Rhythms are sequences repeated at relatively regular intervals. In the biological and medical literature a lot of work has been done in the area of endogenous and exogenous rhythms (e.g., Kurt, 1991; Turner, 1980; Camargo et al., 1999; Cederlund and Lemnell, 1980; Borbely and Tobler, 1996). Most of this work has been done concerning the day/night rhythm. Wildlife animals are influenced by a wide range of natural and human influenced rhythms. Table 2.3 provides some examples which go far beyond a simple light/dark phase analysis.
| Type | Examples |
| astronomical | 24h day, day-night, moon phase, year, tides (amphidromic points, cotidal contours), sunset, sunrise, altitude of sun above horizon, twilight start, end and length (civil, nautical and astronomical), day length, moon rise, moon set, percentage of moon illuminated, altitude of moon above horizon, moon phase |
| meteorological | weather (rain showers, wind, freezing, drying, rainy-seasons, snow cover), temperature |
| biological (a-spatial) | diving times, physiological circadian rhythms (e.g. fruit fly hatching) |
| biological (spatial) | abies fruiting, rutting season, migration |
| complex | tides |
| human | hunting season, working hours, traffic rush hours, week, holidays, vacations, spare time activities, train schedule (30'/first-last), closing time of restaurants or bars, second, minute, hour, calendar month, decade, century |
| technical | ``weir flooding'' |
Many of these rhythms are linked to quite complex spatial phenomena which require a lot of complex calculations to be analyzed, a task often beyond the capacity of a wildlife researcher. This is probably the main reason why such analysis is almost never performed in field research.
Rhythm is closely related to time lags. Analyzing rhythms and their origin often requires the inclusion of time lags. In some cases the amount of time used in an analysis to explore effects with a time lag might be known. In others it is unknown whether such an effect exists at all. In such cases good guessing or a tremendous amount of work might reveal such an effect. This makes the analysis task even more complex.
The title of this section might imply that all rhythms for the analysis of wildlife data could be determined in advance. This is of course not possible. Hence in a first stage of this research I want to include the most apparent rhythms in the framework, so that patterns caused by these can be easily discovered (Chapter 4). In a second stage, it will be the aim to build a generalized rhythm finder without predefined parameters (Chapter 5).