Poachers of Lockwood Rise

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This suggests that the Chinese market for rhino horn is large and stable. We thus predict that if criminal syndicates are forced to cut costs in the face of a legal trade competitor, they will at minimum reduce their shipping costs. And the main way they will do that is by switching all Vietnamese shipments to the existing shipping method used to transport rhino horn to China: ocean transport.

This lower bound unrealistically leaves the middleman making a zero profit. The actual, positive profit of middlemen can be computed once prices paid by retailers to middlemen are known. The parameter p a represents how effective anti-poaching patrols are at curbing poaching. In the model, p a is the probability that a poaching expedition will be interdicted before it has been able to kill any rhinos. This value is arrived at as follows.

There were poachers arrested in , 36 of which were deceased. There were rhinos poached in in KNP. An estimated poachers entered KNP typically in gangs of three some of these are the same poachers entering more than once. There were contacts with rangers and 80 sightings of poachers by rangers. That translates to poachers interacted with or seen by rangers. There is a 0. Recall that the Pro-active Protection scenario involves greater support for anti-poaching patrols, but without the integrated law enforcement effort and development of legal economic opportunities for people living next to parks.

These latter two initiatives are needed to curb the operations of the criminal networks that sponsor the poaching expeditions because they reduce the ability of these networks to recruit, organize, and fund poaching expeditions in the first place. An individual-based sub-model for the South African rhino meta-population is developed along the lines of the prairie vole Microtus ochrogaster individual-based model of [ 60 ]. As with the model of [ 60 ], the rhino sub-model is stochastic in that one run over a particular time period may not produce the same history of abundance and dispersal as another run over the same period.

Therefore, many replications of the sub-model over the same time period are needed so that at each time point, both the expected value of abundance and extinction probability can be computed. Predicted rainfall is used as a scaled proxy of available vegetation. Predictions are computed using a quasi-periodic rainfall model [ 30 ] fitted to KNP rainfall data from to Average rainfall is a proxy for new vegetation.

We model the age, gender, energetic budget, location, and status of each individual rhino living and dying in enclosed patches. By doing so, we also model interactions of these rhinos with their environment—namely seasonal fluctuations in food availability; and interactions with each other through the effects of their spatial density on their birth and mortality rates [ 30 ]. We use biologically realistic population dynamics parameters and their values in the simulations Table 5. There are two sub-regions KNP and ranches each with four patches.

The initial age distribution is Gaussian with a mean of 7. Policies combating rhino horn trafficking seek to avoid non-sustainable solutions that accelerate poaching-led extinction risks. We first checked what the outcomes would be for the current status quo.

Combating Rhino Horn Trafficking: The Need to Disrupt Criminal Networks

Four additional strategic scenarios required evaluation—intensified protection, improved demand reduction, legal horn trade, or a scenario that integrates all three of these strategic responses. For each of these scenarios we started from the present status quo and used our economic-ecological model to evaluate outcomes over a 35 year horizon Fig 4. We altered the effectiveness of anti-poaching efforts p a and the effectiveness of demand-reduction marketing campaigns p m on a scale of 0 to 1, while allowing t to reflect legal horn trade 1 or not 0. KNP rhinos are in red, and rhinos in South African private ownership are in green.

Predictions are computed with and without strategies aimed at disrupting rhino horn trafficking syndicates. The parameter p a is the effectiveness of anti-poaching efforts and p m is the effectiveness of demand-reduction marketing campaigns on a scale of 0 to 1. The parameter t reflects legal horn trade 1 or not 0. During an estimated white rhinos lived in National Parks, in Provincial Reserves and in Private Reserves [ 17 ].

We re-ran the above scenarios, but applied a disruptive policy to our model. Our disruptive policy is two-phased. The first phase consists of smart analyses to allow creation of actionable intelligence [ 65 ] that informs criminal and civil legal responses [ 66 ]. The second phase is the creation of alternative legal economic opportunities for people living next to parks.

These opportunities in-part consist of the sharing of benefits accrued from protected areas with people alienated by traditional conservation philosophy see [ 5 ]. And, such opportunities should help to combat the exploitation of disenfranchised people by rhino horn trafficking syndicates see [ 67 ]. Disruptive interventions have consequences similar to an aggressive increase in the effectiveness of anti-poaching. Again, we evaluated outcomes over a 35 year horizon. The economic sub-model predicts prices that Asian traders pay to middlemen that are close to the prices observed since Fig 5.

Our economic sub-model thus realistically predicts the changing price dynamics in the illegal supply chain from to Model output solid lines of the price that poachers ask middlemen to pay for a kilogram of rhino horn a , and the price that Asian traders bid to buy a kilogram of rhino horn from middlemen b. Symbols are the observed prices paid with the broken lines representing the range of prices paid. The economic sub-model also predicts the number of poached rhinos which serves as input for the rhino sub-model. See [ 23 ]. Mean absolute percentage error MAPE computations indicate that our rhino abundance model is on average See S3 Text for the definition of this cross-validation statistic.

This amount of error lends some validity to the modeling of rhino abundance interacting with an economic sub-model. Our economic-ecological model is thus a robust reflection of the economic and ecological dynamics associated with rhinos given that it tracks price changes paid to poachers as well as rhino population changes in KNP.

