Complex ecosystem management with limited data


Conservation management sometimes requires introducing threatened species or removing invasive species from ecosystems. Such drastic actions can shake up ecosystems substantially and sometimes the well-intended management creates unwanted and unexpected side-effects. It is therefore crucial that we know the potential for unwanted outcomes of such management actions.

We use a variety of mathematical and statistical techniques, combined with field data, to predict the type and likelihood of ecosystem-wide implications of translocations and eradications. The outcomes of this research help to prepare strategic monitoring plans that anticipate adverse outcomes conservation management, and trigger adaptive management actions to minimise the risk of such adverse outcomes.

We apply these novel approaches to a number of key case studies both enabling us to hone the approach and deliver support to mangers and stakeholders. Case studies include management of the Western Swamp Tortoises to new wetlands in Western Australia, the consequences of  feral cat eradication on the ecosystem of invasive and native species on Christmas Island, the ecosystem loses and benefactors of the translocation of the ecosystem engineering Island Scrub Jay to long uninhabited islands in the Channel Islands off California’s Coast, and understanding potential changes from reintroducing a long lost carnivore into Indigenous lands within Booderee National Park.


Group researchers:

Matthew Adams

Micha Plein

Chris Baker

Matthew Holden

Rosalie Willacy

David Algar

Eve McDonald-Madden



Nigel Bean (Adelaide)

Kate O’Brien (UQ)

Nicki Mitchell (UWA)

Margaret Byrne (DPAW)

Parks Australia Christmas Island Staff

David Lindenmayer (ANU)

Michael Bode (QUT)

Scott Sisson (UNSW)

Nick Dexter (Parks Aust)

Kevin Brown (Oregon State University)

Scott Morrison (TNC)

Scott Sillett (Smithsonian)

Kate Helmstedt (QUT)

Michael Bode (QUT)

Kerrie Mengersen (QUT)



ARC Linkage grant


ARC Centre for Excellence for Mathematical and Statistical Frontiers