Optimal surveillance and eradication of invasive species in heterogeneous landscapes

Spatial prioritization for widespread invasive species control: Trade-offs between current impact and future spread

Spatially explicit prioritization of invasive species control is a complex issue, requiring consideration of trade-offs between immediate and future benefits. This study aimed to prioritize management efforts to account for current and future threats from widespread invasions and examine the strength of the trade-off between these different management goals. As a case study, we identified spatially explicit management priorities for the widespread invasion of introduced willow into riparian and wetland habitats across a 102,145-km2 region in eastern Australia. In addition to targeting places where willow threatens biodiversity now, a second set of management goals was to limit reinfestation and further spread that could occur via two different mechanisms (downstream and by wind). A model of likely willow distribution across the region was combined with spatial data for biodiversity (native vegetation, threatened species and communities), ecological conditions, management costs, and two potential dispersal layers. We used systematic conservation planning software (Zonation) to prioritize where willow management should be focussed across more than 100,000 catchments for a range of different scenarios that reflected different weights between management goals. For willow invasion, we found that we could prioritize willow management to reduce the future threat of dispersal downstream with little reduction in the protection of biodiversity. However, accounting for future threats from wind dispersal resulted in a stronger trade-off with protection of threatened biodiversity. The strongest trade-off was observed when both dispersal mechanisms were considered together. This study shows that considering current and future goals together offers the potential to substantially improve conservation outcomes for invasive species management. Our approach also informs land managers about the relative trade-offs among different management goals under different control scenarios, helping to make management decisions more transparent. This approach can be used for other widespread invasive species to help improve invasive species management decisions.

Optimizing strategies for slowing the spread of invasive species

Invasive species are spreading worldwide, causing damage to ecosystems, biodiversity, agriculture, and human health. A major question is, therefore, how to distribute treatment efforts cost-effectively across space and time to prevent or slow the spread of invasive species. However, finding optimal control strategies for the complex spatial-temporal dynamics of populations is complicated and requires novel methodologies. Here, we develop a novel algorithm that can be applied to various population models. The algorithm finds the optimal spatial distribution of treatment efforts and the optimal propagation speed of the target species. We apply the algorithm to examine how the results depend on the species' demography and response to the treatment method. In particular, we analyze (1) a generic model and (2) a detailed model for the management of the spongy moth in North America to slow its spread via mating disruption. We show that, when utilizing optimization approaches to contain invasive species, significant improvements can be made in terms of cost-efficiency. The methodology developed here offers a much-needed tool for further examination of optimal strategies for additional cases of interest.

Optimal allocation of resources among general and species-specific tools for plant pest biosecurity surveillance

This paper proposes a surveillance model for plant pests that can optimally allocate resources among survey tools with varying properties. While some survey tools are highly specific for the detection of a single pest species, others are more generalized. There is considerable variation in the cost and sensitivity of these tools, but there are no guidelines or frameworks for identifying which tools are most cost-effective when used in surveillance programs that target the detection of newly invaded populations. To address this gap, we applied our model to design a trapping surveillance program in New Zealand for bark- and wood-boring insects, some of the most serious forest pests worldwide. Our findings show that exclusively utilizing generalized traps (GTs) proves to be highly cost-effective across a wide range of scenarios, particularly when they are capable of capturing all pest species. Implementing surveillance programs that only employ specialized traps (ST) is cost-effective only when these traps can detect highly damaging pests. However, even in such cases, they significantly lag in cost-effectiveness compared to GT-only programs due to their restricted coverage. When both GTs and STs are used in an integrated surveillance program, the total expected cost (TEC) generally diminishes when compared to programs relying on a single type of trap. However, this relative reduction in TEC is only marginally larger than that achieved with GT-only programs, as long as highly damaging species can be detected by GTs. The proportion of STs among the optimal required traps fluctuates based on several factors, including the relative pricing of GTs and STs, pest arrival rates, potential damage, and, more prominently, the coverage capacity of GTs. Our analysis suggests that deploying GTs extensively across landscapes appears to be more cost-effective in areas with either very high or very low levels of relative risk density, potential damage, and arrival rate. Finally, STs are less likely to be required when the pests that are detected by those tools have a higher likelihood of successful eradication because delaying detection becomes less costly for these species.

Optimizing the use of suppression zones for containment of invasive species

Despite efforts to prevent their establishment, many invasive species continue to spread and threaten food production, human health, and natural biodiversity. Slowing the spread of established species is often a preferred strategy; however, it is also expensive and necessitates treatment over large areas. Therefore, it is critical to examine how to distribute management efforts over space cost-effectively. Here we consider a continuous-space bioeconomic model and we develop a novel algorithm to find the most cost-effective allocation of treatment efforts throughout a landscape. We show that the optimal strategy often comprises eradication in the yet-uninvaded area, and under certain conditions, it also comprises maintaining a "suppression zone," an area between the invaded and the uninvaded areas, where treatment reduces the invading population but without eliminating it. We examine how the optimal strategy depends on the demographic characteristics of the species and reveal general criteria for deciding when a suppression zone is cost effective.

