 - http://www.newsday.co.zw/2013/07/12/storage-space-for-elephant-tusks-runs-out/ In addition, four of the countries listed in Table 11 are amongst those that declared significant stockpiles to CITES at the end of 2007 (Milliken 2010), and which are thought to have accumulated stocks from all sources since then. Based on estimates made by Gobush (2013) on rates of stockpile accumulation from natural mortality and PAC, the approximate range of weights at the end of 2014 are also included in the table. Milliken did not include in his report the 84 MT of ivory that have been stored in Burundi since the 1980s under tight security (Milledge and Nuwamanya 2004)4. This has been included in Table 11. Table 11. Estimates of 2014 ivory stockpiles based on 2007 declared stockpiles

Country End of 2007 (MT) End of 2014 (MT)

Botswana 54.6 90-110

Burundi 84.0 84

Namibia 44.5 53- 60

South Africa 8.4 48- 52

Zambia 30.0 35- 40

TOTAL 221.5 310-346

Source: Milliken (2010)

The total amount of ivory that should be held in just ten African countries is estimated to constitute from 639 to 700 MT at the end of 2014 (Table 10+Table 11). Other African countries contained small stockpile quantities (Milliken 2010), but because of political instability and corruption there has probably been significant leakage, and in six other countries stockpile destruction events have destroyed over 39 MT. The quantities in other countries are considered to be relatively small, totaling approximately 20 MT. To give a figure, we take the average of 639-700 MT, which is 670 MT, and add 20 MT from the rest of Africa, to arrive at 690 MT for the total in the African stockpiles.

To calculate the quantity accumulated since 2002, the pre-2002 stockpile amount needs to be deducted from the 690 MT. Of the ten countries, five have never participated in CITES-authorized ivory sales and therefore should hold pre-2002 ivory. Only ivory accumulated after 2002 is of interest in this study. Based on declared ivory stockpiles made in 1998 to CITES (1998), in 2002 Burundi held 84 MT and Kenya should have held roughly 12 MT, Malawi 6 MT, Tanzania 78 MT and Zambia 10 MT. This 190 MT total of pre-2002 ivory should be subtracted from the overall stockpile estimates, which results in 2002-2014 accumulations of 500 MT.

4Unfortunately, this ivory may recently have started to leak into illegal trade (Ainebyoona and Tajuba 2015).

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Since 2002, ivory has been added to government stockpiles from confiscations made in the field from poaching and illegal trade incidents, and in seizures made in attempted illegal import and export cases. These additions are included in Table 10 and the 20 MT for the rest of Africa, but must be added for those in Table 11 for 2007 to 2014. ETIS does not publish comprehensive country seizure weights. The five countries in Table 11 might hold 10 MT of ivory seized leaving or entering the country, plus another 10 MT seized from poachers since 2007. This 20 MT addition to the total African stockpile is cancelled out by the ivory that is lost to the stockpiles in a few countries (e.g. Zimbabwe, South Africa, Namibia) by legal sales to private parties. This is estimated to total 20 MT from 2002 to 2014.

Therefore, 500 MT should be considered the approximate accumulation to sub-Saharan Africa stockpiles since 20025, with 690 MT being the total held in 2014. These figures include ivory lost to legal sales and leakages. These estimates differ from figures derived from modeling ivory production and assuming proportions lost to non-pickup in the field and leakages at different points in transfer to government storerooms (see Table 19), but they fall within a 10% error of each other.

The CITES (1997a and b) ivory stockpile documents also contain data on privately held ivory stocks in 1997. The documents report that there were approximately 223 MT of legal ivory in private hands in 13 sub-Saharan countries in 1997. This ivory could have been the source of some of the legal (pre-Convention and non-commercial) and/or illegal exports after 2002. This ivory is not included in the analysis in this report as it is all clearly pre-2002 ivory. However, this could add more than 200 MT of ivory to the estimate that is made of ivory imported into China-HK and Japan during the 2002-2014 period.

Table 12 presents a summary of all government stockpiles of ivory estimated to be in China, Hong Kong and Africa in 2002 and in 2014. Japan stated that it keeps no government stockpile of seized ivory (CITES 2014b), which begs the question of what do they do with seized ivory. According to ETIS (2002), Japan had seized approximately 5 MT of ivory up to 2002 and had made 239 ivory seizures up to the end of 2009 (Milliken et al. 2010). Where is the ivory?

5A CITES document estimated that African ivory stocks exceed 800 MT (CITES 2014c), but this figure includes privately held stocks.

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Table 12. Ivory estimated to be in government stockpiles in China, Hong Kong and Africa in 2002 and in 2014 (metric tons).

1 – Source: O’Connell-Rodwell and Parry-Jones (2002). About 40 MT was seized illegal ivory and approximately 20 MT was legal ivory held by Beijing and Daxin ivory carving factories, both under government control. 2 – Source: ETIS (2002). 3 – Approximately 500 MT accumulated since 2002.

2.c. Ivory held in processing (factory) warehouses in China and Japan

China

Table 13 presents the quantities of raw and worked ivory reported by the 13 legal processing enterprises that responded to our request for data. Almost 41 MT of ivory are held by these factories. The only other factories with significant amounts of ivory are the Beijing Ivory Carving Factory and Beijing Mammoth Art Co., but other than pre-Convention ivory purchases, all the ivory should be included in the government 34 MT declared stockpile.

Table 13. Weight in kg of raw and worked ivory in processing factories in China

Province No. Name Raw Worked

Beijing 4 Chinese Arts and Crafts (Group) Company 23,500

Heilongjiang 10 Harbin Ice Age. Everest Trade Ltd. 181 69

Shanghai 11 Shanghai Fengxiang Old Jade Carved Ivory 117 42 12 Shanghai Arts and Crafts Co., Ltd. 331 102 13 Shanghai Changjiang Enterprise Development Cooperation 109 87

Jiangsu 17 Changzhou Boya Sword Engraved Art Ltd. 101 112 Fujian 21 Fujian Zhen Hong Kong Ltd. 180

24 Putian City Mae Arts and Industries Limited 75 25 Putian City, the US-up Lee Arts and Crafts Co., Ltd. 231 27 Xianyou Mori Arts Sengoku Technology Limited 62

Guangdong 28 Guangzhou Daxin Ivory Craft Factory 15,400

31 Foshan City of God 23 109 Guangxi 34 Guangxi Fire Kun Ivory Handicraft Processing Ltd. 132 150 TOTAL 39,828 964

2002 2014 China 50-601 58

Hong Kong 42 33

Africa 225 6903

TOTAL 279-289 781

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The 11 factories not holding government stockpile ivory in Table 13 (CAC and Daxin are the two holding government ivory) hold a total of 2,213 kg, or an average of about 200 kg per factory. If this average holds for the 21 factories not reporting for this study, they hold 4.2 MT of raw and worked ivory. The 4.2 MT added to the 40.8 MT reported in Table 13 yields a total of about 43 MT. But 34 of the 38.9 MT held by CAC and Daxin is government ivory, so to avoid double-counting it needs to be subtracted from the 43 MT, leaving 9 MT in the other factories. Therefore, there should be a total of approximately 43 MT (34+9) of legal ivory in China factories.

The large amount of ivory still held by these factories and in government stockpiles six years after receiving the 62 MT from southern Africa questions the view propounded by many NGOs and the media that there is ‘instatiable demand’ amongst consumers for worked ivory in China. Between 2009 and mid 2013 only 13.78 MT of ivory were processed out of 18 MT made available (Moyle & Conrad in press; Moyle 2014a). Figure 7 shows the relatively small amount of legal ivory processed between 2005 and 2014. Production spiked to 6 MT in 2010 as the legal worked ivory entered the system, declining to 5 MT in 2011, 4.8 MT in 2012, 4.2 MT in 2013 and still declining currently. Government policy until recently was to release an average of 5 MT a year to legal factories, but even this relatively modest amount has not been processed since 2011. Consumer demand for legal worked ivory appears to be falling.

Source: Lidan (2015)

Figure 7. Legal worked ivory production in China 2005-2014

The amount of ivory held in China in illegal ivory processing factories, and their number, are critical questions, but because of the cryptic nature of the business they remain unanswered. Martin and Vigne (2011), Vigne and Martin (2014) and IFAW (2012) did not locate any illegal factories in their surveys, indicating that they are well hidden. Judging by the types of ivory seen for sale in shops and online, it appears that illegal factories concentrate on the smaller, cheaper ‘trinkets’ and jewelry end of the market. Great numbers of these kinds of items can be produced quickly by using machines (Stiles 2003; Gao and Clark 2014). Martin

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and Vigne (2011) found that many carvers who work for legal factories retire in their 40s to work from home. Stiles (Martin and Stiles 2003, p. 65) photographed such an elderly carver on Daxin Street in Guangzhou in 2002, indicating that such a practice is not new. Tusks weighing >10 kg and small pieces (<2 kg) of illegal raw ivory can easily be found through personal contacts or online, which has spurred a surge of small-scale entrepreneurs to that mass-produce ivory bangles, beads, pendants, seal blanks, chopsticks, etc. from home with machines that are sold online (Gao and Clark 2014). Large factories are not needed to supply the local illegal market, though Stiles obtained the names of several located in Guangdong and Fujian provinces in 2002 that were said to employ from 12 to 20 craftspersons each (Martin and Stiles 2003).

The quantities of ivory that the hundreds or thousands of small ‘mom-and-pop’ workshops process annually are also unknown, but the statistical analysis presented under Objective 3 below estimates that there is an annual demand for 6-8 MT of worked ivory in the illegal sector. Most of the craftspersons working the ivory probably do not keep large stocks, but rather buy just enough raw ivory to satisfy the demand of clients as they go. The larger illegal workshops, especially in Guangdong and Fujian provinces, that employ 20 or so carvers will want to stockpile raw ivory for the future. These workshops employ more skilled craftspersons that focus on the illegal export market, producing figurines and netsukes, sometimes manufacturing fake antiques (Stiles 2015a). The amounts cannot be estimated because of the secrecy of their operations. Japan

As of May 2014, private Japanese ivory dealers and manufacturers held 340.6 MT of raw ivory (CITES 2014b). They also held 3,705,061 pieces of worked ivory, but many of these would have been in retail shops; no breakdown or weight was given (CITES 2014b). The weight of the worked ivory pieces is estimated to be 37.5 MT at 100 g (3.5 oz) average per piece (about the weight of a netsuke). This is a conservative estimate given that over 150,000 items reported by the Japanese were not in the small item categories. The extraordinary total of ~378 MT has remarkably raised few eyebrows amongst NGOs monitoring the ivory trade. 2.d. Ivory held in large holdings in China and Hong Kong This comprises mainly the illegal speculator stockpiles, as explained in the Methodology section. The results will be presented in the DISCUSSION section, since the estimate involves the statistical modeling results obtained in Objective 3. 2.e. Ivory held in shops in China and Hong Kong

China

The amount of ivory held in legal and illegal shops cannot be quantified with available data, but the surveys carried out by Martin and Vigne (2011, 2014) and Conrad and Moyle (2013) show a significant difference between the two types of shops. Legal shops tend to sell the

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larger, high quality, more expensive items that take a considerable time to produce, made by skilled craftspersons, while illegal shops sell more of the smaller, cheaper, mechanically mass-produced items. This difference might well explain why demand is so low in the legal outlets, as demonstrated by the low raw ivory processing figures noted above and in Figure 7. The high prices suppress overall demand, although the Veblen effect conversely attracts a minority of wealthy consumers wishing to demonstrate their status by purchasing very expensive commodities. Indeed, Vigne and Martin (2014) found that >50 cm-long carved tusks – a favorite display item for the wealthy – ranged from USD 79,000 to USD 295,000 each in Beijing. This natural economic measure to reduce demand for ivory could be much easier and more effective to implement than expensive demand reduction campaigns. Simply make all worked ivory expensive.

The ivory held in the 130 legal shops should equal the 13.8 MT processed up to 2014, plus the 2 MT processed in 2014, minus sales. One can assume that most of the worked stock held prior to 2008 had already sold, so there would be little holdover from before the southern Africa ivory arrived in early 2009. At the end of 2014, therefore, there would have been considerably less than 16 MT of worked ivory in the legal shops.

The 48 legal shops in Beijing and Shanghai that Vigne and Martin (2014) surveyed displayed an average of 151 and 105 ivory pieces respectively. Of the total number of items, only 20% in Beijing and 18% in Shanghai were types that would weigh over 1 kg each (e.g. figurines, carved tusks). The dozens of photographs in the report showed the tusks to be in the 5-15 kg range, say an average of 10 kg. The worked tusks made up 3% of the items in both cities, which means about 159 in Beijing and 41 in Shanghai, a total of 200, multiplied by 10 kg, making 2 MT of worked tusks. The figurines range from about 0.5 kg to 4 kg, say an average of 2 kg each. Figurines were 17% (N=899) in Beijing and 15 % (N=205) in Shanghai, a total of 1104 figurines, multiplied by 2 kg, making about 2.2 MT of ivory. The remaining ~80% (N= 5,320) of worked items were generally small, averaging perhaps 200 gm each, which equals about 1 MT of ivory. A rough estimate, therefore, is that Beijing and Shanghai had about 5 MT of legal worked ivory on display in May 2014. This total works out to an average of 104 kg of ivory per outlet. If this average is applied to all of the 130 legal outlets, a total of approximately 13.5 MT is calculated. This figure is no doubt too high, because Beijing and Shanghai are two of the largest cities in China and therefore would be expected to contain more ivory than other, smaller cities with legal outlets. The total will therefore be reduced to 10 MT. This methodology is admittedly ‘back of the envelope’, but in the absence of better data it is arguably the best that can currently be achieved.

