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

Young, Nicholas E.; Jarnevich, Catherine S.; Sofaer, Helen R.; Pearse, Ian S.; Sullivan, J S J Daniel; Engelstad, Peder; Stohlgren, Thomas J.

doi:10.1371/journal.pone.0229253

Cited by 21 publications

(28 citation statements)

References 65 publications

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“…The top three predictors (Table 3) were either consistently important (i.e., within the top three at least 30% of the time), occasionally important (in the top three 10% to 30% of the time), or rarely influential on model predictions (in the top three <10% of the time). Minimum winter temperature was the most common important variable across species models (66% of the top three variables across all 15 models), which is consistent with other work modeling invasive species habitat suitability (Young et al 2020). We also found summer precipitation (47%), summer maximum (41%) temperatures, and percent tree canopy cover (31%) consistently important across species' models.…”

Section: Model Characteristics and Important Variablessupporting

confidence: 88%

Assessing the performance and accuracy of invasive plant habitat suitability models in detecting new observations in Wisconsin

Jorgensen

Renz

2021

Invasive plant sci. manag.

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Land managers require tools that improve understanding of suitable habitat for invasive plants and be incorporated into survey efforts to improve efficiency. Habitat suitability models contain attributes that can meet these requirements, but it is not known how well they perform as they are rarely field tested for accuracy. We developed ensemble habitat suitability models in the state of Wisconsin for 15 species using five algorithms (boosted regression trees, generalized linear models, multivariate regression splines, MaxEnt, and random forests), evaluated performance, determined variables that drive suitability, and tested accuracy. All models had good model performance during the development phase (AUC>0.7 and TSS>0.4). While variable importance and directionality was species specific, the most important predictor variables across all of the species’ models were mean winter minimum temperatures, total summer precipitation and tree canopy cover. Post model development we obtained 5,005 new occurrence records from community science observations for all 15 focal species to test the models’ abilities to accurately predict results. Using a correct classification rate of 80%, just 8 of the 15 species correctly predicted suitable habitat (α≤0.05). Exploratory analyses found the number of reporters of these new data and the total number of new occurrences reported per species contributed to increasing correct classification. Results suggest that while some models perform well on evaluation metrics, relying on these metrics alone is not sufficient and can lead to errors when utilized for surveying. We recommend any model should be tested for accuracy in the field prior to use to avoid this potential issue.

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Section: Model Characteristics and Important Variablessupporting

confidence: 88%

Assessing the performance and accuracy of invasive plant habitat suitability models in detecting new observations in Wisconsin

Jorgensen

Renz

2021

Invasive plant sci. manag.

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“…We selected predictors appropriate for winter annual species from a national library of variables representing climate, land cover, human disturbances, fire history and soils created by Young et al. (2020). We summarized climatic conditions over periods physiologically important to winter annual plants.…”

Section: Methodsmentioning

confidence: 99%

“…We developed species distribution models following the methodology of Young et al. (2020) using the Software for Assisted Habitat Modeling (SAHM; Morisette et al., 2013). Briefly, we selected background locations following two methodologies: probabilistically placed based on a kernel density estimator formed around the presence points (Elith et al., 2010) or using a target background approach (SAHM; Morisette et al., 2013; Phillips et al., 2009).…”

Section: Methodsmentioning

confidence: 99%

“…Because of the lack of data on species’ abundance across large geographies (Bradley, Allen, et al., 2018), modelling efforts designed to inform management decisions often fit species distribution models to estimate suitability for presence (e.g. Cook et al., 2019; Fernandes et al., 2019; Hansen et al., 2013; Young et al., 2020). A key question is how models of presence versus abundance align in their implications for management, particularly for on‐the‐ground resource prioritization.…”

Section: Introductionmentioning

confidence: 99%

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Modelling presence versus abundance for invasive species risk assessment