Biodiversity and spatial distribution of ascidian using environmental DNA metabarcoding

Monitoring studies are necessary to understand the biodiversity of marine ecosystems and are useful for identifying and managing rare or invasive species. Because monitoring has traditionally relied only on visual surveys (e.g., trapping, netting, electrofishing, and SCUBA diving) with limited time and physical resources, environmental DNA (eDNA) analysis is being applied as an efficient monitoring method. This study compared whether the eDNA metabarcoding technique can replace the traditional visual survey in an ascidian fauna study. We designed ascidian-specific primers and identified a clear gap (3.75%) by barcoding gap analysis. Then, we collected seawater samples for eDNA analysis during the summer (August-September) of 2021 at three sites (Mokpo, Yeosu, and Uljin) in South Korea. In the survey sites of this study, 25 species were observed through literature and visual survey, among which 9 species were detected by metabarcoding and 16 species were not detected. On the other hand, 10 species were detected only by metabarcoding, and one of them was identified as Pyura mirabilis, an unrecorded species in South Korea. This study succeeded in detecting cryptic or rare species with one seawater collection, which can be used to determine their unexplored habitat. Therefore, we conclude that monitoring using eDNA is more efficient than visual surveys for detecting rare or cryptic ascidian species. We also suggest that, when combined with traditional monitoring methods, it could be a tool to complement ascidian fauna studies.

Info-gap theory to determine cost-effective eradication of invasive species

Invasive species eradication campaigns often fail due to stochastic arrival events, unpredictable detectability and incorrect resource allocation. Severe uncertainty in model parameter estimates may skew the eradication policy results. Using info-gap decision theory, this research aims to provide managers with a method to quantify their confidence in realizing successful eradication of particular invasive species within their specified eradication budgets (i.e. allowed eradication cost) in face of information-gaps. The potential introduction of the Asian house gecko Hemidactylus frenatus to Barrow Island, Australia is used as a case study to illustrate the model. Results of this research demonstrate that, more robustness to uncertainty in the model parameters can be earnt by (1) increasing the allowed eradication cost (2) investment in pre-border quarantine and border inspection (i.e. prevention) or (3) investment in post-border detection surveillance. The combination of a post-border spatial dispersal model and info-gap decision theory demonstrates a novel and spatially efficient method for managers to evaluate the robustness of eradication policies for incursion of invasive species with unexpected behaviour. These methods can be used to provide insight into the success of management goals, in particular the eradication of invasive species on islands or in broader mainland areas. These insights will assist in avoiding eradication failure and wasteful budget allocation and labour investment.

Using 'sentinel' plants to improve early detection of invasive plant pathogens

Infectious diseases of plants present an ongoing and increasing threat to international biosecurity, with wide-ranging implications. An important challenge in plant disease management is achieving early detection of invading pathogens, which requires effective surveillance through the implementation of appropriate monitoring programmes. However, when monitoring relies on visual inspection as a means of detection, surveillance is often hindered by a long incubation period (delay from infection to symptom onset) during which plants may be infectious but not displaying visible symptoms. 'Sentinel' plants-alternative susceptible host species that display visible symptoms of infection more rapidly-could be introduced to at-risk populations and included in monitoring programmes to act as early warning beacons for infection. However, while sentinel hosts exhibit faster disease progression and so allow pathogens to be detected earlier, this often comes at a cost: faster disease progression typically promotes earlier onward transmission. Here, we construct a computational model of pathogen transmission to explore this trade-off and investigate how including sentinel plants in monitoring programmes could facilitate earlier detection of invasive plant pathogens. Using Xylella fastidiosa infection in Olea europaea (European olive) as a current high profile case study, for which Catharanthus roseus (Madagascan periwinkle) is a candidate sentinel host, we apply a Bayesian optimisation algorithm to determine the optimal number of sentinel hosts to introduce for a given sampling effort, as well as the optimal division of limited surveillance resources between crop and sentinel plants. Our results demonstrate that including sentinel plants in monitoring programmes can reduce the expected prevalence of infection upon outbreak detection substantially, increasing the feasibility of local outbreak containment.

Forest Insect Biosecurity: Processes, Patterns, Predictions, Pitfalls

The economic and environmental threats posed by non-native forest insects are ever increasing with the continuing globalization of trade and travel; thus, the need for mitigation through effective biosecurity is greater than ever. However, despite decades of research and implementation of preborder, border, and postborder preventative measures, insect invasions continue to occur, with no evidence of saturation, and are even predicted to accelerate. In this article, we review biosecurity measures used to mitigate the arrival, establishment, spread, and impacts of non-native forest insects and possible impediments to the successful implementation of these measures. Biosecurity successes are likely under-recognized because they are difficult to detect and quantify, whereas failures are more evident in the continued establishment of additional non-native species. There are limitations in existing biosecurity systems at global and country scales (for example, inspecting all imports is impossible, no phytosanitary measures are perfect, knownunknowns cannot be regulated against, and noncompliance is an ongoing problem). Biosecurity should be a shared responsibility across countries, governments, stakeholders, and individuals.

Lymantria dispar (L.) (Lepidoptera: Erebidae): Current Status of Biology, Ecology, and Management in Europe with Notes from North America

The European Spongy moth, Lymantria dispar (L.) (Lepidoptera: Erebidae), is an abundant species found in oak woods in Central and Southern Europe, the Near East, and North Africa and is an important economic pest. It is a voracious eater and can completely defoliate entire trees; repeated severe defoliation can add to other stresses, such as weather extremes or human activities. Lymantria dispar is most destructive in its larval stage (caterpillars), stripping away foliage from a broad variety of trees (>500 species). Caterpillar infestation is an underestimated problem; medical literature reports that established populations of caterpillars may cause health problems to people and animals. Inflammatory reactions may occur in most individuals after exposure to setae, independent of previous exposure. Currently, chemical and mechanical methods, natural predators, and silvicultural practices are included for the control of this species. Various insecticides have been used for its control, often through aerial sprayings, which negatively affect biodiversity, frequently fail, and are inappropriate for urban/recreational areas. However, bioinsecticides based on various microorganisms (e.g., entomopathogenic viruses, bacteria, and fungi) as well as technologies such as mating disruption using sex pheromone traps have replaced insecticides for the management of L. dispar.

