Bio-economic optimisation of surveillance to confirm broadscale eradications of invasive pests and diseases

Although pest eradications from islands have been successful and impart biodiversity benefits, eradications at regional/national scales are more challenging. Such broadscale eradications incur high repeated costs (e.g. control and surveillance effort) because the entire area cannot be treated at one time, and a progressive ‘treat-evaluate-move on’ approach must be employed. We describe a two-stage model to analyse surveillance data for assessing progress and declaring success of broadscale eradications, and to identify optimal cost-efficient surveillance strategies. Stage I modelling coincides or follows population control within a subset area or management zone (MZ). Surveillance data are analysed to quantify the probability of freedom for a treated MZ (i.e. local eradication), which is used to inform an operational decision to reallocate resources to other MZs, and progress across the region. Importantly, freedom declared individually in all MZs is not necessarily equivalent to a high probability of eradication over the broadscale area, because each MZ will have a probability of being erroneously declared free. After a MZ has been operationally declared free, Stage II surveillance commences to detect MZ-level failures, and to estimate the broadscale surveillance sensitivity and a corresponding probability of eradication. We developed a computer algorithm to identify cost-optimal Stage I and II surveillance strategies for a hypothetical large area. We assessed the following: (1) the balance between local surveillance intensity and spatial coverage; (2) the number of years to declare success in Stages I and II; (3) the stopping probability of freedom (Stage I); and (4) the optimal strategy given variation in the starting-over cost, should a MZ be erroneously declared free. This two-stage approach provides an objective basis for decision-making in wildlife pest/disease eradication, and guidance for implementing optimal bio-economic surveillance strategies.     

The Optimal Number of Surveys when Detectability Varies

The survey of plant and animal populations is central to undertaking field ecology. However, detection is imperfect, so the absence of a species cannot be determined with certainty. Methods developed to account for imperfect detectability during surveys do not yet account for stochastic variation in detectability over time or space. When each survey entails a fixed cost that is not spent searching (e.g., time required to travel to the site), stochastic detection rates result in a trade-off between the number of surveys and the length of each survey when surveying a single site. We present a model that addresses this trade-off and use it to determine the number of surveys that: 1) maximizes the expected probability of detection over the entire survey period; and 2) is most likely to achieve a minimally-acceptable probability of detection. We illustrate the applicability of our approach using three practical examples (minimum survey effort protocols, number of frog surveys per season, and number of quadrats per site to detect a plant species) and test our model''s predictions using data from experimental plant surveys. We find that when maximizing the expected probability of detection, the optimal survey design is most sensitive to the coefficient of variation in the rate of detection and the ratio of the search budget to the travel cost. When maximizing the likelihood of achieving a particular probability of detection, the optimal survey design is most sensitive to the required probability of detection, the expected number of detections if the budget were spent only on searching, and the expected number of detections that are missed due to travel costs. We find that accounting for stochasticity in detection rates is likely to be particularly important for designing surveys when detection rates are low. Our model provides a framework to do this.     

Quantifying the success of feral cat eradication, San Nicolas Island, California

It is usually uncertain when to declare success and stop control in pest eradication operations that rely on successive reductions of the population. We used the data collected during a project to eradicate feral cats from San Nicolas Island, California to estimate both the number of cats remaining towards the end of the project, and the amount and type of surveillance effort required to declare successful eradication after the last known cat was removed. Fifty seven cats were removed between June 2009 and April 2010 and our model estimated that there was a 95% chance that a further 1 to 4 cats remained, with 1 cat being the most likely number. After this time a further two cats were detected and removed and the model predicted this outcome with a probability of 0.25. If managers wished to confirm eradication success at this point, we estimated that 55 km of effort searching for recent evidence of cats over the whole island without detecting any would provide 99% certainty that no cats remained (stopping rule 1). Alternatively, the optimal amount of search effort for evidence that minimized the joint cost of searching and the cost of wrongly declaring eradication was 75 km (stopping rule 2). The equivalent amount of camera-nights (26 cameras were available) required to declare successful eradication were 416 (stopping rule 1) and 1196 camera nights (stopping rule 2). During the confirmation phase, 270 km of sign search effort and 3294 camera-nights surveillance were used from late June 2010, when the last cat was removed, through August 2010, without detecting signs of survivors. Managers can be very confident that eradication has been successful.     

