33 research outputs found
Weighting of individuating information elements and base rates in a nursing decision-making task involving non-diagnostic case information
No full textThis thesis was scanned from the print manuscript for digital preservation and is copyright the author.
Researchers can access this thesis by asking their local university, institution or public library to
make a request on their behalf. Monash staff and postgraduate students can use the link in the References field
Skog som vern mot naturfarer : kunnskapssammenstilling og tilpasning til Natur i Norge (NiN) :
No full textRapporten presenterer forslag til enhetlig beskrivelse av de egenskapene ved skog som har vesentlig betydning for skogens funksjon som vern mot naturfare
Forest as protection against natural hazard : classification of knowledge and adaption to the classification system of Nature in Norway (NiN)
No full textRapporten presenterer forslag til enhetlig beskrivelse av de egenskapene ved skog som har vesentlig betydning for skogens funksjon som vern mot naturfare
Capacity building for Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES). Progress report 2011: Indo- Norwegian pilot project on capacity building in biodiversity informatics for enhanced decision making, improved nature conservation and sustainable development
No full textHanssen, F., Mathur, V., Athreya, V., Bakkestuen, V., Chavan, V., Lindgaard, A., Mehlum, F.,
González-Talaván, A., Vang, R. & Valland, N. 2012. Capacity building for Intergovernmental
Platform for Biodiversity and Ecosystem Services (IPBES). Progress report 2011:
Indo- Norwegian pilot project on capacity building in biodiversity informatics for enhanced
decision making, improved nature conservation and sustainable development. - NINA Report
801. 24 pp.
This report describes the activities and achievements in 2011 of the Indo- Norwegian pilot project on capacity building in biodiversity informatics for enhanced decision making, improved conservation and sustainable development in India. The pilot project is initiated and funded by the Norwegian Ministry for Foreign affairs, the Norwegian Environmental Ministry and the Norwegian Directorate for Nature Management. The pilot project is also highly welcomed and explicitly supported by the Government of India. Capacity building has been identified as an essential component of IPBES. The Norwegian Government acknowledges the need for capacity building and has developed and initiated several projects addressing capacity building needs in partner countries. The Norwegian Institute for Nature research (NINA) were during the spring 2011 invited by the Norwegian Directorate for Nature Management to initiate and coordinate a pilot project on capacity building under IPBES. India was early identified as an ideal partner country for the realization of a capacity building pilot project both because of the rich biodiversity in the country and because of the recent development towards establishment of the Indian Biodiversity Information Facility (INBIF). Coordinated by the Wildlife Institute of India (WII), INBIF is the national node for linkage with the Global Biodiversity Information Facility (GBIF). In the context of INBIF, WII has the mandate from the Indian Ministry of Environment and Forests (MoEF) to build capacity for effective biodiversity information management. The main objective of the pilot project is to build capacity to enable free sharing, access and dissemination of biodiversity and ecosystem data in India to be used in policy and knowledge-based decision-making. This also includes a mapping of relevant biodiversity data originating from India and held in the Norwegian natural museum`s collections. The project is led, coordinated and partially executed by NINA and the Norwegian Biodiversity Information Centre (NBIC). NINA will provide its expertise in managing camera-trap projects and together with NBIC and the Norwegian GBIF- node provide the expertise acquired from building the Norwegian biodiversity infrastructure. The Indian counterpart WII will be responsible for the implementation and progress of the project nationally within India. The GBIF Secretariat in Copenhagen will provide guidance about international data standards, training and capacity building on Biodiversity Informatics
Indicative distribution maps for Ecological Functional Groups - Level 3 of IUCN Global Ecosystem Typology
No full textThis dataset includes the original version of the indicative distribution maps and profiles for Ecological Functional Groups - Level 3 of IUCN Global Ecosystem Typology (v1.1). Please refer to Keith et al. (submitted). -- THIS VERSION IS LACKING MAPS FOR SOME FUNCTIONAL GROUPS, OTHER MAPS HAVE BEEN REPLACED; CHECK NEWEST VERSION --
The descriptive profiles provide brief summaries of key ecological traits and processes for each functional group of ecosystems to enable any ecosystem type to be assigned to a group.
