Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms

McGarvey, Richard; Burch, P; Matthews, Janet

doi:10.1890/14-1973.1

Cited by 7 publications

(20 citation statements)

References 48 publications

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“…The literature shows that stratified-random sampling design performs better than random sampling design to estimate relative abundance of clustered taxa (i.e. higher accuracy in cover estimates in our case ; Cochran, 1946;McGarvey et al, 2016). When a community tends towards a homogeneous spatial distribution pattern, the different methods tend to perform equally.…”

Section: Accounting For Spatial Distribution Of Benthic Taxamentioning

confidence: 81%

“…heterogeneous distribution of the individuals). This can impact the effectiveness of spatially-structured sampling methods (Cochran, 1946;Dutilleul, 1993;Legendre et al, 2002;McGarvey et al, 2016) such as the way count points are projected on the images. The literature shows that stratified-random sampling design performs better than random sampling design to estimate relative abundance of clustered taxa (i.e.…”

Section: Accounting For Spatial Distribution Of Benthic Taxamentioning

confidence: 99%

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Optimizing image-based protocol to monitor macroepibenthic communities colonizing artificial structures

Taormina

Marzloff

Desroy

et al. 2020

ICES Journal of Marine Science

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Underwater imagery is increasingly used as an effective and repeatable method to monitor benthic ecosystems. Nevertheless, extracting ecologically relevant information from a large amount of raw images remains a time-consuming and somewhat laborious challenge. Thus, underwater imagery processing needs to strike a compromise between time-efficient image annotation and accuracy in quantifying benthic community composition. Designing and implementing robust image sampling and image annotation protocols are therefore critical to rationally address these trade-offs between ecological accuracy and processing time. The aim of this study was to develop and to optimize a reliable image scoring strategy based on the point count method using imagery data acquired on tide-swept macroepibenthic communities. Using a stepwise approach, we define an underwater imagery processing protocol that is effective in terms of (i) time allocated to overall image, (ii) reaching a satisfactory accuracy to estimate the occurrence of dominant benthic taxa, and (iii) adopting a sufficient taxonomic resolution to describe changes in community composition. We believe that our method is well adapted to investigate the composition of epibenthic communities on artificial reefs and can be useful in surveying colonization of other human structures (wind turbine foundations, pipelines, etc.) in coastal areas. Our strategy meets the increasing demand for inexpensive and time-effective tools for monitoring changes in benthic communities in a context of increasing coastal artificialization pressures.

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Section: Accounting For Spatial Distribution Of Benthic Taxamentioning

confidence: 81%

Section: Accounting For Spatial Distribution Of Benthic Taxamentioning

confidence: 99%

Optimizing image-based protocol to monitor macroepibenthic communities colonizing artificial structures

Taormina

Marzloff

Desroy

et al. 2020

ICES Journal of Marine Science

View full text Add to dashboard Cite

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“…As well as the density of an indicator across an area, its spatial distribution also plays a role in the power to detect changes in populations through time, with more aggregated distributions requiring increased sampling effort to achieve high precision and power (McGarvey et al 2016, Perkins et al 2016). Aggregation could be the result of covariate associations and the patchiness of those covariates or to behavioral aspects of species, such as schooling.…”

Section: Discussionmentioning

confidence: 99%

“…Spatial point process modeling indicated that for brown rockfish there was not a strong aggregation of brown rockfish across any of our sites. Where aggregation does occur, sampling effort needs to be higher to achieve the same level of precision in estimates (e.g., McGarvey et al 2016, Perkins et al 2016). The spatial parameters found in this study for brown rockfish were similar to other species, including canary rockfish ( Sebastes pinniger ), kelp greenling ( Hexagrammos decagrammus ), and lingcod ( Ophiodon elongatus ) that were modeled across the same sites ( unpublished data ).…”

Section: Discussionmentioning

confidence: 99%

“…The randomized systematic survey design employed in our study has been shown to be capable of relatively high precision, even when distributions are more highly clustered than those in our simulations (McGarvey et al 2016). Over many simulations our design covers a wide range of covariates, thereby further reducing the effect of the uncertainty around covariate coefficient importance.…”

Section: The Importance Of Covariates and Spatial Distributionsmentioning

confidence: 93%

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Quantifying the statistical power of monitoring programs for marine protected areas

Perkins

Prall

Chakraborty

et al. 2020

Ecological Applications

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Marine Protected Areas (MPAs) are increasingly established globally as a spatial management tool to aid in conservation and fisheries management objectives. Assessing whether MPAs are having the desired effects on populations requires effective monitoring programs. A cornerstone of an effective monitoring program is an assessment of the statistical power of sampling designs to detect changes when they occur. We present a novel approach to power assessment that combines spatial point process models, integral projection models (IPMs) and sampling simulations to assess the power of different sample designs across a network of MPAs. We focus on the use of remotely operated vehicle (ROV) video cameras as the sampling method, though the results could be extended to other sampling methods. We use empirical data from baseline surveys of an example indicator fish species across three MPAs in California, USA as a case study. Spatial models simulated time series of spatial distributions across sites that accounted for the effects of environmental covariates, while IPMs simulated expected trends over time in abundances and sizes of fish. We tested the power of different levels of sampling effort (i.e., the number of 500-m ROV transects) and temporal replication (every 1-3 yr) to detect expected post-MPA changes in fish abundance and biomass. We found that changes in biomass are detectable earlier than changes in abundance. We also found that detectability of MPA effects was higher in sites with higher initial densities. Increasing the sampling effort had a greater effect than increasing sampling frequency on the time taken to achieve high power. High power was best achieved by combining data from multiple sites. Our approach provides a powerful tool to explore the interaction between sampling effort, spatial distributions, population dynamics, and metrics for detecting change in previously fished populations.

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Efficiency of spatial sampling designs in estimating abundance and species richness of carabids at the landscape level

et al. 2023

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Context Declining biodiversity in agricultural landscapes has increased the need for research and monitoring of insect abundance and diversity at the landscape level. Objectives We investigated the accuracy of different spatial sampling designs in estimating landscape-level abundance and species richness of carabids in agricultural landscapes and, further, which sample size per landscape section was required and whether dominating land use or landscape subdivision affected accuracy and required sample size. Methods We developed a simulation model that created raster maps of agricultural landscapes, compiled local carabid communities (species composition and abundances) within raster cells and simulated the sampling of carabids with pitfall traps using different spatial sampling designs and sample sizes between 4 and 49 sampling points per landscape section. Spatial sampling designs included random, systematic grid-based, stratified and clustered schemes. Results To estimate landscape-level abundance, area-proportional stratified random sampling was most accurate followed by systematic grid-based designs. A sample size of 25 appeared to be the best trade-off between accuracy and sampling cost. Accuracy was not affected substantially by landscape characteristics in most cases. With respect to species richness, all designs except for clustered sampling had comparable accuracies, but even 49 samples were not sufficient to detect 80% of the species. Conclusion Systematic grid-based designs are generally recommendable for sampling of carabids in agricultural landscapes and, in case a carabid-specific habitat classification is available, area-proportional stratified random sampling provides optimal accuracy for estimating landscape-level abundance. For assessment of species richness, large sample size is more important than spatial sampling design.

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Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms

Cited by 7 publications

References 48 publications

Optimizing image-based protocol to monitor macroepibenthic communities colonizing artificial structures

Optimizing image-based protocol to monitor macroepibenthic communities colonizing artificial structures

Quantifying the statistical power of monitoring programs for marine protected areas

Efficiency of spatial sampling designs in estimating abundance and species richness of carabids at the landscape level

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