Evaluation of weed eradication programs: containment and extirpation

Weed eradication programs often require 10 years or more to achieve their objective. It is important that progress is evaluated on a regular basis so that programs that are 'on track' can be distinguished from those that are unlikely to succeed. Earlier research has addressed conformity of eradication programs to the delimitation criterion. In this paper evaluation in relation to the containment and extirpation criteria is considered. Because strong evidence of containment failure (i.e. spread from infestations targeted for eradication) is difficult to obtain, it generally will not be practicable to evaluate how effective eradication programs are at containing the target species. However, chronic failure of containment will be reflected in sustained increases in cumulative infested area and thus a failure to delimit a weed invasion. Evaluating the degree of conformity to the delimitation and extirpation criteria is therefore sufficient to give an appraisal of progress towards the eradication objective. A significant step towards eradication occurs when a weed is no longer readily detectable at an infested site, signalling entry to the monitoring phase. This transition will occur more quickly if reproduction is prevented consistently. Where an invasion consists of multiple infestations, the monitoring profile (frequency distribution of time since detection) provides a summary of the overall effectiveness of the eradication program in meeting the extirpation criterion. Eradication is generally claimed when the target species has not been detected for a period equal to or greater than its seed longevity, although there is often considerable uncertainty in estimates of the latter. Recently developed methods, which take into consideration the cost of continued monitoring vs. the potential cost of damage should a weed escape owing to premature cessation of an eradication program, can assist managers to decide when to terminate weed eradication programs.     

Modelling tropical fire ant (<Emphasis Type="Italic">Solenopsis geminata</Emphasis>) dynamics and detection to inform an eradication project

Invasive species threaten endangered species worldwide and substantial effort is focused on their control. Eradication projects require critical resource allocation decisions, as they affect both the likelihood of success and the overall cost. However, these complex decisions must often be made within data-poor environments. Here we develop a mathematical framework to assist in resource allocation for invasive species control projects and we apply it to the proposed eradication of the tropical fire ant (Solenopsis geminata) from the islands of Ashmore Reef in the Timor Sea. Our framework contains two models: a population model and a detection model. Our stochastic population model is used to predict ant abundance through time and allows us to estimate the probability of eradication. Using abundance predictions from the population model, we use the detection model to predict the probability of ant detection through time. These models inform key decisions throughout the project, which include deciding how many baiting events should take place, deciding whether to invest in detector dogs and setting surveillance effort to confirm eradication following control. We find that using a combination of insect growth regulator and toxins are required to achieve a high probability of eradication over 2 years, and we find that using two detector dogs may be more cost-effective than the use of lure deployment, provided that they are used across the life of the project. Our analysis lays a foundation for making decisions about control and detection throughout the project and provides specific advice about resource allocation.     

Evaluating the “recovery level” of endangered species without prior information before alien invasion

For maintaining social and financial support for eradication programs of invasive species, quantitative assessment of recovery of native species or ecosystems is important because it provides a measurable parameter of success. However, setting a concrete goal for recovery is often difficult owing to lack of information prior to the introduction of invaders. Here, we present a novel approach to evaluate the achievement level of invasive predator management based on the carrying capacity of endangered species estimated using long‐term monitoring data. In Amami‐Oshima Island, Japan, where the eradication project of introduced small Indian mongoose is ongoing since 2000, we surveyed the population densities of four endangered species threatened by the mongoose (Amami rabbit, the Otton frog, Amami tip‐nosed frog, and Amami Ishikawa's frog) at four time points ranging from 2003 to 2011. We estimated the carrying capacities of these species using the logistic growth model combined with the effects of mongoose predation and environmental heterogeneity. All species showed clear tendencies toward increasing their density in line with decreased mongoose density, and they exhibited density‐dependent population growth. The estimated carrying capacities of three endangered species had small confidence intervals enough to measure recovery levels by the mongoose management. The population density of each endangered species has recovered to the level of the carrying capacity at about 20–40% of all sites, whereas no individuals were observed at more than 25% of all sites. We propose that the present approach involving appropriate monitoring data of native organism populations will be widely applicable to various eradication projects and provide unambiguous goals for management of invasive species.     

