Optimal sampling design for estimating spatial distribution and abundance of a freshwater mussel population

2005 ◽  
Vol 24 (3) ◽  
pp. 525-537 ◽  
Author(s):  
Penelope S. Pooler ◽  
David R. Smith
Keyword(s):  
Spatial Distribution ◽  
Sampling Design ◽  
Freshwater Mussel ◽  
Optimal Sampling ◽  
Mussel Population ◽  
Optimal Sampling Design

Spatial distribution of sardine (Sardinops sagax) eggs and larvae: an application of geostatistics and resampling to survey data

1999 ◽  
Vol 56 (6) ◽  
pp. 907-914 ◽  
Author(s):  
W J Fletcher ◽  
Neil R Sumner
Keyword(s):  
Spatial Distribution ◽  
Survey Data ◽  
Ordinary Kriging ◽  
Sampling Design ◽  
Optimal Sampling ◽  
Bootstrap Resampling ◽  
Two Dimensional ◽  
Optimal Sampling Design ◽  
Sardinops Sagax ◽  
Eggs And Larvae

The abundance of eggs and larvae of sardine (pilchard) (Sardinops sagax) off the south coast of Western Australia was investigated using geostatistical and bootstrap resampling techniques. A two-dimensional correlogram showed that the patch of newly spawned pilchard eggs had dimensions of 8 nautical miles (M) in diameter in the offshore direction and 10 M in the alongshore direction. For older stages, the size of the patch tended to increase and the level of cohesion decrease. It was more difficult to determine the size of the patch from directional variograms. The abundance of 1- and 2-day-old eggs in the study area was estimated using ordinary kriging. A bootstrapping analysis suggested that the optimal sampling design for day 1 eggs should use 4 × 10 M grids, an increase in intensity on the 5 × 15 M grids used in previous surveys.

The spatial distribution of salmon and steelhead redds and optimal sampling design

2015 ◽  
Vol 72 (3) ◽  
pp. 434-446 ◽  
Author(s):  
Martin C. Liermann ◽  
Dan Rawding ◽  
George R. Pess ◽  
Bryce Glaser
Keyword(s):  
Spatial Distribution ◽  
Spatial Structure ◽  
Random Sampling ◽  
Sampling Design ◽  
Pacific Salmon ◽  
Simple Random Sampling ◽  
Optimal Sampling ◽  
Optimal Sampling Design ◽  
Peak Count ◽  
Spatially Balanced Sampling

Redd surveys are used extensively to estimate spawner population size for Pacific salmon (Onchorynchus spp.). Because redds tend to be spatially aggregated, estimates of total redds based on subsamples of the potential spawning grounds can be uncertain unless the spatial structure is accounted for. Here we use known redd locations for three populations over several years to compare five different probability sampling designs through simulation. The coefficient of variation (CV) for estimates based on simple random sampling was high, with values well over 15% when sampling a third of the reaches. Moving to a spatially balanced sampling design (generalized random tessellation stratified; GRTS) produced improvements in two of the three watersheds (16%–22% reduction in CV). Estimates based on a stratified GRTS design and a GRTS design that included a census of all reaches close to the peak count had higher accuracy, with an approximate CV of one-half to one-third of GRTS alone. We show how these improvements are predicted by theory and under which conditions the different approaches are likely to perform well.

scholarly journals Optimal sampling design for spatial capture‐recapture

Ecology ◽  
2020 ◽  
Author(s):  
Gates Dupont ◽  
J. Andrew Royle ◽  
Muhammad Ali Nawaz ◽  
Chris Sutherland
Keyword(s):  
Sampling Design ◽  
Optimal Sampling ◽  
Optimal Sampling Design ◽  
Capture Recapture

Optimal sampling design and minimal effort for soil charcoal analyses considering the soil type and forest history

2017 ◽  
Vol 26 (6) ◽  
pp. 627-637 ◽  
Author(s):  
Thomas Feiss ◽  
Hélène Horen ◽  
Boris Brasseur ◽  
Jonathan Lenoir ◽  
Jérôme Buridant ◽  
...  
Keyword(s):  
Soil Type ◽  
Sampling Design ◽  
Forest History ◽  
Optimal Sampling ◽  
Soil Charcoal ◽  
Optimal Sampling Design ◽  
Minimal Effort

scholarly journals Planning of experiments for a nonautonomous ornstein-uhlenbeck process

