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Imperfect observations in ecological studies

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Imperfect observations in ecological studies. / Shimadzu, Hideyasu; Foster, Scott D; Darnell, Ross.

In: Environmental and Ecological Statistics, Vol. 23, No. 3, 09.2016, p. 337-358.

Research output: Contribution to journalArticle

Harvard

Shimadzu, H, Foster, SD & Darnell, R 2016, 'Imperfect observations in ecological studies' Environmental and Ecological Statistics, vol. 23, no. 3, pp. 337-358. https://doi.org/10.1007/s10651-016-0342-2

APA

Shimadzu, H., Foster, S. D., & Darnell, R. (2016). Imperfect observations in ecological studies. Environmental and Ecological Statistics, 23(3), 337-358. https://doi.org/10.1007/s10651-016-0342-2

Vancouver

Shimadzu H, Foster SD, Darnell R. Imperfect observations in ecological studies. Environmental and Ecological Statistics. 2016 Sep;23(3):337-358. https://doi.org/10.1007/s10651-016-0342-2

Author

Shimadzu, Hideyasu ; Foster, Scott D ; Darnell, Ross. / Imperfect observations in ecological studies. In: Environmental and Ecological Statistics. 2016 ; Vol. 23, No. 3. pp. 337-358.

Bibtex - Download

@article{b94afa968b2f4b589919361c3cde90e6,
title = "Imperfect observations in ecological studies",
abstract = "Every ecological data set is the result of sampling the biota at sampling locations. Such samples are rarely a census of the biota at the samplinglocations and so will inherently contain biases. It is crucial to account for the bias induced by sampling if valid inference on biodiversity quantities isto be drawn from the observed data. The literature on accounting for sampling effects is large, but most are dedicated to the specific type of inferencerequired, the type of analysis performed and the type of survey undertaken. There is no general and systematic approach to sampling. Here, we explorethe unification of modelling approaches to account for sampling. We focus on individuals in ecological communities as the fundamental sampling element,and show that methods for accounting for sampling at the species level can be equated to individual sampling effects. Particular emphasis is given to the casewhere the probability of observing an individual, when it is present at the site sampled, is less than one. We call these situations ‘imperfect observations’.The proposed framework is easily implemented in standard software packages. We highlight some practical benefits of this formal framework: the ability ofpredicting the true number of individuals using an expectation that conditions on the observed data, and designing appropriate survey plans accounting for",
keywords = "Compound distributions, Detection probability, Ecological modelling, Marine surveys, Sampling, Species Distribution Models (SDMs)",
author = "Hideyasu Shimadzu and Foster, {Scott D} and Ross Darnell",
year = "2016",
month = "9",
doi = "10.1007/s10651-016-0342-2",
language = "English",
volume = "23",
pages = "337--358",
journal = "Environmental and Ecological Statistics",
issn = "1352-8505",
publisher = "Springer",
number = "3",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Imperfect observations in ecological studies

AU - Shimadzu, Hideyasu

AU - Foster, Scott D

AU - Darnell, Ross

PY - 2016/9

Y1 - 2016/9

N2 - Every ecological data set is the result of sampling the biota at sampling locations. Such samples are rarely a census of the biota at the samplinglocations and so will inherently contain biases. It is crucial to account for the bias induced by sampling if valid inference on biodiversity quantities isto be drawn from the observed data. The literature on accounting for sampling effects is large, but most are dedicated to the specific type of inferencerequired, the type of analysis performed and the type of survey undertaken. There is no general and systematic approach to sampling. Here, we explorethe unification of modelling approaches to account for sampling. We focus on individuals in ecological communities as the fundamental sampling element,and show that methods for accounting for sampling at the species level can be equated to individual sampling effects. Particular emphasis is given to the casewhere the probability of observing an individual, when it is present at the site sampled, is less than one. We call these situations ‘imperfect observations’.The proposed framework is easily implemented in standard software packages. We highlight some practical benefits of this formal framework: the ability ofpredicting the true number of individuals using an expectation that conditions on the observed data, and designing appropriate survey plans accounting for

AB - Every ecological data set is the result of sampling the biota at sampling locations. Such samples are rarely a census of the biota at the samplinglocations and so will inherently contain biases. It is crucial to account for the bias induced by sampling if valid inference on biodiversity quantities isto be drawn from the observed data. The literature on accounting for sampling effects is large, but most are dedicated to the specific type of inferencerequired, the type of analysis performed and the type of survey undertaken. There is no general and systematic approach to sampling. Here, we explorethe unification of modelling approaches to account for sampling. We focus on individuals in ecological communities as the fundamental sampling element,and show that methods for accounting for sampling at the species level can be equated to individual sampling effects. Particular emphasis is given to the casewhere the probability of observing an individual, when it is present at the site sampled, is less than one. We call these situations ‘imperfect observations’.The proposed framework is easily implemented in standard software packages. We highlight some practical benefits of this formal framework: the ability ofpredicting the true number of individuals using an expectation that conditions on the observed data, and designing appropriate survey plans accounting for

KW - Compound distributions

KW - Detection probability

KW - Ecological modelling

KW - Marine surveys

KW - Sampling

KW - Species Distribution Models (SDMs)

U2 - 10.1007/s10651-016-0342-2

DO - 10.1007/s10651-016-0342-2

M3 - Article

VL - 23

SP - 337

EP - 358

JO - Environmental and Ecological Statistics

T2 - Environmental and Ecological Statistics

JF - Environmental and Ecological Statistics

SN - 1352-8505

IS - 3

ER -

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ID: 240936839