Bayesian Belief Networks as a tool for evidence-based conservation management
Introduction
Recent years have witnessed growing interest in evidence-based approaches to conservation, reflecting widespread concern that much conservation practice is based on tradition or the experience of practitioners, rather than on the results of scientific research (Pullin & Knight (2001), Pullin & Knight (2003); Pullin, Knight, Stone, & Charman, 2004; Sutherland, Pullin, Dolman, & Knight, 2004). This process has been inspired by the ‘effectiveness revolution’ that has occurred in medicine during the past 20 years, aimed at incorporating the results of medical research into medical practice (Egger, Smith, & Altman, 2003; Stevens & Milne, 1997). Evidence-based frameworks have subsequently developed in other areas of public policy, including psychology (Petticrew, 2001), education (Nye, Schwartz, & Turner, 2005), social welfare (Stagner, Ehrle, & Reardon-Anderson, 2003), and criminology (Gadon, Cooke, & Johnstone, 2005).
The common element of these evidence-based frameworks is the process of ‘systematic review’. All reviews are retrospective, observational research studies and are therefore subject to systematic and random error (Cook, Mulrow, Haynes, & Brian, 1997). The quality of a review therefore depends on the extent to which scientific methods have been used to minimise error and bias. Systematic reviews locate data from published and unpublished sources, critically appraise methods using pre-defined criteria and synthesise evidence to provide empirical answers to research questions. They differ from conventional reviews in that they follow a strict methodological and statistical protocol making them more comprehensive, minimising the chance of bias and improving transparency, repeatability, and reliability. Rather than reflecting the views of authors or being based on a (possibly biased) restricted sample of literature, they provide a comprehensive assessment and summary of available evidence (Cook et al., 1997).
Guidelines for the production of ecological systematic reviews have been established (Pullin & Stewart, 2006) and the results of the first systematic reviews of conservation evidence are now becoming available (Stewart, Coles, & Pullin, 2005; Tyler, Pullin, & Stewart, 2006). Further unpublished and ongoing reviews can be obtained from http://www.cebc.bham.ac.uk/. These reviews have focused on the identification of experimental and monitoring evidence most analogous to the controlled trials, observational studies, and diagnostic test trials synthesised in medical meta-analyses (Egger et al., 2003). Criteria for inclusion in such meta-analyses include the presence of a quantitative comparator before and after application of an experimental treatment, and/or between experimental treatment and controls.
These initial systematic reviews of conservation evidence have highlighted the fact that experimental investigations meeting these criteria are relatively rare, even for management approaches that are widely used. For example, of 317 articles with relevant titles concerning the impact of burning on blanket bog, only eight (2.5%) had comparators allowing quantitative synthesis (Stewart, Coles, & Pullin, 2004). Similarly, reviews regarding burning of dry heathland, the impact of wind farms on bird abundance, and bracken control utilised 1.7%, 12%, and 4.2% of material with relevant titles respectively (www.cebc.bham.ac.uk). As a consequence, the results of meta-analyses can lack statistical power (as a consequence of small sample sizes), especially when numerous effect modifiers are included in the analysis. Despite the rigour underlying the methodology, review outcomes are therefore often highly tentative, allowing few firm conclusions to be drawn. Such reviews can clearly be used to identify experimental knowledge-gaps, allowing priority areas for needs-led research to be identified. However, additional forms of evidence or information exist, and can be retrieved using systematic review methods (for example, published studies that fail to meet the criteria for inclusion in a meta-analysis, perhaps because a suitable comparator or control was not included). The inclusion of such information could increase the utility of reviews to practitioners, allowing tentative management recommendations to be made on all the available evidence rather than just a subset.
Methods are therefore required that would enable additional forms of evidence to be incorporated into analyses. Although experimental investigations of conservation management interventions are relatively few, much information is collected during environmental monitoring activities. Conservation practitioners often also possess deep working knowledge of the ecological communities with which they are familiar, based on the results of their practical experience and anecdotal observations of the outcome of management interventions. An analytical approach is needed that would enable such types of information to be analysed together with more rigorous scientific evidence, in an integrated manner. Ideally, the outcomes of such analyses should be made available in a form that can readily support decision-making, including the development and implementation of appropriate conservation policies.