Consider our Disruptive, Integrated Response scenario illustrated in Fig 4. This scenario consists of authorities pursuing aggressive disruption of trafficking syndicates while providing economic opportunities for people living next to parks. The main conclusion of this article is that under this scenario, the South African rhino population is sustainable. The sensitivity analysis reported in S4 Text indicates that this conclusion is not unduly affected by possible parameter misspecification. Intensified anti-poaching, demand reduction, and competitive horn trade policies result in non-sustainable solutions Fig 4.

Under the status quo, extinction probability increases rapidly from onwards. Purist intensified protection and demand reduction policies result in extinction risk rapidly increasing by —a 10 year extension of rhino existence. A purist trade policy will only buy one extra year—extinction risks increase rapidly from onwards. Combining these three policies still results in rapid extinction risk increase by But what happens if each of these policies is complemented with disruptive interventions that target organized crime syndicates while at the same time providing economic opportunities for people living next to parks?

In those scenarios, sustainable solutions are obtained irrespective of the policy implemented Fig 4. Curbing wildlife trafficking has become a key international focus [ 68 ]. Authorities have several approaches at their disposal to address the illegal trade in wildlife products [ 8 ]. For rhinos, most policies that have been debated combine increased protection with either demand reduction as championed by anti-trade proponents [ 9 — 11 ]—or legal supply options championed by pro-trade activists [ 12 — 14 ]. Our simulations suggest that protecting rhinos in-situ , reducing demand, and providing legal supply as policies to curb threats of rhino horn trafficking all result in ultimate rhino extinction.

The author of [ 69 ] illustrated that most anti-poaching methods failed to protect rhinos. Our analysis also shows that demand reduction and legal supply policies carry failure risks. Therefore, policies need complimentary responses. Present interventions have not curbed the rhino poaching onslaught. Poaching rates continue to rise since [ 17 ] and key rhino populations may decline in the future [ 25 ].

At present, rhino management policies allow for no legal horn trade [ 70 ], but permit live African rhino trade and legal sport hunting [ 31 ]. In addition, rhinos have high ecotourism value [ 71 ] and stimulate a vibrant wildlife industry [ 31 , 72 ]. Rising Asian demand for horn is associated with economic well-being of eastern countries [ 73 ]. Present rhino populations are relatively small [ 17 ] and threatened by the rising onslaught of poaching.

This present scenario and associated dynamics predicts continued decline in rhino population size with rapid increases in extinction risks of rhinos by anytime from to [ 30 ]. The existence of multiple solutions in the dynamics of rhino horn trade introduces uncertainties that fuel trade versus no-trade debates [ 48 ]. Agencies that inform international agreements such as CITES, suspect multiple solutions and evoke precaution [ 74 ] when they believe there is a chance that reality will settle into a non-sustainable solution.

But continuation of the status quo as a result of political and bureaucratic inertia also risks a non-sustainable solution. A consequence is that various policy options arise [ 8 ], but assessment of the outcomes of these is relatively rare. For example, the African Rhino Action Plan [ 75 ] inherently evaluates extinction risks and then proposes several actions. But the potential for these actions to achieve stated objectives is not explicitly assessed.

When conducted, expert-based risk analyses indicate that benefits consistently exceed risks for those policies that in some or other format legally match supply and demand [ 76 ]. These analyses do, however, contain high levels of uncertainty [ 76 ]. And, they are premised on the two assumptions that a the scale and drivers of demand are known and can be managed; and b demand will redirect itself towards legally traded horn only. In addition, most proposed legal trade models e.

The trade, illegal or legal, in wildlife goods like rhino horn has many features that challenge the assumptions of such simple economic models [ 48 ].

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Our study has attempted to overcome some of these uncertainties. Individual-based ecological [ 60 ] and agent-based economic [ 27 ] modeling can track the trends in prices paid to poachers at various levels of the supply chain, but more importantly can track rhino population changes. Predictions are thus based on approaches that track historical patterns, thus reducing some of the uncertainty surrounding the prediction that introducing legal trade in rhino horn to the current status quo may lead to non-sustainable rhino populations.

This is also the outcome of introducing more aggressive rhino protection and demand reduction campaigns. Our results have some parallels with a recent study examining the socioeconomic drivers of poaching, based on data collected from to [ 14 ].

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Increases in anti-poaching enforcement and penalties for violators can maintain rhinos, but may carry high financial costs. Our model predicts sustainable rhino populations only when intensified protection, demand reduction, or legal trade in rhino horn are complimented with trafficking syndicate disruption combined with creation of alternative economic options for people living next to parks. This prediction challenges the current global support for the increased development of militaristically-oriented anti-poaching tactics see [ 67 , 78 ] for critiques of this militaristically focused approach to biodiversity protection.

Our prediction is also at odds with proposals to increase sanctions [ 79 ] that ultimately result in further alienation of stakeholders living next to protected areas [ 43 ]; or potentially, across a nation [ 80 ]. Our findings may appear to contradict that of [ 14 ] who conclude increased protection funded through rhino horn sales revenue can save rhinos.

Increased protection would require crime disruption—similar to our analyses. In addition, lasting outcomes of reduced crime is more likely in environments that provide equal rights in fair benefit sharing [ 81 ]. The authors of [ 14 ] imply the same key requirements as what our analyses highlight. It is the funding of these initiatives that dichotomize the debate. Given that rhino horns are part of a suite of commodities that organized crime targets [ 16 ] authorities that deal with organized crime will also deal with rhino horn trafficking.

