Reconstructions of population histories can shed light on the impact of citizen surveillance on managed populations. This can inform efforts to make best use of citizen surveillance as an alternative or complement to organised surveillance by public agencies in conservation, sustainable harvesting and control programs. We reconstructed the history of an invasive population of red-imported fire ants (Solenopsis invicta) and the management actions taken to eradicate the population, some of which were influenced by citizen reports. The invasion histories were represented as a graph that considered relationships between citizen reports, invader population dynamics and management actions. We estimated that citizen reports slowed the invasion's spread and/or reduced the cost of containment. Our approach can readily be extended to estimate the impact of citizen surveillance support programs, and thereby provide a rigorous empirical basis for the design of such programs.

Accurate delimitation of the geographic range of a species is important for control of biological invasions, conservation of threatened species, and understanding species range dynamics under environmental change. However, estimating range boundaries is challenging because monitoring methods are imperfect, the area that might contain individuals is often incompletely surveyed, and species may have patchy distributions. In these circumstances, large areas can be surveyed without finding individuals despite occupancy extending beyond surveyed areas, resulting in underestimation of range limits. We developed a delimitation method that can be applied with imperfect survey data and patchy distributions. The approach is to construct polygons indicative of the geographic range of a species. Each polygon is associated with a specific probability such that each interior point of the polygon has at least that posterior probability of being interior to the true boundary according to a Bayesian model. The method uses the posterior distribution of latent quantities derived from an agent-based Bayesian model and calculates the posterior distribution of the range as a derived quantity from Markov chain Monte Carlo samples. An application of this method described here informed the Australian campaign to eradicate red imported fire ants (Solenopsis invicta).

This paper presents a practical model for optimally allocating a budget across different biosecurity threats and measures (e.g. prevention or border quarantine, active surveillance for early detection, and containment and eradication measures) to ensure the highest rate of return. Our portfolio model differs from the common principle, which ranks alternative projects by their benefit cost ratios and picks the one that generates the highest average benefit cost ratio. The model we propose, instead, aims to allocate shares of the budget to the species where it is most cost-effective, and consequently determine the optimal scale of the control program for each threat under varying budget constraints. The cost-effectiveness of each block of budget spent on a threat is determined by minimising its expected total cost, including the damages it inflicts, and the control expenditures incurred in preventing or mitigating damages. As an illustration, the model is applied to the optimal allocation of a budget across four of Australia's most dangerous pests and diseases: red imported fire ants; foot-and-mouth disease; papaya fruit fly; and orange hawkweed. The model can readily be extended to consider more species and activities, and more complex settings including cases where detailed spatial and temporal information needs to be considered.

Fisheries management strategies usually do not consider secondary consequences of environmental shocks. A recent viral disease outbreak that decimated blacklip abalone populations in southern Australia had a much smaller impact on the region's less-abundant greenlip abalone populations. Decreases in total allowable commercial catch (TACC) for blacklip abalone were partially offset by a transient increase in greenlip abalone TACC that peaked in 2010 and reduced to zero in 2013. A hypothesis that the greenlip abalone stock had declined was supported by a Bayesian analysis of catch rates (catch per unit effort, CPUE, as a proxy for biomass). The estimated decline is consistent with depletion of the relatively small and fragmented greenlip stock as an indirect consequence of the disease impact. Our analysis demonstrates a need to jointly monitor and manage substitute fisheries when one of the fisheries experiences depletion as a result of a natural disturbance event. Considerations for Management Hierarchical Bayesian models can determine whether a shock to a natural resource indirectly affects related resources via a transfer of exploitation effort; Greenlip abalone stocks in western Victoria were generally poor in quality and infrequently exploited prior to the disease outbreak. Their low productivity rendered them vulnerable to the sudden increase in exploitation pressure, which could not be sustained once CPUE declined to 40-50kg h(-1), after which fishing activity ceased; A CPUE of 40kg h(-1) could be a precautionary limit reference point for triggering future greenlip abalone fishery closures; An increase in greenlip abalone legal minimum length (LML) to at least 140mm would improve quality and profit; It is important to protect substitute resources from secondary exploitation when imposing restrictions on exploitation of resources that are adversely impacted by natural disturbance events.

