@phdthesis{Schwager2005, author = {Schwager, Monika}, title = {Climate change, variable colony sizes and temporal autocorrelation : consequences of living in changing environments}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-5744}, school = {Universit{\"a}t Potsdam}, year = {2005}, abstract = {Natural and human induced environmental changes affect populations at different time scales. If they occur in a spatial heterogeneous way, they cause spatial variation in abundance. In this thesis I addressed three topics, all related to the question, how environmental changes influence population dynamics. In the first part, I analysed the effect of positive temporal autocorrelation in environmental noise on the extinction risk of a population, using a simple population model. The effect of autocorrelation depended on the magnitude of the effect of single catastrophic events of bad environmental conditions on a population. If a population was threatened by extinction only, when bad conditions occurred repeatedly, positive autocorrelation increased extinction risk. If a population could become extinct, even if bad conditions occurred only once, positive autocorrelation decreased extinction risk. These opposing effects could be explained by two features of an autocorrelated time series. On the one hand, positive autocorrelation increased the probability of series of bad environmental conditions, implying a negative effect on populations. On the other hand, aggregation of bad years also implied longer periods with relatively good conditions. Therefore, for a given time period, the overall probability of occurrence of at least one extremely bad year was reduced in autocorrelated noise. This can imply a positive effect on populations. The results could solve a contradiction in the literature, where opposing effects of autocorrelated noise were found in very similar population models. In the second part, I compared two approaches, which are commonly used for predicting effects of climate change on future abundance and distribution of species: a "space for time approach", where predictions are based on the geographic pattern of current abundance in relation to climate, and a "population modelling approach" which is based on correlations between demographic parameters and the inter-annual variation of climate. In this case study, I compared the two approaches for predicting the effect of a shift in mean precipitation on a population of the sociable weaver Philetairus socius, a common colonially living passerine bird of semiarid savannahs of southern Africa. In the space for time approach, I compared abundance and population structure of the sociable weaver in two areas with highly different mean annual precipitation. The analysis showed no difference between the two populations. This result, as well as the wide distribution range of the species, would lead to the prediction of no sensitive response of the species to a slight shift in mean precipitation. In contrast, the population modelling approach, based on a correlation between reproductive success and rainfall, predicted a sensitive response in most model types. The inconsistency of predictions was confirmed in a cross-validation between the two approaches. I concluded that the inconsistency was caused, because the two approaches reflect different time scales. On a short time scale, the population may respond sensitively to rainfall. However, on a long time scale, or in a regional comparison, the response may be compensated or buffered by a variety of mechanisms. These may include behavioural or life history adaptations, shifts in the interactions with other species, or differences in the physical environment. The study implies that understanding, how such mechanisms work, and at what time scale they would follow climate change, is a crucial precondition for predicting ecological consequences of climate change. In the third part of the thesis, I tested why colony sizes of the sociable weaver are highly variable. The high variation of colony sizes is surprising, as in studies on coloniality it is often assumed that an optimal colony size exists, in which individual bird fitness is maximized. Following this assumption, the pattern of bird dispersal should keep colony sizes near an optimum. However, I showed by analysing data on reproductive success and survival that for the sociable weaver fitness in relation to colony size did not follow an optimum curve. Instead, positive and negative effects of living in large colonies overlaid each other in a way that fitness was generally close to one, and density dependence was low. I showed in a population model, which included an evolutionary optimisation process of dispersal that this specific shape of the fitness function could lead to a dispersal strategy, where the variation of colony sizes was maintained.}, subject = {Populationsbiologie}, language = {en} } @article{JeltschMoloneySchurretal.2008, author = {Jeltsch, Florian and Moloney, Kirk A. and Schurr, Frank Martin and K{\"o}chy, Martin and Schwager, Monika}, title = {The state of plant population modelling in light of environmental change}, issn = {1433-8319}, doi = {10.1016/j.ppees.2007.11.004}, year = {2008}, abstract = {Plant population modelling has been around since the 1970s, providing a valuable approach to understanding plant ecology from a mechanistic standpoint. It is surprising then that this area of research has not grown in prominence with respect to other approaches employed in modelling plant systems. In this review, we provide an analysis of the development and role of modelling in the field of plant population biology through an exploration of where it has been, where it is now and, in our opinion, where it should be headed. We focus, in particular, on the role plant population modelling could play in ecological forecasting, an urgent need given current rates of regional and global environmental change. We suggest that a critical element limiting the current application of plant population modelling in environmental research is the trade-off between the necessary resolution and detail required to accurately characterize ecological dynamics pitted against the goal of generality, particularly at broad spatial scales. In addition to suggestions how to overcome the current shortcoming of data on the process-level we discuss two emerging strategies that may offer a way to overcome the described limitation: (1) application of a modern approach to spatial scaling from local processes to broader levels of interaction and (2) plant functional-type modelling. Finally we outline what we believe to be needed in developing these approaches towards a 'science of forecasting'.}, language = {en} } @article{JeltschTewsBroseetal.2004, author = {Jeltsch, Florian and Tews, J{\"o}rg and Brose, Ulrich and Grimm, Volker and Tielb{\"o}rger, Katja and Wichmann, Matthias and Schwager, Monika}, title = {Animal species diversity driven by habitat heterogeneity/diversity : the importance of keystone structures}, year = {2004}, abstract = {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.}, language = {en} } @article{GrimmRevillaGroeneveldetal.2005, author = {Grimm, Volker and Revilla, Eloy and Groeneveld, J{\"u}rgen and Kramer-Schadt, Stephanie and Schwager, Monika and Tews, J{\"o}rg and Wichmann, Matthias and Jeltsch, Florian}, title = {Importance of buffer mechanisms for population viability analysis}, year = {2005}, language = {en} } @inproceedings{RossmanithBlaumKeiletal.2006, author = {Rossmanith, Eva and Blaum, Niels and Keil, Manfred and Langerwisch, F. and Meyer, Jork and Popp, Alexander and Schmidt, Michael and Schultz, Christoph and Schwager, Monika and Vogel, Melanie and Wasiolka, Bernd and Jeltsch, Florian}, title = {Scaling up local population dynamics to regional scales}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-7320}, year = {2006}, abstract = {In semi-arid savannas, unsustainable land use can lead to degradation of entire landscapes, e.g. in the form of shrub encroachment. This leads to habitat loss and is assumed to reduce species diversity. In BIOTA phase 1, we investigated the effects of land use on population dynamics on farm scale. In phase 2 we scale up to consider the whole regional landscape consisting of a diverse mosaic of farms with different historic and present land use intensities. This mosaic creates a heterogeneous, dynamic pattern of structural diversity at a large spatial scale. Understanding how the region-wide dynamic land use pattern affects the abundance of animal and plant species requires the integration of processes on large as well as on small spatial scales. In our multidisciplinary approach, we integrate information from remote sensing, genetic and ecological field studies as well as small scale process models in a dynamic region-wide simulation tool.