Using habitat suitability models to target invasive plant species surveys

Variation in Oceanographic Resistance of the World's Coastlines to Invasion by Species With Planktonic Dispersal

For marine species with planktonic dispersal, invasion of open ocean coastlines is impaired by the physical adversity of ocean currents moving larvae downstream and offshore. The extent species are affected by physical adversity depends on interactions of the currents with larval life history traits such as planktonic duration, depth and seasonality. Ecologists have struggled to understand how these traits expose species to adverse ocean currents and affect their ability to persist when introduced to novel habitat. We use a high-resolution global ocean model to isolate the role of ocean currents on the persistence of a larval-producing species introduced to every open coastline of the world. We find physical adversity to invasion varies globally by several orders of magnitude. Larval duration is the most influential life history trait because increased duration prolongs species' exposure to ocean currents. Furthermore, variation of physical adversity with life history elucidates how trade-offs between dispersal traits vary globally.

Modeling the risk of aquatic species invasion spread through boater movements and river connections

Aquatic invasive species (AIS) are one of the greatest threats to the functioning of aquatic ecosystems worldwide. Once an invasive species has been introduced to a new region, many governments develop management strategies to reduce further spread. Nevertheless, managing AIS in a new region is challenging because of the vast areas that need protection and limited resources. Spatial heterogeneity in invasion risk is driven by environmental suitability and propagule pressure, which can be used to prioritize locations for surveillance and intervention activities. To better understand invasion risk across aquatic landscapes, we developed a simulation model to estimate the likelihood of a waterbody becoming invaded with an AIS. The model included waterbodies connected via a multilayer network that included boater movements and hydrological connections. In a case study of Minnesota, we used zebra mussels (Dreissena polymorpha) and starry stonewort (Nitellopsis obtusa) as model species. We simulated the impacts of management scenarios developed by stakeholders and created a decision-support tool available through an online application provided as part of the AIS Explorer dashboard. Our baseline model revealed that 89% of new zebra mussel invasions and 84% of new starry stonewort invasions occurred through boater movements, establishing it as a primary pathway of spread and offering insights beyond risk estimates generated by traditional environmental suitability models alone. Our results highlight the critical role of interventions applied to boater movements to reduce AIS dispersal.

A within-lake occupancy model for starry stonewort, Nitellopsis obtusa, to support early detection and monitoring

To efficiently detect aquatic invasive species early in an invasion when control may still be possible, predictions about which locations are likeliest to be occupied are needed at fine scales but are rarely available. Occupancy modeling could provide such predictions given data of sufficient quality and quantity. We assembled a data set for the macroalga starry stonewort (Nitellopsis obtusa) across Minnesota and Wisconsin, USA, where it is a new and high-priority invader. We used these data to construct a multi-season, single-species spatial occupancy model that included biotic, abiotic, and movement-related predictors. Distance to the nearest access was an important occurrence predictor, highlighting the likely role boats play in spreading starry stonewort. Fetch and water depth also predicted occupancy. We estimated an average detection probability of 63% at sites with mean non-N. obtusa plant cover, declining to ~ 38% at sites with abundant plant cover, especially that of other Characeae. We recommend that surveyors preferentially search for starry stonewort in areas of shallow depth and high fetch close to boat accesses. We also recommend searching during late summer/early fall when detection is likelier. This study illustrates the utility of fine-scale occupancy modeling for predicting the locations of nascent populations of difficult-to-detect species.

