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| Titel: |
Using Covariance to Unravel the Effects of Meteorological Factors and Daily and Seasonal Rhythms |
| Auteur: |
Nevill, Alan M. Teixeira, Laura V. Marques, Mirian D. Waterhouse, James M. |
| Verschenen in: |
Biological rhythm research |
| Paginering: | Jaargang 35 (2004) nr. 1-2 pagina's 159-169 |
| Jaar: |
2004-02 |
| Inhoud: |
Many factors contribute to the activity of animals in the wild. Whilst daily and seasonal rhythms are likely to be present, and to represent underlying biological functions, these will normally be modified by several factors in the environment. Important amongst these are light, temperature, humidity and whether or not it is raining. There is also the problem that the factors might interact, the effect of, say, time of day, being modified by the concomitant temperature. Separating out the effects of these different factors experimentally can be extremely arduous, if not impossible. An alternative approach is to treat the environmental factors as covariants, and then to separate out their effects from the biological ones by statistical means, using Analysis of Covariance, ANCOVA. The potential of this method is illustrated in the current report by a consideration of exits and entries of a colony of bees from their hive. Hourly measurements of this behaviour were taken during the daylight hours for three consecutive days in 11 consecutive months of the year. At the same time, ambient temperature, light intensity, humidity and whether or not it was raining were recorded. ANCOVA enabled the effects of temperature, humidity, light and rainfall upon the exits from the hive and entries back into it to be separated from the effects of time of day and time of the year. The analyses allowed those climatic variables, in addition to time-of-day and time-of-year effects, that influenced behaviour to be identified. Such climatic variables have not been previously isolated, and this might have lead to a misinterpretation of similar results in the past. Having separated out any effects of climatic variables (the covariates), the interaction between time of day and time of the year could then be investigated. Furthermore it has been possible to quantify the effects upon behaviour of each covariate. Rainfall was shown to decrease activity by more than 80%. For the other variables (temperature, humidity and light intensity), the statistical model allowed for the possibility that an increase in the variable initially produced a rise in activity but that this was followed, if the variable continued to rise, by a fall in activity. For light intensity, only a very modest increase in activity was found, and this continued throughout the range of intensities measured. However, for both temperature and humidity, the effects were more marked and showed 'turning points'. That is, activity increased as the ambient temperature rose until activity peaked at about 33-35°C; after which activity began to fall. Similarly, entries into the hive rose with increasing humidity up to a value of 48%, but fell thereafter. By contrast, exits from the hive increased with increasing humidity throughout the range measured. In the biological system tested, this form of analysis has produced valuable information about the way different factors influence activity in a field study. The results strongly suggest that the proposed methodology has a much wider and more general application. The way in which this type of analysis might be elaborated is discussed. |
| Uitgever: |
Taylor & Francis |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 14 van 14 gevonden artikelen
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