It is unrealistic to expect the provision of good governance to depend on revenue generated by the use of a natural resource like rhino horn. Our findings also contradict the opinions of authors who advocate the sale of rhino horn as a way to curb rhino poaching through a mechanism using a Central Selling Organization e.

We do not explicitly model the mechanism of trade—we simply ask whether trade, in whatever format, will have consequences for rhinos. Even so, a key challenge for any mechanism of legal trade aimed at curbing rhino horn trafficking would be the ability to undercut the prices offered by illegal traders. Criminal networks are able to engage in predatory pricing undercutting with legal traders because criminal networks have less overhead and can access capital without the need for borrowing [ 82 ].

Note that a government-run central selling organization would need to borrow money through bonds if its operation was more expensive than its tax receipt revenues—identical to any legal, private trader. Criminals undercut legal traders for a range of commodities including gambling services [ 83 ], cigarettes [ 84 ], Pacific crab harvesting [ 85 — 86 ] and the international timber trade [ 87 ].

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PDF | Habitat loss, climate change, poaching and illegal wildlife trafficking are threatening a large rise in poaching of protected species and illegal wildlife. The move comes as officers try to deal with a rise in the number of incidents. have met officers to discuss ways of preventing poaching and other crime. Paddy Tomlinson, farm manager at Lockwood Estate in Scampton.

An extreme form of such criminal predation is the take-over of all trade in a commodity by a criminal organization via their practice of infiltrating a legal business and eventually taking control [ 88 ]. These examples challenge the potential of trade mechanisms being able to force criminal syndicates to operate at a loss.

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Our results clearly illustrate that disrupting organized crime is a key element disregarding whether authorities allow trade in rhino horn or not, as well as whatever mechanism authorities use if trade is allowed. We do not know of a documented case of a legal trade mechanism driving out a black market without the assistance of strong and effective law enforcement. Indeed, black markets appear to be very resilient in the face of legal competition. In addition to the cases cited above, one can look at the recent record of marijuana legalization in some states in the United States.

The author of [ 89 ] delineates how legalization has failed to shutdown illegal marijuana sales. For example, several years into legal marijuana in some states, 2. In this case, illicit marijuana continues to be produced in large quantities in the face of a lower profit margin for its smugglers. Tobacco is another case of an illicit market co-existing with a legal one. A well-known example of legal trade apparently driving illegal traders out of a market is the repeal of Prohibition in the United States. But the actual mechanism involved in this case only serves to reinforce the importance of disrupting criminal syndicates.

As a case in point, just after Prohibition was repealed, the Governor of the state of Washington in the United States assigned Admiral Gregory the task of shutting down the illicit alcohol trade in that state [ 89 ]. Gregory's successful strategy consisted of the following five steps.

The present illicit trade in rhino horn does not appear to share three key aspects of this strategy. First, wildlife traffickers are typically criminal syndicates engaged in a variety of illegal activities. To simply declare them to be legal without any criminal prosecution of their other business activities would be illegal under South African law. Second, the world's record of successfully shutting down illegal traders has clearly been a failure. Third, given that two-thirds of South African rhinos live on reserves, it seems unlikely that private rhino owners would be able to achieve enough trade volume as to make illegal trade unprofitable.

For private owners, the only way to set a lower price that is still profitable would be through high-volume production, i. These policies carry risks to sustainability because their implementation would incentivize intensive breeding of rhinos, a practice that has been shown to carry significant challenges for other species [ 92 — 93 ]. The authors of [ 94 ] provide evidence to support their conclusion that the legal sale in elephant ivory from through the one-time sale of ivory stocks in likely produced the recent, catastrophic rise in poaching.

This is the opposite of what would be expected if the phenomenon was governed by classical static economics. These authors suggest that several factors could be operating. These are a ineffectual disruption of ivory trafficking by law enforcement, b fencing opportunities created by legal traders, c market expansion due to the removal of the stigma surrounding ivory consumption, and d perceptions by poachers that the ivory market is growing. But could legal trade in ivory be sustainable? The authors of [ 95 ] employ an elephant population dynamics model coupled to data-based forecasts of ivory demand to address this question.

Their modeling results suggest that demand is much too great to supply it with a sustainable elephant harvesting strategy. We emphasize that disruptive policies is not simply increasing traditional anti-poaching effort. If within-reserve poacher interception linearly relates to funds spent on anti-poaching, then budgets have to increase 15 times, a financially challenging way to increase anti-poaching effectiveness enough to achieve sustainability see [ 14 ].

Such an increase in effectiveness can only be realistically achieved through the enactment of two parallel initiatives. First, disruption of the entire syndicate's operations from the poacher on the ground up through the four levels of middlemen. Second, a reduction in the economic need to poach rhinos by people living next to parks through the development of alternative economic opportunities for them. How may such criminal network disruption be achieved? Crime networks engaged in wildlife trafficking span beyond those protected areas [ 16 ] that are strongholds of species of interest such as African rhinos.

Crime networks also extend over several countries beyond the jurisdiction of any one law enforcement authority. Federated databases can overcome disjoint information kept in different databases, while social network analyses can provide law enforcers with targeted responses that maximally disrupt a criminal network [ 20 ]. The entrance of trans-national organized crime syndicates into rhino horn trafficking [ 16 ] has produced challenges that wildlife protectors have never faced before. Our simulations illustrate that traditional policies [ 8 ], fueling lengthy debates in some instances [ 13 , 48 ], is not enough to curb rhino horn trafficking [ 14 , 67 ].

