Long-term strategy for the statistical design of a forest health monitoring system

Schreuder, Hans T.; Czaplewski, Raymond L.

doi:10.1007/bf00551347

Cited by 23 publications

(5 citation statements)

References 10 publications

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“…Inference.-Sampling design is another important aspect of a monitoring protocol (Schreuder and Czaplewski 1993). The ability to draw useful inferences from monitoring data is affected by site selection.…”

Section: Discussionmentioning

confidence: 99%

“…Consistency is one of the most important aspects of a monitoring protocol (Schreuder and Czaplewski 1993). Sampling procedures that cannot be consistently applied across space (e.g., among sites) and time can (1) affect data quality and impede the ability to detect change, (2) limit comparisons across space and time, and (3) introduce systematic bias into the data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Analysis of Potential Stream Fish and Fish Habitat Monitoring Procedures for the Inland Northwest: Annual Report 1999.

Peterson¹,

Wollrab²

1999

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Analysis of Potential Stream Fish and Fish Habitat Monitoring Procedures for the Inland Northwest: Annual Report 1999.

Peterson¹,

Wollrab²

1999

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“…This requires stringent criteria in the survey. Some or all of the following criteria and thoughts should generally be considered for any reasonable survey (Schreuder and Czaplewski 1992):…”

Section: Information To Be Collectedmentioning

confidence: 99%

Statistical techniques for sampling and monitoring natural resources

Schreuder¹,

Ernst²,

Ramírez-Maldonado³

2004

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We present the statistical theory of inventory and monitoring from a probabilistic point of view. We start with the basics and show the interrelationships between designs and estimators illustrating the methods with a small artificial population as well as with a mapped realistic population. For such applications, useful open source software is given in Appendix 4. Various sources of ancillary information are described and applications of the sampling strategies are discussed. Classical and bootstrap variance estimators are discussed also. Numerous problems with solutions are given, often based on the experiences of the authors. Key additional references are cited as needed or desired. Problem: Two random samples of size n are taken without replacement from a population. By pure chance, the two samples are identical. Are the 2 estimates independent? Answer: Yes, they are. Problem. In the above example, how would you go about combining the two samples? Answer. The most sensible solution would probably be to treat it as a sample of size 2n with replacement even though each sample in itself was taken without replacement. The advantage of this is that the variance estimate would typically be an overestimate of the actual variance.

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“…Simplicity of design is an important component of an effective monitoring protocol (Schreuder and Czaplewski 1993, Schreuder 1994, Overton and Stehman 1996. Although previous studies have compared various partial replacement designs with simpler designs (e.g., Urquhart et al 1993, Urquhart andKincaid 1999), none have incorporated a combination of temporally related factors such as changes in density, distribution, and spatial clustering.…”

Section: Plot Selection Across Timementioning

confidence: 99%

Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations.

Thompson

2001

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Abstract. Monitoring population numbers is important for assessing trends and meeting various legislative mandates. However, sampling across time introduces a temporal aspect to survey design in addition to the spatial one. For instance, a sample that is initially representative may lose this attribute if there is a shift in numbers and/or spatial distribution in the underlying population that is not reflected in later sampled plots. Plot selection methods that account for this temporal variability will produce the best trend estimates. Consequently, I used simulation to compare bias and relative precision of estimates of population change among stratified and unstratified sampling designs based on permanent, temporary, and partial replacement plots under varying levels of spatial clustering, density, and temporal shifting of populations. Permanent plots produced more precise estimates of change than temporary plots across all factors. Further, permanent plots performed better than partial replacement plots except for high density (5 and 10 individuals per plot) and 25% -50% shifts in the population. Stratified designs always produced less precise estimates of population change for all three plot selection methods, and often produced biased change estimates and greatly inflated variance estimates under sampling with partial replacement. Hence, stratification that remains fixed across time should be avoided when monitoring populations that are likely to exhibit large changes in numbers and/or spatial distribution during the study period.

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Long-term strategy for the statistical design of a forest health monitoring system

Cited by 23 publications

References 10 publications

An Analysis of Potential Stream Fish and Fish Habitat Monitoring Procedures for the Inland Northwest: Annual Report 1999.

An Analysis of Potential Stream Fish and Fish Habitat Monitoring Procedures for the Inland Northwest: Annual Report 1999.

Statistical techniques for sampling and monitoring natural resources

Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations.

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