A critique of the use of jackknife and related non-parametric techniques to estimate species richness
Abstract Species richness in an assemblage is frequently used as a measure of biological diversity. However, observed species richness is strongly dependent on sample size. If more samples are collected, then more species are observed. Non-parametric species richness estimators, such as the jackknif...
Ausführliche Beschreibung
Autor*in: |
Melo, A. S. [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2004 |
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Anmerkung: |
© Akadémiai Kiadó, Budapest 2004 |
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Übergeordnetes Werk: |
Enthalten in: Community ecology - Budapest : Akadémiai Kiadó, 2000, 5(2004), 2 vom: Juni, Seite 149-157 |
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Übergeordnetes Werk: |
volume:5 ; year:2004 ; number:2 ; month:06 ; pages:149-157 |
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DOI / URN: |
10.1556/ComEc.5.2004.2.1 |
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Katalog-ID: |
SPR03875519X |
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100 | 1 | |a Melo, A. S. |e verfasserin |4 aut | |
245 | 1 | 2 | |a A critique of the use of jackknife and related non-parametric techniques to estimate species richness |
264 | 1 | |c 2004 | |
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520 | |a Abstract Species richness in an assemblage is frequently used as a measure of biological diversity. However, observed species richness is strongly dependent on sample size. If more samples are collected, then more species are observed. Non-parametric species richness estimators, such as the jackknife 1 and 2 and the Chao 1 and 2, are indicated in the literature as potential solutions to the problem of dependence of observed species richness on sampling effort. These methods are intended to estimate the total species richness in an area or assemblage with small sampling effort. Non-parametric estimators are based on the number of species observed, and the number of rare species in a sample, i.e., that occurred in one and/or two sampling units, or with one and/or two individuals. High estimates are produced when samples contain large proportions of rare species. Using a range of real datasets, 1 show that estimates produced by non-parametric methods are generally dependent on observed species richness. An implicit assumption of these non-parametric techniques is that the rare species curve should present high values at small sample sizes and decreasing values as sampling effort is increased. This assumption was observed in only one out of eight datasets presented. Instead, the rare species curve generally flattens off around a constant value as sampling effort increases. I conclude that non-parametric estimators are not reliable to estimate species richness in an assemblage when the rare species curve does not show a decreasing trend. Comments are made on the possibilities of using non-parametric estimators in the comparison of species assemblages. | ||
650 | 4 | |a Community ecology |7 (dpeaa)DE-He213 | |
650 | 4 | |a Diversity |7 (dpeaa)DE-He213 | |
650 | 4 | |a Log-series |7 (dpeaa)DE-He213 | |
650 | 4 | |a Rare species |7 (dpeaa)DE-He213 | |
650 | 4 | |a Sample size |7 (dpeaa)DE-He213 | |
650 | 4 | |a Species accumulation curve |7 (dpeaa)DE-He213 | |
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10.1556/ComEc.5.2004.2.1 doi (DE-627)SPR03875519X (SPR)ComEc.5.2004.2.1-e DE-627 ger DE-627 rakwb eng Melo, A. S. verfasserin aut A critique of the use of jackknife and related non-parametric techniques to estimate species richness 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest 2004 Abstract Species richness in an assemblage is frequently used as a measure of biological diversity. However, observed species richness is strongly dependent on sample size. If more samples are collected, then more species are observed. Non-parametric species richness estimators, such as the jackknife 1 and 2 and the Chao 1 and 2, are indicated in the literature as potential solutions to the problem of dependence of observed species richness on sampling effort. These methods are intended to estimate the total species richness in an area or assemblage with small sampling effort. Non-parametric estimators are based on the number of species observed, and the number of rare species in a sample, i.e., that occurred in one and/or two sampling units, or with one and/or two individuals. High estimates are produced when samples contain large proportions of rare species. Using a range of real datasets, 1 show that estimates produced by non-parametric methods are generally dependent on observed species richness. An implicit assumption of these non-parametric techniques is that the rare species curve should present