%0 Journal Article %1 clough_generalized_2012 %A Clough, Yann %D 2012 %J Ecology %K Bayesian Carlo, Linear Markov Models, Monte \_tablet, analytics, chain effects, equation hierarchical indirect inference, model, modeling path structural %R 10.1890/11-1899.1 %T A generalized approach to modeling and estimating indirect effects in ecology %U http://www.esajournals.org/doi/abs/10.1890/11-1899.1 %X The need to model and test hypotheses about complex ecological systems has led to a steady increase in use of path analytical techniques, which allow the modeling of multiple multivariate dependencies reflecting hypothesized causation and mechanisms. The aim is to achieve the estimation of direct, indirect and total effects of one variable on another and to assess the adequacy of whole models. Path analytical techniques based on maximum likelihood currently used in ecology are rarely adequate for ecological data, which are often sparse, multi-level, and may contain non-linear relationships as well as non-normal response data such as counts or proportion data. Here I introduce a more flexible approach in the form of the joint application of hierarchical Bayes, Markov-Chain Monte Carlo algorithms, Shipley's d-sep test and the potential outcomes framework to fit path models as well as decompose and estimate effects. An example based on the direct and indirect interactions between ants, two insect herbivore and a plant species demonstrates the implementation of these techniques, using freely available software. Read More: http://www.esajournals.org/doi/abs/10.1890/11-1899.1

@article{clough_generalized_2012, abstract = {The need to model and test hypotheses about complex ecological systems has led to a steady increase in use of path analytical techniques, which allow the modeling of multiple multivariate dependencies reflecting hypothesized causation and mechanisms. The aim is to achieve the estimation of direct, indirect and total effects of one variable on another and to assess the adequacy of whole models. Path analytical techniques based on maximum likelihood currently used in ecology are rarely adequate for ecological data, which are often sparse, multi-level, and may contain non-linear relationships as well as non-normal response data such as counts or proportion data. Here I introduce a more flexible approach in the form of the joint application of hierarchical Bayes, Markov-Chain Monte Carlo algorithms, Shipley's d-sep test and the potential outcomes framework to fit path models as well as decompose and estimate effects. An example based on the direct and indirect interactions between ants, two insect herbivore and a plant species demonstrates the implementation of these techniques, using freely available software. Read More: http://www.esajournals.org/doi/abs/10.1890/11-1899.1}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Clough, Yann}, biburl = {https://www.bibsonomy.org/bibtex/2750bc45ae450540b33de0e1ccbe1f9f3/yourwelcome}, doi = {10.1890/11-1899.1}, interhash = {2fa3f74bb4621c12568d4e0de7ed8664}, intrahash = {750bc45ae450540b33de0e1ccbe1f9f3}, issn = {0012-9658}, journal = {Ecology}, keywords = {Bayesian Carlo, Linear Markov Models, Monte \_tablet, analytics, chain effects, equation hierarchical indirect inference, model, modeling path structural}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {A generalized approach to modeling and estimating indirect effects in ecology}, url = {http://www.esajournals.org/doi/abs/10.1890/11-1899.1}, year = 2012 }