Optimizing management of invasions in an uncertain world using dynamic spatial models

Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions.

Automated Surveillance of Lepidopteran Pests with Smart Optoelectronic Sensor Traps

Several lepidopterans are pests in horticulture and pose biosecurity risks to trading countries worldwide. Efficient species-specific semiochemical lures are available for some of these pests, facilitating the implementation of surveillance programmes via trapping networks. These networks have a long history of success in detecting incursions of invasive species; however, their reliance on manual trap inspections makes these surveillance programmes expensive to run. Novel smart traps integrating sensor technology are being developed to detect insects automatically but are so far limited to expensive camera-based sensors or optoelectronic sensors for fast-moving insects. Here, we present the development of an optoelectronic sensor adapted to a delta-type trap to record the low wing-beat frequencies of Lepidoptera, and remotely send real-time digital detection via wireless communication. These new smart traps, combined with machine-learning algorithms, can further facilitate diagnostics via species identification through biometrics. Our laboratory and field trials have shown that moths flying in/out of the trap can be detected automatically before visual trap catch, thus improving early detection. The deployment of smart sensor traps for biosecurity will significantly reduce the cost of labour by directing trap visits to the locations of insect detection, thereby supporting a sustainable and low-carbon surveillance system.

Optimal management of stochastic invasion in a metapopulation with Allee effects

Invasive species account for incalculable damages worldwide, in both ecological and bioeconomic terms. The question of how a network of invasive populations can be optimally managed is one that deserves further exploration. A study accounting for partial observability and imperfect detection, in particular, could yield useful insights into species eradication efforts. Here, we generalized a simple model system that we developed in previous work. This model consists of three interacting populations with underlying strong Allee effects and stochastic dynamics, inhabiting distinct locations connected by dispersal, which can generate bistability. To explore the stochastic dynamics, we formulated an individual-based modeling approach. Next, using the theory of continuous-time Markov chains, we approximated the original high-dimensional model by a Markov chain with eight states, with each state corresponding to a combination of population thresholds. We then used the reduced model as the core for a powerful decision-making tool, referred to as a Partially Observable Markov Decision Process (POMDP). Analysis of this POMDP indicates when the system results in optimal management outcomes.

Unified system describing factors related to the eradication of an alien plant species

Background

In the field of biological invasions science, a problem of many overlapping terms arose among eradication assessment frameworks. Additionally there is a need to construct a universally applicable eradication evaluation system. To unify the terminology and propose an eradication feasibility assessment scale we created the Unified System for assessing Eradication Feasibility (USEF) as a complex tool of factors for the analysis of eradications of alien (both invasive and candidate) plant species. It compiles 24 factors related to eradication success probability reported earlier in the literature and arranges them in a hierarchical system (context/group/factor/component) with a possibility to score their influence on eradication success.

Methodology

After a literature survey we analyzed, rearranged and defined each factor giving it an intuitive name along with the list of its synonyms and similar and/or related terms from the literature. Each factor influencing eradication feasibility is ascribed into one of four groups depending on the context that best matches the factor: location context (size and location of infestation, ease of access), species context (fitness and fecundity, detectability), human context (knowledge, cognition and resources to act) and reinvasion context (invasion pathways). We also devised a simple ordinal scale to assess each factor's influence on eradication feasibility.

Conclusions

The system may be used to report and analyze eradication campaign data in order to (i) prioritize alien species for eradication, (ii) create the strategy for controlling invasive plants, (iii) compare efficiency of different eradication actions, (iv) find gaps in knowledge disabling a sound eradication campaign assessment. The main advantage of using our system is unification of reporting eradication experience data used by researchers performing different eradication actions in different systems.

Optimal surveillance against bioinvasions: a sample average approximation method applied to an agent-based spread model

Trade-offs exist between the point of early detection and the future cost of controlling any invasive species. Finding optimal levels of early detection, with post-border active surveillance, where time, space and randomness are explicitly considered, is computationally challenging. We use a stochastic programming model to find the optimal level of surveillance and predict damages, easing the computational challenge by combining a sample average approximation (SAA) approach and parallel processing techniques. The model is applied to the case of Asian Papaya Fruit Fly (PFF), a highly destructive pest, in Queensland, Australia. To capture the non-linearity in PFF spread, we use an agent-based model (ABM), which is calibrated to a highly detailed land-use raster map (50 m × 50 m) and weather-related data, validated against a historical outbreak. The combination of SAA and ABM sets our work apart from the existing literature. Indeed, despite its increasing popularity as a powerful analytical tool, given its granularity and capability to model the system of interest adequately, the complexity of ABM limits its application in optimizing frameworks due to considerable uncertainty about solution quality. In this light, the use of SAA ensures quality in the optimal solution (with a measured optimality gap) while still being able to handle large-scale decision-making problems. With this combination, our application suggests that the optimal (economic) trap grid size for PFF in Queensland is ˜0.7 km, much smaller than the currently implemented level of 5 km. Although the current policy implies a much lower surveillance cost per year, compared with the $2.08 million under our optimal policy, the expected total cost of an outbreak is $23.92 million, much higher than the optimal policy of roughly $7.74 million.

