project UMO43 Developing Risk-based Approaches for Managing Contaminants in

the Wimmera River, Victoria- is the forth in a series of five produced by LWA/MDB

A State-Transition Dbn For Management Of Willows In An American Heritage River Catchment

Expansion of willows in the naturally mixed landscape of vegetation types in the Upper St. Johns River Basin in Florida, USA, im- pacts upon biodiversity, aesthetic and recre- Ational values. Managers need an inte- grated knowledge base to support decisions on where, when and how to control willows. Modelling the spread of willows over space and time requires spatially explicit data on willow occupancy, an understanding of dis- persal mechanisms and how the various life- history stages of willows respond to envi- ronmental factors and management actions. We describe an architecture for a manage- ment tool that integrates environmental spa- Tial data from GIS, dispersal dynamics from a process model and Bayesian Networks (BNs) for modelling the inuence of environmen- Tal and management actions on the key life- history stages of willows. In this paper we fo- cus on modelling temporal changes in willow stages using a form of Dynamic Bayesian Net- work (DBN). Starting from a state-transition (ST) model of the willow\u27s lifecyle, from ger- mination to seed-producing adult, we de- scribe the expert elicitation process used to develop a ST-DBN structure, that follows the template described by Nicholson and Flores (2011). We present a scenario-based evalua- Tion of the prototype ST-DBN model

An Object-Oriented Spatial And Temporal Bayesian Network For Managing Willows In An American Heritage River Catchment

Willow encroachment into the naturally mixed landscape of vegetation types in the Upper St. Johns River Basin in Florida, USA, impacts upon biodiversity, aesthetic and recreational values. To control the ex- Tent of willows and their rate of expansion into other extant wetlands, spatial context is critical to decision making. Modelling the spread of willows requires spatially ex- plicit data on occupancy, an understanding of seed production, dispersal and how the key life-history stages respond to environmental factors and management actions. Nichol- son et al. (2012) outlined the architecture of a management tool to integrate GIS spa- Tial data, an external seed dispersal model and a state-transition dynamic Bayesian net- work (ST-DBN) for modelling the inuence of environmental and management factors on temporal changes in willow stages. That paper concentrated on the knowledge en- gineering and expert elicitation process for the construction and scenario-based evalua- Tion of the prototype ST-DBN. This paper extends that work by using object-oriented techniques to generalise the knowledge or- ganisational structure of the willow ST-DBN and to construct an object-oriented spatial Bayesian network (OOSBN) for modelling the neighbourhood spatial interactions that underlie seed dispersal processes. We present an updated architecture for the management tool together with algorithms for implement- ing the dispersal OOSBN and for combining all components into an integrated tool

Modelling Spatial And Temporal Changes With Gis And Spatial And Dynamic Bayesian Networks

State-and-transition models (STMs) have been successfully combined with Dynamic Bayesian Networks (DBNs) to model temporal changes in managed ecosystems. Such models are useful for exploring when and how to intervene to achieve the desired management outcomes. However, knowing where to intervene is often equally critical. We describe an approach to extend state-and-transition dynamic Bayesian networks (ST-DBNs) - incorporating spatial context via GIS data and explicitly modelling spatial processes using spatial Bayesian networks (SBNs). Our approach uses object-oriented (OO) concepts and exploits the fact that ecological systems are hierarchically structured. This allows key phenomena and ecological processes to be represented by hierarchies of components that include similar, repetitive structures. We demonstrate the generality and power of our approach using two models - one developed for adaptive management of eucalypt woodland restoration in south-eastern Australia, and another developed to manage the encroachment of invasive willows into marsh ecosystems in east-central Florida

The Impact of Surgical Experience on Major Intraoperative Aneurysm Rupture and Their Consequences on Outcome: A Multivariate Analysis of 538 Microsurgical Clipping Cases

<div><p>The incidence and associated mortality of major intraoperative rupture (MIOR) in intracranial aneurysm surgery is diverse. One possible reason is that many studies failed to consider and properly adjust the factor of surgical experience in the context. We conducted this study to clarify the role of surgical experience on MIOR and associated outcome. 538 consecutive intracranial aneurysm surgeries performed on 501 patients were enrolled in this study. Various potential predictors of MIOR were evaluated with stratified analysis and multivariate logistic regression. The impact of surgical experience and MIOR on outcome was further studied in a logistic regression model with adjustment of each other. The outcome was evaluated using the Glasgow Outcome Scale one year after the surgery. Surgical experience and preoperative Glasgow Coma Scale (GCS) were identified as independent predictors of MIOR. Experienced neurovascular surgeons encountered fewer cases of MIOR compared to novice neurosurgeons (MIOR, 18/225, 8.0% vs. 50/313, 16.0%, P = 0.009). Inexperience and MIOR were both associated with a worse outcome. Compared to experienced neurovascular surgeons, inexperienced neurosurgeons had a 1.90-fold risk of poor outcome. On the other hand, MIOR resulted in a 3.21-fold risk of unfavorable outcome compared to those without it. Those MIOR cases managed by experienced neurovascular surgeons had a better prognosis compared with those managed by inexperienced neurosurgeons (poor outcome, 4/18, 22% vs. 30/50, 60%, P = 0.013).</p></div

Towards open, reliable, and transparent ecology and evolutionary biology

Unreliable research programmes waste funds, time, and even the lives of the organisms we seek to help and understand. Reducing this waste and increasing the value of scientific evidence require changing the actions of both individual researchers and the institutions they depend on for employment and promotion. While ecologists and evolutionary biologists have somewhat improved research transparency over the past decade (e.g. more data sharing), major obstacles remain. In this commentary, we lift our gaze to the horizon to imagine how researchers and institutions can clear the path towards more credible and effective research programmes

Univariate analysis for risk factors of MIOR.

<p>Univariate analysis for risk factors of MIOR.</p