We propose that Bayesian Belief Networks (BBN) offer a uniquely powerful tool to address these problems, by providing a structured combination of diverse lines of evidence. BBNs have developed at the interface between statistics, applied artificial intelligence, and expert system development (Pearl (1986), Pearl (1988)). BBNs are graphical models that encode probabilistic relationships among variables of interest (Bøttcher & Dethlefsen, 2003; Heckerman, 1996), and may be considered as tools for graphically representing the relationships among a set of variables (Castillo, Gutierrez, & Hadi, 1997). A BBN comprises a network of nodes connected by directed links, with a probability function attached to each node (Jensen, 2001). BBNs are therefore statistical models of a domain. The network of a BBN is referred to as a directed acyclic graph (DAG), which is used to model a domain containing uncertainty, and therefore provides a tool for reasoning under uncertainty (Jensen, 2001). This uncertainty can arise owing to an imperfect understanding of the domain, incomplete knowledge of the state of the domain, randomness in the mechanisms governing the behaviour of the domain, or any combination of these.
Bayesian networks evolved in the early 1990s, based on a deep body of theory developed for graphical models in general. Statistical graphical models have a history that can be traced back to Wright (1934) who developed them in the context of path analysis. In order to develop the directed graphs used in BBNs, several challenges had to be overcome, as described by Spiegelhalter, Dawid, Lauritzen, and Cowell (1993). The calculus of probability underlying Bayesian networks was at one time considered to be both epistemologically inadequate and computationally infeasible for complex domains. The principal difficulty was that complex applications require the specification of what can be huge joint probability distributions. In addition, evidence propagation within such a framework requires the computation of probabilities for events of interest that are conditional on what could be arbitrary configurations of other variables. The computational hurdles have now been overcome, due in large part to the advances derived from the seminal work of Pearl (1986), Pearl (1988). Later, Pearl (1995) showed how graphical models can be used for causal inference, thus strengthening the underlying justification behind contemporary applications of BBNs that use expert knowledge to determine both the structure and parameters of the networks (Spiegelhalter & Cowell, 1993). As a result of software developments and the increased availability of computing power, construction of large BBNs is now feasible (Neil, Fenton, & Nielson, 2000), and the method can readily be implemented on a personal computer.
Contemporary software programs for implementing BBNs are extremely flexible. BBNs can be built directly from knowledge of the domain of interest. Alternatively, it is now possible for both the structure and the parameters of a BBN to be learnt directly from a data set, and for this reason, the method is widely used for automated data mining, particularly for market research. However, many of the issues identified by conservation managers (see Sutherland et al., 2006) do not generate the large quantities of replicated data needed for such data mining. In such circumstances, it is best to view BBNs as decision-support tools helpful for combining expert knowledge with available empirical data (Marcot, Holthausen, Raphael, Rowland, & Wisdom, 2001).
Bayesian analytical techniques have received growing interest from ecological researchers since publication of a special edition of Ecological Applications in 1996 (Crome, Thomas, & Moore, 1996; Ellison, 1996; Gertner & Zhu, 1996). Typically, Bayesian statistics are used to find parameter values when the stochastic component of a model is represented by one or more continuous probability density functions. The directed acyclical graphs used to represent these models can follow the same formalisms as BBNs. However, examples of the use of BBNs in ecology or resource management are few. Examples include predicting density of mountain aspen suckers (Haas, 1991), assessing population trends in aquatic and terrestrial vertebrate species (Marcot et al., 2001; Rieman et al., 2001), integrated water resource planning (Bromley, Jackson, Clymer, Giacomello, & Jensen, 2005), social aspects of resource management (Cain, Batchelor, & Waughray, 1999) and assessing the impact of commercialising non-timber forest products on livelihoods (Newton et al., 2006). We are not aware of any previous attempt to explore the potential value of BBNs specifically to evidence-based conservation management.
In this paper, we explore the application of BBNs to evidence-based conservation management through consideration of four case studies. These illustrate a range of different conservation issues and management options, and vary in the type and quality of evidence available. In two of the examples, BBNs are constructed that incorporate the results of systematic surveys of the conservation literature, and in one case, this form of evidence is contrasted with that based on the experience of conservation practitioners. In each case, BBNs are used to assess the potential impact of management interventions on some outcome or variable of conservation interest. The four examples are: (i) impacts of deer grazing on saltmarsh vegetation; (ii) impacts of burning on upland bog vegetation; (iii) control of Rhododendron ponticum; and (iv) management of lowland heathland by burning. Each of these themes is currently a significant conservation issue in the UK, and yet the potential outcome of management interventions is uncertain.
Section snippets
Case 1. Impacts of deer grazing on saltmarsh vegetation
Poole harbour, located on the south coast of England, is internationally recognised as a site for large numbers of wintering wildfowl and waders, which feed and roost on intertidal mudflats and saltmarshes. The saltmarshes are also important during the early summer months as breeding sites for waders, gulls, and terns. In consequence, the harbour has been designed as a Special Protection Area (SPA) under the European Birds Directive and as a Ramsar site. The saltmarsh vegetation is dominated by
Construction of BBNs
BBNs were constructed using Hugin Developer 6.3, a commercial software package developed and distributed by Hugin Expert A/S, Aalborg, Denmark (http://www.hugin.com/). In each case, variables were represented as nodes in the networks, and connected by arrows (directed links), which are indications of conditional dependence. A link between two nodes, from node A (parent node) to node B (child node), indicates that A and B are functionally related, or that A and B are statistically correlated.
Case 1. Impacts of deer grazing on saltmarsh vegetation
In this case study, the variables included Sika deer density, Spartina volume, floristic diversity of vegetation, and abundance of macro-invertebrates (Fig. 1). In each of these nodes, CPTs were based on the experimental data provided by Diaz et al. (2005). In this investigation, data were not collected on redshank (Tringa tetanus) nest sites, numbers, or reproductive success, but these have been included in the network so that the potential impacts of deer grazing on bird populations can be
Discussion
BBNs possess a number of features that make them particularly valuable as a tool for supporting evidence-based conservation management. First, the graphical interface of a BBN provides a highly intuitive means of representing the features of a system of interest. The first step in producing a BBN is to illustrate the system (or domain) as a diagram, in which variables (nodes) are represented as ellipses. These nodes are then connected by arrows, which indicate conditional dependencies between
Acknowledgements
Many thanks to the practitioners who responded to the questionnaire survey on control methods in Rhododendron and to Claire Tyler who disseminated it. Parts of this work were carried out with support from English Nature and NERC.
References (58)
- et al.
The use of Hugin to develop Bayesian networks as an aid to integrated water resource planning
Environmental Modelling and Software
(2005) - et al.
Grazing of lowland heath in England: Management methods and their effects on heathland vegetation
Biological Conservation
(1997) - et al.
Modeling ecological and economic systems with STELLA: Part III
Ecological Modelling
(2001) - et al.
Ecological impacts of Sika deer on Poole Harbour saltmarshes
- et al.
Bayesian estimation in forest surveys when samples or prior information are fuzzy
Fuzzy Sets and Systems
(1996) - et al.
Using Bayesian Belief Networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement
Forest Ecology and Management
(2001) - et al.
Hugin: A system creating adaptive causal probabilistic networks
Fusion, propagation and structuring in belief networks
Artificial Intelligence
(1986)- et al.
Support for decision making in conservation practice: An evidence-based approach
Journal for Nature Conservation
(2003) - et al.
Do conservation managers use scientific evidence to support their decision-making?
Biological Conservation
(2004)
Evaluation of potential effects of federal land management alternatives on trends of salmonids and their habitats in the interior Columbia River Basin
Forest Ecology and Management
Changes on the heathlands in Dorset, England, between 1987 and 1996
Biological Conservation
Applying evidence-based practice in conservation management: Lessons from the first systematic review and dissemination projects
Biological Conservation
The need for evidence-based conservation
Trends in Ecology and Evolution
Risks and decisions for conservation and environmental management
Belief networks: A framework for the participatory development of natural resource management strategies
Environment, Development and Sustainability
Expert systems and probabilistic network models
Systematic reviews: Synthesis of best evidence for clinical decisions
Annals of Internal Medicine
Fast retraction of evidence in a probabilistic expert system
Statistics and Computing
A novel Bayesian approach to assessing impacts of rain forest logging
Ecological Applications
Biological flora of the British Isles: Rhododendron ponticum L
The Journal of Ecology
Review of heather and grass burning regulations and code of practice in England. A consultation document – September 2005
Systematic reviews in healthcare: Meta-analysis in context
An introduction to Bayesian inference for ecological research and environmental decision-making
Ecological Applications
Institutional violence: A systematic review and meta-analysis of the impact of situational factors on violence
Campbell Collaboration Systematic Review Protocol
A Bayesian Belief Network advisory system for Aspen regeneration
Forest Science
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