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57 results for “deadwood”
The impact of beech deadwood on soil properties and microbial diversity
<p><span><span>Our research is an attempt to determine the role of decaying wood in shaping the properties of forest soils in mountain ecosystems.</span></span><span><span> </span></span></p>
Data and code from: Climate-based prediction of carbon fluxes from deadwood in Australia
This repository contains the code for the publication 'Climate-based prediction of carbon fluxes from deadwood in Australia'.
Forest deadwood in Europe
<p>This dataset considers information from 3,243 ICP-Forests Level I plots collected between 2006 and 2008 in 19 European Countries: France, Belgium, Germany, Italy, United Kingdom, Ireland, Denmark, Spain, Sweden, Austria, Finland, Hungary, Poland, Slovak Republic, Lithuania, Czech Republic, Slovenia, Latvia, Cyprus. The dataset provides standardized estimates of deadwood volume distinguished in five different deadwood components: standing and lying dead trees, snags, coarse woody debris, and stumps.</p> <p>The data allowed a traditional set of forest parameters to be extracted and that may be used to build relationship with other forest attributes, both quantitative and qualitative, to strength the knowledge of forest structure, diversity and species interactions naturally occurring in European forests. To the best of our knowledge it is the first dataset reporting measurements on deadwood at such large spatial scale in forest stands in Europe.</p>
Figure 1 in Deadwood and saproxylic beetle diversity in naturally disturbed and managed spruce forests in Nova Scotia
Figure 1. Non-metric multidimensional scaling ordination diagrams of forest sites in two-dimensional space defined by (A) beetle assemblage and (B) habitat structures. Symbols signify disturbance history class of forests: 1= CLEARCUT, D= THINNED, ·= FIRE, and Ɨ= WIND. Numbers identify the specific forest. Forest number 25 was excluded as an outlier in (B) due to much higher deadwood volumes than all other sites.
Network analysis reflects the trophic relationship between microbial colonizers and deadwood resources - supporting information
<p>Supporting tables S2 - S5 of "Network analysis reflects the trophic relationship between microbial colonizers and deadwood resources".</p> <p>The file Table_Legends_S2-S5.txt contains all legends, as given below:</p> <p>Table S2: Module-associated trees and OTUs, their relative abundances and identities for the fungal sapwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p> <p>Table S3: Module-associated trees and OTUs, their relative abundances and identities for the fungal heartwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p> <p>Table S4: Module-associated trees and OTUs, their relative abundances and identities for the prokaryotic sapwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p> <p>Table S5: Module-associated trees and OTUs, their relative abundances and identities for the prokaryotic heartwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .</p>
Data from: Fungal communities are important determinants of bacterial community composition in deadwood
<p>Fungal-bacterial interactions play a key role in the functioning of many ecosystems. Thus, understanding their interactive dynamics is of central importance for gaining predictive knowledge on ecosystem functioning. However, it is challenging to disentangle the mechanisms behind species associations from observed co-occurrence patterns and little is known about the directionality of such interactions. Here we apply joint species distribution modelling to high-throughput sequencing data on co-occurring fungal and bacterial communities in deadwood to ask whether fungal and bacterial co-occurrences result from shared habitat use (i.e. dead wood's properties), or whether there are fungal-bacterial interactive associations after habitat characteristics are taken into account. Moreover, we test the hypothesis that the interactions are mainly modulated through fungal communities influencing bacterial communities. For that, we quantified how much the predictive power of the joint species distribution models for bacterial and fungal community improved when accounting for the other community. Our results show that fungi and bacteria form tight association networks (i.e. some species pairs co-occur more frequently and other species pairs co-occur less frequently than expected by chance) in deadwood that include common (or opposite) responses to the environment, as well as (potentially) biotic interactions. Additionally, we show that information about the fungal occurrences and abundances increased the power to predict the bacterial abundances substantially, whereas information about the bacterial occurrences and abundances increased the power to predict the fungal abundances much less. Our results suggest that fungal communities may mainly affect bacteria in deadwood.