A remarkable finding was that much more ivory is displayed in the legal than in the illegal outlets. There were many more illegal outlets (Beijing 121 and Shanghai 106) than legal ones, but they hold very little ivory; an average of 8 per shop in both Beijing and Shanghai. It is possible that in some illegal outlets ivory pieces were hidden from view because of fears of law enforcement inspections.

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Another interesting finding that was not pointed out in the Vigne and Martin (2014) report was the relative slow growth and small scale of the ivory markets in Beijing and Shanghai. In fact, the report itself and the Save the Elephants (2014) press release gave just the opposite impression from the reality by stating, “The illegal ivory trade is exploding in China…”, “skyrocketing demand for ivory in China…” and so on.

The actual numbers indicate a slow expansion of the open retail market in Beijing from 2002 onwards – about 7% annual growth – and stagnation in Shanghai. The average number of items displayed per outlet has actually decreased since 2002. The 7% ivory market growth in Beijing is less than the 9.8% average economic growth rate for China during the 2002-2014 period. The increase in item numbers can be explained largely by the increased availability of legal ivory, which was very limited in 2002. Data would be needed from the black market to support a claim that consumer demand for worked ivory has skyrocketed, or that demand was insatiable, but 8 ivory pieces per illegal outlet hardly suggests insatiable demand. Even if pieces were hidden from view, which E. Martin thinks unlikely (pers. comm. to D. Stiles, September 2015), the quantity would not be significant.

Retail market data do not seem to support a conclusion that ivory market growth has ‘exploded’ since 2002; growth in the physical outlets sector has been surprisingly small. Beijing and Shanghai combined – cities with a total population of about 37 million – had 25% less ivory than Manhattan, USA, in 2006, a city of about 1.6 million. The scale of the open market in Beijing and Shanghai lags behind Bangkok (Thailand), Cairo (Egypt), Khartoum (Sudan), Lagos (Nigeria), Luanda (Angola) and probably other cities outside China. The low numbers are reinforced by observations the authors make on consumer attitudes and behavior that ivory is of little interest to the average Chinese citizen (see Stiles 2015b for a more complete analysis). Changing consumer attitudes about buying ivory in response to demand reduction campaigns appear to be working to reduce ivory demand even further (WildAid et al. 2015). Hong Kong A recent WWF publication stated that the AFCD reported that the private registered stockpile stood at 111.3 MT at the end of 2014 (Lo and Edwards 2015). This is down from 256 MT in 2002, a drop of 144.7 MT in 13 years. However, not all of this drop was from ivory consumption through sales. In 2009 the government changed its system of registration for privately held ivory. Previously, all privately held ivory stocks were required to be declared. After 2009, only commercial ivory stocks needed to be declared annually. The stocks registered in 2009 fell by 54 tonnes from 2008, and in 2010 they fell a further 57 tonnes (Martin and Vigne 2015). While from 2000 to 2008 ivory stocks declined by about 3.6 tonnes per year on average, between 2009 and 2010 about 111 tonnes disappeared from the books. If the consumption average of 3.6 tonnes remained stable, it means that approximately 104 tonnes of ivory became ‘non-commercial’ during this period.

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If we subtract the 104 tonnes removed from the registration books by owners from the 144.7 tonnes drop 2002-2014, that means 40.7 tonnes of ivory was consumed 2002-2014, an average of 3.1 tonnes a year. Martin and Vigne (2015) at the end of 2014 found 72 outlets selling 30,856 ivory items, about the same as in their earlier survey in 2011, with mainland Chinese buyers forming over 90% of the clientele. This suggests that Chinese were smuggling over 2.8 MT of worked ivory a year into China from Hong Kong (i.e. 90% of 3.1 MT). The Martin and Vigne (2015) data can be used to estimate ivory demand by Chinese consumers in Hong Kong. The Hong Kong Tourism Board reported that 54.3 million mainland Chinese visited Hong Kong in 2014, the main purpose being to shop for luxury merchandise. If 2.8 MT of ivory were smuggled back to mainland China, that works out to a per capita purchase of about 5 gm of ivory in a year, the weight of a ring. This finding supports the Martin and Vigne (2015) observation, “We saw few customers in the retail shops, however, and the small handful of Chinese people who actually bought any ivory chose bangles and pendants”. This is consistent with other evidence of low demand for worked ivory by Chinese consumers6.

Hong Kong also imported about 3 MT of pre-Convention legal raw ivory a year up to the end of 2014, most of which was re-exported to the mainland, but this has declined substantially in 2015 (Dentex, pers. com. to D. Stiles, 2015). The CITES Trade Database indicates that in recent years an average of over 2 MT a year was imported into China-HK, which suggests that there is underreporting (see below under Objective 3). Hong Kong has by far the largest ivory market of any city in the world.

2.f. Ivory held by online retailers in China

There is a thriving illegal ivory trading enterprise being carried out in China on the Internet by thousands of small-scale entrepreneurs and probably a few larger scale businesspersons. Many Chinese workers who are employed in Africa in infrastructure projects or small businesses return to China with smuggled ivory, or they send it through the post or courier services. The press is full of stories of those who are caught in airports with illicit ivory. Those who successfully smuggle the ivory sell it online through chat rooms and commercial websites. There are most likely storage facilities for stocks of ivory sold online, located either in warehouses or in homes.

The 15-31 May 2015 survey of online ivory vendors found 1,039 items for sale (Table 14). This number differs substantially from a one-week online survey conducted by IFAW (2012) in January 2012, in which 17,847 ivory pieces were found on 13 websites and social forums.

6 If the actual AFCD figures are used for the 2013 and 2014 stockpiles (a drop of 5.8 tonnes), the calculation would yield 96 gm per capita purchased in 2014 by Chinese shoppers.

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IFAW found that 97% of the pieces were on two websites that trade in collectibles and antiques. The China State Forestry Administration (SFA) on 15 December, 2011 issued the Urgent Notice on the Auction of Wildlife Products, which effectively halted ivory being auctioned. The IFAW survey was conducted soon after auction sales almost ceased, so it is possible that the pieces that were planned for auction shifted to online outlets (Figure 8).

There has been a significant change in 2015 from what IFAW found in 2012. The present study found no ivory items at all on the two websites (http://www.gucn.com/, http://trade.findart.com.cn) where IFAW counted 16,902 ivory items. Every conceivable search term was used – no ivory turned up. As a recent TRAFFIC study found, ivory and other illegal wildlife products have moved from e-retailer websites to online social forums and mobile phone accounts (Yu and Jia 2015).

IFAW (2012) reported on three of the sites seen in Table 14. Table 15 shows the comparison.

Table 14. Number of ivory items and types for sale online, 15-30 May, 2015

Type Wechat QQ Weibo Baidu Post Bar Taobao Ali Baba

Whole raw tusks 45 23 0 24 10 7

Cut tusks 35 199 0 12 0 0

Semi-worked raw pieces 0 0 0 28 0 0 Worked ivory jewelry 19 34 4 32 89 75 seal blanks 0 15 0 52 0 0 trinkets 17 33 2 63 0 4 netsukes 13 0 0 0 0 9 carved objects 13 67 8 45 5 43 carved tusks 0 0 0 0 0 0 polished tusks 0 0 9 5 0 0

TOTAL 142 371 23 261 104 138

Table 15. Numbers of ivory items seen online by IFAW in 2012 and this study in 2015

Online Site IFAW 2012 This study 2015

Baidu Post Bar 407 261 Taobao 202 104

Ali Baba 19 138

TOTAL 628 503 There was somewhat less ivory for sale online in these three outlets in late May 2015 than in January 2012, but one would have expected even less given pledges made by the websites to control illegal wildlife sales – especially in Ali Baba’s case. TRAFFIC (Yu and Jia 2015) found thousands of ivory items on one social media platform over the course of one month, but unfortunately they did not name the platform, give the data collection dates, or break

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down the worked items into types, although the report did say that over 115 were whole tusks and more than 276 were raw ivory pieces. In March 2014, Gao and Clark conducted an online survey concentrating on Baidu Post Bar (Baidu Tieba), one of the largest online communication platforms operated by the Chinese search engine Baidu. Social forums existed to trade expressly in ivory. They visited the top five, each having on average of 4,680 members, but people interested in ivory no doubt belonged to more than one forum. They read about 2,000 posts made between 2008 and 2014, which were mostly advertisements of raw or worked ivory. Raw ivory typically weighed less than 2 kg and worked ivory items were mainly jewelry, such as pendants, rings, bangles, and bead necklaces, followed by simple figurines. Dealers active in these forums were middlemen with access to raw tusks in Africa or middlemen who sold home-processed, semi-worked and worked ivory with machines that they had purchased online for about USD 4,000. These people posted ivory photos and information about the size and price of the pieces. Gao and Clark do not report on the total number of pieces they found in any given period. In addition, they only looked at samples, not at the total number of ivory posts on any given site (Yufang Gao, in litt. to D. Stiles, 2 July 2015).

The most significant finding of this study was the great drop in raw ivory prices in a little over one year.

Table 16 compares the prices found by Gao and Clark and this study.

Table 16. Raw ivory prices, March 20141 and May 2015

Type Whole Tusk With Taobao Omitted

Number G&C This study

7 109

99

Mean weight kg G&C This study

1.2 14.1

14.2

Mean price USD/kg G&C This study

2,160 921

974

1 Gao and Clark 2014

The average illegal raw ivory price of USD 921/kg seems unusually low, but we consulted with the Chairman of DTX Far East, a European and Hong Kong firm with extensive experience selling legal ivory to China and Hong Kong, and he replied that it, “…sounds credible and more or less in line with my info about illegal raw ivory prices.” He actually had heard that illegal raw ivory prices had dropped to 460-575/kg in China. The prices found on Taobao (literally “Searching for Treasure”) online shopping website were even lower than the prices Dentex reported, averaging USD 385/kg. These prices seem too low. With the 10 Taobao tusks omitted, the average price rises to USD 974/kg and average tusk weight remains almost unchanged at 14.2 kg. The average black market price in May 2015 was 55% lower

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than that found in March 2014 by Gao and Clark, and also by Vigne and Martin (2014) in May 2014, and this was for much larger tusks.

DTX Far East reported in September 2015 that China is no longer processing requests for imports of legal pre-Convention tusks and that demand for legal raw ivory imports in Hong Kong has virtually evaporated.

The price of polished tusks has also taken a hit. Gao and Clark (2014) found only one, which cost USD 2,930/kg. E. Martin (2015) stated that the average online price was USD 2,650/kg in the first quarter of 2015. This study found 14 polished tusks weighing >10 kg at USD 1,628-1,943/kg. Polished tusks are very popular with more wealthy buyers. The price had dropped by a third in only three months.

A third significant finding is the change in average size of ivory items for sale. Whereas in 2014 the raw ivory items were very small, now they are much larger. The average tusk size has risen from 1.2 kg to 14.2 kg. This suggests that ivory speculators are now selling investment tusks.

The final difference that might be significant is the increased desire by sellers to offload their ivory in May 2015. The Chinese researcher (WJ) conducting the online survey, posing as a prospective buyer, reported that the sellers seemed anxious to sell their stocks and made price concessions easily.

Evidence seems to suggest that a speculative bubble on ivory investment has burst, but further research would be needed to confirm this. 2.g. Ivory held by auction houses in China

Auction houses do not hold stocks of ivory, but rather sell items on consignment for private owners. The only ivory items sold currently are verifiable antique pieces. In 2012 only 444 ivory items were sold, in 2013, 136 items and in 2014, only 55 ivory pieces were sold in 22 auction houses, down from a peak of 6,280 items in 2011 (Artron, pers. comm. to Wei Ji, May 2015). The crash in numbers is due to the Urgent Notice on the Auction of Wildlife Products made by the China State Forestry Administration (SFA) on 15 December, 2011. The notice prohibited the sale of any protected wildlife species product, except for antiques (pre-1949) (Gao and Clark 2014). The SFA and the State Administration of Cultural Heritage must approve any items for auction. The number of items sold in all auction houses in 2011 is very close the number of items seen online by IFAW about three weeks after auction sales almost ceased, suggesting that auction ivory is now sold illegally online (Figure 8).

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Source: Gao & Clark 2014

Figure 8. Trend in sales of ivory items by auction, 2002-2013 The pattern from 2008 to the end of 2011 is indicative of speculative behavior and the spike in art market ivory sales coincides with the Global Financial Crisis (GFC). The Objective 3 section below explains that as the stock and property markets crashed, investors sought other places to put their money. Luxury investment commodities such as gold, jewelry, jade, paintings – and ivory – enjoyed a high growth in sales and increase in prices. But to profit from commodity investments, they must be sold. Closing the auction houses to most ivory items has made their sale more difficult and risky, as online sales are illegal and buyers are suspicious of fakes. The closure of the auction ivory outlets seems to have served to suppress demand from ivory art investors.

