Population size and dispersal patterns for a Drimeotus (Coleoptera, Leiodidae, Leptodirini) cave population

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Abstract

Drimeotus viehmanni (Coleoptera, Leiodidae) is abundant in the cave Peştera cu Apă din Valea Leşului (Western Carpathians) and was chosen for a mark-release-resight experiment. The aims of the experiment were to estimate the size of the population and to analyze the dispersal patterns inside the cave, for conservation purposes. During the three years’ study, the observed abundance of D. viehmanni was significantly higher in summer compared to the winter season. The seasonal dynamics can not be explained by climate features such as temperature and air relative humidity which had low or no variation during all seasons. Few marked beetles were re-seen during the mark-resight experiment proving the existence of an important cave/subterranean population, which was estimated between 5,084 and 533,033 individuals. The marked individuals moved between neighbouring patches on a distance of 10 m over the same amount of time as on distances longer than 200 m. Dispersal inside the cave occurs during the winter months, which indicates non-continuous behaviour triggered by environmental features and involving only a negligible part of the population in the studied cave.

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Census error and the detection of density dependence.

Sally E. Green, Robert Freckleton, Brandon R. Sutherland … (2006)

1. Studies aiming to identify the prevalence and nature of density dependence in ecological populations have often used statistical analysis of ecological time-series of population counts. Such time-series are also being used increasingly to parameterize models that may be used in population management. 2. If time-series contain measurement errors, tests that rely on detecting a negative relationship between log population change and population size are biased and prone to spuriously detecting density dependence (Type I error). This is because the measurement error in density for a given year appears in the corresponding change in population density, with equal magnitude but opposite sign. 3. This effect introduces bias that may invalidate comparisons of ecological data with density-independent time-series. Unless census error can be accounted for, time-series may appear to show strongly density-dependent dynamics, even though the density-dependent signal may in reality be weak or absent. 4. We distinguish two forms of census error, both of which have serious consequences for detecting density dependence. 5. First, estimates of population density are based rarely on exact counts, but on samples. Hence there exists sampling error, with the level of error depending on the method employed and the number of replicates on which the population estimate is based. 6. Secondly, the group of organisms measured is often not a truly self-contained population, but part of a wider ecological population, defined in terms of location or behaviour. Consequently, the subpopulation studied may effectively be a sample of the population and spurious density dependence may be detected in the dynamics of a single subpopulation. In this case, density dependence is detected erroneously, even if numbers within the subpopulation are censused without sampling error. 7. In order to illustrate how process variation and measurement error may be distinguished we review data sets (counts of numbers of birds by single observers) for which both census error and long-term variance in population density can be estimated. 8. Tests for density dependence need to obviate the problem that measured population sizes are typically estimates rather than exact counts. It is possible that in some cases it may be possible to test for density dependence in the presence of unknown levels of census error, for example by uncovering nonlinearities in the density response. However, it seems likely that these may lack power compared with analyses that are able to explicitly include census error and we review some recently developed methods.