Cost-effective surveillance of invasive species using info-gap theory

Invasive species can lead to community-level damage to the invaded ecosystem and extinction of native species. Most surveillance systems for the detection of invasive species are developed based on expert assessment, inherently coming with a level of uncertainty. In this research, info-gap decision theory (IGDT) is applied to model and manage such uncertainty. Surveillance of the Asian House Gecko, Hemidactylus frenatus Duméril and Bibron, 1836 on Barrow Island, is used as a case study. Our research provides a novel method for applying IGDT to determine the population threshold (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K$$\end{document}K) so that the decision can be robust to the deep uncertainty present in model parameters. We further robust-optimize surveillance costs rather than minimize surveillance costs. We demonstrate that increasing the population threshold for detection increases both robustness to the errors in the model parameter estimates, and opportuneness to lower surveillance costs than the accepted maximum budget. This paper provides guidance for decision makers to balance robustness and required surveillance expenditure. IGDT offers a novel method to model and manage the uncertainty prevalent in biodiversity conservation practices and modelling. The method outlined here can be used to design robust surveillance systems for invasive species in a wider context, and to better tackle uncertainty in protection of biodiversity and native species in a cost-effective manner.

A framework for allocating conservation resources among multiple threats and actions

Land managers decide how to allocate resources among multiple threats that can be addressed through multiple possible actions. Additionally, these actions vary in feasibility, effectiveness, and cost. We sought to provide a way to optimize resource allocation to address multiple threats when multiple management options are available, including mutually exclusive options. Formulating the decision as a combinatorial optimization problem, our framework takes as inputs the expected impact and cost of each threat for each action (including do nothing) and for each overall budget identifies the optimal action to take for each threat. We compared the optimal solution to an easy to calculate greedy algorithm approximation and a variety of plausible ranking schemes. We applied the framework to management of multiple introduced plant species in Australian alpine areas. We developed a model of invasion to predict the expected impact in 50 years for each species-action combination that accounted for each species' current invasion state (absent, localized, widespread); arrival probability; spread rate; impact, if present, of each species; and management effectiveness of each species-action combination. We found that the recommended action for a threat changed with budget; there was no single optimal management action for each species; and considering more than one candidate action can substantially increase the management plan's overall efficiency. The approximate solution (solution ranked by marginal cost-effectiveness) performed well when the budget matched the cost of the prioritized actions, indicating that this approach would be effective if the budget was set as part of the prioritization process. The ranking schemes varied in performance, and achieving a close to optimal solution was not guaranteed. Global sensitivity analysis revealed a threat's expected impact and, to a lesser extent, management effectiveness were the most influential parameters, emphasizing the need to focus research and monitoring efforts on their quantification.

Approaches for estimating benefits and costs of interventions in plant biosecurity across invasion phases

Nonnative plant pests cause billions of dollars in damages. It is critical to prevent or reduce these losses by intervening at various stages of the invasion process, including pathway risk management (to prevent pest arrival), surveillance and eradication (to counter establishment), and management of established pests (to limit damages). Quantifying benefits and costs of these interventions is important to justify and prioritize investments and to inform biosecurity policy. However, approaches for these estimations differ in (1) the assumed relationship between supply, demand, and prices, and (2) the ability to assess different types of direct and indirect costs at invasion stages, for a given arrival or establishment probability. Here we review economic approaches available to estimate benefits and costs of biosecurity interventions to inform the appropriate selection of approaches. In doing so, we complement previous studies and reviews on estimates of damages from invasive species by considering the influence of economic and methodological assumptions. Cost accounting is suitable for rapid decisions, specific impacts, and simple methodological assumptions but fails to account for feedbacks, such as market adjustments, and may overestimate long-term economic impacts. Partial equilibrium models consider changes in consumer and producer surplus due to pest impacts or interventions and can account for feedbacks in affected sectors but require specialized economic models, comprehensive data sets, and estimates of commodity supply and demand curves. More intensive computable general equilibrium models can account for feedbacks across entire economies, including capital and labor, and linkages among these. The two major considerations in choosing an approach are (1) the goals of the analysis (e.g., consideration of a single pest or intervention with a limited range of impacts vs. multiple interventions, pests or sectors), and (2) the resources available for analysis such as knowledge, budget and time.

Developing a non-destructive metabarcoding protocol for detection of pest insects in bulk trap catches

Metabarcoding has the potential to revolutionise insect surveillance by providing high-throughput and cost-effective species identification of all specimens within mixed trap catches. Nevertheless, incorporation of metabarcoding into insect diagnostic laboratories will first require the development and evaluation of protocols that adhere to the specialised regulatory requirements of invasive species surveillance. In this study, we develop a multi-locus non-destructive metabarcoding protocol that allows sensitive detection of agricultural pests, and subsequent confirmation using traditional diagnostic techniques. We validate this protocol for the detection of tomato potato psyllid (Bactericera cockerelli) and Russian wheat aphid (Diuraphis noxia) within mock communities and field survey traps. We find that metabarcoding can reliably detect target insects within mixed community samples, including specimens that morphological identification did not initially detect, but sensitivity appears inversely related to community size and is impacted by primer biases, target loci, and sample indexing strategy. While our multi-locus approach allowed independent validation of target detection, lack of reference sequences for 18S and 12S restricted its usefulness for estimating diversity in field samples. The non-destructive DNA extraction proved invaluable for resolving inconsistencies between morphological and metabarcoding identification results, and post-extraction specimens were suitable for both morphological re-examination and DNA re-extraction for confirmatory barcoding.

