Spatially balanced survey designs for natural resources

Olsen, Anthony R.; Kincaid, Thomas M.; Payton, Quinn

doi:10.1017/cbo9781139022422.010

Cited by 44 publications

(25 citation statements)

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“…Although there was little theoretical support for it at the time, it has been useful to respond to a wide range of questions of interest to decision makers , it is similar to designs adopted by other researchers faced with sampling biodiversity over large areas (e.g. McDonald 2012, Olsen et al 2012, and it is a design that has since been approved by experienced modelers for continent-wide monitoring (e.g. Franklin 2009, Johnson 2012, Reynolds 2012.…”

Section: Discussionmentioning

confidence: 99%

“…We found that the only way to obtain integrated data collection was to provide standardized infrastructure that could be used by most researchers to answer a wide variety of questions. Henle et al (2006) present a European example of this approach, and Olsen et al (2012) give several examples from the USA. Most researchers had not adequately budgeted for field infrastructure and were happy to use what was available.…”

Section: Introductionmentioning

confidence: 99%

“…This dichotomy has been recognized by many researchers responsible for nationwide monitoring of biodiversity (e.g. Johnson 2012, Olsen et al 2012), but has only recently been included in reviews of best monitoring practice from a statistical perspective (e.g. Buckland et al 2011, Connolly and Dornelas 2011, Reynolds 2012), most of which had previously concentrated on idiosyncratic planning of projects with a common source of funding (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty and the design of in-situ biodiversity-monitoring programs

Haila

Henle²,

Apostolopoulou³

et al. 2014

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Citation: Magnusson WE (2014) Uncertainty and the design of in-situ biodiversity-monitoring programs. Nature Conservation 8: 77-94. doi: 10.3897/natureconservation.8.5929 AbstractThere are many techniques to deal with uncertainty when modeling data. However, there are many forms of uncertainty that cannot be dealt with mathematically that have to be taken into account when designing a biodiversity monitoring system. Some of these can be minimized by careful planning and quality control, but others have to be investigated during monitoring, and the scale and methods adjusted when necessary to meet objectives. Sources of uncertainty include uncertainty about stakeholders, who will monitor, what to sample, where to sample, causal relationships, species identifications, detectability, distributions, relationships with remote sensing, biotic concordance, complementarity, validity of stratification, and data quality and management. Failure to take into account any of these sources of uncertainty about how the data will be used can make monitoring nothing more than monitoring for the sake of monitoring, and I make recommendations as to how to reduce uncertainties. Some form of standardization is necessary, despite the multiple sources of uncertainty, and experience from RAPELD and other monitoring schemes indicates that spatial standardization is viable and helps reduce many sources of uncertainty.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty and the design of in-situ biodiversity-monitoring programs

Haila

Henle²,

Apostolopoulou³

et al. 2014

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“…Wadeable, perennial streams with Strahler orders 1 through 4 were the target population for the West Virginia Department of Environmental Protection's (WVDEP's) spatially balanced, probabilistic survey (WVDEP, ). This was a generalized random tessellation stratified (GRTS) survey design (Olsen et al ., ). The sample frame from which the probability sample was drawn was the geographic information system (GIS) shapefile of the National River and Streams Assessment based on the 1:100,000 NHDPlus (Michael Whitman, WVDEP Environmental Resources Analyst, November 5, 2012, personal communication).…”

Section: Methodsmentioning

confidence: 99%

“…For spatially balanced probabilistic stream surveys, ecoregions or watersheds are incorporated into the survey design either through stratification or unequal probability selection (Stevens and Olsen, ; Kincaid and Olsen, ; Olsen et al ., ). Consequently, such designs allow the estimates based on the probabilistically drawn sites from the linear stream network to be associated with areal units, such as ecoregions or watersheds.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Condition Assessment of Watersheds with Linked Micromaps

McManus

Pond

Reynolds

et al. 2016

J American Water Resour Assoc

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A challenge for statewide stream monitoring is visualizing the spatial and statistical characteristics of such data to compare the biotic condition of watersheds and relate that condition to watershed‐level estimates of instream variables. We used linked micromaps on stream survey data of 25 subbasins (766‐5,982 km2) for biotic condition, nine water quality, and two habitat variables. Subbasin biotic condition was negatively correlated with conductivity, magnesium and sulfate concentrations, and weakly positively correlated with habitat scores of sedimentation and embeddedness, with higher scores indicating better habitat. Positive spatial autocorrelation was detected among the subbasins in both habitat variables, iron concentration, pH, and exceedances of fecal coliform criteria as shown in linked micromaps. A spatial principal components analysis reduced the 11 environmental variables to two principal axes. The first axis synthesized a gradient of water quality and habitat scores among the subbasins. Subbasin biotic condition regressed on first axis subbasin scores had a significant, negative slope and accounted for 55% of the variation. Subbasins in degraded biotic condition had elevated conductivities and ion concentrations in northern and southern subbasins, and low habitat scores in western subbasins. Through linked micromaps, we compared the biotic condition among subbasins and identified stressors prevalent among subbasins that affected biotic condition.

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Micromaps

Symanzik¹,

Carr²,

McManus³

et al. 2017

Wiley StatsRef: Statistics Reference Online

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Micromap visualizations are an important tool for the geovisualization of areal data that are based on a series of small maps, the micromaps. The three major variations of micromap visualization designs are linked micromap (LM) plots, conditioned micromaps, and comparative micromaps. The most widely used are LM plots. These are based on several panel columns that consist of multiple small maps, that is, the micromaps, name identifiers of the area units, plotting symbols, and one or more statistical data columns. LM plots overcome some of the limitations of choropleth maps that are frequently used for the display of areal data, but that are usually limited to only one statistical variable at a time and that do not work well for visualizing small areas. Micromaps were originally created for the United States, but have seen extended use for countries in Europe (France and Germany), Asia (Korea and China), and South America (Brazil and Argentina) as well as for different nonadministrative boundaries, such as watersheds and ecoregions. Micromaps have been used for all kinds of areal data, such as environmental, agricultural, and medical, to name only a few.

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Spatially balanced survey designs for natural resources

Cited by 44 publications

References 0 publications

Uncertainty and the design of in-situ biodiversity-monitoring programs

Uncertainty and the design of in-situ biodiversity-monitoring programs

Multivariate Condition Assessment of Watersheds with Linked Micromaps

Micromaps

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