*David Hankin, Michael S. Mohr, and Kenneth B. Newman*

- Published in print:
- 2019
- Published Online:
- December 2019
- ISBN:
- 9780198815792
- eISBN:
- 9780191853463
- Item type:
- book

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198815792.001.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Ecology

We present a rigorous but understandable introduction to the field of sampling theory for ecologists and natural resource scientists. Sampling theory concerns itself with development of procedures ...
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We present a rigorous but understandable introduction to the field of sampling theory for ecologists and natural resource scientists. Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and statistically sound inferences about the population as a whole. The inferences fall into two broad categories: (a) estimation of simple descriptive population parameters, such as means, totals, or proportions, for variables of interest, and (b) estimation of uncertainty associated with estimated parameter values. Although the targets of estimation are few and simple, estimates of means, totals, or proportions see important and often controversial uses in management of natural resources and in fundamental ecological research, but few ecologists or natural resource scientists have formal training in sampling theory. We emphasize the classical design-based approach to sampling in which variable values associated with units are regarded as fixed and uncertainty of estimation arises via various randomization strategies that may be used to select samples. In addition to covering standard topics such as simple random, systematic, cluster, unequal probability (stressing the generality of Horvitz–Thompson estimation), multi-stage, and multi-phase sampling, we also consider adaptive sampling, spatially balanced sampling, and sampling through time, three areas of special importance for ecologists and natural resource scientists. The text is directed to undergraduate seniors, graduate students, and practicing professionals. Problems emphasize application of the theory and R programming in ecological and natural resource settings.Less

We present a rigorous but understandable introduction to the field of sampling theory for ecologists and natural resource scientists. Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and statistically sound inferences about the population as a whole. The inferences fall into two broad categories: (a) estimation of simple descriptive population parameters, such as means, totals, or proportions, for variables of interest, and (b) estimation of uncertainty associated with estimated parameter values. Although the targets of estimation are few and simple, estimates of means, totals, or proportions see important and often controversial uses in management of natural resources and in fundamental ecological research, but few ecologists or natural resource scientists have formal training in sampling theory. We emphasize the classical *design-based* approach to sampling in which variable values associated with units are regarded as fixed and uncertainty of estimation arises via various randomization strategies that may be used to select samples. In addition to covering standard topics such as simple random, systematic, cluster, unequal probability (stressing the generality of Horvitz–Thompson estimation), multi-stage, and multi-phase sampling, we also consider adaptive sampling, spatially balanced sampling, and sampling through time, three areas of special importance for ecologists and natural resource scientists. The text is directed to undergraduate seniors, graduate students, and practicing professionals. Problems emphasize application of the theory and R programming in ecological and natural resource settings.

*David G. Hankin, Michael S. Mohr, and Ken B. Newman*

- Published in print:
- 2019
- Published Online:
- December 2019
- ISBN:
- 9780198815792
- eISBN:
- 9780191853463
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198815792.003.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Ecology

Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make ...
More

Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and statistically sound inferences about the population as a whole. The Introduction orients the reader to the essential content and structure of the text, including (a) an overview of the nature and objectives of design-based sampling theory as well an explanation for why we emphasize the design-based approach to sampling, (b) organization of content by chapters, (c) summary of notational conventions adopted throughout, (d) suggestions for instructors using the text for a class, (e) assumed level of mathematics and statistics background expected from readers, and (f) discussion of how this text differs from other published sampling texts. The chapter concludes with a brief review of the history and development of sampling theory, including both the design-based theory (that is stressed in the text), and model-based prediction theory (that is motivated and illustrated in Chapter 7). The Introduction notes that the text presents small sample space numerical illustrations of the performance of many sampling strategies, and that the text adopts a logical and consistent notation which clarifies presentation of sampling theory concepts. The ecological/environmental/natural resources orientation of the text is most evident in problem exercises associated with most chapters, many of which have been motivated by practical resource applications.Less

Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and statistically sound inferences about the population as a whole. The Introduction orients the reader to the essential content and structure of the text, including (a) an overview of the nature and objectives of design-based sampling theory as well an explanation for why we emphasize the design-based approach to sampling, (b) organization of content by chapters, (c) summary of notational conventions adopted throughout, (d) suggestions for instructors using the text for a class, (e) assumed level of mathematics and statistics background expected from readers, and (f) discussion of how this text differs from other published sampling texts. The chapter concludes with a brief review of the history and development of sampling theory, including both the design-based theory (that is stressed in the text), and model-based prediction theory (that is motivated and illustrated in Chapter 7). The Introduction notes that the text presents small sample space numerical illustrations of the performance of many sampling strategies, and that the text adopts a logical and consistent notation which clarifies presentation of sampling theory concepts. The ecological/environmental/natural resources orientation of the text is most evident in problem exercises associated with most chapters, many of which have been motivated by practical resource applications.

*Leon A. Barmuta*

- Published in print:
- 2018
- Published Online:
- February 2019
- ISBN:
- 9780198766384
- eISBN:
- 9780191820908
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198766384.003.0003
- Subject:
- Biology, Aquatic Biology, Biodiversity / Conservation Biology

Ecological questions are better addressed by framing multiple working hypotheses and designing sampling to assess their relative merits, often using multiple lines of evidence. Most applications in ...
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Ecological questions are better addressed by framing multiple working hypotheses and designing sampling to assess their relative merits, often using multiple lines of evidence. Most applications in conservation are likely to differ from ‘pure’ ecology by preferring predictive rather than explanatory approaches. Often the complexity of freshwater ecosystems dictate combining both model- and design-based sampling strategies. Recent developments provide several options to improve ‘standard’ designs. Increasingly accessible geospatial databases have facilitated habitat-based strategies which can improve sampling efficiencies for both standing water bodies and dendritic networks of river systems. Modern organism-centric strategies strongly emphasise estimating detectability of the target species. This helps refine estimates for capture-mark-recapture (CMR) methods and sampling strategies for rare taxa. It also provides the basis for occupancy modelling, which is becoming increasingly popular for broad-scale issues. New technologies in remote sensing, videography, camera trapping, and eDNA will likely further accelerate specialised, more cost-effective sampling strategies.Less

Ecological questions are better addressed by framing multiple working hypotheses and designing sampling to assess their relative merits, often using multiple lines of evidence. Most applications in conservation are likely to differ from ‘pure’ ecology by preferring predictive rather than explanatory approaches. Often the complexity of freshwater ecosystems dictate combining both model- and design-based sampling strategies. Recent developments provide several options to improve ‘standard’ designs. Increasingly accessible geospatial databases have facilitated habitat-based strategies which can improve sampling efficiencies for both standing water bodies and dendritic networks of river systems. Modern organism-centric strategies strongly emphasise estimating detectability of the target species. This helps refine estimates for capture-mark-recapture (CMR) methods and sampling strategies for rare taxa. It also provides the basis for occupancy modelling, which is becoming increasingly popular for broad-scale issues. New technologies in remote sensing, videography, camera trapping, and eDNA will likely further accelerate specialised, more cost-effective sampling strategies.