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The impact of temporally correlated fluctuating environments (coloured noise) on the extinction risk of populations has become a main focus in theoretical population ecology. In this study we particularly focus on the extinction risk in strongly autocorrelated environments. Here, in contrast to moderate autocorrelation, we found the extinction risk to be highly dependent on the process of noise generation, in particular on the method of variance scaling. Such variance scaling is commonly applied to avoid variance-driven biases when comparing the extinction risk for white and coloured noise. In this study we found an often-used scaling technique to lead to high variability in the resulting variances of different time series for strong auto-correlation eventually leading to deviations in the projected extinction risk. Therefore, we present an alternative method that always delivers the target variance, even in the case of strong temporal correlation. Furthermore, in contrast to the earlier method, our very intuitive method is not bound to auto-regressive processes but can be applied to all types of coloured noises. We recommend the method introduced here to be used when the target of interest is the effect of noise colour on extinction risk not obscured by any variance effects.
Predicting the breeding success of large raptors in arid southern Africa : a first assessment
(2006)
Raptors are often priorities for conservation efforts and breeding success is a target measure for assessing their conservation status. The breeding success of large raptors in and southern Africa is thought to be higher in years of high rainfall. While this correlation has been found in several studies, it has not yet been shown for data from a wider geographical area. In conservation research, it is important to explore the differences between spatially- separated populations to estimate and to compare their conservation status, and to deduce specific management strategies. Using a theoretical approach, we develop a simplistic model to explain the breeding success-rainfall relationship in large African raptors at larger spatial scales. Secondly, we validate this model and we show that the inclusion of field data leads to consistent predictions. In particular, we recommend that the average size of the 'effective territory' should be included in the relationship between annual rainfall and breeding success of raptors in and southern Africa. Accordingly, we suggest that breeding success is a function of precipitation and inter- nest distance. We present a new measure of territory quality depending on rainfall and territory size. We suggest that our model provides a useful first approach to assess breeding success in large raptors of and southern Africa. However, we strongly emphasise the need to gather more data to further verify our model. A general problem in conservation research is to compare the status of populations assessed in different study areas under changing environmental conditions. Our simplistic approach indicates that this problem can be overcome by using a weighted evaluation of a target measure (i.e. breeding success), taking regional differences into account
In a selected literature survey we reviewed studies on the habitat heterogeneity-animal species diversity relationship and evaluated whether there are uncertainties and biases in its empirical support. We reviewed 85 publications for the period 1960-2003. We screened each publication for terms that were used to define habitat heterogeneity, the animal species group and ecosystem studied, the definition of the structural variable, the measurement of vegetation structure and the temporal and spatial scale of the study. The majority of studies found a positive correlation between habitat heterogeneity/diversity and animal species diversity. However, empirical support for this relationship is drastically biased towards studies of vertebrates and habitats under anthropogenic influence. In this paper we show that ecological effects of habitat heterogeneity may vary considerably between species groups depending on whether structural attributes are perceived as heterogeneity or fragmentation. Possible effects may also vary relative to the structural variable measured. Based upon this, we introduce a classification framework that may be used for across-studies comparisons. Moreover, the effect of habitat heterogeneity for one species group may differ in relation to the spatial scale. In several studies, however, different species groups are closely linked to 'keystone structures' that determine animal species diversity by their presence. Detecting crucial keystone structures of the vegetation has profound implications for nature conservation and biodiversity management.
The predicted climate change causes deep concerns on the effects of increasing temperatures and changing precipitation patterns on species viability and, in turn, on biodiversity. Models of Population Viability Analysis (PVA) provide a powerful tool to assess the risk of species extinction. However, most PVA models do not take into account the potential effects of behavioural adaptations. Organisms might adapt to new environmental situations and thereby mitigate negative effects of climate change. To demonstrate such mitigation effects, we use an existing PVA model describing a population of the tawny eagle (Aquila rapax) in the southern Kalahari. This model does not include behavioural adaptations. We develop a new model by assuming that the birds enlarge their average territory size to compensate for lower amounts of precipitation. Here, we found the predicted increase in risk of extinction due to climate change to be much lower than in the original model. However, this "buffering" of climate change by behavioural adaptation is not very effective in coping with increasing interannual variances. We refer to further examples of ecological "buffering mechanisms" from the literature and argue that possible buffering mechanisms should be given due consideration when the effects of climate change on biodiversity are to be predicted. (c) 2004 Elsevier B.V. All rights reserved