Opening the floodgates for invasion-modelling the distribution dynamics of invasive alien fishes in India

Invasive alien species have become the second major threat to biodiversity affecting all three major ecosystems (terrestrial, marine, and freshwater). Increasing drivers such as habitat destruction, expanding horticulture and aquaculture industries, and global pet and food trade have created pathways for exotic species to be introduced leading to severe impacts on recipient ecosystems. Although relatively less studied than terrestrial ecosystems, freshwater ecosystems are highly susceptible to biological invasions. In India, there has been a noticeable increase in the introduction of alien fish species in freshwater environments. In the current study, we aimed to understand how climate change can affect the dynamics of the biological invasion of invasive alien fishes in India. We also evaluated the river-linking project's impact on the homogenization of biota in Indian freshwater bodies. We used species occurrence records with selected environmental variables to assess vulnerable locations for current and future biological invasion using species distribution models. Our study has identified and mapped the vulnerable regions to invasion in India. Our research indicates that the interlinking of rivers connects susceptible regions housing endangered fish species with invasive hotspots. Invasive alien fishes from the source basin may invade vulnerable basins and compete with the native species. Based on the results, we discuss some of the key areas for the management of these invasive alien species in the freshwater ecosystems.

The possibilities of explicit Striga (Striga asiatica) risk monitoring using phenometric, edaphic, and climatic variables, demonstrated for Malawi and Zambia

Food insecurity continues to affect more than two-thirds of the population in sub-Saharan Africa (SSA), particularly those depending on rain-fed agriculture. Striga, a parasitic weed, has caused yield losses of cereal crops, immensely affecting smallholder farmers in SSA. Although earlier studies have established that Striga is a constraint to crop production, there is little information on the spatial extent of spread and infestation severity of the weed in some SSA countries like Malawi and Zambia. This study aimed to use remotely sensed vegetation phenological (n = 11), climatic (n = 3), and soil (n = 4) variables to develop a data-driven ecological niche model to estimate Striga (Striga asiatica) spatial distribution patterns over Malawi and Zambia, respectively. Vegetation phenological variables were calculated from 250-m enhanced vegetation index (EVI) timeline data, spanning 2013 to 2016. A multicollinearity test was performed on all 18 predictor variables using the variance inflation factor (VIF) and Pearson's  correlation approach. From the initial 18 variables, 12 non-correlated predictor variables were selected to predict Striga risk zones over the two focus countries. The variable "start of the season" (start of the rainy season) showed the highest model relevance, contributing 26.8% and 37.9% to Striga risk models for Malawi and Zambia, respectively. This indicates that the crop planting date influences the occurrence and the level of Striga infestation. The resultant occurrence maps revealed interesting spatial patterns; while a very high Striga occurrence was predicted for central Malawi and eastern Zambia (mono-cultural maize growing areas), lower occurrence rates were found in the northern regions. Our study shows the possibilities of integrating various ecological factors with a better spatial and temporal resolution for operational and explicit monitoring of Striga-affected areas in SSA. The explicit identification of Striga "hotspot" areas is crucial for effectively informing intervention activities on the ground.

Predicting distributions of blacklegged ticks (Ixodes scapularis), Lyme disease spirochetes (Borrelia burgdorferi sensu stricto) and human Lyme disease cases in the eastern United States

Lyme disease is the most commonly reported vector-borne disease in the United States (US), with approximately 300,000 -to- 40,000 cases reported annually. The blacklegged tick, Ixodes scapularis, is the primary vector of the Lyme disease-causing spirochete, Borrelia burgdorferi sensu stricto, in high incidence regions in the upper midwestern and northeastern US. Using county-level records of the presence of I. scapularis or presence of B. burgdorferi s.s. infected host-seeking I. scapularis, we generated habitat suitability consensus maps based on an ensemble of statistical models for both acarological risk metrics. Overall accuracy of these suitability models was high (AUC = 0.76 for I. scapularis and 0.86 for B. burgdorferi s.s. infected-I. scapularis). We sought to compare which acarological risk metric best described the distribution of counties reporting high Lyme disease incidence (≥10 confirmed cases/100,000 population) by setting the models to a fixed omission rate (10%). We compared the percent of high incidence counties correctly classified by the two models. The I. scapularis consensus map correctly classified 53% of high and low incidence counties, while the B. burgdorferi s.s. infected-I. scapularis consensus map classified 83% correctly. Counties classified as suitable by the B. burgdorferi s.s. map showed a 91% overlap with high Lyme disease incidence counties with over a 38-fold difference in Lyme disease incidence between high- and low-suitability counties. A total of 288 counties were classified as highly suitable for B. burgdorferi s.s., but lacked records of infected-I. scapularis and were not classified as high incidence. These counties were considered to represent a leading edge for B. burgdorferi s.s. infection in ticks and humans. They clustered in Illinois, Indiana, Michigan, and Ohio. This information can aid in targeting tick surveillance and prevention education efforts in counties where Lyme disease risk may increase in the future.

