A test of a nonparametric distance-based estimator for seedling density

1989 ◽  
Vol 19 (9) ◽  
pp. 1169-1173 ◽  
Author(s):  
Stephen E. Fairweather ◽  
John F. Amrhein
Keyword(s):  
Spatial Distribution ◽  
Sampling Methods ◽  
Distance Sampling ◽  
Nonparametric Estimator ◽  
Seedling Density ◽  
Quadrat Sampling ◽  
Clear Cut ◽  
Central Pennsylvania ◽  
Unbiased Estimates ◽  
Simulated Sampling

Two sampling methods were used to estimate the number of seedlings per acre on a 15-month-old clear-cut in central Pennsylvania. The seedling population was very dense, and the spatial distribution was highly aggregated. The first method used distance sampling and a nonparametric estimator, and the second method used conventional quadrat sampling (milacre plots (1 milacre = 0.0004 ha)). Based on simulated sampling trials on the mapped seedling population, the distance sampling estimator was severely biased, whereas quadrat sampling yielded unbiased estimates of density.

scholarly journals Distance Sampling in R

2016 ◽  
Author(s):  
David L Miller ◽  
Eric Rexstad ◽  
Len Thomas ◽  
Laura Marshall ◽  
Jeffrey L Laake
Keyword(s):  
Spatial Distribution ◽  
Sampling Methods ◽  
Distance Sampling ◽  
Cutting Edge ◽  
Plant Populations ◽  
Migration Pathway ◽  
Conservation And Management

AbstractEstimating the abundance and spatial distribution of animal and plant populations is essential for conservation and management. We introduce the R package Distance that implements distance sampling methods to estimate abundance. We describe how users can obtain estimates of abundance (and density) using the package as well documenting the links it provides with other more specialized R packages. We also demonstrate how Distance provides a migration pathway from previous software, thereby allowing us to deliver cutting-edge methods to the users more quickly.

Erratum to: Distance Sampling: Methods and Applications

2017 ◽  
pp. E3-E4
Author(s):  
S. T. Buckland ◽  
E. A. Rexstad ◽  
T. A. Marques ◽  
C. S. Oedekoven
Keyword(s):  
Sampling Methods ◽  
Distance Sampling

A SEVEN-YEAR COMPARISON OF RELATIVE-ABUNDANCE AND DISTANCE-SAMPLING METHODS

The Auk ◽  
2003 ◽  
Vol 120 (4) ◽  
pp. 1013 ◽  
Author(s):  
Russell E. Norvell ◽  
Frank P. Howe ◽  
Jimmie R. Parrish
Keyword(s):  
Relative Abundance ◽  
Sampling Methods ◽  
Distance Sampling

scholarly journals Spatial distribution and abundance of nonindigenous coral genus Tubastraea (Cnidaria, Scleractinia) around Ilha Grande, Brazil

2005 ◽  
Vol 65 (4) ◽  
pp. 661-673 ◽  
Author(s):  
A. F. Paula ◽  
J. C. Creed
Keyword(s):  
Spatial Distribution ◽  
Coastal Waters ◽  
Sampling Methods ◽  
Geographic Range ◽  
Shallow Waters ◽  
Rocky Shores ◽  
Visual Estimation ◽  
Hard Substratum ◽  
Coral Genus ◽  
Different Depths

The distribution and abundance of azooxanthellate coral Tubastraea Lesson, 1829 were examined at different depths and their slope preference was measured on rocky shores on Ilha Grande, Brazil. Tubastraea is an ahermatypic scleractinian nonindigenous to Brazil, which probably arrived on a ship's hull or oil platform in the late 1980's. The exotic coral was found along a great geographic range of the Canal Central of Ilha Grande, extending over a distance of 25 km. The abundance of Tubastraea was quantified by depth, using three different sampling methods: colony density, visual estimation and intercept points (100) for percentage of cover. Tubastraea showed ample tolerance to temperature and desiccation since it was found more abundantly in very shallow waters (0.1-0.5 m), despite the fact that hard substratum is available at greater depths at all the stations sampled. At most sites, 1 to 5 colonies per 0.25 m² were found most frequently, but occasionally more than 50 colonies were found per 0.25 m², indicating a somewhat gregarious spatial distribution for this coral. The coral Tubastraea was found to occupy slopes of every possible angle in the Canal Central of Ilha Grande, but more colonies were found occupying slopes of 80 to 100°. Therefore, its insensitivity to angles of recruitment and its tolerance for different depths makes it an organism with great ecological tolerance, with a potential to colonize new areas and increase its current range in Brazil's coastal waters.