Innovative collapse of trans-national crime syndicates is a prerequisite disruptive policy that, in association with establishing alternative economies for people living next to parks may substantially reduce the risks of rhino extinction. The present debate about whether to allow legal trade in rhino horn gives low importance to two critical aspects of the rhino slaughter crisis.

These are the efficient criminal networks that fund the slaughter; and the attractiveness to people with no legal economic alternatives to participate in such criminal operations. The debate needs to change to include multi-faceted strategies that contest the slaughter across all of its drivers and at all of its levels. These strategies ultimately depend on good robust governance [ 7 ], which, when implemented, could create several options to use natural resources sustainably.

We have shown through our data-validated modeling results how such strategies can be evaluated against their likelihood of avoiding the extinction of the rhino. Hence, we recommend that a model such as ours be used to assess the potential success of any policy that is intended to promote survival of the rhino. More generally, our modeling and evaluation approach could be applied to any species for which anthropogenic pressures are putting its survival at risk.

Travel for Timothy C. We do not have any conflicts of interest. Data curation: SMF. Funding acquisition: SMF. Methodology: TCH. Project administration: SMF. Software: TCH. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Introduction Unprecedented wildlife trafficking threatens biodiversity [ 1 ]. Materials and Methods Study population Our focus is on southern white rhinos Ceratotherium simum simum in South Africa.

Data collection Data used to validate our economic-ecological model described below includes prices paid to poachers at various levels of the supply chain. Download: PPT. Table 1. Prices paid to participants at various levels of illegal rhino horn supply chains from to Table 2.

Asian population estimates noted during and predicted up to Analytical approach We consider three products associated with white rhinos traded in three largely separate markets: 1 horn for Asian consumers [ 16 ], 2 live rhinos for the South African recreational tourism and hunting market [ 31 ], and 3 the international market for reducing anxiety about rhino extinction [ 32 ]. Fig 1.

Conceptual graph of how events flow through the economic-ecological model through time. Economic modeling with interacting agents. Rhino horn traders as agents.

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Modeling syndicate supply chain dynamics. Characterizing and estimating demand We assume that the illegal rhino horn trade operates a standard supply chain having a non-zero stockpile or inventory. Asian population trend estimate of demand. Table 3. Number of rhinos poached and rhino horn retail prices. Estimating intra-syndicate prices.

Parameter values to represent effectiveness of anti-poaching measures The parameter p a represents how effective anti-poaching patrols are at curbing poaching. Rhino individual-based model An individual-based sub-model for the South African rhino meta-population is developed along the lines of the prairie vole Microtus ochrogaster individual-based model of [ 60 ]. Rainfall predictions and available vegetation.

Fig 3. Predicted average rainfall in KNP per week centimeters. Parameter values. Table 5. Simulating future scenarios. Fig 4. Summary of strategic response scenarios and consequences for white rhino populations. Results Model validation The economic sub-model predicts prices that Asian traders pay to middlemen that are close to the prices observed since Fig 5. Fig 5. Table 6. Comparison of rhino population estimates derived from the economic-ecological model with estimates obtained through aerial surveys.

Scenario predictions Intensified anti-poaching, demand reduction, and competitive horn trade policies result in non-sustainable solutions Fig 4. Discussion Curbing wildlife trafficking has become a key international focus [ 68 ]. Make the entry cost for a legal trader very low and impose very low taxes initially. Allow illegal traders to voluntarily switch to being legal traders without any legal penalties as long as they agree to pay a tax on each liquor sale.

Allow legal liquor producers many legal outlets for their product so that they can use their economy of scale production system to set a low selling price but still turn a profit. Have rigid, effective law enforcement: successfully detect any illegal trader and wield the necessary legal authority to permanently shut down that trader's business. Raise the tax only after all illegal traders have left the market. Conclusions The present debate about whether to allow legal trade in rhino horn gives low importance to two critical aspects of the rhino slaughter crisis.

Supporting Information. Current fisheries regulations have a minimum-size limit, protecting the younger oysters. Lockwood suggests maximum or slot limits could be part of a rethinking of regulations and other conservation actions to encourage protection of the breeding grandma oysters. We also raise larva in the lab, then release them in the Bay.

Worthy as such spat-centric initiatives are, Lockwood points out that the odds for the survival of any one oyster larva are in the neighborhood of one in 10, — under the best of conditions. Lockwood suggested increasing the numbers and sizes of oyster sanctuaries in the Bay. She also advises siting sanctuaries and replanting sites with regard to anticipated sea-level rise. Oyster preserves, off-limits to harvesting, would not only allow the residents to grow to their full potential, but might also help to mitigate the effects of the two diseases plaguing native oysters in the Chesapeake.

She said that the disease tolerance is a result of natural selection. Oysters in protected grounds could be expected to not only grow to grandma size, but also to pass on to their multitudinous offspring some of those genes that convey disease tolerance. She cited the example of sea turtles, where populations have rebounded more quickly when conservation strategies were switched from a focus on nest sites and hatching events to protecting adults from poaching and boat strikes. Oysters are really struggling, and I would like to see us shift our funding into approaches that quantitatively appear that they would yield a lot more success.

