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Journal articles on the topic 'Distance sampling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Kulkarni, Mandar M. "Mahalanobis Distance-based Over-Sampling Technique." Journal of Advanced Research in Dynamical and Control Systems 12, SP8 (2020): 874–82. http://dx.doi.org/10.5373/jardcs/v12sp8/20202591.

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2

Richardson, Alice M. "Advanced Distance Sampling." Ecology 89, no. 12 (2008): 3550–51. http://dx.doi.org/10.1890/0012-9658-89.12.3550.

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3

Barry, Simon C., and A. H. Welsh. "Distance sampling methodology." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63, no. 1 (2001): 23–31. http://dx.doi.org/10.1111/1467-9868.00274.

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4

Buckland, S. T., C. S. Oedekoven, and D. L. Borchers. "Model-Based Distance Sampling." Journal of Agricultural, Biological, and Environmental Statistics 21, no. 1 (2015): 58–75. http://dx.doi.org/10.1007/s13253-015-0220-7.

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5

Marques, Tiago. "Distance sampling: estimating animal density." Significance 6, no. 3 (2009): 136–37. http://dx.doi.org/10.1111/j.1740-9713.2009.00380.x.

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6

Howe, Eric J., Stephen T. Buckland, Marie‐Lyne Després‐Einspenner, and Hjalmar S. Kühl. "Distance sampling with camera traps." Methods in Ecology and Evolution 8, no. 11 (2017): 1558–65. http://dx.doi.org/10.1111/2041-210x.12790.

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7

Clark, Robert Graham. "Statistical Efficiency in Distance Sampling." PLOS ONE 11, no. 3 (2016): e0149298. http://dx.doi.org/10.1371/journal.pone.0149298.

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8

Miller, Mark W. "Distance Sampling: Methods and Applications." Bird Study 63, no. 1 (2016): 152–53. http://dx.doi.org/10.1080/00063657.2016.1148352.

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9

Nielson, Ryan M., Robert T. Sugihara, Thomas J. Boardman, and Richard M. Engeman. "Optimization of ordered distance sampling." Environmetrics 15, no. 2 (2004): 119–28. http://dx.doi.org/10.1002/env.627.

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10

Borchers, David, Tiago Marques, Thorvaldur Gunnlaugsson, and Peter Jupp. "Estimating Distance Sampling Detection Functions When Distances Are Measured With Errors." Journal of Agricultural, Biological, and Environmental Statistics 15, no. 3 (2010): 346–61. http://dx.doi.org/10.1007/s13253-010-0021-y.

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11

Royle, J. Andrew, Deanna K. Dawson, and Scott Bates. "MODELING ABUNDANCE EFFECTS IN DISTANCE SAMPLING." Ecology 85, no. 6 (2004): 1591–97. http://dx.doi.org/10.1890/03-3127.

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12

Buckland, Stephen T., Robin E. Russell, Brett G. Dickson, Victoria A. Saab, Donal N. Gorman, and William M. Block. "Analyzing designed experiments in distance sampling." Journal of Agricultural, Biological, and Environmental Statistics 14, no. 4 (2009): 432–42. http://dx.doi.org/10.1198/jabes.2009.08030.

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13

Laake, J. L., B. A. Collier, M. L. Morrison, and R. N. Wilkins. "Point-Based Mark-Recapture Distance Sampling." Journal of Agricultural, Biological, and Environmental Statistics 16, no. 3 (2011): 389–408. http://dx.doi.org/10.1007/s13253-011-0059-5.

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14

Swann, Don E., Roy C. Averill-Murray, and Cecil R. Schwalbe. "Distance Sampling for Sonoran Desert Tortoises." Journal of Wildlife Management 66, no. 4 (2002): 969. http://dx.doi.org/10.2307/3802929.

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15

Williams, M. S., H. T. Valentine, J. H. Gove, and M. J. Ducey. "Additional results for perpendicular distance sampling." Canadian Journal of Forest Research 35, no. 4 (2005): 961–66. http://dx.doi.org/10.1139/x05-023.

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Over the last decade a number of new methods have been proposed to sample coarse woody debris. Of the new methods, both field trials and computer simulations suggest that perpendicular distance sampling is often the most efficient method for estimating the volume and surface area of coarse woody debris. As with any new sampling technique, further research and field testing are required to address some of the practical problems associated with the implementation of perpendicular distance sampling. This paper provides further results associated with the sampling of curved and multistemmed logs a

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16

Herrero, J., A. García Serrano, C. Prada, and O. Fernández Arberas. "Using block counts and distance sampling to estimate populations of chamois." Pirineos 166 (July 14, 2011): 123–33. http://dx.doi.org/10.3989/pirineos.2011.166006.