When freshwater resources become scarce there is a trade-off between human resource demands and environmental sustainability. The cost of conserving freshwater ecosystems can potentially be reduced by implementing institutional reforms that endow environmental water managers with a permanent water entitlement and the capacity to store, trade and release water. Australia's Murray Darling Basin Plan (MDBP) includes one of the world's most ambitious programs to recover water for the environment, supported by institutional reforms that allow environmental water managers to operate in water markets. One of the anticipated benefits of the Plan is to improve the health of flood-dependent forests, which are among the most endangered ecosystems globally because of river regulation and land clearance. However, periodic flooding to conserve floodplain ecosystems in the MDB creates losses to riparian landowners such as damage to fencing and temporary loss of access to flooded land. To reduce these losses reservoir operators restrict daily water release volumes. Using a model of optimal water management in Australia's southern MDB we estimate that current reservoir operating restrictions will substantially reduce the ecological benefits of investments made to recover water for the environment. The reduction in benefits is largest if floodplain forests decline rapidly without periodic inundation. In the latter circumstances, ecological losses cannot significantly be reduced by allowing environmental water managers to operate in water markets. Our findings demonstrate that the recovery of large volumes of water for environmental purposes and water market reforms are insufficient for conserving flood-dependent ecosystems without coordination and cooperation among multiple stakeholders responsible for water and land management.

Most successful invasive species eradication programs were applied to invasions confined to a small area. Invasions occupying large areas at a low density can potentially be eradicated if individual infestations can be found at affordable cost. The development of low cost aerial surveillance methods allows for larger areas to be monitored but such methods often have lower sensitivity than conventional surveillance methods, making their cost-effectiveness uncertain. Here, we consider the cost-effectiveness of including a new aerial monitoring method in Australia's largest eradication program, the campaign to eradicate red imported fire ants (Solenopsis invicta). The program previously relied on higher sensitivity ground surveillance and broadcast treatment. The high cost of those methods restricted the total area that could be managed with available resources below the level required to prevent ongoing expansion of the invasion. By increasing the area that can be monitored and thereby improving the targeting of treatment and ground surveillance, we estimate that remote sensing could substantially reduce eradication costs despite the method's low sensitivity. The development of low cost monitoring methods could potentially lead to substantially improved management of invasive species.

Invasive species eradication programs can fail by applying management strategies that are not robust to potentially large but nonquantified risks. A more robust strategy can succeed over a larger range of possible values for non-quantified risk. This form of robustness analysis is often not undertaken in eradication program evaluations. The main nonquantified risk initially facing Australia's fire ant eradication program was that the invasion had spread further than expected. Earlier consideration of this risk could have led to a more robust strategy involving a larger area managed in the program's early stages. This strategy could potentially have achieved eradication at relatively low cost without significantly increasing known and quantified risks. Our findings demonstrate that focusing on known and quantifiable risks can increase the vulnerability of eradication programs to known but non-quantified risks. This highlights the importance of including robustness to potentially large but non-quantified risks as a mandatory criterion in evaluations of invasive species eradication programs.

Eradication of invasive species can have substantial benefits but programs often fail and have high costs. Costs can be reduced by substituting lower cost management actions for higher cost actions that have a similar impact on the probability of eradication. A first step towards minimizing costs is to determine all combinations of management actions that achieve eradication with the same probability, which is a form of trade-off analysis. Trade-off analysis can have a high computational cost when large numbers of management alternatives are compared with a complex spatial simulation model. Here, we apply a practical method for conducting trade-off analysis in which statistical methods are applied to data generated by a complex spatial model to derive a simpler model (the meta-model) that is used to determine trade-offs. We demonstrate this approach with a case study focusing on Australia’s largest eradication program, the campaign to eradicate red imported fire ants (Solenopsis invicta). Trade-offs were estimated for two surveillance methods, remote surveillance and ground surveillance, that are currently used in the eradication program. The methods are applied adaptively, with remote surveillance applied over all potentially infested locations (“area-wide surveillance”) and ground surveillance applied near remotely sensed detection points to find and remove remaining undetected individuals (“local surveillance”). The meta-modelling approach provided useful insights for management. When area-wide and local surveillance methods are applied adaptively, increases in the sensitivity of area-wide surveillance can allow for large reductions in the area of local surveillance and treatment. This can potentially result in substantial cost savings in circumstances where local search methods have a high cost.

