McGill2003a

Référence

McGill, B.J. (2003) Does Mother Nature really prefer rare species or are log-left-skewed SADs a sampling artefact? Ecology Letters, 6(8):766-773.

Résumé

Intensively sampled species abundance distributions (SADs) show left-skew on a log scale. That is, there are too many rare species to fit a lognormal distribution. I propose that this log-left-skew might be a sampling artefact. Monte Carlo simulations show that taking progressively larger samples from a log-unskewed distribution (such as the lognormal) causes log-skew to decrease asymptotically (move towards -infinity) until it reaches the level of the underlying distribution (zero in this case). In contrast, accumulating certain types of repeated small samples results in a log-skew that becomes progressively more log-left-skewed to a level well beyond the underlying distribution. These repeated samples correspond to samples from the same site over many years or from many sites in 1 year. Data from empirical datasets show that log-skew generally goes from positive (right-skewed) to negative (left-skewed) as the number of temporally or spatially replicated samples increases. This suggests caution when interpreting log-left-skew as a pattern that needs biological interpretation.

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@ARTICLE { McGill2003a,
    AUTHOR = { McGill, B.J. },
    TITLE = { Does Mother Nature really prefer rare species or are log-left-skewed SADs a sampling artefact? },
    JOURNAL = { Ecology Letters },
    YEAR = { 2003 },
    VOLUME = { 6 },
    PAGES = { 766-773 },
    NUMBER = { 8 },
    MONTH = { aug },
    ABSTRACT = { Intensively sampled species abundance distributions (SADs) show left-skew on a log scale. That is, there are too many rare species to fit a lognormal distribution. I propose that this log-left-skew might be a sampling artefact. Monte Carlo simulations show that taking progressively larger samples from a log-unskewed distribution (such as the lognormal) causes log-skew to decrease asymptotically (move towards -infinity) until it reaches the level of the underlying distribution (zero in this case). In contrast, accumulating certain types of repeated small samples results in a log-skew that becomes progressively more log-left-skewed to a level well beyond the underlying distribution. These repeated samples correspond to samples from the same site over many years or from many sites in 1 year. Data from empirical datasets show that log-skew generally goes from positive (right-skewed) to negative (left-skewed) as the number of temporally or spatially replicated samples increases. This suggests caution when interpreting log-left-skew as a pattern that needs biological interpretation. },
    OWNER = { brugerolles },
    TIMESTAMP = { 2007.12.18 },
}

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