Distance Sampling: Methods and Applications

Bird Study ◽  
2016 ◽  
Vol 63 (1) ◽  
pp. 152-153
Author(s):  
Mark W. Miller
Keyword(s):  
Sampling Methods ◽  
Distance Sampling

scholarly journals Using mark-recapture distance sampling methods on line transect surveys

2014 ◽  
Vol 5 (11) ◽  
pp. 1180-1191 ◽  
Author(s):  
Mary Louise Burt ◽  
David L. Borchers ◽  
Kurt J. Jenkins ◽  
Tiago A. Marques
Keyword(s):  
Sampling Methods ◽  
Distance Sampling ◽  
Line Transect ◽  
Mark Recapture ◽  
On Line

scholarly journals Monitoring arthropods in protected grasslands: comparing pitfall trapping, quadrat sampling and video monitoring

Web Ecology ◽  
2015 ◽  
Vol 15 (1) ◽  
pp. 15-23 ◽  
Author(s):  
J. G. Zaller ◽  
G. Kerschbaumer ◽  
R. Rizzoli ◽  
A. Tiefenbacher ◽  
E. Gruber ◽  
...  
Keyword(s):  
Sampling Methods ◽  
Natural Populations ◽  
Video Camera ◽  
Video Monitoring ◽  
Pitfall Traps ◽  
Pitfall Trapping ◽  
Quadrat Sampling ◽  
Advantages And Disadvantages ◽  
Metal Frame ◽  
Non Destructive

Abstract. When monitoring the activity and diversity of arthropods in protected areas it is ethically advisable to use non-destructive methods in order to avoid detrimental effects on natural populations and communities. The aim of this study was to compare the efficiency of three methods for potential use for arthropod monitoring in a protected grassland: pitfall trapping, quadrat sampling and video monitoring. Pitfall trapping was conducted either during the day or over night (cup diameter 6.5 cm, unfenced, without preservation fluid). Quadrat sampling was conducted within a metal frame (width × length × height: 50 × 50 × 30 cm). Video monitoring was done on a 68 × 37 cm area using a digital high-density video camera mounted on a tripod. The study site was located in a semi-dry grassland northwest of Vienna, Austria (305 m a.s.l., 48°27′ E, 16°34′ N); the three methods were replicated five times. Across the sampling methods a total of 24 arthropod orders were observed with Hymenoptera being the most abundant, followed by Diptera and Coleoptera. The sampling methods differed considerably in number of arthropods recorded: video monitoring (2578 individuals) followed by quadrat sampling (202 individuals), nocturnal (43 individuals) and diurnal pitfall trapping (12 individuals). Diversity of arthropod assemblages varied highly significantly among the tested methods with quadrat sampling yielding the highest diversity 0.70 ± 0.22 (Gini–Simpson index, mean ±SD) followed by video monitoring (0.57 ± 0.15), diurnal pitfall sampling (0.35 ± 0.28) and nocturnal pitfall sampling (0.17 ± 0.24). Video surveillance of the pitfall traps showed that out of a total of 151 individuals crawling in the vicinity of pitfall traps none of them were actually trapped. A tabular comparison listing the advantages and disadvantages of the sampling methods is presented. Taken together, our results suggest that video monitoring has a great potential as a supplementary method for quantitative and qualitative assessments of arthropod activity and diversity in grasslands.

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