Counting Cats: The integration of expert and citizen science data for unbiased inference of population abundance

McDonald, Jenni L.; Hodgson, Dave

doi:10.1002/ece3.7330

Cited by 5 publications

(8 citation statements)

References 70 publications

(76 reference statements)

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“…The individual records were maintained as replicate counts within each cluster. Clustering of 500 m has also been shown to provide reasonable estimates in an urban area with high expert coverage (91%), where you would not anticipate cat numbers to be significantly inflated above those observed by experts 25 . In the absence of expert data, the effect of violating this assumption (i.e.…”

Section: Methodsmentioning

confidence: 93%

“…We applied an integrated abundance model (IAM) within a Bayesian framework that combines count data across sites from two forms of citizen science data and expert data 25 . The hierarchical structure of the IAM enables it to borrow strength from the sites with expert data to inform detection biases of citizen science data, including detection probability of an unowned cat and false positives due to misidentification of an owned cat as unowned.…”

Section: Methodsmentioning

confidence: 99%

“…We extend the original development of an IAM 25 described above to model the log the site specific mean ( λ i ) as a linear function of covariates ( x ) using the following linear relationship: where x j , I are the values of the j th covariate across sites i , βs are the regression coefficients for each covariate and ɛ is the residual site-specific variation providing estimates of unexplained variance. We also fitted a model without covariate effects to gain an estimate of total site-specific variance.…”

Section: Methodsmentioning

confidence: 99%

“…To address these challenges, we provide an alternative approach using an Integrated Abundance Model (IAM). This model accounts for both the underdetection of unowned cats and misidentification of owned cats as unowned by integrating data from residents with expert data that apply robust protocols that more accurately identifies an unowned cat with no risk of double counting 25 . Integrating citizen science data with expert data of higher quality from a subset of sites within IAMs has been shown to produce model parameter estimates that are both precise and accurate 25 .…”

Section: Introductionmentioning

confidence: 99%

“…This model accounts for both the underdetection of unowned cats and misidentification of owned cats as unowned by integrating data from residents with expert data that apply robust protocols that more accurately identifies an unowned cat with no risk of double counting 25 . Integrating citizen science data with expert data of higher quality from a subset of sites within IAMs has been shown to produce model parameter estimates that are both precise and accurate 25 . Applying this model framework to data collected across a broad range of urban areas allows both the quantification of the unowned cat population and importantly the identification of conditions that enable populations to persist.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Human influences shape the first spatially explicit national estimate of urban unowned cat abundance

McDonald

Skillings²

2021

Sci Rep

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Globally, unowned cats are a common element of urban landscapes, and the focus of diverse fields of study due to welfare, conservation and public health concerns. However, their abundance and distribution are poorly understood at large spatial scales. Here, we use an Integrated Abundance Model to counter biases that are inherent in public records of unowned cat sightings to assess important drivers of their abundance from 162 sites across five urban towns and cities in England. We demonstrate that deprivation indices and human population densities contribute to the number of unowned cats. We provide the first spatially explicit estimates of expected distributions and abundance of unowned cats across a national scale and estimate the total UK urban unowned cat population to be 247,429 (95% credible interval: 157,153 to 365,793). Our results provide a new baseline and approach for studies on unowned cats and links to the importance of human-mediated effects.

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Section: Methodsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Human influences shape the first spatially explicit national estimate of urban unowned cat abundance

McDonald

Skillings²

2021

Sci Rep

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The implications of estimating rarity in Brazilian reptiles from GBIF data based on contributions from citizen science versus research institutions

Forti,

da Silva,

Ferreira

et al. 2024

Integrative Conservation

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Understanding the distribution of rare species is important for conservation prioritisation. Traditionally, museums and other research institutions have served as depositories for specimens and biodiversity information. However, estimating abundance from these sources is challenging due to spatiotemporally biased collection methods. For instance, large‐bodied reptiles that are found near research institutions or in popular, easily accessible sites tend to be overrepresented in collections compared to smaller species found in remote areas. Recently, a substantial number of observations have been amassed through citizen (or community) science initiatives, which are invaluable for monitoring purposes. Given the unstructured nature of this sampling, these datasets are often affected by biases, such as taxonomic, spatial and temporal preferences. Therefore, analysing data from these two sources can lead to different abundance estimates. This study compiled data on Brazilian reptile species from the Global Information Biodiversity Facility (GBIF). It employed a community‐ecology approach to analyse data from research institutions and citizen science initiatives, separately and collectively, to assess taxonomic and spatial species coverage and predict species rarity. Using a 1‐degree hexagonal grid, we analysed the spatial distribution of reptile communities and calculated rarity indices for 754 reptile species. Our findings reveal that 87 species were exclusively recorded in the citizen science subset, while 212 were recorded only by research institutions. The number of observations per species in the citizen science data followed a Gambin distribution, which aligns with the expected pattern of abundance in natural communities, unlike the data from research institutions. This suggests that citizen science data may be a more accurate source for estimating species abundance and rarity. The discrepancies in rarity classifications between the datasets were likely due to differences in sample size and potentially other sampling parameters. Nevertheless, combining data collected by both research institutions and citizen science initiatives can help to fill knowledge gaps in reptile species occurrence, thus enhancing the foundation for conservation efforts on a national scale.

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Analysis of simulated trap counts arising from correlated and biased random walks

Alqubori

Petrovskii

2022

Ecological Modelling

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Counting Cats: The integration of expert and citizen science data for unbiased inference of population abundance

Cited by 5 publications

References 70 publications

Human influences shape the first spatially explicit national estimate of urban unowned cat abundance

Human influences shape the first spatially explicit national estimate of urban unowned cat abundance

The implications of estimating rarity in Brazilian reptiles from GBIF data based on contributions from citizen science versus research institutions

Analysis of simulated trap counts arising from correlated and biased random walks

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