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Record W4392922287 · doi:10.3389/finsc.2024.1378061

Editorial: Forest insect invasions – risk mapping approaches and applications

2024· editorial· en· W4392922287 on OpenAlexaffabout
Kishan R. Sambaraju, Vivek Srivastava, Brittany S. Barker, Melody A. Keena, Michael Ormsby, Allan L. Carroll

Bibliographic record

VenueFrontiers in Insect Science · 2024
Typeeditorial
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsGovernment of British ColumbiaNatural Resources CanadaUniversity of British ColumbiaCanadian Forest Service
Fundersnot available
KeywordsInsectInvasive speciesEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Forests across the globe face unprecedented threats from biotic and abiotic factors that challenge the overall health of ecosystems and potentially contribute to warming climates through positive feedback to atmospheric CO2 (1-4). Presently, some of the greatest challenges pertain to the rapid spread and resultant tree mortality caused by non-native forest insects (5, 6). Increased global trade and more conducive climates due to climate change have facilitated establishment of non-native forest insects in new areas where conditions were previously unsuitable for growth and survival (7, 8). The lack of co-evolutionary history of native trees with invasive insects, combined with an absence of natural enemies in the invaded habitats, has allowed aggressive non-native forest insects to colonize or kill trees rapidly and expand their invaded range in a short period of time (9). Invasions and expansions of certain non-native forest insects such as the spongy moth [Lymantria dispar (L.)], hemlock woolly adelgid [Adelges tsugae (Annand)], and emerald ash borer (Agrilus planipennis Fairmaire) can have both short- and long-term impacts on forest health via disruption of a range of ecological processes and ecosystem services (5, 9, 10). For example, some non-native forest insects may cause extensive defoliation or tree mortality (5), alter plant community composition (11), change soil hydrology (12), and influence carbon and nutrient cycling (13, 14). Socio-economic costs and human health-related impacts of invasive forest insects can be very significant (15, 16). These impacts will remain a concern owing to the consistently large number of non-native insects being intercepted at ports of entry (17-19). Given the far-reaching consequences of non-native forest insect invasions on the environment and the economy, it is critical to address this threat for the long-term sustainability of urban and natural forest ecosystems. Pest risk models and maps are pivotal tools in assessing the risk posed by such threats as they enable quantification and visualization of the invasion and damage potential of non-native pests (20). They may incorporate transport pathways, known or presumed responses to one or more environmental drivers and/or dispersal capacity of invasive species to forecast their seasonal activities (phenology) and population dynamics or to predict the likelihood of pest introduction, establishment, and spread (21, 22). A broad range of modelling methods are available including correlative (e.g., ecological niche models), semi-mechanistic (e.g., CLIMEX), and process-based approaches (e.g., insect phenology models) (20, 22, 23). When integrated with impact assessments, pest risk models may be used for decision support to guide management and surveillance strategies (21). This editorial aims to summarize published articles covering the above-mentioned aspects under the research topic, Forest Insect Invasions – Risk Mapping Approaches and Applications, highlighting the latest work in predictive modeling and pest surveillance. Maps that forecast the phenology of invasive insects may support efforts to detect and control populations because decision-makers often target life stages that are more observable (e.g., larvae vs. adults of wood-boring beetles) or more vulnerable to control tactics such as pesticide treatments (24, 25). Likewise, maps that forecast the establishment risk and spread of invasive insects can support surveillance programs by identifying areas that have both suitable environments for population persistence and a high likelihood of pest arrival (26-28). In this special issue, Takeuchi et al. [hyperlink] present a web-based spatial analytic framework that produces forecasts of phenology, climate suitability, and spread of high-priority invasive insects and diseases that threaten forested and agricultural ecosystems in the United States (US). The Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS) is publicly available and was developed to support surveillance efforts conducted by the US Department of Agriculture, Animal and Plant Inspection Service, Plant Protection and Quarantine (USDA-APHIS-PPQ) program in the continental US. The utility of the SAFARIS system was demonstrated using two invasive insect species that threaten forests in the US, the oak ambrosia beetle (Platypus quercivorus Murayama, 1925) and the spongy moth. In a related study, Barker et al. [hyperlink] developed and validated a spatial model that combines forecasts of phenology and establishment risk for emerald ash borer (EAB) to help with the development and implementation of effective management strategies against this major invasive pest of ash (Fraxinus spp.) in North America and other regions such as Europe. The model for EAB is one of 16 models developed for use in the Degree-Days, Risk, and Phenological event mapping (DDRP) platform, which serves as an open-source modeling tool to help detect, monitor, and manage invasive threats (29). Near real-time model forecasts for EAB for the continental US are available at two websites to provide decision-support for the detection of new establishments and for controlling existing populations with pesticide treatments and parasitoid introductions.Certain invasive insect species can be monitored via natural methods such as through assessments of prey catches by predatory insects. This approach is particularly useful when traditional monitoring methods are laborious and expensive such as with EAB. A study by Rutledge and Clark [hyperlink] examined EAB catches by a predatory wasp, Cerceris fumipennis Say, to describe the occurrences and proportional abundances of EAB among all buprestids caught by C. fumipennis. The paper presents ten years of biosurveillance data of EAB in Connecticut, US, identifying the time from first detection to a population decline, which was nine years on average. Outward expansion of EAB from an epicentre assessed through the prey capture methodology support findings regarding EAB dispersal studied using other frameworks (e.g., using tree infestations) (30). Trotter et al. [hyperlink] introduce the Asian Longhorned Beetle Hazard Management and Monitoring (ALBHMM) 2.0 tool, which offers a structured approach to track progress toward eradication and optimization of future management efforts for the Asian longhorned beetle [Anoplophora glabripennis (Motschulsky)], an invasive wood borer from China and Southeast Asia that attacks multiple hardwood species (17, 31). Asian longhorned beetle has been introduced into the US, Canada, and Europe (17-19) raising phytosanitary concerns that have led to the adoption of policies aimed at preventing its spread and eradicating established populations (32). It poses a threat to both urban treed and forested landscapes, making eradication efforts crucial. The ALBHMM 2.0 tool integrates information on beetle dispersal, surveys, and management activities (tree removals) to quantify changes in infestation risk at a landscape scale, allowing for measurement of changes in infestation risk over time to monitor eradication progress. The tool is demonstrated using infestation data from three US states with varying beetle dispersal behaviors and eradication program histories. In summary, this research topic sought to collate contributions toward describing novel techniques and recent modeling advancements to assess and lower the risk posed by non-native forest insect invasions. The studies published here introduced innovative tools (SAFARIS, DDRP, and ALBHMM 2.0) to support and improve strategic and tactical decisions for insect surveillance and management, as well as a novel biosurveillance approach for population monitoring (EAB monitoring using a predatory wasp). The modelling approaches included in this research topic provide unique perspectives into pest risk assessments owing to differences in their modeling framework, yet they may be complementary. For instance, the DDRP system, which was used to model EAB in Barker et al. [hyperlink], is complementary to SAFARIS in that model forecasts can support decision-making for the surveillance of invasive pests, as well as for managing invasive pests that have already established. The adoption of pest forecasting tools and map products requires engagement with end users such as pest control managers, government officials, and the general public (33). However, model complexity, lack of training opportunities for end users, and insufficient outreach may hinder a broader uptake of these tools and products. Therefore, providing educational opportunities, requesting user feedback, and improving the delivery and formats of map products based on the feedback received are recommended (33). Considering potential future invasions of destructive forest insects under climate change, utilizing new and powerful technologies into modeling pest risk and filling gaps in end-user outreach and training are critical to safeguarding forest health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.217
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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