Distance sampling vs. plot sampling for monitoring population abundances of the Pyrenean rock ptarmigan

2020 ◽  
Vol 66 (4) ◽  
Author(s):  
Gaël Aleix-Mata ◽  
Marc Mossoll-Torres ◽  
Evelyn Marty ◽  
Mathieu Boos ◽  
Antonio Sánchez ◽  
...  
2018 ◽  
Vol 40 (1) ◽  
pp. 93 ◽  
Author(s):  
Gregory W. Lollback ◽  
J. Ernest Dunwoody ◽  
Rachel Mebberson ◽  
Jonathan D. Shuker ◽  
Tahlie Page ◽  
...  

A traditional design-reliant estimate of abundance is calculated by multiplying a density estimate obtained from transects to reflect the size of the study area. This type of estimate tells nothing about the nature of a species’ distribution between the samples. In contrast, model-based inference can better estimate abundance by interpolating transect estimates over the study area with the aid of covariates. This study used density surface modelling (DSM) to predict spatial distribution of greater bilby (Macrotis lagotis) and rabbit (Oryctolagus cuniculus) pellets within a predator-proof enclosure at Currawinya National Park, south-west Queensland. Pellets and latrines were counted using distance sampling and plot sampling on 30 PPBio plots during 2012 and 2014. Pellets and latrines were not strongly associated with habitat features, reflecting the generalist nature of both species. Bilby pellets were found on 23 plots in 2012 and 5 plots in 2014. Rabbit pellets were present on 29 plots in 2012 and 16 plots during 2014. These substantial declines in pellet abundances coincided with invasion of the feral cat (Felis catus) into the enclosure. While DSM modelling can allow managers to make informed decisions about applying survey effort or management practices, it is not suited to all species or situations.


2011 ◽  
Vol 76 (2) ◽  
pp. 308-316 ◽  
Author(s):  
Åshild Ø. Pedersen ◽  
Bård-Jørgen Bårdsen ◽  
Nigel G. Yoccoz ◽  
Nicolas Lecomte ◽  
Eva Fuglei

Author(s):  
Cornelia S. Oedekoven ◽  
Tiago A. Marques ◽  
Danielle Harris ◽  
Len Thomas ◽  
Aaron M. Thode ◽  
...  

AbstractVarious methods for estimating animal density from visual data, including distance sampling (DS) and spatially explicit capture-recapture (SECR), have recently been adapted for estimating call density using passive acoustic monitoring (PAM) data, e.g., recordings of animal calls. Here we summarize three methods available for passive acoustic density estimation: plot sampling, DS, and SECR. The first two require distances from the sensors to calling animals (which are obtained by triangulating calls matched among sensors), but SECR only requires matching (not localizing) calls among sensors. We compare via simulation what biases can arise when assumptions underlying these methods are violated. We use insights gleaned from the simulation to compare the performance of the methods when applied to a case study: bowhead whale call data collected from arrays of directional acoustic sensors at five sites in the Beaufort Sea during the fall migration 2007–2014. Call detections were manually extracted from the recordings by human observers simultaneously scanning spectrograms of recordings from a given site. The large discrepancies between estimates derived using SECR and the other two methods were likely caused primarily by the manual detection procedure leading to non-independent detections among sensors, while errors in estimated distances between detected calls and sensors also contributed to the observed patterns. Our study is among the first to provide a direct comparison of the three methods applied to PAM data and highlights the importance that all assumptions of an analysis method need to be met for correct inference.


2014 ◽  
Vol 78 (2) ◽  
pp. 359-368 ◽  
Author(s):  
Sara Franceschi ◽  
Luca Nelli ◽  
Caterina Pisani ◽  
Alessandro Franzoi ◽  
Lorenzo Fattorini ◽  
...  

1994 ◽  
Vol 11 (1) ◽  
pp. 12-16 ◽  
Author(s):  
Veronica Lessard ◽  
David D. Reed ◽  
Nicholas Monkevich

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.


2005 ◽  
Vol 35 (10) ◽  
pp. 2295-2303 ◽  
Author(s):  
Maria João Paulo ◽  
Margarida Tomé ◽  
Albert Otten ◽  
Alfred Stein

The cork oak (Quercus suber L.) is an evergreen oak that has the ability to produce a continuous layer of cork tissue which regenerates after being removed. Cork oak stands can be diverse in structure. Young stands are often regularly spaced, whereas older stands usually show clustering and can be mixed with other species. Farmers assessing cork value use a zigzag sampling procedure within a stand. In this study we compare zigzag sampling with two other sampling methods, fixed-radius plot sampling and n-tree distance sampling, using a model for the costs of sampling. We used data from two cork oak stands in Portugal as well as data from six types of simulated stands. We found that zigzag is the poorest sampling method, as in most situations it produces estimators with larger bias and larger standard errors than that produced by the other two procedures. Fixed-radius plot sampling and n-tree distance sampling produce comparable results; however, fixed-radius plot sampling is preferred because it produces unbiased estimators.


2019 ◽  
Vol 49 (1) ◽  
pp. 41-52 ◽  
Author(s):  
P. Corona ◽  
R.M. Di Biase ◽  
L. Fattorini ◽  
M. D’Amati

Non-detection of trees is an important issue when using single-scan TLS in forest inventories. A hybrid inference approach is adopted. Quoting from distance sampling, a detection function is assumed, so that the inclusion probability of each tree included within each plot can be determined. A simulation study is performed to compare the TLS-based estimators corrected and uncorrected for non-detection with the Horvitz–Thompson estimator based on conventional plot sampling, in which all the trees within plots are recorded. Results show that single-scan TLS provides more efficient estimators with respect to those provided by the conventional plot sampling in the case of low-density forests when no distance sampling correction is performed. In low-density forests, uncorrected estimators lead to a small bias (1%–6%), increasing with plot size. Therefore, care must be taken in enlarging the plot radius too much. The bias increases in forests with clustered spatial structures and in dense forests, where the bias levels (30%–50%) deteriorate the performance of uncorrected estimators. Even if the bias-corrected estimators prove to be effective in reducing the bias (below 15%), these reductions are not sufficient to outperform conventional plot sampling. Therefore, there is no convenience in using TLS-based estimation in high-density forests.


Author(s):  
Robert Montgomerie ◽  
Karen Holder
Keyword(s):  

Author(s):  
Katherine C Kral-O’Brien ◽  
Adrienne K Antonsen ◽  
Torre J Hovick ◽  
Ryan F Limb ◽  
Jason P Harmon

Abstract Many methods are used to survey butterfly populations, with line transect and area surveys being prominent. Observers are typically limited to search within 5 or 10 m from the line, while observers are unrestricted in larger specified search regions in area surveys. Although methods differ slightly, the selection is often based on producing defendable data for conservation, maximizing data quality, and minimizing effort. To guide method selection, we compared butterfly surveys using 1) line versus area methods and 2) varying width transects (5 m, 10 m, or unrestricted) using count data from surveys in North Dakota from 2015 to 2018. Between line and area surveys, we detected more individuals with area surveys, even when accounting for effort. However, both methods accumulated new species at similar rates. When comparing transect methodology, we detected nearly 60% more individuals and nine more species when transect width increased from 5 m to unrestricted, despite similar effort across methodology. Overall, we found line surveys slightly less efficient at detecting individuals, but they collected similar species richness to area surveys when accounting for effort. Additionally, line surveys allow the use of unrestricted-width transects with distance sampling procedures, which were more effective at detecting species and individuals while providing a means to correct count data over the same transect length. Methods that reduce effort and accurately depict communities are especially important for conservation when long-term datasets are unavailable.


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