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- Institut für Biochemie und Biologie (128) (remove)
Zooplankton carcasses are ubiquitous in marine and freshwater systems, implicating the importance of non-predatory mortality, but both are often overlooked in ecological studies compared with predatory mortality. The development of several microscopic methods allows the distinction between live and dead zooplankton in field samples, and the reported percentages of dead zooplankton average 11.6 (minimum) to 59.8 (maximum) in marine environments, and 7.4 (minimum) to 47.6 (maximum) in fresh and inland waters. Common causes of non-predatory mortality among zooplankton include senescence, temperature change, physical and chemical stresses, parasitism and food-related factors. Carcasses resulting from non-predatory mortality may undergo decomposition leading to an increase in microbial production and a shift in microbial composition in the water column. Alternatively, sinking carcasses may contribute significantly to vertical carbon flux especially outside the phytoplankton growth seasons, and become a food source for the benthos. Global climate change is already altering freshwater ecosystems on multiple levels, and likely will have significant positive or negative effects on zooplankton non-predatory mortality. Better spatial and temporal studies of zooplankton carcasses and non-predatory mortality rates will improve our understanding of this important but under-appreciated topic.
Over the last two decades, macroecology the analysis of large-scale, multi-species ecological patterns and processes has established itself as a major line of biological research. Analyses of statistical links between environmental variables and biotic responses have long and successfully been employed as a main approach, but new developments are due to be utilized. Scanning the horizon of macroecology, we identified four challenges that will probably play a major role in the future. We support our claims by examples and bibliographic analyses. 1) Integrating the past into macroecological analyses, e.g. by using paleontological or phylogenetic information or by applying methods from historical biogeography, will sharpen our understanding of the underlying reasons for contemporary patterns. 2) Explicit consideration of the local processes that lead to the observed larger-scale patterns is necessary to understand the fine-grain variability found in nature, and will enable better prediction of future patterns (e.g. under environmental change conditions). 3) Macroecology is dependent on large-scale, high quality data from a broad spectrum of taxa and regions. More available data sources need to be tapped and new, small-grain large-extent data need to be collected. 4) Although macroecology already lead to mainstreaming cutting-edge statistical analysis techniques, we find that more sophisticated methods are needed to account for the biases inherent to sampling at large scale. Bayesian methods may be particularly suitable to address these challenges. To continue the vigorous development of the macroecological research agenda, it is time to address these challenges and to avoid becoming too complacent with current achievements.
In this opinion article we propose a scenario detailing how two crucial components have evolved simultaneously to ensure the transition of glycogen to starch in the cytosol of the Archaeplastida last common ancestor: (i) the recruitment of an enzyme from intracellular Chlamydiae pathogens to facilitate crystallization of alpha-glucan chains; and (ii) the evolution of novel types of polysaccharide (de)phosphorylating enzymes from preexisting glycogen (de)phosphorylation host pathways to allow the turnover of such crystals. We speculate that the transition to starch benefitted Archaeplastida in three ways: more carbon could be packed into osmotically inert material; the host could resume control of carbon assimilation from the chlamydial pathogen that triggered plastid endosymbiosis; and cyanobacterial photosynthate export could be integrated in the emerging Archaeplastida.
In ecology, biodiversity-ecosystem functioning (BEE) research has seen a shift in perspective from taxonomy to function in the last two decades, with successful application of trait-based approaches. This shift offers opportunities for a deeper mechanistic understanding of the role of biodiversity in maintaining multiple ecosystem processes and services. In this paper, we highlight studies that have focused on BEE of microbial communities with an emphasis on integrating trait-based approaches to microbial ecology. In doing so, we explore some of the inherent challenges and opportunities of understanding BEE using microbial systems. For example, microbial biologists characterize communities using gene phylogenies that are often unable to resolve functional traits. Additionally, experimental designs of existing microbial BEE studies are often inadequate to unravel BEE relationships. We argue that combining eco-physiological studies with contemporary molecular tools in a trait-based framework can reinforce our ability to link microbial diversity to ecosystem processes. We conclude that such trait-based approaches are a promising framework to increase the understanding of microbial BEE relationships and thus generating systematic principles in microbial ecology and more generally ecology.
Aim Biotic interactions within guilds or across trophic levels have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of species interaction distribution models (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities.