Maps are indicative of global distribution patterns are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Most maps were prepared using a coarse-scale template (e.g. ecoregions), but some were compiled from higher resolution spatial data where available (see details in profiles). Higher resolution mapping is planned in future publications.
We emphasise that spatial representation of Ecosystem Functional Groups does not follow higher-order groupings described in respective ecoregion classifications. Consequently, when Ecosystem Functional Groups are aggregated into functional biomes (Level 2 of the Global Ecosystem Typology), spatial patterns may differ from those of biogeographic biomes. Differences reflect the distinctions between functional and biogeographic interpretations of the term, “biome”.This dataset is part of the publication:
David A. Keith, Jose R. Ferrer, Emily Nicholson, Melanie J. Bishop, Beth A. Polidoro, Eva Ramirez-Llodra, Mark G. Tozer, Jeanne L. Nel, Ralph Mac Nally, Edward J. Gregr, Kate E. Watermeyer, Franz Essl, Don Faber-Langendoen, Janet Franklin, Caroline E. R. Lehmann, Andres Etter, Dirk Roux, Jonathan S. Stark, Jessica A. Rowland, Neil A. Brummitt, Ulla C. Fernandez-Arcaya, Iain M. Suthers, Susan K. Wiser, Ian Donohue, Leland J. Jackson, R. Toby Pennington, Nathalie Pettorelli, Angela Andrade, Arild Lindgaard, Teemu Tahvanainan, Aleks Terauds, James E. M. Watson , Michael A Chadwick, Nicholas J. Murray, Justin Moat, Patricio Pliscoff, Irene Zager, Richard T. Kingsford
'Earth's ecosystems: a function-based typology for conservation and sustainable management'
(*submitted*)
The PLuS Alliance supported a workshop in London to initiate development. DAK, EN, RTK, JRFP, JAR & NJM were supported by ARC Linkage Grants LP170101143 and LP180100159 and the MAVA Foundation. The IUCN Commission on Ecosystem Management supported travel for DAK to present aspects of the research to peers and stakeholders at International Congresses on Conservation Biology in 2017 and 2019, and at meetings in Africa, the middle east and Europe
Capacity building for Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES). Final report. Indo- Norwegian pilot project on capacity building in biodiversity informatics for enhanced decision making, improved nature conservation and sustainable development.
No full textHanssen, F. (editor), Mathur, V.B. (editor), Athreya, V., Barve, V., Bhardwaj, R., Boumans, L., Cadman, M., Chavan, V., Ghosh, M., Lindgaard, A., Lofthus, Ø., Mehlum, Pandav, B., Punjabi, G. A., F., González Talaván, A., Talukdar, G., Valland, N. and Vang, R. Capacity building for Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES). Final report. Indo- Norwegian pilot project on capacity building in biodiversity informatics for enhanced decision making, improved nature conservation and sustainable development. - NINA Report 1079. 116 pp. Dette pilotprosjektet har vært koordinert av Norsk Institutt for Naturforskning (NINA) i nært
samarbeid med Wildlife Insitutute of India (WII), Artsdatabanken, Naturhistorisk Museum ved
Universitetet i Oslo, Wildlife Conservation Society- India Program (WCS) og Centre for Wildlife
Studies (CWF) i India. Prosjektet er finansiert av den Norske Regjering med støtte fra den og
India.
Prosjektet har samarbeidet med Global Biodiversity Information Facility (GBIF) og har
implementert flere av deres kapasitetsbyggende verktøy, standarder og tjenester. I tillegg er WII
og Naturhistorisk Museum nasjonale GBIF- noder. Prosjektet er nært knyttet til indiske og
internasjonale strategier for utvikling av biodiversitetsinfrastruktur.
