Assessing the ecological and economic impacts of non-native species is crucial to providing managers and policymakers with the information necessary to respond effectively. Most non-native species have minimal impacts on the environment in which they are introduced, but a small fraction are highly deleterious. The definition of ‘damaging’ or ‘high-impact’ varies based on the factors determined to be valuable by an individual or group, but interpretations of whether non-native species meet particular definitions can be influenced by the interpreter’s bias or level of expertise, or lack of group consensus. Uncertainty or disagreement about an impact classification may delay or otherwise adversely affect policymaking on management strategies. One way to prevent these issues would be to have a detailed, nine-point impact scale that would leave little room for interpretation and then divide the scale into agreed upon categories, such as low, medium, and high impact. Following a previously conducted, exhaustive search regarding non-native, conifer-specialist insects, the authors independently read the same sources and scored the impact of 41 conifer-specialist insects to determine if any variation among assessors existed when using a detailed impact scale. Each of the authors, who were selected to participate in the working group associated with this study because of their diverse backgrounds, also provided their level of expertise and uncertainty for each insect evaluated. We observed 85% congruence in impact rating among assessors, with 27% of the insects having perfect inter-rater agreement. Variance in assessment peaked in insects with a moderate impact level, perhaps due to ambiguous information or prior assessor perceptions of these specific insect species. The authors also participated in a joint fact-finding discussion of two insects with the most divergent impact scores to isolate potential sources of variation in assessor impact scores. We identified four themes that could be experienced by impact assessors: ambiguous information, discounted details, observed versus potential impact, and prior knowledge. To improve consistency in impact decision-making, we encourage groups to establish a detailed scale that would allow all observed and published impacts to fall under a particular score, provide clear, reproducible guidelines and training, and use consensus-building techniques when necessary.
more »
« less
Options for reducing uncertainty in impact classification for alien species
Impact assessment is an important and cost‐effective tool for assisting in the identification and prioritization of invasive alien species. With the number of alien and invasive alien species expected to increase, reliance on impact assessment tools for the identification of species that pose the greatest threats will continue to grow. Given the importance of such assessments for management and resource allocation, it is critical to understand the uncertainty involved and what effect this may have on the outcome. Using an uncertainty typology and insects as a model taxon, we identified and classified the causes and types of uncertainty when performing impact assessments on alien species. We assessed 100 alien insect species across two rounds of assessments with each species independently assessed by two assessors. Agreement between assessors was relatively low for all three impact classification components (mechanism, severity, and confidence) after the first round of assessments. For the second round, we revised guidelines and gave assessors access to each other’s assessments which improved agreement by between 20% and 30% for impact mechanism, severity, and confidence. Of the 12 potential reasons for assessment discrepancies identified a priori, 11 were found to occur. The most frequent causes (and types) of uncertainty (i.e., differences between assessment outcomes for the same species) were as follows: incomplete information searches (systematic error), unclear mechanism and/or extent of impact (subjective judgment due to a lack of knowledge), and limitations of the assessment framework (context dependence). In response to these findings, we identify actions that may reduce uncertainty in the impact assessment process, particularly for assessing speciose taxa with diverse life histories such as Insects. Evidence of environmental impact was available for most insect species, and (of the non‐random original subset of species assessed) 14 of those with evidence were identified as high impact species (with either major or massive impact). Although uncertainty in risk assessment, including impact assessments, can never be eliminated, identifying, and communicating its cause and variety is a first step toward its reduction and a more reliable assessment outcome, regardless of the taxa being assessed.
more » « less- Award ID(s):
- 1638702
- NSF-PAR ID:
- 10451046
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecosphere
- Volume:
- 12
- Issue:
- 4
- ISSN:
- 2150-8925
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Diseases and insects, particularly those that are non-native and invasive, arguably pose the most destructive threat to North American forests. Currently, both exotic and native insects and diseases are producing extensive ecological damage and economic impacts. As part of an effort to identify United States tree species and forests most vulnerable to these epidemics, we compiled a list of the most serious insect and disease threats for 419 native tree species and assigned a severity rating for each of the 1378 combinations between mature tree hosts and 339 distinct insect and disease agents. We then joined this list with data from a spatially unbiased and nationally consistent forest inventory to assess the potential ecological impacts of insect and disease infestations. Specifically, potential host species mortality for each host/agent combination was used to weight species importance values on approximately 132,000 Forest Inventory and Analysis (FIA) plots across the conterminous 48 United States. When summed on each plot, these weighted importance values represent an estimate of the proportion of the plot’s existing importance value at risk of being lost. These plot estimates were then used to identify statistically significant geographic hotspots and coldspots and of potential forest impacts associated with insects and diseases in total, and for different agent types. In general, the potential impacts of insects and diseases were greater in the West, where there are both fewer agents and less diverse forests. The impact of non-native invasive agents, however, was potentially greater in the East. Indeed, the impacts of current exotic pests could be greatly magnified across much of the Eastern United States if these agents are able to reach the entirety of their hosts’ ranges. Both the list of agent/host severities and the spatially explicit results can inform species-level vulnerability assessments and broad-scale forest sustainability reporting efforts, and should provide valuable information for decision-makers who need to determine which tree species and locations to target for monitoring efforts and pro-active management activities.more » « less
-
more » « less
Abstract Invasive forest insects can induce tree mortality in two ways: (a) by directly harming trees; or (b) by influencing forest owners to pre‐emptively harvest threatened trees. This study investigates forest owners’ intentions to harvest trees threatened by invasive insects.