Spatially explicit removal strategies increase the efficiency of invasive plant species control

Effective management strategies are needed to control expansion of invasive alien plant species and attenuate economic and ecological impacts. While previous theoretical studies have assessed optimal control strategies that balance economic costs and ecological benefits, less attention has been paid to the ways in which the spatial characteristics of individual patches may mediate the effectiveness of management strategies. We developed a spatially explicit cellular automaton model for invasive species spread, and compared the effectiveness of seven control strategies. These control strategies used different criteria to prioritize the removal of invasive species patches from the landscape. The different criteria were related to patch size, patch geometry, and patch position within the landscape. Effectiveness of strategies was assessed for both seed dispersing and clonally expanding plant species. We found that, for seed-dispersing species, removal of small patches and removal of patches that are isolated within the landscape comprised relatively effective control strategies. For clonally expanding species, removal of patches based on their degree of isolation and their geometrical properties comprised relatively effective control strategies. Subsequently, we parameterized the model to mimic the observed spatial distribution of the invasive species Antigonon leptopus on St. Eustatius (northern Caribbean). This species expands clonally and also disperses via seeds, and model simulations showed that removal strategies focusing on smaller patches that are more isolated in the landscape would be most effective and could increase the effectiveness of a 10-yr control strategy by 30-90%, as compared to random removal of patches. Our study emphasizes the potential for invasive plant species management to utilize recent advances in remote sensing, which enable mapping of invasive species at the high spatial resolution needed to quantify patch geometries. The presented results highlight how this spatial information can be used in the design of more effective invasive species control strategies.

Towards Building a Sustainable Future: Positioning Ecological Modelling for Impact in Ecosystems Management

As many ecosystems worldwide are in peril, efforts to manage them sustainably require scientific advice. While numerous researchers around the world use a great variety of models to understand ecological dynamics and their responses to disturbances, only a small fraction of these models are ever used to inform ecosystem management. There seems to be a perception that ecological models are not useful for management, even though mathematical models are indispensable in many other fields. We were curious about this mismatch, its roots, and potential ways to overcome it. We searched the literature on recommendations and best practices for how to make ecological models useful to the management of ecosystems and we searched for 'success stories' from the past. We selected and examined several cases where models were instrumental in ecosystem management. We documented their success and asked whether and to what extent they followed recommended best practices. We found that there is not a unique way to conduct a research project that is useful in management decisions. While research is more likely to have impact when conducted with many stakeholders involved and specific to a situation for which data are available, there are great examples of small groups or individuals conducting highly influential research even in the absence of detailed data. We put the question of modelling for ecosystem management into a socio-economic and national context and give our perspectives on how the discipline could move forward.

Optimal invasive species surveillance in the real world: practical advances from research

When alien species make incursions into novel environments, early detection through surveillance is critical to minimizing their impacts and preserving the possibility of timely eradication. However, incipient populations can be difficult to detect, and usually, there are limited resources for surveillance or other response activities. Modern optimization techniques enable surveillance planning that accounts for the biology and expected behavior of an invasive species while exploring multiple scenarios to identify the most cost-effective options. Nevertheless, most optimization models omit some real-world limitations faced by practitioners during multi-day surveillance campaigns, such as daily working time constraints, the time and cost to access survey sites and personnel work schedules. Consequently, surveillance managers must rely on their own judgments to handle these logistical details, and default to their experience during implementation. This is sensible, but their decisions may fail to address all relevant factors and may not be cost-effective. A better planning strategy is to determine optimal routing to survey sites while accounting for common daily logistical constraints. Adding site access and other logistical constraints imposes restrictions on the scope and extent of the surveillance effort, yielding costlier but more realistic expectations of the surveillance outcomes than in a theoretical planning case.

Combining multiple tactics over time for cost-effective eradication of invading insect populations

Because of the profound ecological and economic impacts of many non-native insect species, early detection and eradication of newly founded, isolated populations is a high priority for preventing damages. Though successful eradication is often challenging, the effectiveness of several treatment methods/tactics is enhanced by the existence of Allee dynamics in target populations. Historically, successful eradication has often relied on the application of two or more tactics. Here, we examine how to combine three treatment tactics in the most cost-effective manner, either simultaneously or sequentially in a multiple-annum process. We show that each tactic is most efficient across a specific range of population densities. Furthermore, we show that certain tactics inhibit the efficiency of other tactics and should therefore not be used simultaneously; but since each tactic is effective at specific densities, different combinations of tactics should be applied sequentially through time when a multiple-annum eradication programme is needed.

Optimising risk-based surveillance for early detection of invasive plant pathogens

Emerging infectious diseases (EIDs) of plants continue to devastate ecosystems and livelihoods worldwide. Effective management requires surveillance to detect epidemics at an early stage. However, despite the increasing use of risk-based surveillance programs in plant health, it remains unclear how best to target surveillance resources to achieve this. We combine a spatially explicit model of pathogen entry and spread with a statistical model of detection and use a stochastic optimisation routine to identify which arrangement of surveillance sites maximises the probability of detecting an invading epidemic. Our approach reveals that it is not always optimal to target the highest-risk sites and that the optimal strategy differs depending on not only patterns of pathogen entry and spread but also the choice of detection method. That is, we find that spatial correlation in risk can make it suboptimal to focus solely on the highest-risk sites, meaning that it is best to avoid ‘putting all your eggs in one basket’. However, this depends on an interplay with other factors, such as the sensitivity of available detection methods. Using the economically important arboreal disease huanglongbing (HLB), we demonstrate how our approach leads to a significant performance gain and cost saving in comparison with conventional methods to targeted surveillance.

