Optimal sampling design for spatial capture-recapture Article Swipe
Gates Dupont
,
J. Andrew Royle
,
Muhammad Ali Nawaz
,
Chris Sutherland
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.04.16.045740
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.04.16.045740
Spatial capture-recapture (SCR) has emerged as the industry standard for estimating population density by leveraging information from spatial locations of repeat encounters of individuals. The precision of density estimates depends fundamentally on the number and spatial configuration of traps. Despite this knowledge, existing sampling design recommendations are heuristic and their performance remains untested for most practical applications. To address this issue, we propose a genetic algorithm that minimizes any sensible, criteria-based objective function to produce near-optimal sampling designs. To motivate the idea of optimality, we compare the performance of designs optimized using three model-based criteria related to the probability of capture. We use simulation to show that these designs out-perform those based on existing recommendations in terms of bias, precision, and accuracy in the estimation of population size. Our approach allows conservation practitioners and researchers to generate customized and improved sampling designs for wildlife monitoring.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.04.16.045740
- https://www.biorxiv.org/content/biorxiv/early/2020/07/30/2020.04.16.045740.full.pdf
- OA Status
- green
- Cited By
- 6
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3016346478
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3016346478Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2020.04.16.045740Digital Object Identifier
- Title
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Optimal sampling design for spatial capture-recaptureWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-04-18Full publication date if available
- Authors
-
Gates Dupont, J. Andrew Royle, Muhammad Ali Nawaz, Chris SutherlandList of authors in order
- Landing page
-
https://doi.org/10.1101/2020.04.16.045740Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2020/07/30/2020.04.16.045740.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2020/07/30/2020.04.16.045740.full.pdfDirect OA link when available
- Concepts
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Sampling (signal processing), Mark and recapture, Computer science, Heuristic, Sampling design, Spatial analysis, Population, Data mining, Statistics, Artificial intelligence, Mathematics, Demography, Computer vision, Filter (signal processing), SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2025: 1, 2023: 2, 2022: 2, 2020: 1Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.size. | 128 |
| abstract_inverted_index.terms | 117 |
| abstract_inverted_index.their | 50 |
| abstract_inverted_index.these | 108 |
| abstract_inverted_index.those | 111 |
| abstract_inverted_index.three | 93 |
| abstract_inverted_index.using | 92 |
| abstract_inverted_index.allows | 131 |
| abstract_inverted_index.design | 45 |
| abstract_inverted_index.issue, | 61 |
| abstract_inverted_index.number | 34 |
| abstract_inverted_index.repeat | 21 |
| abstract_inverted_index.traps. | 39 |
| abstract_inverted_index.Despite | 40 |
| abstract_inverted_index.Spatial | 1 |
| abstract_inverted_index.address | 59 |
| abstract_inverted_index.compare | 86 |
| abstract_inverted_index.density | 13, 28 |
| abstract_inverted_index.depends | 30 |
| abstract_inverted_index.designs | 90, 109, 142 |
| abstract_inverted_index.emerged | 5 |
| abstract_inverted_index.genetic | 65 |
| abstract_inverted_index.produce | 75 |
| abstract_inverted_index.propose | 63 |
| abstract_inverted_index.related | 96 |
| abstract_inverted_index.remains | 52 |
| abstract_inverted_index.spatial | 18, 36 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.accuracy | 122 |
| abstract_inverted_index.approach | 130 |
| abstract_inverted_index.capture. | 101 |
| abstract_inverted_index.criteria | 95 |
| abstract_inverted_index.designs. | 78 |
| abstract_inverted_index.existing | 43, 114 |
| abstract_inverted_index.function | 73 |
| abstract_inverted_index.generate | 137 |
| abstract_inverted_index.improved | 140 |
| abstract_inverted_index.industry | 8 |
| abstract_inverted_index.motivate | 80 |
| abstract_inverted_index.sampling | 44, 77, 141 |
| abstract_inverted_index.standard | 9 |
| abstract_inverted_index.untested | 53 |
| abstract_inverted_index.wildlife | 144 |
| abstract_inverted_index.algorithm | 66 |
| abstract_inverted_index.estimates | 29 |
| abstract_inverted_index.heuristic | 48 |
| abstract_inverted_index.locations | 19 |
| abstract_inverted_index.minimizes | 68 |
| abstract_inverted_index.objective | 72 |
| abstract_inverted_index.optimized | 91 |
| abstract_inverted_index.practical | 56 |
| abstract_inverted_index.precision | 26 |
| abstract_inverted_index.sensible, | 70 |
| abstract_inverted_index.customized | 138 |
| abstract_inverted_index.encounters | 22 |
| abstract_inverted_index.estimating | 11 |
| abstract_inverted_index.estimation | 125 |
| abstract_inverted_index.knowledge, | 42 |
| abstract_inverted_index.leveraging | 15 |
| abstract_inverted_index.population | 12, 127 |
| abstract_inverted_index.precision, | 120 |
| abstract_inverted_index.simulation | 104 |
| abstract_inverted_index.information | 16 |
| abstract_inverted_index.model-based | 94 |
| abstract_inverted_index.monitoring. | 145 |
| abstract_inverted_index.optimality, | 84 |
| abstract_inverted_index.out-perform | 110 |
| abstract_inverted_index.performance | 51, 88 |
| abstract_inverted_index.probability | 99 |
| abstract_inverted_index.researchers | 135 |
| abstract_inverted_index.conservation | 132 |
| abstract_inverted_index.individuals. | 24 |
| abstract_inverted_index.near-optimal | 76 |
| abstract_inverted_index.applications. | 57 |
| abstract_inverted_index.configuration | 37 |
| abstract_inverted_index.fundamentally | 31 |
| abstract_inverted_index.practitioners | 133 |
| abstract_inverted_index.criteria-based | 71 |
| abstract_inverted_index.recommendations | 46, 115 |
| abstract_inverted_index.capture-recapture | 2 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5078591197 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I24603500 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.72159646 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |