AbstractThe need for tools and technologies for understanding and quantifying invasive species has never been greater. Rates of infestation vary on the species or organism being examined across the United States, and notable examples can be found. For example, from 2001 to 2003 alone, ash (Fraxinusspp.) mortality progressed at a rate of 12.97 km year−1(Siegert et al. 2014), and cheatgrass (Bromus tectorum) is expected to increase dominance on 14% of Great Basin rangelands (Boyte et al. 2016). The magnitude and scope of problems that invasive species present suggest novel approaches for detection and management are needed, especially those that enable more cost-effective solutions. The advantages of using technologically advanced approaches and tools are numerous, and the quality and quantity of available information can be significantly enhanced by their use. They can also play a key role in development of decision-support systems; they are meant to be integrated with other systems, such as inventory and monitoring, because often the tools are applied after a species of interest has been detected and a threat has been identified. In addition, the inventory systems mentioned in Chap.10are regularly used in calibrating and validating models and decision-support systems. For forested areas, Forest Inventory and Analysis (FIA) data are most commonly used (e.g., Václavík et al. 2015) given the long history of the program. In non-forested systems, national inventory datasets have not been around as long (see Chap.10), but use of these data to calibrate and validate spatial models is growing. These inventory datasets include the National Resources Inventory (NRI) (e.g., Duniway et al. 2012) and the Assessment Inventory and Monitoring program (AIM) (e.g., McCord et al. 2017). Similarly, use of the Nonindigenous Aquatic Species (NAS) database is growing as well (e.g., Evangelista et al. 2017). The consistent protocols employed by these programs prove valuable for developing better tools, but the data they afford are generally limited for some tools because the sampling intensity is too low.

Bark beetle outbreaks in the Rocky Mountains caused substantial tree mortality starting in the late 1990s, and continued into the 2000s, with the most severe mortality occurring from 2002 to 2012. Over the same time period, concentrations of dissolved copper in the Big Thompson River (BTR), Colorado, USA, increased significantly and are high enough to negatively affect aquatic life. We examined correlations between dissolved copper and tree mortality in the BTR. Two sites, one consisting of water from the western side of the continental divide and one consisting of water from the eastern side, demonstrated a positive relationship between percentage tree mortality and dissolved copper. The relationships were similar except that the best relationship occurred with a 3-year lag between tree mortality and subsequent dissolved copper levels at the eastern site and with a 5-year lag at the western site. The differential time lag is potentially the result of different levels of carbon in the soil in the watersheds associated with each site because carbon can affect copper mobility. Our results suggest that bark beetle-induced tree mortality may contribute significantly to dissolved copper levels in the BTR.

Invasive species risk maps provide broad guidance on where to allocate resources for pest monitoring and regulation, but they often present individual risk components (such as climatic suitability, host abundance, or introduction potential) as independent entities. These independent risk components are integrated using various multicriteria analysis techniques that typically require prior knowledge of the risk components’ importance. Such information is often nonexistent for many invasive pests. This study proposes a new approach for building integrated risk maps using the principle of a multiattribute efficient frontier and analyzing the partial order of elements of a risk map as distributed in multidimensional criteria space. The integrated risks are estimated as subsequent multiattribute frontiers in dimensions of individual risk criteria. We demonstrate the approach with the example of Agrilus biguttatus Fabricius, a high‐risk pest that may threaten North American oak forests in the near future. Drawing on U.S. and Canadian data, we compare the performance of the multiattribute ranking against a multicriteria linear weighted averaging technique in the presence of uncertainties, using the concept of robustness from info‐gap decision theory. The results show major geographic hotspots where the consideration of tradeoffs between multiple risk components changes integrated risk rankings. Both methods delineate similar geographical regions of high and low risks. Overall, aggregation based on a delineation of multiattribute efficient frontiers can be a useful tool to prioritize risks for anticipated invasive pests, which usually have an extremely poor prior knowledge base.

The construction of the 2006 National Insect and Disease Risk Map, compiled by the USDA Forest Service, State and Private Forestry Area, Forest Health Protection Unit, resulted in the development of a GIS-based, multicriteria approach for insect and disease risk mapping that can account for regional variations in forest health concerns and threats. This risk mapping framework, used by all nine Forest Service regions and 49 States, provides a consistent, repeatable, transparent process through which interactive spatial and temporal risk assessments can be conducted at various levels to aid in decisionmaking. The national framework was designed to be highly iterative, using input from a wide range of sources including subject area experts. The framework consists of a five-step process: (1) identify agents of concern (insects and diseases) and target-host species; (2) identify, rank, and weight criteria that determine the susceptibility (potential for introduction and establishment) and vulnerability (potential for tree mortality to occur if an agent is established) to each agent; (3) standardize criteria values, and combine the resultant maps using a series of weighted overlays; (4) convert modeled values for each agent to predicted basal area (BA) loss over a 15-year period; and (5) identify regions at risk of encountering a 25-percent or greater loss of total basal area in the next 15 years. This potentially interactive threshold was set by the National Risk Map Oversight team for the national risk map product. The National Insect and Disease Risk Map resulted in the integration into a national map of 186 forest insect and disease models, individually run and assembled on a central server located at the Forest Health Technology Enterprise Team (FHTET) in Fort Collins, Colorado. The national framework also enables local knowledge and data to be entered into models, allowing for quick, large-scale assessments. The development of this national framework is described here.

Nonindigenous species have caused significant impacts to North American forests despite past and present international phytosanitary efforts. Though broadly acknowledged, the risks of pest invasions are difficult to quantify as they involve interactions between many factors that operate across a range of spatial and temporal scales: the transmission of invading organisms via various pathways, their spread and establishment in new environments. Our study presents a stochastic simulation approach to quantify these risks and associated uncertainties through time in a unified fashion. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. We simulate new potential entries of S. noctilio as a stochastic process, based on recent volumes of marine shipments of commodities from countries where S. noctilio is established, as well as the broad dynamics of foreign marine imports. The results are then linked with a spatial model that simulates the spread of S. noctilio within the geographical distribution of its hosts (pines) while incorporating existing knowledge about its behavior in North American landscapes. Through replications, this approach yields a spatial representation of S. noctilio risks and uncertainties in a single integrated product. The approach should also be appealing to decisionmakers, since it accounts for projected flows of commodities that may serve as conduits for pest entry. Our 30‐year forecasts indicate high establishment probability in Ontario, Quebec, and the northeastern United States, but further southward expansion of S. noctilio is uncertain, ultimately depending on the impact of recent international treatment standards for wood packing materials.

Worldwide, a large number of potential pest species are introduced to locations outside their native ranges; under the best possible prevention scheme, some are likely to establish one or more localized populations. A comprehensive early detection and rapid-response protocol calls for surveillance to determine if a pest has invaded additional locations outside its original area of introduction. In this manuscript, we adapt and spatially extend a two-stage sampling technique to determine the required sample size to substantiate freedom from an invasive pest with a known level of certainty. The technique, derived from methods for sampling livestock herds for disease presence, accounts for the fact that pest activity may be low at a coarse spatial scale (i.e., among forested landscapes) but high at a fine scale (i.e., within a given forested landscape). We illustrate the utility of the approach by generating a national-scale survey based on a risk map for a hypothetical forest pest species threatening the United States. These techniques provide a repeatable, cost-effective, practical framework for developing broad-scale surveys to substantiate freedom from non-native invasive forest pests with known statistical power.