Spatially explicit spreadsheet modelling for optimising the efficiency of reducing invasive animal density

Abstract

Summary1. Invasive ungulates with eruptive population dynamics can degrade sensitive habitats, harbour disease-causing pathogens and facilitate the spread of weedy plants. Hence there is a need globally for cost-effective density reduction and damage mitigation strategies. User-friendly software tools that facilitate effective decision making by managers (who are not usually scientists) can help in understanding uncertainty and maximising benefits to native biodiversity within a constrained budget. 2. We designed an easy-to-use spreadsheet model -the Spatio-Temporal Animal Reduction (STAR) model -for strategic management of large feral ungulates (pigs, swamp buffalo and horses) within the World Heritage Kakadu National Park in Australia. The main goals of the model are to help park managers understand the landscape and population dynamics that influence the number and distribution of feral ungulates in time and space. 3. The model is a practical tool and methodological advance that provides a forecast of the effects and financial costs of proposed management plans. Feral animal management in the park is complex because populations cover an extensive area comprised of diverse and difficult-to-access habitats. There are also large reservoir populations in the regions surrounding the park, and these can provide immigrants even after within-park control operations. To provide the optimal outcomes for the reduction of feral animals, STAR is spatially explicit in relation to habitat, elevation and regions of culling, and applies density-feedback models in a lattice framework (multi-layer grid) to determine the optimal cost-benefit ratio of control choices. A series of spatial and nonspatial optimisation routines yielding the best cost-benefit approaches to culling are provided. 4. The spreadsheet module is flexible and adaptable to other regions and species, and is made available for testing and modifying. Users can operate STAR without having prior expert knowledge of animal management theory and application. The intuitive spreadsheet format could render it effective as a teaching or training tool for undergraduate students and landscape managers who might not have detailed ecological backgrounds.

Using Spatio - Temporal Modelling as a Decision Support Tool for Management of a Native Pest Herbivore

“…commercial harvesting). A comprehensive explanation of specific cell mechanics is presented in McMahon et al, (2010), where each cell in the grid acts as a specific unit within the overall dynamics of the study area and can be influenced by surrounding cell parameters. Thus, the model is habitat driven in that animal densities are ultimately linked to the habitat quality of the region of interest.…”

“…Specific details of model structure, coding and user manual access are described in McMahon et al (2010). The model presented here has been modified to incorporate the landscape configuration of our study site in Scottsdale, north-eastern Tasmania, Australia (41 o 06'S, 147 35'E), based on a cellular lattice framework, and is thus spatially explicit with respect to habitat type, elevation and landscape terrain relevant to this, and similar agricultural-forest mosaic areas across the Tasmanian landscape .…”

“…These features can have important implications on management approaches (and therefore success) as heterogeneous landscapes can alter access to, and costs associated with, animal management in both targeted and adjacent cells. The model structure uses density-feedback and economic cost functions to calculate the cost-benefit ratios of proposed management scenarios (detailed in McMahon et al, 2010) and has been updated to incorporate cost-benefit functions relative to the proposed management scenarios currently practiced in Tasmania (Wiggins et al, 2010). Briefly, animal density is represented as a numeric response between the following parameters in the STAR model: habitat carrying capacity, rate of recruitment, rate of immigration and emigration, culling rate, spatial and temporal variation in landscape features and management-specific costs, location, time-frame, frequency and benefits (i.e.…”

“…By taking immigration (and emigration) movements into account, management can be targetspecific to problematic areas. Additionally, simulation models allow for updates to control strategies as new knowledge is acquired (Haule et al, 2002;McMahon et al, 2010). It is desirable, and often essential, for pest management strategies to minimise the cost-benefit ratio of the control strategy.…”

“…The management of pest species generally attempts to decrease population numbers below a critical damage threshold level (Shea et al, 1998;McMahon et al, 2010). In order to achieve this, determination of an acceptable level of damage and its relationship to pest density across the landscape is required (Buckley et al, 2004;Edwards et al, 2004), in combination with the selection of the most cost effective management strategies available (Buckley et al, 2004).The suppression of population densities of native pest herbivores remains difficult across the Australian landscape, and the critical damage threshold may shift in response to localised features, as (i) native herbivore species have large population densities at the interface of managed landscapes, necessitating extensive, large-scale and highly coordinated management approaches to keep population numbers low ; (ii) the cost of management increases with decreasing animal densities (Bayliss and Yeomans, 1989); (iii) pest herbivore species may display cryptic and/or nocturnal behaviour, which can make access and control strategies challenging and costly (Coleman et An improved approach to the control of population densities of native pest herbivores may be aided by the development of decision support tools using spatiotemporal explicit models which can identify near optimal cost-effective removal strategies of pest species.…”