high values at small sample sizes and decreasing values as sampling effort is increased. This assumption was observed in only one out of eight datasets presented. Instead, the rare species curve generally flattens off around a constant value as sampling effort increases. I conclude that non-parametric estimators are not reliable to estimate species richness in an assemblage when the rare species curve does not show a decreasing trend. Comments are made on the possibilities of using non-parametric estimators in the comparison of species assemblages. Community ecology (dpeaa)DE-He213 Diversity (dpeaa)DE-He213 Log-series (dpeaa)DE-He213 Rare species (dpeaa)DE-He213 Sample size (dpeaa)DE-He213 Species accumulation curve (dpeaa)DE-He213 Enthalten in Community ecology Budapest : Akadémiai Kiadó, 2000 5(2004), 2 vom: Juni, Seite 149-157 (DE-627)486725421 (DE-600)2187759-2 1588-2756 nnns volume:5 year:2004 number:2 month:06 pages:149-157 https://dx.doi.org/10.1556/ComEc.5.2004.2.1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 5 2004 2 06 149-157 |
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10.1556/ComEc.5.2004.2.1 doi (DE-627)SPR03875519X (SPR)ComEc.5.2004.2.1-e DE-627 ger DE-627 rakwb eng Melo, A. S. verfasserin aut A critique of the use of jackknife and related non-parametric techniques to estimate species richness 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest 2004 Abstract Species richness in an assemblage is frequently used as a measure of biological diversity. However, observed species richness is strongly dependent on sample size. If more samples are collected, then more species are observed. Non-parametric species richness estimators, such as the jackknife 1 and 2 and the Chao 1 and 2, are indicated in the literature as potential solutions to the problem of dependence of observed species richness on sampling effort. These methods are intended to estimate the total species richness in an area or assemblage with small sampling effort. Non-parametric estimators are based on the number of species observed, and the number of rare species in a sample, i.e., that occurred in one and/or two sampling units, or with one and/or two individuals. High estimates are produced when samples contain large proportions of rare species. Using a range of real datasets, 1 show that estimates produced by non-parametric methods are generally dependent on observed species richness. An implicit assumption of these non-parametric techniques is that the rare species curve should present high values at small sample sizes and decreasing values as sampling effort is increased. This assumption was observed in only one out of eight datasets presented. Instead, the rare species curve generally flattens off around a constant value as sampling effort increases. I conclude that non-parametric estimators are not reliable to estimate species richness in an assemblage when the rare species curve does not show a decreasing trend. Comments are made on the possibilities of using non-parametric estimators in the comparison of species assemblages. Community ecology (dpeaa)DE-He213 Diversity (dpeaa)DE-He213 Log-series (dpeaa)DE-He213 Rare species (dpeaa)DE-He213 Sample size (dpeaa)DE-He213 Species accumulation curve (dpeaa)DE-He213 Enthalten in Community ecology Budapest : Akadémiai Kiadó, 2000 5(2004), 2 vom: Juni, Seite 149-157 (DE-627)486725421 (DE-600)2187759-2 1588-2756 nnns volume:5 year:2004 number:2 month:06 pages:149-157 https://dx.doi.org/10.1556/ComEc.5.2004.2.1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 5 2004 2 06 149-157 |
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10.1556/ComEc.5.2004.2.1 doi (DE-627)SPR03875519X (SPR)ComEc.5.2004.2.1-e DE-627 ger DE-627 rakwb eng Melo, A. S. verfasserin aut A critique of the use of jackknife and related non-parametric techniques to estimate species richness 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest 2004 Abstract Species richness in an assemblage is frequently used as a measure of biological diversity. However, observed species richness is strongly dependent on sample size. If more samples are collected, then more species are observed. Non-parametric species richness estimators, such as the jackknife 1 and 2 and the Chao 1 and 2, are indicated in the literature as potential solutions to the problem of dependence of observed species richness on sampling effort. These methods are intended to estimate the total species richness in an area or assemblage with small sampling effort. Non-parametric estimators are based on the number of species observed, and the number of rare species in a sample, i.e., that occurred in one and/or two sampling units, or with one and/or two individuals. High estimates are produced when samples contain large proportions of rare species. Using a range of real datasets, 1 show that estimates produced by