The Biodiversity Knowledge Framework supports the Biodiversity 2037 MERF by explaining how to identify knowledge gaps, so that we can better invest in research, monitoring and data collection.
The Biodiversity Knowledge Framework uses modelling (see an example of one of these models below) to describe the relationship between biodiversity values and management actions in different scenarios. Experts assess these models and identify what we don’t yet know about these relationships. Given different levels of uncertainty, the Biodiversity Knowledge Framework suggests what the best- and worst-case scenario might be for different actions.
Using a calculation called Relative Benefit of Knowledge (how much benefit we may get from investing in one piece of research over another), we determine which knowledge gaps, when filled, have the greatest potential to help biodiversity management and achieve the vision of Biodiversity 2037. This guides DELWP in prioritising investment in research.
Read the full framework:
Read a short summary:
Below is a causal model demonstrating the best-case impacts of goat control on plants. In ‘best-case’ scenarios, uncertainty is geared positively, such that conservation outcomes may be better than expected. In ‘worst-case’ scenarios, management outcomes are unlikely to be effective. The orange box represents the key action or disturbance, green boxes are the outcomes we are interested in, and grey are additional factors which exist as part of the system. Arrows have negative (-) symbols to indicate a negative relationship of varying strength, and green arrows indicate those relationships have some associated positive uncertainty (these arrows are red in worst-case scenarios), grey arrows are certain in this model.
What is currently being informed by the Knowledge Framework?
86 causal models have been produced as of May 2021. These have been translated to 9 research questions, of which 3 are active projects. Causal models have informed management across the state including Biodiversity Bushfire Response and cultural works caring for Country.
Interact with the story map below highlighting key projects for Biodiversity Knowledge Acquisition, including those directly informed by the Biodiversity Knowledge Framework.
Frequently Asked Questions
What is the Biodiversity Knowledge Framework?
The Biodiversity Knowledge Framework supports the Biodiversity 2037 MERF by providing a method for identify knowledge gaps, so that we can better invest in research, monitoring and data collection. The interactive Knowledge Portal makes this more accessible and dynamic.
How does the Biodiversity Knowledge Framework connect with Biodiversity 2037?
The Biodiversity Knowledge Framework is part of the Biodiversity 2037 MERF and it provides the process for identifying knowledge gaps and prioritising them for research investment. This way, we’re using our time and resources as effectively as possible.
Why do we need a Knowledge Framework?
Targeted data collection is critical for effective evidence-based decision making. We know that there are knowledge gaps that will be critical to effective management; our challenge is to identify these knowledge gaps and prioritise them for research investment. New information is most valuable when it addresses large portions of uncertainty that, once resolved, could lead to highly beneficial actions.
Resources for biodiversity conservation are always limited and often contested. Ideally, the majority of program resources will go towards directly delivering on-ground outcomes, but this can easily slip to much less than this, particularly if efficient support processes are not used. For example:
On ground actions
Similarly, information is always limited and there are many opportunities for improvement that are unlikely to be realised. Biodiversity 2037 champions the use of decision-science approaches to help make sound choices about which opportunities to pursue. This starts by bringing together available evidence and expert opinion in structured processes to consider insights, gaps, and consequences. This allows us to be strategic in selecting new research projects.
Why is the Knowledge Framework important?
The Knowledge Framework provides an approach to support investors by ranking the potential value of filling knowledge gaps. Rather than asking what researchers would like to do, then assessing whether this aligns with strategic biodiversity information needs, Expressions of Interest are sought for projects/approaches to directly address identified needs. This process creates more focused investment to generate more benefit for more species, rather than spreading the same budget over many smaller, less powerful projects.
The information and priorities identified using the Knowledge Framework processes (i.e. the Relative Benefit of Knowledge and causal maps) can also provide a guide for other investors (such as Parks Victoria) and researchers.
