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 62 publications

(81 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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Effects of ignoring survey design information for data reuse

Foster

Vanhatalo

Trenkel

et al. 2021

Ecological Applications

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Data are currently being used, and reused, in ecological research at an unprecedented rate. To ensure appropriate reuse however, we need to ask the question: "Are aggregated databases currently providing the right information to enable effective and unbiased reuse?" We investigate this question, with a focus on designs that purposefully favour the selection of sampling locations (upweighting the probability of selection of some locations). These designs are common and examples are those designs that have uneven inclusion probabilities or are stratified. We perform a simulation experiment by creating datasets with progressively more uneven inclusion probabilities, and examine the resulting estimates of the average number of individuals per unit area (density). The effect of ignoring the survey design can be profound, with biases of up to 250% in density estimates when naive analytical methods are used. This density estimation bias is not reduced by adding more data. Fortunately, the estimation bias can be mitigated by using an appropriate estimator or an appropriate model that incorporates the design information. These are only available however, when essential information about the survey design is available: the sample location selection process (e.g. inclusion probabilities), and/or covariates used in their specification. The results suggest that such information must be stored and served with the data to support meaningful inference and data reuse.

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Making Messy Data Work for Conservation

Cited by 62 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

Effects of ignoring survey design information for data reuse

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