non-parametric methods are generally dependent on observed species richness. An implicit assumption of these non-parametric techniques is that the rare species curve should present high values at small sample sizes and decreasing values as sampling effort is increased. This assumption was observed in only one out of eight datasets presented. Instead, the rare species curve generally flattens off around a constant value as sampling effort increases. I conclude that non-parametric estimators are not reliable to estimate species richness in an assemblage when the rare species curve does not show a decreasing trend. Comments are made on the possibilities of using non-parametric estimators in the comparison of species assemblages. Community ecology (dpeaa)DE-He213 Diversity (dpeaa)DE-He213 Log-series (dpeaa)DE-He213 Rare species (dpeaa)DE-He213 Sample size (dpeaa)DE-He213 Species accumulation curve (dpeaa)DE-He213 Enthalten in Community ecology Budapest : Akadémiai Kiadó, 2000 5(2004), 2 vom: Juni, Seite 149-157 (DE-627)486725421 (DE-600)2187759-2 1588-2756 nnns volume:5 year:2004 number:2 month:06 pages:149-157 https://dx.doi.org/10.1556/ComEc.5.2004.2.1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 5 2004 2 06 149-157 |
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10.1556/ComEc.5.2004.2.1 doi (DE-627)SPR03875519X (SPR)ComEc.5.2004.2.1-e DE-627 ger DE-627 rakwb eng Melo, A. S. verfasserin aut A critique of the use of jackknife and related non-parametric techniques to estimate species richness 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest 2004 Abstract Species richness in an assemblage is frequently used as a measure of biological diversity. However, observed species richness is strongly dependent on sample size. If more samples are collected, then more species are observed. Non-parametric species richness estimators, such as the jackknife 1 and 2 and the Chao 1 and 2, are indicated in the literature as potential solutions to the problem of dependence of observed species richness on sampling effort. These methods are intended to estimate the total species richness in an area or assemblage with small sampling effort. Non-parametric estimators are based on the number of species observed, and the number of rare species in a sample, i.e., that occurred in one and/or two sampling units, or with one and/or two individuals. High estimates are produced when samples contain large proportions of rare species. Using a range of real datasets, 1 show that estimates produced by non-parametric methods are generally dependent on observed species richness. An implicit assumption of these non-parametric techniques is that the rare species curve should present high values at small sample sizes and decreasing values as sampling effort is increased. This assumption was observed in only one out of eight datasets presented. Instead, the rare species curve generally flattens off around a constant value as sampling effort increases. I conclude that non-parametric estimators are not reliable to estimate species richness in an assemblage when the rare species curve does not show a decreasing trend. Comments are made on the possibilities of using non-parametric estimators in the comparison of species assemblages. Community ecology (dpeaa)DE-He213 Diversity (dpeaa)DE-He213 Log-series (dpeaa)DE-He213 Rare species (dpeaa)DE-He213 Sample size (dpeaa)DE-He213 Species accumulation curve (dpeaa)DE-He213 Enthalten in Community ecology Budapest : Akadémiai Kiadó, 2000 5(2004), 2 vom: Juni, Seite 149-157 (DE-627)486725421 (DE-600)2187759-2 1588-2756 nnns volume:5 year:2004 number:2 month:06 pages:149-157 https://dx.doi.org/10.1556/ComEc.5.2004.2.1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 5 2004 2 06 149-157 |
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10.1556/ComEc.5.2004.2.1 doi (DE-627)SPR03875519X (SPR)ComEc.5.2004.2.1-e DE-627 ger DE-627 rakwb eng Melo, A. S. verfasserin aut A critique of the use of jackknife and related non-parametric techniques to estimate species richness 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest 2004 Abstract Species richness in an assemblage is frequently used as a measure of biological diversity. However, observed species richness is strongly dependent on sample size. If more samples are collected, then more species are observed. Non-parametric species richness estimators, such as the jackknife 1 and 2 and the Chao 1 and 2, are indicated in the literature as potential solutions to the problem of dependence of observed species richness on sampling effort. These methods are intended to estimate the total species richness in an area or assemblage with small sampling effort. Non-parametric estimators are based on the number of species observed, and the number of rare species in a sample, i.e., that occurred in one and/or two sampling units, or