Our first objective is to identify and characterize agent functional types (AFTs) of family forest owners in the northeastern United States using a set of contingent behaviour questions contained in a mail survey. We establish AFTs as a form of dimension reduction, effectively casting landowners into a typology in which each type (AFT) has distinct probabilities of tree harvesting in response to forest insects. Our analysis identifies three functional types of landowners: ‘Cutters’ (46% of respondents; high intent to harvest trees impacted by invasive forest insects), ‘Responsive Cutters’ (42% of respondents; intent sensitive to insect impact severity), and ‘Non‐cutters’ (12% of respondents; low intent to cut).
Our second objective is to model AFT membership to predict the distribution of AFTs across the landscape. Predictors are chosen from a set of survey, geographic and demographic features. Our best AFT‐prediction model has three predictor variables: parcel size (hectares of forest), geographical region, and town‐level forested fraction. Application of the model provides a high‐resolution probability distribution of AFTs across the landscape.
By coupling human and insect behaviour, our results allow for holistic assessments of how invasive forest insects disturb forests, inclusive of the management response to these pests.
A free
Plain Language Summary can be found within the Supporting Information of this article. -
more » « less
Abstract Insect populations are changing rapidly, and monitoring these changes is essential for understanding the causes and consequences of such shifts. However, large‐scale insect identification projects are time‐consuming and expensive when done solely by human identifiers. Machine learning offers a possible solution to help collect insect data quickly and efficiently.
Here, we outline a methodology for training classification models to identify pitfall trap‐collected insects from image data and then apply the method to identify ground beetles (Carabidae). All beetles were collected by the National Ecological Observatory Network (NEON), a continental scale ecological monitoring project with sites across the United States. We describe the procedures for image collection, image data extraction, data preparation, and model training, and compare the performance of five machine learning algorithms and two classification methods (hierarchical vs. single‐level) identifying ground beetles from the species to subfamily level. All models were trained using pre‐extracted feature vectors, not raw image data. Our methodology allows for data to be extracted from multiple individuals within the same image thus enhancing time efficiency, utilizes relatively simple models that allow for direct assessment of model performance, and can be performed on relatively small datasets.
The best performing algorithm, linear discriminant analysis (LDA), reached an accuracy of 84.6% at the species level when naively identifying species, which was further increased to >95% when classifications were limited by known local species pools. Model performance was negatively correlated with taxonomic specificity, with the LDA model reaching an accuracy of ~99% at the subfamily level. When classifying carabid species not included in the training dataset at higher taxonomic levels species, the models performed significantly better than if classifications were made randomly. We also observed greater performance when classifications were made using the hierarchical classification method compared to the single‐level classification method at higher taxonomic levels.
The general methodology outlined here serves as a proof‐of‐concept for classifying pitfall trap‐collected organisms using machine learning algorithms, and the image data extraction methodology may be used for nonmachine learning uses. We propose that integration of machine learning in large‐scale identification pipelines will increase efficiency and lead to a greater flow of insect macroecological data, with the potential to be expanded for use with other noninsect taxa.
-
Non-native, invasive plants are projected to shift their ranges with climate change, creating hotspots of risk where a multitude of novel species may soon establish and spread. The Northeast U.S. is one such hotspot. However, because monitoring for novel species is costly, these range-shifting invasive plants need to be prioritized. Preventing negative impacts is a key goal of management, thus, comparing the potential impacts of range-shifting invasive species could inform this prioritization. Here, we adapted the environmental impacts classification for alien taxa protocol to evaluate potential impacts of 100 invasive plants that could establish either currently or by 2050 in the states of New York, Massachusetts, Connecticut, or Rhode Island. We searched Web of Science for each species and identified papers reporting ecological, economic, human health, or agricultural impacts. We scored ecological impacts from 1 (‘minimal concern’) to 4 (‘major’) and socio-ecological impacts as present or absent. We evaluated 865 impact studies and categorized 20 species as high-impact, 36 as medium-impact, and 26 as low-impact. We further refined high-impact invasive species based on whether major impacts affect ecosystems found in Northeast U.S. and identified five high-priority species: Anthriscus caucalis, Arundo donax, Avena barbata, Ludwigia grandiflora, and Rubus ulmifolius. Additional research is needed for 18 data-deficient species, which had no studies reporting impacts. Identifying and prioritizing range-shifting invasive plants provides a unique opportunity for early detection and rapid response that targets future problem species before they can establish and spread. This research illustrates the feasibility of using impacts assessments on range-shifting invasive species in order to inform proactive policy and management.more » « less