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17

RT, Torres, Valente AM, Marques TA, and C. Fonseca. "Estimating red deer abundance using the pellet-based distance sampling method." Journal of Forest Science 61, No. 10 (2016): 422–30. http://dx.doi.org/10.17221/52/2015-jfs.

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18

Magnussen, Steen, Nicholas Picard, and Christoph Kleinn. "A Gamma-Poisson Distribution of Point to k Nearest Event Distance." Forest Science 54, no. 4 (2008): 429–41. http://dx.doi.org/10.1093/forestscience/54.4.429.

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Abstract Distance sampling of events in natural or seminatural populations often indicates a larger variance in the distance to the kth nearest event than expected for events distributed completely at random. Overdispersion contributes to the well-known bias problem of distance sampling density estimators. Distance distribution models that accommodate overdispersion in the data should lead to more robust estimators of density. To this end we propose a gamma-Poisson distribution model for distances from a point to k nearest events. The model assumes a gamma distribution of local densities of ra

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19

Nadir Engin, Uzun, and Yeon Kyoo-Seok. "The correlation of linguistic and geographic distance: A sampling of Altaic languages." Language and Linguistics 94 (November 30, 2021): 133–58. http://dx.doi.org/10.20865/20219406.

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20

Sprent, P., S. T. Buckland, D. R. Anderson, K. P. Burnham, and J. L. Laake. "Distance Sampling-Estimating Abundance of Biological Populations." Journal of Applied Ecology 31, no. 4 (1994): 789. http://dx.doi.org/10.2307/2404172.

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21

Ibaragi, Eijirou, and Masanori Toi. "Distance Relay Using High Speed Sampling Technology." IEEJ Transactions on Power and Energy 114, no. 7-8 (1994): 701–7. http://dx.doi.org/10.1541/ieejpes1990.114.7-8_701.

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22

Buckland, S. T. "Perpendicular Distance Models for Line Transect Sampling." Biometrics 41, no. 1 (1985): 177. http://dx.doi.org/10.2307/2530653.

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23

Thompson, S. K., S. T. Buckland, D. R. Anderson, K. P. Burnham, and J. L. Laake. "Distance Sampling: Estimating Abundance of Biological Populations." Biometrics 50, no. 3 (1994): 891. http://dx.doi.org/10.2307/2532812.

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24

Sollmann, Rahel, Beth Gardner, Richard B. Chandler, J. Andrew Royle, and T. Scott Sillett. "An open-population hierarchical distance sampling model." Ecology 96, no. 2 (2015): 325–31. http://dx.doi.org/10.1890/14-1625.1.

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25

Howe, Eric J., Stephen T. Buckland, Marie‐Lyne Després‐Einspenner, and Hjalmar S. Kühl. "Model selection with overdispersed distance sampling data." Methods in Ecology and Evolution 10, no. 1 (2018): 38–47. http://dx.doi.org/10.1111/2041-210x.13082.

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26

Miller, David L., and Len Thomas. "Mixture Models for Distance Sampling Detection Functions." PLOS ONE 10, no. 3 (2015): e0118726. http://dx.doi.org/10.1371/journal.pone.0118726.

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27

Borchers, David L., and Tiago A. Marques. "From distance sampling to spatial capture–recapture." AStA Advances in Statistical Analysis 101, no. 4 (2017): 475–94. http://dx.doi.org/10.1007/s10182-016-0287-7.

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28

Mandal, B. N., Rajender Parsad, V. K. Gupta, and U. C. Sud. "A family of distance balanced sampling plans." Journal of Statistical Planning and Inference 139, no. 3 (2009): 860–74. http://dx.doi.org/10.1016/j.jspi.2008.05.039.

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29

Oedekoven, C. S., S. T. Buckland, M. L. Mackenzie, R. King, K. O. Evans, and L. W. Burger. "Bayesian Methods for Hierarchical Distance Sampling Models." Journal of Agricultural, Biological, and Environmental Statistics 19, no. 2 (2014): 219–39. http://dx.doi.org/10.1007/s13253-014-0167-0.

