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

Mandeville, Caitlin P.; Nilsen, Erlend B.; Finstad, Anders G.

doi:10.1002/2688-8319.12185

Cited by 10 publications

(9 citation statements)

References 91 publications

(138 reference statements)

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“…Finally, ease of access to collection sites is another factor adding geospatial bias to observation datasets. For example, on large scales, CS projects tend to source more data from nontropical countries and more accessible and densely populated parts of nontropical countries (Tiago et al., 2017), whereas on more local scales, sampling is often concentrated around easily accessible trails (Mandeville et al., 2022).…”

Section: Challenges and Solutionsmentioning

confidence: 99%

The power of citizen science to advance fungal conservation

Haelewaters,

Quandt,

Bartrop

et al. 2024

Conservation Letters.

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Fungal conservation is gaining momentum globally, but many challenges remain. To advance further, more data are needed on fungal diversity across space and time. Fundamental information regarding population sizes, trends, and geographic ranges is also critical to accurately assess the extinction risk of individual species. However, obtaining these data is particularly difficult for fungi due to their immense diversity, complex and problematic taxonomy, and cryptic nature. This paper explores how citizen science (CS) projects can be leveraged to advance fungal conservation efforts. We present several examples of past and ongoing CS‐based projects to record and monitor fungal diversity. These include projects that are part of broad collecting schemes, those that provide participants with targeted sampling methods, and those whereby participants collect environmental samples from which fungi can be obtained. We also examine challenges and solutions for how such projects can capture fungal diversity, estimate species absences, broaden participation, improve data curation, and translate resulting data into actionable conservation measures. Finally, we close the paper with a call for professional mycologists to engage with amateurs and local communities, presenting a framework to determine whether a given project would likely benefit from participation by citizen scientists.

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Section: Challenges and Solutionsmentioning

confidence: 99%

The power of citizen science to advance fungal conservation

Haelewaters,

Quandt,

Bartrop

et al. 2024

Conservation Letters.

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“…When those species' abundances or distributions are the variables of analytic interest, preferential sampling naturally results in a positive ddc (McClure & Rolek, 2023 ). On the other hand, naturalists might be constrained to visiting and collecting data in, say, built‐up areas, which are easier to access than remote locations (Geldmann et al, 2016 ; Hughes et al, 2020 ; Mandeville et al, 2022 ). Built‐up areas generally have low‐quality habitats, meaning that species are less likely to occupy them in large numbers and that the ddc might be negative.…”

Section: Introductionmentioning

confidence: 99%

Descriptive inference using large, unrepresentative nonprobability samples: An introduction for ecologists

Boyd,

Stewart,

Pescott

2024

Ecology

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Biodiversity monitoring usually involves drawing inferences about some variable of interest across a defined landscape from observations made at a sample of locations within that landscape. If the variable of interest differs between sampled and non‐sampled locations, and no mitigating action is taken, then the sample is unrepresentative and inferences drawn from it will be biased. It is possible to adjust unrepresentative samples so that they more closely resemble the wider landscape in terms of “auxiliary variables”. A good auxiliary variable is a common cause of sample inclusion and the variable of interest, and if it explains an appreciable portion of the variance in both, then inferences drawn from the adjusted sample will be closer to the truth. We applied six types of survey sample adjustment—subsampling, quasi‐randomisation, poststratification, superpopulation modelling, a “doubly robust” procedure, and multilevel regression and poststratification—to a simple two‐part biodiversity monitoring problem. The first part was to estimate mean occupancy of the plant Calluna vulgaris in Great Britain in two time‐periods (1987‐1999 and 2010‐2019); the second was to estimate the difference between the two (i.e. the trend). We estimated the means and trend using large, but (originally) unrepresentative, samples from a citizen science dataset. Compared to the unadjusted estimates, the means and trends estimated using most adjustment methods were more accurate, although standard uncertainty intervals generally did not cover the true values. Completely unbiased inference is not possible from an unrepresentative sample without knowing and having data on all relevant auxiliary variables. Adjustments can reduce the bias if auxiliary variables are available and selected carefully, but the potential for residual bias should be acknowledged and reported.This article is protected by copyright. All rights reserved.

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“…When those species' abundances or distributions are the variables of analytic interest, preferential sampling naturally results in a positive ddc (McClure & Rolek, 2023). On the other hand, naturalists might be constrained to visiting and collecting data in, say, built-up areas, which are easier to access than remote locations (Geldmann et al, 2016;Hughes et al, 2020;Mandeville et al, 2022). Built-up areas generally have low-quality habitats, meaning that species are less likely to occupy them in large numbers and that the ddc might be negative.…”

Section: Introductionmentioning

confidence: 99%

Descriptive inference using large, unrepresentative nonprobability samples: An introduction for ecologists

Boyd¹,

Stewart²,

Pescott³

2023

Preprint

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In the age of big data, it is essential to remember that the size of a dataset is not all that matters. This is particularly true where the goal is to draw inferences about some wider population, in which case it is far more important that the data are representative of that population. It is possible to adjust unrepresentative samples so that they more closely resemble the population in terms of “auxiliary variables”. If the auxiliaries predict sample inclusion and/or the variable of interest well, then the adjusted sample estimates will be closer to the truth. Several survey sampling techniques exist to perform such adjustments, but most are not familiar to ecologists. We applied five types of adjustment—subsampling, quasi-randomisation, poststratification, superpopulation modelling, and multilevel regression and poststratification—to a simple two-part biodiversity monitoring problem. The first part was to estimate mean occupancy of the plant Calluna vulgaris in Great Britain in two time-periods (1987-1999 and 2010-2019); the second was to estimate the difference between the two (i.e. the trend). Calluna vulgaris is an attractive case study because we have good estimates of its true distribution in both time-periods. We estimated the means and trend using large, but (originally) unrepresentative, samples. Compared to the unadjusted estimates, the means and trends estimated using most adjustment methods were more accurate, although their uncertainty intervals generally did not cover the true values. Quasi-randomisation performed especially poorly, and we explain why. Most adjustments were far more successful at bringing the distributions of the auxiliary variables in the samples closer to those in the population than they were at improving the estimates of population means and trends. This implies that the major challenge for adjusting unrepresentative samples in biodiversity monitoring is assembling a suitable set of auxiliary variables (i.e. predictors of sample inclusion and the variable of interest). This challenge will be particularly acute for poorly studied taxa and those whose habitat requirements or sampling biases are not reflected in available data.

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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

Cited by 10 publications

References 91 publications

The power of citizen science to advance fungal conservation

The power of citizen science to advance fungal conservation

Descriptive inference using large, unrepresentative nonprobability samples: An introduction for ecologists

Descriptive inference using large, unrepresentative nonprobability samples: An introduction for ecologists

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