It is likely that a high percentage of the ivory items sold at auction in the 250 approved auction houses up to December 2011 was produced both in the legal and illegal factories from ivory imported after 2002. The legal amount is captured in the production statistics in Figure 7. The illegal quantity would have been relatively small, as the skilled carvers necessary to produce investment grade ivory are few in the illegal sector. About 29,000 ivory items were auctioned 2002-2014. We will assume the average weight per piece was 5 kg and 5% were made on illegal ivory, yielding a total weight of 7,275 kg (~7.3 MT) of recently imported illegal ivory sold in the 2002-2012 period.

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Objective 3. A statistical analysis of trends in ivory poaching in Africa and demand drivers in China

The markets for ivory in China and Hong Kong are complex. They stretch from sources in Africa and Asia, using ivory imported in both legal and illegal streams, and are used to produce diverse products. The markets are divided by geography and product-type. Ivory is not just used to make carvings, but this report presents evidence that it is also used as a speculative good in its raw form. We need to understand many aspects of this trade.

The first aspect is the jump to unsustainable poaching, beginning in about 2007. There was a very rapid increase in poaching from 2009-2011 (Figure 9), with an initial spike in 2007 picked up in the ivory production simulation (Figures 2 & 3). While illegal killing does appear to have decreased since the 2011 peak, it is still unsustainable (Wittemyer et al. 2014; CITES 2015; Objective 1 results of the present report). What has generated this increase from the China-Hong Kong demand perspective is the subject of this analysis.

Source: CITES MIKE Programme.

Figure 9. PIKE estimates of poaching intensity The second aspect is the shift in seizures. The amount of ivory seized as worked-items, and reported to ETIS since 1996, has been relatively stable. Generally it is less than 4 MT per year. The growth in raw ivory seizures has, however, undergone a massive increase. Total annual seizures of 30-50 MT are now occurring, as compared to the roughly 10 MT per year in the 1990s and early 2000s. Within that increase in raw ivory is another pattern. Almost all of the increase is due to large shipments (Figure 10). Additionally, seizures of raw ivory items less than 100 kg also exhibit no discernible upward trend (Underwood et al. 2013; CITES 2014a), reinforcing the conclusion that the increase in large shipments is a unique trend.

There has not therefore been a simple scale increase of the illegal traffic since the mid-2000s. Along with the increase in poaching has been a dramatic shift in the patterns of illegal trade. It is seen largely as a growth in raw ivory traffic. And this growth is almost entirely

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manifested in very large shipments (>1000 kg). These are typically made in shipping containers. Within that trend, all of these large seizures from 2010 onwards have been en route to, or within, East Asia (Figure 11).

This upward trend in illegal raw ivory seizures is mirrored by the import of tusks from pre-Convention sources (Table 17). These legal imports into China also show a marked increase. In the 2002-2007 period, the annual average import of tusks was an estimated 40 kg. From 2008-2010 this had increased to an estimated annual average of 658 kg. From 2011-2013 it had jumped again to an estimated 2,021 kg.

Figure 10. Ivory Seizure Data

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Figure 11. Destinations of Large Shipments (Source: CITES 2014a)

Table 17. Ivory Imports into China

Year Tusks- Europe

(number) Raw Ivory- All Sources

(kg) 2002 3 20 2003 1 100 2004 1 35 2005 0 10 2006 0 75 2007 4 20 2008 85 990 2009 32 190 2010 46 794 2011 44 437 2012 33 2124 2013 237 3503 2014 0 132 Total 486 8,430

Source: CITES Trade Database, excludes 2008 sale of ivory under CITES It appears, therefore, that the increase in poaching is largely associated with demand for raw ivory (especially large stocks of it) rather than demand for imported African worked items in China, the main final destination country. Chinese manufacture of legal raw ivory for carvings has not risen to match this increase. In fact, there have been quite large declines by 2014 in the legal sector (see Figure 7 above). Unfortunately, there are few data on the manufacture of illegal raw ivory. Recent studies by Vigne and Martin (2014) and Martin and

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Vigne (2011, 2015) indicate that very little illegal worked ivory is sold in physical outlets in China and Hong Kong. Evidence suggests that larger quantities of illegal ivory are sold online and in person-to-person sales, but the total weight is unknown (IFAW 2012; Gao and Clark 2014; Yu and Jia 2015).

This report considers the statistical and economic evidence for the dynamics and structure of the trade in ivory. Some models are generated to link the drivers of the illegal traffic with the important events of the time in China-Hong Kong, Africa and globally. However, it is important to understand some of the limitations of statistical tools in this context.

Statistical Methodology

There are several data problems to be aware of. The first is that the data sets are relatively small. For instance, it is necessary for statistical analysis to have a metric of poaching activity. This metric needs to be broad enough to cover all the range states in Africa. It also has to cover enough years to cover the events we are concerned with. The only data series that meets these criteria is the PIKE data. Nonetheless, PIKE data only exist from 2002 to 2014. That provides 13 annual observations at an aggregate level. That is too small for credible analysis. This low number of observations can be improved by using regional PIKE data, which expands this to 52 annual observations. Nevertheless, the amount of information available to detect relationships is still limited. This is exacerbated by the fact that the intense poaching years are clustered at the end of the series. This gives fewer observations to test explanations for this high poaching rate.

The second data problem is that many variables we are interested in examining are highly correlated. This means they have a close statistical relationship. They move together. They exhibit what is termed multicollinearity. For example, rises in GDP tend to be correlated with increases in household incomes, increases in the population size of high and middle-income earners, reductions in absolute poverty and infrastructure improvements (such as access to the Internet, tourism to other countries) and direct investment in other countries.

Another example is corruption in a country, which is correlated to factors such as poverty, governance, incomes and education.

Hence it can be problematic determining what variables are causing increases in illegal demand for ivory. A variable may be capturing the effects of other correlated variables. This means it is not truly causal and the actual relationship is spurious. If two highly correlated variables are used in analysis, it is probable that neither will appear statistically significant. The model struggles to separate the influence of the two variables and this is manifested in increased uncertainty about the true, underlying impact of the two variables. In statistics that uncertainty is reflected in a much larger standard error for the coefficients. In small data sets that face multicollinearity the general solution is be parsimonious (if increase in sample size is not possible). Parsimony means employing only one variable in the model, recognising it implicitly captures the effect of other variables. In short, it is rarely possible to include all the demand variables into a single model because nothing will appear causal.

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The third problem is that of a lack of data. The illegal trade in ivory is a cryptic activity. Many issues we are directly interested in we simply have no data for. Smugglers and poachers do not file statistical returns on their activity. One strategy to overcome this is to find instrumental variables. These behave the same way as the variable we are really interested in, so can be substituted for it. For instance, Moyle & Conrad (in press) use gold, silver and jewelry sales in China as an instrument for ivory retail demand. The first variable is collected and published by the Chinese government. The second is not. However, by showing retail orders for ivory carvings to factories is synchronised with gold, silver and jewellery retail sales, it becomes a suitable instrument. The underlying economic reason is that both categories are artisanal, display items that appeal to similar (or the same) consumers. Indeed some companies in China produce and display both categories, side by side.

A fourth data problem is bias. They may systematically under-estimate or over-estimate the true value of a variable. For instance, Underwood et al. (2013) show that the ETIS seizure rates are biased down for many countries. This is because their reporting rates and detection rates are not on par with the other countries. Nonetheless, most of the seizures (by weight of ivory) do come in large container intercepts and underreporting is less of an issue with this category. Further, the bias from year to year in aggregate is much smaller than the bias within each year. Thus while bias prevents a simple comparison of countries in any given year, the problem is less acute from year to year, especially if trends are our main interest.

Further, the statistical tests can accommodate this bias. For instance, if enforcement effort increases then most ivory seizure data will show higher than expected values. By expected values we mean those seizures we would predict would occur, given the values of the other variables. For example, declines in shipping costs would also cause an increase in seizures if criminal firms shipped more ivory in response (and seizure rates were stable). An increase in enforcement would lead to higher seizures than that predicted by shipping costs variables alone. Nonetheless, these higher values lead to a systematic higher error across the different categories of ivory. This change to the error can be incorporated into the models.

A fifth data problem is causality. The general trend is for poaching and ivory seizures to be increasing. There are periods when these have decreased also. Nonetheless, the overall pattern is for increasing poaching, albeit with observed declines after 2011. This generates an unfortunate problem. Any variable we select that has also grown over the same period will show a positive correlation with poaching. Thus, exports of dairy products from New Zealand have a statistical, but spurious, relationship with poaching. Statistical fit is not a good reason by itself to include a variable in a poaching model. As Underwood et al. (2013) pointed out: “Because CITES decisions are implemented in a constantly changing, complex socio-economic environment, a full causal analysis is required that considers all other potential drivers of ivory trade and their interactions along the trade chain. Without this comprehensive analysis the impact of an individual driver may be confounded with the effects of other drivers.”

Related to causality is the direction of the relationship. For instance, the recent spate of ivory stockpile destructions began in 2011. This was in response to the poaching crisis. Similarly,

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international and coordinated campaigns have been undertaken to destroy and disrupt criminal organizations. These include Operation Cobra (2013), Cobra II (2014) and Cobra III (2015). Because these interventions have occurred during periods of high poaching (2011-2014) rather than relatively low poaching (2002-2008) they appear to have a positive correlation to poaching. This is not because they cause high poaching, but rather that they are in response to poaching. It is the poaching levels that cause these interventions, not the interventions that caused the poaching, even though they are highly positively correlated. Ideally their effects on reducing poaching will be strong enough to be identified. It is also possible to see if the effect is delayed. Nonetheless, the best solution is to increase the sample size and that is not yet possible.

Panel-data regression is the only statistical technique that is able to incorporate both the time and geographical aspects of the data analyzed here (Hsiao 1985). It is the ability to analyze the data across a geographical dimension and a time dimension at the same time that gives the technique the greatest scope from the other methods available. Model selection tests were based on the Residual Sum of Squares, which are derived from single equation Ordinary Least Squares/Generalized Least Squares (OLS/GLS) estimators (Verbeen 2000). This makes standard selection tests a little harder to interpret – here we estimated four equations and linked them by the behavior of the residuals. The variables were selected from a pool of those that had been identified in prior research or hypothesized as relevant. They were also restricted to variables for which data were available for all or most of the years of interest. The methodology used was a Hendry Complex-to-Simple modeling approach (Hendry 2002). Over-parametized models were estimated and then variables that appeared redundant were omitted. This is the preferred methodology in econometrics to simplify in order not to weight the report down with output tables. The final results of this process are reported here.

Analysis

There are existing models that look to explain poaching. One of these is Underwood et al. (2013), which links Chinese household expenditures (demand driver) and governance in Africa (supply driver), as measured currently by ‘rule of law’, a World Bank governance indicator, to poaching. A combination of high consumer spending in China and poor governance in Africa is associated with elephant poaching. This emphasises a point that has to be recognised. There are some long-term factors that are driving poaching. These could be powerful enough to overwhelm any short-term interventions or events. The rise in Chinese consumer-wealth is powerful and being mimicked by growth in other East Asian economies, such as Vietnam, Indonesia and Cambodia.

The second model that tries to explain the rise in illegal activity is Moyle (2014b). This took a global perspective and examined smuggling activity. Like Underwood et al. (2013) it found that governance issues in Africa contribute to poaching. The metric used in this model was refugee numbers as an instrument for instability. The strength of this metric is that it explains why poaching in the Central African region is so much higher than the rest of Africa. Refugee numbers out of those range states, in aggregate, have increased greatly. The timing of this increase is also significant (Figure 12). The major jump was over 2008-2009, whereupon it continued to increase until stabilising in 2011-2012. Note that Moyle (2014b) only showed that raw ivory seizures were linked to refugee numbers. Worked ivory is not,

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Figure 12. Refugee Numbers

largely because such seizures have stayed low and stable in comparison. To avoid biases in the models presented below, it will be necessary to control for African supply-side effects also.

Moyle (2014b) also tested shipping costs as an explanation for increasing smuggling. The conspicuous feature of the black market trade already noted is the increase in large raw ivory seizures detected in shipping containers. Given criminal organizations are profit-orientated firms and cognisant of their costs, this should influence their smuggling decisions. Indeed, many such containers are filled with cheap materials as obfuscation, simply to save on costs. The results of this paper showed that shipping costs did matter, and that it was the large shipments of ivory (over 1000kg) that were most sensitive to shipping costs. The conspicuous feature of shipping costs is they reached their peak in 2008 (and interdictions dipped strongly) before collapsing after the Global Financial Crisis (GFC). The drop in shipping costs coincides with the rapid rise in large ivory seizures in containers. This period of elevated poaching is also associated with low shipping costs (Figure 13).

