Building a synthesis of economic costs of biological invasions in New Zealand

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Ecology

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Introduction

Methods

Data collection

Estimating total costs

  1. “Method_reliability”: a conservative but objective evaluation of the traceability of the cost estimation (“High” vs “Low”). This is based on the type of publication and method of estimation i.e., peer-reviewed or other official documents from the grey literature are likely validated prior to publication so are classified as “High” reliability. Other materials were only classified as “High” if the original sources, assumptions, and methods were accessible and fully described.

  2. “Implementation”: referring to whether the cost estimate was actually realised in the invaded habitat (“Observed”) or whether it was extrapolated (“Potential”).

  3. “Environment_IAS”: whether the cost was incurred from biota that are either “Aquatic”, “Terrestrial” or “Diverse/Unspecified” (i.e., either unspecified or a combination of species and hence biomes).

  4. “Type_of_cost_merged”: collation of costs according to principal categories: (a) “Damage”, referring to damages or losses incurred by invasion (e.g., costs for damage repair, resource losses); (b) “Management”, comprising management-related expenditure (e.g., monitoring, prevention, control, eradication); and (c) “Mixed” costs, including a mixture of damage and management costs. We also used the “Management_type” column to compare management expenditure between pre- and post-invasion actions. Here, pre-invasion management comprised monetary investments for preventing successful invasions in an area including quarantine or border inspection, risk analyses, biosecurity management etc.; and post-invasion management includes money spent on managing invaded areas, and so includes control, eradication, or containment. Additional categories comprised: (a) “knowledge/funding”—money allocated to any action or operation that could be relevant to management at pre- or post-invasion stages, but is not specifically attributed within the source e.g., administration, communication, education or research costs;  (b) “mixed”—costs that included at least (and without possibility to disentangle the specific proportion of) two of the previous categories; and (c) “unspecified”—costs where the exact nature was not clearly defined. 

  5. “Impacted_sector”: the activity, societal or market sector that was impacted by the cost (e.g., “Agriculture”, “Health” or “Authorities and Stakeholders”—this latter category representing official structures and organisations allocating efforts to manage IAS). Individual cost entries not allocated to a single sector were classified as “Mixed”, and records without an identifiable sector, or those that were unreported, were classified as ”Unspecified”. These are relatively broad groupings as the level of granularity provided within references varied substantially. Importantly, there was very rarely sufficient detail to attribute costs to, for example, specific stakeholders, communities or specific health impacts, even if these were mentioned, rather than to the collective sector groupings detailed above.

  6. “Species”: the taxonomic nomenclature of the species causing the cost.

Results

Discussion

Supplemental Information

Abstract (in Maori)

DOI: 10.7717/peerj.13580/supp-1

Supplementary Tables and Figures

DOI: 10.7717/peerj.13580/supp-2

InvaCost v4 Dataset for New Zealand used in this study

DOI: 10.7717/peerj.13580/supp-3

Additional Information and Declarations

Competing Interests

The authors declare there are no competing interests.

Author Contributions

Thomas W. Bodey conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Zachary T. Carter conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Phillip J. Haubrock conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Ross N. Cuthbert conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Melissa J. Welsh conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.

Christophe Diagne conceived and designed the experiments, performed the experiments, authored or reviewed drafts of the article, and approved the final draft.

Franck Courchamp conceived and designed the experiments, performed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The raw data are available in the Supplementary Files.

Funding

This work was funded by the French National Research Agency (ANR-14-CE02-0021) and the BNP-Paribas Foundation Climate Initiative for funding the InvaCost project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the AXA Research Fund Chair of Invasion Biology and is part of the AlienScenario project funded by BiodivERsA and Belmont-Forum call 2018 on biodiversity scenarios. Thomas W. Bodey was funded by European Union’s Horizon 2020 research and innovation programme Marie Skłodowska-Curie fellowship (Grant No. 747120). Zachary T. Carter was funded by a New Zealand International Doctoral Research scholarship. Ross N. Cuthbert was funded by Humboldt Fellowship funding from the Alexander von Humboldt Foundation. Christophe Diagne was funded by the BiodivERsA-Belmont Forum Project “Alien Scenarios” (BMBF/PT DLR 01LC1807C). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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