Stratified Sampling for Field Survey of Environmental Gradients in the Mojave Desert Ecoregion

Franklin, Janet; Keeler-Wolf, Todd; Thomas, Kathryn A.; Shaari, David; Stine, Peter; Michaelsen, Joel; Miller, Jennifer A.

doi:10.1007/978-1-4615-1523-4_14

Cited by 21 publications

(17 citation statements)

References 27 publications

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“…Current practice with respect to the data model is to emphasise the use of stratified sampling (Austin and Heyligers, 1989;Guisan and Zimmermann, 2000;Iverson and Pasad, 1998;Franklin et al, 2002;Cawsey et al, 2002), and to use either presence Á/absence data or cover/abundance values. Guisan and Harrell (2000) indicate that better statistical models (ordinal regression) are required for use with cover/abundance data.…”

Section: Data Model and Statistical Methodsmentioning

confidence: 99%

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

Austin¹

2002

Ecological Modelling

1,509

1,275

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Neglect of ecological knowledge is a limiting factor in the use of statistical modelling to predict species distribution. Three components are needed for statistical modelling, an ecological model concerning the ecological theory to be used or tested, a data model concerning the collection and measurement of the data, and a statistical model concerning the statistical theory and methods used. This component framework is reviewed with emphasis on ecological theory. The expected shape of a species response curve to an environmental gradient is a central assumption on which agreement has yet to be reached. The nature of the environmental predictors whether indirect variables, e.g. latitude that have no physiological impact on plants, or direct variables, e.g. temperature also influence the type of response expected. Straight-line relationships between organisms and environment are often used uncritically. Many users of canonical correlation analysis use linear (straight-line) functions to relate ordination axes to variables such as slope and aspect though this is not a necessary part of the method. Some statisticians have used straight lines for species/environment relationships without testing, when evaluating new statistical procedures. Assumptions used in one component often conflict with those in another component. Statistical models can be used to explore ecological theory. Skewed species response curves predominate contrary to the symmetric unimodal curves assumed by some statistical methods. Improvements in statistical modelling can be achieved based on ecological concepts. Examples include incorporating interspecific competition from dominant species; more proximal predictors based on water balance models and spatial autocorrelation procedures to accommodate non-equilibrium vegetation. #

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Section: Data Model and Statistical Methodsmentioning

confidence: 99%

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

Austin¹

2002

Ecological Modelling

1,509

1,275

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“…3). A community classification also is a first step in vegetation mapping (Franklin et al, 2001). There is currently great interest in vegetation mapping in the Mojave Desert for biological conservation and resource management in the face of climate change and proposed broad-scale renewable energy development (Abella, 2010).…”

Section: Applicationsmentioning

confidence: 99%

A hierarchical analysis of vegetation on a Mojave Desert landscape, USA

Abella

Prengaman

Embrey

et al. 2012

Journal of Arid Environments

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“…Stratified random sampling has long been advocated for biodiversity surveys (Austin and Heyligers 1989;Franklin et al 2001) and ensures an adequate representation of smaller regions or strata, which would not be sufficiently represented in a random sample to allow for statistical analysis of trends (Metzger et al 2013). At the same time, the sampling effort in larger regions can be reduced to limit the collection of redundant data.…”

Section: Where To Monitor? Sampling Design Scale and Landscape Grainmentioning

confidence: 99%

State-of-the-art practices in farmland biodiversity monitoring for North America and Europe

Herzog

Franklin

2016

Ambio

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Policy makers and farmers need to know the status of farmland biodiversity in order to meet conservation goals and evaluate management options. Based on a review of 11 monitoring programs in Europe and North America and on related literature, we identify the design choices or attributes of a program that balance monitoring costs and usefulness for stakeholders. A useful program monitors habitats, vascular plants, and possibly faunal groups (ecosystem service providers, charismatic species) using a stratified random sample of the agricultural landscape, including marginal and intensive regions. The size of landscape samples varies with the grain of the agricultural landscape; for example, samples are smaller in Europe and larger in North America. Raw data are collected in a rolling survey, which distributes sampling over several years. Sufficient practical experience is now available to implement broad monitoring schemes on both continents. Technological developments in remote sensing, metagenomics, and social media may offer new opportunities for affordable farmland biodiversity monitoring and help to lower the overall costs of monitoring programs.

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Stratified Sampling for Field Survey of Environmental Gradients in the Mojave Desert Ecoregion

Cited by 21 publications

References 27 publications

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

A hierarchical analysis of vegetation on a Mojave Desert landscape, USA

State-of-the-art practices in farmland biodiversity monitoring for North America and Europe

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