FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1971233073> ?p ?o ?g. }

• W1971233073 endingPage "10" @default.
• W1971233073 startingPage "5" @default.
• W1971233073 abstract "At an estimated $7–10 billion annually, the global trade in illegal wildlife parts is comparable in economic value to human trafficking, and the smuggling of weapons and drugs (Wasser et al. 2008; Wyler & Sheikh 2013). Basic economic principles of supply and demand ensure that, as target species become ever rarer, their market value continues to rise, gradually pushing them towards extinction (Courchamp et al. 2006; Nowell 2012a). One particular problem is that anti-poaching rangers often arrive too late at crime scenes to arrest criminals, making poaching a low-risk and high-gains enterprise (Wyler & Sheikh 2013). Here, we identify an opportunity to address this fundamental problem – we propose that cutting-edge tracking technology could be harnessed to implement effective ‘real-time poaching-alert systems’. Animals would be fitted with miniature electronic devices (‘biologgers’) that can detect a poaching event, establish its exact location and relay data remotely to ground teams. Such systems should considerably increase the chances of successful interception, and thereby, escalate the actual and perceived risks of poaching, establishing a powerful new deterrent. In combination with other mitigation strategies (reviewed below), this innovative approach could lead to a much-needed breakthrough in the increasingly desperate fight against wildlife crime. While a wide range of species is targeted for illegal trading, we focus here on the poaching of large mammals, as these are often particularly vulnerable due to their naturally low population densities and reproductive rates. Three case studies serve to illustrate the urgency of implementing effective anti-poaching measures (cf. Nowell 2012b), but our novel approach would no doubt benefit many other species. Rhinos are currently experiencing unprecedented poaching pressure (Fig. 1), with rates of one animal killed every 13 hours in some areas, and are fast heading towards wholesale extinction in the wild (Biggs et al. 2013). In fact, following a precipitous, poaching-induced population crash in the 1960s (Emslie & Brooks 1999), the African western black rhino was declared extinct by the International Union for Conservation of Nature (IUCN) in 2011 (Biggs et al. 2013). As the price of ivory is rising, elephants fare little better and could be virtually extinct across most of their African range by 2020, unless poaching off-take is considerably reduced (Wasser et al. 2008; see also Maisels et al. 2013). Finally, tigers are another group under extreme pressure (Nowell & Xu 2007; Walston et al. 2010), with three subspecies having already been lost in the last 70 years, and a lack of confirmed sightings from southern China likely signalling another extinction event (Tilson, Traylor-Holzer & Jiang 1997). Many anti-poaching measures have been explored over the years (Sutherland 2008), including the following: environmental education programmes, to reduce demand for wildlife parts in East Asia (Lee & Tilbury 1998; Nowell & Xu 2007); legalization of high-value products, such as ivory or rhino horn, to control trade dynamics (Gillson & Lindsay 2003; Martin et al. 2012; Biggs et al. 2013); targeted monitoring of money-laundering activities, to hamper illegal trading (as highlighted by a recent international summit; Coghlan 2014); drastic in situ management of threatened animal populations, such as large-scale dehorning of rhinos, to reduce poaching opportunities (Lindsey & Taylor 2011); and ‘militarization’ of nature reserves (Milliken & Shaw 2012; see below), to facilitate arrests and deter criminal activities. As we have illustrated above, however, illegal trade in wildlife products remains rife, and novel solutions are urgently needed. Our proposal aims at increasing the effectiveness of a widely used approach for protecting the most critically endangered species, the deployment of mobile, armed anti-poaching units (Milliken & Shaw 2012). While these teams are often highly trained and well equipped, they generally have no way of knowing the exact time and location of poaching events. Since many target species are wide ranging and live in inaccessible habitats, this means that carcasses are often only found days or weeks after death (Martin 2001). As a result, arrests of poachers are rare and resources are mainly being focussed on securing evidence (Wasser et al. 2008), which is often insufficient for successful prosecution. Our proposed real-time poaching-alert systems would enable rangers to head towards crime scenes with rapid response times, substantially increasing the chances of apprehending suspects. In conjunction with legislation that ensures the severe punishment of convicted poachers, these altered risk dynamics should substantially reduce the economic attractiveness of poaching, giving heavily persecuted animal populations time to recover. In fact, even a temporary slowing of harvest rates would be valuable, as it would allow longer-term measures – such as educational programmes – to deliver benefits. The rationale of our proposed biologging system is straightforward (for a schematic illustration, see Fig. 2, and for a summary of key challenges, see Table 1). Animals are fitted