with one and/or two individuals. High estimates are produced when samples contain large proportions of rare species. Using a range of real datasets, 1 show that estimates produced by non-parametric methods are generally dependent on observed species richness. An implicit assumption of these non-parametric techniques is that the rare species curve should present high values at small sample sizes and decreasing values as sampling effort is increased. This assumption was observed in only one out of eight datasets presented. Instead, the rare species curve generally flattens off around a constant value as sampling effort increases. I conclude that non-parametric estimators are not reliable to estimate species richness in an assemblage when the rare species curve does not show a decreasing trend. Comments are made on the possibilities of using non-parametric estimators in the comparison of species assemblages. Community ecology (dpeaa)DE-He213 Diversity (dpeaa)DE-He213 Log-series (dpeaa)DE-He213 Rare species (dpeaa)DE-He213 Sample size (dpeaa)DE-He213 Species accumulation curve (dpeaa)DE-He213 Enthalten in Community ecology Budapest : Akadémiai Kiadó, 2000 5(2004), 2 vom: Juni, Seite 149-157 (DE-627)486725421 (DE-600)2187759-2 1588-2756 nnns volume:5 year:2004 number:2 month:06 pages:149-157 https://dx.doi.org/10.1556/ComEc.5.2004.2.1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 5 2004 2 06 149-157 |
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Enthalten in Community ecology 5(2004), 2 vom: Juni, Seite 149-157 volume:5 year:2004 number:2 month:06 pages:149-157 |
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S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A critique of the use of jackknife and related non-parametric techniques to estimate species richness</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2004</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Akadémiai Kiadó, Budapest 2004</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Species richness in an assemblage is frequently used as a measure of biological diversity. However, observed species richness is strongly dependent on sample size. If more samples are collected, then more species are observed. Non-parametric species richness estimators, such as the jackknife 1 and 2 and the Chao 1 and 2, are indicated in the literature as potential solutions to the problem of dependence of observed species richness on sampling effort. These methods are intended to estimate the total species richness in an area or assemblage with small sampling effort. Non-parametric estimators are based on the number of species observed, and the number of rare species in a sample, i.e., that occurred in one and/or two sampling units, or with one and/or two individuals. High estimates are produced when samples contain large proportions of rare species. Using a range of real datasets, 1 show that estimates produced by non-parametric methods are generally dependent on observed species richness. 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Melo, A. S. misc Community ecology misc Diversity misc Log-series misc Rare species misc Sample size misc Species accumulation curve A critique of the use of jackknife and related non-parametric techniques to estimate species richness |
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A critique of the use of jackknife and related non-parametric techniques to estimate species richness Community ecology (dpeaa)DE-He213 Diversity (dpeaa)DE-He213 Log-series (dpeaa)DE-He213 Rare species (dpeaa)DE-He213 Sample size (dpeaa)DE-He213 Species accumulation curve (dpeaa)DE-He213 |
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A critique of the use of jackknife and related non-parametric techniques to estimate species richness |
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A critique of the use of jackknife and related non-parametric techniques to estimate species richness |
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critique of the use of jackknife and related non-parametric techniques to estimate species richness |
title_auth |
A critique of the use of jackknife and related non-parametric techniques to estimate species richness |
abstract |
