People

** Do not hesitate to contact me to discuss student and collaborative research opportunities **

** Si mis lineas de trabajo encajan con tus intereses, no dudes en contactarme para comentar ideas y oportunidades de colaboración, bien sea a través de supervisión de tesis si eres estudiante, o más en general a través de proyectos conjuntos. **

Current students

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August Hao

August (Tianxiao) Hao [MSc BioSciences, University of Melbourne, co-supervised with José Lahoz-Monfort and Jane Elith]. August is interested in Species Distribution Modelling and the value of ensembles of models fitted with different techniques as a tool to reduce model uncertainty.

 

 

 

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Els van Burm

Els van Burm [PhD candidate, University of Melbourne, co-supervised with Mick McCarthy and Brendan Wintle]. Els is studying how many data are needed to make reliable conservation decisions. Her two main case studies are the management of a metapopulation of an endangered frog around Melbourne, and the design of surveys for invasive yellow crazy ants in Christmas Island.

 

 

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David Wilkinson

David Wilkinson [PhD candidate, University of Melbourne, co-supervised with Mick McCarthy, Reid Tingley and Nick  Golding]. David’s research focuses in the emerging field of Joint Species Distribution Modelling (JSDMs). He is interested in testing and comparing methods, as well as in developing model extensions.

 

 

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Roozbeh Valavi

Roozbeh Valavi [PhD candidate, University of Melbourne, co-supervised with Jane Elith and José Lahoz-Monfort]. Roozbeh is interested in Species Distribution Modelling and the prediction of range dynamics under environmental change. Currently, he is accounting for spatial autocorrelation in model evaluation and developing methods/tools to investigate transferability of species distribution models in space and time.

 

 

Students from other institutions

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Nick Deere

Nick Deere [PhD candidate, Univ. Kent, co-supervised with Matthew Struebig (main), Zoe Davies and Glen Reynolds]. Nick’s PhD research assesses the spatial congruence between mammalian diversity and forest carbon stocks at a fine landscape scale. In so doing, Nick aims to understand the ecological response of tropical forest mammals to a gradient of landscape disturbance, but also the extent to which this variation in mammal diversity is associated with the carbon provisions prioritized by REDD+ policies.

Past students

  • Leanne Greenwood [Honours, 2016, Deakin Univ., co-supervised leanne_400x400with Euan Ritchie (main), Dale Nimmo and Emily Nicholson]. Leanne evaluated the performance of alternative monitoring strategies for detecting changes in occupancy for a range of species in Wilson’s Promontory National Park, in Victoria (Australia). She based her assessment on analyses of data from camera-trap surveys
  • Nicolás Gálvez [PhD, 2015, Univ. Kent, co-supervised with Zoe Davies (main), Robert Smith, Freya NGalvez_400x400St. John, David McDonald and Byron Morgan]. Nico investigated güiña (Leopardus guigna) dynamics in agricultural landscapes in southern Chile, to inform conservation interventions. He collected camera-trap data over 4 seasons across 145 sample units and conducted questionnaires with over 230 farmers to ascertain how frequently they encounter the species, and how tolerant they are of it.

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  • Hayley Geyle [Honours, 2015, Deakin Univ., co-supervised with Emily Nicholson (main), Euan Ritchie, Dale Nimmo and Brett Murphy]. Hayley modelled the occupancy and detectability of the threatened brush-tailed rabbit-rat Conilurus penicillatus on the Tiwi Islands (NT), one of the last remaining population strongholds. Hayley used the knowledge gained from the model to assess the declines we can expect to detect in the occurrence of this species under alternative monitoring regimes.

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  • Madeleine Gorsuch (MEnv, 2013, Univ. Melbourne, co-supervised with Mick McCarthy). Maddy conducted field detection experiments. Targets were planted in four big plots (50x50m) and several observers were set to find the targets using transect and quadrat searchers. Maddy used these data to test a mathematical model of detection.
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