Roger A. Close, Serjoscha W. Evers, John Alroy & Richard =
J. Butler (2018)
v>
1.To infer genuine patterns of biodiversity change in the fossil rec=
ord, we must be able to accurately estimate relative differences in numbers=
of taxa (richness) despite considerable variation in sampling between time=
intervals. Popular subsampling (=3Dinterpolation) methods aim to standardi=
se diversity samples by rarefying the data to equal sample size or equal sa=
mple completeness (=3Dcoverage). Standardising by sample size is misleading=
because it compresses richness ratios, thereby flattening diversity curves=
. However, standardising by coverage reconstructs relative richness ratios =
with high accuracy. Asymptotic richness extrapolators are widely used in ec=
ology, but rarely applied to fossil data. However, a recently developed par=
ametric extrapolation method, TRiPS (True Richness estimation using Poisson=
Sampling), specifically aims to estimate the true richness of fossil assem=
blages.
2.Here, we examine the suit=
ability of a range of richness estimators (both interpolators and extrapola=
tors) for fossil datasets, using simulations and a novel method for compari=
ng the performance of richness estimators with empirical data. We construct=
ed sampling-standardised discovery curves (SSDCs) for two datasets, each sp=
anning 150 years of palaeontological research: Mesozoic dinosaurs at global=
scale, and Mesozoic=E2=80=93early Cenozoic tetrapods from North America. T=
hese approaches reveal how each richness estimator responds to both simulat=
ed best-case and empirical real-world accumulation of fossil occurrences.div>
3.We find that extrapolators can onl=
y truly standardise diversity data once sampling is sufficient for richness=
estimates to have asymptoted. Below this point, directly comparing extrapo=
lated estimates derived from samples of different sizes may not accurately =
reconstruct relative richness ratios. When abundance distributions are not =
perfectly flat and sampling is moderate to good, but not perfect, TRiPS doe=
s not extrapolate, because it overestimates binomial sampling probabilities=
. Coverage-based interpolators, by contrast, generally yield more stable su=
bsampled diversity estimates, even in the face of dramatic increases in fac=
e-value counts of species richness. Richness estimators that standardise by=
coverage are among the best currently-available methods for reconstructing=
deep-time biodiversity patterns. However, we recommend the use of sampling=
-standardised discovery curves to understand how biased reporting of fossil=
occurrences may affect sampling-standardised diversity estimates.
iv>
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_______________________________________________
Hangout mailing list
Hangout-at-nylxs.com
http://lists.mrbrklyn.com/mailman/listinfo/hangout
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Ben Creisler
bcreisler-at-gmail.com
Two new papers:
Free pdf:
Jonathan P. Tennant=E2=80=8B, Alfio Alessandro Chiarenza=E2=80=8B & Matthew=
Baron (2018)
How has our knowledge of dinosaur diversity through geologic time changed
through research history?