Emerging infectious diseases of plants continue to devastate ecosystems and livelihoods worldwide. By linking a mathematical model of pest spread with a computational optimisation routine, this study identifies where to look for invasive pests if we wish to detect them at an early stage; this method improves upon conventional methods of risk-based surveillance and is robust to model misspecification.

Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty

Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13-20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.

Optimal surveillance against foot-and-mouth disease: A sample average approximation approach

Decisions surrounding the presence of infectious diseases are typically made in the face of considerable uncertainty. However, the development of models to guide these decisions has been substantially constrained by computational difficulty. This paper focuses on the case of finding the optimal level of surveillance against a highly infectious animal disease where time, space and randomness are fully considered. We apply the Sample Average Approximation approach to solve our problem, and to control model dimension, we propose the use of an infection tree model, in combination with sensible ‘tree-pruning’ and parallel processing techniques. Our proposed model and techniques are generally applicable to a number of disease types, but we demonstrate the approach by solving for optimal surveillance levels against foot-and-mouth disease using bulk milk testing as an active surveillance protocol, during an epidemic, among 42,279 farms, fully characterised by their location, livestock type and size, in the state of Victoria, Australia.

Testing multiple substrates for terrestrial biodiversity monitoring using environmental DNA metabarcoding

Biological surveys based on visual identification of the biota are challenging, expensive and time consuming, yet crucial for effective biomonitoring. DNA metabarcoding is a rapidly developing technology that can also facilitate biological surveys. This method involves the use of next generation sequencing technology to determine the community composition of a sample. However, it is uncertain as to what biological substrate should be the primary focus of metabarcoding surveys. This study aims to test multiple sample substrates (soil, scat, plant material and bulk arthropods) to determine what organisms can be detected from each and where they overlap. Samples (n = 200) were collected in the Pilbara (hot desert climate) and Swan Coastal Plain (hot Mediterranean climate) regions of Western Australia. Soil samples yielded little plant or animal DNA, especially in the Pilbara, probably due to conditions not conducive to long-term preservation. In contrast, scat samples contained the highest overall diversity with 131 plant, vertebrate and invertebrate families detected. Invertebrate and plant sequences were detected in the plant (86 families), pitfall (127 families) and vane trap (126 families) samples. In total, 278 families were recovered from the survey, 217 in the Swan Coastal Plain and 156 in the Pilbara. Aside from soil, 22%-43% of the families detected were unique to the particular substrate, and community composition varied significantly between substrates. These results demonstrate the importance of selecting appropriate metabarcoding substrates when undertaking terrestrial surveys. If the aim is to broadly capture all biota then multiple substrates will be required.

Will growing invasive arthropod biodiversity outpace our ability for eradication?

The Global Eradication Database documents 811 eradication attempts against invasive arthropods since 1890, in 104 countries. Eradication programs show a greater than exponential increase in the number of programs started in recent decades. In addition, there is a trend of a rapidly diversifying burden of the most severe threats. The species richness showed a three-fold increase in number of species under eradication in the last 50 yr, and all taxonomic levels rose dramatically. The increase in number of eradication programs shows that current management measures for constraining the spread of invasive species are inadequate. A similar surge in the number of governments trying to prevent the establishment of new pests has occurred. Increased biodiversity of arthropod eradication targets includes new pest groups with fewer tools developed for management. We argue that a rapid increase in biodiversity of invasive and economically or environmentally damaging organisms represents a substantial and underestimated challenge for managers wanting to prevent their establishment, requiring a shift in research focus to accelerate delimitation and suppression options with less reliance on insecticides.

Managing biological invasions in urban environments with the acceptance sampling approach

Detections of invasive species outbreaks are often followed by the removal of susceptible host organisms in order to slow the spread of the invading pest population. We propose the acceptance sampling approach for detection and optional removal of susceptible host trees to manage an outbreak of the emerald ash borer (EAB), a highly destructive forest pest, in Winnipeg, Canada. We compare the strategy with two common delimiting survey techniques that do not consider follow-up management actions such as host removal. Our results show that the management objective influences the survey strategy. The survey-only strategies maximized the capacity to detect new infestations and prioritized sites with high likelihood of being invaded. Comparatively, the surveys with subsequent host removal actions allocated most of the budget to sites where complete host removal would minimize the pest's ability to spread to uninvaded locations. Uncertainty about the pest's spread causes the host removal measures to cover a larger area in a uniform spatial pattern and extend to farther distances from already infested sites. If a decision maker is ambiguity-averse and strives to avoid the worst-case damages from the invasion, the optimal strategy is to survey more sites with high host densities and remove trees from sites at farther distances, where EAB arrivals may be uncertain, but could cause significant damage if not detected quickly. Accounting for the uncertainty about spread helps develop a more robust pest management strategy. The approach is generalizable and can support management programs for new pest incursions.

Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance

Trap-based surveillance strategies are widely used for monitoring of invasive insect species, aiming to detect newly arrived exotic taxa as well as track the population levels of established or endemic pests. Where these surveillance traps have low specificity and capture non-target endemic species in excess of the target pests, the need for extensive specimen sorting and identification creates a major diagnostic bottleneck. While the recent development of standardized molecular diagnostics has partly alleviated this requirement, the single specimen per reaction nature of these methods does not readily scale to the sheer number of insects trapped in surveillance programmes. Consequently, target lists are often restricted to a few high-priority pests, allowing unanticipated species to avoid detection and potentially establish populations. DNA metabarcoding has recently emerged as a method for conducting simultaneous, multi-species identification of complex mixed communities and may lend itself ideally to rapid diagnostics of bulk insect trap samples. Moreover, the high-throughput nature of recent sequencing platforms could enable the multiplexing of hundreds of diverse trap samples on a single flow cell, thereby providing the means to dramatically scale up insect surveillance in terms of both the quantity of traps that can be processed concurrently and number of pest species that can be targeted. In this review of the metabarcoding literature, we explore how DNA metabarcoding could be tailored to the detection of invasive insects in a surveillance context and highlight the unique technical and regulatory challenges that must be considered when implementing high-throughput sequencing technologies into sensitive diagnostic applications.

