39 research outputs found
Determining factors that influence the dispersal of a pelagic species: A comparison between artificial neural networks and evolutionary algorithms
No full textD.R. Pontin, S. Schliebs, S.P. Worner, M.J. Watt
Estimating the risk of insect species invasion: Kohonen self-organising maps versus k-means clustering
No full textMichael J. Watts and S.P. Worne
Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species
No full textThe use of multi-layer perceptrons (MLP) to determine the relative significance of climatic variables to the establishment of insect pest species is described. Results show that the MLP are able to learn to accurately predict the establishment of a pest species within a specific geographic region. Analysis of the MLP yielded insights into the contribution of the individual input variables and allowed for the identification of those variables that were most significant in either encouraging or inhibiting establishment.Michael J. Watts and S.P. Worne
Scientific workflow management with ADAMS: building and data mining a database of crop protection and related data
No full textData mining is said to be a field that encourages data to speak for itself rather than “forcing” data to conform to a pre-specified model, but we have to acknowledge that what is spoken by the data may well be gibberish. To obtain meaning from data it is important to use techniques systematically, to follow sound experimental procedure and to examine results expertly. This paper presents a framework for scientific discovery from data with two examples from the biological sciences. The first case is a re-investigation of previously published work on aphid trap data to predict aphid phenology and the second is a commercial application for identifying and counting insects captured on sticky plates in greenhouses. Using support vector machines rather than neural networks or linear regression gives better results in case of the aphid trap data. For both cases, we use the open source machine learning workbench WEKA for predictive modelling and the open source ADAMS workflow system for automating data collection, preparation, feature generation, application of predictive models and output generation
Prediction of global distribution of insect pest species in relation to climate using an ecological informatics method.
No full text
Using MLP to Determine Abiotic Factors In uencing the Establishment of Insect Pest Species
No full textNotogaster wornerae Fernandez-Triana & Ward 2020, sp. nov.
No full textNotogaster wornerae Fernandez-Triana & Ward, sp. nov. (Figs 21 A–H) Holotype. Male (LUNZ), NEW ZEALAND, Stewart Island, Table Hill, Rakeahua track, -47.0032 167.8004, sweeping, 8/2/1991, S.P. Worner. Diagnostic description. Mesosoma entirely to mostly dark (dark brown or black), lighter areas very small and localized; metasoma dorsally entirely dark (brown or black); gena with relatively large and clear pale spot; tegula dark; first two pairs of legs mostly dark (brown to black); metacoxa dark; metafemur mostly pale (yellow, orange or light yellow-brown); metatibia mostly yellow; T 2 mostly sculptured. See Table 1 for additional morphological measurements. Notogaster wornerae is probably the most distinguishable species in the genus, based on the trapezoidal shape of T 2 (its width at posterior margin 1.2 × its median length) and fore wing vein 1CUa comparatively very short (0.33 × as long as vein 1CUb). It is also the only species known from Stewart Island. Molecular data. There are no DNA barcode sequences in BOLD for this species. Distribution. Only known from a single specimen from Stewart Island (Fig. 9). Etymology. Named after Sue Worner, the collector of the type specimen, for her contribution to entomology in New Zealand.Published as part of Fernández-Triana, Jose L. & Ward, Darren F., 2020, Notogaster, a new genus of Microgastrinae (Hymenoptera: Braconidae) from New Zealand, pp. 251-279 in Zootaxa 4801 (2) on page 275, DOI: 10.11646/zootaxa.4801.2.3, http://zenodo.org/record/390044
Using MLP to Determine Abiotic Factors Influencing the Establishment of Insect Pest Species
No full textThe use of multi-layer perceptrons (MLP) to determine the significance of climatic variables to the establishment of insect pest species is described. Results show that the MLP are able to learn to accurately predict the establishment of a pest species within a specific geographic region. Analysis of the MLP yielded insights into the contribution of the individual input variables and allowed for the identification of those variables that were most significant in either encouraging or inhibiting establishment.Michael J. Watts and S. P. Worne
Null-model validation of MLP input contribution analysis in ecology
No full textA method is presented for applying a null-model analysis to the verification of the significance of the input neurons of Multi-Layer Perceptrons (MLP). This method was applied to a problem from ecology, namely the establishment of invasive insect pest species. Previous work has described how the MLP were trained to predict species establishment from climate data, and to identify which climatic factors are significant. The null-model analysis method described here was used to validate these predictions.Michael J. Watts and S. P. Worne