PeerJ 6:e4417
doi: https://urldefense.proofpoint.com/v2/url?u=3Dhttps-3A__doi.org_10.771=
7_peerj.4417&d=3DDwIFaQ&c=3DclK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=
=3DRy_mO4IFaUmGof_Yl9MyZgecRCKHn5g4z1CYJgFW9SI&m=3D6_PnexkIowZrvYWCcQXKPLoR=
tA9V1KhzNsKZOWv8JsA&s=3DMZ_I1dDM0-sQm5L5M8vS3PaplOJXBfjwBkHYuWg2J9U&e=3D
https://urldefense.proofpoint.com/v2/url?u=3Dhttps-3A__peerj.com_articles_4=
417_&d=3DDwIFaQ&c=3DclK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=3DRy_mO4I=
FaUmGof_Yl9MyZgecRCKHn5g4z1CYJgFW9SI&m=3D6_PnexkIowZrvYWCcQXKPLoRtA9V1KhzNs=
KZOWv8JsA&s=3Dw6WLKOEOgAypyA0vPgAt0BZ3GOTPaZseP4a-52whWhM&e=3D
https://urldefense.proofpoint.com/v2/url?u=3Dhttps-3A__peerj.com_articles_4=
417.pdf&d=3DDwIFaQ&c=3DclK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=3DRy_m=
O4IFaUmGof_Yl9MyZgecRCKHn5g4z1CYJgFW9SI&m=3D6_PnexkIowZrvYWCcQXKPLoRtA9V1Kh=
zNsKZOWv8JsA&s=3DZowWN0VX2njgGUNZFVRaCIznz8KPvDUsinl0PSTjWPA&e=3D
Assessments of dinosaur macroevolution at any given time can be biased by
the historical publication record. Recent studies have analysed patterns in
dinosaur diversity that are based on secular variations in the numbers of
published taxa. Many of these have employed a range of approaches that
account for changes in the shape of the taxonomic abundance curve, which
are largely dependent on databases compiled from the primary published
literature. However, how these =E2=80=98corrected=E2=80=99 diversity patter=
ns are
influenced by the history of publication remains largely unknown. Here, we
investigate the influence of publication history between 1991 and 2015 on
our understanding of dinosaur evolution using raw diversity estimates and
shareholder quorum subsampling for the three major subgroups: Ornithischia,
Sauropodomorpha, and Theropoda. We find that, while sampling generally
improves through time, there remain periods and regions in dinosaur
evolutionary history where diversity estimates are highly volatile (e.g.
the latest Jurassic of Europe, the mid-Cretaceous of North America, and the
Late Cretaceous of South America). Our results show that historical changes
in database compilation can often substantially influence our
interpretations of dinosaur diversity. =E2=80=98Global=E2=80=99 estimates o=
f diversity
based on the fossil record are often also based on incomplete, and distinct
regional signals, each subject to their own sampling history. Changes in
the record of taxon abundance distribution, either through discovery of new
taxa or addition of existing taxa to improve sampling evenness, are
important in improving the reliability of our interpretations of dinosaur
diversity. Furthermore, the number of occurrences and newly identified
dinosaurs is still rapidly increasing through time, suggesting that it is
entirely possible for much of what we know about dinosaurs at the present
to change within the next 20 years.
=3D=3D=3D=3D=3D=3D=3D=3D
Also for dinosaurs....
Roger A. Close, Serjoscha W. Evers, John Alroy & Richard J. Butler (2018)
How should we estimate diversity in the fossil record? Testing richness
estimators using sampling-standardised discovery curves.
Methods in Ecology and Evolution (advance online publication)
DOI: 10.1111/2041-210X.12987
https://urldefense.proofpoint.com/v2/url?u=3Dhttp-3A__onlinelibrary.wiley.c=
om_doi_10.1111_2041-2D210X.12987_full&d=3DDwIFaQ&c=3DclK7kQUTWtAVEOVIgvi0NU=
5BOUHhpN0H8p7CSfnc_gI&r=3DRy_mO4IFaUmGof_Yl9MyZgecRCKHn5g4z1CYJgFW9SI&m=3D6=
_PnexkIowZrvYWCcQXKPLoRtA9V1KhzNsKZOWv8JsA&s=3DJ-xBui0Wux3j2VvkD5bybcXMOCyA=
wxies-y7NygnIrs&e=3D
Summary
1.To infer genuine patterns of biodiversity change in the fossil record, we
must be able to accurately estimate relative differences in numbers of taxa
(richness) despite considerable variation in sampling between time
intervals. Popular subsampling (=3Dinterpolation) methods aim to standardise
diversity samples by rarefying the data to equal sample size or equal
sample completeness (=3Dcoverage). Standardising by sample size is misleadi=
ng
because it compresses richness ratios, thereby flattening diversity curves.
However, standardising by coverage reconstructs relative richness ratios
with high accuracy. Asymptotic richness extrapolators are widely used in
ecology, but rarely applied to fossil data. However, a recently developed
parametric extrapolation method, TRiPS (True Richness estimation using
Poisson Sampling), specifically aims to estimate the true richness of
fossil assemblages.
