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Ecologists urgently need a better ability to predict how environmental change affects biodiversity. We examine individual-based ecology (IBE), a research paradigm that promises better a predictive ability by using individual-based models (IBMs) to represent ecological dynamics as arising from how individuals interact with their environment and with each other. A key advantage of IBMs is that the basis for predictions-fitness maximization by individual organisms-is more general and reliable than the empirical relationships that other models depend on. Case studies illustrate the usefulness and predictive success of long-term IBE programs. The pioneering programs had three phases: conceptualization, implementation, and diversification. Continued validation of models runs throughout these phases. The breakthroughs that make IBE more productive include standards for describing and validating IBMs, improved and standardized theory for individual traits and behavior, software tools, and generalized instead of system-specific IBMs. We provide guidelines for pursuing IBE and a vision for future IBE research.
Red, orange or green snow is the macroscopic phenomenon comprising different eukaryotic algae. Little is known about the ecology and nutrient regimes in these algal communities. Therefore, eight snow algal communities from five intensively tinted snow fields in western Spitsbergen were analysed for nutrient concentrations and fatty acid (FA) composition. To evaluate the importance of a shift from green to red forms on the FA-variability of the field samples, four snow algal strains were grown under nitrogen replete and moderate light (+N+ML) or N-limited and high light (-N+HL) conditions. All eight field algal communities were dominated by red and orange cysts. Dissolved nutrient concentration of the snow revealed a broad range of NH4+ (<0.005-1.2 mg NI-1) and only low PO43- (< 18 mu g P I-1) levels. The external nutrient concentration did not reflect cellular nutrient ratios as C:N and C:P ratios of the communities were highest at locations containing relatively high concentrations of NH4- and PO43-. Molar N:P ratios ranged from 11 to 21 and did not suggest clear limitation of a single nutrient. On a per carbon basis, we found a 6-fold difference in total FA content between the eight snow algal communities, ranging from 50 to 300 mg FA g C-1. In multivariate analyses total FA content opposed the cellular N:C quota and a large part of the FA variability among field locations originated from the abundant FAs C181n-9, C18 2n-6, and C183n-3. Both field samples and snow algal strains grown under -N+HL conditions had high concentrations of C181n-9. FAs possibly accumulated due to the cessation of growth. Differences in color and nutritional composition between patches of snow algal communities within one snow field were not directly related to nutrient conditions. We propose that the highly patchy distribution of snow algae within and between snow fields may also result from differences in topographical and geological parameters such as slope, melting water rivulets, and rock formation.
The heterogeneity in species assemblages of epigeal spiders was studied in a natural forest and in a managed forest. Additionally the effects of small-scale microhabitat heterogeneity of managed and unmanaged forests were determined by analysing the spider assemblages of three different microhabitat structures (i. vegetation, ii. dead wood. iii. litter cover). The spider were collected in a block design by pitfall traps (n=72) in a 4-week interval. To reveal key environmental factors affecting the spider distribution abiotic and biotic habitat parameters (e.g. vegetation parameters, climate parameters, soil moisture) were assessed around each pitfall trap. A TWINSPAN analyses separated pitfall traps from the natural forest from traps of the managed forest. A subsequent discriminant analyses revealed that the temperature, the visible sky, the plant diversity and the mean diameter at breast height as key discriminant factors between the microhabitat groupings designated by the TWINSPAN analyses. Finally a Redundant analysis (RDA) was done revealing similar environmental factors responsible for the spider species distribution, as a good separation of the different forest types as well as the separation of the microhabitat groupings from the TWINSPAN. Overall the study revealed that the spider communities differed between the forest types as well as between the microhabitat structures and thus species distribution changed within a forest stand on a fine spatial scale. It was documented that the structure of managed forests affects the composition of spider assemblages compared to natural forests significantly and even small scale-heterogeneity seems to influence the spider species composition.