with miniature electronic tags that detect poaching events and transmit relevant information remotely to anti-poaching units on the ground. In terms of technological implementation, the integration of a few existing, well-tested components would enable an effective three-step process for raising an alarm: detection –location – transmission/alert. Exact system specifications will depend on a wide range of factors, including the size, behaviour and habitat preferences of the species in question, as well as the availability of local infrastructure and other resources, but the following description outlines key principles. A range of sensors could be used to detect when an animal is shot or trapped, including accelerometers or heart-rate sensors (Rutz & Hays 2009; see Table 1). To avoid false alarms, sensors would require careful calibration before system deployment and could even be combined within a single tag, to enable redundant event-triggering (i.e. multiple sensors must trigger before the tag raises an alarm) or remote validation – for example, an accelerometer could trigger an integrated video camera (Fig. 2; Rutz et al. 2007; Watanabe & Takahashi 2013) or microphone (Lynch et al. 2013). Once the tag's sensors have confirmed a poaching event, an on-board GPS receiver is booted up (Tomkiewicz et al. 2010), to establish the position of the trapped, injured or dead animal. State-of-the-art systems can estimate coordinates of suitable accuracy (within tens of metres) within split-seconds, with minimal power requirements (e.g. Fastloc). In the final step, the tag communicates the event – that is, animal ID, trigger time, sensor readings and GPS coordinate information – to a mission control centre and/or directly to rangers in the field. This could be achieved through various routes, including satellite uplinks (e.g. Iridium), UHF transmission, or pre-existing or ad hoc mobile phone networks. We estimate that a well-designed system could raise an alarm within ca. 10 s, which in the majority of scenarios will be faster than poachers could reach the animal and destroy its tag. Anti-poaching units often have helicopters at their disposal, ensuring that crime scenes could be reached within minutes, or tens of minutes, after receiving an alert (Fig. 2), even in vast and inaccessible patrol areas. Where helicopters are not available, reserves would at least be warned of ongoing poaching activity, enabling them to focus ranger resources spatially, patrol park perimeters and conduct targeted vehicle checks, greatly increasing the chances of apprehending poachers. The proposed technology should not be confused with standard satellite tracking, as routinely used with endangered species (e.g. Galanti et al. 2006). Although conventional GPS loggers could in principle be employed to infer poaching events from animals’ movement trajectories, costly time delays – to establish whether a stationary animal is merely resting or has indeed been injured or killed – would rule out their utility for guiding ad hoc intervention. Furthermore, constant sampling and relaying of positional data would quickly deplete batteries (in cases where solar power is not an option), which is not an issue with the ‘one-shot’ tags we envisage here. Likewise, the marking of animals with PIT/RFID chips (Casey 2014), or with cutting-edge life-history tags (e.g. Horning & Mellish 2012), only enables the post hoc identification of mortalities, but cannot support a real-time response, which lies at the heart of our proposal (for the use of real-time ‘listening’ stations, to detect illegal logging, see Gross 2014). We can think of many ways to tailor system specifications to suit particular species or deployment contexts, or to extend basic system functionality. For example, event-triggering could be combined very effectively with another anti-poaching technology that is currently being developed – unmanned aerial systems, or ‘drones’ (Marks 2013, 2014; Casey 2014; Gross 2014; Mulero-Pázmány et al. 2014). Rather than putting (tagged) animals under intermittent or constant drone surveillance, however, as currently planned, poaching-alert tags could guide drones selectively to confirmed crime scenes, for collection of still-image or video evidence until anti-poaching units arrive on the ground. Such targeted monitoring should considerably increase the effectiveness of drone-based projects, while reducing their logistical complexity and running costs. It is useful to explore briefly the practicalities of implementing our approach (cf. Table 1). Assuming that the engineering challenges of constructing suitable tags can be met, a key requirement is adequate tagging effort. Our approach aims at escalating the potential risks involved in committing poaching crimes, driving an unfavourable cost-benefit ratio for poachers. This can only be achieved if a substantial proportion of local animal populations is marked with poaching-alert tags or is at least being perceived to be marked, forcing poachers to take an increased risk, every time they pull the trigger or check a snare. It would of course be desirable if tags were difficult to see at a distance, because they are either very small or well hidden (e.g. in the horn of rhinos, or in ankle bracelets that cannot be seen in high grass; see Fig. 1), but where this is impossible (e.g. because tags need to be mounted on a