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Journal articles on the topic 'Distance sampling'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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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 (July 30, 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 (December 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 (February 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 (September 3, 2015): 58–75. http://dx.doi.org/10.1007/s13253-015-0220-7.

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5

Clark, Robert Graham. "Statistical Efficiency in Distance Sampling." PLOS ONE 11, no. 3 (March 7, 2016): e0149298. http://dx.doi.org/10.1371/journal.pone.0149298.

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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 (May 10, 2017): 1558–65. http://dx.doi.org/10.1111/2041-210x.12790.

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7

Nielson, Ryan M., Robert T. Sugihara, Thomas J. Boardman, and Richard M. Engeman. "Optimization of ordered distance sampling." Environmetrics 15, no. 2 (February 20, 2004): 119–28. http://dx.doi.org/10.1002/env.627.

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8

Miller, Mark W. "Distance Sampling: Methods and Applications." Bird Study 63, no. 1 (January 2, 2016): 152–53. http://dx.doi.org/10.1080/00063657.2016.1148352.

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9

Marques, Tiago. "Distance sampling: estimating animal density." Significance 6, no. 3 (August 24, 2009): 136–37. http://dx.doi.org/10.1111/j.1740-9713.2009.00380.x.

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10

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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11

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 (June 3, 2016): 422–30. http://dx.doi.org/10.17221/52/2015-jfs.

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12

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 (March 24, 2010): 346–61. http://dx.doi.org/10.1007/s13253-010-0021-y.

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13

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

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14

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 (December 2009): 432–42. http://dx.doi.org/10.1198/jabes.2009.08030.

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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 (April 1, 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 and field techniques for both slope correction and the measurement of elevated logs.

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16

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 (March 26, 2011): 389–408. http://dx.doi.org/10.1007/s13253-011-0059-5.

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17

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

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18

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 (September 1994): 891. http://dx.doi.org/10.2307/2532812.

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19

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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20

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 (February 2015): 325–31. http://dx.doi.org/10.1890/14-1625.1.

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21

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

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22

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 (March 2009): 860–74. http://dx.doi.org/10.1016/j.jspi.2008.05.039.

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23

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 (November 1994): 789. http://dx.doi.org/10.2307/2404172.

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24

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 (September 20, 2018): 38–47. http://dx.doi.org/10.1111/2041-210x.13082.

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25

Miller, David L., and Len Thomas. "Mixture Models for Distance Sampling Detection Functions." PLOS ONE 10, no. 3 (March 20, 2015): e0118726. http://dx.doi.org/10.1371/journal.pone.0118726.

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26

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 (February 27, 2019): e22962. http://dx.doi.org/10.1002/ajp.22962.

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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 (January 10, 2017): 475–94. http://dx.doi.org/10.1007/s10182-016-0287-7.

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28

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 (February 19, 2014): 219–39. http://dx.doi.org/10.1007/s13253-014-0167-0.

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29

Magnussen, S., C. Kleinn, and N. Picard. "Two new density estimators for distance sampling." European Journal of Forest Research 127, no. 3 (December 5, 2007): 213–24. http://dx.doi.org/10.1007/s10342-007-0197-z.

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30

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

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31

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 (July 1995): 628. http://dx.doi.org/10.2307/3802478.

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32

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 (August 1, 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, the primary advantage is that a design-unbiased estimator of CWD volume is achieved without ever actually measuring the volume of any logs. This method, referred to as perpendicular distance sampling (PDS), is compared with three existing sampling techniques for CWD using a simulation study on a series of artificial populations. In every case, the variance of the PDS estimator of CWD volume was smaller than the variance of the competing methods, but the difference in the variance was not large between PDS and two of the competing methods. When estimating the number of pieces of CWD, the variance of the PDS estimator was one of the largest amongst the tested methods. An equally important result is that the variant of line intersect sampling used in this study, where the orientation of the line is the same at all sample points, performed poorly in every situation. This and other problems suggest that the suitability of this sampling technique for estimating CWD is questionable.

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33

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 (December 16, 2014): 063015. http://dx.doi.org/10.1117/1.jei.23.6.063015.

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34

Borchers, David Louis, and Martin James Cox. "Distance sampling detection functions: 2D or not 2D?" Biometrics 73, no. 2 (October 17, 2016): 593–602. http://dx.doi.org/10.1111/biom.12581.

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35

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

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36

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 (December 14, 2017): 1456–64. http://dx.doi.org/10.1111/1365-2664.13053.

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37

Gregoire, John J., and Jeffery A. Sparkas. "Sampling from a Distance Took In-house Insight." Opflow 32, no. 10 (October 2006): 28–31. http://dx.doi.org/10.1002/j.1551-8701.2006.tb01895.x.

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38

Pavanato, Heloise J., Leonardo L. Wedekin, Fernando R. Guilherme-Silveira, Márcia H. Engel, and Paul G. Kinas. "Estimating humpback whale abundance using hierarchical distance sampling." Ecological Modelling 358 (August 2017): 10–18. http://dx.doi.org/10.1016/j.ecolmodel.2017.05.003.

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39

Oedekoven, Cornelia S., Jeffrey L. Laake, and Hans J. Skaug. "Distance sampling with a random scale detection function." Environmental and Ecological Statistics 22, no. 4 (March 22, 2015): 725–37. http://dx.doi.org/10.1007/s10651-015-0316-9.

