Message-ID: <CAEsSYzxN=sbTLdottsZHU1090fYPx1d+5p8-P_WyaKr_7Q_pXA@mail.gmail.com>
Date: 2025-12-03T16:15:41Z
From: Oliver Hooker
Subject: Analysing Ecological Data with Detection Error
Better Ecological Inference ? Even When You Don?t Detect Every Animal
Analysing Ecological Data with Detection Error
Learn to analyse ecological field data with detection error using R. Work
with point counts, ARU data, N-mixture models, distance sampling and
time-removal methods.
https://prstats.org/course/analysing-ecological-data-with-detection-error-aedd01/
*Analysing Ecological Data with Detection Error (AEED01)* is a live online
course designed for ecologists, conservation biologists, and wildlife
researchers who collect field data where *imperfect detection*, observation
error, or non-detection are part of the reality. The course teaches you how
to recognise and properly model detection error, so your inferences about
presence, abundance, occupancy, or distribution are robust, defensible, and
scientifically sound.
What You Will Learn
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Why ignoring detection error often leads to *biased or misleading
conclusions* ? especially important when working with mobile, elusive,
or rare species.
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How to apply statistical methods and models that account for *imperfect
detection*, such as occupancy models, detection-adjusted count or
abundance models, and other approaches implemented in R (e.g., using
packages inspired by detection-error frameworks).
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When and how to use *single-visit or repeat-survey designs, distance
sampling, removal methods, or hierarchical models* to properly estimate
real population parameters rather than naive indices.
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How to incorporate *environmental covariates, observer variation,
habitat heterogeneity, and detection heterogeneity* to improve model
realism and ecological inference.
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Best practices for *designing surveys and data collection* to make
detection-error modelling feasible and effective ? including guidance on
repeated visits, survey protocols, sampling effort, and data structure.
Why This Course Matters
Many animals ? especially marine mammals, large mammals, cryptic species,
or rare taxa ? are difficult to detect reliably. Observations often miss
individuals, or detectability varies with environment, behavior, observer
effort, or time. Without accounting for detection error, analyses of
presence/absence, occupancy, abundance, distribution, habitat use, or
population trends can be seriously flawed. Recent ecological research warns
that ignoring complex observation processes can lead to ?biased inference
and poor predictions.?
By learning detection-error-aware methods, you can:
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Estimate *true occupancy or density* rather than undercounted indices.
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Quantify *uncertainty around presence or abundance*, essential for
conservation, impact assessment, and monitoring.
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Better understand patterns of habitat use, distribution, and behaviour ?
even when detection is imperfect.
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Make more defensible claims in reports, publications, or management
recommendations.
Course Format & Who Should Attend
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Live-online course (dates & duration published on course website) ?
delivered in clear, accessible sessions combining theory, case studies, and
hands-on coding.
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Suitable for researchers, field ecologists, conservation biologists,
postgraduate students ? anyone working with observational ecological data
in R or planning to.
-
Participants should ideally have some experience with ecological data,
survey work or field studies ? but the course will guide you through the
statistical and practical challenges from first principles.
Please email oliver at prstats.org with any questions.
--
Oliver Hooker PhD.
PR stats
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