An example for the overall Biodiversity 2037 approach is managing the impact of deer on biodiversity. The approach is described below:
- Strategic Management Prospects (SMP) identifies that deer impact is a widespread issue;
- There are current control methods, but their level of effectiveness is not yet known
- A causal map (fuzzy cognitive map) of deer control and biodiversity outcomes is developed by a group of experts
- Knowledge gaps are identified. Currently available knowledge is used to indicate how effective, affordable and feasible a new method would need to be in order to justify investment to fund further research into it. Knowledge gaps are ranked and then prioritised against others for investment.
Who uses the Knowledge Framework?
Anyone interested in the methods behind DELWP’s knowledge acquisition process can read the Knowledge Framework, though it is particularly targeted at:
- Research scientists, universities and other organisations who are interested in addressing priority knowledge gaps
- Land managers to understand implications of their management strategies and the relationships between actions
- Investors to help them decide which projects to fund
How can I contribute to the Knowledge Framework?
Everyone will be able to contribute to the implementation of the Framework through the Biodiversity Knowledge Portal. There will be an annual review and update of models. Portal users will be able to interact with the models and external groups will be able to indicate where they are doing or planning to do research. Currently, queries and contributions can be directed to firstname.lastname@example.org
When does the Knowledge Framework come into effect?
It is currently in use. The Biodiversity Knowledge Framework has already been used as the basis of the 2019 Knowledge Grants.
Is the Biodiversity Knowledge Portal available?
The Biodiversity Knowledge Portal was released in May 2021. The portal provides access to all available models.
How are knowledge gaps and priority research investment identified?
To identify knowledge gaps and prioritise research investment, there needs to be a consistent, quantifiable and systematic approach. Part of this process is the development causal maps. Causal maps show the relationship between important biodiversity values and intervention actions. This ensures that assessment of knowledge gaps uses a whole-of-ecosystem view.
This approach also allows knowledge gaps to be ranked in accordance with an index of Relative Benefit of Knowledge (the benefit to biodiversity gained from doing a particular piece of research compared to another). The technique we have chosen to develop the causal models is Fuzzy Cognitive Maps. Fuzzy Cognitive Maps are an established method in ecology and conservation to explore the impacts of different management scenarios and to combine knowledge from different sources. We have been collaborating with key experts in the decision science space at the University of Melbourne and Arthur Rylah Institute to design the process and develop the initial maps. Importantly, our approach has been designed to be draw on benefit and uncertainty data from the expert elicitation conducted to support Strategic Management Prospects (SMP) and Specific Needs Assessments.
These tools are explicitly designed to capture uncertainty about the effectiveness of an action (in response to a threat) and the outcome for species, or groups of species. However, there is still uncertainty surrounding the impact of an action on a species (e.g. are were uncertain about fox control because of uncertainty surrounding the method or because of uncertainty around the impact of reduced fox density on the target species?). The intention is that the causal models will be easily accessible, reviewed, tested and updated.
How do we acquire new knowledge?
There are a range of biodiversity knowledge improvement approaches, each with strengths and weaknesses. The most appropriate approach should be used to address the question we are interested in, rather than setting up all the different approaches. For example:
- Adaptive management studies are desirable because they can be designed to provide feedback on specific treatments/outcomes, but due to the challenges of measuring change in complex systems and discriminating between correlation and causation, under-funded studies may provide limited insights
- Remote sensing provides repeatable overviews that can cost-effectively show trends in natural resource features, but these are typically general rather than species-specific
- Long-term monitoring sites can detect gradual changes, but these may be slow and expensive to identify, and depending on the number and layout of sites, typically do not provide any insights to management effectiveness or specific/urgent conservation issues (e.g. Forest Monitoring Plots Project has not been useful for solving Leadbeater’s Possum or Greater Glider issues)
- Broad scale citizen science projects (e.g. bird atlas) are good for engagement due to the ease of observing and identifying species, but generally confirm things we already know (e.g. movements due to seasons/climate variations; populations crashes due to droughts/fires; local extinctions due to loss of habitat) rather than improve our choices about what to do next
Page last updated: 07/06/21