2.Here, we examine the suitability of a range of richness estimators (both
interpolators and extrapolators) for fossil datasets, using simulations and
a novel method for comparing the performance of richness estimators with
empirical data. We constructed sampling-standardised discovery curves
(SSDCs) for two datasets, each spanning 150 years of palaeontological
research: Mesozoic dinosaurs at global scale, and Mesozoic=E2=80=93early Ce=
nozoic
tetrapods from North America. These approaches reveal how each richness
estimator responds to both simulated best-case and empirical real-world
accumulation of fossil occurrences.
3.We find that extrapolators can only truly standardise diversity data once
sampling is sufficient for richness estimates to have asymptoted. Below
this point, directly comparing extrapolated estimates derived from samples
of different sizes may not accurately reconstruct relative richness ratios.
When abundance distributions are not perfectly flat and sampling is
moderate to good, but not perfect, TRiPS does not extrapolate, because it
overestimates binomial sampling probabilities. Coverage-based
interpolators, by contrast, generally yield more stable subsampled
diversity estimates, even in the face of dramatic increases in face-value
counts of species richness. Richness estimators that standardise by
coverage are among the best currently-available methods for reconstructing
deep-time biodiversity patterns. However, we recommend the use of
sampling-standardised discovery curves to understand how biased reporting
of fossil occurrences may affect sampling-standardised diversity estimates.
signature-3Futm-5Fmedium-3Demail-26utm-5Fsource-3Dlink-26utm-5Fcampaign-3Ds=
ig-2Demail-26utm-5Fcontent-3Dwebmail&d=3DDwIFaQ&c=3DclK7kQUTWtAVEOVIgvi0NU5=
BOUHhpN0H8p7CSfnc_gI&r=3DRy_mO4IFaUmGof_Yl9MyZgecRCKHn5g4z1CYJgFW9SI&m=3D6_=
PnexkIowZrvYWCcQXKPLoRtA9V1KhzNsKZOWv8JsA&s=3DwFmcVnFXbbcYynQ9gqJYiHhBtF6vf=
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Virus-free.
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PnexkIowZrvYWCcQXKPLoRtA9V1KhzNsKZOWv8JsA&s=3DwFmcVnFXbbcYynQ9gqJYiHhBtF6vf=
-D_dVesjO4PVi8&e=3D>
<#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>
--001a1149534abc366e056593571f
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Content-Transfer-Encoding: Quoted-printable
Two new papers:
Fr=
ee pdf:
Jonathan P. Tennant=E2=
=80=8B, Alfio Alessandro Chiarenza=E2=80=8B & Matthew Baron (2018)>
How has our knowledge of dinosaur diversity through geologic time cha=
nged through research history?=C2=A0
PeerJ 6:e4417
v2/url?u=3Dhttps-3A__peerj.com_articles_4417.pdf&d=3DDwMFaQ&c=3DclK7kQUTWtA=
VEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=3DRy_mO4IFaUmGof_Yl9MyZgecRCKHn5g4z1CYJg=
FW9SI&m=3D6_PnexkIowZrvYWCcQXKPLoRtA9V1KhzNsKZOWv8JsA&s=3DZowWN0VX2njgGUNZF=
VRaCIznz8KPvDUsinl0PSTjWPA&e=3D">https://peerj.com/articles/4417.pdfiv>
Assessments of dinosau=
r macroevolution at any given time can be biased by the historical publicat=
ion record. Recent studies have analysed patterns in dinosaur diversity tha=
t are based on secular variations in the numbers of published taxa. Many of=
these have employed a range of approaches that account for changes in the =
shape of the taxonomic abundance curve, which are largely dependent on data=
bases compiled from the primary published literature. However, how these =
=E2=80=98corrected=E2=80=99 diversity patterns are influenced by the histor=
y of publication remains largely unknown. Here, we investigate the influenc=
e of publication history between 1991 and 2015 on our understanding of dino=
saur evolution using raw diversity estimates and shareholder quorum subsamp=
ling for the three major subgroups: Ornithischia, Sauropodomorpha, and Ther=
opoda. We find that, while sampling generally improves through time, there =
remain periods and regions in dinosaur evolutionary history where diversity=
estimates are highly volatile (e.g. the latest Jurassic of Europe, the mid=
-Cretaceous of North America, and the Late Cretaceous of South America). Ou=
r results show that historical changes in database compilation can often su=
bstantially influence our interpretations of dinosaur diversity. =E2=80=98G=
lobal=E2=80=99 estimates of diversity based on the fossil record are often =
also based on incomplete, and distinct regional signals, each subject to th=
eir own sampling history. Changes in the record of taxon abundance distribu=
tion, either through discovery of new taxa or addition of existing taxa to =
improve sampling evenness, are important in improving the reliability of ou=
r interpretations of dinosaur diversity. Furthermore, the number of occurre=
nces and newly identified dinosaurs is still rapidly increasing through tim=
e, suggesting that it is entirely possible for much of what we know about d=
inosaurs at the present to change within the next 20 years.