Abstract Species richness in an assemblage is frequently used as a measure of biological diversity. However, observed species richness is strongly dependent on sample size. If more samples are collected, then more species are observed. Non-parametric species richness estimators, such as the jackknife 1 and 2 and the Chao 1 and 2, are indicated in the literature as potential solutions to the problem of dependence of observed species richness on sampling effort. These methods are intended to estimate the total species richness in an area or assemblage with small sampling effort. Non-parametric estimators are based on the number of species observed, and the number of rare species in a sample, i.e., that occurred in one and/or two sampling units, or with one and/or two individuals. High estimates are produced when samples contain large proportions of rare species. Using a range of real datasets, 1 show that estimates produced by non-parametric methods are generally dependent on observed species richness. An implicit assumption of these non-parametric techniques is that the rare species curve should present high values at small sample sizes and decreasing values as sampling effort is increased. This assumption was observed in only one out of eight datasets presented. Instead, the rare species curve generally flattens off around a constant value as sampling effort increases. I conclude that non-parametric estimators are not reliable to estimate species richness in an assemblage when the rare species curve does not show a decreasing trend. Comments are made on the possibilities of using non-parametric estimators in the comparison of species assemblages. © Akadémiai Kiadó, Budapest 2004 |
abstractGer |
Abstract Species richness in an assemblage is frequently used as a measure of biological diversity. However, observed species richness is strongly dependent on sample size. If more samples are collected, then more species are observed. Non-parametric species richness estimators, such as the jackknife 1 and 2 and the Chao 1 and 2, are indicated in the literature as potential solutions to the problem of dependence of observed species richness on sampling effort. These methods are intended to estimate the total species richness in an area or assemblage with small sampling effort. Non-parametric estimators are based on the number of species observed, and the number of rare species in a sample, i.e., that occurred in one and/or two sampling units, or with one and/or two individuals. High estimates are produced when samples contain large proportions of rare species. Using a range of real datasets, 1 show that estimates produced by non-parametric methods are generally dependent on observed species richness. An implicit assumption of these non-parametric techniques is that the rare species curve should present high values at small sample sizes and decreasing values as sampling effort is increased. This assumption was observed in only one out of eight datasets presented. Instead, the rare species curve generally flattens off around a constant value as sampling effort increases. I conclude that non-parametric estimators are not reliable to estimate species richness in an assemblage when the rare species curve does not show a decreasing trend. Comments are made on the possibilities of using non-parametric estimators in the comparison of species assemblages. © Akadémiai Kiadó, Budapest 2004 |
abstract_unstemmed |
Abstract Species richness in an assemblage is frequently used as a measure of biological diversity. However, observed species richness is strongly dependent on sample size. If more samples are collected, then more species are observed. Non-parametric species richness estimators, such as the jackknife 1 and 2 and the Chao 1 and 2, are indicated in the literature as potential solutions to the problem of dependence of observed species richness on sampling effort. These methods are intended to estimate the total species richness in an area or assemblage with small sampling effort. Non-parametric estimators are based on the number of species observed, and the number of rare species in a sample, i.e., that occurred in one and/or two sampling units, or with one and/or two individuals. High estimates are produced when samples contain large proportions of rare species. Using a range of real datasets, 1 show that estimates produced by non-parametric methods are generally dependent on observed species richness. An implicit assumption of these non-parametric techniques is that the rare species curve should present high values at small sample sizes and decreasing values as sampling effort is increased. This assumption was observed in only one out of eight datasets presented. Instead, the rare species curve generally flattens off around a constant value as sampling effort increases. I conclude that non-parametric estimators are not reliable to estimate species richness in an assemblage when the rare species curve does not show a decreasing trend. Comments are made on the possibilities of using non-parametric estimators in the comparison of species assemblages. © Akadémiai Kiadó, Budapest 2004 |
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title_short |
A critique of the use of jackknife and related non-parametric techniques to estimate species richness |
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https://dx.doi.org/10.1556/ComEc.5.2004.2.1 |
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|
score |
7.401636 |