Traditional trapping methods outperform eDNA sampling for introduced semi-aquatic snakes

Given limited resources for managing invasive species, traditional survey methods may not be feasible to implement at a regional scale. Environmental DNA (eDNA) sampling has proven to be an effective method for detecting some invasive species, but comparisons between the detection probability of eDNA and traditional survey methods using modern occupancy modeling methods are rare. We developed a qPCR assay to detect two species of watersnake (Nerodia fasciata and Nerodia sipedon) introduced to California, USA, and we compared the efficacy of eDNA and aquatic trapping. We tested 3–9 water samples each from 30 sites near the known range of N. fasciata, and 61 sites near the known range of N. sipedon. We also deployed aquatic funnel traps at a subset of sites for each species. We detected N. fasciata eDNA in three of nine water samples from just one site, but captured N. fasciata in traps at three of ten sites. We detected N. sipedon eDNA in five of six water samples from one site, which was also the only site of nine at which this species was captured in traps. Traditional trapping surveys had a higher probability of detecting watersnakes than eDNA surveys, and both survey methods had higher detection probability for N. sipedon than N. fasciata. Occupancy models that integrated both trapping and eDNA surveys estimated that 5 sites (95% Credible Interval: 4–10) of 91 were occupied by watersnakes (both species combined), although snakes were only detected at four sites (three for N. fasciata, one for N. sipedon). Our study shows that despite the many successes of eDNA surveys, traditional sampling methods can have higher detection probability for some species. We recommend those tasked with managing species invasions explicitly compare eDNA and traditional survey methods in an occupancy framework to inform their choice of the best method for detecting nascent populations.

Iterative Models for Early Detection of Invasive Species across Spread Pathways

Species distribution models can be used to direct early detection of invasive species, if they include proxies for invasion pathways. Due to the dynamic nature of invasion, these models violate assumptions of stationarity across space and time. To compensate for issues of stationarity, we iteratively update regionalized species distribution models annually for European gypsy moth (Lymantria dispar dispar) to target early detection surveys for the USDA APHIS gypsy moth program. We defined regions based on the distances from the invasion spread front where shifts in variable importance occurred and included models for the non-quarantine portion of the state of Maine, a short-range region, an intermediate region, and a long-range region. We considered variables that represented potential gypsy moth movement pathways within each region, including transportation networks, recreational activities, urban characteristics, and household movement data originating from gypsy moth infested areas (U.S. Postal Service address forwarding data). We updated the models annually, linked the models to an early detection survey design, and validated the models for the following year using predicted risk at new positive detection locations. Human-assisted pathways data, such as address forwarding, became increasingly important predictors of gypsy moth detection in the intermediate-range geographic model as more predictor data accumulated over time (relative importance = 5.9%, 17.36%, and 35.76% for 2015, 2016, and 2018, respectively). Receiver operating curves showed increasing performance for iterative annual models (area under the curve (AUC) = 0.63, 0.76, and 0.84 for 2014, 2015, and 2016 models, respectively), and boxplots of predicted risk each year showed increasing accuracy and precision of following year positive detection locations. The inclusion of human-assisted pathway predictors combined with the strategy of iterative modeling brings significant advantages to targeting early detection of invasive species. We present the first published example of iterative species distribution modeling for invasive species in an operational context.

Accounting for spatially heterogeneous conditions in local-scale surveillance strategies: case study of the biosecurity insect pest, grape phylloxera (Daktulosphaira vitifoliae (Fitch))

BACKGROUND

Surveillance strategies are often standardized and completed on grid patterns to detect pest incursions quickly; however, it may be possible to improve surveillance through more targeted observation that accounts for landscape heterogeneity, dispersal and the habitat requirements of the invading organism. We simulated pest spread at a local scale, using grape phylloxera (Daktulosphaira vitifoliae (Fitch)) as a case study, and assessed the influence of incorporating spatial heterogeneity into surveillance compared with current, standard surveillance strategies.

RESULTS

Time to detection and spread within and beyond the vineyard were reduced by conducting surveys that target sampling effort in soil that is highly suitable for the invading pest in comparison with standard surveillance strategies. However, these outcomes were dependent on the virulence level of phylloxera because phylloxera is a complex pest with multiple genotypes that influence spread and detectability.

CONCLUSION

Targeting surveillance strategies based on local-scale spatial heterogeneity can decrease the time to detection without increasing the survey cost, and surveillance that targets highly suitable soil is the most efficient strategy for detecting new incursions. In addition, combining targeted surveillance strategies with buffer zones and hygiene procedures, and updating surveillance strategies as additional species information becomes available, will further decrease the risk of pest spread. © 2018 Society of Chemical Industry.