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Figure 13. Shipping Costs

The demand for ivory could also have a speculative component. Ivory could be imported into East Asia, including China, as an investment good or asset. Ivory is durable and can be stored at little cost (Moyle 2014b). In other words, speculators in Asia regard ivory as a good investment and this is consistent with the shift in demand towards seizures of large quantities in raw form. This has the additional implication that only relatively small amounts of the ivory are being used to manufacture finished pieces. For instance, Moyle and Conrad (in press) show that the largest known factory system (37 legal factories in China in 2014) had a throughput of 28-30 tusks per month from mid 2009 to early 2014. While this is deliberately slow to focus on higher-quality pieces, the amount of raw ivory being exported from Africa is many orders of magnitude greater than this. During this period of intense elephant poaching, up to 200 MT of ivory could have been entering China a year, which at 10 kg average per tusk (a high estimate) yields 20,000 tusks. Approximately 1,667 tusks a month would have to be processed to use it all. Even if such factories sacrifice quality for quantity, there is still an impossible gap to make up between observed raw ivory and estimated raw ivory production.

The growth in raw ivory being smuggled while worked ivory is not (Figure 14), is another indicator that demand for ivory has shifted towards speculation. If the demand for ivory is largely speculative, then it must also be responsive to interest rates. This merits an aside on investment behavior. Since Markowitz introduced portfolio theory in 1952 it is recognised that investors are motivated by two factors. The first is the financial return on an asset, and the second is its level of risk. There is a trade-off between these two components. Hence, investors are only willing to hold risky assets if the return is high enough to compensate them for that risk. An ideal portfolio will hold a mix of assets, including assets like government

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bonds or bank deposits. These are held, not because they have a high rate of return, but because they have a very low risk. In this context, changes in either the risk or return of an asset will change its demand. Hence investors will demand, say, more government bonds, if other financial assets have become riskier. This reduces their portfolio’s overall level of risk. Similarly, if the relative return on these financial assets changes, investors demand more of the asset with the relatively higher rate of return.

Figure 14. Raw versus Worked Ivory

In addition, interest rates are used as the benchmark for many financial transactions. For example, mortgage lending rates are often based on cash rates set by central banks or government bond rates. For international transactions, the London Inter-Bank Offer Rate (LIBOR) has been the benchmark for decades. Such interest rates represent not just a component of a portfolio, but also the benchmark for other lending contracts with higher levels of return.

The speculative hypothesis thus generates a strong prediction. If interest rates are high, then demand for ivory as an investment should be lower. Investors can get a relatively good return by buying other financial assets. If interest rates are low, then ivory becomes more attractive. Moyle (2014b) uses the LIBOR as the metric for interest rates because of its international relevance. This is continued in the models reported below.

It is shown that ivory shipments that are investment-grade (in other words, large shipments of raw ivory that could not be easily transformed into carvings) are sensitive to interest rates. Following the GFC the LIBOR rapidly collapsed in an attempt to stimulate economies mired

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in recession (Figure 15). Investors would be unable to obtain much of a return by leaving their money in the bank. For unscrupulous investors, ivory offered higher annual returns and this would be improved if ivory was seen as an increasingly scarce commodity (which the CITES sales ban guaranteed). The inverse relationship between interest rates and poaching goes back to the early 2000s. Moyle (2014b) demonstrates this link, and this is replicated in the models generated below.

Figure 15. Global Interest Rates and Poaching Rates Portfolio theory identifies two important factors that drive investment in assets. One is the relative return on the asset. This means relative to other assets that could be included in the portfolio instead. The other factor is the relative risk, usually measured as the variance in the returns, of an asset. It is useful to look also at the possible returns from investing in Asian stock exchanges. So could ivory speculators make a better return by investing in stock exchanges? If we consider the Shanghai Stock Exchange Index, since the GFC to the end of 2014, the answer is no (Figure 16). The Shanghai exchange’s return was deeply negative and from 2009-2014 the returns have been largely negative. If raw ivory has been increasing in price in the black market, then it will be a very attractive asset to investors. Indeed, compared to the Stock Exchange ivory would produce a better return even if its price did not go up. It is perhaps useful to think of ivory as akin to gold. Gold can be, and is, used to manufacture jewelry. Gold however, also has value as plain ingots or bars, and is traded amongst speculators as such. Similarly, while ivory can be used to make carvings and jewellery, it can also be traded as tusks or pieces amongst those that see it as an investment.

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Figure 16. Shanghai Stock Exchange Composite Index

A recent conference paper by Farah and Boyce (2015) on the impact of mammoth ivory imports has been generated. This posits that the import of mammoth ivory into China has reduced the growth in demand for elephant ivory. The metric for illegal activity was the ETIS data and thus goes back to the 1990s. Ordinary Least Squares and Instrumental Variable Regressions were run on seizures, using Chinese per capita income as the main demand variable. The hypothesis was that mammoth ivory, in overall terms, would be a substitute for illegal elephant ivory. Thus demand for illegal ivory would, all else being equal, be lower if more mammoth ivory was imported.

The models supported this hypothesis. In general terms, every MT of mammoth ivory appeared to reduce demand for illegal ivory by 0.8 MT. Thus, without the importation of mammoth ivory, illegal activity would be even higher than it is now. The legal import of elephant ivory (e.g. pre-Convention ivory and CITES approved sales) was also included in their model as another substitute. The effect of this variable was not robust. In only one of the four models in which this variable was included, did it have a statistical effect. The effect however, was negative. This also implied that a substitution effect was operating.

It is worth noting that the model did not show that the CITES approved sale of ivory in 2008 had any stimulatory effect on poaching (as measured by ETIS seizures). That mammoth ivory had a strong link with seizures may indicate that both have a similar investment component. With average Russian export of 60 MT of mammoth ivory per year from 2000-2013, much of this may not be being carved, but rather stored for speculative reasons. Their modelling suggests that while poaching and smuggling has risen, it would be even higher without these mammoth ivory imports.

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One of the issues with Underwood et al. (2013) and Moyle (2014b) is the dependence on ETIS data. Lags in academic publication and ETIS reporting meant that they depended on data available in 2013. ETIS data at that time was only available up to the end of 2011. The years from 2012 to 2014 are missing from the analyses. Nonetheless, it is important to know what variables should be included in the models we generate below. These past papers inform this process.

In this section PIKE data are used as the indicator of elephant poaching. It is available up until the end of 2014 (CITES 2015). To increase the statistical power of the study, a panel model is used. This takes the PIKE estimates from each region, such that each region is explained separately but combined into one, larger data set. This means we explicitly recognise that poaching has a spatial-component and a time-component. For instance, the Central African region is not treated the same as the Southern African region. We also link the statistical residuals in each equation across the same time period to recognise that these are not independent.

All variables are converted to quarterly data. The conversion depends on the variable in question. For instance, some of the financial data is recorded almost daily. This is averaged for each quarter. Some of the series, such as the throughput of containers in East Asian ports, or the Chinese retail spending on gold, silver and jewellery, the monthly data is summed. The PIKE data is annual and two methods were used to convert it to quarterly. One was to spread the PIKE data over the whole period, so that each quarter had the same value. This gives the year an unbiased estimate overall. The other was to create a moving average from this data. This created a smoother series but at the cost of losing some observations. This ended up having more of an adverse effect than assuming that the PIKE statistics were the same in each quarter.

To capture the cross-section effects of changes the errors are linked via a White function. For instance, PIKE figures may change in the same direction in each region because of random, exogenous effects that affect all regions simultaneously. This information about the errors can be used across each time-period to increase the fidelity of the model. The annual growth in expenditures of Chinese retail spending on gold, silver and jewellery is used as an instrument for demand for ivory. It has a much finer level of resolution than GDP. Note that the gold, silver, jewellery growth declined significantly in 2014 and matches the PIKE decline shown in Fig. 17.

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Figure 17: PIKE and Gold, Silver and Jewelry Sales

Fixed-Effects Panel Regressions are used to predict the poaching levels in each year, and in each region, with using the PIKE variable as the indicator of poaching activity. Model 1 below is somewhat over-specified. Refugee numbers (REFUGEE) for each region, as measured by those fleeing a range State, are used as each region’s explanatory variable. It is expected this will have a positive coefficient. This measures instability, and relates to changes in GDP, poverty and enforcement. The variable GOLD measures the growth in retail spending on gold, silver and jewelry in China. It is employed as the most suited instrumental variable to measure demand for ivory in China. It is expected to have a positive coefficient. The LIBOR is the interest rates and is expected to have a negative coefficient. Shipping costs are measured as the Baltic Dry Index (BDIY), which is the global benchmark for shipping costs. It is expected to have a positive coefficient. R3CN is the recorded ETIS seizures of raw seizures in China above 100 kg, while seizures of small, worked pieces of ivory in China are recorded as W1CN. These are used to test the hypotheses that speculative demand for ivory is driving poaching (in which case R3CN should have a positive coefficient) and that demand for small carvings is driving poaching (in which case W1CN should have a negative coefficient.

Refugee numbers lacks a statistical effect, probably through multicollinearity effects. It is correlated with both the LIBOR and BDIY variable. It is included as it has already been demonstrated as important (Moyle 2014b), hence its omission cannot be justified on theoretical grounds. Only two ETIS seizure series were included for the same multicollinearity reasons. It does however, provide a useful test. Demand for large shipments of raw ivory does affect poaching in Africa. Demand for small, worked pieces of ivory in China has no effect on poaching levels. The model shows that growth in retail spending on gold, silver and jewelry does correlate with increased poaching. Hence the growth in the East Asian economies is a powerful demand factor influencing poaching.

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Interest rates and the shipments of raw ivory, both linked to speculative demand, are statistically significant. Shipping costs also correlate positively with poaching activity. Model 1: REFUGEE and other variables

Dependent Variable: PIKE Method: Panel Least Squares N=156

Variable Coefficient Std. Error Prob. C 0.248341 0.053708 0.0000

REFUGEE_ -6.02E-08 2.35E-07 0.7980 GOLD 0.004270 0.000627 0.0000 LIBOR -0.021340 0.006701 0.0018 BDIY 1.16E-05 6.62E-06 0.0820 R3CN 0.072269 0.008512 0.0000 W1CN 2.45E-05 3.64E-05 0.5012

R-squared 0.677745

Adjusted R-squared 0.657880 F-statistic 34.11746 Prob(F-statistic) 0.000000

In model 2 a more parsimonious specification is tried. The shipping cost factor is replaced by the throughput of containers in Singapore. Singapore is a major East Asian hub and reflects shipping activity throughout the whole region. It is used instead of the BDIY.

MODEL 2: Replacing shipping costs with Singapore container throughput

Dependent Variable: PIKE_ Variable Coefficient Std. Error Prob. C -0.021900 0.045903 0.6339

REFUGEE_ -1.28E-07 1.51E-07 0.3971 GOLD 0.000532 0.000860 0.5367 LIBOR -0.018453 0.006039 0.0026

SGCONT 8.14E-05 8.24E-06 0.0000 R-squared 0.677942

Adjusted R-squared 0.665417 F-statistic 54.12934 Prob(F-statistic) 0.000000

Model 2 shows that container throughput does have a positive coefficient. Nonetheless, the high R-squared value and the lack of significance of most variables (excluding LIBOR) is an

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informal indicator of deep problems with multicollinearity. We tend to replace the BDIY with the Singapore container throughput as the main measure for regional shipping activity to take advantage of the better model fit.

Model 3 is included as an example of the challenges of analyzing post-2010 interventions. Operation Cobra and Operation Cobra II are tested to see if they reduced poaching as measured by PIKE. In other words, can any of the decline in PIKE since 2011 be attributed to these enforcement operations? While they have the hoped for negative sign this is not statistically significant and the sign is sensitive to model specification. Other model-specifications have positive signs that are also not significant. With so few observations in this end of the PIKE series, there is simply not enough information to see if interventions have any significant effect. The 2008 CITES-agreed one-off sale also has no discernible effect on poaching. Basically, so many other drivers of poaching changed for the worse in the same period that the one-off sale adds nothing to the explanation for poaching increases. This is consistent with the findings of MIKE (CITES 2013a), ETIS (CITES 2013b) and Farah and Boyce (2015) reported above.

MODEL 3: PIKE and law enforcement effort

Dependent Variable: PIKE_ Variable Coefficient Std. Error Prob. C -0.021562 0.065039 0.7406

REFUGEE_ -1.26E-07 1.57E-07 0.4227 LIBOR -0.015535 0.008701 0.0759 GOLD 0.000371 0.000902 0.6812

SGCONT 8.00E-05 1.43E-05 0.0000 COBRA -0.039131 0.038066 0.3054

CITES_2008 0.021382 0.039757 0.5914 R-squared 0.680175

Adjusted R-squared 0.664005 S.E. of regression 0.125232 Sum squared resid 2.791593 Log likelihood 128.9635 F-statistic 42.06169 Prob(F-statistic) 0.000000

In Model 4 the main Chinese stock exchange is tested. This includes the annual percentage change in the value of the Shanghai Stock Exchange Composite Index up to the end of 2014. If speculation is important then this should have a negative coefficient. The BDIY replaces the SGCONT variable here to reduce the problem of multicollinearity. We note that the percentage change in the Shanghai index has the expected sign, but is not quite significant at a 10% level. In this instance, multicollinearity is a known risk and does reduce the power of our statistical tests. Hence the effect may exist.