Incorporating risk mapping at multiple spatial scales into eradication management plans

The success of pro-active management of invasive plants depends on the ability to rapidly detect invasive populations and individuals. However, the factors important for detection depend on the spatial scale examined. We propose a protocol for developing risk maps at national, landscape, and local scales to improve detection rates of invasive plant species. We test this approach in the context of developing an eradication plan for the invasive tree Acacia stricta in South Africa. At a national scale we used bioclimatic models coupled with the most likely sites of introduction (i.e. forestry nursery plantations) to identify areas where national-scale surveillance should be focussed. At the landscape and local scales we correlated the presence of A. stricta populations to various attributes. Regional populations were found in forestry plantations only, and mostly on highly used graded roads along which seeds are spread by road maintenance vehicles. Locally, previously recorded plant localities accurately predicted individuals in subsequent surveys. Using these variables, we produced a map of high-risk areas that facilitated targeted searches—which reduced the required search effort by ca. 83 %—and developed recommendations for site-specific surveying. With the high visibility of plants, and relatively small seed banks, long-term annual clearing should achieve eradication. We propose that such multi-scale risk mapping is valuable for prioritising management and surveillance efforts, though caution that the approach is correlative and so it does not represent all the sites that can be invaded.     

The importance of incorporating imperfect detection in biodiversity assessments: a case study of small mammals in an Australian region

Aim Assessments of biodiversity are an essential requirement of conservation management planning. Species distributional modelling is a popular approach to quantifying biodiversity whereby occurrence data are related to environmental covariates. An important confounding factor that is often overlooked in the development of such models is uncertainty due to imperfect detection. Here, I demonstrate how an analytical approach that accounts for the bias due to imperfect detection can be applied retrospectively to an existing biodiversity survey data set to produce more realistic estimates of species distributions and unbiased covariate relationships. Location Pilbara biogeographic region, Australia. Methods As a component of the Pilbara survey, presence/absence (i.e. undetected) data on small ground‐dwelling mammals were collected. I applied a multiseason occupancy modelling approach to six of the most common species encountered during this survey. Detection and occupancy rates, as well as extinction and colonization probabilities, were determined, and the influence of covariates on these parameters was examined using the multi‐model inference approach. Results Detection probabilities for all six species were considerably lower than 1.0 and varied across time and species. Naïve estimates of occupancy underestimated occupancy rates corrected for species detectability by up to 45%. Seasonal variation in occupancy status was attributed to changes in detection for two of the focal species, while reproductive events explained variation in occupancy in three others. Covariates describing the substrate strongly influenced site occupancy for most of the species modelled. A comparison of the occupancy model with a generalized linear model, assuming perfect detection, showed that the effects of the covariates were underestimated in the latter model. Main conclusions The application of the multiseason occupancy modelling approach to the Pilbara mammal data set demonstrated a powerful framework for examining changes in site occupancy, as well as local colonization and extinction rates of species which are not confounded by variable species detection rates.     

Costing eradications of alien mammals from islands

The ability to estimate costs of alien species eradications is essential for a rigorous assessment of priorities for island restoration. Using a global data file from 41 islands, mostly gleaned from the 'grey' literature, we show that the cost of vertebrate eradications can be satisfactorily predicted if island area and species to be eradicated are known. About 72% of the variation in cost can be explained by island area, whereas, for a given area, rodent eradications are 1.7–3.0 times more expensive than ungulate eradications. Costs per hectare decrease with island size. Restricting the analysis to roughly half the data set, the relatively homogeneous half concerned with New Zealand islands, we identify two further influences on cost: date of eradication and distance to the main airport (an indicator of remoteness). For a given area, costs have declined over time but increase with island remoteness. This information therefore provides conservation planners with a robust, if preliminary, estimate of the cost of any proposed eradication programme.     

