TY - JOUR A1 - Moenickes, Sylvia A1 - Schneider, Anne-Kathrin A1 - Muehle, Lesley A1 - Rohe, Lena A1 - Richter, Otto A1 - Suhling, Frank T1 - From population-level effects to individual response: modelling temperature dependence in Gammarus pulex JF - The journal of experimental biology N2 - Population-level effects of global warming result from concurrent direct and indirect processes. They are typically described by physiologically structured population models (PSPMs). Therefore, inverse modelling offers a tool to identify parameters of individual physiological processes through population-level data analysis, e. g. the temperature dependence of growth from size-frequency data of a field population. Here, we make use of experiments under laboratory conditions, in mesocosms and field monitoring to determine the temperature dependence of growth and mortality of Gammarus pulex. We found an optimum temperature for growth of approximately 17 degrees C and a related temperature coefficient, Q(10), of 1.5 degrees C(-1), irrespective of whether we classically fitted individual growth curves or applied inverse modelling based on PSPMs to laboratory data. From a comparison of underlying data sets we conclude that applying inverse modelling techniques to population-level data results in meaningful response parameters for physiological processes if additional temperature-driven effects, including within-population interaction, can be excluded or determined independently. If this is not the case, parameter estimates describe a cumulative response, e. g. comprising temperature-dependent resource dynamics. Finally, fluctuating temperatures in natural habitats increased the uncertainty in parameter values. Here, PSPM should be applied for virtual monitoring in order to determine a sampling scheme that comprises important dates to reduce parameter uncertainty. KW - temperature response KW - temperature coefficient KW - Q(10) KW - optimum temperature KW - parameter estimation KW - inverse modelling Y1 - 2011 U6 - https://doi.org/10.1242/jeb.061945 SN - 0022-0949 VL - 214 IS - 21 SP - 3678 EP - 3687 PB - Company of Biologists Limited CY - Cambridge ER - TY - JOUR A1 - Beck, Jan A1 - Ballesteros-Mejia, Liliana A1 - Buchmann, Carsten M. A1 - Dengler, Jürgen A1 - Fritz, Susanne A. A1 - Gruber, Bernd A1 - Hof, Christian A1 - Jansen, Florian A1 - Knapp, Sonja A1 - Kreft, Holger A1 - Schneider, Anne-Kathrin A1 - Winter, Marten A1 - Dormann, Carsten F. T1 - What's on the horizon for macroecology? JF - Ecography : pattern and diversity in ecology ; research papers forum N2 - 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. Y1 - 2012 U6 - https://doi.org/10.1111/j.1600-0587.2012.07364.x SN - 0906-7590 SN - 1600-0587 VL - 35 IS - 8 SP - 673 EP - 683 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Schneider, Anne-Kathrin A1 - Schröder-Esselbach, Boris T1 - Perspectives in modelling earthworm dynamics and their feedbacks with abiotic soil properties JF - Applied soil ecology : a section of agriculture, ecosystems & environment N2 - Effects of earthworms on soil abiotic properties are well documented from several decades of laboratory and mesocosm experiments, and they are supposed to affect large-scale soil ecosystem functioning. The prediction of the spatiotemporal occurrence of earthworms and the related functional effects in the field or at larger scales, however, is constrained by adequate modelling approaches. Correlative, phenomenological methods, such as species distribution models, facilitate the identification of factors that drive species' distributions. However, these methods ignore the ability of earthworms to select and modify their own habitat and therefore may lead to unreliable predictions. Understanding these feedbacks between earthworms and abiotic soil properties is a key requisite to better understand their spatiotemporal distribution as well as to quantify the various functional effects of earthworms in soil ecosystems. Process-based models that investigate either effects or responses of earthworms on soil environmental conditions are mostly applied in ecotoxicological and bioturbation studies. Process-based models that describe feedbacks between earthworms and soil abiotic properties explicitly are rare. In this review, we analysed 18 process-based earthworm dynamic modelling studies pointing out the current gaps and future challenges in feedback modelling. We identify three main challenges: (i) adequate and reliable process identification in model development at and across relevant spatiotemporal scales (individual behaviour and population dynamics of earthworms), (ii) use of information from different data sources in one model (laboratory or field experiments, earthworm species or functional type) and (iii) quantification of uncertainties in data (e.g. spatiotemporal variability of earthworm abundances and soil hydraulic properties) and derived parameters (e.g. population growth rate and hydraulic conductivity) that are used in the model. KW - Oligochaeta KW - Feedback biotic-abiotic KW - Functional effect KW - Population dynamics KW - Modeling KW - Ecosystem engineer Y1 - 2012 U6 - https://doi.org/10.1016/j.apsoil.2012.02.020 SN - 0929-1393 VL - 58 IS - 1 SP - 29 EP - 36 PB - Elsevier CY - Amsterdam ER -