Can ecosystems recover under a changing wildfire regime in the Greater Blue Mountains World Heritage Area? (with WWF Eyes on Recovery, Blue Mountains City Council, Greater Sydney Local Land Services and National Parks and Wildlife)

Ecosystems are collapsing. The unprecedented pace of global environmental change in our new geological epoch—the Anthropocene—is coupled with increases in disturbance events, such as extreme wildfires. Australia’s 2019/20 wildfire season was unprecedented, but just how ecosystems respond to dramatic disturbances is highly uncertain. Nor do we understand the complex responses of ecosystems to increases in fire frequency and severity because of climate change. Current ecosystem assessment processes focus on species or habitat loss, but often occur too late to save species of high ecological or conservation value and therefore result in a loss of critical function from the system. To aid post-fire recovery of native species, we must understand the complexity of the interactions among threats from increased fire frequency, severity and introduced predators and how these interactions affect function. This project aims to investigate how the fire severity and frequency affect the trajectory of change in species composition from the 2019/20 mega-fires in the greater Blue Mountains and thus provide new insights into how communities may respond under changing wildfire regimes.

ARC Training Centre for Data Analytics for Resources and Environments (DARE)

The ARC Industrial Transformation Training Centre for Data Analytics for Resources and Environments (DARE) will develop and deliver the data science skills and tools for Australia’s natural resource industries and managers; to be expert users of data and models; to quantify, explain and understand uncertainty; and to make the best possible evidence-based decisions in exploiting and stewarding the nations’ natural resources and environment.

Maximising the resilience of pastures to grazing and extreme drought events (with DroughtNet and Sydney Institute of Agriculture)

This project aims to address the significant knowledge gap of how species composition may change due to extreme drought, and in-turn, quantify the loss of ecosystem function resulting from species turnover. Further, this project will identify species that contribute the most to function.

Funded by the Hermon Slade Foundation.

More information here.

ARC Near-term iterative ecological forecasts of biodiversity change

This project will advance ecosystem forecasting by accounting for how legacy effects from extreme environmental events – prolonged droughts, floods, heatwaves and fires – persist into future years in vulnerable dryland ecosystems. As highly stressed environments are expected to leave increasingly large impacts on flora and fauna and exacerbate desertification, answers are urgently needed to understand and mitigate these impacts. This project will foster new appreciation of ecosystem features that build resilience to change, or that lead to collapse. Benefits include better forecasting tools to manage ecosystems at risk, improved security of biodiversity and food production in Australian rangelands, and training of early career researchers.

The role of feral predators in disrupting small vertebrate communities in arid South Australia (with NESP Threatened Species Hub)

This project is investigating why native species persist in some refuge areas of South Australia but not others, and the role of habitat condition and especially feral predators in restricting their populations. The kowari and fawn hopping mouse are threatened and other species such as the plains mouse and crest-tailed mulgara are restricted in range.

Populations of the Kowari in South Australia are in decline and may need to be listed as Endangered. Photo Billy La Marca.

All species of northern South Australia are also at risk of fox and cat predation, less so where predator activity is suppressed by dingoes, particularly on vast stony plains where cover is inadequate for prolonged cat and fox occupation.

When the impacts of predators are understood, rebuilding ecological function may be possible through translocation of threatened species.

Download the project summary.

Download the research factsheet..

Help save the kowari. Join Team Kowari here.

DigiFarm. A digitally enabled durable agroecosystem (with Sydney Institute of Agriculture)

L’lara, Narrabri. Photo by Kieran Shephard

Investment over the past century by the University of Sydney is culminating in an integrated approach to its farming education and research activities. DigiFarm is an important stage in this activity, bringing together the community, farmers and environmental stakeholders. We aim to develop a digitally enabled network which will simultaneously monitor crop and animal production (including native flora and fauna), and soil and ecosystem health. The network will enable the triple bottom line framework of social, environmental and financial accounting to optimally manage a production ecosystem. Building on current investments in Narrabri, we shall build a physical and virtual DigiFarm hub and satellite farm network for north-west NSW providing digital dashboards of ‘health, production and social’ metrics. We will create an education platform at Narrabri for farmers, agribusiness, schools, environmental stakeholders to experience the latest ag-innovation thinking.

Can machine learning be used to accurately identify wildlife in remote camera trap images? (with WildCount, National Parks and Wildlife Service, NSW Government)

Kindly provided by WildCount, National Parks and Wildlife Service, NSW

Motion-active or remote camera traps are now commonly used in wildlife studies around the globe. They are a powerful and cost-effective method to survey wildlife due to their ease in deployment and ability to continually monitor populations across time. However, a common limitation of camera traps is that they capture millions of images that need to be processed visually by an observer. Machine learning techniques provide a powerful and exciting opportunity to automate image processing; thereby reducing analysis and reporting time. The time gained by implementing an automated image processing pipeline and increase speed of reporting results can be used for on-ground species conservation management.

This project will work closely with WildCount, a large-scale wildlife monitoring program run by the National Parks and Wildlife Service, NSW Government and the School of Life and Environmental Sciences, University of Sydney. It will test the feasibility of using machine learning algorithms for identifying species in camera trap images.

International long term ecological research network (iLTER): Simpson Desert, Australia (with the Desert Ecology Research Group at The University of Sydney, Australia)

Spinifex (Triodia basedowii) in the Simpson Desert, Qld. Photo by Aaron Greenville.

This project aims to quantify inter-relationships between the frequency and intensity of increased climate extremes, wildfire and introduced species and their effects on species dynamics.

The web of death: evaluating the ecosystem effects of carcasses (with Global Ecology Lab at The University of Sydney)

Project OzScav’s main directive is to investigate the role of carrion in ecological communities in Australia. Follow project up-dates on Twitter

Technology for ecology and environmental sciences (with Royal Botanic Gardens Sydney)

Advances in technology, such as drones, remote camera traps and more recently open-source hardware and software have revolutionised data collection for cryptic species and surveys in remote locations. I have started a research theme investigating how scientists can use open-source hardware (Raspberry Pi and Arduino platforms) within their research programs, such as by building remote environmental sensor loggers. This project uses the latest innovations in technology from computer science, engineering and electronics to build custom devices with a reproducible workflow.

For more information see:

Greenville, A.C. and Emery, N.J. (2016). Gathering lots of data on a small budget. Science, 353: 1360-1361.