Mitchell Lyons

Dr Mitchell Lyons

Role: Research Fellow | Lecturer

Bio: I am a postdoc in the Centre for Ecosystem Science, and my research can be described as a mixture of Ecology, Geography and Statistics. I finished my PhD in 2013, at the University of Queensland, which focused on developing new remote sensing methods for long term monitoring and change detection in terrestrial and marine ecosystems. After this I focused on automated monitoring of seagrass environments using remote sensing and autonomous underwater vehicles (AUVs). On moving to the University of New South Wales, I shifted focus to application of modern statistical and modelling approaches for large scale vegetation classification and mapping problems, with a side interest in drone-acquired image data. I also teach remote sensing in some of the courses in the School of Biological, Earth and Environmental Sciences, as well as programming and statistics in various short courses and workshops (http://environmentalcomputing.net/).

Technically speaking, my expertise lies in remote sensing and ecological modelling (statistics and machine learning), and I generally take a computational programming (R and Python specifically) approach. Non-technically speaking, I love getting into the bush or into the ocean, love bare feet on grass and I have a penchant for cricket and homebrewing.

Research field keywords: ecology, remote sensing and GIS, ecological modelling, vegetation science, statistical ecology

Publications: see my Google Scholar profile (http://scholar.google.com.au/citations?user=9PnIKHYAAAAJ), and please contact me if you would like a copy of any of my papers.

Code + software: see my github page (https://github.com/mitchest/), and check out my R packages if you are so inclined:

optimus - model-based clustering diagnostics - https://cran.r-project.org/web/packages/optimus/index.html

c2c - comparing classification and clustering solutions to eachother - https://cran.r-project.org/web/packages/c2c/index.html

 


phone: +61 2 9385 2797 | email: mitchell.lyons@unsw.edu.aulocation: level 5, E26 (biological sciences south) | twitter: @mitchest

Publications

Author Date Title Link PDF
Callaghan et al. 2017 Assessing the reliability of avian biodiversity measures of urban greenspaces using eBird citizen science data

ABSTRACT. Urban greenspaces are important areas for biodiversity, serving multiple uses, sometimes including conservation and biodiversity management. Citizen science provides a cheap and potentially effective method of assisting biodiversity management in urban greenspaces. Despite this potential, the minimum amount of citizen science data required to adequately represent a community is largely untested. We used eBird data to test the minimum sampling effort required to be confident in results for three biological metrics, species richness, Shannon diversity, and community composition (Bray-Curtis similarity). For our data, from 30 urban greenspaces in North America, for a 90% threshold level, a minimum mean number of 210, 33, and 58 checklists were necessary for species richness, Shannon diversity, and community composition, respectively. However, when we eliminated those species that were present in fewer than 5% of checklists at a given site, there was a marked decrease in mean minimum number of checklists required (17, 9, and 52, respectively). Depending on the ecological questions of interest, eBird data may be a potentially reliable data source in urban greenspaces. We provide a validation methodology using eBird data, with its associated code in the R statistical environment, to provide confidence for land managers and community groups managing urban greenspaces.

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