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Species distribution models for and policy approaches to invasive plant ecology and management

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dc.contributor.authorTeich, Nathan Benjamin, author
dc.contributor.authorBrown, Cynthia S., advisor
dc.contributor.authorJarnevich, Catherine, committee member
dc.contributor.authorPearse, Ian, committee member
dc.contributor.authorEvangelista, Paul, committee member
dc.date.accessioned2024-05-27T10:32:52Z
dc.date.available2025-05-20
dc.date.issued2024
dc.description.abstractThe ability of abundance-based Species Distribution Models (SDMs) to predict where invasive plants can be abundant, and to what degree, is a powerful research and management tool. Often, invasive plant abundance-based SDMs are created using similar inputs and approaches as occurrence SDMs. However, invasive plant ecology literature suggests that the factors found to control invasive plant abundance are more diverse and contextual, and therefore not entirely interchangeable with factors that control invasive plant occurrence. To ensure invasive plant abundance-based SDMs are leveraging the robust body of knowledge, this paper aims to highlight and summarize the ecological factors underpinning invasive plant abundance and reviews how those factors can be represented within abundance-based SDMs. I find that while the inclusion of invasive plant abundance governing factors often improves abundance-based SDM performance, certain governing factors are ubiquitously represented while others are less commonly accounted for in model creation despite their ecological importance. Barriers to incorporating invasive plant abundance governing factors into abundance-based SDMs often include data limitations or methodological uncertainty. Finally, we provide future research directions that would help address certain barriers and improve our ability to integrate abundance governing factors into SDMs. Invasive plants, when they become dominant components of a plant community, threaten native species and ecosystem processes. Abundance-based SDMs are gaining traction as a geospatial tool to predict where invasive plants can become abundant and have negative impacts. Biotic interactions influence invasive plant abundance locally but are often not included within the abundance-based SDM creation process. At present, it is unknown to what degree local-scale biotic interactions with other plant species determine locations where invasive plant species can become abundant. Using data from large-scale abundance observations of the invasive plant cheatgrass (Bromus tectorum) paired with data from plant communities in the western United States, we quantified the degree to which biotic interactions explain where cheatgrass is abundant beyond what would be anticipated from an abundance-based SDM created with abiotic and landscape context predictors alone. To this end, we fit Generalized Linear Models (GLMs) for different categories of cheatgrass abundance and used the predicted suitability SDM outputs alongside biotic variables, representing known competitive and facilitative interactions, to determine if including biotic interactions improved a model's explanatory power. The addition of biotic variables marginally improved GLMs for low (5-25%) and medium (25+-50%) cheatgrass abundance but displayed greater improved performance for high (50+%) cheatgrass abundance. Most notable amongst the specific biotic variables was the cover of perennial graminoid cover, representing known competitors of cheatgrass, which interacted with SDM environmental suitability to strongly reduce the probability of high cheatgrass abundance. These findings suggest that considering biotic interactions alongside SDM predicted suitability may indeed improve our ability to predict abundance locations of invasive plant species, but potentially only in specific contexts such as where that species can already achieve high abundance. Invasive plants cost the US billions of dollars each year due to ecological and economic impacts as well as management costs. One of the most common pathways of introduction and spread of invasive plants is through ornamental plant sales. While solutions such as regulations and voluntary self-bans have been implemented in some instances to mitigate this problem, widespread adoption has not occurred. As such, new alternatives should be explored. Opt-in labeling programs are commonly used throughout the agricultural industry to better inform customers about the products they are purchasing. An opt-in labeling program that consists of a partnership between retailers and governments or non-profit organizations could help reduce the spread of invasive plants by influencing customer behavior. This approach would be less costly to retailers than regulations, create new invasive plant prevention opportunities for governments and non-profits, and better inform consumers about specific invasive plant species.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierTeich_colostate_0053A_18276.pdf
dc.identifier.urihttps://hdl.handle.net/10217/238500
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.rights.accessEmbargo expires: 05/20/2025.
dc.subjectecology
dc.subjectinvasive plant
dc.subjectspecies distribution model
dc.subjectinvasive ornamental plant
dc.subjectabundance
dc.subjectpolicy
dc.titleSpecies distribution models for and policy approaches to invasive plant ecology and management
dc.typeText
dcterms.embargo.expires2025-05-20
dcterms.embargo.terms2025-05-20
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineAgricultural Biology
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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