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Model 4: Influence of Shanghai Stock Exchange Dependent Variable: PIKE_

N=182 Variable Coefficient Std. Error Prob. C 0.283154 0.048191 0.0000

REFUGEE_ 6.50E-07 1.95E-07 0.0010 BDIY -7.08E-06 6.24E-06 0.2587

PCSHCOMP -0.147473 0.097340 0.1316 GOLD 0.003517 0.001040 0.0009

R-squared 0.528653

Adjusted R-squared 0.509690 S.E. of regression 0.150336 Sum squared resid 3.932536 Log likelihood 90.71291 F-statistic 27.87921

Discussion

Moyle and Conrad (in press) estimate at a basic level the market for ivory carvings in China using the weight of ivory as the metric. This takes the throughput of tusks, provided by the Chinese SFA as the main indicator (Figure 18).

Within that model, four interrelated equations are used to take into account the different size categories of tusks (tusk pieces, small tusks, medium-sized tusks, large-tusks). The scale of demand for carvings is a function of the demand for gold, silver and jewelry sales. Note that supply constraints in production mean that the demand for carvings estimated in this model could not be met by legal carvings. Inserting the gold, silver and jewelry sales values into these four equations provides estimates of demand of 48.39 MT from 2008 to 2014. This is an average of about 7 MT per year, but of course, has not been a constant 7 MT. In 2010 and 2011 it is in the order of 11 to 12 MT per year. According to the model, this is when legal demand for ivory carvings in China peaked. That this peak also coincides with the peak in poaching reported with the PIKE data improves the credibility of this model. Using the data on gold, silver and jewelry sales (Figure 17) the demand for carvings can be estimated. Note that as we extrapolate further from the dates used in this model (2010 to early 2014), estimates of demand become increasingly unreliable. In other words, the model is unlikely to be a good fit for 2005 because this is a long way from the sample-period. It is likely much better for 2009 or 2014 as this is just one year out of the sample.

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Figure 18. Tusk throughput by Weight

This model suggests the demand for legal carvings is in the order of 3-4 MT per year, which fits very well with the processing figures in Figure 7. The demand for illegal ivory is therefore approximately 6-8 MT per year. This does not appear in line with street market surveys in China (Martin and Vigne 2011; Vigne and Martin 2014), which although they locate more illegal outlets than legal, the actual quantities for sale are small, with only 8 items on average per shop (Table 18).

Table 18. Legal versus Illegal Sellers

Location Beijing Shanghai Legal Outlets 35 13 Items for sale 5286 1366

Average 151 105 Illegal Outlets 121 106

Items for sale 986 806 Average 8 8

Given also that illegal sellers stock predominately small items of 50 g or less in weight, this sample adds up to less than 100 kg of illegal ivory on display in Beijing and Shanghai. This does not make much inroad into the estimated 6 to 8 MT per year of illegal demand for ivory

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carvings, even if some pieces are kept out of sight in illegal shops. Therefore, most of the illegal worked ivory must be sold online and in person-to-person sales, which is supported by the TRAFFIC online sales report (Yu and Jia 2015).

All estimates are based on the information we have, which is limited for reasons outlined above. Hence, such estimates are liable to be revised upon the discovery of new information and the addition of new data.

The models shown here and others discussed above, indicate that most of the raw ivory being smuggled out of Africa is to meet a speculative demand by investors, not consumer demand for worked ivory. Poaching is linked to important financial, investment variables. There is no good reason why the demand for carvings would respond to interest rates or returns in stock markets because for most items they are simply a consumption good. The scale of this speculative demand is difficult to estimate as much of it is cryptic. It is being stored, not displayed in shops. Nor is it straightforward to apportion it between speculators in China versus those outside. If we assume the legal demand for pre-Convention and other legal tusks (Table 17) is largely for speculation (and given it follows ETIS large raw ivory seizures, this is likely the case), then speculative demand has undergone roughly a 50-fold increase since the GFC. That is, comparing average imports from 2005-2008, to imports from 2008-2013.

It is possible to estimate how much is being stored in the short-term, however. This is partly dependent on the discount rates and the planning horizon of such investors. In this case we limit short-term to ivory stored for production of carvings. Given the SFA tries to limit legal price increases to 10% per annum, 10% could be the discount factor in this market. If there is a planning horizon of 5 years, and 4 MT of ivory per annum is the planned production, then such short-term investment would sum to 19.2 MT. If the planning horizon is 10 years, then such investors would want to hold stocks of 28.6 MT, to be assured of stocks in the event supply was cut off. With firms using up less of their allocations (approximately 75% had been used by mid-2013, Moyle and Conrad (in press)), and over 8 MT of legal ivory (Table 17) imported from sources like pre-Convention ivory, these estimates appear plausible. Most of the rest of the ivory being smuggled into China would be intended for long-term storage.

There is further evidence of speculative demand for raw ivory. MIKE (CITES 2014a) found that mammoth ivory imports to China-Hong Kong did not seem to follow the law of supply and demand, which expects demand to decrease as price increases. Instead, in recent years the amount of mammoth ivory imported into China-Hong Kong has been increasing in direct proportion to price. The quantities also appear to greatly exceed that needed to supply consumer demand for worked items, averaging 60 MT per year for 2001-2013 (Farah and Boyce 2015). Mammoth ivory seems to offer a good proxy for elephant ivory and has been following a similar pattern to its import and price rise. The pattern is consistent with a hypothesis that both elephant and mammoth raw ivory are being accumulated for speculative purposes. A chilling thought is that these speculators see scarcity value in elephant ivory for the same reason they perceive it for mammoth – supply is limited by extinction of the source of production.

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The models generated here show that the most important causes of the increase in poaching are economic. The GFC wrought major changes to the global economy and it appears simply coincidental that the start of it was the same year (2007) the one-off sale was approved. Rather than poachers and traffickers in Africa responding to supply ‘signals’, speculators in China appear to be sensitive to economic demand drivers. The instigation of the CITES moratorium on future legal raw ivory sales in 2007 for at least a decade reinforced the perception that increased scarcity would drive prices higher. Transportation is also a factor. Key ports facing the east, from Mombasa in Kenya to Maputo in Mozambique have undergone expansions. Foreign Direct Investment and aid money have gone to improve roads (especially from China). Crucially, the cost of sea transport has fallen dramatically. It is not surprising that the growth in smuggling, as indicated by ETIS seizures, is heavily focused on the large shipments in containers. While it is difficult to judge the effectiveness of stockpile destructions and other demand reduction campaigns aimed at the retail consumer market, these approaches do not seem to tackle the main motivations that drive speculators (‘t Sas-Rolfs et al. 2014). The speculative market in raw ivory only has weak links to the market for carvings and may be largely autonomous, except for ivory factory owners that may be engaged in it. The 29 May 2015 announcement by the SFA that the legal domestic ivory market in China will be phased out will have uncertain consequences, depending on how consumers and the illegal sector perceive it. Most Chinese consumers already buy their ivory in illegal outlets and online. Will closing the 130 legal outlets change their purchasing habits? Of more importance will be how speculators react to it. Speculation is based on perceptions of future market conditions. If speculators believe that closing the legal market sector will adversely affect future consumer demand for ivory, they could stop buying poached ivory and even begin selling. Conversely, if they believe closing legal outlets will simply drive previously legal buyers into the illegal market, thus increasing demand, the SFA decision could bolster speculation. A third possibility is that speculators do not believe the announcement, which will result in no change. Online illegal ivory prices and quantities of raw ivory offered for sale should be closely monitored to assess any changes. The reported declines in poaching in the PIKE data may be linked to the increased enforcement intensity. In 2012 over 40 MT of raw ivory were interdicted in large shipments, rising to almost 60 MT in 2014 (CITES 2014a). The Operation Cobras are seizing ivory and harming criminal organizations. This starts to make ivory riskier as an investment. The returns are being diluted by the increasing losses to interdictions. Nonetheless, the slowing Chinese economy and especially the sector the ivory market operates in does suffice to explain the poaching decline also. Another factor affecting a decline in PIKE values in some areas could be the result of a substantial reduction in the elephant population, making it more difficult for poachers to find suitable targets in such areas, as posited by MIKE (CITES 2014a).

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The surge in poaching beginning in about 2007 can be largely explained by economic events. Essentially, the most important drivers for poaching converged and worsened – dramatically – in the wake of the GFC. Interest rates collapsed and property and stock markets deteriorated. This was augmented by an increase in instability in Africa at the time, propelling poaching rates higher. Criminals could also take advantage of the lower shipping costs to move ivory into stockpiles in Asia more easily. The 2007 CITES CoP in the Hague voted a moratorium on future sales of legal ivory until 2016 at the earliest, signaling to speculators a clear opportunity to gain from increased scarcity of raw ivory supply to China. Perversely, the moratorium on further CITES-approved imports should increase the confidence in ivory investments. The ivory antique market surged in auction houses beginning in 2007 and peaked in 2011 as investors saw opportunity (Gao and Clark 2014). Within China property markets did not do well up to early 2015. According to Bloomberg research, publically traded real-estate firms with debt-equity ratios over 100% increased to 135 out of 336 at the end of 2014 from 57 in 20077. Since the beginning of 2015 the situation has changed. House prices declined in most cities in 2014, but in 2015 are making a recovery.8 Stock markets up to June 2015 were also booming, with Shanghai up substantially and Shenzen almost doubling since January (Swanson 2015), shown in in Figure 16. A “tidal wave of money from mainland China has brought Hong Kong stocks to record highs”9. There have been corrections and volatility, but it is clear that Chinese investors are returning to property and stock markets, which helps explain the huge drop in raw ivory prices found in this study. Anti-corruption drives within China have further dampened demand for the more expensive items that were commonly given as “gifts” by high-level government and business personalities. Figure 19 shows the gold price in China over the past ten years. The silver price graph is similar, with both showing a rise beginning about 2006, peaking at the end of 2011 for about a year, and then from 2013 showing a sharp decline trend back to 2010 price levels in mid 2015. This is similar to the pattern that raw ivory price has shown. Raw ivory prices appear to be behaving in similar fashion to other investment commodity price trends in China, though further monitoring is needed to confirm whether this is a trend or a fluctuation, with a return to higher prices on the horizon.

7 Source: http://www.bloomberg.com/news/articles/2015-01-25/china-property-agony-deepens-as-trust-loan-lifelines-cut 8 Source: http://www.bloomberg.com/news/articles/2015-05-17/homebuyers-return-driving-tentative-property-recovery-in-china 9 http://qz.com/412975/a-tidal-wave-of-chinese-money-is-causing-chaos-in-hong-kongs-stock-market/

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Figure 19. Gold prices in China 2005-2015. (Source: http://goldprice.org/gold-price-china.html)

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DISCUSSION

Ideally, we would now need to estimate the total amount of ivory known to be ‘in the system’ in China-Hong Kong and Japan and estimate the amount that has entered these countries from illegal ivory production 2002-2014. The difference would suggest how much ivory that may be stockpiled for speculative purposes (Objective 2.d). But, as stated above, we do not know how much ivory is held by illegal shops and informal market sellers (online, P2P).

The only quantified estimates we have for China are declared government stocks (34 MT), legal factory stocks (~9 MT), worked ivory stocks in legal outlets (~10 MT), and illegal ivory sold at auction 2002-2014 (~7.3 MT), totaling 60.3 MT. Any legal pre-Convention ivory imported since 2002 would be included in the declared government and legal factory stocks. An additional 111.3 MT in legal private stocks was in Hong Kong at the end of 2014 and at least 378 MT in Japan (ostensibly legal raw and worked ivory held by private dealers). Only 89 MT of ivory have been legally imported into Japan since 1990 in the two CITES-authorized sales, so where did the additional ivory come from?

Sakamoto (2004) reported that the total registered private whole tusk stocks in Japan in 2002 amounted to 153 MT. Japan reported to CITES that private dealers held 340.6 MT of whole tusks in 2014 (CITES 2014 b). That is a difference of 187.6 MT, more than double the 89 MT imported from the two CITES-authorized sales, and this does not even take into account the estimated 130 MT that should have been processed 2002-2014. Vigne and Martin (2010) reported that in 2001 Japan processed about 13 MT a year, declining to 7 MT a year in 2009. Taking the average of 10 MT consumption a year, 130 MT should have been processed 2002-2014, leaving 23 MT of raw ivory remaining in 2014. Yet there was more than 340 MT, a discrepancy of an estimated 317 MT.

There are mitigating possibilities to explain the large discrepancy between 2002 tusk stocks and the 2014 declared amount in Japan. Whole tusks only needed to be registered in 2002 if the owner intended to sell them (Sakamoto 2004), therefore there could have been many more undeclared whole tusks in stock, as was the case with the 104 MT of ivory removed from registration in Hong Kong described above. Could it have equaled 317 MT? The CITES Trade Database reports that between 2002 and 2011, only 31 whole tusks and 240 kg of tusks were imported, apparently legally. There are no imports reported after 2011. This quantity is insignificant and cannot help explain the disparity.