INHABIT: A web-based decision support tool for invasive plant species habitat visualization and assessment across the contiguous United States

Narrowing the communication and knowledge gap between producers and users of scientific data is a longstanding problem in ecological conservation and land management. Decision support tools (DSTs), including websites or interactive web applications, provide platforms that can help bridge this gap. DSTs can most effectively disseminate and translate research results when producers and users collaboratively and iteratively design content and features. One data resource seldom incorporated into DSTs are species distribution models (SDMs), which can produce spatial predictions of habitat suitability. Outputs from SDMs can inform management decisions, but their complexity and inaccessibility can limit their use by resource managers or policy makers. To overcome these limitations, we present the Invasive Species Habitat Tool (INHABIT), a novel, web-based DST built with R Shiny to display spatial predictions and tabular summaries of habitat suitability from SDMs for invasive plants across the contiguous United States. INHABIT provides actionable science to support the prevention and management of invasive species. Two case studies demonstrate the important role of end user feedback in confirming INHABIT's credibility, utility, and relevance.

A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales

Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. To ensure efficiency, we used largely automated modeling approaches and human input only at key junctures. We explore and present uncertainty by using two alternative sources of background samples, including five statistical algorithms, and constructing model ensembles. We demonstrate the use and efficiency of the Software for Assisted Habitat Modeling [SAHM 2.1.2], a package in VisTrails, which performs the majority of the modeling analyses. Our workflow includes solicitation of expert feedback on model outputs such as spatial prediction results and variable response curves, and iterative improvement based on new data availability and directed field validation of initial model results. We highlight the utility of the models for decision-making at regional and local scales with case studies of two plant species that invade natural areas: fountain grass (Pennisetum setaceum) and goutweed (Aegopodium podagraria). By balancing model automation with human intervention, we can efficiently provide land managers with mapped predicted distributions for multiple invasive species to inform decisions across spatial scales.

Experimental evidence of chemical attraction in the mutualistic zebra mussel-killer shrimp system

Invasion facilitation, whereby one species has a positive effect on the establishment of another species, could help explain the rapid colonisation shown by some freshwater invasive species, but the underlying mechanisms remain unclear. We employed two-choice test arenas to test whether the presence of zebra mussel (Dreissena polymorpha) could facilitate the establishment of the killer shrimp (Dikerogammarus villosus). Killer shrimp preferred to settle on mats of zebra mussel, but this was unrelated to mat size, and was not different from attraction shown to artificial grass, suggesting that zebra mussel primarily provides substrate and refuge to the killer shrimp. Killer shrimp were strongly attracted to water scented by zebra mussel, but not to water scented by fish. Chemical attraction to the zebra mussel's scent did not differ between sympatric and allopatric populations of killer shrimp, suggesting that chemical attraction is not an acquired or learned trait. Our study shows, for the first time, chemical attraction between two highly invasive freshwater species, thereby providing a plausible mechanism for invasion facilitation. This has implications for managing the spread of killer shrimp, and perhaps other freshwater invasive species, because chemical attraction could significantly increase establishment success in mutualistic systems. Failure to consider invasion facilitation may underestimate the risk of establishment, and likely also the impact of some aquatic invaders.

Development and Delivery of Species Distribution Models to Inform Decision-Making

Information on where species occur is an important component of conservation and management decisions, but knowledge of distributions is often coarse or incomplete. Species distribution models provide a tool for mapping habitat and can produce credible, defensible, and repeatable information with which to inform decisions. However, these models are sensitive to data inputs and methodological choices, making it important to assess the reliability and utility of model predictions. We provide a rubric that model developers can use to communicate a model's attributes and its appropriate uses. We emphasize the importance of tailoring model development and delivery to the species of interest and the intended use and the advantages of iterative modeling and validation. We highlight how species distribution models have been used to design surveys for new populations, inform spatial prioritization decisions for management actions, and support regulatory decision-making and compliance, tying these examples back to our model assessment rubric.

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.

Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research

Opportunistic citizen science (CS) programs allow volunteers to report species observations from anywhere, at any time, and can assemble large volumes of historic and current data at faster rates than more coordinated programs with standardized data collection. This can quickly provide large amounts of species distributional data, but whether this focus on participation comes at a cost in data quality is not clear. Although automated and expert vetting can increase data reliability, there is no guarantee that opportunistic data will do anything more than confirm information from professional surveys. Here, we use eButterfly, an opportunistic CS program, and a comparable dataset of professionally collected observations, to measure the amount of new distributional species information that opportunistic CS generates. We also test how well opportunistic CS can estimate regional species richness for a large group of taxa (>300 butterfly species) across a broad area. We find that eButterfly contributes new distributional information for >80% of species, and that opportunistically submitting observations allowed volunteers to spot species ~35 days earlier than professionals. Although eButterfly did a relatively poor job at predicting regional species richness by itself (detecting only about 35-57% of species per region), it significantly contributed to regional species richness when used with the professional dataset (adding ~3 species that had gone undetected in professional surveys per region). Overall, we find that the opportunistic CS model can provide substantial complementary species information when used alongside professional survey data. Our results suggest that data from opportunistic CS programs in conjunction with professional datasets can strongly increase the capacity of researchers to estimate species richness, and provide unique information on species distributions and phenologies that are relevant to the detection of the biological consequences of global change.

Modeling the distributions of tegu lizards in native and potential invasive ranges

Invasive reptilian predators can have substantial impacts on native species and ecosystems. Tegu lizards are widely distributed in South America east of the Andes, and are popular in the international live animal trade. Two species are established in Florida (U.S.A.) - Salvator merianae (Argentine black and white tegu) and Tupinambis teguixin sensu lato (gold tegu) – and a third has been recorded there— S. rufescens (red tegu). We built species distribution models (SDMs) using 5 approaches (logistic regression, multivariate adaptive regression splines, boosted regression trees, random forest, and maximum entropy) based on data from the native ranges. We then projected these models to North America to develop hypotheses for potential tegu distributions. Our results suggest that much of the southern United States and northern México probably contains suitable habitat for one or more of these tegu species. Salvator rufescens had higher habitat suitability in semi-arid areas, whereas S. merianae and T. teguixin had higher habitat suitability in more mesic areas. We propose that Florida is not the only state where these taxa could become established, and that early detection and rapid response programs targeting tegu lizards in potentially suitable habitat elsewhere in North America could help prevent establishment and abate negative impacts on native ecosystems.

Species distribution model transferability and model grain size - finer may not always be better

Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.

Can species distribution models really predict the expansion of invasive species?

Predictive studies are of paramount importance for biological invasions, one of the biggest threats for biodiversity. To help and better prioritize management strategies, species distribution models (SDMs) are often used to predict the potential invasive range of introduced species. Yet, SDMs have been regularly criticized, due to several strong limitations, such as violating the equilibrium assumption during the invasion process. Unfortunately, validation studies–with independent data–are too scarce to assess the predictive accuracy of SDMs in invasion biology. Yet, biological invasions allow to test SDMs usefulness, by retrospectively assessing whether they would have accurately predicted the latest ranges of invasion. Here, we assess the predictive accuracy of SDMs in predicting the expansion of invasive species. We used temporal occurrence data for the Asian hornet Vespa velutina nigrithorax, a species native to China that is invading Europe with a very fast rate. Specifically, we compared occurrence data from the last stage of invasion (independent validation points) to the climate suitability distribution predicted from models calibrated with data from the early stage of invasion. Despite the invasive species not being at equilibrium yet, the predicted climate suitability of validation points was high. SDMs can thus adequately predict the spread of V. v. nigrithorax, which appears to be—at least partially–climatically driven. In the case of V. v. nigrithorax, SDMs predictive accuracy was slightly but significantly better when models were calibrated with invasive data only, excluding native data. Although more validation studies for other invasion cases are needed to generalize our results, our findings are an important step towards validating the use of SDMs in invasion biology.