The third member of the panel is Jacquelyn Gill of the University of Maine. Search Submit Search. In the subsequent cycle, the retailer receives as input of 5 q p kg of rhino horn. The authors of [ 44 ] show that firms in a multi-tier supply chain stand to make higher profits through vertical integration. Specifically for a three tier supply chain, the retailer and the supplier both increase their profits if the tasks of the middleman can be taken on by one or both, and the middleman removed disintermediation. Because the middleman acts only to connect suppliers to retailers, the middleman adds little value to the product.

Middlemen do develop suppliers in emerging markets such as rhino horn trafficking but, as the poachers gain experience, this development function ceases to be necessary. Retailers also rely on middlemen in an emerging market to seek out suppliers. Again, once these suppliers become established, this liaison role becomes unnecessary.

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Theoretical justification for this tendency can be found in [ 45 ]. These authors find that once a supply chain is stable, there are economic reasons for eliminating the middleman. This result implies that before the supply chain becomes stable, i. In this mechanism, poachers gradually increase their asking price for horn, while retailers gradually reduce their bid price for horn that they purchase from the middleman. This mechanism is represented in our economic sub-model by having within each cycle, the retailer offer an incrementally lower price to the middleman.

And having the poachers in turn, place an incrementally higher price on the rhino horn that they have poached. We assume that the illegal rhino horn trade operates a standard supply chain having a non-zero stockpile or inventory. Discovery of large, private, illegal stockpiles of rhino horn in Asia might indicate that criminal syndicates are engaging in speculative rhino horn purchasing hoarding in anticipation of rhino extinction.

The speculation is that if the rhino were to become extinct, the price for stockpiled rhino horn would be high enough for a speculator to make a profit by selling such horn post-extinction. Of course, this price would need to be high enough to allow for the lost interest from not selling the stockpile prior to the extinction event [ 47 ].

In our model, the criminal syndicate maintains two rhino horn stockpiles: one to supply rhino horn retailers, called here their inventory ; and one for speculation purposes, called here their speculation stockpile. Let D T be the random variable modeling demand for rhino horn in period T.

Let Y T be the random variable that denotes the amount of rhino horn placed into inventory at the beginning of period T. Let Q T be the supply of rhino horn from poaching. Let R T be the amount of this supply that is placed into the speculation stockpile. No evidence has been reported since that indicates criminal syndicates are building such stockpiles.

Because we have chosen a small value for this parameter, the results of our simulations, below should be viewed as conservative. In other words, if the actual hoarding rate is higher, the activities of rhino horn trafficking syndicates are having an even greater impact on rhino sustainability. Some apparent hoarding may be due to inaccurate short-term demand forecasts by illegal traders, but with our assumption of a small hoarding rate to begin with, we do not see the need to model or estimate this distinction.

Statistical estimators of trafficked horn supply, demand, and average price that are derived from modern statistical practice would be ideal, but need data before they can be implemented. We describe several such estimators in S2 Text. In lieu of such a statistical estimator, we describe the demand estimator we use in our economic sub-model that is based on United Nations estimates of population growth on the Asian continent.

Asian black market retail prices for a kilogram of rhino horn Table 3 exhibit exponential growth.

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If all of the rhino horns supplied from poached rhinos were sold at the prices given, and there was no customer recruitment through time, then the market demand curve based on this data would be strongly upward sloping. The author of [ 54 ] also reports that there are elite customers who use trusted buyers to purchase entire rhino horns for their family.

There are techniques for detecting fake horn that these trusted buyers are familiar with. One conclusion that can be drawn from [ 54 ] is that the fake horn is manufactured for the low-price customers in order to hold in reserve the genuine horn for the elite customers. This anecdotal evidence suggests that the demand for rhino horn is significantly higher than the supply.

Using only this information, a quantitative estimate of demand is not possible. What can be said is that demand is higher than sales of genuine rhino horn as long as a significant amount of imitation rhino horn is sold. This implies that with the black market in rhino horn, D t is almost always larger than, rather than equal to Y t.

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Therefore, because we lack the necessary data to execute any of the sampling-based estimates of rhino horn demand see S2 Text , we proceed instead as follows. The assumption of insatiable demand at current illegal production levels is represented by initializing the consumer population as follows.

Create enough consumers to purchase all rhino horn poached under the maximum poaching rate of 30 rhinos per week across South Africa 20 in KNP, and 10 on private ranches. Because each pair of rhino horns weigh on average 5 kg , these numbers are multiplied by 5. For policies that include a legal trading scheme operating in parallel to the illegal trade, this consumer pool is doubled.

Because demand for rhino horn in the near future is predicted to be about four times current sales [ 3 ], doing so is well-within current demand forecasts. This value is increased in proportion to the entries in Table 2 to a maximum of in the year At the per-kilogram prices mentioned above, this would be between 33 g and 57 g of rhino horn.

Other individuals of course may purchase other amounts of rhino horn. By doing so, we ignore the variability in the amount of purchased rhino horn and in-effect, lump approximately 18 to 30 real-life purchase events into one purchase event. Hence, our sub-model's purchase event time series should be viewed as the aggregate behavior of groups of approximately 18 to 30 real-life individuals.

Next, consumer behaviors start with the decision to enter the rhino horn market or not. If there is a media campaign aimed at potential rhino horn consumers that delivers a message that rhino horn has no medicinal value, some of the potential consumers may decide to not try to purchase rhino horn. This media campaign effect is represented as follows. Let n pc be the number of potential consumers each week. Let p m be the effectiveness of a horn-is-not-medicine media campaign run in the country where the consumers live.