Understanding the interactions of predators and their prey and their responses to environmental changes is one of the striking features of ecological research. In this thesis, spring dynamics of phytoplankton and its consumers, zooplankton, were considered in dependence on the environmental conditions in a deep lake (Lake Constance) and a shallow marine water (mesocosms from Kiel Bight), using descriptive statistics, multiple regression models, and process-oriented dynamic simulation models. The development of the spring phytoplankton bloom, representing a dominant feature in the plankton dynamics in temperate and cold oceans and lakes, may depend on temperature, light, and mixing intensity, and the success of over-wintering phyto- and zooplankton. These factors are often correlated in the field. Unexpectedly, irradiance often dominated algal net growth rather than vertical mixing even in deep Lake Constance. Algal net losses from the euphotic layer to larger depth were induced by vertical mixing, but were compensated by the input from larger depth when algae were uniformly distributed over the water column. Dynamics of small, fast-growing algae were well predicted by abiotic variables, such as surface irradiance, vertical mixing intensity, and temperature. A simulation model additionally revealed that even in late winter, grazing may represent an important loss factor of phytoplankton during calm periods when losses due to mixing are small. The importance of losses by mixing and grazing changed rapidly as it depended on the variable mixing intensity. Higher temperature, lower global irradiance and enhanced mixing generated lower algal biomass and primary production in the dynamic simulation model. This suggests that potential consequences of climate change may partly counteract each other. The negative effect of higher temperatures on phytoplankton biomass was due to enhanced temperature-sensitive grazing losses. Comparing the results from deep Lake Constance to those of the shallow mesocosm experiments and simulations, confirmed the strong direct effect of light in contrast to temperature, and the importance of grazing already in early spring as soon as moderate algal biomasses developed. In Lake Constance, ciliates dominated the herbivorous zooplankton in spring. The start of ciliate net growth in spring was closely linked to that of edible algae, chlorophyll a and the vertical mixing intensity but independent of water temperature. The duration of ciliate dominance in spring was largely controlled by the highly variable onset of the phytoplankton bloom, and little by the less variable termination of the ciliate bloom by grazing of meta-zooplankton. During years with an extended spring bloom of algae and ciliates, they coexisted at relatively high biomasses over 15-30 generations, and internally forced species shifts were observed in both communities. Interception feeders alternated with filter feeders, and cryptomonads with non-cryptomonads in their relative importance. These dynamics were not captured by classical 1-predator-1-prey models which consistently predict pronounced predator-prey cycles or equilibria with either the predator or the prey dominating or suppressed. A multi-species predator-prey model with predator species differing in their food selectivity, and prey species in their edibility reproduced the observed patterns. Food-selectivity and edibility were related to the feeding and growth characteristics of the species, which represented ecological trade-offs. For example, the prey species with the highest edibility also had the highest maximum growth rate. Data and model revealed endogenous driven ongoing species alternations, which yielded a higher variability in species-specific biomasses than in total predator and prey biomass. This holds for a broad parameter space as long as the species differ functionally. A more sophisticated model approach enabled the simulation of a continuum of different functional types and adaptability of predator and prey communities to altered environmental conditions, and the maintenance of a rather low model complexity, i.e., low number of equations and free parameters. The community compositions were described by mean functional traits --- prey edibility and predator food-selectivity --- and their variances. The latter represent the functional diversity of the communities and thus, the potential for adaptation. Oscillations in the mean community trait values indicated species shifts. The community traits were related to growth and grazing characteristics representing similar trade-offs as in the multi-species model. The model reproduced the observed patterns, when nonlinear relationships between edibility and capacity, and edibility and food availability for the predator were chosen. A constant minimum amount of variance represented ongoing species invasions and thus, preserved a diversity which allows adaptation on a realistic time-span.
One of the most striking features of ecological systems is their ability to undergo sudden outbreaks in the population numbers of one or a small number of species. The similarity of outbreak characteristics, which is exhibited in totally different and unrelated (ecological) systems naturally leads to the question whether there are universal mechanisms underlying outbreak dynamics in Ecology. It will be shown into two case studies (dynamics of phytoplankton blooms under variable nutrients supply and spread of epidemics in networks of cities) that one explanation for the regular recurrence of outbreaks stems from the interaction of the natural systems with periodical variations of their environment. Natural aquatic systems like lakes offer very good examples for the annual recurrence of outbreaks in Ecology. The idea whether chaos is responsible for the irregular heights of outbreaks is central in the domain of ecological modeling. This question is investigated in the context of phytoplankton blooms. The dynamics of epidemics in networks of cities is a problem which offers many ecological and theoretical aspects. The coupling between the cities is introduced through their sizes and gives rise to a weighted network which topology is generated from the distribution of the city sizes. We examine the dynamics in this network and classified the different possible regimes. It could be shown that a single epidemiological model can be reduced to a one-dimensional map. We analyze in this context the dynamics in networks of weighted maps. The coupling is a saturation function which possess a parameter which can be interpreted as an effective temperature for the network. This parameter allows to vary continously the network topology from global coupling to hierarchical network. We perform bifurcation analysis of the global dynamics and succeed to construct an effective theory explaining very well the behavior of the system.