Accounting for Imperfect Detection in Ecology: A Quantitative Review

Detection in studies of species abundance and distribution is often imperfect. Assuming perfect detection introduces bias into estimation that can weaken inference upon which understanding and policy are based. Despite availability of numerous methods designed to address this assumption, many refereed papers in ecology fail to account for non-detection error. We conducted a quantitative literature review of 537 ecological articles to measure the degree to which studies of different taxa, at various scales, and over time have accounted for imperfect detection. Overall, just 23% of articles accounted for imperfect detection. The probability that an article incorporated imperfect detection increased with time and varied among taxa studied; studies of vertebrates were more likely to incorporate imperfect detection. Among articles that reported detection probability, 70% contained per-survey estimates of detection that were less than 0.5. For articles in which constancy of detection was tested, 86% reported significant variation. We hope that our findings prompt more ecologists to consider carefully the detection process when designing studies and analyzing results, especially for sub-disciplines where incorporation of imperfect detection in study design and analysis so far has been lacking.     

Minimizing the cost of environmental management decisions by optimizing statistical thresholds

Environmental management decisions are prone to expensive mistakes if they are triggered by hypothesis tests using the conventional Type I error rate (α) of 0.05. We derive optimal α‐levels for decision‐making by minimizing a cost function that specifies the overall cost of monitoring and management. When managing an economically valuable koala population, it shows that a decision based on α = 0.05 carries an expected cost over $5 million greater than the optimal decision. For a species of such value, there is never any benefit in guarding against the spurious detection of declines and therefore management should proceed directly to recovery action. This result holds in most circumstances where the species’ value substantially exceeds its recovery costs. For species of lower economic value, we show that the conventional α‐level of 0.05 rarely approximates the optimal decision‐making threshold. This analysis supports calls for reversing the statistical ‘burden of proof’ in environmental decision‐making when the cost of Type II errors is relatively high.     

Improving Aquatic Warbler Population Assessments by Accounting for Imperfect Detection

Monitoring programs designed to assess changes in population size over time need to account for imperfect detection and provide estimates of precision around annual abundance estimates. Especially for species dependent on conservation management, robust monitoring is essential to evaluate the effectiveness of management. Many bird species of temperate grasslands depend on specific conservation management to maintain suitable breeding habitat. One such species is the Aquatic Warbler (Acrocephalus paludicola), which breeds in open fen mires in Central Europe. Aquatic Warbler populations have so far been assessed using a complete survey that aims to enumerate all singing males over a large area. Because this approach provides no estimate of precision and does not account for observation error, detecting moderate population changes is challenging. From 2011 to 2013 we trialled a new line transect sampling monitoring design in the Biebrza valley, Poland, to estimate abundance of singing male Aquatic Warblers. We surveyed Aquatic Warblers repeatedly along 50 randomly placed 1-km transects, and used binomial mixture models to estimate abundances per transect. The repeated line transect sampling required 150 observer days, and thus less effort than the traditional ‘full count’ approach (175 observer days). Aquatic Warbler abundance was highest at intermediate water levels, and detection probability varied between years and was influenced by vegetation height. A power analysis indicated that our line transect sampling design had a power of 68% to detect a 20% population change over 10 years, whereas raw count data had a 9% power to detect the same trend. Thus, by accounting for imperfect detection we increased the power to detect population changes. We recommend to adopt the repeated line transect sampling approach for monitoring Aquatic Warblers in Poland and in other important breeding areas to monitor changes in population size and the effects of habitat management.     

Modelling the policy and economic context for detection- based monitoring tools: the costs and benefits of detecting last survivors and first re-colonisers

Establishing and maintaining the success of pest or weed eradication programmes requires interpretation of failures to detect survivors and first re-colonisers. Recent developments provide statistical frameworks that allow sequences of such failures to be interpreted in a probabilistic context. For example, application of these methods allow managers of eradication programmes to decide a priori an acceptable risk of programme failure, and to use this decision to design monitoring regimes that deliver this level of certainty, given the detection characteristics of the search techniques at their disposal. Similar methods could be use to design monitoring regimes to detect an incursion by a previously eradicated species (i.e. an eradication breakdown), which have an acceptable risk of failure. However, the availability of these methods begs questions about how “acceptable” risks of eradication failure or breakdown should be specified, and the consequent effort that should be expended to locate last survivors and first re- colonisers. We use a risk-based bioeconomic framework to model and analyse these decisions. The analysis demonstrates critical trade-offs between the cost and efficacy of the detection techniques available, the “value” of the eradication programme, and the perceived risk that a breakdown can occur. While our focus is on island pest eradication, we suggest how the bioeconomic framework used could be usefully applied to the detection of rare, at risk species, and the management of sporadically frequent diseases.     