If p m is close to 1. Sample once from this distribution to find n c , the number of consumers for that week who enter the market for rhino horn. To represent the absence of demand reduction campaigns, p m is set to 0. Because there is little evidence that these campaigns are effective [ 57 ], we set p m to 0. Now, rhino horn purchases may be simulated. The number of kilograms of rhino horns the illegal trader sells each week need not equal five times the number of rhino horn pairs poached the previous week.

This is because it is assumed that the illegal trader maintains a buffer stock of rhino horn. Doing so appears to be reasonable because an Asian retailer would convert their currency, say USD to Rand in order to purchase the rhino horn from the port exporter. Extracted from [ 28 ].

This cost is equal to the price charged by port exporters for a kilogram of rhino horn. The author of [ 59 ] reports that a portion of the rhino horn shipped to Vietnam is re-shipped to China. This suggests that the Chinese market for rhino horn is large and stable. We thus predict that if criminal syndicates are forced to cut costs in the face of a legal trade competitor, they will at minimum reduce their shipping costs.

And the main way they will do that is by switching all Vietnamese shipments to the existing shipping method used to transport rhino horn to China: ocean transport. This lower bound unrealistically leaves the middleman making a zero profit. The actual, positive profit of middlemen can be computed once prices paid by retailers to middlemen are known. The parameter p a represents how effective anti-poaching patrols are at curbing poaching. In the model, p a is the probability that a poaching expedition will be interdicted before it has been able to kill any rhinos.

This value is arrived at as follows. There were poachers arrested in , 36 of which were deceased. There were rhinos poached in in KNP. An estimated poachers entered KNP typically in gangs of three some of these are the same poachers entering more than once. There were contacts with rangers and 80 sightings of poachers by rangers. That translates to poachers interacted with or seen by rangers. There is a 0. Recall that the Pro-active Protection scenario involves greater support for anti-poaching patrols, but without the integrated law enforcement effort and development of legal economic opportunities for people living next to parks.

These latter two initiatives are needed to curb the operations of the criminal networks that sponsor the poaching expeditions because they reduce the ability of these networks to recruit, organize, and fund poaching expeditions in the first place. An individual-based sub-model for the South African rhino meta-population is developed along the lines of the prairie vole Microtus ochrogaster individual-based model of [ 60 ]. As with the model of [ 60 ], the rhino sub-model is stochastic in that one run over a particular time period may not produce the same history of abundance and dispersal as another run over the same period.

Therefore, many replications of the sub-model over the same time period are needed so that at each time point, both the expected value of abundance and extinction probability can be computed. Predicted rainfall is used as a scaled proxy of available vegetation. Predictions are computed using a quasi-periodic rainfall model [ 30 ] fitted to KNP rainfall data from to Average rainfall is a proxy for new vegetation.

We model the age, gender, energetic budget, location, and status of each individual rhino living and dying in enclosed patches. By doing so, we also model interactions of these rhinos with their environment—namely seasonal fluctuations in food availability; and interactions with each other through the effects of their spatial density on their birth and mortality rates [ 30 ]. We use biologically realistic population dynamics parameters and their values in the simulations Table 5. There are two sub-regions KNP and ranches each with four patches.

The initial age distribution is Gaussian with a mean of 7. Policies combating rhino horn trafficking seek to avoid non-sustainable solutions that accelerate poaching-led extinction risks. We first checked what the outcomes would be for the current status quo. Four additional strategic scenarios required evaluation—intensified protection, improved demand reduction, legal horn trade, or a scenario that integrates all three of these strategic responses.

For each of these scenarios we started from the present status quo and used our economic-ecological model to evaluate outcomes over a 35 year horizon Fig 4. We altered the effectiveness of anti-poaching efforts p a and the effectiveness of demand-reduction marketing campaigns p m on a scale of 0 to 1, while allowing t to reflect legal horn trade 1 or not 0. KNP rhinos are in red, and rhinos in South African private ownership are in green. Predictions are computed with and without strategies aimed at disrupting rhino horn trafficking syndicates.

The parameter p a is the effectiveness of anti-poaching efforts and p m is the effectiveness of demand-reduction marketing campaigns on a scale of 0 to 1. The parameter t reflects legal horn trade 1 or not 0. During an estimated white rhinos lived in National Parks, in Provincial Reserves and in Private Reserves [ 17 ]. We re-ran the above scenarios, but applied a disruptive policy to our model. Our disruptive policy is two-phased. The first phase consists of smart analyses to allow creation of actionable intelligence [ 65 ] that informs criminal and civil legal responses [ 66 ].

The second phase is the creation of alternative legal economic opportunities for people living next to parks. These opportunities in-part consist of the sharing of benefits accrued from protected areas with people alienated by traditional conservation philosophy see [ 5 ]. And, such opportunities should help to combat the exploitation of disenfranchised people by rhino horn trafficking syndicates see [ 67 ].

Disruptive interventions have consequences similar to an aggressive increase in the effectiveness of anti-poaching. Again, we evaluated outcomes over a 35 year horizon. The economic sub-model predicts prices that Asian traders pay to middlemen that are close to the prices observed since Fig 5. Our economic sub-model thus realistically predicts the changing price dynamics in the illegal supply chain from to Model output solid lines of the price that poachers ask middlemen to pay for a kilogram of rhino horn a , and the price that Asian traders bid to buy a kilogram of rhino horn from middlemen b.

