Authored by Nicole Benjamin-Fink*
Abstract
The ecologist’s dilemma is worldwide and straightforward; providing wildlife management recommendations, while constrained by scarce resources and limited data. We apply Object Oriented Bayesian Networks (OOBNs) to quantify complexities within their ecological context. We generate and utilize expert understanding of uncertainties. Key variables are identified and clustered within spatial, biological, and market domains. Specifically, (i) blue wildebeest male to black wildebeest male ratio, and (ii) spatial connectivity.
We put forth two OOBNs. Varying in their scope, each accurately represents the wicked problem concerning wildebeest hybridization and addresses different objectives faced by decision-makers. A detailed OOBN quantifies spillover implications, while a skeleton OOBN assesses associated risks. We equip ecologist with effective tools to resume informed decisions and maximize resources. Lastly, we offer a decision tree that promotes the usage of OOBNs as user friendly tools aimed at solving for the ecologist’s dilemma while addressing similar ecological and environmental uncertainties worldwide.
Keywords: Applied resources management; Ecologist’s dilemma; Hybridization; Object-oriented-bayesian-network; Policy making; Wicked problems; Wildebeest
Introduction
The challenge in “Wicked” Problems
Researchers are increasingly faced with ‘Wicked’ problems; multifaceted complex challenges that span disciplines, knowledge bases, and value systems [1]. They are sequential to other problems, lack clear solution, and may be easily misrepresented as an isolated concern [2]. Ecological and environmental wicked challenges are often rooted in spillover effects from biodiversity, ecosystem services, social and environmental aspects, management implications, and economic profitability goals [3-6].
The ecologist’s decision-maker’s dilemma
We coin the phrase ‘The ecologist’s dilemma’. On the one horn, ecologists are task with providing wildlife managers and environmental agencies with guidelines aimed at managing populations. On the other hand, scarce data and limited resources pose significant constraints. More often than not, the complexity of this dilemma is intensified by multidisciplinary linkages, unpredictability, and competing stakeholder objectives. Consequentially, many current paradigms leverage existing best practices (i.e., “grandfathered-in” solutions) [7]. However, this may result in “Type III error” whereby an effective solution is employed to address the wrong problem [8].
A precise understanding of key and embedded variables is required for an effective and relevant solution [9,10]. However, traditional liner modelling may be ineffective in the face of imperfect information [11]. There is a need to employ an innovative approach which accounts for tradeoffs with the purpose to standardize the procedure of ecology-based decision-making. Solving for the ecological decision-makers’ dilemma will help to advance policy and resource management implications worldwide. A noble approach should be multi-disciplinary and recognize spillover effects of wildlife management decisions into other domains, user friendly, and capable of accounting for all aspects of the overarching wicked problem.
The wicked case Study of hybridization
Hybridization is one of the primary wicked problems of our time [4]. Accelerating at an alarming rate, it is responsible for the genetic extinction of endangered and indigenous species worldwide (Box 1). Here, we demonstrate the applicability of Object-Oriented Bayesian Networks (OOBNs) to solve for the ecologist’s dilemma (i.e., constraints posed by limited data and scares resources). This platform for standardizing decision making is presented within the contextual framework of the case study concerning hybridization between the endemic black wildebeest (Connochaetes gnou) and the more common blue wildebeest (Connochaetes taurinus) in South Africa.
To date, genetic markers that differentiate pure breed wildebeest individuals from varying generations of hybrids have not yet been identified, and qualitative data are imprecise [12-14]. Ecologists are forced to provide guidelines for wildlife populations and landscapes management with economic profitability in mind, and under limited resources. Although such decisions are made on an ongoing basis, they are often “grandfathered in” [15] cautions that “grandfathered in” practices (which address inbreeding and outbreeding concerns) should be rigorously evaluated and adapted, considering hybridization concerns.
BNs are underutilized in conservation research due to computational expense
Bayesian Networks (BNs) are one of the most useful tools within the probabilistic knowledge representation and reasoning discipline applicable to modelling for reliability, dependency, risk analysis, diagnosis, and monitoring [16-18]. Recently, BNs have been used to model diverse ecology-based problems of high complexity, mainly concerning water, fisheries management, crop disease, and conservation [19]. However, during 1990-2010 a merely 4.2% of peer reviewed research, related to the environmental field utilized them, suggesting that this predictive tool remains unexploited [20].
BNs’ underutilization is primarily attributed to associated computational expense and a gradual learning curve [3,11]. Deriving a BNs structure and populating it with expert knowledge is proven to be difficult and time consuming [21].
Despite their underutilization, BNs are a useful tool for addressing the wicked nature of ecology and environmental research [22]. Specifically, with bidirectional flow enables ecologists to populate networks with data as it becomes available; thus, serving as a tool that optimizes informed decisions given scarce, and seemingly insufficient, data. Here, we provide a platform for setting conservation standards to solve for the ecologist’s dilemma, and thereby promote informed decision making and aid resource management in similar situations worldwide.
Goal
We address the long-standing ecologist’s dilemma. We put forth two frameworks that informed decision in the face of scarce data and limited resources. We illustrate the applicability of Bayesian modeling and inference within the ecological and environmental discipline by solving for the case study of hybridization between black wildebeest and blue wildebeest.
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