A Species‐Specific Approach to Modeling Biological Communities and Its Potential for Conservation

By Julian D. Olden. 2003

93 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/9f1bb946e377aff056d4e755c1ce10b503a4aba4

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Citations in the corpus

Multiscale effects of flow regime and habitat and their interaction on fish assemblage structure in eastern Australia (2007, methodology): “We refer the reader to Olden (2003) and Olden et al. (2006a) for more methodological details.”
A review of existing fish assemblage indicators and methodologies (2007, unknown): “There are drawbacks with both approaches (e.g. Chessman 1999; Olden 2003).”
Spatial modelling of biodiversity at the community level (2006, methodology): “These extended techniques include multiresponse neural networks (Olden 2003), vector generalized linear (or additive) models (Yee & Mackenzie 2002) and a multiresponse implementation of multivariate adaptive regression splines (Leathwick et al. 2005).”
An empirical model for estimating aquatic invertebrate respiration (2010, background): “…applied to a variety of tasks in aquatic ecology ranging from taxonomic identifica- tion to diversity pattern prediction (e.g., Brey, Jarre-Teich- mann, & Borlich 1996; Lek & Guégan 2000; Belgrano, Malmgren, & Lindahl 2001, Olden 2003, Pei et al. 2004, Dedecker et al. 2005;Willems et al. 2008).”
The Homogocene: a research prospectus for the study of biotic homogenisation (2018, background): “Community-level models also provide a promising way forward because they facilitate the modelling of all species, including the rare or poorly-sampled ones (Olden 2003), while expanding the possibility to account for the observation process (Iknayan et al.”
Selecting thresholds of occurrence in the prediction of species distributions (2005, unknown): “Ozesmi and Ozesmi (1999), Brosse et al. (1999), Manel et al. (1999), Olden and Jackson (2001), Berry et al. (2002), Pearson et al. (2002, 2004) and Olden (2003) , as a modeling technique better than other traditional techniques in modeling complex phenomena with non-linear relationships.”
Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines (2007, background): “Multiresponse models are effective for presence–absence data (Olden, 2003; Leathwick et al ., 2006) and conceptually are likely to be useful for presence-only data, in which some species may be poorly represented.”
Machine Learning Methods Without Tears: A Primer for Ecologists (2008, background): “Model complexity can be varied by altering the transfer function or the inner architecture of the network through an increase in the number of hidden neurons or layers to enhance data fitting, or by increasing the number of output neurons to model multiple ecological response variables, such as mult”
A multi‐parameter artificial neural network model to estimate macrobenthic invertebrate productivity and production (2012, background): “Ecological applications of ANN range from taxonomic identification to diversity pattern prediction (e.g., Olden 2003, Willems et al. 2008).”
Error and uncertainty in habitat models (2006, methodology): “Methods include generalized dissimilarity modelling (Ferrier et al. 2002), neural networks ( Olden 2003 ), and multivariate adaptive regression splines (Leathwick et al. 2005).”
Modelling stream fish species distribution in a river network: the relative effects of temperature versus physical factors (2008, result): “Secondly, our results confirm those of some rare previous studies (e.g., Olden 2003; Pont et al. 2005) showing the need to use a species-specific modelling approach.”