Exploiting Data and Human Knowledge for Predicting Wildlife Poaching

Poaching continues to be a significant threat to the conservation of wildlife and the associated ecosystem. Estimating and predicting where the poachers have committed or would commit crimes is essential to more effective allocation of patrolling resources. The real-world data in this domain is often sparse, noisy and incomplete, consisting of a small number of positive data (poaching signs), a large number of negative data with label uncertainty, and an even larger number of unlabeled data. Fortunately, domain experts such as rangers can provide complementary information about poaching activity patterns. However, this kind of human knowledge has rarely been used in previous approaches. In this paper, we contribute new solutions to the predictive analysis of poaching patterns by exploiting both very limited data and human knowledge. We propose an approach to elicit quantitative information from domain experts through a questionnaire built upon a clustering-based division of the conservation area. In addition, we propose algorithms that exploit qualitative and quantitative information provided by the domain experts to augment the dataset and improve learning. In collaboration with World Wild Fund for Nature, we show that incorporating human knowledge leads to better predictions in a conservation area in Northeastern China where the charismatic species is Siberian Tiger. The results show the importance of exploiting human knowledge when learning from limited data.Comment: COMPASS 201

Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation

Understanding the human dimensions of coexistence between carnivores and people: A case study in Namibia

Many carnivore populations were in decline throughout much of the 20th century, but due to recent conservation policies, their numbers are stabilising or even increasing in some areas of the world. This, compounded with human population growth, has caused increased livestock depredation by carnivores, which threatens farmer livelihoods, particularly those in developing countries such as Namibia. How to resolve this so-called “conflict” between carnivores and livestock farmers remains challenging, in part because some mitigation strategies have proven somewhat ineffective or unacceptable. By using a case-study approach on the commercial farmlands of northcentral Namibia, I aimed to understand the complexity of the human dimensions affecting coexistence between carnivores and people in an unprotected working landscape. Specifically, my objectives were to 1) develop a participatory decisionmaking exercise to analyse the views of stakeholders on how they would like carnivores to be managed in unprotected lands, 2) understand how the media framed financial incentives to improve human-carnivore coexistence, and 3) determine if there were any underlying social, economic or political causes of negative human-carnivore interactions on commercial livestock farms. To answer objective 1, I developed a new decision-making exercise that combined Q-methodology and the Delphi technique to determine whether a diverse group of stakeholders could agree on how to manage carnivores on commercial farmland. A strong agreement was reached by participants: providing conservation education and training on livestock husbandry were acceptable and effective ways to improve coexistence with carnivores. This new also method highlighted areas of disagreement between stakeholders and showed that there were two different narratives on how carnivores should be managed. This method could be used by policy makers to help with participatory decision-making for resolving other conservation conflicts. To answer objective 2, I undertook content analysis of national newspapers to determine how the media framed articles on financial incentives to mitigate this conservation conflict. The most common (30%) financial incentive discussed was compensation - many (61%) of these articles framed compensation positively. However, upon categorising these articles into those where respondents were enrolled in compensation schemes compared with those who were not, a clear pattern emerged: articles were more likely (89%) to be framed ambivalently or negatively when respondents had experience of this financial incentive compared with respondents that did not. These results can help conservationists plan more effective communication interventions and anticipate issues that can affect the success of mitigation strategies. To answer objective 3, I undertook eight months of participant observation on livestock farms and interviewed 69 respondents and found that reported livestock depredation was associated with increased instances of poaching of wildlife and stealing of livestock. This association appeared to be partly due to farmer-worker relations: when employees felt happy, respected and were paid a liveable wage, they were incentivised to perform well in their job. This resulted in livestock that were managed more effectively and therefore less likely to be killed by predators. Furthermore, these well-paid employees were not incentivised to steal or poach to supplement their income, which limited the extent of game poaching and livestock theft on the farm. These findings underline the fact that this conservation conflict is extremely complicated, driven by many social, economic and political factors that may not be apparent initially. In conclusion, this thesis has found that the conflict between carnivores and livestock farmers is a truly wicked problem, affected by a multitude of complex layers. Only by exploring the entangled web of drivers will we ever begin to create positive, lasting change for both people and predators. Niki Rust © 201

Navigating Obstacles to Environmental Conservation: How NGOs Emerge as Effective Environmental Stewards