collar, as with tigers), the strategic use of cheap dummy tags could considerably reduce programme costs (dummy tags are often used in biologging projects, to assess tagging effects; e.g. Bridger & Booth 2003). Trapping effort would admittedly pose significant challenges for large populations, but is unlikely to be an issue in those areas where intervention is most urgently needed: this is because critically endangered populations are often heavily managed, with large numbers of subjects being routinely trapped for ID marking (Ngene et al. 2011) and health checks. As with any new technology employed in antagonistic contexts, one particular concern is the possible development of counter measures. In our case, this could involve, for example, technology to jam tags’ two-way communication with satellites. We think that such an ‘arms race’ is unlikely, at least in the short term, given the required levels of technological expertise, and the substantial costs involved, which would quickly diminish criminals’ profit margins. For two main reasons, we are surprised that real-time poaching-alert systems have not been implemented yet. First, the fight against most other types of crime heavily relies on the use of event-triggered technology. While large-scale CCTV surveillance, and regular police patrols, may lead to reductions in crime rates (e.g. Levitt 1997), the success of policing is no doubt dramatically enhanced by systems that raise alarms in real-time and enable arrests at crime scenes. This includes house and car alarms, panic buttons and rape alarms, and perhaps most importantly, the victims’ ability in many circumstances to phone the police directly. We see no reason why this powerful route of ‘self-reporting’ could not be emulated in the desperate fight against poaching crime. To our knowledge, this opportunity has so far been overlooked, despite increasing interest in technology-driven approaches (see above). Secondly, over the last 10 years or so, significant advances have been made in biologging science, producing tags of unprecedented miniaturization, sophistication and integration (Rutz & Hays 2009) – while major engineering challenges lie ahead (see Table 1), the construction of real-time poaching-alert systems is well within reach of current expertise. We hope others will join us in our efforts to implement the ideas outlined in this essay. To start with, we invite biologging engineers – many of whom already have keen interests in conservation biology (Cooke 2008; Bograd et al. 2010) – to collaborate with us on system development, as free sharing of expertise and other resources will be essential to making rapid progress. But, success will also depend on support from wildlife biologists and ranger teams on the ground, and on the willingness of governments and other authorities to issue permits for system deployment, to facilitate the cross-border pursuit of criminal suspects and to put in place robust legislation for the sentencing of convicted poachers (cf. Maisels et al. 2013; see Table 1). Given that many target species are fast heading towards extinction, we need to explore all available anti-poaching tools with utmost urgency, aiming for intervention at every stage of the trade chain. While we are fully aware that our reactive, technology-based approach does not provide an all-encompassing solution, it should – through its contribution to improving arrest rates and establishing an effective deterrent – buy crucial time until longer-term, preventive measures have gained sufficient traction. We thank: Mark Johnson for discussing with us the technical feasibility of different engineering solutions; Tom Collier for permission to use his rhino photographs in Fig. 1; Steve Thompson for preparing Fig. 2; the Aspinall Foundation, Kent, UK, for allowing us to trial prototype tags on their captive rhinos; Mark Calkin for providing technical assistance during trial deployments; and the Associate Editor and two anonymous reviewers for thoughtful and constructive comments on an earlier draft. This work was supported by the Biotechnology and Biological Sciences Research Council (grant BB/G023913/2 to CR). PO initiated this collaboration; PO and CR conceived ideas; PO and CR conducted research; and CR drafted the manuscript, which was edited by PO and CR. The authors have started developing prototype poaching-alert tags, but do not intend to exploit the technology commercially. Data have not been archived because this article does not contain data. Paul O'Donoghue is an applied ecologist with a PhD from the University of Sheffield. Through his work on black rhinos in Namibia and South Africa, he has gained considerable ‘front-line’ experience of fighting poaching crime. Christian Rutz is an evolutionary ecologist with a DPhil from the University of Oxford. He uses cutting-edge ‘biologging’ technologies extensively in his field projects and has pioneered the use of miniature video cameras and proximity loggers for studying wild birds. By pooling their diverse practical expertise, Paul and Christian hope to make a contribution to the development of innovative real-time anti-poaching tools." @default.
• W1971233073 created "2016-06-24" @default.
• W1971233073 creator A5011714390 @default.
• W1971233073 creator A5045118643 @default.
• W1971233073 date "2015-07-24" @default.
• W1971233073 modified "2023-10-06" @default.
