The consecutive disparity index, D
Temporal variability in ecological processes has attracted the attention of many disciplines in ecology, which has resulted in the development of several quantitative indices. The coefficient of variation (CV = standard deviation × mean-1) is still one of the most commonly used indices to assess tem...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:200033 |
| Acceso en línea: | https://ddd.uab.cat/record/200033 https://dx.doi.org/urn:doi:10.1002/ecs2.2527 |
| Access Level: | acceso abierto |
| Palabra clave: | Coefficient of variation Disparity Proportional variability Temporal dynamics |
| Sumario: | Temporal variability in ecological processes has attracted the attention of many disciplines in ecology, which has resulted in the development of several quantitative indices. The coefficient of variation (CV = standard deviation × mean-1) is still one of the most commonly used indices to assess temporal variability, despite being known to present several problems on its assessment (e.g., mean dependence or high sensitivity to rare events). The proportional variability (PV) index was developed to solve some of the CV's drawbacks, but, so far, no variability index takes into account the chronological order of the values in time series. In this paper, we introduce the consecutive disparity index (D), a temporal variability index that takes into account the chronological order of the values, assessing the average rate of change between consecutive values. We used computer simulations and empirical data for fruit production in trees, bird counts, and rodent captures to compare the behavior of D, PV, and CV under different scenarios. D was sensitive to changes in temporal autocorrelation in the negative autocorrelation range, and CV and PV were sensitive in the positive autocorrelation range despite not considering the chronological order of the values. The CV, however, was highly dependent on the mean of the time series, while D and PV were not. Our results demonstrate that, like PV, D solves many of the problems of the CV index while taking into account the chronological order of values in time series. The mathematical and statistical features of D make it a suitable index for analyzing temporal variability in a wide range of ecological studies. |
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