Cost analysis in choosing group size when group testing for Potato virus Y in the presence of classification errors

In many areas of the world, Potato virus Y (PVY) is one of the most economically important disease problems in seed potatoes. In Taiwan, generation 2 (G2) class certified seed potatoes are required by law to be free of detectable levels of PVY. To meet this standard, it is necessary to perform accurate tests at a reasonable cost. We used a two‐stage testing design involving group testing which was performed in Taiwan's Seed Improvement and Propagation Station to identify plants infected with PVY. At the first stage of this two‐stage testing design, plants are tested in groups. The second stage involves no retesting for negative test groups and exhaustive testing of all constituent individual samples from positive test groups. In order to minimise costs while meeting government standards, it is imperative to estimate optimal group size. However, because of limited test accuracy, classification errors for diagnostic tests are inevitable; to get a more accurate estimate, it is necessary to adjust for these errors. Therefore, this paper describes an analysis of diagnostic test data in which specimens are grouped for batched testing to offset costs. The optimal batch size is determined by various cost parameters as well as test sensitivity, specificity and disease prevalence. Here, the Bayesian method is employed to deal with uncertainty in these parameters. Moreover, we developed a computer program to determine optimal group size for PVY tests such that the expected cost is minimised even when using imperfect diagnostic tests of pooled samples. Results from this research show that, compared with error free testing, when the presence of diagnostic testing errors is taken into account, the optimal group size becomes smaller. Higher diagnostic testing costs, lower costs of false negatives or smaller prevalence can all lead to a larger optimal group size. Regarding the effects of sensitivity and specificity, optimal group size increases as sensitivity increases; however, specificity has little effect on determining optimal group size. From our simulated study, it is apparent that the Bayesian method can truly update the prior information to more closely approximate the intrinsic characteristics of the parameters of interest. We believe that the results of this study will be useful in the implementation of seed potato certification programmes, particularly those which require zero tolerance for quarantine diseases in certified tubers.     

Eradications and People: Lessons from the Plant Eradication Program in Galapagos

It is often argued that the benefit of eradication of an invasive species—a one-off injection of funds and the problem is solved—far outweighs the cost of a perennial control program. Furthermore, these are very attractive projects for funding agencies as outcomes are clear and easy to assess. Galapagos is in the early stage of the invasion process, with most alien species not yet naturalized and still restricted to gardens and farms. These species should be easy targets for early and cost-effective eradication projects, which would prevent many future problems. We review 30 plant eradication projects covering 23 potentially invasive species with limited distributions on four of the Galapagos Islands. Of the 30 projects, only four were successful: these were all less than 1 ha in net area, on land with a single owner and did not have persistent seed banks. Of the other 26 projects, most failed due to a lack of support from institutions that did not offer continuity of resources, from land owners who denied permission to carry out the work or from being too ambitious. As a result of these problems, 64.3% of the funding secured for the program was spent on discontinued projects. We highlight lessons learned to inform plant eradication programs in the future.     

Impacts of rodent eradication on seed predation and plant community biomass on a tropical atoll

Invasive rodent eradications are frequently undertaken to curb island biodiversity loss. However, the breadth of rodents’ ecological impact, even after eradication, is not always fully recognized. For example, the most widespread invasive rodent, the black rat (Rattus rattus), while omnivorous, eats predominantly seeds and fruit. Yet, the effects of seed predation release after eradication on plant communities and ecological functions are not well understood, posing a gap for island restoration. We examined the role of seed predation release following black rat eradication in changes to tree composition and aboveground biomass across an islet network (Palmyra Atoll) in the Central Pacific. We conducted repeated surveys of seed, juvenile, and adult tree biomass and survival in permanent vegetation plots before and after the eradication of rats. We observed a 95% reduction in seed predation for an introduced, previously cultivated tree population (Cocos nucifera). Juvenile tree biomass of all species increased 14‐fold, with C. nucifera increasing the most, suggesting that eradication increased this tree's competitive advantage. Indeed, based on stage‐structured demographic models, rat eradication led to a 10% increase in C. nucifera population growth rate. The effect of invasive rodent seed predation varies considerably among the plant species in a community and can shift competitive dynamics, sometimes in favor of invasive plants. These bottom‐up effects should be considered in evaluating the costs and benefits of eradication. Documenting the variation in invasive rodent diet items, along with long‐term surveys, can help prioritize island eradications where restoration is most likely to be successful.     

