Making Messy Data Work for Conservation

Dobson, Andrew D. M.; Milner‐Gulland, E.J.; Aebischer, Nicholas J.; Beale, Colin M.; Brozovic, Robert; Coals, Peter; Critchlow, Rob; Dancer, Anthony; Greve, Michelle; Hinsley, Amy; Ibbett, Harriet; Johnston, Alison; Kuiper, Timothy; Comber, Steven Le; Mahood, Simon P.; Moore, J. F.; Nilsen, Erlend B.; Pocock, Michael J. O.; Quinn, Anthony; Travers, Henry; Wilfred, Paulo; Wright, Joss; Keane, Aidan

doi:10.1016/j.oneear.2020.04.012

Cited by 74 publications

(90 citation statements)

References 92 publications

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“…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 ). For analysts and policy makers there are clear limitations to how these data can be used, but there is no denying the practical usefulness of additional detections to plant health officials (Ryan et al 2018 ).…”

Section: Introductionmentioning

confidence: 92%

“…Passive surveillance occurs as and when an observer (a member of the public, land-user or member of a citizen science scheme), both notices something of concern and is aware of, and uses, the mechanisms available to notify authorities. The resulting data is unstructured and “messy” in nature (Dobson et al 2020 ). Interpreting and using them to estimate the prevalence of a pest and inform biosecurity measures is thus challenging.…”

Section: Introductionmentioning

confidence: 99%

“…Interpreting and using them to estimate the prevalence of a pest and inform biosecurity measures is thus challenging. The use of species population data collected by citizen scientists has been the focus of recent reviews across ecology and conservation (Dobson et al 2020 ; Larson et al 2020 ; Isaac and Pocock 2015 ; Pocock et al 2017a ; Dickinson et al 2010 ; Crall et al 2015 ). We build on these foundations to highlight the role of passive surveillance data in plant health.…”

Section: Introductionmentioning

confidence: 99%

“…Passive surveillance has been defined as “any encounter with a pest by members of the public that is reported to the relevant authority” (Hester and Cacho 2017 ). Increased access to websites and smartphone technology has greatly eased the ability of the public to engage with surveillance in recent years (Dobson et al 2020 ; Pocock et al 2017a ; Dickinson et al 2010 ). Although such reports, via phone, email and letter writing have long played an important role in the detection and diagnosis of tree pests and diseases (Gilbert et al 2005 ; Potter et al 2011 ).…”

Section: Introductionmentioning

confidence: 99%

“…At this point reporting ceases to be purely opportunistic, with additional structures such as citizen science programmes favouring certain types of reporting behaviour (Boakes et al 2016 ). In fact, most volunteer reporting methods currently available will contain both passive and active components (Hester and Cacho 2017 ), and individual programmes will present specific challenges to analyst’s dependent on the methods employed (Dobson et al 2020 ). In this review we aim to characterise current approaches across the ‘passive to active surveillance spectrum’ (Hester and Cacho 2017 ) (Fig.…”

Section: Introductionmentioning

confidence: 99%

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The role of passive surveillance and citizen science in plant health

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The role of passive surveillance and citizen science in plant health

et al. 2020

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Spatial distribution of biodiversity citizen science in a natural area depends on area accessibility and differs from other recreational area use

Mandeville

Nilsen

Finstad

2022

Ecol Sol and Evidence

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1. Opportunistic citizen science produces large amounts of primary biodiversity data but is underutilized in the conservation and management of protected areas despite these areas' status as citizen science hotspots. Application of these data may be limited by the challenge of understanding sampling patterns associated with opportunistic data at a scale relevant to local area management. An improved understanding of citizen science activity patterns within protected areas could strengthen both data analysis and the local promotion and guidance of citizen science activity.2. We investigated local-scale patterns of citizen science activity, using a case study approach to examine citizen science activity in a recreationally popular natural area that serves as a regional citizen science hotspot. We modelled the relationship between local citizen science activity and 10 spatial covariates broadly related to ease of access and natural interest, factors which have been shown to drive citizen science activity at regional scales. We further compared the distribution of citizen science activity with that of professional data collection and recreational visitor activity in the study area.3. We found that citizen science data largely complement rather than replicate openly available professional data. Citizen science participation was primarily driven by ease of access, especially the presence of trails. However, citizen science use of the trail network differed from other types of recreational trail use, including a weaker preference for well-established trails and a stronger association with developed areas.4. This improved understanding of patterns in citizen science participation may be used to better account for spatial biases in citizen science data and to manage natural areas in a way that supports and guides future citizen science activity.

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Community science can inform invasive species management: Melaleuca (Myrtaceae) in South Africa

Potgieter,

ter Huurne,

Richardson

2024

Ecol Sol and Evidence

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Community science initiatives are revolutionising our ability to detect and respond to biological invasions. Non‐native trees and shrubs are among the world's most damaging invasive species and community science data can be used to inform protocols for managing these invasions. This study explores the utility of iNaturalist in informing management practices for the widely cultivated and naturalised genus Melaleuca L. (Myrtaceae; here including the genus Callistemon) in South Africa. We applied data from iNaturalist to assess the distribution and invasion ecology of Melaleuca species in South Africa. Melaleucas, first recorded in South Africa in 1882, have been widely used as garden ornamentals and street trees in the country for over 50 years. Naturalisation of melaleucas in South Africa was first reported in 1998 and the first records of naturalisation/invasiveness for other species are accumulating rapidly. Data on all Melaleuca species in South Africa were downloaded from iNaturalist and analysed using Geographic Information System software. In September 2023, iNaturalist had 3221 records of melaleucas across the country. After checking and filtering, and applying criteria to increase reliability, 2815 records remained, with confirmed identifications of 26 species. These species were recorded in a total of 138 quarter‐degree cells (QDCs) in South Africa (7% of the country); Research Grade (RG, wild‐growing) records occurred in 21 QDCs, records of cultivated plants in 75 QDCs, and 42 QDCs had records of both cultivated and wild‐growing plants. An Invasiveness Index was calculated for each species in the country, provinces, and municipalities, to show which species are already invasive or have substantial invasion debt. Thirty‐two percent of the filtered records were RG (naturalised). The municipality with the highest number of records is the City of Cape Town, with 43% RG records. iNaturalist provided useful information on the occurrence of five Melaleuca species for which no information was available before this study was undertaken. Case studies of invaded habitats highlight that melaleucas have the potential to alter ecosystems incurring substantial control costs. Practical implications: Our study highlights the value of community science data in the detection, monitoring, and management of invasive plant species.

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Making Messy Data Work for Conservation

Cited by 74 publications

References 92 publications

The role of passive surveillance and citizen science in plant health

The role of passive surveillance and citizen science in plant health

Spatial distribution of biodiversity citizen science in a natural area depends on area accessibility and differs from other recreational area use

Community science can inform invasive species management: Melaleuca (Myrtaceae) in South Africa

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