Interaction between transect design and animal distribution in distance sampling of deer Article Swipe
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/wsb.1368
We conducted a simulation study to evaluate the consequences of violating statistical assumptions of distance sampling (DS) on the bias and precision of population estimates of white‐tailed deer ( Odocoileus virginianus ). Distance sampling is a method for estimating the density of organisms using a distribution of observed distances to individuals. A key assumption of DS is that sampling transects are randomly located with respect to the population being sampled. Most DS transects used in National Parks follow roads and trails, which are not positioned randomly. We constructed spatially explicit simulations of surveys using 7 different types of deer spatial distributions and 3 survey designs. A significant interaction between survey type and deer distribution type explained most of the variation in population estimates across simulations and this interaction was also a significant predictor of the coefficient of variation (CV) of population estimates. Simulation results suggested that 1) non‐road surveys were more robust to bias than were road surveys, 2) effectiveness of each of 3 survey types was dependent on the way deer were distributed across the landscape, and 3) non‐road surveys produced unbiased estimates of populations affected by roads, whereas road surveys did not. The results of our study suggest that DS surveys using pre‐existing roads and trails have potential biases resulting from study design, something that could be considered by land managers when constructing surveys.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/wsb.1368
- OA Status
- hybrid
- Cited By
- 1
- References
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- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4308804337Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/wsb.1368Digital Object Identifier
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Interaction between transect design and animal distribution in distance sampling of deerWork title
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articleOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-11-11Full publication date if available
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Nicholas S. Green, Mark L. Wildhaber, Janice L. AlbersList of authors in order
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https://doi.org/10.1002/wsb.1368Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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- Concepts
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Transect, Odocoileus, Distance sampling, Sampling (signal processing), Population, Geography, Statistics, Sampling design, Population density, Environmental science, Distribution (mathematics), Survey methodology, Physical geography, Ecology, Mathematics, Biology, Computer science, Demography, Sociology, Filter (signal processing), Computer vision, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.1) | 147 |
| abstract_inverted_index.2) | 159 |
| abstract_inverted_index.3) | 179 |
| abstract_inverted_index.DS | 56, 72, 202 |
| abstract_inverted_index.We | 1, 87 |
| abstract_inverted_index.be | 219 |
| abstract_inverted_index.by | 188, 221 |
| abstract_inverted_index.in | 75, 121 |
| abstract_inverted_index.is | 35, 57 |
| abstract_inverted_index.of | 10, 14, 23, 26, 42, 47, 55, 92, 98, 118, 134, 137, 140, 161, 163, 185, 197 |
| abstract_inverted_index.on | 18, 169 |
| abstract_inverted_index.to | 6, 50, 66, 153 |
| abstract_inverted_index.The | 195 |
| abstract_inverted_index.and | 21, 80, 102, 112, 126, 178, 207 |
| abstract_inverted_index.are | 61, 83 |
| abstract_inverted_index.did | 193 |
| abstract_inverted_index.for | 38 |
| abstract_inverted_index.key | 53 |
| abstract_inverted_index.not | 84 |
| abstract_inverted_index.our | 198 |
| abstract_inverted_index.the | 8, 19, 40, 67, 119, 135, 170, 176 |
| abstract_inverted_index.was | 129, 167 |
| abstract_inverted_index.way | 171 |
| abstract_inverted_index.(CV) | 139 |
| abstract_inverted_index.(DS) | 17 |
| abstract_inverted_index.Most | 71 |
| abstract_inverted_index.also | 130 |
| abstract_inverted_index.bias | 20, 154 |
| abstract_inverted_index.deer | 28, 99, 113, 172 |
| abstract_inverted_index.each | 162 |
| abstract_inverted_index.from | 213 |
| abstract_inverted_index.have | 209 |
| abstract_inverted_index.land | 222 |
| abstract_inverted_index.more | 151 |
| abstract_inverted_index.most | 117 |
| abstract_inverted_index.not. | 194 |
