Biology subjects

Poschlod, P.

Publications and source records attributed to Poschlod, P..

4 recordsLinked to original sources

bioRxiv2022

ReSurveyGermany: Vegetation-plot time-series over the past hundred years in Germany

Vegetation-plot resurvey data are a main source of information on terrestrial biodiversity change, with records reaching back more than one century. Although more and more data from re-sampled plots have been published, there is not yet a comprehensive open-access dataset available for analysis. Here, we compiled and harmonised vegetation-plot resurvey data from Germany covering almost 100 years. We show the distribution of the plot data in space, time and across habitat types of the European Nature Information System (EUNIS). In addition, we include metadata on geographic location, plot size and vegetation structure. The data allow calculating temporal biodiversity change at the community scale and reach back further into the past than most comparable data yet available. They also enable tracking changes in the incidence and distribution of individual species across Germany. In summary, the data come at a level of detail that holds promise for broadening our understanding of the mechanisms and drivers behind plant diversity change over the last century.

ecology↗

bioRxiv2022

Machine learning algorithms predict soil seed bank persistence from easily available traits

QuestionSoil seed banks (SSB), i.e., pools of viable seeds in the soil and its surface, play a crucial role in plant biology and ecology. Information on seed persistence in soil is of great importance for fundamental and applied research, yet compiling datasets on this trait still requires enormous efforts. We asked whether the machine learning (ML) approach could be used to infer and predict SSB properties of a regional flora based on easily available data. LocationEighteen calcareous grasslands located along an elevational gradient of almost 2000 m in the Bavarian Alps, Germany. MethodsWe compared a commonly used ML model (random forest) with a conventional model (linear regression model) as to their ability to predict SSB presence/absence and density using empirical data on SSB characteristics (environmental, seed traits and phylogenetic predictors). Further, we identified the most important determinants of seed persistence in soil for predicting qualitative and quantitative SSB characteristics using the ML approach. ResultsWe demonstrated that the ML model predicts SSB characteristics significantly better than the linear regression model. A single set of predictors (either environment, or seed traits, or phylogenetic eigenvectors) was sufficient for the ML model to achieve high performance in predicting SSB characteristics. Importantly, we established that a few widely available SSB predictors can achieve high predictive power in the ML approach, suggesting a high flexibility of the developed approach for use in various study systems. ConclusionsOur study provides a novel methodological approach that combines empirical knowledge on the determinants of SSB characteristics with a modern, flexible statistical approach based on ML. It clearly demonstrates that ML can be developed into a key tool to facilitate labor-intensive, costly and time-consuming functional trait research.

ecology↗

bioRxiv2021

Inferring community assembly processes from functional seed trait variation along temperature gradient

O_LIAssembly of plant communities has long been scrutinized through the lens of trait-based ecology. Studies generally analyze functional traits related to the vegetative growth, survival and resource acquisition and thus ignore how ecological processes may affect plants at other stages of their lifecycle, particularly when seeds disperse, persist in soil and germinate. C_LIO_LIHere, we analyzed an extensive data set of 16 traits for 167 species measured in-situ in 36 grasslands located along an elevational gradient and compared the impact of abiotic filtering, biotic interactions and dispersal on traits reflecting different trait categories: plant vegetative growth, germination, dispersal, and seed morphology. For each community, we quantified community weighted mean (CWM) and functional diversity (FD) for all traits and established their relationships to mean annual temperature. C_LIO_LIThe seed traits were weakly correlated to vegetative traits and thus constituted independent axes of plant phenotypical variation that were affected differently by the ecological processes considered. Abiotic filtering impacted mostly the vegetative traits and to a lesser extent on seed germination and morphological traits. Increasing low-temperature stress towards colder sites selected for short-stature, slow-growing and frost-tolerant species that produce small quantity of smaller seeds with higher degree of dormancy, high temperature requirements for germination and comparatively low germination speed. C_LIO_LIBiotic interactions, specifically competition in the lowlands and facilitation in uplands, also filtered certain functional traits in the study communities. The benign climate in lowlands promoted plant with competitive strategies including fast growth and resource acquisition (vegetative growth traits) and early and fast germination (germination traits), whereas the effects of facilitation on the vegetative and germination traits were cancelled out by the strong abiotic filtering. C_LIO_LIThe changes in the main dispersal vector from zoochory to anemochory along the gradient strongly affected the dispersal and the seed morphological trait structure of the communities. Specifically, stronger vertical turbulence and moderate warm-upwinds combined with low grazing intensity selected for light and non-round shaped seeds with lower terminal velocity and endozoochorous potential. C_LIO_LISynthesis. We clearly demonstrate that, in addition to vegetation traits, seed traits can substantially contribute to functional structuring of plant communities along environmental gradients. Thus, the, hard seed traits related to germination and dispersal are critical to detect multiple, complex community assembly rules. Consequently, such traits should be included in core lists of plant traits and, when applicable, be incorporated into analysis of community assembly. C_LI

ecology↗

bioRxiv2021

An objective-based prioritization approach to improve trophic complexity through ecological restoration

O_LIReassembling ecological communities and rebuilding habitats through active restoration treatments requires curating the selection of plant species to use in seeding and planting mixes. Ideally, these mixes should be assembled based on attributes that support ecosystem function and services, promote plant and animal species interactions and ecological networks in restoration while balancing project constraints. Despite these critical considerations, it is common for species mixes to be selected opportunistically. Reframing the selection of seed mixes for restoration around ecological objectives is essential for success but accessible methods and tools are needed to support this effort. C_LIO_LIWe developed a framework to optimize species seed mixes based on prioritizing plant species attributes to best support different objectives for ecosystem functions, services, and trophic relationships such as pollination, seed dispersal, and herbivory. We compared results to approaches where plant species are selected to represent plant taxonomic richness, dominant species, and at random. We tested our framework for 176 plant species found in European alpine grasslands and identified 163 associated attributes affiliated to trophic relationships, ecosystem functions, and services. C_LIO_LIIn all cases, trophic relationships, ecosystem functions, and services can be captured more efficiently through objective-based prioritization using the functional identity of plant species. Solutions (plant species lists) can be compared quantitatively, in terms of costs, species, or objectives. We confirm that a random draw of plant species from the regional plant species pool cannot be assumed to support other trophic groups and ecosystem functions and services. C_LIO_LISynthesis and Applications. Our framework is presented as a proof of concept to help restoration practitioners better apply quantitative decision-support to plant species selection in order to meet ecological restoration outcomes. Our approach may be tailored to any restoration initiative and habitat where seeding or planting mixes will be applied in active treatments. As global priority and resources are increasingly placed into restoration, this approach could be advanced to help make efficient decisions for many stages of the restoration process. C_LI

ecology↗