Application of techniques for the quantitative analysis of complex surveillance systems to disease and wildlife management

Scenario tree modelling and associated methods provide tools to quantify the combined value of multiple complex surveillance activities over time. The outputs of this analysis are an estimate of the sensitivity of a surveillance system, and the cumulative probability of disease freedom that surveillance provides over time. The ability to quantify the performance of complex surveillance systems provides a number of new opportunities in the design and application of search and detection activities. One of these is the ability to objectively compare alternative detection strategies. The sensitivity of a strategy (the probability that the target biota would be detected, given that they are present at a defined level) may be balanced against cost and practicality considerations to determine the most effective strategy for a given situation. The paper provides an example of the comparison of two surveillance strategies (structured surveys and abattoir surveillance) for disease detection in animal health (Classical Swine Fever). These techniques may also play an important role in the certification of success in pest eradication operations. The ability to use multiple sources of evidence to evaluate success means that a higher level of confidence can often be achieved at lower cost. Examples of the application of scenario tree modelling in plant health and invasive pests are provided to illustrate its use within eradication programs.     

Population probability models for insect eradication

One of the greatest challenges in eradicating pest species is determining when no further individuals remain: terminating the control programme too early means failure to eradicate, whereas continuing for too long can add considerable expense. Since monitoring tools are usually only qualitative and invariably imperfect, there may be considerable uncertainty about when and if eradication has been achieved. However, it is possible to quantify the efficacy of monitoring tools and to use this together with knowledge of the basic ecology of the target pest to robustly quantify the probability of successful eradication over time. Here, I describe one such approach and demonstrate its use in the large-scale eradication of painted apple moth (Teia anartoides) from Auckland, New Zealand. A population model for the production of male moths was used in conjunction with spatially-explicit pheromone trap locations and attraction radii to determine the daily probability of detecting a hypothetical wild population at a particular location. Over time, these probabilities compounded to decrease the likelihood of painted apple moth presence given an ongoing lack of detection. In this way, spatio-temporal risk maps were produced to inform managers and to suggest when eradication had been achieved to a predetermined level of certainty. The model suggested that eradication was likely to have been successful in the main infestation areas by mid 2005, with subsequent catches likely to represent further small incursions, as corroborated by evidence from mitochondrial DNA and stable isotope markers. While it was plausible that a wild population was present in the Otahuhu area in 2005, it was very unlikely that it remained by the end of 2006. Population probability models have potential for much wider use in border biosecurity and establishment of area freedom, particularly in combination with future automated trapping systems.     

Network theory as a method for developing biosecurity preparedness: Netweork topology of Paratrechina longicornis in the Pacific and within New Zealand

One of the greatest challenges in eradicating pest species is determining when no further individuals remain: terminating the control programme too early means failure to eradicate, whereas continuing for too long can add considerable expense. Since monitoring tools are usually only qualitative and invariably imperfect, there may be considerable uncertainty about when and if eradication has been achieved. However, it is possible to quantify the efficacy of monitoring tools and to use this together with knowledge of the basic ecology of the target pest to robustly quantify the probability of successful eradication over time. Here, I describe one such approach and demonstrate its use in the large-scale eradication of painted apple moth (Teia anartoides) from Auckland, New Zealand. A population model for the production of male moths was used in conjunction with spatially-explicit pheromone trap locations and attraction radii to determine the daily probability of detecting a hypothetical wild population at a particular location. Over time, these probabilities compounded to decrease the likelihood of painted apple moth presence given an ongoing lack of detection. In this way, spatio-temporal risk maps were produced to inform managers and to suggest when eradication had been achieved to a predetermined level of certainty. The model suggested that eradication was likely to have been successful in the main infestation areas by mid 2005, with subsequent catches likely to represent further small incursions, as corroborated by evidence from mitochondrial DNA and stable isotope markers. While it was plausible that a wild population was present in the Otahuhu area in 2005, it was very unlikely that it remained by the end of 2006. Population probability models have potential for much wider use in border biosecurity and establishment of area freedom, particularly in combination with future automated trapping systems.