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Statements

Subject Item
n2:RIV%2F60076658%3A12310%2F13%3A43885124%21RIV14-MSM-12310___
rdf:type
n7:Vysledek skos:Concept
rdfs:seeAlso
http://link.springer.com/article/10.1007%2Fs00442-012-2434-5
dcterms:description
A key challenge in the estimation of tropical arthropod species richness is the appropriate management of the large uncertainties associated with any model. Such uncertainties had largely been ignored until recently, when we attempted to account for uncertainty associated with model variables, using Monte Carlo analysis. This model is restricted by various assumptions. Here, we use a technique known as probability bounds analysis to assess the influence of assumptions about (1) distributional form and (2) dependencies between variables, and to construct probability bounds around the original model prediction distribution. The original Monte Carlo model yielded a median estimate of 6.1 million species, with a 90 % confidence interval of [3.6, 11.4]. Here we found that the probability bounds (p-bounds) surrounding this cumulative distribution were very broad, owing to uncertainties in distributional form and dependencies between variables. Replacing the implicit assumption of pure statistical independence between variables in the model with no dependency assumptions resulted in lower and upper p-bounds at 0.5 cumulative probability (i.e., at the median estimate) of 2.9-12.7 million. From here, replacing probability distributions with probability boxes, which represent classes of distributions, led to even wider bounds (2.4-20.0 million at 0.5 cumulative probability). Even the 100th percentile of the uppermost bound produced (i.e., the absolutely most conservative scenario) did not encompass the well-known hyper-estimate of 30 million species of tropical arthropods. This supports the lower estimates made by several authors over the last two decades. A key challenge in the estimation of tropical arthropod species richness is the appropriate management of the large uncertainties associated with any model. Such uncertainties had largely been ignored until recently, when we attempted to account for uncertainty associated with model variables, using Monte Carlo analysis. This model is restricted by various assumptions. Here, we use a technique known as probability bounds analysis to assess the influence of assumptions about (1) distributional form and (2) dependencies between variables, and to construct probability bounds around the original model prediction distribution. The original Monte Carlo model yielded a median estimate of 6.1 million species, with a 90 % confidence interval of [3.6, 11.4]. Here we found that the probability bounds (p-bounds) surrounding this cumulative distribution were very broad, owing to uncertainties in distributional form and dependencies between variables. Replacing the implicit assumption of pure statistical independence between variables in the model with no dependency assumptions resulted in lower and upper p-bounds at 0.5 cumulative probability (i.e., at the median estimate) of 2.9-12.7 million. From here, replacing probability distributions with probability boxes, which represent classes of distributions, led to even wider bounds (2.4-20.0 million at 0.5 cumulative probability). Even the 100th percentile of the uppermost bound produced (i.e., the absolutely most conservative scenario) did not encompass the well-known hyper-estimate of 30 million species of tropical arthropods. This supports the lower estimates made by several authors over the last two decades.
dcterms:title
Estimating global arthropod species richness: refining probabilistic models using probability bounds analysis Estimating global arthropod species richness: refining probabilistic models using probability bounds analysis
skos:prefLabel
Estimating global arthropod species richness: refining probabilistic models using probability bounds analysis Estimating global arthropod species richness: refining probabilistic models using probability bounds analysis
skos:notation
RIV/60076658:12310/13:43885124!RIV14-MSM-12310___
n7:predkladatel
n17:orjk%3A12310
n4:aktivita
n8:Z n8:I n8:P
n4:aktivity
I, P(EE.2.3.20.0064), P(GA206/09/0115), P(LH11008), Z(AV0Z50070508)
n4:cisloPeriodika
2
n4:dodaniDat
n10:2014
n4:domaciTvurceVysledku
n13:4876954
n4:druhVysledku
n19:J
n4:duvernostUdaju
n14:S
n4:entitaPredkladatele
n16:predkladatel
n4:idSjednocenehoVysledku
73245
n4:idVysledku
RIV/60076658:12310/13:43885124
n4:jazykVysledku
n20:eng
n4:klicovaSlova
Uncertainty; Monte Carlo; Model; Host specificity
n4:klicoveSlovo
n5:Monte%20Carlo n5:Host%20specificity n5:Model n5:Uncertainty
n4:kodStatuVydavatele
US - Spojené státy americké
n4:kontrolniKodProRIV
[4F2C47989041]
n4:nazevZdroje
Oecologia
n4:obor
n11:EH
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
11
n4:projekt
n18:EE.2.3.20.0064 n18:GA206%2F09%2F0115 n18:LH11008
n4:rokUplatneniVysledku
n10:2013
n4:svazekPeriodika
171
n4:tvurceVysledku
Miller, Scott E. Waters, Edward K. Yen, Jian D. L. Weiblen, George D. Samuelson, G. Allan Grimbacher, Peter S. Stork, Nigel E. Novotný, Vojtěch Basset, Yves Hamilton, Andrew J. Benke, Kurt K.
n4:wos
000313800600006
n4:zamer
n9:AV0Z50070508
s:issn
0029-8549
s:numberOfPages
9
n12:doi
10.1007/s00442-012-2434-5
n22:organizacniJednotka
12310