</p> <p><b>Importance</b></p> <p>Understanding the interactive dynamics between fungal and bacterial communities is important to gain predictive knowledge on ecosystem functioning. However little is known about the mechanisms behind fungal-bacterial associations and the directionality of species interactions. Applying joint species distribution modelling to high throughput sequencing data on co-occurring fungal-bacterial communities in deadwood, we found evidence that non-random fungal-bacterial associations derive from shared habitat use, as well as (potentially) biotic interactions. Importantly,<i> </i>the combination of cross-validations and conditional cross-validations helped us to answer the question about the directionality of the biotic interactions, providing evidence that suggests that fungal communities may mainly affect bacteria in deadwood. Our modelling approach may help gaining insight into the directionality of interactions between different components of the microbiome in other environments.</p>
Hierarchical trait filtering at different spatial scales determines beetle assemblages in deadwood: Additional data
<p>Contains data used in the following publication:</p> <p>Felix Neff, Jonas Hagge, Rafael Achury, Didem Ambarlı, Christian Ammer, Peter Schall, Sebastian Seibold, Michael Staab, Wolfgang W. Weisser, Martin M. Gossner (2022). <em>Hierarchical trait filtering at different spatial scales determines beetle assemblages in deadwood. </em>Functional Ecology. <a href="https://doi.org/10.1111/1365-2435.14186">https://doi.org/10.1111/1365-2435.14186</a></p> <p>These are complementary data, which are needed to reproduce the analyses. Most data are archived in the Biodiversity Exploratories Information System (<a href="https://doi.org/10.17616/R32P9Q">https://doi.org/10.17616/R32P9Q</a>).</p> <p>The following data are included:</p> <ul> <li><strong>BELongDead_Subplots_Normal.csv</strong>: List of <em>normal</em> subplots within the BELongDead projects (only these were included in the analyses)</li> <li><strong>Body_length.csv</strong>: Body length data for study species assembled from Freude et al. (1965-1998)</li> <li><strong>Lightness_completion.csv</strong>: Colour lightness recorded from measured individuals and photos from coleonet.de (Lompe, 2002)</li> <li><strong>Name_standardisation.csv</strong>: Dataset used to standardise taxonomic names from different sources</li> <li><strong>Saproxylic_species_sub.csv</strong>: List of saproxylic species (according to Schmidl & Bussler (2004)), which were recorded in the project</li> <li><strong>Similar_Species.csv</strong>: List of similar species for species with missing traits. Based on these, missing traits were estimated</li> </ul> <p><strong>References</strong></p> <p>Freude, H., Harde, K. W., & Lohse, G. A. (1965–1998). <em>Die Käfer Mitteleuropas Band 1-15</em>. Goecke und Evers.</p> <p>Lompe, A. (2002). <em>Käfer Europas</em>. <a href="http://coleonet.de/">http://coleonet.de/</a></p> <p>Schmidl, J., & Bussler, H. (2004). Ökologische Gilden xylobionter Käfer Deutschlands. <em>Naturschutz und Landschaftsplanung</em>, <em>36</em>(7), 202–218.</p>
FIG. 4 in New and noteworthy records of deadwood dwelling bryophyte species for Turkey and Southwest Asia
FIG. 4. — Scapania apiculata Spruce: A, shoot; B, leaf; C, D, median leaf cells and oil-bodies; E, gemmae. Scale bars: A, 1 mm; B, 200 μm; C-E, 50 μm.
FIG. 3 in New and noteworthy records of deadwood dwelling bryophyte species for Turkey and Southwest Asia
FIG. 3. — Crossocalyx hellerianus (Nees ex Lindenb.) Meyl.: A, habitus; B, shoots; C, leaf; D, median leaf cells and oil bodies; E, gemmae. Scale bars: A, 400 μm; B, 200 μm; C, 100 μm; D, E, 50 μm.
FIG. 2 in New and noteworthy records of deadwood dwelling bryophyte species for Turkey and Southwest Asia
FIG. 2. — Calypogeia suecica (Arnell & J.Perss.) Müll.Frib.: A, habitus; B-C, underleaves; D, leaf; E, median leaf cells and oil-bodies. Scale bars: A, 3 mm; B-D, 200 μm; E, 50 μm.
