Using Covariance to Unravel the Effects of Meteorological Factors and Daily and Seasonal Rhythms

Abstract

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 m...

Differences of the Daily Flight Activity Rhythm in two Neotropical Stingless Bees (Hymenoptera, Apidae)

“…The species of Meliponini studied did not leave the nest before the temperature reached 19°C, but for M. scutellaris acrophase times generally occurred when the average temperature was around 24°C and the acrophases of F. doederleini occurred at higher temperature values (average 27°C). Similar results have been obtained for Frieseomelitta flight activity in other localities, such as in Ribeirão Preto (São Paulo State) in a study of F. doederleini (under laboratory controlled conditions) (Almeida, 2004) and of F. varia (under field conditions) (Nevill et al, 2004). Furthermore, positive correlation between bee activities of nest entrance with and without pollen and tempera-of nest entrance with and without pollen and tempera-and temperature may be observed in the studies of Souza et al (2006), in relation to M. asilvai in Pedra Branca.…”

“…When studying the Trigona mosquito (=Plebeia mosquito) (Smith), Lutz (1931) observed that light intensity interfered at the beginning and end of daily activities. Further-Furthermore, in Ribeirão Preto (SP), Nevill et al (2004) confirmed that this abiotic factor had a positive influence on the external activities of F. varia. According Corbet et al (1993) the daily activities of social bees would start at sunrise and would be more correlated with light intensity than the temperature.…”

The VENUS cabled observatory as a method to observe fish behaviour and species assemblages in a hypoxic fjord, Saanich Inlet (British Columbia, Canada)

“…While observations in the lab can bring important insights into the biology of individual species, they cannot supplement observations in the field, where the complexity of the ecosystem is influenced by species interactions and the unpredictability of the environment Nevill et al 2004). Thus, activity rhythms of animals can be masked by factors like food availability (Fernandéz de Miguel and Aréchiga 1994), changes in water masses DO concentrations (Schurmann et al 1998;Matabos et al 2011).…”

“…Thus, activity rhythms of animals can be masked by factors like food availability (Fernandéz de Miguel and Aréchiga 1994), changes in water masses DO concentrations (Schurmann et al 1998;Matabos et al 2011). The need to statistically separate the effect of meteorological factors and activity rhythms was recognized by Nevill et al (2004). His method allows one to determine how species activity covary with changing environmental conditions but is not applicable at the system level when dealing with a multispecies assemblage to understand ecosystem dynamics as whole.…”