At all levels, from national governments to individual researchers, there are obstacles faced by those trying to conserve the Earth’s biodiversity. Non-government organizations are in a relatively unique position where they have to interact with actors from all of these levels and navigate the roadblocks that come with them. Non-government organizations are in a relatively unique position where they have to interact with actors from all of these levels as well as community members to navigate the roadblocks that come with them. Because of their commitment to doing things for the right reasons, international reach, and engagement with local communities, NGOs emerge as environmental stewards, filling in gaps and doing the work that governments will not do and individuals cannot undertake on their own

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Towards a science of security games

Abstract. Security is a critical concern around the world. In many domains from counter-terrorism to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or allocated or deployed), while simultaneously taking into account the impor-tance of different targets, the responses of the adversaries to the security posture, and the potential uncertainties in adversary payoffs and observations, etc. Com-putational game theory can help generate such security schedules. Indeed, casting the problem as a Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for scheduling of secu-rity resources. These applications are leading to real-world use-inspired research in the emerging research area of “security games”. The research challenges posed by these applications include scaling up security games to real-world sized prob-lems, handling multiple types of uncertainty, and dealing with bounded rationality of human adversaries.

Testing the Efficacy and Potential Consequences of Fencing As A Wildlife Management Tool

This dissertation examines how various anthropogenic barriers affect wildlife movement, and in particular, how fencing affects movement and behavior of both migratory prey and predators in semi-porous environments. I chose to examine this subject as our planets last remaining ecosystems are threatened by human encroachment due to population pressure, agriculture, and a myriad of other ecological stressors. In order to mitigate the encroachment, conservation fencing is rapidly becoming the norm even though constraining wildlife movement is fraught with ecological issues. My interest in conservation fencing was to examine the potentially hidden or understudied consequences of its usage. The introduction discusses human-wildlife conflicts and the role of fences. Chapters 1 and 2 review the literature concerning animal movement and landscape ecology and set the general framework from which follows the series of specific studies in Chapters 3-6. Chapter 3 compares basic monitoring methods that lie at the core of the studies that follow. In this chapter, a comparison of traditional track monitoring to modern camera trapping methods demonstrated the power of mechanical vigilance but also the importance of timely monitoring for managerial decisions. Chapter 4 examines the effectiveness of fence-gaps, a wildlife management tool designed to compromise between complete isolation by fencing and an open landscape. The results of this study showed that most of the species in situ have indeed discovered these fence-gaps. Chapter 5 explores the potentially unintended consequences of the creation of fence-gaps as these structures funnel migration movement and thus could act as prey-traps. Using a spatial analysis of carcass locations, the results of this study demonstrated that predation locations did not cluster near the fence-gaps. Chapter 6 examines predation near the perimeter fencing and within fenced areas designed to exclude elephant. Results showed that lion predation was not over-represented near the perimeter fences and that exclosures provided good hunting grounds for lion but these exclosures did not create prey-traps. The dissertation concludes that fencing is a useful conservation tool that requires reliable monitoring to understand how wildlife functions with fencing, and to permit managers to react to issues through an adaptive management framework

Characterization of Illegal Wildlife Trade Networks

The legal and illegal trade in wild animals and their products is a multi-billion dollar industry that threatens the health and well-being of humans and animals alike. The management of the wildlife trade is a crisis-driven area, where decisions are made quickly, and, often, inefficiently. In particular, the regulation and control of the illegal wildlife trade is hampered by a dearth of formal quantitative analysis of the nature of the trade. This thesis represents a preliminary attempt to rectify that knowledge gap. It describes an investigation into the factors that support and promote the trade and is based upon information in two databases: CITES (the legal trade) and HealthMap (the illegal trade). The study 1) quantified the relationship between the illegal wildlife trade and several key factors thought to contribute to the illegal wildlife trade, namely road development, unemployment, and Corruption Perception Index (a score related to the perceived level of corruption); 2) measured the extent to which the product types, origins, destinations, and trade routes in the legal and the illegal wildlife trade are alike; and 3) identified locations to place resources to (a) restrict trade by causing the greatest network destabilization and (b) disseminate an educational message that would cause the greatest impact to the network. Several key factors and the legal trade were associated with the magnitude of various indices of the illegal trade at a country-level, but no generalizable findings can be asserted at this time. With regard to the best placement of regulatory resources, China was key with respect to network disruption and information dissemination targets. This thesis has begun the urgently needed analysis of the complex relationships of the illegal wildlife trade and identified specific ways to bring about change using network science. These findings offer hope for regulatory and enforcement agencies, NGOs, and governments that it will be possible to find more effective ways of combating the illegal wildlife trade and problems it brings with it