• W1971233073 title "Real-time anti-poaching tags could help prevent imminent species extinctions" @default.
• W1971233073 cites W1980449587 @default.
• W1971233073 cites W1989093689 @default.
• W1971233073 cites W1997548708 @default.
• W1971233073 cites W2022008002 @default.
• W1971233073 cites W2038372522 @default.
• W1971233073 cites W2047005779 @default.
• W1971233073 cites W2049247140 @default.
• W1971233073 cites W2053228669 @default.
• W1971233073 cites W2062210325 @default.
• W1971233073 cites W2065296205 @default.
• W1971233073 cites W2072981395 @default.
• W1971233073 cites W2074193087 @default.
• W1971233073 cites W2079940262 @default.
• W1971233073 cites W2088264916 @default.
• W1971233073 cites W2105311661 @default.
• W1971233073 cites W2131455567 @default.
• W1971233073 cites W2134182643 @default.
• W1971233073 cites W2152029777 @default.
• W1971233073 cites W2156732810 @default.
• W1971233073 cites W2165011500 @default.
• W1971233073 cites W2170530388 @default.
• W1971233073 doi "https://doi.org/10.1111/1365-2664.12452" @default.
• W1971233073 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4949716" @default.
• W1971233073 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27478204" @default.
• W1971233073 hasPublicationYear "2015" @default.
• W1971233073 type Work @default.
• W1971233073 sameAs 1971233073 @default.
• W1971233073 citedByCount "41" @default.
• W1971233073 countsByYear W19712330732016 @default.
• W1971233073 countsByYear W19712330732017 @default.
• W1971233073 countsByYear W19712330732018 @default.
• W1971233073 countsByYear W19712330732019 @default.
• W1971233073 countsByYear W19712330732020 @default.
• W1971233073 countsByYear W19712330732021 @default.
• W1971233073 countsByYear W19712330732022 @default.
• W1971233073 countsByYear W19712330732023 @default.
• W1971233073 crossrefType "journal-article" @default.
• W1971233073 hasAuthorship W1971233073A5011714390 @default.
• W1971233073 hasAuthorship W1971233073A5045118643 @default.
• W1971233073 hasBestOaLocation W19712330731 @default.
• W1971233073 hasConcept C101991246 @default.
• W1971233073 hasConcept C151730666 @default.
• W1971233073 hasConcept C18903297 @default.
• W1971233073 hasConcept C205649164 @default.
• W1971233073 hasConcept C2776217272 @default.
• W1971233073 hasConcept C29376679 @default.
• W1971233073 hasConcept C86803240 @default.
• W1971233073 hasConceptScore W1971233073C101991246 @default.
• W1971233073 hasConceptScore W1971233073C151730666 @default.
• W1971233073 hasConceptScore W1971233073C18903297 @default.
• W1971233073 hasConceptScore W1971233073C205649164 @default.
• W1971233073 hasConceptScore W1971233073C2776217272 @default.
• W1971233073 hasConceptScore W1971233073C29376679 @default.
• W1971233073 hasConceptScore W1971233073C86803240 @default.
• W1971233073 hasFunder F4320334629 @default.
• W1971233073 hasIssue "1" @default.
• W1971233073 hasLocation W19712330731 @default.
• W1971233073 hasLocation W19712330732 @default.
• W1971233073 hasLocation W19712330733 @default.
• W1971233073 hasLocation W19712330734 @default.
• W1971233073 hasLocation W19712330735 @default.
• W1971233073 hasOpenAccess W1971233073 @default.
• W1971233073 hasPrimaryLocation W19712330731 @default.
• W1971233073 hasRelatedWork W1533119345 @default.
• W1971233073 hasRelatedWork W2166529861 @default.
• W1971233073 hasRelatedWork W2748952813 @default.
• W1971233073 hasRelatedWork W2899084033 @default.
• W1971233073 hasRelatedWork W3011018279 @default.
• W1971233073 hasRelatedWork W3036133106 @default.
• W1971233073 hasRelatedWork W3122749228 @default.
• W1971233073 hasRelatedWork W3149790621 @default.
• W1971233073 hasRelatedWork W42944143 @default.
• W1971233073 hasRelatedWork W596302018 @default.
• W1971233073 hasVolume "53" @default.
• W1971233073 isParatext "false" @default.
• W1971233073 isRetracted "false" @default.
• W1971233073 magId "1971233073" @default.
• W1971233073 workType "article" @default.