2012 ◽  
Vol 51 (1) ◽  
pp. 101-113 ◽  
Author(s):  
Vladimír Lacko
Keyword(s):  
Sampling Design ◽  
Information Matrix ◽  
Mean Reversion ◽  
Time Dependent ◽  
Optimal Designs ◽  
Optimal Sampling ◽  
Uhlenbeck Process ◽  
Optimal Sampling Design ◽  
Ornstein Uhlenbeck Process ◽  
Planning Of Experiments

ABSTRACT We study exact optimal designs for processes governed by mean- -reversion stochastic differential equations with a time dependent volatility and known mean-reversion speed. It turns out that any mean-reversion It¯o process has a product covariance structure.We prove the existence of a nondegenerate optimal sampling design for the parameter estimation and derive the information matrix corresponding to the observation of the full path. The results are demonstrated on a process with exponential volatility.

scholarly journals A Proposal of Optimal Sampling Design Using Infrastructure Modularity

2017 ◽  
Vol 186 ◽  
pp. 559-566
Author(s):  
Antonietta Simone ◽  
Luigi Berardi ◽  
Daniele Laucelli ◽  
Orazio Giustolisi
Keyword(s):  
Sampling Design ◽  
Optimal Sampling ◽  
Optimal Sampling Design

scholarly journals A comparative study of optimization methods and conventional methods for sampling design in fishery-independent surveys

2009 ◽  
Vol 66 (9) ◽  
pp. 1873-1882 ◽  
Author(s):  
Yong Liu ◽  
Yong Chen ◽  
Jiahua Cheng
Keyword(s):  
Comparative Study ◽  
Sample Size ◽  
Simulation Study ◽  
Sampling Design ◽  
Optimization Methods ◽  
The Other ◽  
Optimal Sampling ◽  
Optimal Sampling Design ◽  
Conventional Methods ◽  
Better Than

Abstract Liu, Y., Chen, Y., and Cheng, J. 2009. A comparative study of optimization methods and conventional methods for sampling design in fishery-independent surveys. – ICES Journal of Marine Science, 66: 1873–1882. We have introduced and evaluated a procedure, the constrained spatial simulated annealing method, for developing an optimal sampling design for fishery-independent surveys. We used two criterion functions, minimization of the mean of the shortest distance (MMSD) and uniform distribution of point pairs for variogram estimation (WM), and three arrangements of the two criteria, all WM, all MMSD, and a combination of MMSD (2/3 of samples) and WM (1/3), to construct three optimized sampling designs (denoted as Designs I, II, and III, respectively). These three designs were compared in a simulation study with systematic sampling (Design IV) and stratified random sampling designs (Design V), commonly used in fishery-independent surveys. Three levels of sample size (small, medium, and large) were considered in the simulation study developed using a geostatistical approach. The results showed that for parameter estimation of the spatial covariance function, Design III was better than the other designs at relatively small sample size and Design II performed better than the other designs at relatively large sample size. For estimating fish stock abundance, the performance of the designs considered in this study can be ranked as follows: Design II > Design IV > Design III > Design V > Design I. It is clearly important to evaluate and improve sampling design based on historical survey data. Such a study allows us to identify an optimal sampling design to balance the quality of the data collected and the costs of the sampling programme, leading to the development and optimization of a sustainable and fishery-independent monitoring programme.

scholarly journals The optimal sampling design for littoral habitats modelling: A case study from the north-western Mediterranean

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0197234 ◽  
Author(s):  
Maria Elena Cefalì ◽  
Enric Ballesteros ◽  
Joan Lluís Riera ◽  
Eglantine Chappuis ◽  
Marc Terradas ◽  
...  
Keyword(s):  
Sampling Design ◽  
Western Mediterranean ◽  
Optimal Sampling ◽  
The North ◽  
Optimal Sampling Design ◽  
North Western ◽  
Littoral Habitats

Optimal sampling design to survey riparian bird populations with low detection probability

Bird Study ◽  
2020 ◽  
Vol 67 (2) ◽  
pp. 160-167
Author(s):  
Frank J. N. D'Amico ◽  
Claire Kermorvant ◽  
José M. Sánchez ◽  
Juan Arizaga
Keyword(s):  
Detection Probability ◽  
Sampling Design ◽  
Optimal Sampling ◽  
Bird Populations ◽  
Optimal Sampling Design
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