Evidence map›Paper›PMID 33748770›Full record

ReviewCABI agriculture and bioscience2020

The role of passive surveillance and citizen science in plant health.

Nathan Brown, Ana Pérez-Sierra, Peter Crow, Stephen Parnell

Open access · goldAbstract readReview
In one paragraph

Review in CABI agriculture and bioscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.

  1. Pooled it
  2. Citizen science in plant disease surveillance.Frontiers in plant science · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Nathan BrownWoodland Heritage, P.O. Box 1331, Cheltenham, GL50 9AP UK.ORCID 0000-0003-3590-0538
Ana Pérez-SierraTree Health Diagnostics and Advisory Service, Forest Research, Alice Holt Lodge, Farnham, Surrey, GU10 4LH UK.
Peter CrowObservatree, Forest Research, Alice Holt Lodge, Farnham, Surrey, GU10 4LH UK.
Stephen ParnellSchool of Science Engineering and Environment, University of Salford, Salford, M5 4WT UK.
Forest Research · GBEnglish Heritage · GBUniversity of Salford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The early detection of plant pests and diseases is vital to the success of any eradication or control programme, but the resources for surveillance are often limited. Plant health authorities can however make use of observations from individuals and stakeholder groups who are monitoring for signs of ill health. Volunteered data is most often discussed in relation to citizen science groups, however these groups are only part of a wider network of professional agents, land-users and owners who can all contribute to significantly increase surveillance efforts through "passive surveillance". These ad-hoc reports represent chance observations by individuals who may not necessarily be looking for signs of pests and diseases when they are discovered. Passive surveillance contributes vital observations in support of national and international surveillance programs, detecting potentially unknown issues in the wider landscape, beyond points of entry and the plant trade. This review sets out to describe various forms of passive surveillance, identify analytical methods that can be applied to these "messy" unstructured data, and indicate how new programs can be established and maintained. Case studies discuss two tree health projects from Great Britain (TreeAlert and Observatree) to illustrate the challenges and successes of existing passive surveillance programmes. When analysing passive surveillance reports it is important to understand the observers' probability to detect and report each plant health issue, which will vary depending on how distinctive the symptoms are and the experience of the observer. It is also vital to assess how representative the reports are and whether they occur more frequently in certain locations. Methods are increasingly available to predict species distributions from large datasets, but more work is needed to understand how these apply to rare events such as new introductions. One solution for general surveillance is to develop and maintain a network of tree health volunteers, but this requires a large investment in training, feedback and engagement to maintain motivation. There are already many working examples of passive surveillance programmes and the suite of options to interpret the resulting datasets is growing rapidly.

Indexed as

Citizen scienceEarly warningSurveillanceTree healthUnstructured data

Identifiers

PMID33748770
PMCPMC7596624
OpenAlexW3097514288

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.