Decision Support Systems for rabbit management

Decision Support Systems (DSS) can help land managers and advisory staff (such as Local Land Service staff) with decisions of when, where and how to manage rabbits, guided by best practice, using interactive tools that provide information, model analysis and decision guides. DSS need to be fit for purpose to ensure their relevance, and use software that is accurate, easy to use, and adaptable, so that new information can be easily incorporated.

The former Invasive Animals CRC in collaboration with Landcare Research NZ developed two purpose built DSS to support rabbit management under a participatory approach that focuses on the needs of stakeholders, who are involved at all stages of the development process. The DSS are:

  1. Rabbit Conservation DSS (downloadable excel spreadsheet)–to support decisions under limited funding allocation for rabbit management in public lands of the Australian Capital Territory (ACT).
  2. Production land DSS (online program) – a learning tool that illustrates the cost-benefits of alternative control protocols on wool production farms in the Central Tablelands of New South Wales (NSW), Australia.

These DSS have been developed for specific end-users, but they are provided as open-source tools so they can be adapted to similar situations elsewhere.

DSSs are important tools that can support decision making, knowledge management, collaboration and learning. However, effective rabbit management also depends on additional social, economic, and legislative factors. For example, community involvement to carry out coordinated landscape-level control programs is more effective than localised efforts; legislation encourages individuals to manage rabbits on their properties; training provides technical experience on how to carry out control. Consideration of these additional factors, not supported by DSS, is important in ensuring the success of a program.

We therefore propose that projects aimed at supporting rabbit management (including DSS development projects) should use an outcomes-based approach for project management and evaluation commonly known as ‘Theory of Change’. This approach guides projects to focus on what difference they are making (outcomes) rather than what they are doing (outputs), and paints a big picture of where the project’s activities and outputs fit to achieve these outcomes. For rabbit management to be effective we need to keep in mind that the ultimate outcomes are protecting and enhancing environmental, social and economic assets, not just killing rabbits.

How to use the DSS

Conservation land DSS

A Decision Support System (DSS) has been developed to guide funding allocation for rabbit management on public lands in the Australian Capital Territory (ACT). Although designed specifically for use by ACT Parks and Conservation Service (ACTPCS), this DSS can be downloaded and adapted by other agencies that need to make decisions on where to allocate limited funding to achieve the best rabbit management outcomes. The DSS prioritises areas for rabbit management based on relative conservation, economic and social assets, current levels of rabbit abundance, and prior investment on rabbit management.

The DSS is available for download as an Excel spreadsheet so that it is easily accessible, easy to use and adaptable beyond the life of the project. Values relating to the rabbit problem at specific sites, site attributes, previous control activities and resources available are entered into an input form. Output tables are then automatically generated that rank the sites for priority future management.

Every year, ACT Parks and Conservation Service decides which areas of public land receive funding allocation for rabbit management. Decisions to allocate funding are based on prior government commitments for rabbit management that year, prior investments in rabbit management, current rabbit numbers and the relative importance of available conservation, economic and social assets for each area of public land. The decision tree below, developed collectively with ACTPCS staff, details the steps in the decision-making process:


As shown in the decision tree, if an area has received funding for rabbit management in the last two years it automatically gets assigned to receive funding for follow-up control. The remaining areas are assessed for their levels of rabbit infestation (using the modified McLean scale, spotlight or warren counts) and those with levels above a given threshold (provided by ACTPCS) are then ranked based on their conservation, economic and social assets.

As part of the DSS development process, we (with ACTPCS staff) identified that the decision-making process had the following limitations:

  1. no knowledge of current rabbit numbers in areas that had not been managed for rabbits previously,
  2. no process to rank areas based on conservation, economic and social assets.

