Competition may explain the fine-scale spatial patterns and genetic structure of two co-occurring plant congeners

1. The spatial distribution of individual plants within a population and the population’s genetic structure are determined by several factors, like dispersal, reproduction mode or biotic interactions. The role of interspecific interactions in shaping the spatial genetic structure of plant populations remains largely unknown. 2. Species with a common evolutionary history are known to interact more closely with each other than unrelated species due to the greater number of traits they share. We hypothesize that plant interactions may shape the fine genetic structure of closely related congeners. 3. We used spatial statistics (georeferenced design) and molecular techniques (ISSR markers) to understand how two closely related congeners, Thymus vulgaris (widespread species) and T. loscosii (narrow endemic) interact at the local scale. Specific cover, number of individuals of both study species and several community attributes were measured in a 10 × 10 m plot. 4. Both species showed similar levels of genetic variation, but differed in their spatial genetic structure. Thymus vulgaris showed spatial aggregation but no spatial genetic structure, while T. loscosii showed spatial genetic structure (positive genetic autocorrelation) at short distances. The spatial pattern of T. vulgaris’ cover showed significant dissociation with that of T. loscosii. The same was true between the spatial patterns of the cover of T. vulgaris and the abundance of T. loscosii and between the abundance of each species. Most importantly, we found a correlation between the genetic structure of T. loscosii and the abundance of T. vulgaris: T. loscosii plants were genetically more similar when they were surrounded by a similar number of T. vulgaris plants. 5. Synthesis. Our results reveal spatially complex genetic structures of both congeners at small spatial scales. The negative association among the spatial patterns of the two species and the genetic structure found for T. loscosii in relation to the abundance of T. vulgaris indicate that competition between the two species may account for the presence of adapted ecotypes of T. loscosii to the abundance of a competing congeneric species. This suggests that the presence and abundance of close congeners can influence the genetic spatial structure of plant species at fine scales

Phenotipic integration does not constrain phenotypic plasticity: differential plasticity of traits is associated to their integration across environments

ResearchUnderstanding constraints to phenotypic plasticity is key given its role on the response of organisms to environmental change. It has been suggested that phenotypic integration, the structure of trait covariation, could limit trait plasticity. However, the relationship between plasticity and integration is far from resolved. Using a database of functional plasticity to drought of a Mediterranean shrub that included 20 ecophysiological traits, we assessed environmentally-induced changes in phenotypic integration and whether integration constrained the expression of plasticity, accounting for the within-environment phenotypic variation of traits. Furthermore, we provide the first test of the association between differential trait plasticity and trait integration across an optimum and a stressful environment. Phenotypic plasticity was positively associated with phenotypic integration in both environments, but this relationship was lost when phenotypic variation was considered. The similarity in the plastic response of two traits predicted their integration across environments, with integrated traits having more similar plasticity. Such variation in the plasticity of traits partly explained the lower phenotypic integration found in the stressful environment. We found no evidence that integration may constitute an internal constraint to plasticity. Rather, we present the first empirical demonstration that differences in plastic responses may involve a major reorganization of the relationships among traits, and challenge the notion that stress generally induces a tighter phenotypeinfo:eu-repo/semantics/publishedVersio

An Automatic Palindrome Generator

In 1984 Dan Hoey, a US naval mathematician, wrote a computer which he used to create a 540-word expansion of Leigh Mercer\u27s Panama palindrome (PD). It began A man, a plan, a caret, a ban, a myriad, a sum, a lac... and ended ...a calmus, a diaryman, a bater, a canal Panama. (For the full PD, plus additional information, see http://www2.vo.lu/homepages/phahn/anagrams/panama/htm.

Cross-realm assessment of climate change impacts on species' abundance trends

Climate change, land-use change, pollution and exploitation are among the main drivers of species' population trends; however, their relative importance is much debated. We used a unique collection of over 1,000 local population time series in 22 communities across terrestrial, freshwater and marine realms within central Europe to compare the impacts of long-term temperature change and other environmental drivers from 1980 onwards. To disentangle different drivers, we related species' population trends to species- and driver-specific attributes, such as temperature and habitat preference or pollution tolerance. We found a consistent impact of temperature change on the local abundances of terrestrial species. Populations of warm-dwelling species increased more than those of cold-dwelling species. In contrast, impacts of temperature change on aquatic species' abundances were variable. Effects of temperature preference were more consistent in terrestrial communities than effects of habitat preference, suggesting that the impacts of temperature change have become widespread for recent changes in abundance within many terrestrial communities of central Europe.Additionally, we appreciate the open access marine data provided by the International Council for the Exploration of the Sea. We thank the following scientists for taxonomic or technical advice: C. Brendel, T. Caprano, R. Claus, K. Desender, A. Flakus, P. R. Flakus, S. Fritz, E.-M. Gerstner, J.-P. Maelfait, E.-L. Neuschulz, S. Pauls, C. Printzen, I. Schmitt and H. Turin, and I. Bartomeus for comments on a previous version of the manuscript. R.A. was supported by the EUproject LIMNOTIP funded under the seventh European Commission Framework Programme (FP7) ERA-Net Scheme (Biodiversa, 01LC1207A) and the long-term ecological research program at the Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB). R.W.B. was supported by the Scottish Government Rural and Environment Science and Analytical Services Division (RESAS) through Theme 3 of their Strategic Research Programme. S.D. acknowledges support of the German Research Foundation DFG (grant DO 1880/1-1). S.S. acknowledges the support from the FP7 project EU BON (grant no. 308454). S.K., I.Kü. and O.S. acknowledge funding thorough the Helmholtz Association’s Programme Oriented Funding, Topic ‘Land use, biodiversity, and ecosystem services: Sustaining human livelihoods’. O.S. also acknowledges the support from FP7 via the Integrated Project STEP (grant no. 244090). D.E.B. was funded by a Landes–Offensive zur Entwicklung Wissenschaftlich–ökonomischer Exzellenz (LOEWE) excellence initiative of the Hessian Ministry for Science and the Arts and the German Research Foundation (DFG: Grant no. BO 1221/23-1).Peer Reviewe