Prosjektet har fokusert på nasjonale brukerbehov, viltkamerametodikk, dataforvaltning, åpen
datadeling og barrierer for åpen datadeling. Seks casestudier har vist hvordan biodiversitetsinformatikk,
bruk av viltkamera, datamobilisering og strategier for deling av data kan bidra til
forbedrede beslutningsprosesser. Dette har ført til en bedre forståelse for bruk av viltkamera,
occupancy-modellering, DNA-analyser, artsutbredelse, rovvilt/samfunn konflikter, effekter av
menneskelig aktivitet på ville dyr, habitatrestaurering, behov knyttet til forvaltning av tigre, samt
etterforskning av ulovlig jakt på tiger.
Prosjektet har gjennomført en mindre datarepatrieringsøvelse ved de norske naturhistoriske museene.
Kapasitetsbyggingskomponenten i dette arbeidet overfor internasjonale museumssamlinger
ligger primært i beskrivelsen av hvordan repatrierte data kan mobiliseres gjennom GBIF.
WII har utviklet en nasjonal database og en webportal for mobilisering av viltkameradata. Dette
utviklingsarbeidet er et viktig skritt i retning av å utvikle et nasjonalt åpent system for forvaltning
av viltkamerabilder og tilhørende metadata. Prosjektet har også utviklet en Best Practice Guide
(BPG) for publisering av biodiversitetsdata avledet fra viltkamerabilder. Denne guiden vil bli vedlikeholdt
av GBIF i fremtiden.
Dette prosjektet har vist høy relevans i forhold til de kapasitetsbyggingsbehov som er identifisert
av IPBES. Som prosjektet viser er det store internasjonale synergier innen kapasitetsbygging
knyttet til biodiversitetsinformatikk, bruk av viltkamera, datamobilisering, datarepatriering, dataforvaltning
og forbedrede strategier for datadeling. I avslutningsfasen av dette pilotprosjektet har
prosjektpartnerne bestemt seg for å se etter nye samarbeidsmuligheter under IPBES
An adaptive floating node based formulation for the analysis of multiple delaminations under high cycle fatigue loading
No full textA novel efficient numerical formulation for the analysis of multiple fatigue-driven delamination cracks is presented. A cohesive zone model is used in combination with an Adaptive Refinement Scheme (ARS) and an Adaptive Floating Node Method (A-FNM) element that refine the model effectively during the analysis. Novel techniques are proposed to track the positions of multiple crack tips and calculate the mode decomposed energy release rates for the individual crack tips using the J-integral. The method has been implemented in a Matlab finite element code and validated with single and multiple delamination cases with varying mode mixities. Comparisons with theoretically based predictions and available experimental data showcase the high accuracy of the method. The presented method lowers the computational time compared to standard, fully refined finite element models by a factor of 4–5.Aerospace Structures & Computational Mechanic
Developing and Maintaining a National Biodiversity Data Infrastructure – An example from Norway
No full textBiodiversity data infrastructures are fundamental to halting the ongoing loss of species and habitats. Here we provide an overview of the national biodiversity data infrastructure developed and implemented in Norway by The Norwegian Biodiversity Information Centre (NBIC). Key elements and properties of this infrastructure are highlighted and directions for future development are outlined.The overarching objective for the infrastructure is to make data on habitats and species in Norway available for policy and decision makers, researchers and the general public. Here we will focus on data on species and their distribution. The infrastructure is built as a modular system but with a high level of integration between the components. NBIC has the main responsibility for developing and managing the infrastructure in collaboration with natural history museums, research organizations, private companies and non-governmental organizations (NGOs), including the Norwegian node of the Global Biodiversity Information Facility (GBIF). The infrastructure includes a citizen science portal that gives both amateurs and professionals the possibility to report species sightings. The data from this portal together with data from natural history museums and other data providers are then made available though the Species Map Service website. The infrastructure also includes the Norwegian taxonomic backbone database and a trait bank that is under development. The trait bank is planned to contain both ecological traits but also other information about species such as Red List status. The infrastructure builds on a few simple principles. The exchange of data is based on the Darwin Core Standard and its extensions, or other open data standards. Each data owner is responsible for quality control, secure storage and management of their own data. The data owners publish the data using the Integrated Publishing Toolkit (IPT) developed by GBIF. NBIC then harvests all observations with spatial coordinates that fall within Norway and adds information about, for example, status on the Norwegian Red List before the data are made available in the map. The core parts of the infrastructure only handle data that are open and adhere to the FAIR principles, i.e. that the data are Findable, Accessible, Interoperable, and