Doublethink and scale mismatch polarize policies for an invasive tree

Mismatches between invasive species management policies and ecological knowledge can lead to profound societal consequences. For this reason, natural resource agencies have adopted the scientifically-based density-impact invasive species curve to guide invasive species management. We use the density-impact model to evaluate how well management policies for a native invader (Juniperus virginiana) match scientific guidelines. Juniperus virginiana invasion is causing a sub-continental regime shift from grasslands to woodlands in central North America, and its impacts span collapses in endemic diversity, heightened wildfire risk, and crashes in grazing land profitability. We (1) use land cover data to identify the stage of Juniperus virginiana invasion for three ecoregions within Nebraska, USA, (2) determine the range of invasion stages at individual land parcel extents within each ecoregion based on the density-impact model, and (3) determine policy alignment and mismatches relative to the density-impact model in order to assess their potential to meet sustainability targets and avoid societal impacts as Juniperus virginiana abundance increases. We found that nearly all policies evidenced doublethink and policy-ecology mismatches, for instance, promoting spread of Juniperus virginiana regardless of invasion stage while simultaneously managing it as a native invader in the same ecoregion. Like other invasive species, theory and literature for this native invader indicate that the consequences of invasion are unlikely to be prevented if policies fail to prioritize management at incipient invasion stages. Theory suggests a more realistic approach would be to align policy with the stage of invasion at local and ecoregion management scales. There is a need for scientists, policy makers, and ecosystem managers to move past ideologies governing native versus non-native invader classification and toward a framework that accounts for the uniqueness of native species invasions, their anthropogenic drivers, and their impacts on ecosystem services.

Do an invasive organism's dispersal characteristics affect how we should search for it?

We investigated how an invading organism's dispersal characteristics affect the efficacy of different surveillance strategies aimed at detecting that organism as it spreads following a new incursion. Specifically, we assessed whether, out of the surveillance strategies tested, the best surveillance strategy for an organism varied depending on the way it disperses. We simulated the spread of invasive organisms with different dispersal characteristics including leptokurtic and non-leptokurtic kernels with different median dispersal distances and degrees of kurtosis. We evaluated surveillance strategies with different sampling arrangements, densities and frequencies. Surveillance outcomes compared included the time to detection, the total spread of the invasion and the likelihood of the invasion reaching new areas. Overall, dispersal characteristics affected the surveillance outcomes, but the grid surveillance arrangement consistently performed best in terms of early detection and reduced spread within and between fields. Additionally, the results suggest that dispersal characteristics may influence spread to new areas and surveillance strategies. Therefore, knowledge on an invasive organism's dispersal characteristics may influence how we search for it and how we manage the invasion to prevent spread to new areas.

Early detection monitoring for aquatic non-indigenous species: Optimizing surveillance, incorporating advanced technologies, and identifying research needs

Following decades of ecologic and economic impacts from a growing list of nonindigenous and invasive species, government and management entities are committing to systematic early- detection monitoring (EDM). This has reinvigorated investment in the science underpinning such monitoring, as well as the need to convey that science in practical terms to those tasked with EDM implementation. Using the context of nonindigenous species in the North American Great Lakes, this article summarizes the current scientific tools and knowledge - including limitations, research needs, and likely future developments - relevant to various aspects of planning and conducting comprehensive EDM. We begin with the scope of the effort, contrasting target-species with broad-spectrum monitoring, reviewing information to support prioritization based on species and locations, and exploring the challenge of moving beyond individual surveys towards a coordinated monitoring network. Next, we discuss survey design, including effort to expend and its allocation over space and time. A section on sample collection and analysis overviews the merits of collecting actual organisms versus shed DNA, reviews the capabilities and limitations of identification by morphology, DNA target markers, or DNA barcoding, and examines best practices for sample handling and data verification. We end with a section addressing the analysis of monitoring data, including methods to evaluate survey performance and characterize and communicate uncertainty. Although the body of science supporting EDM implementation is already substantial, research and information needs (many already actively being addressed) include: better data to support risk assessments that guide choice of taxa and locations to monitor; improved understanding of spatiotemporal scales for sample collection; further development of DNA target markers, reference barcodes, genomic workflows, and synergies between DNA-based and morphology-based taxonomy; and tools and information management systems for better evaluating and communicating survey outcomes and uncertainty.

A modeling framework for the establishment and spread of invasive species in heterogeneous environments

Natural and human-induced events are continuously altering the structure of our landscapes and as a result impacting the spatial relationships between individual landscape elements and the species living in the area. Yet, only recently has the influence of the surrounding landscape on invasive species spread started to be considered. The scientific community increasingly recognizes the need for broader modeling framework that focuses on cross-study comparisons at different spatiotemporal scales. Using two illustrative examples, we introduce a general modeling framework that allows for a systematic investigation of the effect of habitat change on invasive species establishment and spread. The essential parts of the framework are (i) a mechanistic spatially explicit model (a modular dispersal framework-MDIG) that allows population dynamics and dispersal to be modeled in a geographical information system (GIS), (ii) a landscape generator that allows replicated landscape patterns with partially controllable spatial properties to be generated, and (iii) landscape metrics that depict the essential aspects of landscape with which dispersal and demographic processes interact. The modeling framework provides functionality for a wide variety of applications ranging from predictions of the spatiotemporal spread of real species and comparison of potential management strategies, to theoretical investigation of the effect of habitat change on population dynamics. Such a framework allows to quantify how small-grain landscape characteristics, such as habitat size and habitat connectivity, interact with life-history traits to determine the dynamics of invasive species spread in fragmented landscape. As such, it will give deeper insights into species traits and landscape features that lead to establishment and spread success and may be key to preventing new incursions and the development of efficient monitoring, surveillance, control or eradication programs.