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40

Fewster, Rachel M., Stephen T. Buckland, Kenneth P. Burnham, David L. Borchers, Peter E. Jupp, Jeffrey L. Laake, and Len Thomas. "Estimating the Encounter Rate Variance in Distance Sampling." Biometrics 65, no. 1 (March 24, 2008): 225–36. http://dx.doi.org/10.1111/j.1541-0420.2008.01018.x.

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41

Lynch, Thomas B. "A mirage boundary correction method for distance sampling." Canadian Journal of Forest Research 42, no. 2 (February 2012): 272–78. http://dx.doi.org/10.1139/x11-185.

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42

Affleck, David L. R. "On the efficiency of line intersect distance sampling." Canadian Journal of Forest Research 40, no. 6 (June 2010): 1086–94. http://dx.doi.org/10.1139/x10-063.

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Sampling strategies commonly used for coarse woody debris (CWD) inventories, including line intersect sampling (LIS), typically require large sample sizes to estimate aggregate volume with reasonable precision. Line intersect distance sampling (LIDS) is a recently developed strategy based on a probability proportional-to-volume design and a linear sampling unit. In principle, the design augments the precision of volume estimators by increasing the intensity with which bulkier particles are sampled, while the transect-based protocol facilitates the search for qualifying particles. This study reports on the relative performances of LIDS and LIS in seven stands in Montana, USA. Particles selected by LIDS were consistently less numerous but larger in cross section than those selected at the same locations by LIS. In timed field trials, LIDS required more time than LIS, but CWD volume estimates from LIDS were generally more precise, more than offsetting the time differential. Conversely, aggregate length and abundance of CWD were generally estimated more efficiently with LIS. Results suggest that LIDS permits more efficient use of survey resources than LIS where CWD inventories focus on parameters relating to volume, biomass, or carbon. However, the constant volume factor design of LIDS is not advantageous where CWD frequency is of central interest.

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43

Iles, Kim, and David Hugh Harrison Carter. "“Distance-variable” estimators for sampling and change measurement." Canadian Journal of Forest Research 37, no. 9 (September 2007): 1669–74. http://dx.doi.org/10.1139/x07-029.

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The estimation procedure described is a simple technique that is applicable to virtually any plot-based sampling method and virtually any measured variable. It can be retrofitted to any existing fixed or variable plot over time by simply knowing the distance from the sampled object to the sample point. These estimators are illustrated for sampling over time as the plot size changes. An example is variable-plot sampling in forestry. Traditional estimates from sample plots can be geometrically viewed as a series of “disc shapes” where the same estimate is used for an object no matter how near the sample point is to that selected object. “Distance-variable” (DV) or “shaped” estimators have the same average value over the plot area, with some very important advantages. We believe that the DV estimate will be shown to reduce the variance of growth measurement compared with simple difference estimators. Traditional “disc” estimators are a special case of the more general DV estimators. There are no difficulties with the use of current edge-effect correction techniques, and the calculation of statistics is virtually identical to traditional methods.

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44

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

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45

Baccaro, F. B., and G. Ferraz. "Estimating density of ant nests using distance sampling." Insectes Sociaux 60, no. 1 (December 13, 2012): 103–10. http://dx.doi.org/10.1007/s00040-012-0274-2.

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46

Lundqvist, Anders. "On the Distance Between Some πps Sampling Designs." Acta Applicandae Mathematicae 97, no. 1-3 (April 5, 2007): 79–97. http://dx.doi.org/10.1007/s10440-007-9134-x.

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47

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 (September 3, 2012): 119–28. http://dx.doi.org/10.1093/forestry/cps059.

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48

Thomas, Len, Stephen T. Buckland, Eric A. Rexstad, Jeff L. Laake, Samantha Strindberg, Sharon L. Hedley, Jon R. B. Bishop, Tiago A. Marques, and Kenneth P. Burnham. "Distance software: design and analysis of distance sampling surveys for estimating population size." Journal of Applied Ecology 47, no. 1 (February 2010): 5–14. http://dx.doi.org/10.1111/j.1365-2664.2009.01737.x.

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49

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 (May 28, 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 compare the performance of distance sampling and spatial capture-recapture abundance estimators. Additionally, we estimated the abundance of North Atlantic right whales in the southeastern United States with two formulations of each model and compared the estimates. Spatial capture-recapture abundance estimates had lower root mean squared error than distance sampling estimates. Spatial capture-recapture 95% credible intervals for abundance had nominal coverage, i.e., contained the simulating value for abundance in 95% of simulations, whereas distance sampling credible intervals had below nominal coverage. Moreover, North Atlantic right whale abundance estimates from distance sampling models were more sensitive to model specification compared to spatial capture-recapture estimates. When estimating abundance from line transect data, researchers should consider using search encounter spatial capture-recapture when individuals in the population of interest are identifiable, when line transects are surveyed over multiple occasions, when there is imperfect detection of individuals located on the line transect, and when it is safe to assume the population of interest is closed demographically. When line transects are surveyed over multiple occasions, researchers should be aware that individual space use may induce spatial autocorrelation in counts across transects. This is not accounted for in common distance sampling estimators and leads to overly precise abundance estimates.

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50

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 (March 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 acre produced by n-tree distance sampling are biased, but when a bias correction factor is applied to the northern hardwood estimates, the results are equivalent to estimates from point and plot sampling. Investigation of bias in the plantation and clumped forests is ongoing. N-tree distance sampling is cost-competitive with the more traditional point and plot northern hardwoods. North. J. Appl. For. 11(1):12-16.

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