Enhancing plant biosecurity with citizen science monitoring: comparing methodologies using reports of acute oak decline

Baker, Emily; Jeger, M.J.; Mumford, John; Brown, Nigel L.

doi:10.1007/s10109-018-0285-2

Cited by 12 publications

(21 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…a Shows NBN reports before 1987 (when Shirt published a red data book for insects (Shirt 1987 )) and b shows all NBN reports to 2017; c shows Forest Research records before 2009 (when Denman and Webber first described AOD (Denman and Webber 2009 )) and d shows Forest Research reports up to 2017. Data are discussed further by Baker et al ( 2018 ) …”

Section: Understanding and Interpreting Data From Passive Surveillancmentioning

confidence: 90%

“…Encouraging reporting by raising awareness of current outbreaks, as well as species thought likely to arrive or which are high risk, can improve detection by general surveillance programmes. Training materials and programmes may be best when focused toward land managers, as these individuals are most likely to encounter affected trees (Baker et al 2018 ; Marzano et al 2016 ), although there are also many amateur naturalists who are regularly engaged in reporting sightings of species through organisations like the National Biodiversity Network (NBN) and significant efforts have been made to raise awareness of invasive species through its associated GB‐NNSIP programme (Roy et al 2015 ). The production of training materials and direct engagement with observers, begins to move even general surveillance away from being a purely passive process, and the distinctions with citizen science begin to blur.…”

Section: The Nature Of Passive Surveillancementioning

confidence: 99%

“…The continued expansion of passive surveillance schemes in plant health (Pocock et al 2017a ; Brown et al 2017a ; Baker et al 2018 ; Meentemeyer et al 2015 ; Ryan et al 2018 ; Caley et al 2019 ; Rallapalli et al 2015 ) presents an opportunity for a step change in availability of surveillance data and pest detection capabilities. However, data from passive surveillance are generally “messy” (Dobson et al 2020 ) or “noisy” (Isaac and Pocock 2015 ) and can be difficult to understand.…”

Section: Understanding and Interpreting Data From Passive Surveillancmentioning

confidence: 99%

“…Finally, if an eradication attempt has occurred, detection surveys may once again be undertaken to determine success of interventions. Members of the public or land managers who report disease symptoms to the plant health authorities increase the likelihood of detection in surveillance programmes (Hester and Cacho 2017 ; Baker et al 2018 ) and have a potential role across the full range of surveillance objectives. For example, the first detection of the oriental chestnut gall wasp ( Dryocosmus kuriphilus ) on sweet chestnuts in England was made by an amateur gall enthusiast who submitted samples to the official laboratory for identification (Morath et al 2015 ).…”

Section: Introductionmentioning

confidence: 99%

“…Detections that occur through passive surveillance represent fortuitous contributions that depend on individuals both detecting an issue within the environment and deciding they should report the discovery to the authorities. These reports occur at times and places where it is convenient for the public to participate and, as such, are unstructured and potentially biased in distribution (Isaac and Pocock 2015 ; Baker et al 2018 ; Johnston et al 2020 ). This systematic error contravenes a fundamental assumption of most statistical approaches (that data is a representative random sample of the wider population) (Dobson et al 2020 ; August et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The role of passive surveillance and citizen science in plant health

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Understanding and Interpreting Data From Passive Surveillancmentioning

confidence: 90%

Section: The Nature Of Passive Surveillancementioning

confidence: 99%

Section: Understanding and Interpreting Data From Passive Surveillancmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The role of passive surveillance and citizen science in plant health

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

An analysis about the accuracy of geographic profiling in relation to the number of observations and the buffer zone

Santosuosso

Papini

2022

J Geogr Syst

View full text Add to dashboard Cite

Geographic Profiling (GP) attempts to reconstruct the spreading centre of a series of events due to the same cause. The result of the analysis provides an approximated localization of the spreading centre within an area (often represented as a red red), where the probability of finding it is higher than a given threshold (typically 95%). The analysis has as an assumption that the events will be likely to occur at very low probability around the spreading centre, in a ring-shaped zone called the buffer zone. Obvious examples are series of crimes perpetrated by an offender (unwilling to perpetrate offences close to home), or the localities of spread of an invasive species, where the buffer zone, if present, depends on the biological features of the species. Our first aim was to show how the addition of new events may change the preliminary approximate localization of the spreading centre. The analyses of the simulated data showed that if B, the parameter used to represent the radius of the buffer zone, varies within a range of 10% from the real value, after a low number of events (7–8), the method yields converging results in terms of distance between the barycentre of the red zone and the “real” user provided spreading centre of a simulated data set. The convergence occurs more slowly with the increase in inaccuracy of B. These results provide further validity to the method of the GP, showing that even an approximate choice of the B value can be sufficient for an accurate location of the spreading centre. The results allow also to quantify how many samples are needed in relation to the uncertainty of the chosen parameters, to obtain feasible results.

show abstract

Identifying, reducing, and communicating uncertainty in community science: a focus on alien species

et al. 2022

View full text Add to dashboard Cite

Community science (also often referred to as citizen science) provides a unique opportunity to address questions beyond the scope of other research methods whilst simultaneously engaging communities in the scientific process. This leads to broad educational benefits, empowers people, and can increase public awareness of societally relevant issues such as the biodiversity crisis. As such, community science has become a favourable framework for researching alien species where data on the presence, absence, abundance, phenology, and impact of species is important in informing management decisions. However, uncertainties arising at different stages can limit the interpretation of data and lead to projects failing to achieve their intended outcomes. Focusing on alien species centered community science projects, we identified key research questions and the relevant uncertainties that arise during the process of developing the study design, for example, when collecting the data and during the statistical analyses. Additionally, we assessed uncertainties from a linguistic perspective, and how the communication stages among project coordinators, participants and other stakeholders can alter the way in which information may be interpreted. We discuss existing methods for reducing uncertainty and suggest further solutions to improve data reliability. Further, we make suggestions to reduce the uncertainties that emerge at each project step and provide guidance and recommendations that can be readily applied in practice. Reducing uncertainties is essential and necessary to strengthen the scientific and community outcomes of community science, which is of particular importance to ensure the success of projects aimed at detecting novel alien species and monitoring their dynamics across space and time.

show abstract

Enhancing plant biosecurity with citizen science monitoring: comparing methodologies using reports of acute oak decline

Cited by 12 publications

References 44 publications

The role of passive surveillance and citizen science in plant health

The role of passive surveillance and citizen science in plant health

An analysis about the accuracy of geographic profiling in relation to the number of observations and the buffer zone

Identifying, reducing, and communicating uncertainty in community science: a focus on alien species

Contact Info

Product

Resources

About