1. The health of managed and wild honeybee colonies appears to have declined substantially in Europe and the United States over the last decade. Sustainability of honeybee colonies is important not only for honey production, but also for pollination of crops and wild plants alongside other insect pollinators. A combination of causal factors, including parasites, pathogens, land use changes and pesticide usage, are cited as responsible for the increased colony mortality. 2. However, despite detailed knowledge of the behaviour of honeybees and their colonies, there are no suitable tools to explore the resilience mechanisms of this complex system under stress. Empirically testing all combinations of stressors in a systematic fashion is not feasible. We therefore suggest a cross-level systems approach, based on mechanistic modelling, to investigate the impacts of (and interactions between) colony and land management. 3. We review existing honeybee models that are relevant to examining the effects of different stressors on colony growth and survival. Most of these models describe honeybee colony dynamics, foraging behaviour or honeybee - varroa mite - virus interactions. 4. We found that many, but not all, processes within honeybee colonies, epidemiology and foraging are well understood and described in the models, but there is no model that couples in-hive dynamics and pathology with foraging dynamics in realistic landscapes. 5. Synthesis and applications. We describe how a new integrated model could be built to simulate multifactorial impacts on the honeybee colony system, using building blocks from the reviewed models. The development of such a tool would not only highlight empirical research priorities but also provide an important forecasting tool for policy makers and beekeepers, and we list examples of relevant applications to bee disease and landscape management decisions.
Winter is an important season for many limnological processes, which can range from biogeochemical transformations to ecological interactions. Interest in the structure and function of lake ecosystems under ice is on the rise. Although limnologists working at polar latitudes have a long history of winter work, the required knowledge to successfully sample under winter conditions is not widely available and relatively few limnologists receive formal training. In particular, the deployment and operation of equipment in below 0 degrees C temperatures pose considerable logistical and methodological challenges, as do the safety risks of sampling during the ice-covered period. Here, we consolidate information on winter lake sampling and describe effective methods to measure physical, chemical, and biological variables in and under ice. We describe variation in snow and ice conditions and discuss implications for sampling logistics and safety. We outline commonly encountered methodological challenges and make recommendations for best practices to maximize safety and efficiency when sampling through ice or deploying instruments in ice-covered lakes. Application of such practices over a broad range of ice-covered lakes will contribute to a better understanding of the factors that regulate lakes during winter and how winter conditions affect the subsequent ice-free period.
Compared to the well-studied open water of the "growing" season, under-ice conditions in lakes are characterized by low and rather constant temperature, slow water movements, limited light availability, and reduced exchange with the surrounding landscape. These conditions interact with ice-cover duration to shape microbial processes in temperate lakes and ultimately influence the phenology of community and ecosystem processes. We review the current knowledge on microorganisms in seasonally frozen lakes. Specifically, we highlight how under-ice conditions alter lake physics and the ways that this can affect the distribution and metabolism of auto-and heterotrophic microorganisms. We identify functional traits that we hypothesize are important for understanding under-ice dynamics and discuss how these traits influence species interactions. As ice coverage duration has already been seen to reduce as air temperatures have warmed, the dynamics of the under-ice microbiome are important for understanding and predicting the dynamics and functioning of seasonally frozen lakes in the near future.
Background In angiosperm evolution, autogamously selfing lineages have been derived from outbreeding ancestors multiple times, and this transition is regarded as one of the most common evolutionary tendencies in flowering plants. In most cases, it is accompanied by a characteristic set of morphological and functional changes to the flowers, together termed the selfing syndrome. Two major areas that have changed during evolution of the selfing syndrome are sex allocation to male vs. female function and flower morphology, in particular flower (mainly petal) size and the distance between anthers and stigma.
Scope A rich body of theoretical, taxonomic, ecological and genetic studies have addressed the evolutionary modification of these two trait complexes during or after the transition to selfing. Here, we review our current knowledge about the genetics and evolution of the selfing syndrome.
Conclusions We argue that because of its frequent parallel evolution, the selfing syndrome represents an ideal model for addressing basic questions about morphological evolution and adaptation in flowering plants, but that realizing this potential will require the molecular identification of more of the causal genes underlying relevant trait variation.
Potassium (K+) is inevitable for plant growth and development. It plays a crucial role in the regulation of enzyme activities, in adjusting the electrical membrane potential and the cellular turgor, in regulating cellular homeostasis and in the stabilization of protein synthesis. Uptake of K+ from the soil and its transport to growing organs is essential for a healthy plant development. Uptake and allocation of K+ are performed by K+ channels and transporters belonging to different protein families. In this review we summarize the knowledge on the versatile physiological roles of plant K+ channels and their behavior under stress conditions in the model plant Arabidopsis thaliana.