Pollen sleuthing for terrestrial plant surveys: Locating plant populations by exploiting pollen movement

PREMISE OF THE STUDY

We present an innovative technique for sampling, identifying, and locating plant populations that release pollen, without extensive ground surveys. This method (1) samples pollen at random locations within the target species' habitat, (2) detects species' presence using morphological pollen analysis, and (3) uses kriging to predict likely locations of populations to focus future search efforts.

METHODS

To demonstrate, we applied the pollen sleuthing system to search for artificially constructed populations of Brassica rapa in an old field. Population size varied from 0-100 flowers labeled with artificial pollen (paint pellets). After characterizing the landscape, we pan-trapped 2762 potential insect vectors from random locations across the field and washed particulate matter from their bodies to assess artificial pollen abundance with a microscope.

RESULTS

Population size greatly influenced artificial pollen detection success; following random pollen trap sampling and interpolation, ground surveys would be best focused on identified areas with high pollen density and low variation in pollen density. Sampling sites most successfully detected artificial pollen when they were located at higher elevations, near showy flowering plants that were not grasses.

DISCUSSION

Detection of nascent populations using the proposed system is possible but accuracy will depend on local environmental factors (e.g., wind, elevation). Conservation and invasive species control programs may be improved by using this approach.

Integrating subsistence practice and species distribution modeling: assessing invasive elodea's potential impact on Native Alaskan subsistence of Chinook salmon and whitefish

Alaska has one of the most rapidly changing climates on earth and is experiencing an accelerated rate of human disturbance, including resource extraction and transportation infrastructure development. Combined, these factors increase the state's vulnerability to biological invasion, which can have acute negative impacts on ecological integrity and subsistence practices. Of growing concern is the spread of Alaska's first documented freshwater aquatic invasive plant Elodea spp. (elodea). In this study, we modeled the suitable habitat of elodea using global and state-specific species occurrence records and environmental variables, in concert with an ensemble of model algorithms. Furthermore, we sought to incorporate local subsistence concerns by using Native Alaskan knowledge and available statewide subsistence harvest data to assess the potential threat posed by elodea to Chinook salmon (Oncorhynchus tshawytscha) and whitefish (Coregonus nelsonii) subsistence. State models were applied to future climate (2040-2059) using five general circulation models best suited for Alaska. Model evaluations indicated that our results had moderate to strong predictability, with area under the receiver-operating characteristic curve values above 0.80 and classification accuracies ranging from 66 to 89 %. State models provided a more robust assessment of elodea habitat suitability. These ensembles revealed different levels of management concern statewide, based on the interaction of fish subsistence patterns, known spawning and rearing sites, and elodea habitat suitability, thus highlighting regions with additional need for targeted monitoring. Our results suggest that this approach can hold great utility for invasion risk assessments and better facilitate the inclusion of local stakeholder concerns in conservation planning and management.

Ecologically sustainable weed management: How do we get from proof-of-concept to adoption?