available for Reuse. An important exception to this principle is observations of threatened species that are particularly vulnerable to human disturbance. Sensitive data are managed in a separate, secure system and with a restricted access portal for data viewing.Data in the Species Map Service are also available through a public API, which can be used to harvest and import the data or subsets of it into other systems and services. To give an example, the forestry industry integrates data on Red Listed species into their planning systems tools and has even an option to display a subset of species occurrences on their forestry machine computers.NBIC has in cooperation with NGOs established a system in which experts validate observations that are reported through the citizen science portal. As it is not possible to validate all observations, species of particular interest for environmental management and conservation are given priority. A future solution could be to develop a system that can identify observations with a low likelihood of being correct using, for example, statistical models that describe the likelihood of an occurrence in a given space and environment. If photos are available, image recognition based on machine learning can be used to spot species that are likely to be misidentified. It is also possible/conceivable to use a more heuristic approach based on an ontology for organisms and ecological traits (for example, "this is a fish, fish are always aquatic organisms, this observation was made on dry land and is thus unlikely to be true"). The development of improved tools and systems to improve data quality is a major task for future development of the infrastructure.In Norway, spatial planning processes and decisions are required to integrate information made available through the infrastructure. If new information about the occurrence of Red Listed species or other species of special interest for environmental management and conservation are discovered through the planning process, the developer is responsible for archiving these observations in a publicly available database.Key factors for the success of the biodiversity data infrastructure described here are: (i) the close cooperation between NBIC, data providers, NGO's and data users, (ii) the modularity of the infrastructure, (iii) the use of open and flexible standards for data exchange, and (iv) the integration of the infrastructure in the legal framework for spatial planning requiring that data from e.g., environmental impact assessments are made available through the infrastructure
Roles of the Red List of Ecosystems in the Kunming-Montreal Global Biodiversity Framework
No full textThe Kunming-Montreal Global Biodiversity Framework (GBF) of the UN Convention on Biological Diversity set the agenda for global aspirations and action to reverse biodiversity loss. The GBF includes an explicit goal for maintaining and restoring biodiversity, encompassing ecosystems, species and genetic diversity (goal A), targets for ecosystem protection and restoration and headline indicators to track progress and guide action1. One of the headline indicators is the Red List of Ecosystems2, the global standard for ecosystem risk assessment. The Red List of Ecosystems provides a systematic framework for collating, analysing and synthesizing data on ecosystems, including their distribution, integrity and risk of collapse3. Here, we examine how it can contribute to implementing the GBF, as well as monitoring progress. We find that the Red List of Ecosystems provides common theory and practical data, while fostering collaboration, cross-sector cooperation and knowledge sharing, with important roles in 16 of the 23 targets. In particular, ecosystem maps, descriptions and risk categories are key to spatial planning for halting loss, restoration and protection (targets 1, 2 and 3). The Red List of Ecosystems is therefore well-placed to aid Parties to the GBF as they assess, plan and act to achieve the targets and goals. We outline future work to further strengthen this potential and improve biodiversity outcomes, including expanding spatial coverage of Red List of Ecosystems assessments and partnerships between practitioners, policy-makers and scientists.No Full Tex
An adaptive floating node based formulation for the analysis of multiple delaminations under quasi-static loading
No full textA novel and efficient numerical formulation for the modelling of multiple delaminations growth in laminated composite materials subjected to quasi-static loading is presented. The proposed formulation alleviates the high computational cost associated with models featuring cohesive elements by using a novel Adaptive Refinement Scheme and an Adaptive Floating Node Method Element to refine the model effectively during the analysis without modifying the global finite element connectivity. The formulation has been implemented in a MATLAB finite element code and validated with single and multiple delamination numerical models with varying mode mixities. The new formulation provides accurate results comparable to standard fully refined finite element models while drastically lowering the computational time of the analysis.Aerospace Structures & Computational Mechanic