Symbols are the observed prices paid with the broken lines representing the range of prices paid. The economic sub-model also predicts the number of poached rhinos which serves as input for the rhino sub-model. See [ 23 ]. Mean absolute percentage error MAPE computations indicate that our rhino abundance model is on average See S3 Text for the definition of this cross-validation statistic. This amount of error lends some validity to the modeling of rhino abundance interacting with an economic sub-model.

Our economic-ecological model is thus a robust reflection of the economic and ecological dynamics associated with rhinos given that it tracks price changes paid to poachers as well as rhino population changes in KNP. Consider our Disruptive, Integrated Response scenario illustrated in Fig 4. This scenario consists of authorities pursuing aggressive disruption of trafficking syndicates while providing economic opportunities for people living next to parks. The main conclusion of this article is that under this scenario, the South African rhino population is sustainable.

The sensitivity analysis reported in S4 Text indicates that this conclusion is not unduly affected by possible parameter misspecification. Intensified anti-poaching, demand reduction, and competitive horn trade policies result in non-sustainable solutions Fig 4. Under the status quo, extinction probability increases rapidly from onwards.

Purist intensified protection and demand reduction policies result in extinction risk rapidly increasing by —a 10 year extension of rhino existence. A purist trade policy will only buy one extra year—extinction risks increase rapidly from onwards. Combining these three policies still results in rapid extinction risk increase by But what happens if each of these policies is complemented with disruptive interventions that target organized crime syndicates while at the same time providing economic opportunities for people living next to parks?

In those scenarios, sustainable solutions are obtained irrespective of the policy implemented Fig 4. Curbing wildlife trafficking has become a key international focus [ 68 ]. Authorities have several approaches at their disposal to address the illegal trade in wildlife products [ 8 ]. For rhinos, most policies that have been debated combine increased protection with either demand reduction as championed by anti-trade proponents [ 9 — 11 ]—or legal supply options championed by pro-trade activists [ 12 — 14 ].

Our simulations suggest that protecting rhinos in-situ , reducing demand, and providing legal supply as policies to curb threats of rhino horn trafficking all result in ultimate rhino extinction. The author of [ 69 ] illustrated that most anti-poaching methods failed to protect rhinos. Our analysis also shows that demand reduction and legal supply policies carry failure risks. Therefore, policies need complimentary responses. Present interventions have not curbed the rhino poaching onslaught. Poaching rates continue to rise since [ 17 ] and key rhino populations may decline in the future [ 25 ].

At present, rhino management policies allow for no legal horn trade [ 70 ], but permit live African rhino trade and legal sport hunting [ 31 ]. In addition, rhinos have high ecotourism value [ 71 ] and stimulate a vibrant wildlife industry [ 31 , 72 ]. Rising Asian demand for horn is associated with economic well-being of eastern countries [ 73 ]. Present rhino populations are relatively small [ 17 ] and threatened by the rising onslaught of poaching. This present scenario and associated dynamics predicts continued decline in rhino population size with rapid increases in extinction risks of rhinos by anytime from to [ 30 ].

The existence of multiple solutions in the dynamics of rhino horn trade introduces uncertainties that fuel trade versus no-trade debates [ 48 ]. Agencies that inform international agreements such as CITES, suspect multiple solutions and evoke precaution [ 74 ] when they believe there is a chance that reality will settle into a non-sustainable solution. But continuation of the status quo as a result of political and bureaucratic inertia also risks a non-sustainable solution.

A consequence is that various policy options arise [ 8 ], but assessment of the outcomes of these is relatively rare. For example, the African Rhino Action Plan [ 75 ] inherently evaluates extinction risks and then proposes several actions. But the potential for these actions to achieve stated objectives is not explicitly assessed. When conducted, expert-based risk analyses indicate that benefits consistently exceed risks for those policies that in some or other format legally match supply and demand [ 76 ]. These analyses do, however, contain high levels of uncertainty [ 76 ].

And, they are premised on the two assumptions that a the scale and drivers of demand are known and can be managed; and b demand will redirect itself towards legally traded horn only. In addition, most proposed legal trade models e. The trade, illegal or legal, in wildlife goods like rhino horn has many features that challenge the assumptions of such simple economic models [ 48 ]. Our study has attempted to overcome some of these uncertainties. Individual-based ecological [ 60 ] and agent-based economic [ 27 ] modeling can track the trends in prices paid to poachers at various levels of the supply chain, but more importantly can track rhino population changes.

Predictions are thus based on approaches that track historical patterns, thus reducing some of the uncertainty surrounding the prediction that introducing legal trade in rhino horn to the current status quo may lead to non-sustainable rhino populations. This is also the outcome of introducing more aggressive rhino protection and demand reduction campaigns. Our results have some parallels with a recent study examining the socioeconomic drivers of poaching, based on data collected from to [ 14 ].

Increases in anti-poaching enforcement and penalties for violators can maintain rhinos, but may carry high financial costs. Our model predicts sustainable rhino populations only when intensified protection, demand reduction, or legal trade in rhino horn are complimented with trafficking syndicate disruption combined with creation of alternative economic options for people living next to parks.

This prediction challenges the current global support for the increased development of militaristically-oriented anti-poaching tactics see [ 67 , 78 ] for critiques of this militaristically focused approach to biodiversity protection. Our prediction is also at odds with proposals to increase sanctions [ 79 ] that ultimately result in further alienation of stakeholders living next to protected areas [ 43 ]; or potentially, across a nation [ 80 ]. Our findings may appear to contradict that of [ 14 ] who conclude increased protection funded through rhino horn sales revenue can save rhinos.

