TY - JOUR A1 - Sporbert, Maria A1 - Jakubka, Desiree A1 - Bucher, Solveig Franziska A1 - Hensen, Isabell A1 - Freiberg, Martin A1 - Heubach, Katja A1 - König, Andreas A1 - Nordt, Birgit A1 - Plos, Carolin A1 - Blinova, Ilona A1 - Bonn, Aletta A1 - Knickmann, Barbara A1 - Koubek, Tomáš A1 - Linstädter, Anja A1 - Mašková, Tereza A1 - Primack, Richard B. A1 - Rosche, Christoph A1 - Shah, Manzoor A. A1 - Stevens, Albert-Dieter A1 - Tielbörger, Katja A1 - Träger, Sabrina A1 - Wirth, Christian A1 - Römermann, Christine T1 - Functional traits influence patterns in vegetative and reproductive plant phenology - a multi-botanical garden study JF - New phytologist N2 - Phenology has emerged as key indicator of the biological impacts of climate change, yet the role of functional traits constraining variation in herbaceous species' phenology has received little attention. Botanical gardens are ideal places in which to investigate large numbers of species growing under common climate conditions. We ask whether interspecific variation in plant phenology is influenced by differences in functional traits. We recorded onset, end, duration and intensity of initial growth, leafing out, leaf senescence, flowering and fruiting for 212 species across five botanical gardens in Germany. We measured functional traits, including plant height, absolute and specific leaf area, leaf dry matter content, leaf carbon and nitrogen content and seed mass and accounted for species' relatedness. Closely related species showed greater similarities in timing of phenological events than expected by chance, but species' traits had a high degree of explanatory power, pointing to paramount importance of species' life-history strategies. Taller plants showed later timing of initial growth, and flowered, fruited and underwent leaf senescence later. Large-leaved species had shorter flowering and fruiting durations. Taller, large-leaved species differ in their phenology and are more competitive than smaller, small-leaved species. We assume climate warming will change plant communities' competitive hierarchies with consequences for biodiversity. KW - botanical gardens KW - first flowering day KW - growing season length KW - leaf KW - traits KW - PhenObs phenological network KW - phylogeny Y1 - 2022 U6 - https://doi.org/10.1111/nph.18345 SN - 0028-646X SN - 1469-8137 VL - 235 IS - 6 SP - 2199 EP - 2210 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Kissling, W. D. A1 - Dormann, Carsten F. A1 - Groeneveld, Juergen A1 - Hickler, Thomas A1 - Kühn, Ingolf A1 - McInerny, Greg J. A1 - Montoya, Jose M. A1 - Römermann, Christine A1 - Schiffers, Katja A1 - Schurr, Frank Martin A1 - Singer, Alexander A1 - Svenning, Jens-Christian A1 - Zimmermann, Niklaus E. A1 - O'Hara, Robert B. T1 - Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents JF - Journal of biogeography N2 - 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. KW - Community ecology KW - ecological networks KW - global change KW - guild assembly KW - multidimensional complexity KW - niche theory KW - prediction KW - species distribution model KW - species interactions KW - trait-based community modules Y1 - 2012 U6 - https://doi.org/10.1111/j.1365-2699.2011.02663.x SN - 0305-0270 VL - 39 IS - 12 SP - 2163 EP - 2178 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Dunker, Susanne A1 - Boyd, Matthew A1 - Durka, Walter A1 - Erler, Silvio A1 - Harpole, W. Stanley A1 - Henning, Silvia A1 - Herzschuh, Ulrike A1 - Hornick, Thomas A1 - Knight, Tiffany A1 - Lips, Stefan A1 - Mäder, Patrick A1 - Švara, Elena Motivans A1 - Mozarowski, Steven A1 - Rakosy, Demetra A1 - Römermann, Christine A1 - Schmitt-Jansen, Mechthild A1 - Stoof-Leichsenring, Kathleen A1 - Stratmann, Frank A1 - Treudler, Regina A1 - Virtanen, Risto A1 - Wendt-Potthoff, Katrin A1 - Wilhelm, Christian T1 - The potential of multispectral imaging flow cytometry for environmental monitoring JF - Cytometry part A N2 - Environmental monitoring involves the quantification of microscopic cells and particles such as algae, plant cells, pollen, or fungal spores. Traditional methods using conventional microscopy require expert knowledge, are time-intensive and not well-suited for automated high throughput. Multispectral imaging flow cytometry (MIFC) allows measurement of up to 5000 particles per second from a fluid suspension and can simultaneously capture up to 12 images of every single particle for brightfield and different spectral ranges, with up to 60x magnification. The high throughput of MIFC has high potential for increasing the amount and accuracy of environmental monitoring, such as for plant-pollinator interactions, fossil samples, air, water or food quality that currently rely on manual microscopic methods. Automated recognition of particles and cells is also possible, when MIFC is combined with deep-learning computational techniques. Furthermore, various fluorescence dyes can be used to stain specific parts of the cell to highlight physiological and chemical features including: vitality of pollen or algae, allergen content of individual pollen, surface chemical composition (carbohydrate coating) of cells, DNA- or enzyme-activity staining. Here, we outline the great potential for MIFC in environmental research for a variety of research fields and focal organisms. In addition, we provide best practice recommendations. KW - environmental monitoring KW - imaging flow cytometry KW - plant traits Y1 - 2022 U6 - https://doi.org/10.1002/cyto.a.24658 SN - 1552-4922 SN - 1552-4930 VL - 101 IS - 9 SP - 782 EP - 799 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Dormann, Carsten F. A1 - Schymanski, Stanislaus J. A1 - Cabral, Juliano Sarmento A1 - Chuine, Isabelle A1 - Graham, Catherine A1 - Hartig, Florian A1 - Kearney, Michael A1 - Morin, Xavier A1 - Römermann, Christine A1 - Schröder-Esselbach, Boris A1 - Singer, Alexander T1 - Correlation and process in species distribution models: bridging a dichotomy JF - Journal of biogeography N2 - Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlativeprocess spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions. KW - Hypothesis generation KW - mechanistic model KW - parameterization KW - process-based model KW - species distribution model KW - SDM KW - uncertainty KW - validation Y1 - 2012 U6 - https://doi.org/10.1111/j.1365-2699.2011.02659.x SN - 0305-0270 VL - 39 IS - 12 SP - 2119 EP - 2131 PB - Wiley-Blackwell CY - Hoboken ER -