Weed management is a critically important activity on both agricultural and non-agricultural lands, but it is faced with a daunting set of challenges: environmental damage caused by control practices, weed resistance to herbicides, accelerated rates of weed dispersal through global trade, and greater weed impacts due to changes in climate and land use. Broad-scale use of new approaches is needed if weed management is to be successful in the coming era. We examine three approaches likely to prove useful for addressing current and future challenges from weeds: diversifying weed management strategies with multiple complementary tactics, developing crop genotypes for enhanced weed suppression, and tailoring management strategies to better accommodate variability in weed spatial distributions. In all three cases, proof-of-concept has long been demonstrated and considerable scientific innovations have been made, but uptake by farmers and land managers has been extremely limited. Impediments to employing these and other ecologically based approaches include inadequate or inappropriate government policy instruments, a lack of market mechanisms, and a paucity of social infrastructure with which to influence learning, decision-making, and actions by farmers and land managers. We offer examples of how these impediments are being addressed in different parts of the world, but note that there is no clear formula for determining which sets of policies, market mechanisms, and educational activities will be effective in various locations. Implementing new approaches for weed management will require multidisciplinary teams comprised of scientists, engineers, economists, sociologists, educators, farmers, land managers, industry personnel, policy makers, and others willing to focus on weeds within whole farming systems and land management units.

An iterative and targeted sampling design informed by habitat suitability models for detecting focal plant species over extensive areas

Prioritizing areas for management of non-native invasive plants is critical, as invasive plants can negatively impact plant community structure. Extensive and multi-jurisdictional inventories are essential to prioritize actions aimed at mitigating the impact of invasions and changes in disturbance regimes. However, previous work devoted little effort to devising sampling methods sufficient to assess the scope of multi-jurisdictional invasion over extensive areas. Here we describe a large-scale sampling design that used species occurrence data, habitat suitability models, and iterative and targeted sampling efforts to sample five species and satisfy two key management objectives: 1) detecting non-native invasive plants across previously unsampled gradients, and 2) characterizing the distribution of non-native invasive plants at landscape to regional scales. Habitat suitability models of five species were based on occurrence records and predictor variables derived from topography, precipitation, and remotely sensed data. We stratified and established field sampling locations according to predicted habitat suitability and phenological, substrate, and logistical constraints. Across previously unvisited areas, we detected at least one of our focal species on 77% of plots. In turn, we used detections from 2011 to improve habitat suitability models and sampling efforts in 2012, as well as additional spatial constraints to increase detections. These modifications resulted in a 96% detection rate at plots. The range of habitat suitability values that identified highly and less suitable habitats and their environmental conditions corresponded to field detections with mixed levels of agreement. Our study demonstrated that an iterative and targeted sampling framework can address sampling bias, reduce time costs, and increase detections. Other studies can extend the sampling framework to develop methods in other ecosystems to provide detection data. The sampling methods implemented here provide a meaningful tool when understanding the potential distribution and habitat of species over multi-jurisdictional and extensive areas is needed for achieving management objectives.

A generic risk-based surveying method for invading plant pathogens

Invasive plant pathogens are increasing with international trade and travel, with damaging environmental and economic consequences. Recent examples include tree diseases such as sudden oak death in the Western United States and ash dieback in Europe. To control an invading pathogen it is crucial that newly infected sites are quickly detected so that measures can be implemented to control the epidemic. However, since sampling resources are often limited, not all locations can be inspected and locations must be prioritized for surveying. Existing approaches to achieve this are often species specific and rely on detailed data collection and parameterization, which is difficult, especially when new arrivals are unanticipated. Consequently regulatory sampling responses are often ad hoc and developed without due consideration of epidemiology, leading to the suboptimal deployment of expensive sampling resources. We introduce a flexible risk-based sampling method that is pathogen generic and enables available information to be utilized to develop epidemiologically informed sampling programs for virtually any biologically relevant plant pathogen. By targeting risk we aim to inform sampling schemes that identify high-impact locations that can be subsequently treated in order to reduce inoculum in the landscape. This "damage limitation" is often the initial management objective following the first discovery of a new invader. Risk at each location is determined by the product of the basic reproductive number (R0), as a measure of local epidemic size, and the probability of infection. We illustrate how the risk estimates can be used to prioritize a survey by weighting a random sample so that the highest-risk locations have the highest probability of selection. We demonstrate and test the method using a high-quality spatially and temporally resolved data set on Huanglongbing disease (HLB) in Florida, USA. We show that even when available epidemiological information is relatively minimal, the method has strong predictive value and can result in highly effective targeted surveying plans.