| abstract_inverted_index.road | 157, 191 |
| abstract_inverted_index.than | 155 |
| abstract_inverted_index.that | 58, 146, 201, 217 |
| abstract_inverted_index.this | 127 |
| abstract_inverted_index.type | 111, 115 |
| abstract_inverted_index.used | 74 |
| abstract_inverted_index.were | 150, 156, 173 |
| abstract_inverted_index.when | 224 |
| abstract_inverted_index.with | 64 |
| abstract_inverted_index.Parks | 77 |
| abstract_inverted_index.being | 69 |
| abstract_inverted_index.could | 218 |
| abstract_inverted_index.roads | 79, 206 |
| abstract_inverted_index.study | 5, 199, 214 |
| abstract_inverted_index.types | 97, 166 |
| abstract_inverted_index.using | 44, 94, 204 |
| abstract_inverted_index.which | 82 |
| abstract_inverted_index.across | 124, 175 |
| abstract_inverted_index.biases | 211 |
| abstract_inverted_index.follow | 78 |
| abstract_inverted_index.method | 37 |
| abstract_inverted_index.roads, | 189 |
| abstract_inverted_index.robust | 152 |
| abstract_inverted_index.survey | 104, 110, 165 |
| abstract_inverted_index.trails | 208 |
| abstract_inverted_index.between | 109 |
| abstract_inverted_index.density | 41 |
| abstract_inverted_index.design, | 215 |
| abstract_inverted_index.located | 63 |
| abstract_inverted_index.respect | 65 |
| abstract_inverted_index.results | 144, 196 |
| abstract_inverted_index.spatial | 100 |
| abstract_inverted_index.suggest | 200 |
| abstract_inverted_index.surveys | 93, 149, 181, 192, 203 |
| abstract_inverted_index.trails, | 81 |
| abstract_inverted_index.whereas | 190 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Distance | 33 |
| abstract_inverted_index.National | 76 |
| abstract_inverted_index.affected | 187 |
| abstract_inverted_index.designs. | 105 |
| abstract_inverted_index.distance | 15 |
| abstract_inverted_index.evaluate | 7 |
| abstract_inverted_index.explicit | 90 |
| abstract_inverted_index.managers | 223 |
| abstract_inverted_index.observed | 48 |
| abstract_inverted_index.produced | 182 |
| abstract_inverted_index.randomly | 62 |
| abstract_inverted_index.sampled. | 70 |
| abstract_inverted_index.sampling | 16, 34, 59 |
| abstract_inverted_index.surveys, | 158 |
| abstract_inverted_index.surveys. | 226 |
| abstract_inverted_index.unbiased | 183 |
| abstract_inverted_index.conducted | 2 |
| abstract_inverted_index.dependent | 168 |
| abstract_inverted_index.different | 96 |
| abstract_inverted_index.distances | 49 |
| abstract_inverted_index.estimates | 25, 123, 184 |
| abstract_inverted_index.explained | 116 |
| abstract_inverted_index.organisms | 43 |
| abstract_inverted_index.potential | 210 |
| abstract_inverted_index.precision | 22 |
| abstract_inverted_index.predictor | 133 |
| abstract_inverted_index.randomly. | 86 |
| abstract_inverted_index.resulting | 212 |
| abstract_inverted_index.something | 216 |
| abstract_inverted_index.spatially | 89 |
| abstract_inverted_index.suggested | 145 |
| abstract_inverted_index.transects | 60, 73 |
| abstract_inverted_index.variation | 120, 138 |
| abstract_inverted_index.violating | 11 |
| abstract_inverted_index.Odocoileus | 30 |
| abstract_inverted_index.Simulation | 143 |
| abstract_inverted_index.assumption | 54 |
| abstract_inverted_index.considered | 220 |
| abstract_inverted_index.estimates. | 142 |
| abstract_inverted_index.estimating | 39 |
| abstract_inverted_index.landscape, | 177 |
| abstract_inverted_index.non‐road | 148, 180 |
| abstract_inverted_index.population | 24, 68, 122, 141 |
| abstract_inverted_index.positioned | 85 |
| abstract_inverted_index.simulation | 4 |
| abstract_inverted_index.assumptions | 13 |
| abstract_inverted_index.coefficient | 136 |
| abstract_inverted_index.constructed | 88 |
| abstract_inverted_index.distributed | 174 |
| abstract_inverted_index.interaction | 108, 128 |
| abstract_inverted_index.populations | 186 |
| abstract_inverted_index.significant | 107, 132 |
| abstract_inverted_index.simulations | 91, 125 |
| abstract_inverted_index.statistical | 12 |
| abstract_inverted_index.virginianus | 31 |
| abstract_inverted_index.consequences | 9 |
| abstract_inverted_index.constructing | 225 |
| abstract_inverted_index.distribution | 46, 114 |
| abstract_inverted_index.individuals. | 51 |
| abstract_inverted_index.distributions | 101 |
| abstract_inverted_index.effectiveness | 160 |
| abstract_inverted_index.pre‐existing | 205 |
| abstract_inverted_index.white‐tailed | 27 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5040961514 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I172980758 |
| citation_normalized_percentile.value | 0.58939572 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |