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The onslaught on the World's wildlife continues despite numerous initiatives aimed at curbing it. We build a model that integrates rhino horn trade with rhino population dynamics in order to evaluate the impact of various management policies on rhino sustainability. In our model, an agent-based sub-model of horn trade from the poaching event up through a purchase of rhino horn in Asia impacts rhino abundance. A data-validated, individual-based sub-model of the rhino population of South Africa provides these abundance values. We evaluate policies that consist of different combinations of legal trade initiatives, demand reduction marketing campaigns, increased anti-poaching measures within protected areas, and transnational policing initiatives aimed at disrupting those criminal syndicates engaged in horn trafficking. Simulation runs of our model over the next 35 years produces a sustainable rhino population under only one management policy. This policy includes both a transnational policing effort aimed at dismantling those criminal networks engaged in rhino horn trafficking-coupled with increases in legal economic opportunities for people living next to protected areas where rhinos live. This multi-faceted approach should be the focus of the international debate on strategies to combat the current slaughter of rhino rather than the binary debate about whether rhino horn trade should be legalized. This approach to the evaluation of wildlife management policies may be useful to apply to other species threatened by wildlife trafficking.

Business is both the main driver of the planet's current catastrophic loss of biodiversity and the key to stemming it. To address this challenge, a business strategy is developed wherein firms launch profitable business lines that harness market forces to fund projects that result in the enhancement of biodiversity. This strategy leverages existing systems for managing at-risk ecosystems and introduces a new procedure for minimizing the costs of such projects while maximizing their positive impacts on biodiversity. These business lines are built by first, understanding the political context of a particular biodiversity threat; second, designing a profitable product or service that is tied to a minimum-cost project that enhances biodiversity; and third, reporting the current and future impact of the project to those customers who want to know if their purchases are actually curbing the destruction. A new parameter learning algorithm within a political-ecological system simulator is used to model how the actions of firms change the beliefs of people to the point where they adopt ecosystem-preserving behaviors. The newly developed and available software that implements this procedure is applied to the conservation of white (Ceratotherium simum) and black (Diceros bicornis) rhinoceroses in South Africa.

Conservation management is of increasing importance in ecology as most ecosystems nowadays are essentially managed ecosystems. Conservation managers work within a political-ecological system when they develop and attempt to implement a conservation plan that is designed to meet particular conservation goals. In this article, we develop a decision support tool that can identify a conservation policy for a managed wildlife population that is both sustainable and politically feasible. Part of our tool consists of a simulation model composed of interacting influence diagrams. We build, fit, and use our tool on the case of rhino horn trafficking between South Africa and Asia. Using these diagrams, we show how a rhino poacher’s belief system can be modified by such a policy and locate it in a perceived risks-benefits space before and after policy implementation. We statistically fit our model to observations on group actions and rhino abundance. We then use this fitted model to compute a politically feasible conservation policy.

Wildlife trafficking is driving many species to extinction and is overwhelming law enforcement efforts to stop it. At least a 2-fold increase in the number of traffickers who are put out of business is needed to help avoid these extinctions. A cybersecurity-based solution described in this article consists of a large international confederation of criminal investigators collecting intelligence on persons involved in wildlife trafficking, analyzing it, and then recommending to law enforcement (a) cybercriminals to detain, (b) cybercriminals to surveil, and (c) where and when to intercept cybercriminal-initiated wire transfers and shipments of wildlife products. Wildlife traffickers nowadays often use the internet to commit their cybercrimes. Prosecuting such crimes is challenging. Indeed, one of the top five challenges in cybersecurity is to develop methods for pursuing cybercriminals and bringing them to justice through the acquisition of digital evidence that links specific individuals to specific illegal acts. The proposed confederation finds two lists of wildlife cybercriminals to remove. The first is found by computing centrality measures on the statistically estimated (reconstructed) current social network of wildlife cybercriminals to identify those criminals whose removal would, according to social network theory, maximally disrupt the syndicate’s operations. This list contains criminals identified as kingpins, and/or information brokers. The second list consists of those m criminals whose removal results in the largest simulator-computed drop in poaching of the trafficked species over the next year. Database access control is a form of information security (InfoSec), or data security—a chief component of cybersecurity. Here, a distributed form of information security is developed for keeping a confederation’s criminal intelligence database secure from unauthorized access and insider threats. This procedure uses only peer-to-peer transactions. The illegal trade in rhino horn is used to illustrate how this confederation would use criminal intelligence from several countries to first build a simulation of the political–ecological system that contains the trafficking operation, and then use this statistically fitted simulator to identify those traffickers to remove, wire transfers to block, and wildlife product shipments to seize. All software to implement this federated database and its access control procedure is freely available.

Explores computer-intensive probability and statistics for ecosystem management decision making Simulation is an accessible way to explain probability and stochastic model behavior to beginners. This book introduces probability and statistics to future and practicing ecosystem managers by providing a comprehensive treatment of these two areas. The author presents a self-contained introduction for individuals involved in monitoring, assessing, and managing ecosystems and features intuitive, simulation-based explanations of probabilistic and statistical concepts. Mathematical programming details are provided for estimating ecosystem model parameters with Minimum Distance, a robust and computer-intensive method. The majority of examples illustrate how probability and statistics can be applied to ecosystem management challenges. There are over 50 exercises – making this book suitable for a lecture course in a natural resource and/or wildlife management department, or as the main text in a program of self-study.

The decision to implement environmental protection options is a political one. These, and other political and social decisions affect the balance of the ecosystem and how the point of equilibrium desired is to be reached. This book develops a stochastic, temporal model of how political processes influence and are influenced by ecosystem processes and looks at how to find the most politically feasible plan for managing an at-risk ecosystem. Finding such a plan is accomplished by first fitting a mechanistic political and ecological model to a data set composed of observations on both political actions that impact an ecosystem and variables that describe the ecosystem. The parameters of this fitted model are perturbed just enough to cause human behaviour to change so that desired ecosystem states occur. This perturbed model gives the ecosystem management plan needed to reach desired ecosystem states. To construct such a set of interacting models, topics from political science, ecology, probability, and statistics are developed and explored. Key features: Explores politically feasible ways to manage at-risk ecosystems. Gives agent-based models of how social groups affect ecosystems through time. Demonstrates how to fit models of population dynamics to mixtures of wildlife data. Presents statistical methods for fitting models of group behaviour to political action data. Supported by an accompanying website featuring datasets and JAVA code. This book will be useful to managers and analysts working in organizations charged with finding practical ways to sustain biodiversity or the physical environment. Furthermore this book also provides a political roadmap to help lawmakers and administrators improve institutional environmental management decision making.

Before massive numbers of students can take online courses for college credit, the challenges of providing tutoring support, answers to student-posed questions, and the control of cheating will need to be addressed. These challenges are taken up here by developing an online course delivery system that runs in a cluster computing environment and is designed to support the delivery of a course having 10K or more students. This delivery system enhances synchronous and asynchronous lectures, provides an online intelligent tutoring system, and detects plagiarism. The free software system is shown to provide fast response times when run on a mid-range cluster computer. The system's automatic plagiarism detection system is shown to be able to detect multiple authors of course assignments when used to analyze the work of actual online students. Use of this system on a large scale would allow most colleges to reduce their faculty size by 20 to 40%.

A prediction method is given for a first- and second-order nonstationary spatio-temporal process. The predictor uses local data only and consists of a two-stage generalized regression estimate of the local drift at the prediction location added to a kriging prediction of the residual process at that location. This predictor is applied to observations on seasonal, rainfall-deposited sulfate over the conterminous United States between summer 1986 and summer 1992. Analyses suggest that predictions and estimated prediction standard errors have negligible to small biases, there is spatially heterogeneous temporal drift, and temporal covariance is negligible.

The deposition of heightened levels of sulfuric and nitric acid through rainfall in the United States may adversely affect the environment. For example, soils may become toxic to native tree species because of soil acidification. Ecological effects models being built to study these potential problems have a need for regional deposition estimates with associated measures of uncertainty. However, statistical estimation of the deposition process is complicated by a strong spatial trend (mean nonstationarity) and apparently a spatial covariance structure dependent on location (covariance nonstationarity). The available data for calculating deposition estimates consist of several hundred point observations at irregularly spaced sampling locations across the United States. The spatial estimation technique of kriging is the foundation of four deposition estimation methods evaluated in this study. These are lognormal kriging with a single model of the spatial covariance structure (the variogram); single-estimate lognormal kriging within a moving window using a local model of the variogram; ordinary kriging within a moving window, also with a local variogram model; and planar regression with spatially correlated errors within a moving window used to estimate residuals that are then input to the moving window ordinary kriging algorithm to calculate a (local) trend-adjusted estimate. Each of these four methods has some capacity for accommodating mean and covariance nonstationarity. The moving window methods are new. It is shown that the log transform may not be the correct transform for the deposition process. However, confidence intervals found from a lognormal kriging method do not include a negative interval, as can frequently occur with intervals found from ordinary kriging. Assuming that a partially negative confidence interval for deposition is interpretable, the method of local planar regressions followed by residual kriging gives confidence interval widths that are the most resistant to inflation due to trend effects on the variogram and trend-dependent variance.