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30

Lutz, Scott, S. T. Buckland, D. R. Anderson, K. P. Burnham, and J. L. Laake. "Distance Sampling: Estimating Abundance of Biological Populations." Journal of Wildlife Management 59, no. 3 (1995): 628. http://dx.doi.org/10.2307/3802478.

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31

Magnussen, S., C. Kleinn, and N. Picard. "Two new density estimators for distance sampling." European Journal of Forest Research 127, no. 3 (2007): 213–24. http://dx.doi.org/10.1007/s10342-007-0197-z.

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32

Boumenir, A. "Sampling the miss-distance and transmission function." Journal of Mathematical Analysis and Applications 310, no. 1 (2005): 197–208. http://dx.doi.org/10.1016/j.jmaa.2005.02.001.

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33

Cappelle, Noémie, Marie‐Lyne Després‐Einspenner, Eric J. Howe, Christophe Boesch, and Hjalmar S. Kühl. "Validating camera trap distance sampling for chimpanzees." American Journal of Primatology 81, no. 3 (2019): e22962. http://dx.doi.org/10.1002/ajp.22962.

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34

Patterson, Shane, Jonathan Harris, Stephen Dinsmore, and Karen Kinkead. "Evaluating differences in density estimation for central Iowa butterflies using two methodologies." PeerJ 11 (October 9, 2023): e16165. http://dx.doi.org/10.7717/peerj.16165.

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The Pollard-Yates transect is a widely used method for sampling butterflies. Data from these traditional transects are analyzed to produce density estimates, which are then used to make inferences about population status or trends. A key assumption of the Pollard-Yates transect is that detection probability is 1.0, or constant but unknown, out to a fixed distance (generally 2.5 m on either side of a transect line). However, species-specific estimates of detection probability would allow for sampling at farther distances, resulting in more detections of individuals. Our objectives were to (1) e

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35

Morgan, David G., John R. Gibbens, Ed T. Conway, Graham Hepworth, and James Clough. "Wildlife density estimation by distance sampling: A novel technique with movement compensation." PLOS ONE 19, no. 10 (2024): e0310020. http://dx.doi.org/10.1371/journal.pone.0310020.

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Estimates of population density are fundamental to wildlife conservation and management. Distance sampling from line transects is a widely used sample count method and is most often analysed using Distance software. However, this method has limited capabilities with mobile populations (e.g., birds), which tend to encounter an observer more often than immobile ones. This paper presents a novel distance sampling method based on a different set of models and assumptions, named WildlifeDensity after its associated software. It is based on mechanistic modelling of visual detections of individuals o

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36

Williams, M. S., and J. H. Gove. "Perpendicular distance sampling: an alternative method for sampling downed coarse woody debris." Canadian Journal of Forest Research 33, no. 8 (2003): 1564–79. http://dx.doi.org/10.1139/x03-056.

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Coarse woody debris (CWD) plays an important role in many forest ecosystem processes. In recent years, a number of new methods have been proposed to sample CWD. These methods select individual logs into the sample using some form of unequal probability sampling. One concern with most of these methods is the difficulty in estimating the volume of each log. A new method of sampling CWD that addresses this issue is proposed. This method samples each log with probability proportional to the volume of each piece of CWD. While this method generally has a smaller variance than the existing methods, t

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37

Openshaw, Garet, and Brent Chamberlain. "Optimizing Viewpoint Selection for Route-Based Experiences: Assessing the Role of Viewpoints on Viewshed Accuracy." Land 11, no. 8 (2022): 1324. http://dx.doi.org/10.3390/land11081324.

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A visual analysis is useful to assess potential impacts to our surroundings. There has been tremendous progress toward the optimization, accuracy, and techniques of these analyses. Viewshed analyses are a common type of visual analysis. The purpose of this study was to identify the optimal trade-off between the number of viewpoints needed to generate an accurate viewshed for a given route. In this study, we focused on identifying how a viewshed differs based on the sampling distance (interval) of viewpoints, topography, and distance of analysis. We employed the Geospatial Route Analysis and Vi

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38

Li, Xiao Yong. "Statistic Analyses of Correlation Distances of Soil Parameters." Key Engineering Materials 480-481 (June 2011): 1412–17. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.1412.

