Simple, policy friendly, ecological interaction models from uncertain data and expert opinion

In the marine environment, humans exploit natural ecosystems for food and economic benefit. Challenging policy goals have been set to protect resources, species, communities and habitats, yet ecologists often have sparse data on interactions occurring in the system to assess policy outcomes. This paper presents a technique, loosely based on Bayesian Belief Networks, to create simple models which 1) predict whether individual species within a community will decline or increase in population size, 2) encapsulate uncertainty in the predictions in an intuitive manner and 3) require limited knowledge of the ecosystem and functional parameters required to model it. We develop our model for a UK rocky shore community, to utilise existing knowledge of species interactions for model validation purposes. However, we also test the role of expert opinion, without full scientific knowledge of species interactions, by asking non-UK based marine scientists to derive parameters for the model (non-UK scientists are not familiar with the exact communities being described and will need to extrapolate from existing knowledge in a similar manner to model a poorly studied system). We find these differ little from the parameters derived by ourselves and make little difference to the final model predictions. We also test our model against simple experimental manipulations, and find that the most important changes in community structure as a result of manipulations correspond well to the model predictions with both our, and non-UK expert parameterisation. The simplicity of the model, nature of the outputs, and the user-friendly interface makes it potentially suitable for policy, conservation and management work on multispecies interactions in a wide range of marine ecosystems

Bayesian networks and adaptive management of wildlife habitat

Adaptive management is an iterative process of gathering new knowledge regarding a system's behavior and monitoring the ecological consequences of management actions to improve management decisions. Although the concept originated in the 1970s, it is rarely actively incorporated into ecological restoration. Bayesian networks (BNs) are emerging as efficient ecological decision-support tools well suited to adaptive management, but examples of their application in this capacity are few. We developed a BN within an adaptive-management framework that focuses on managing the effects of feral grazing and prescribed burning regimes on avian diversity within woodlands of subtropical eastern Australia. We constructed the BN with baseline data to predict bird abundance as a function of habitat structure, grazing pressure, and prescribed burning. Results of sensitivity analyses suggested that grazing pressure increased the abundance of aggressive honeyeaters, which in turn had a strong negative effect on small passerines. Management interventions to reduce pressure of feral grazing and prescribed burning were then conducted, after which we collected a second set of field data to test the response of small passerines to these measures. We used these data, which incorporated ecological changes that may have resulted from the management interventions, to validate and update the BN. The network predictions of small passerine abundance under the new habitat and management conditions were very accurate. The updated BN concluded the first iteration of adaptive management and will be used in planning the next round of management interventions. The unique belief-updating feature of BNs provides land managers with the flexibility to predict outcomes and evaluate the effectiveness of management interventions. Copyright © 1999–2011 John Wiley & Sons, Inc. All Rights Reserved

Estimating the influence of land management change on weed invasion potential using expert knowledge

Aim To develop and test a general framework for estimating weed invasion potential (suitability and susceptibility) that utilized expert knowledge of dispersal, establishment and persistence and considered the influence of land management. Location The semi-arid Desert Channels Region of Queensland, Australia (476,000 km2). Methods We developed a general framework that integrated knowledge and empirical data of the environmental and land management variables influencing the dispersal, establishment and persistence of the invasive shrub parkinsonia (Parkinsonia aculeata) using a Bayesian network linked to a Geographic Information System (GIS). We evaluated the influence of different land management scenarios on landscape suitability for parkinsonia. Model performance was assessed by comparing predicted landscape suitability with mapped parkinsonia locations and estimated parkinsonia density. Results Our predictions of moderate to high suitability corresponded reasonably well with mapped parkinsonia locations (71% match) and areas of common to abundant estimated density (92% match). They also suggested that parkinsonia has not reached its potential distribution within the study region. Under current land management conditions, 77,000 km2 of land was found to be highly or moderately suitable for parkinsonia. Scenario analysis indicated that maintaining moderate herbaceous ground cover levels, and using sheep to browse juvenile parkinsonia, reduced the predicted moderate to high suitability area to 27,000 km2, offering a potential management strategy for limiting parkinsonia invasion. Main conclusions Weed invasion potential can be reasonably estimated using expert knowledge of dispersal, establishment and persistence, integrated using a Bayesian network linked to a GIS. This modelling approach can be an alternative to process-based and phenomenological modelling, which can be problematic for modelling new and emerging weed invasions, particularly where data are patchy. The modelling approach also allows the influence of land management change on invasion potential to be investigated through scenario analysis