Study of biological communities subject to imperfect detection: bias and precision of community N-mixture abundance models in small-sample situations

Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy model...

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Bibliographic Details
Published inEcological research Vol. 31; no. 3; pp. 289 - 305
Main Authors Yamaura, Yuichi, Kéry, Marc, Andrew Royle, J
Format Journal Article
LanguageEnglish
Published Tokyo Springer Japan 01.05.2016
Blackwell Publishing Ltd
Subjects
Behavioral Sciences
Biomedical and Life Sciences
Community ecology
Count data
Data augmentation
Ecology
Estimating techniques
Evolutionary Biology
False negative
Forestry
Life Sciences
Mathematical models
Miyadi Award
Performance assessment
Plant Sciences
Population density
probability
Species diversity
Species richness
Zoology
β (beta) diversity
Data augmentation
Count data
β (beta) diversity
False negative
Species richness
Online AccessGet full text
ISSN0912-3814
1440-1703
DOI10.1007/s11284-016-1340-4

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Abstract Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance [Formula: see text]: 0.1, 0.5, 1, 5), detection probability [Formula: see text]: 0.1, 0.2, 0.5), and number of sampling sites (n ₛᵢₜₑ : 10, 20, 40) and visits (n ᵥᵢₛᵢₜ : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables [Formula: see text], [Formula: see text], n ₛᵢₜₑ , n ᵥᵢₛᵢₜ) increased. Detection probability [Formula: see text] was most important for the estimates of mean abundance, while [Formula: see text] was most influential for covariate effect and species richness estimates. For all parameters, increasing n ₛᵢₜₑ was more beneficial than increasing n ᵥᵢₛᵢₜ . Minimal conditions for obtaining adequate performance of community abundance models were n ₛᵢₜₑ ≥ 20, [Formula: see text] ≥ 0.2, and [Formula: see text] ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
AbstractList (ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image).Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance ...: 0.1, 0.5, 1, 5), detection probability ...: 0.1, 0.2, 0.5), and number of sampling sites (n site : 10, 20, 40) and visits (n visit : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables ..., ..., n site , n visit ) increased. Detection probability ... was most important for the estimates of mean abundance, while ... was most influential for covariate effect and species richness estimates. For all parameters, increasing n site was more beneficial than increasing n visit . Minimal conditions for obtaining adequate performance of community abundance models were n site greater than or equal to 20, ... greater than or equal to 0.2, and ... greater than or equal to 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate beta diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance [Formula: see text]: 0.1, 0.5, 1, 5), detection probability [Formula: see text]: 0.1, 0.2, 0.5), and number of sampling sites (n ₛᵢₜₑ : 10, 20, 40) and visits (n ᵥᵢₛᵢₜ : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables [Formula: see text], [Formula: see text], n ₛᵢₜₑ , n ᵥᵢₛᵢₜ) increased. Detection probability [Formula: see text] was most important for the estimates of mean abundance, while [Formula: see text] was most influential for covariate effect and species richness estimates. For all parameters, increasing n ₛᵢₜₑ was more beneficial than increasing n ᵥᵢₛᵢₜ . Minimal conditions for obtaining adequate performance of community abundance models were n ₛᵢₜₑ ≥ 20, [Formula: see text] ≥ 0.2, and [Formula: see text] ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance [Formula: see text]: 0.1, 0.5, 1, 5), detection probability [Formula: see text]: 0.1, 0.2, 0.5), and number of sampling sites (n ₛᵢₜₑ : 10, 20, 40) and visits (n ᵥᵢₛᵢₜ : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables [Formula: see text], [Formula: see text], n ₛᵢₜₑ , n ᵥᵢₛᵢₜ) increased. Detection probability [Formula: see text] was most important for the estimates of mean abundance, while [Formula: see text] was most influential for covariate effect and species richness estimates. For all parameters, increasing n ₛᵢₜₑ was more beneficial than increasing n ᵥᵢₛᵢₜ . Minimal conditions for obtaining adequate performance of community abundance models were n ₛᵢₜₑ ≥ 20, [Formula: see text] ≥ 0.2, and [Formula: see text] ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
Community N -mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance ( λ ¯ : 0.1, 0.5, 1, 5), detection probability ( p ¯ : 0.1, 0.2, 0.5), and number of sampling sites ( n site : 10, 20, 40) and visits ( n visit : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables ( λ ¯ , p ¯ , n site , n visit ) increased. Detection probability p ¯ was most important for the estimates of mean abundance, while λ ¯ was most influential for covariate effect and species richness estimates. For all parameters, increasing n site was more beneficial than increasing n visit . Minimal conditions for obtaining adequate performance of community abundance models were n site  ≥ 20, p ¯  ≥ 0.2, and λ ¯  ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N -mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
(ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image) Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance ...: 0.1, 0.5, 1, 5), detection probability ...: 0.1, 0.2, 0.5), and number of sampling sites (n ^sub site^: 10, 20, 40) and visits (n ^sub visit^: 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables ..., ..., n ^sub site^, n ^sub visit^) increased. Detection probability ... was most important for the estimates of mean abundance, while ... was most influential for covariate effect and species richness estimates. For all parameters, increasing n ^sub site^ was more beneficial than increasing n ^sub visit^. Minimal conditions for obtaining adequate performance of community abundance models were n ^sub site^ [greater than or equal to] 20, ... [greater than or equal to] 0.2, and ... [greater than or equal to] 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate [beta] diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