The DSS development process addressed each limitation as follows:

Assessing rabbit numbers in areas not previously managed

Spotlight counts are carried out by ACTPCS in some public lands but several areas remain unmonitored due to funding and time constraints. Knowledge of rabbit numbers is a crucial step in the decision-making process so implementing the DSS required the additional development of a rapid and cost-effective method for estimating relative rabbit abundance, which could easily be used by ACTPCS rangers monitoring public lands. We (in agreement with ACTPCS staff) opted for the modified McLean scale as a cost-effective and rapid method for estimating the relative levels of rabbit infestation in a particular area. The modified McLean scale is currently part of the good practice guidelines for national pest rabbit monitoring and control in New Zealand [1]. This scale has been tested and widely used by management agencies in New Zealand for decades [2,3], and was recently modified (in 2012) to clarify some terminology. The scale provides a relative index of rabbit abundance in a defined area by gauging the average distance between ‘buck heaps’ or ‘rabbit dung latrines’ that contain fresh or recent dung deposits (


This scale was tested for use by ACTPCS staff in collaboration with Landcare Research and Environment Canterbury, New Zealand, who provided training to ACTPCS rangers on using the scale in the field under a wide range of scenarios. ACT Parks and Conservation Service plan to train additional staff and have added the scale as part of their preferred toolkit of monitoring techniques for assessing rabbit numbers. They are in the process of validating the scale against spotlight counts.

Ranking areas based on their assets

To rank areas based on their conservation, economic and social assets, we use the Analytical Hierarchy Process , a type of multi-criteria analysis [4]. Multi-criteria analysis is an explicit approach for decision making that allows transparency and is based on clear objectives and criteria provided by the decision-making team. The DSS uses three criteria for ranking an area: conservation, economic and social values. The criteria are firstly weighted against each other by the user based on their relative importance. Then, each site is scored by the user under each criterion, using the scoring tables provided in the DSS. The DSS uses the weighted criteria scores to rank areas in decreasing order of priority.

Production land DSS

This decision support system (DSS) was developed by Landcare Research, New Zealand and the Invasive Animals Cooperative Research Centre (IA-CRC), in collaboration with NSW Local Land Service and grazing production farmers in the Centre Tablelands region of NSW. The DSS is designed as a learning tool that demonstrates the potential cost-benefits of rabbit control under alternative scenarios, and encourages the use of best practice. The DSS allows the users to vary pre-set inputs depending on the scenario they want to simulate including the size of the target area prone to rabbits, the initial amount of grass available and density of rabbits present, and the rabbit control methods to be applied, as well as how often control will be repeated. The main outputs are estimates of stock production and the cost-benefits of the chosen rabbit control strategy versus undertaking no rabbit control.

This DSS has been developed in the R programming environment and made easily accessible using the Shiny web application framework.  A detailed description of the DSS and the R source code is also available from this website so anyone can update it or adapt it as required.

Ecological Model

The DSS is based on an ecological model developed by Choquenot (1998) and detailed in Thompson (2000). This model was used for a previous (currently unused) DSS developed by the Centre for Agricultural & Regional Economics Pty Ltd (CARE) for the Bureau of Rural Sciences. The model uses data collected from rabbit control trials at multiple sheep production farms in the Centre Tablelands region of NSW over a three year period (Choquenot 1998). The data were used to develop a seasonal herbivore-resource model in which rabbits and stock interact through shared pasture biomass.

The simulation of rabbit control using the DSS proceeds by applying starting values for pasture biomass, rabbit and stock density, and rainfall, to the ecological model and simulating their monthly values over time for a specified number of years. Control and no-control are simulated using the same initial rainfall values and the outputs then summarised for comparison. The general structure of the DSS is arranged into specific steps performed on the pasture and herbivore (stock and rabbit) components of the model (see Fig).

Figure. DSS simulation structure. The DSS is separated into a sequence of steps performed on the herbivore-resource model. Based on starting values, the DSS calculates monthly values, continuing until the end of the simulation.