Ostensibly legal ivory has also been exported from Zimbabwe to China and Japan; ‘legal’ only because it is reported in the CITES Trade Database and is classified as ‘non-commercial’ (see LionAid 2013). Many of the cases look questionably legal. Over 9.7 MT plus 6,110 ivory carvings and pieces have gone to China and 15.7 MT plus 6,172 ivory carvings and pieces have gone to Japan 2002-2013. If other countries are included, Zimbabwe alone has exported over 25 MT of worked or semi-worked ivory 2002-2014. Assuming an average weight of 100 g per piece, 61 kg is added for China and 62 kg is added for Japan for the combined 12,282 pieces. One could estimate 25.5 MT of worked ivory (mostly name seal

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blanks, jewelry, chopsticks and cut raw ivory) entered China-HK and Japan in this way. We will assume that the entire quantity is included in the stockpiles reported for China-HK and Japan, recognizing that this may not be the case. Private individuals may well have imported some of the ivory, which is sold illegally on the Internet or through personal networks, but without evidence this cannot be assumed.

The total known ivory being held legally ‘in the system’ in China-HK and Japan was approximately 576.3 MT in 2014 (60.3 MT in China, 111.3 MT in Hong Kong and 378 MT in Japan).

The total ivory estimated to have been smuggled into China and Hong Kong 2002-2014 is presented in Table 19. The total illegal ivory available from poaching is estimated to be about 2,748 MT and the quantity making it to China-HK/Japan is estimated to be about 1,737 MT, or an average of 134 MT a year. From 2008 on, the average would have been over 200 MT. This quantity of ivory is highly unlikely to have all been processed illegally to sell to consumers because, as this report has shown, (1) there is no carver capacity to process that amount and (2) the consumer demand level does not justify it. It should be recalled that pre-ban legal ivory and legal ivory imported since early 2009 was also available to satisfy consumer demand, in addition to the illegal ivory. There is also the 223 MT of legal ivory held in private hands in 1997, some of which could have made its way to East Asia.

CONCLUSIONS

The final conclusion of this study is that there has been stockpiling of raw ivory for speculative purposes. It is conceivable that more than 1,000 MT of illegal raw ivory remains stored in Chinese warehouses, and additional ivory is probably stored in Africa and in other Asian countries (e.g. Malaysia, Vietnam). With the apparent collapse of raw ivory prices in China documented in this study it would appear that the speculators may be regretting their gamble on ivory. More research is needed, however, to confirm that prices and demand are on the way down.

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Table 19. Ivory Flow Balance Sheet 2002-2014 (see Figure 1 Flow Diagram)

IVORY PRODUCTION IN AFRICA # kg Notes ILLEGAL Illegal Hunting 1 2,747,977 See Table 9 LEGAL Natural mortality 3 223,510 Problem Animal Control

4 530,139 Set at 0.5% of total population Legal harvesting 5 (13,740) Included in #4

Confiscations 6 100,000 Not added to legal production, origin in #1. Trophy hunting 7 385,100

TOTAL LEGAL IVORY 2 1,138,749 #3,4,7 TOTAL IVORY PRODUCTION 1+2 3,886,726

LEAKAGES Potential Potential

853,649 Sum of 3,4,6 Field losses – 50% of NM and 10% of PAC&C

8 174,769 Ivory which doesn’t reach local repositories Ivory reaching local repositories 678,880 Leakage from local repositories 9 135,776 Assumed 20% of stocks

Input to Government Ivory Stores 10 543,104

Input to Government stores from SEIZURES

Africa

18 140,000 Estimate from CITES (2014a)

GOVT IVORY STORES Existing stocks

200,000 In 2002 based on CITES (1998) plus Inputs 10 & 18 883,104

less 2008 One-off sale under CITES 13 105,400 less Legal Sales to local buyers (ivormarkets)

14 20,000 Guess, in the countries that allow/allowed it less Stockpile Destructions in Africa 12 39,135 Data provided by WCS and press reports

Balance in national storerooms 718,569 Leakages 11 143,714 Assumed 20%

Balance in Government Ivory Stores 574,855 500,000 kg was estimated from in Objective 2.a. Plus input from 6 100,000

Total in Government Stores 674,855 690,000 kg was estimated in Objective 2.a.

ILLEGAL IVORY MARKETS IN AFRICA 15+16 560,000 Hunter et al (2004) average annual consumption

LEGAL IVORY EXPORTS FROM AFRICA 2008 One-off sale under CITES 13 105,400

Worked ivory exports 14 30,000 ‘non-commercial purposes’ Sport hunted trophies 7 385,100

TOTAL LEGAL EXPORTS 520,500 Sum of 13, 14, 7

ILLEGAL IVORY EXPORTS FROM AFRICA Illegally hunted 1 2,647,977 Minus #6, 100,000 kg confiscated in Africa

Field leakages 8 174,769 Leakage from local repositories 9 135,776

Leakage from Government Ivory Stores 11 143,714 Subtotal 3,102,236

Less Seizures 18 140,000 Less African Illegal Ivory Markets 15+16 560,000

NET ILLEGAL EXPORTS 19 2,402,236 Assumes no stockpiling in Africa IVORY ENTERING ASIA

Available for Illegal Import 19 2,402,236 Out of Africa seizures S 222,000 CITES 2014a, CITES 2013

Fig. 8, it should be 34 MT (TRAFFIC 2013)

After seizures available for import 2,180,236 Amount Destined for China-HK/Japan 20-22 1,744,189 Assumes 80%

less China Stockpile destructions 6,812 AMOUNT REACHING CHINA-HK/Japan 1,737,337 Minus S and destructions

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Appendix 1

The Population Simulation Model

The population simulation model used in this study is similar to that used for the Namibian Elephant Management Plan (Martin 2004), which was refined in the Elephant Management Plan for Savé Valley Conservancy in Zimbabwe (Martin 2006), further modified in a study of trophy hunting in Botswana (Craig et al. 2011), improved in 2012 for a review of an FAO project in Zimbabwe and applied to the new Elephant Management Plan for Zimbabwe.

The model consists of 12 linked spreadsheets which operate as a ‘birth-pulse’ system (Fig.A1.1). It behaves in a manner similar to the Leslie matrix (Leslie 1984), but the calculations of births and deaths are separated into successive operations because it is designed to cycle within the row operations of a computer spreadsheet. A single key-press causes the model to carry out all the calculations for one year and advance to the next year.

The functioning of the spreadsheets shown in the figure are as follows:

A – The main reproductive, management and financial parameters are defined here and, for any particular simulation run, different management functions can be enabled or disabled.

B – The starting population is defined here by setting the overall size of the population and the numbers in all male and female age classes. The model runs on a counter for each iteration and in the first year it uses the population defined here. In all successive years, the ‘running cohort’ is the population from the year before (obtained after completion of the mortality calculations on sheet H). Various options are provided for the starting population. It can have a stable age structure or be any ‘custom’ population defined by the user. The overall sex ratio of the population can be adjusted to give any desired ratio of males to females.

C – The annual breeding of the population is done on this sheet. The intercalving interval is specified in months and converted to a fecundity value (12/ICI) which is the average number of calves (of both sexes) produced per year per female. The mean age at first conception is specified together with a standard deviation which allows a spread of ages either side of the mean.

The decline in fecundity with old age is catered for by specifying the mean age for the onset of decline and a standard deviation which determines how sharply fecundity declines with increasing old age. The mean value used for the age at first parturition is 12 years (with a standard deviation of 2) and an intercalving interval of 4 years has been used. A full review of values for these parameters is given in Appendix 2 and sensitivity analyses were performed to examine their influence on the population rate of increase. The values chosen in this study are the most typical for savanna populations. The next three spreadsheets each deal with a particular management activity. All of these spreadsheets contain algorithms that ensure that an integer number of animals is deducted from each age class.

D – Illegal Hunting removes a percentage of the total population or a fixed number of animals in each year. The new selectivity function developed for this study (Appendix 4) appears here and works with the offtake of a specified percentage of the population.

E – Culling entails the removal of animals in breeding herds (all males up to the age of 10 years and all female age classes). This sheet allows the specification of a percentage offtake, or a fixed number from the population, or the surplus above a specified carrying capacity to be removed in any selected year. The financial returns from ivory, meat and skin derived from culling are calculated.

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F – Problem animal control (PAC) includes males and females older than 14 years of age in the simulation model. probability template is used to bias the PAC offtake towards younger males and fewer females.

G – Trophy Hunting: This provides the selectivity for trophy hunting and removes the annual quota. The impact of trophy hunting on the elephant population is negligible in biological terms. Trophy hunting has no effect whatsoever on limiting population growth and is not a management tool to replace culling when an elephant population is judged to be overabundant. In this study the actual numbers estimated to have been hunted (Table 10) were used rather than modeled numbers.

This completes the spreadsheets analyzing the management regime. For each sheet the total and net income to be expected from sale of meat, skin and ivory is calculated and, for trophy hunting, the income generated from trophy fees and daily rates is estimated.

H – Emigration: This sheet provides for emigration from the population when its density exceeds a certain threshold. It has not been used in this study.

I – Natural mortality is the last of the spreadsheets in which population numbers are modified in the course of an annual cycle. The model assumes that age-specific mortality is the same for both sexes in the absence of any data which might indicate otherwise. A mortality of 8% for juveniles in their first year of life, 0.5% for animals from 10-40 years and an escalating curve for senescent mortality which begins at the age of 40 has been used here (see Craig et al. (2011, Appendix 2). The age-specific mortality schedule is set by means of a template and the analysis is performed simply by multiplying the numbers in each age class by the age-specific mortality template. Moss (2001) found male mortality was significantly higher than female mortality for all age classes in the Amboseli elephants in Kenya. However, much of the male mortality is anthropogenic rather than ‘natural’ in the sense used here. From the low Amboseli population growth rate it can be deduced that the elephants are probably at a density where self-regulating mechanisms are operating.

This sheet also calculates the total amount and value of ivory both on the living elephants and from the animals expected to die naturally.

After natural mortality has been applied, the final population vectors (males and females) become the starting population for the next year.

J – This sheet summarises each year of management giving the numbers at the start of the year, the numbers of animals dying in each category of management and through natural mortality, and the population at the end of the year. The population growth rate is calculated and the results are displayed graphically.

The spreadsheet software is set to ‘manual operation’, and the model is advanced one year at a time by a keypress. By keeping the key depressed, the model runs continuously within the speed limitations of the computer. Any particular run of the model is terminated on this page by the Reset button.

K – This sheet keeps an overall record of the results for each year in any simulation run, including all of the population dynamics information, the management information (offtakes, mean tusk weights etc.) and a full financial record of all gross and net income earned from management.

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Figure A1.1: Population Simulation Model

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Appendix 2

Reproductive Parameters 1

The biological parameters that determine the population dynamics of elephants and which are used in the population simulation model (Appendix 1) are summarised below.

Longevity

Elephants are generally assumed to live to about 60 years old (Laws 1966). Recently, Moss (2001) recorded the death of an adult female whose age was over 60 years.

Gestation

The gestation period for elephants is well-established as 22 months (Smithers 1983). This together with the lactational anoestrus period which follows parturition determines the intercalving interval.

Seasonal breeding

Although elephants may produce calves in any month of the year, most populations have a distinct breeding peak during the rains.

Sex ratio

Sex ratio at birth is 1:1 with minor variations recorded in the literature, usually in small populations. The overall sex ratio in the population may vary slightly in favor of females depending on the history of management and illegal hunting. Moss (2001) recorded significantly higher mortalities for males (which included anthropogenic mortality) than for females over their entire lifetime. In the population simulation model it has been assumed that natural mortality for males and females is the same throughout their lifetimes.

The next four parameters are the main determinants of the rate of increase of elephant populations. Van Aarde (2008, Tables 1A, 1B, 2,3) gives summary tables of values for these parameters recorded from elephant populations in South Africa and elsewhere in Africa. However, the data in these tables are not as useful as they might be. They include values from small populations and from short-term studies and these are given equal weight to values from large elephant populations and long-term studies.2 The values given for survival are not sufficiently precise to allow meaningful values of adult mortality to be derived.3

The Botswana population is the largest in Africa. We required values for the key reproductive parameters that were consistent with values derived from other large populations in Africa. To this end, the studies of elephants in Hwange National Park (Williamson 1976), Etosha National Park (Lindeque 1988), Kruger National Park (Smuts 1975, Whyte 2001, Freeman et al 2008), Luangwa Valley (Hanks 1972a) and the Zambezi Vally in Zimbabwe (Dunham 1988) appear to be the most relevant. 1. Appendix 2 in Craig GC, RB Martin & DA Peake (2011). The Elephants of Northern Botswana: Trophy Hunting, Population Dynamics and Future Management. Research study funded by the Conservation Trust Fund (CTF) of the Ministry of Environment, Wildlife and Tourism. 103pp

2. Martin (2010) cautions about conclusions drawn from small populations over short time periods with inadequate consideration of stable age structures.

3. Typical adult mortality in large elephant populations is usually a fraction of one percent but the data are only given to one percent accuracy.

