Uncertainty in population estimates: A meta‐analysis for petrels

Bird, Jeremy P.; Woodworth, Bradley K.; Fuller, Richard A.; Shaw, Justine

doi:10.1002/2688-8319.12077

Cited by 12 publications

(11 citation statements)

References 63 publications

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“…There is no indication that a switch to model-based analyses will overcome the challenges associated with burrowing seabird population estimation and improve estimates for trend detection. Based upon our results and those of other recent studies (Buxton et al 2016, Bird et al 2021 we advocate that studies aiming to detect population trends should focus on constant-effort monitoring approaches rather than collecting intermittent wholeisland population estimates.…”

Section: Design-based Versus Model-based Analysissupporting

confidence: 71%

“…To evaluate the strength of our unbiased estimators of burrow numbers we simulated declines of 30, 50 and 80% over three generations for Antarctic prions and white-headed petrels following Bird et al (2021) and assessed their power to detect significant differences between repeat estimates (Supporting information).…”

Section: Power Analysesmentioning

confidence: 99%

“…Most studies that estimate burrowing seabird population sizes aim to use their estimate to detect trends (Bird et al 2021). For this, stratified and/or randomised survey designs are suitable for generating unbiased estimates (Buckland et al 2015).…”

Section: Survey Designmentioning

confidence: 99%

“…This clarifies the importance of bias and precision, which informs survey design and analysis. For example, if the purpose is to provide one estimate for a time series that will be used to detect population trends, minimising bias and high precision are important (Bird et al 2021). Similarly, if the aim is to inform spatially explicit management actions on the ground, or to communicate information about threats and actions, model-based analyses which provide a spatial representation of abundance may be most useful.…”

Section: Recommendations For Future Studiesmentioning

confidence: 99%

“…Typically the number of burrows and the proportion that are occupied are measured separately and combined to obtain an estimate for a given area, often a whole island (Sutherland and Dann 2012). In most cases large uncertainty in these estimates prevents the detection of significant changes in seabird population sizes over time (Bird et al 2021). Uncertainty is usually greater for burrow detection than burrow occupancy, since burrows are often distributed discontinuously across challenging terrain, and their entrances may be obscured by vegetation (Rayner et al 2007, Rodríguez et al 2019.…”

Section: Introductionmentioning

confidence: 99%

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Generating unbiased estimates of burrowing seabird populations

et al. 2022

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Maximising survey efficiency can help reduce the tradeoff between spending limited conservation resources on identifying population changes and responding to those changes through management. Burrow-nesting seabirds are particularly challenging to survey because nests cannot be counted directly. We evaluated a stratified random survey design for generating unbiased population estimates simultaneously for four petrel species nesting on Macquarie Island, Australia, where the survey cue, burrow entrances, is similar for all species. We also compared the use of design-based and model-based analyses for minimising uncertainty in estimates. We recorded 2845 Antarctic prion burrows, 306 white-headed petrel burrows and two blue petrel burrows while distance-sampling along 154 km of transects. For blue petrels and grey petrels, we completed nocturnal searches along a further 71 km and searched 249 km of tracks during follow-up ground searches. We failed to generate unbiased population estimates for two rare and localised species, blue and grey petrels, from our stratified random survey. Only for the most widespread and abundant species, Antarctic prion, did the estimate have reasonable power to detect a rapid population change. Modelbased analyses of the stratified random survey data did not improve upon traditional design-based analyses in terms of uncertainty in population estimates, but they did provide useful spatial representation of current populations. Models that used the targeted survey data did not reflect current population sizes and distributions of the two rare and localised species. We found that when species ecologies, distributions and abundances vary, a multi-method approach to surveys is needed. Species with low abundance that occur patchily across large islands are likely to be best estimated using targeted surveys, whereas widespread and abundant species can be accurately and precisely estimated from randomised surveys using informative model-based analyses.

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Section: Design-based Versus Model-based Analysissupporting

confidence: 71%

Section: Power Analysesmentioning

confidence: 99%

Section: Survey Designmentioning

confidence: 99%

Section: Recommendations For Future Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generating unbiased estimates of burrowing seabird populations

et al. 2022

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Embedding communication concepts in forecasting training increases students' understanding of ecological uncertainty

Woelmer,

Moore,

Lofton

et al. 2023

Ecosphere

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Communicating and interpreting uncertainty in ecological model predictions is notoriously challenging, motivating the need for new educational tools, which introduce ecology students to core concepts in uncertainty communication. Ecological forecasting, an emerging approach to estimate future states of ecological systems with uncertainty, provides a relevant and engaging framework for introducing uncertainty communication to undergraduate students, as forecasts can be used as decision support tools for addressing real‐world ecological problems and are inherently uncertain. To provide critical training on uncertainty communication and introduce undergraduate students to the use of ecological forecasts for guiding decision‐making, we developed a hands‐on teaching module within the Macrosystems Environmental Data‐Driven Inquiry and Exploration (EDDIE; MacrosystemsEDDIE.org) educational program. Our module used an active learning approach by embedding forecasting activities in an R Shiny application to engage ecology students in introductory data science, ecological modeling, and forecasting concepts without needing advanced computational or programming skills. Pre‐ and post‐module assessment data from more than 250 undergraduate students enrolled in ecology, freshwater ecology, and zoology courses indicate that the module significantly increased students' ability to interpret forecast visualizations with uncertainty, identify different ways to communicate forecast uncertainty for diverse users, and correctly define ecological forecasting terms. Specifically, students were more likely to describe visual, numeric, and probabilistic methods of uncertainty communication following module completion. Students were also able to identify more benefits of ecological forecasting following module completion, with the key benefits of using forecasts for prediction and decision‐making most commonly described. These results show promise for introducing ecological model uncertainty, data visualizations, and forecasting into undergraduate ecology curricula via software‐based learning, which can increase students' ability to engage and understand complex ecological concepts.

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Mitigating light attraction

Rodríguez

2023

Conservation of Marine Birds

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Uncertainty in population estimates: A meta‐analysis for petrels

Cited by 12 publications

References 63 publications

Generating unbiased estimates of burrowing seabird populations

Generating unbiased estimates of burrowing seabird populations

Embedding communication concepts in forecasting training increases students' understanding of ecological uncertainty

Mitigating light attraction

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