Red imported fire ants were first detected in Brisbane in February 2001. Since then, the National Red Imported Fire Ant Eradication Program has been collecting data on the locations of detected nests and keeping a record of the areas searched and treated with baits. The result is an exceptionally large and detailed record of both the spread of the ant and the effects of human intervention on the invasion. In recent work, these data have been used to reconstruct the history of the invasion in terms of the trajectories of nest abundance and geographic range. A novel feature of the method is that it explicitly models individual nests and can thus reconstruct the invasion to a high level of spatial and temporal detail. Some important lessons have been learned from this reconstruction. One is that an invasion can continue to expand its geographic range despite a drop in the number of invaders. Another is that immature nests - those not yet able to found new nests and generally too small to be detected - outnumbered mature nests at every stage of the invasion. Both of these lessons highlight the importance of sophisticated models to assist in monitoring an invasion and managing an eradication programme.

Eradication of an invasive species can provide significant environmental, economic, and social benefits, but eradication programs often fail. Constant and careful monitoring improves the chance of success, but an invasion may seem to be in decline even when it is expanding in abundance or spatial extent. Determining whether an invasion is in decline is a challenging inference problem for two reasons. First, it is typically infeasible to regularly survey the entire infested region owing to high cost. Second, surveillance methods are imperfect and fail to detect some individuals. These two factors also make it difficult to determine why an eradication program is failing. Agent-based methods enable inferences to be made about the locations of undiscovered individuals over time to identify trends in invader abundance and spatial extent. We develop an agent-based Bayesian method and apply it to Australia's largest eradication program: the campaign to eradicate the red imported fire ant (Solenopsis invicta) from Brisbane. The invasion was deemed to be almost eradicated in 2004 but our analyses indicate that its geographic range continued to expand despite a sharp decline in number of nests. We also show that eradication would probably have been achieved with a relatively small increase in the area searched and treated. Our results demonstrate the importance of inferring temporal and spatial trends in ongoing invasions. The method can handle incomplete observations and takes into account the effects of human intervention. It has the potential to transform eradication practices.

In many environmental management problems, the construction of occurrence maps of species of interest is a prerequisite to their effective management. However, the construction of occurrence maps is a challenging problem because observations are often costly to obtain (thus incomplete) and noisy (thus imperfect). It is therefore critical to develop tools for designing efficient spatial sampling strategies and for addressing data uncertainty. Adaptive sampling strategies are known to be more efficient than non-adaptive strategies. Here, we develop a model-based adaptive spatial sampling method for the construction of occurrence maps. We apply the method to estimate the occurrence of one of the world’s worst invasive species, the red imported fire ant, in and around the city of Brisbane, Australia. Our contribution is threefold: (i) a model of uncertainty about invasion maps using the classical image analysis probabilistic framework of Hidden Markov Random Fields (HMRF), (ii) an original exact method for optimal spatial sampling with HMRF and approximate solution algorithms for this problem, both in the static and adaptive sampling cases, (iii) an empirical evaluation of these methods on simulated problems inspired by the fire ants case study. Our analysis demonstrates that the adaptive strategy can lead to substantial improvement in occurrence mapping.

Summary Water‐resource management should maintain ecological condition, including population viabilities of aquatic taxa. Many arid and semi‐arid regions have experienced elevated water regulation and face drying and warming climates. We combined stochastic, population dynamics models for four fish species with differing life histories with simulated regulated and unregulated flow regimes to assess the relative robustness of fish population persistence to different scenarios of climate change and water management. Water regulation had a larger effect than differences in climate, negatively affecting one species through increased summer flows, and stabilizing population trajectories for two species that were sensitive to cease‐to‐flow events; the other species was insensitive to regulation or climate. The greater importance of water regulation suggests that management of water regulation and human use can be used to insulate fish, to some degree, from the effects of future climate change. General deductions from our results, such as the importance of inter‐annual variability and the application of demographic modelling tools, are readily transferable to other systems. Synthesis and applications. Our scenario‐based approach was able to assess the population‐level effects of multiple concurrent stressors and represents an effective framework for identifying management strategies that are robust to uncertainty in future environments.

Habitat connectivity is required at large spatial scales to facilitate movement of biota in response to climatic changes and to maintain viable populations of wide-ranging species. Nevertheless, it may require decades to acquire habitat linkages at such scales, and areas that could provide linkages are often developed before they can be reserved. Reserve scheduling methods usually consider only current threats, but threats change over time as development spreads and reaches presently secure areas. We investigated the importance of considering future threats when implementing projects to maintain habitat connectivity at a regional scale. To do so, we compared forward-looking scheduling strategies with strategies that consider only current threats. The strategies were applied to a Costa Rican case study, where many reserves face imminent isolation and other reserves will probably become isolated in the more distant future. We evaluated strategies in terms of two landscape-scale connectivity metrics, a pure connectivity metric and a metric of connected habitat diversity. Those strategies that considered only current threats were unreliable because they often failed to complete planned habitat linkage projects. The most reliable and effective strategies considered the future spread of development and its impact on the likelihood of completing planned habitat linkage projects. Our analyses highlight the critical need to consider future threats when building connected reserve networks over time.