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The correlation distance is one of the important parameters for the application of random field theory to reliability analyses. Soil spatial variability is related to soil point variability with the reduction factor of variance in random field theory, and the reduction factor of variance depends on both soil auto-correlation distance and spatial area. The sampling space effect on auto-correlation distance is studied. The vertical and horizontal correlation distances of typical stratum are analyzed in statistics based on a large amount of investigation data and the representative values of corr

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39

Crum, Nathan J., Lisa C. Neyman, and Timothy A. Gowan. "Abundance estimation for line transect sampling: A comparison of distance sampling and spatial capture-recapture models." PLOS ONE 16, no. 5 (2021): e0252231. http://dx.doi.org/10.1371/journal.pone.0252231.

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Accurate and precise abundance estimation is vital for informed wildlife conservation and management decision-making. Line transect surveys are a common sampling approach for abundance estimation. Distance sampling is often used to estimate abundance from line transect survey data; however, search encounter spatial capture-recapture can also be used when individuals in the population of interest are identifiable. The search encounter spatial capture-recapture model has rarely been applied, and its performance has not been compared to that of distance sampling. We analyzed simulated datasets to

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40

Lessard, Veronica, David D. Reed, and Nicholas Monkevich. "Comparing N-Tree Distance Sampling with Point and Plot Sampling in Northern Michigan Forest Types." Northern Journal of Applied Forestry 11, no. 1 (1994): 12–16. http://dx.doi.org/10.1093/njaf/11.1.12.

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Abstract This study demonstrates the utility of n-tree distance sampling as an alternative to the more common point and plot sampling. This practical demonstration was conducted in Michigan's Upper Peninsula in three forest types: northern hardwood stands, plantation red pine stands, and clumped, mixed hardwood stands. Seven types of field sampling techniques were used: 1/5 ac and 1/10 ac fixed radius plot sampling, BAF 10 and BAF 20 variable radius point sampling, and n-tree distance sampling of 3, 5, and 7 trees. Estimates of mean board foot volume, cords, basal area, and number of trees per

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41

Darras, Kevin, Brett Furnas, Irfan Fitriawan, Yeni Mulyani, and Teja Tscharntke. "Estimating bird detection distances in sound recordings for standardizing detection ranges and distance sampling." Methods in Ecology and Evolution 9, no. 9 (2018): 1928–38. http://dx.doi.org/10.1111/2041-210x.13031.

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42

Ducey, M. J., M. S. Williams, J. H. Gove, S. Roberge, and R. S. Kenning. "Distance-limited perpendicular distance sampling for coarse woody debris: theory and field results." Forestry 86, no. 1 (2012): 119–28. http://dx.doi.org/10.1093/forestry/cps059.

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43

Thomas, Len, Stephen T. Buckland, Eric A. Rexstad, et al. "Distance software: design and analysis of distance sampling surveys for estimating population size." Journal of Applied Ecology 47, no. 1 (2010): 5–14. http://dx.doi.org/10.1111/j.1365-2664.2009.01737.x.

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44

Quinn, Terrance J. "Estimating abundance: a good introduction to distance sampling." Journal of Biogeography 30, no. 4 (2003): 629–30. http://dx.doi.org/10.1046/j.1365-2699.2003.00822.x.

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45

Schweder, Tore. "Advanced Distance Sampling: Estimating Abundance of Biological Populations." Journal of the American Statistical Association 102, no. 478 (2007): 763–64. http://dx.doi.org/10.1198/jasa.2007.s192.

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46

Borchers, David Louis, and Martin James Cox. "Distance sampling detection functions: 2D or not 2D?" Biometrics 73, no. 2 (2016): 593–602. http://dx.doi.org/10.1111/biom.12581.

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47

Qin, Hongxing, XiaoYang Hong, Bin Xiao, Shaoting Zhang, and Guoyin Wang. "Blue noise sampling method based on mixture distance." Journal of Electronic Imaging 23, no. 6 (2014): 063015. http://dx.doi.org/10.1117/1.jei.23.6.063015.

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48

Baccaro, F. B., and G. Ferraz. "Estimating density of ant nests using distance sampling." Insectes Sociaux 60, no. 1 (2012): 103–10. http://dx.doi.org/10.1007/s00040-012-0274-2.

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49

Lundqvist, Anders. "On the Distance Between Some πps Sampling Designs". Acta Applicandae Mathematicae 97, № 1-3 (2007): 79–97. http://dx.doi.org/10.1007/s10440-007-9134-x.

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50

Mizel, Jeremy D., Joshua H. Schmidt, and Mark S. Lindberg. "Accommodating temporary emigration in spatial distance sampling models." Journal of Applied Ecology 55, no. 3 (2017): 1456–64. http://dx.doi.org/10.1111/1365-2664.13053.

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