Community N‐mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance (λ¯: 0.1, 0.5, 1, 5), detection probability (p¯: 0.1, 0.2, 0.5), and number of sampling sites (nsite: 10, 20, 40) and visits (nvisit: 2, 3, 4) on the bias and precision of species‐level parameters (mean abundance and covariate effect) and a community‐level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables (λ¯, p¯, nsite, nvisit) increased. Detection probability p¯ was most important for the estimates of mean abundance, while λ¯ was most influential for covariate effect and species richness estimates. For all parameters, increasing nsite was more beneficial than increasing nvisit. Minimal conditions for obtaining adequate performance of community abundance models were nsite ≥ 20, p¯ ≥ 0.2, and λ¯ ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N‐mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
Community N ‐mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance : 0.1, 0.5, 1, 5), detection probability : 0.1, 0.2, 0.5), and number of sampling sites ( n site : 10, 20, 40) and visits ( n visit : 2, 3, 4) on the bias and precision of species‐level parameters (mean abundance and covariate effect) and a community‐level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables , , n site , n visit ) increased. Detection probability was most important for the estimates of mean abundance, while was most influential for covariate effect and species richness estimates. For all parameters, increasing n site was more beneficial than increasing n visit . Minimal conditions for obtaining adequate performance of community abundance models were n site ≥ 20, ≥ 0.2, and ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N ‐mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
Author Yamaura, Yuichi
Andrew Royle, J.
Kéry, Marc
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  fullname: Kéry, Marc
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Copyright The Author(s) 2016
2016 The Ecological Society of Japan
The Ecological Society of Japan 2016
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ID FETCH-LOGICAL-c4539-cf099ed4609a99556d318854338295175d63733b9dfbb1184585df5944ab9c533
IEDL.DBID 24P
ISSN 0912-3814
IngestDate Thu Jul 10 17:03:59 EDT 2025
Fri Jul 11 16:44:13 EDT 2025
Wed Aug 13 11:16:05 EDT 2025
Thu Apr 24 23:07:57 EDT 2025
Tue Jul 01 04:06:43 EDT 2025
Wed Jan 22 16:22:02 EST 2025
Fri Feb 21 02:40:35 EST 2025
Wed Mar 13 07:57:12 EDT 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 3
Keywords Data augmentation
Count data
β (beta) diversity
False negative
Species richness
Language English
License Attribution
http://creativecommons.org/licenses/by/4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c4539-cf099ed4609a99556d318854338295175d63733b9dfbb1184585df5944ab9c533
Notes http://dx.doi.org/10.1007/s11284-016-1340-4
Yuichi Yamaura is the recipient of the 18th Denzaburo Miyadi Award.
The online version of this article (doi:10.1007/s11284‐016‐1340‐4) contains supplementary material, which is available to authorized users.
Electronic supplementary material
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
OpenAccessLink https://onlinelibrary.wiley.com/doi/abs/10.1007%2Fs11284-016-1340-4
PQID 1782391908
PQPubID 31033
PageCount 17
ParticipantIDs proquest_miscellaneous_1803120137
proquest_miscellaneous_1787978285
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crossref_citationtrail_10_1007_s11284_016_1340_4
crossref_primary_10_1007_s11284_016_1340_4
wiley_primary_10_1007_s11284_016_1340_4_ERE0289
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PublicationDate May 2016
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  year: 2016
  text: May 2016
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PublicationTitle Ecological research
PublicationTitleAbbrev Ecol Res
PublicationYear 2016
Publisher Springer Japan
Blackwell Publishing Ltd
Publisher_xml – name: Springer Japan
– name: Blackwell Publishing Ltd
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Snippet Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within...
Community N -mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within...
Community N‐mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within...
Community N ‐mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within...
(ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image) Community N-mixture abundance models for replicated counts provide a powerful and...
(ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image).Community N-mixture abundance models for replicated counts provide a powerful and...
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SubjectTerms Behavioral Sciences
Biomedical and Life Sciences
Community ecology
Count data
Data augmentation
Ecology
Estimating techniques
Evolutionary Biology
False negative
Forestry
Life Sciences
Mathematical models
Miyadi Award
Performance assessment
Plant Sciences
Population density
probability
Species diversity
Species richness
Zoology
β (beta) diversity
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Title Study of biological communities subject to imperfect detection: bias and precision of community N-mixture abundance models in small-sample situations
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Volume 31
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