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Moss (2001) and Whitehouse & Hall-Martin (2000) are good examples of long-term studies of elephant populations. However, both the Amboseli and the Addo elephant populations are relatively small compared to the larger southern Africa populations. The studies of the Uganda elephant populations (Perry 1953, Laws 1966, Laws 1969, Laws et al 1970) apply to a relatively high rainfall regime and, at the same time, provide the extreme examples of reproductive parameters in populations which have exceeded ‘carrying capacity’.

In the investigation which follows, we are seeking three key values: firstly, the most ‘typical’ value for the parameter; secondly, the value for the parameter which falls within the recorded range of values and which gives the highest rate of growth for the population; and, thirdly, the value for the parameter which falls within the recorded range of values and which gives the lowest rate of growth for the population.4

Fecundity Age at first parturition Williamson (1976) found the mean age of sexual maturity5 in Hwange National Park elephants to be 11 years. Lindeque (1988) gives the mean age at first conception for elephants in Etosha to be 10-12 years (i.e. age at first parturition of 12-14 years). In examining material from culled animals in Kruger National Park (KNP), Freeman et al (2008) found one six-year old female with a developed corpus luteum. The implication of this is that, assuming the animal was killed mid-year, she would have conceived in her sixth year of life. In the same sample, four females aged 10 years old had placental scars indicating that they had conceived at 8-9 years of age.6 Both Smuts (1975) and Freeman et al (2008) found the mean age at first parturition in KNP to be about 12 years. Dunham (1988) estimated that the mean age at which females in the Zambezi Valley attained sexual maturity was 11-12 years in 1969-72 and 13-14 years in 1985 when the population was at a higher density. Hanks (1972a) found the mean age at sexual maturity for the South Luangwa elephants7 to be about 14 years which implies an age at first parturition of about 16 years. Owens & Owens (2009) found the mean age at first birth to be 11.3 years for the North Luangwa elephants.

On the basis of these findings, we have chosen 12 years as the typical age of first parturition for a population which is below carrying capacity. In a population displaying rapid growth, the age of first parturition might be adjusted downwards to about 10 years. Van Aarde (2008) gives numerous examples, mainly from small populations, where this has been achieved. This is our value for the lower end of the range for age at first parturition: there is little justification in the literature for adjusting the value much lower than this.

4. This last value is of little direct relevance to the Botswana population but is used in the sensitivity analysis to compare the relative impact of the four parameters on the rate of increase of the population.

5. Several authors use the ‘attainment of sexual maturity’ as the yardstick for onset of breeding. It poses a quandary for a population modeller needing an estimate of ‘age at first parturition’. Hanks (1972a) defines attainment of sexual maturity as the mean age at which a female first ovulates. Assuming that conception follows shortly after ovulation, the mean age at first parturition would accordingly be about two years later.

6. The earliest age by which females had produced their first calf in Addo National Park was 10 years (Whitehouse & Hall- Martin 2001).

7. The Luangwa Valley elephants were considered to be well in excess of ‘carrying capacity’ in 1972.

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Laws (et al 1975) recorded conception being delayed until about 20 years of age in a high density population in Uganda (Murchison Falls Park South). This is our upper end of the range for age at first parturition. Moss (2001) found the mean age at first birth in the Amboseli population to be 14-15 years – another population perhaps suffering resource shortages.

Intercalving interval Female elephants in southern Africa generally produce a calf every four years throughout their main breeding lifetime. Fecundity declines in the last 10-20 years of life.

Lindeque (1988) derived average fecundities for the female elephants in Etosha National Park in 1983 and 1985 from two shot samples which included 103 and 214 females respectively. His finding was that, over their main breeding life span, the females were producing almost exactly one calf every four years (i.e. a fecundity of 0.25 including calves of both sexes). Williamson (1976) found a similar intercalving interval for the elephants of Hwange National Park. For Kruger National Park, Smuts (1975) estimated the mean calving interval at 4.5 years – which is higher than the mean value of 3.8 years found by Freeman et al (2008). However, Freeman et al (2008, Table 3) found considerable variation in this parameter (2.3-5.3 years) over the years 1976-1995.8 In the Zambezi Valley, Dunham (1988) found a high fecundity (mean calving interval 3.4 years) for the years 1969-1972 and an even higher value for 1984/85 (mean calving interval 2.8 years). However, his values fall within the range recorded by Freeman et al (2008) – which had a mean value of 3.8 years. The high 1984/85 fecundity may have been the result of synchronised breeding following a drought. Hanks (1972a) found a mean calving interval of 4 years for Luangwa Valley elephants between the ages of 20-40 years.9

For a typical southern African elephant population we have selected 4 years as the mean calving interval over the main breeding lifetime and 3.75 years as the minimum value. Although lower mean calving intervals have been recorded, it is doubtful whether they can be sustained for the time required (24 years) to explain the Botswana population rate of increase. The highest recorded mean calving interval is that of 9.1 years reported by Laws et al (1970) for Murchison Falls Park North.

Data on the decline in fecundity in the latter years of an elephant’s lifetime are provided by Hanks (1972a), Williamson (1976), Moss (2001) and Freeman et al (2008). The Luangwa Valley elephants’ fecundity dropped from a value of 0.25 (up to 40 years) to 0.21 between 40-50 years and 0.17 between 50-60 years. The Hwange National Park elephants showed almost no decline in fecundity from 40-49 years but it decreased to 0.08 after 50 years. The Amboseli elephants younger than 40 years showed an average fecundity of about 0.22 (one calf every 4.5 years); this dropped to 0.196 between 40-50 years (one calf every 5.1 years) and to 0.098 between 50-60 years (one calf every 10.2 years). Animals older than 60 years continued to breed. Kruger National Park elephants showed a similar decline in fecundity after 50 years of age. The population simulation model (Appendix 1) takes these values of senescent fecundity into account.

Mortality As a variable, mortality differs from fecundity and age at first parturition. Physically, most elephants will only be able to conceive once they have passed a certain age and will be highly likely to have produced their first calf within a few years of that age threshold. Similarly, it is almost impossible for 8.Smuts (1975) data also show significant variation from year to year over the period 1970-1974.

9.Hanks (1972) found a peak in fecundity for females aged 18-19 years (0.38 which is equivalent to an intercalving interval of 32 months). However, no such similar peak has been recorded in the other literature reviewed for this study. Whitehouse & Hall-Martin (2000) found a minor peak in fecundity for females aged 25-29 years for the Addo National Park elephants.

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them to produce calves more frequently than once every three years and it is most unlikely that the spacing between calves will be much more than six years. Both of these variables probably abide by a normal distribution and can be described with a mean and confidence intervals.

Both juvenile and adult mortality are ‘open-ended’ variables. There is no limit as to how high they can get. In a bad drought year juvenile mortality might be 100%; if a disease sweeps through a population both juvenile and adult mortality might be 100%. Statistically, it is possible to calculate the mean and confidence intervals for mortality in a number of separate elephant populations but there is a need to retain the awareness that the data apply to particular samples. The data tell nothing about populations which have suffered much higher mortalities and died out.

Because of this open-ended nature of mortality as a variable, it is capable of exerting a far greater influence on population growth than either fecundity or age at first conception.10 Using the ‘typical’ fecundity values given earlier in this section, elephant populations cease to increase when the baseline adult mortality reaches about 5%.11 At higher values than this they decline to extinction.12

We use a generic formula for age-specific mortality –

Age-specific mortality = A + B e - C ( Age - 1 ) + D e E ( Age - F )

where A, B, C, D, E and F are constants affecting natural mortality as follows – A is the baseline mortality for all age classes; B is the mortality in the first year of life (to which A is added); C defines the decrease in juvenile mortality as age increases; D sets the amplitude of old age mortality; and E sets the rate of escalation of old age mortality with age; and F is the age of onset of old age mortality. With the constants set at the values shown in Fig. A2.1 (below), the second term in the formula is effectively zero after 6 years of age. The third term in the formula is zero (using a logical function) for all ages up to F.

10. Hanks & McIntosh (1973) viewed the mean calving interval as the key parameter in affecting the rate of increase of elephant populations and assert that juvenile mortality is an important regulating for elephant populations. Moss (2001, quoting Croze et al 1981) emphasizes the importance of age at first parturition. The population modelling work in this study does not support these conclusions.

11. Van Aarde (2008, Table 3) reports adult mortalities well in excess of 5% for certain elephant populations without any elaboration on the significance of this.

12. Laws et al (1970) detected the set of conditions which foretold a population crash in the Murchison Falls Park South elephants.

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Figure A2.1: A generic curve for defining age-specific mortality

Adult mortality Data on adult mortality are scant. The studies on the key populations in Hwange National Park (Williamson 1976) and the Zambezi Valley (Dunham 1988) do not provide age specific survival curves. Lindeque (1988, Table 6.10) gives age-specific survival schedules for Etosha National Park which indicate a baseline mortality of 4.6% for elephants between the ages of 5-48 years. As the population was more or less stationary (not increasing) in 1983-85 this figure is not useful for an increasing population such as that of Botswana. Moss (2001) gives survivorship curves for the Amboseli elephants from which van Aarde (2008) concluded that baseline adult mortality was 1%. However, there is insufficient precision for this figure to be useful and most of the adult mortality was anthropogenic. Whitehouse & Hall-Martin (2000) show age-specific mortality increasing from 0.5% for animals aged 1-9 years to 3.1% for animals aged 40-49 years in Addo National Park. Their mean annual mortality for ages 1-49 years is 1.8%. This, too, is high compared with the natural mortality required to produce population growth rates in excess of 4%. Craig (1992) gives perhaps the most insightful analysis of the rôle of mortality in large increasing elephant populations (the Sebungwe region in Zimbabwe) and shows that it must be of the order of 0.5%. This is the value selected as our ‘typical’ value and as the lower limit for the parameter. As mentioned at the start of this subsection, mortality is an ‘open-ended’ variable so that there is no upper limit to the parameter.

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Juvenile mortality This term is intended to refer to mortality in the first year of life. Data on calf mortality are difficult to collect in the field since carcases of young elephant are usually removed by predators within a few days. Moss (2001) is one of few studies based on direct observations of the disappearance of known individuals and our interpretation of her data suggests a mortality of 8.1% in the first year of life for Amboseli.13 In a similar type of study, Whitehouse & Hall-Martin (2000) give a mortality of 6.2% in the first year of life for Addo National Park.

Juvenile mortality is most often deduced from elephant life tables. Lindeque (1988) found mortality in the first year of life for Etosha elephants to be about 30%: however, this population was not increasing. Dunham (1988) comments on the susceptibility of juveniles to mortality caused by drought. In Craig’s (1992) analysis for the Sebungwe elephants he concludes that juvenile mortality must be low to explain the observed rates of population increase. Van Aarde (2008, Table 3) gives instances in a number of elephant populations where survival in the first year of life is equal to or greater than 94% – which implies a juvenile mortality less than 6%.

Our selected ‘typical’ value for juvenile mortality is 8% per annum and the lowest value for this parameter is 6%. The highest recorded value in the literature is 41% (van Aarde 2008, interpreting Hanks’ (1979) data. However, values up to 100% mortality are possible.

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13. Moss(2001) gives the survival of males up to 5 years of age as 82% and females as 89%. Taking the average of these percentages and fitting the generic mortality curve given on page to the data requires the mortality in the first year of life to be 8.1%.

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Sensitivity analysis In this subsection the population simulation model described in Appendix 1 was used to examine the sensitivity of the population rate of increase to changes in the key parameters. All of the simulation runs on which these figures are based were continued until the population had achieved a stable age distribution. In mathematical terms, the population rate of increase y is a function of adult mortality (x1 ), juvenile mortality (x2 ), intercalving interval (x3 ) and age at first parturition (x4 ), i.e. –

y = Fn ( x1 , x2 , x3 , x4 ) In Table A2.1 (next page) and in Fig. A2.2 we examine the partial derivatives –

∂ y ∂ y ∂ y ∂ y ∂ x1 ∂ x2 ∂ x3 ∂ x4

– obtained by varying each of these parameters in turn, ceteris paribus.14

For each of the parameters, values which result in a rate of population increase greater than zero are shown in the upper of the two tables given for each parameter in Table A2.1. (bottom X axis in each of the four graphs making up Fig. A2.2). The ‘typical’ values are shaded in dark green in the table and the range of recorded values from those giving the highest population rates of increase to those giving the lowest rates are shaded in pale green. Cells shaded in grey are those which fall outside this range. They are included because, for some of the parameters, they demonstrate the very wide range of values which could be tolerated by the population before it actually declined, e.g. intercalving intervals of up to 14 years are sustainable.

Once the value for any population parameter exceeds the threshold at which the population can maintain itself, decline rates expressed as negative percentages have little meaning – the decline is better described in terms of a ‘half-life’ i.e. the time it take the population to halve in numbers. The tables in red font in Table A2.1 and the upper X axes and right-hand Yaxes in Fig.A2.2 show this half life for each parameter. The scales for all left-hand axes in Fig. A2.2 (rate of increase) and all right-hand axes (log scale showing half-life) are identical in all four graphs.