Jarnevich

Sofaer

Engelstad

2021

Diversity and Distributions

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Aim Invasive species prevention and management can be guided by comparisons of invasion risk across space and among species. Species distribution models are widely used to assess invasion risk and typically estimate suitability for species presence. However, suitability for presence may not capture patterns of abundance and impact. We asked how models estimating suitability for presence versus suitability for abundance aligned in their implications for risk assessment. Location Western United States. Methods We developed ensembles of species distribution models for presence and for abundance for four invasive plants. We visualized the distribution of presence and abundance in environmental and geographic space and compared model outputs using criteria relevant for decision‐making: a comparison of risk across management units for each species, and a ranking of risk among species for each management unit. Results We found good overall agreement between models of presence versus abundance in the relative risk across management units and among species. However, the area predicted to be suitable for invasive species presence was often substantially higher than the area predicted to be suitable for abundance, especially within uninvaded management units. Main conclusions Models of suitability for invasive species presence and abundance yielded similar assessments of relative risk in comparisons across space and species. In addition, we found patterns of presence and abundance in environmental space can guide modelling decisions and model interpretation. Suitability for abundance can improve relative risk assessment when abundance locations occupy a well‐defined subset of the environmental space corresponding to presence. Where abundance locations occur throughout this environmental space, as was particularly striking for Taeniatherum caput‐medusae, suitability for presence may better reflect risk of ongoing population increases and spread. This species is at risk of becoming abundant across a substantial portion of the western United States.

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“…A principle of BLM's AIM strategy is the complementary integration of landscape and field-based measures to enhance the spatial and thematic detail of mapped resources to inform management (Toevs et al 2011a(Toevs et al , 2011bTaylor et al 2014). Current data integration efforts use field-based AIM data for training and accuracy assessment of remotely sensed products (e.g., Jones et al 2018;Rigge et al 2020;Zhou et al 2020) and to improve mapping of high priority vegetation species (e.g., Young et al 2020). Using analogous approaches that integrate agency field data with the proposed indicators may increase their utility.…”

Section: The Process For Identifying Indicatorsmentioning

confidence: 99%

Identifying Policy-relevant Indicators for Assessing Landscape Vegetation Patterns to Inform Planning and Management on Multiple-use Public Lands

Carter

Burris

Domschke

et al. 2021

Environmental Management

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Understanding the structure and composition of landscapes can empower agencies to effectively manage public lands for multiple uses while sustaining land health. Many landscape metrics exist, but they are not often used in public land decision-making. Our objectives were to (1) develop and (2) apply a process for identifying a core set of indicators that public land managers can use to understand landscape-level resource patterns on and around public lands. We first developed a process for identifying indicators that are grounded in policy, feasible to quantify using existing data and resources, and useful for managers. We surveyed landscape monitoring efforts by other agencies, gathered science and agency input on monitoring goals, and quantified the prevalence of potential indicators in agency land health standards to identify five landscape indicators: amount, distribution, patch size, structural connectivity, and diversity of vegetation types. We then conducted pilot applications in four bureau of land management (BLM) field offices in Arizona, California, and Colorado to refine procedures for quantifying the indicators and assess the utility of the indicators for managers. Results highlighted the dominance of upland and the limited extent of riparian/wetland vegetation communities, moderate connectivity of priority vegetation patches, and lower diversity of native vegetation types on BLM compared to non-BLM lands. Agency staff can use the indicators to inform the development of quantitative resource management objectives in land use plans, evaluate progress in meeting those objectives, quantify potential impacts of proposed actions, and as a foundation for an all-lands approach to landscape-level management across public lands.

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

Cited by 21 publications

References 65 publications

Assessing the performance and accuracy of invasive plant habitat suitability models in detecting new observations in Wisconsin

Assessing the performance and accuracy of invasive plant habitat suitability models in detecting new observations in Wisconsin

Modelling presence versus abundance for invasive species risk assessment

Identifying Policy-relevant Indicators for Assessing Landscape Vegetation Patterns to Inform Planning and Management on Multiple-use Public Lands

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