{
  "abstract": "Measures to combat invasive alien species (IAS) are costly and time-consuming, making effective and widely available detection methods essential. Wildlife detection dogs (WDD) are effectively used to detect hidden and cryptic species, offering enormous potential for early detection of IAS. While professional WDDs are rare, private dog owners are looking for meaningful activities. Therefore, we combined online dog training, practical seminars and field tests to train 31 companion dogs and their owners as volunteer WDD teams. We selected three IAS, the Fallopia spp. complex, Impatiens glandulifera and Ambrosia artemisiifolia, as target species due to their prevalence and strong impacts on biodiversity, economy and health. We used detailed video documentation and questionnaires to investigate the effects of inherent (social) parameters, training procedures and environmental conditions on performance and stamina of individual dog-handler teams during training in two cohorts. In total, 87.1% of the participants completed online training, 77.4% the practical seminar and 35.5% conducted the final field test. Both cohorts were comparably fast and accurate during online training, but cohort 1 performed better in the practical seminar. Performance during online training was mostly associated with social factors and reward types. In contrast, best results in the practical seminar were achieved for larger plant parts, when the participants had included all plant parts in their previous training and were able to adapt the training plan slightly. Spontaneous generalisation rate from thawed to fresh laid-out plant parts was 56%. Of the 12 teams conducting the field test, 11 teams passed. Neither the performance during online training nor during the practical seminar ultimately affected operational capability, but personal motivations determined whether a team finished the program. Finally, volunteer WDD teams can demonstrate high efficiency in monitoring, but the intensive training must be carefully monitored by professional ecologists and dog trainers.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Conservation Biology and Social-ecological Systems, Helmholtz-Centre for Environmental Research – UFZ, Leipzig, Germany",
    "Independent Institute for Environmental Issues, Halle, Germany",
    "Society for the Resocialisation and Training of Dogs, Berlin, Germany",
    "Department of Evolutionary Ecology, Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
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        0
      ],
      "name": "Annegret Grimm-Seyfarth",
      "orcid": "0000-0003-0577-7508"
    },
    {
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        0
      ],
      "name": "Wiebke Harms"
    },
    {
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        0
      ],
      "name": "Veronika Paulina Koch"
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    {
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        1
      ],
      "name": "Nora Haack"
    },
    {
      "affs": [
        0
      ],
      "name": "Alexander Harpke"
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    {
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        2
      ],
      "name": "Katja Krauss"
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    {
      "affs": [
        3
      ],
      "name": "Anne Berger",
      "orcid": "0000-0001-5765-8039"
    }
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  "bibtex": "https://research.draconitic.com/paper/1trhxnhq2a6p6.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359003",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13734480/",
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  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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      "heading",
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    ],
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      "heading",
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  "pmcid": "PMC13734480",
  "pmid": "42981286",
  "published": "2026-11-04",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/1trhxnhq2a6p6.ris",
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  "title": "Opportunities and challenges in training volunteer wildlife detection dog-handler teams for invasive plant monitoring",
  "title_text": "Opportunities and challenges in training volunteer wildlife detection dog-handler teams for invasive plant monitoring",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:23:59Z",
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