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14. Wikipedia: “Ceteris paribus is a Latin phrase, literally translated as "all other things being equal or held constant." ... A ceteris paribus assumption is often fundamental to the predictive purpose of scientific inquiry. In order to formulate scientific laws, it is usually necessary to rule out factors which interfere with examining a specific causal relationship. Under scientific experiments, the ceteris paribus assumption is realized when a scientist controls all of the independent variables other than the one under study, so that the effect of a single independent variable on the dependent variable can be isolated. By holding all the other relevant factors constant, a scientist is able to focus on the unique effects of a given factor in a complex causal situation.”

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Table A2.1: Changes in the rate of increase of an elephant population caused by varying the key population dynamics parameters

(a) Effects of changes in baseline mortality on population rate of increase

A – Baseline mortality; R – Growth rate; T0.5 – Time for population to halve in numbers

A % 0 0.1 0.2 0.3 0.4 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

R % 5.27 5.17 5.06 4.95 4.85 4.74 4.21 3.68 3.15 2.63 2.10 0.00 1.57 0.51 0.00

Population halving time for mortality exceeding 5% A % 5.1 5.2 5.5 6 7 8 9 10 14 15.3 17.2 20.0 24.4 32.6 52.0

T0.5 538 296 126 64 33 22 16 13 7 6 5 4 3 2 1

(b) Effects of changes in juvenile mortality on population rate of increase

B – Juvenile mortality; R – Growth rate; T0.5 – Time for population to halve in numbers

B % 1 2 4 6 8 10 15 20 30 40 50 60 61 62 63

R % 5.23 5.16 5.02 4.88 4.74 4.60 4.24 3.87 3.10 2.27 1.37 0.37 0.26 0.15 0.04

Population halving time for juvenile mortality exceeding 63% B % 64 65 70 75 80 85 90 95 100

T0.5 1,263 495 129 76 52 46 41 38 34

(c) Effects of changes in intercalving interval on population rate of increase

ICI – Intercalving interval (months); R – Growth rate; T0.5 – Time for population to halve in numbers

ICI 36 39 42 45 48 50 60 70 80 90 100 120 140 160 170

R % 5.99 5.63 5.31 5.02 4.74 4.57 3.83 3.22 2.71 2.27 1.88 1.23 0.70 0.25 0.06

Population halving time for intercalving interval exceeding 170 months ICI 175 180 190 200 210 220 230 240 250 300 350 400 450 500

T0.5 2,276 698 312 210 163 135 116 103 93 67 54 51 45 40

(d) Effects of changes in age at first parturition on population rate of increase

A1P – Age at first parturition (years); R – Growth rate; T0.5 – Time for population to halve in numbers

A1P 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12 15 20 25 30 35 38

R % 5.88 5.73 5.58 5.43 5.28 5.14 5.00 4.87 4.74 4.05 3.10 2.28 1.50 0.69 0.14

Population halving time for age at first parturition exceeding 38 years A1P 39 40 41 42 43 44 45 46 47 48 49 50

T0.5 1,299 282 149 103 88 60 53 49 45 42 40 38

Figure A2.2: Sensitivity analysis of key population dynamics parameters

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Figure A2.2: Sensitivity analysis of key population dynamics parameters

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Interpretation Baseline mortality Table A2.1 (a) and the graph in upper left-hand corner of Fig. A2.2 demonstrate clearly the importance of adult mortality. With the other population parameters set to the default values, an elephant population can tolerate a maximum of 5% adult mortality before it begins to decline. When adult mortality is zero an elephant population will not increase more rapidly than 5.27% per year. In the various recorded cases in the literature where populations appear to have increased at higher rates than this, generally it is because they have not achieved a stable age structure or it is because the data apply to too short a time period. When adult mortality exceeds 5%, the half-life of the population may be very short: a mortality of 10% will cause the population to halve in 13 years; 20% will give a half-life of 4 years.

Juvenile mortality In contrast, elephant populations can tolerate high levels of juvenile mortality. With the other population parameters set to the default values, juvenile mortality would have to exceed 63% before the population ceased to increase. Moreover, the nature of the decline is very different to that arising from high adult mortality. Even when juvenile mortality is as high as 80%, the population has a half-life of more than 50 years. At the other end of the scale, a very low juvenile mortality does not result in a spectacular increase in the population growth rate: if juvenile mortality were as low as 1% (which is highly unlikely), it would only result in a rate of growth rate of 5.23%.

Intercalving interval An intercalving interval of 3 years would give rise to a population rate of increase very close to 6%. However, nothing in the scientific literature indicates that such a value could be sustained for very long. Freeman et al (2009) found the range of intercalving intervals exhibited by the Kruger National Park elephants over the period 1976-1995 was 2.29-5.92. Almost every year where the data showed an ICI less than 3 years was followed by a year where the ICI was greater than 4 years. Their overall mean for the twenty year period was an ICI of 3.8 years. It would appear that, in the majority of studies, annual fluctuations in intercalving intervals obscure the long-term mean for the parameter. Hanks (1972) and Laws (1969) show large variations in the numbers of elephants in different age classes which suggests that age-specific fecundity has varied considerably over the life time of the elephants concerned. It may be that the influence of environmental variables (rainfall in particular) will forever frustrate attempts to define a relatively constant state for population parameters and, hence, population age structures. Whilst low intercalving intervals may, temporarily, give rise to high population growth rates, the converse situation is that elephant populations are remarkably robust to high intercalving intervals. Only when the ICI exceeds 170 months (14 years) would the population begin to decline. And when it does decline, even at an intercalving interval of 40 years (i.e. one calf per female in her lifetime) the half-life of the population is over 40 years. All of this is theoretical: there are no documented cases of inter-calving intervals exceeding 20 years.

Age at first parturition Of all four population parameters, age at first birth has perhaps the least influence on the population growth rate – notwithstanding Moss’s (2001, page 154) assertion that ‘a delay of even one year can slow population growth rates’. It requires little consideration to see why. A long-lived mammal which breeds at a fairly constant rate (one calf every four years) from the age of 12-48 years old is

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likely to produce 9 calves in its lifetime. This number will not be unduly influenced by the age at which it starts breeding up to the point where it is only able to produce 8 calves in its lifetime. For that to happen requires that it starts breeding at the age of 16 years.

If elephant populations produced their first calf at the age of 8 years old, ceteris paribus, it would result in a population growth rate of 5.88%. There no records in the literature of mean ages at first birth much less than 10 years old – which would result in a population growth rate of 5.28%. Far more commonly reported are ages of first birth around 12 years old which give a population growth rate of about 4.74%.

As with intercalving intervals, elephant populations can tolerate very late ages of first birth before they begin to decline. An age of first parturition of 20 years old for Murchison Falls Park South as reported by Laws (1969) still results in a positive rate of population increase of 3% – ignoring changes in other population parameters.

Summary

The population rate of increase obtained by using ‘typical’ parameter values (pages -) and those obtained by using the ‘minimum’ plausible values are shown in Table A2.2 below.

Table A2.2: Population rate of increase for default values and minimum values

MORTALITY FECUNDITY Rate of population

increase (%) Adult (%) Juvenile (%) ICI (months) A1P (years)

Default values 0.5 8 48 12 4.743

Minimum values 0.5 6 45 10 5.739

ICI –Intercalving Interval A1P – Age at First Parturition

The ‘minimum’ values do not cause the Botswana population rate of increase to reach the level of 6.36% shown by the analysis in Craig et al (2011, Appendix1). To use lower ‘minimum’ values for the parameters would place the elephant population outside the range of documented values. This population is large and has shown a sustained growth rate higher than 6% since 1987 – which means that the odd sporadic case where an elephant population has demonstrated a short mean calving interval does not justify the continued use of such a value.

The arguments for a depleted age structure in 1986 for the Botswana population become stronger. Such an age structure produces rates of population increase which are consistent with the Botswana survey estimates.

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Appendix 3

Relationship between Age and Tusk Weight

Attempts have been made using limited data sets to construct the relationship between elephant tusk weight and age (Perry 1954; Elder 1970). The largest data set assembled (1,116 male tusks and 1,399 female tusks) is that collected by R.M. Laws from elephants culled in Kenya, Tanzania and Uganda between 1965-1969. These data were analysed by Pilgram & Western (1986) to give the relationship for a single male elephant tusk –

Age = e ( 1.76 + 0.58 ln weight ) years This converts to –

Tusk weight = 0.04596 Age 1.7241 kg

However, when this relationship is plotted on the scatter diagram with the original regression line (Pilgram & Western 1986, Fig.4) it does not coincide exactly with the line. To achieve a ‘perfect’ match the constants need to be modified very slightly to –

Male tusk weight = 0.04535 Age 1.731 kg . . . . . . . . . . . . . . . . . . (1)

This is the relationship used for male tusks in the population simulation model. The method of simulating hunting requires a measure of the scatter of tusks for any given age. For Pilgram & Western’s data the expressions below give good approximations of the standard deviations above and below the mean (the scatter is not symmetrical about the mean) –

Upper Standard Deviation = 0.0181 . Age 1.731

Lower Standard Deviation = 0.0118 . Age 1.731

The formula given by Pilgram & Western for female tusks does not sit comfortably with the scatter diagram shown in their Fig.13. It has been modified to pass centrally through the distribution of Law’s data using the formula –

Female tusk weight = 0.8 Age 1.53 (1- e -0.00054 ( 96.5 - Age )) kg . . . . . . (2)

Upper Standard Deviation = 0.357 . Female tusk weight

Lower Standard Deviation = 0.306 . Female tusk weight

The 95% confidence intervals given by these formulae are shown in Fig.A3.1. There is an asymmetrical relationship between the upper and lower standard deviations.

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Figure A3.1: Male and female tusk weights versus age

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Figure A4.1: A logistic function

Appendix 4

Selection function

Thinking like a poacher – My name is Actæon and I am a legendary ivory hunter. I want to obtain as much as ivory as possible before I join my forefathers in heaven. It makes sense for me to spend my energies on the oldest elephants because they will carry the largest tusks.

This can help you Actæon. This is a logistic curve spread over the 60-year life span of an elephant that suggests you will spend most of your time hunting the oldest animals.

But you need to take into account the fact that as elephants get older there are fewer and fewer animals in the older age classes.

Let’s ‘weight’ the curve with the proportions of male elephants in all the age classes. But we need to take unto account the fact that you have already been hunting for many years and the numbers in the oldest age classes are less than they were when you started hunting. So, to get the proportions in the age classes closer to the true situation we have assumed the population has an age structure that results from a 1% hunting offtake that has been in place for 50 years.

Figure A4.2: Proportions in male age classes

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So what does this new curve show? It suggests you may be better off hunting elephants around the 20 year old age mark because, although their tusks are not the biggest, there are more of them.

Can’t you include a factor that does take into account the tusk weights for both male and female elephants over their full life span?

We can certainly try that. But you need to be aware that, because of the wide confidence intervals associated with the weight of tusk for any given age of elephant (see Fig.A2.1), the application of mean tusk weights is a crude tool. It is possible to obtain a 45 kg tusk from any male elephant between the ages of 36 and 60 years. Because this population has been hunted for many years, for each age the actual mean tusk weight will be lower than that found in an unhunted population.

I hear you but I don’t see what difference it makes. I accept the fact that it is going get harder and harder to obtain large tusks but I believe there is plenty of opportunity to obtain medium- sized tusks for many years to come.

The mean tusk weights for males and females are shown in Fig. A4.4. Note the large difference between male tusks and female tusks of any given age. Male tusks will disappear first in any intensive illegal hunting regime and an indicator of the severity of illegal hunting is the proportion of female tusks in the harvest.

In Fig. A4.5, the curves are shown which result from ‘weighting’ our previous curve (Fig. A4.3) with mean tusk weights.

Figure A4.3: Combination of logistic curve and age class numbers

Figure A4.4: Relationship between age and mean tusk weight

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These two curves emphasize the difference between the ivory-producing potential of males and females and show the band of age classes that will produce the most ivory at the start of hunting in the year 2002.

There are 60 male age classes and 60 female age classes in the figure. The highest ranking 60 age classes out of these 120 classes are selected and the annual ivory harvest from them is taken.

That seems reasonable to me – it is pretty much how I will carry out my hunting operations for the year – without using your science!

The selected age classes in the first year of hunting (2002) are shown in Fig. A4.6. Male age classes from 14-56 years and female age classes from 19-35 years are included.

The dynamic nature of this selectivity function is emphasized – it is adjusted after every year’s harvest as a result of the changes in the population age structure caused by the hunting.

In Fig A4.7 on the next page the changes in the selected age classes over the full period from 2002-2014 are shown. As the pressure from illegal hunting increases after 2007, more and more of the younger age classes are selected. In order to achieve the required ivory harvest, very large numbers of young animals have to be killed (the black bars in the figure). At the continental level, the population is on the brink of collapse in 2014.

Figure A4.5: Weighted selectivity curve based on a logistic curve, numbers in age classes and mean tusk

Figure A4.6: Age class selection for illegal hunting in 2002

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Figure A4.7: Effect of selectivity function on numbers killed in all age classes 2002-2014

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