<=
/div>
=3D=3D=3D=3D=3D=3D=3D=3D>
Also for dinosaurs....
div>
Roger A. Close, Serjoscha W. Evers, John Alroy & Richard =
J. Butler (2018)
How should we estimate diversity in the fossil r=
ecord? Testing richness estimators using sampling-standardised discovery cu=
rves.
Methods in Ecology and Evolution=C2=A0 (advance online publ=
ication)
DOI: 10.1111/2041-210X.12987
Summary
v>
1.To infer genuine patterns of biodiversity change in the fossil rec=
ord, we must be able to accurately estimate relative differences in numbers=
of taxa (richness) despite considerable variation in sampling between time=
intervals. Popular subsampling (=3Dinterpolation) methods aim to standardi=
se diversity samples by rarefying the data to equal sample size or equal sa=
mple completeness (=3Dcoverage). Standardising by sample size is misleading=
because it compresses richness ratios, thereby flattening diversity curves=
. However, standardising by coverage reconstructs relative richness ratios =
with high accuracy. Asymptotic richness extrapolators are widely used in ec=
ology, but rarely applied to fossil data. However, a recently developed par=
ametric extrapolation method, TRiPS (True Richness estimation using Poisson=
Sampling), specifically aims to estimate the true richness of fossil assem=
blages.
2.Here, we examine the suit=
ability of a range of richness estimators (both interpolators and extrapola=
tors) for fossil datasets, using simulations and a novel method for compari=
ng the performance of richness estimators with empirical data. We construct=
ed sampling-standardised discovery curves (SSDCs) for two datasets, each sp=
anning 150 years of palaeontological research: Mesozoic dinosaurs at global=
scale, and Mesozoic=E2=80=93early Cenozoic tetrapods from North America. T=
hese approaches reveal how each richness estimator responds to both simulat=
ed best-case and empirical real-world accumulation of fossil occurrences.div>
3.We find that extrapolators can onl=
y truly standardise diversity data once sampling is sufficient for richness=
estimates to have asymptoted. Below this point, directly comparing extrapo=
lated estimates derived from samples of different sizes may not accurately =
reconstruct relative richness ratios. When abundance distributions are not =
perfectly flat and sampling is moderate to good, but not perfect, TRiPS doe=
s not extrapolate, because it overestimates binomial sampling probabilities=
. Coverage-based interpolators, by contrast, generally yield more stable su=
bsampled diversity estimates, even in the face of dramatic increases in fac=
e-value counts of species richness. Richness estimators that standardise by=
coverage are among the best currently-available methods for reconstructing=
deep-time biodiversity patterns. However, we recommend the use of sampling=
-standardised discovery curves to understand how biased reporting of fossil=
occurrences may affect sampling-standardised diversity estimates.
iv>
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_______________________________________________
Hangout mailing list
Hangout-at-nylxs.com
http://lists.mrbrklyn.com/mailman/listinfo/hangout
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