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 temporal biodiversity change to be assessed at the community scale, reaching 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

The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe

The dataset presented here was collected by the GenTree project (EU-Horizon 2020), which aims to improve the use of forest genetic resources across Europe by better understanding how trees adapt to their local environment. This dataset of individual tree-core characteristics including ring-width series and whole-core wood density was collected for seven ecologically and economically important European tree species: silver birch (Betula pendula), European beech (Fagus sylvatica), Norway spruce (Picea abies), European black poplar (Populus nigra), maritime pine (Pinus pinaster), Scots pine (Pinus sylvestris), and sessile oak (Quercus petraea). Tree-ring width measurements were obtained from 3600 trees in 142 populations and whole-core wood density was measured for 3098 trees in 125 populations. This dataset covers most of the geographical and climatic range occupied by the selected species. The potential use of it will be highly valuable for assessing ecological and evolutionary responses to environmental conditions as well as for model development and parameterization, to predict adaptability under climate change scenarios

The GenTree Platform: growth traits and tree-level environmental data in 12 European forest tree species

Background: Progress in the field of evolutionary forest ecology has been hampered by the huge challenge of phenotyping trees across their ranges in their natural environments, and the limitation in high-resolution environmental information. Findings: The GenTree Platform contains phenotypic and environmental data from 4,959 trees from 12 ecologically and economically important European forest tree species: Abies alba Mill. (silver fir), Betula pendula Roth. (silver birch), Fagus sylvatica L. (European beech), Picea abies (L.) H. Karst (Norway spruce), Pinus cembra L. (Swiss stone pine), Pinus halepensis Mill. (Aleppo pine), Pinus nigra Arnold (European black pine), Pinus pinaster Aiton (maritime pine), Pinus sylvestris L. (Scots pine), Populus nigra L. (European black poplar), Taxus baccata L. (English yew), and Quercus petraea (Matt.) Liebl. (sessile oak). Phenotypic (height, diameter at breast height, crown size, bark thickness, biomass, straightness, forking, branch angle, fructification), regeneration, environmental in situ measurements (soil depth, vegetation cover, competition indices), and environmental modeling data extracted by using bilinear interpolation accounting for surrounding conditions of each tree (precipitation, temperature, insolation, drought indices) were obtained from trees in 194 sites covering the species’ geographic ranges and reflecting local environmental gradients. Conclusion: The GenTree Platform is a new resource for investigating ecological and evolutionary processes in forest trees. The coherent phenotyping and environmental characterization across 12 species in their European ranges allow for a wide range of analyses from forest ecologists, conservationists, and macro-ecologists. Also, the data here presented can be linked to the GenTree Dendroecological collection, the GenTree Leaf Trait collection, and the GenTree Genomic collection presented elsewhere, which together build the largest evolutionary forest ecology data collection available

Between but not within species variation in the distribution of fitness effects

New mutations provide the raw material for evolution and adaptation. The distribution of fitness effects (DFE) describes the spectrum of effects of new mutations that can occur along a genome, and is therefore of vital interest in evolutionary biology. Recent work has uncovered striking similarities in the DFE between closely related species, prompting us to ask whether there is variation in the DFE among populations of the same species, or among species with different degrees of divergence, i.e., whether there is variation in the DFE at different levels of evolution. Using exome capture data from six tree species sampled across Europe we characterised the DFE for multiple species, and for each species, multiple populations, and investigated the factors potentially influencing the DFE, such as demography, population divergence and genetic background. We find statistical support for there being variation in the DFE at the species level, even among relatively closely related species. However, we find very little difference at the population level, suggesting that differences in the DFE are primarily driven by deep features of species biology, and that evolutionarily recent events, such as demographic changes and local adaptation, have little impact

<scp>ReSurveyEurope</scp>: A database of resurveyed vegetation plots in Europe

AbstractAimsWe introduce ReSurveyEurope — a new data source of resurveyed vegetation plots in Europe, compiled by a collaborative network of vegetation scientists. We describe the scope of this initiative, provide an overview of currently available data, governance, data contribution rules, and accessibility. In addition, we outline further steps, including potential research questions.ResultsReSurveyEurope includes resurveyed vegetation plots from all habitats. Version 1.0 of ReSurveyEurope contains 283,135 observations (i.e., individual surveys of each plot) from 79,190 plots sampled in 449 independent resurvey projects. Of these, 62,139 (78%) are permanent plots, that is, marked in situ, or located with GPS, which allow for high spatial accuracy in resurvey. The remaining 17,051 (22%) plots are from studies in which plots from the initial survey could not be exactly relocated. Four data sets, which together account for 28,470 (36%) plots, provide only presence/absence information on plant species, while the remaining 50,720 (64%) plots contain abundance information (e.g., percentage cover or cover–abundance classes such as variants of the Braun‐Blanquet scale). The oldest plots were sampled in 1911 in the Swiss Alps, while most plots were sampled between 1950 and 2020.ConclusionsReSurveyEurope is a new resource to address a wide range of research questions on fine‐scale changes in European vegetation. The initiative is devoted to an inclusive and transparent governance and data usage approach, based on slightly adapted rules of the well‐established European Vegetation Archive (EVA). ReSurveyEurope data are ready for use, and proposals for analyses of the data set can be submitted at any time to the coordinators. Still, further data contributions are highly welcome.</jats:sec