Increased protection would require crime disruption—similar to our analyses. In addition, lasting outcomes of reduced crime is more likely in environments that provide equal rights in fair benefit sharing [ 81 ]. The authors of [ 14 ] imply the same key requirements as what our analyses highlight. It is the funding of these initiatives that dichotomize the debate. Given that rhino horns are part of a suite of commodities that organized crime targets [ 16 ] authorities that deal with organized crime will also deal with rhino horn trafficking. It is unrealistic to expect the provision of good governance to depend on revenue generated by the use of a natural resource like rhino horn.

Our findings also contradict the opinions of authors who advocate the sale of rhino horn as a way to curb rhino poaching through a mechanism using a Central Selling Organization e. We do not explicitly model the mechanism of trade—we simply ask whether trade, in whatever format, will have consequences for rhinos. Even so, a key challenge for any mechanism of legal trade aimed at curbing rhino horn trafficking would be the ability to undercut the prices offered by illegal traders.

Criminal networks are able to engage in predatory pricing undercutting with legal traders because criminal networks have less overhead and can access capital without the need for borrowing [ 82 ]. Note that a government-run central selling organization would need to borrow money through bonds if its operation was more expensive than its tax receipt revenues—identical to any legal, private trader.

Criminals undercut legal traders for a range of commodities including gambling services [ 83 ], cigarettes [ 84 ], Pacific crab harvesting [ 85 — 86 ] and the international timber trade [ 87 ]. An extreme form of such criminal predation is the take-over of all trade in a commodity by a criminal organization via their practice of infiltrating a legal business and eventually taking control [ 88 ].

These examples challenge the potential of trade mechanisms being able to force criminal syndicates to operate at a loss. Our results clearly illustrate that disrupting organized crime is a key element disregarding whether authorities allow trade in rhino horn or not, as well as whatever mechanism authorities use if trade is allowed. We do not know of a documented case of a legal trade mechanism driving out a black market without the assistance of strong and effective law enforcement.

Indeed, black markets appear to be very resilient in the face of legal competition. In addition to the cases cited above, one can look at the recent record of marijuana legalization in some states in the United States. The author of [ 89 ] delineates how legalization has failed to shutdown illegal marijuana sales. For example, several years into legal marijuana in some states, 2.

In this case, illicit marijuana continues to be produced in large quantities in the face of a lower profit margin for its smugglers. Tobacco is another case of an illicit market co-existing with a legal one. A well-known example of legal trade apparently driving illegal traders out of a market is the repeal of Prohibition in the United States. But the actual mechanism involved in this case only serves to reinforce the importance of disrupting criminal syndicates.

As a case in point, just after Prohibition was repealed, the Governor of the state of Washington in the United States assigned Admiral Gregory the task of shutting down the illicit alcohol trade in that state [ 89 ]. Gregory's successful strategy consisted of the following five steps. The present illicit trade in rhino horn does not appear to share three key aspects of this strategy.

First, wildlife traffickers are typically criminal syndicates engaged in a variety of illegal activities. To simply declare them to be legal without any criminal prosecution of their other business activities would be illegal under South African law.

WEATHER ALERT

Average rainfall is a proxy for new vegetation. In the model, p a is the probability that a poaching expedition will be interdicted before it has been able to kill any rhinos. Management Science. Trafficking syndicates exploit this frustration in order to recruit some of these people for poaching expeditions. Waging a war to save biodiversity: the rise of militarized conservation. The author of [ 69 ] illustrated that most anti-poaching methods failed to protect rhinos.

Second, the world's record of successfully shutting down illegal traders has clearly been a failure. Third, given that two-thirds of South African rhinos live on reserves, it seems unlikely that private rhino owners would be able to achieve enough trade volume as to make illegal trade unprofitable. For private owners, the only way to set a lower price that is still profitable would be through high-volume production, i. These policies carry risks to sustainability because their implementation would incentivize intensive breeding of rhinos, a practice that has been shown to carry significant challenges for other species [ 92 — 93 ].

The authors of [ 94 ] provide evidence to support their conclusion that the legal sale in elephant ivory from through the one-time sale of ivory stocks in likely produced the recent, catastrophic rise in poaching. This is the opposite of what would be expected if the phenomenon was governed by classical static economics. These authors suggest that several factors could be operating. These are a ineffectual disruption of ivory trafficking by law enforcement, b fencing opportunities created by legal traders, c market expansion due to the removal of the stigma surrounding ivory consumption, and d perceptions by poachers that the ivory market is growing.

But could legal trade in ivory be sustainable? The authors of [ 95 ] employ an elephant population dynamics model coupled to data-based forecasts of ivory demand to address this question. Their modeling results suggest that demand is much too great to supply it with a sustainable elephant harvesting strategy. We emphasize that disruptive policies is not simply increasing traditional anti-poaching effort. If within-reserve poacher interception linearly relates to funds spent on anti-poaching, then budgets have to increase 15 times, a financially challenging way to increase anti-poaching effectiveness enough to achieve sustainability see [ 14 ].

Such an increase in effectiveness can only be realistically achieved through the enactment of two parallel initiatives. First, disruption of the entire syndicate's operations from the poacher on the ground up through the four levels of middlemen. Second, a reduction in the economic need to poach rhinos by people living next to parks through the development of alternative economic opportunities for them.