References and further reading

  1. Williams K, Parer I, Coman B, Burley J, Braysher M (1995). Managing vertebrate pests: rabbits. Canberra, Australia: Bureau of Resource Sciences and CSIRO Division of Wildlife and Ecology. 284 p.
  2. Vere DT, Jones RE, Saunders G (2004). The economic benefits of rabbit control in Australian temperate pastures by the introduction of rabbit haemorrhagic diseaseAgricultural Economics 30: 143-155. doi: 10.1111/j.1574-0862.2004.tb00183.x
  3. Williams CK, Moore RJ (1995). Effectiveness and cost-efficiency of control of the wild rabbit, Oryctolagus cuniculus (L.) by combinations of poisoning, ripping, fumigation, and maintenance fumigationWildlife Research 22: 253-269. doi: 10.1071/WR9950253
  4. Cooke B (2002). Rabbit haemorrhagic disease: field epidemiology and the management of wild rabbit populationsRevue scientifique et technique (International Office of Epizootics) 21: 347-358.
  5. Parkes JP, Glentworth B, Sullivan G (2008). Changes in immunity to rabbit haemorrhagic disease virus, and in abundance and rates of increase of wild rabbits in Mackenzie Basin, New ZealandWildlife Research 35: 775-779. doi: 10.1071/WR08008
  6. Cooke BD (2012). Planning landscape-scape rabbit control. Canberra, Australia: Invasive Animal Cooperative Research Centre. 33 p.
  7. Hung S-Y, Ku Y-C, Liang T-P, Lee C-J (2007). Regret avoidance as a measure of DSS success: an exploratory studyDecision Support Systems 42: 2093-2106. doi: 10.1016/j.dss.2006.05.006
  8. Volk M, Lautenbach S, van Delden H, Newham LTH, Seppelt R (2010). How can we make progress with decision support systems in landscape and river basin management? Lessons learned from a comparative analysis of four different decision support systemsEnvironmental Management 46: 834-849. doi: 10.1007/s00267-009-9417-2
  9. Walker DH (2002). Decision support, learning and rural resource managementAgricultural Systems 73: 113-127. doi: 10.1016/S0308-521X(01)00103-2
  10. Hayman PT, Easdown WJ (2002). An ecology of a DSS: reflections on managing wheat crops in the northeastern Australian grains region with WHEATMANAgricultural Systems 74: 57-77. doi: 10.1016/S0308-521X(02)00018-5
  11. Shtienberg D (2013). Will decision-support systems be widely used for the management of plant diseases? Annual Review of Phytopathology 51: 1-16. doi: 10.1146/annurev-phyto-082712-102244
  12. Voinov A, Bousquet F (2010). Modelling with stakeholdersEnvironmental Modelling and Software 25: 1268-1281. doi: 10.1016/j.envsoft.2010.03.007
  13. McCown RL (2001). Learning to bridge the gap between science-based decision support and the practice of farming: Evolution in paradigms of model-based research and intervention from design to dialogueAustralian Journal of Agricultural Research 52: 549-471. doi: 10.1071/AR00119
  14. Jakku E, Thorburn P (2009). A conceptual framework for guiding the participatory development of agricultural development of agricultural decision support systems. Socio-Economics and the Environment in Discussion CSIRO Working Paper Series 2009-12: 1-33.
  15. McCown RL, Carberry PS, Hochman Z, Dalgliesh NP, Foale MA (2009). Re-inventing model-based decision support with Australian dryland farmers. 1. Changing intervention concepts during 17 years of action researchCrop and Pasture Science 60: 1017-1030. doi: 10.1071/CP08455
  16. Allen W, Cruz J, Warburton B. 2017. How Decision Support Systems Can Benefit from a Theory of Change Approach. Environmental management. J, Howard S, Choquenot D, Allen W, Warburton B.
  17. Decision support systems for improving invasive rabbit management in Australia. Proceeding of the 27th vertebrate pest conference, New Port Beach, CA.
  18. NPCA (2012). Pest rabbits monitoring and control good practice guidelines. Wellington, New Zealand: National Pest Control Agencies. 52 p.
  19. Hamilton DJ, Eason CT (1994). Monitoring for 1080 residues in waterways after a rabbit-poisoning operation in Central OtagoNew Zealand Journal of Agricultural Research 37: 195–198. doi: 10.1080/00288233.1994.9513057
  20. O’Keefe JS, Tempero JE, Motha MXJ, Hansen MF, Atkinsona PH (1999). Serology of rabbit haemorrhagic disease virus in wild rabbits before and after release of the virus in New ZealandVeterinary Microbiology 66: 29–40. doi: 10.1016/S0378-1135(98)00307-1
  21. Steele K, Carmel Y, Cross J, Wilcox C (2008). Uses and misuses of multi-criteria decision analysis (MCDA) in environmental decision-making. Sydney, Australia: Australian Centre of Excellence for Risk Analysis. 19

Feature image by Rebecca Zanker