AimAt first detection, little information is typically known about an invader's characteristics, true arrival date or spatial extent. Yet, before management options such as control or eradication can be considered, we need to know where a nuisance species has already spread. This is particularly difficult because of stochastic processes. Here, we develop an approach that requires little a priori information, yet accurately delimits the range of a biological invader.LocationWe used a simulated landscape, subjected to stochasticity inherent in establishment and spread, to test novel theory for delimiting locally spreading populations.MethodsWe distinguish three stages to identify the boundary of an invasion, which we term Approach, Decline, Delimit (ADD). Our ADD algorithm uses general characteristics of the invasion pattern, obtained during a search for occupied sites, in combination with sampling and probability theory to delimit the invasion. We compare ADD against four naive delimitation strategies, for long and normal dispersal kernels.ResultsOur results illustrate the potential difficulty in delimiting invasions. Naive strategies, such as stopping when the invader is absent, typically failed to properly delimit the invasion. In contrast, ADD operated relatively efficiently, and was robust to habitat heterogeneity and knowledge of the true epicentre, but was sensitive to the sparseness of the invasion. For long-distance dispersal kernels, ADD had 80% accurate delimitations when c. 5% or more of the cells were occupied within the invasion boundary; for normal dispersal kernels, ADD had 95% accurate delimitations when c. 2.5% or more of the cells were occupied.Main conclusionsThere is virtually no existing theory for delimiting invasions. ADD is efficient and accurate, even with unknown time of invasion, unknown dispersal kernels, stochastic establishment dynamics and spatial heterogeneity, except for very low invasion densities.

A central theme of the invasion biology literature is to predict the introduction and spread of biological invasions but predictive models rarely are applied to inform invasion management. Here, we demonstrate the utility of a spatio-temporal predictive model that has been used to inform Australia's largest eradication program, the program to eradicate the red imported fire ant (RIFA) from Brisbane. That model has informed eradication efforts in two main ways: estimating the probability of program success and identifying cost-effective control strategies. A third application of the model was to inform research priority setting by identifying uncertain parameters with greatest impact on program outcomes. One of the main findings of our modelling analyses was that due to apparent long distance jump dispersal, it is critical to search large areas at sufficient sensitivity to detect all individuals. However, the required spatial coverage and sensitivity of surveillance cannot be achieved with the currently used surveillance method, visual search by trained personnel, due to that method's high cost. Therefore, it is necessary to consider alternative surveillance methods. One such method, remote sensing, has a substantially lower cost than visual search but has insufficient sensitivity to find all RIFA colonies. Therefore, there is an important role for both methods. The combined use of those methods potentially can allow for all infestations to be found and thereby achieve eradication even when large areas are infested at low density. We demonstrate that an effective strategy for combining those methods is to use them sequentially, with remote sensing used in the first stage to identify general areas of infestation followed by higher sensitivity visual search to detect all individuals within each infested area. Simulation analyses demonstrate that small increases in the sensitivity of remote sensing are equivalent to large increases in the areal extent of visual search within the plausible range of sensitivities of the two methods. Threshold surveillance sensitivities for remote sensing were estimated, below which stipulated eradication probabilities cannot be achieved with available resources. Although treatment of large areas with aerial baiting can contain the invasion for an extended period without the need for extensive surveillance, some surveillance is required to improve information on infested locations. There is a risk that treated RIFA nests will release offspring prior to those nests being treated. Our analyses demonstrate that such reproductive escape, which rarely is considered in invasion spread models, has a large impact on the probability of eradication. Reproductive escape should, therefore, be further studied to improve estimation of eradication feasibility and identification of effective eradication strategies. Our findings demonstrate that even large biological invasions can potentially be eradicated with available resources using readily available surveillance technologies. That will depend on the detectability of individuals or natal sites with those technologies and, more specifically, whether infestations can be detected before they become a source of long distance spread. Predictive models have much to offer eradication programs despite imperfect predictability of invasions.