FIG. 5 in New and noteworthy records of deadwood dwelling bryophyte species for Turkey and Southwest Asia
FIG. 5. — Lophozia guttulata: A, habitus; B, leaf; C, Median leaf cells and oil-boides; D, Gemmae; E, Perianth armature. Scale bars: A, 3 mm; B, 200 μm; C, D, 50 μm; E, 100 μm.
Data to How to best detect threatened deadwood fungi – comparing metabarcoding and fruit body surveys
<p>This data set contains occurence data of threatened (red listed) fungi on deadwood objects in the National Park Bavarian Forest and the Žofínský prales National Nature Reserve to the the publication "How to best detect threatened deadwood fungi – comparing metabarcoding and fruit body surveys" by Rieker et al. (2024). This data is mainly uploaded to recreate analyses. </p> <p>Site: Information on the sampling site</p> <p>ObjectID: ID of the sampled deadwood object</p> <p>Campaign: Sampling year</p> <p>Decaystage: Decaystage of the deadwood object at sampling time (ranging between 1 and 4)</p> <p>Kingdom: Kingdom the eadwood object tree species belongs to</p> <p>Method: sampling method</p> <p>Species: threatend species name </p> <p>Presence: only contains the value "1" for present</p>
Data from: Fungal communities are important determinants of bacterial community composition in deadwood
Open the record for dataset details and reuse information.
Data from: Biogeographical variation in termite distributions alters global deadwood decay
Open the record for dataset details and reuse information.
Figure 2 in Deadwood and saproxylic beetle diversity in naturally disturbed and managed spruce forests in Nova Scotia
Figure 2. Species-sample curves for each forest disturbance class.
Data from: Optimizing enrichment of deadwood for biodiversity by varying sun exposure and tree species: an experimental approach
<p>1. The enrichment of deadwood is essential for the conservation of saproxylic biodiversity in managed forests. However, existing strategies focus on a cost-intensive increase of deadwood amount, while largely neglecting increasing deadwood diversity.</p> <p>2. Deadwood objects, i.e. logs and branches, from six tree species were experimentally sun-exposed, canopy-shaded, and artificially shaded for four years, after which the alpha-, beta-, and gamma-diversity of saproxylic beetles, wood-inhabiting fungi, and spiders were analyzed. Analyses of beta-diversity included the spatial distance between exposed deadwood objects. A random-drawing procedure was used to identify the combination of tree species and sun exposure that yielded the highest gamma-diversity at a minimum of exposed deadwood amount.</p> <p>3. In sun-exposed plots, species numbers in logs were higher than in shaded plots for all taxa, while in branches we observed the opposite for saproxylic beetles. Tree species affected the species numbers only of saproxylic beetles and wood-inhabiting fungi. The beta-diversity of saproxylic beetles and wood-inhabiting-fungi among logs was influenced by sun exposure and tree species, but beta-diversity of spiders by sun exposure only. For all saproxylic taxa recorded in logs, differences between communities increased with increasing spatial distance.</p> <p>4. A combination of canopy-shaded <i>Carpinus</i> logs and sun-exposed <i>Populus</i> logs resulted in the highest species numbers of all investigated saproxylic taxa among all possible combinations of tree species and sun-exposure treatments.</p> <p>5. <i>Synthesis and applications</i>. We recommend incorporating the enrichment of different tree species and particularly the variation in sun exposure into existing strategies of deadwood enrichment. Based on the results of our study, we suggest to combine logs of softwood broadleaf tree species (e.g. <i>Carpinus, Populus</i>), hardwood broadleaf tree species (e.g. <i>Quercus</i>) and coniferous tree species (e.g. <i>Pinus</i>) under different conditions of sun exposure and distribute them spatially in a landscape to maximize the beneficial effects on overall diversity.</p>
Lying deadwood retention affects microhabitat use of martens (Martes spp.) in European mountain forests
<p><span>Biodiversity loss due to intensive timber production is a ubiquitous conservation issue across temperate and boreal forest ecosystems. Retention forestry, the retention of deadwood and old-growth features within production forest, is one management strategy that has been implemented in various countries around the world to conserve a wide range of taxa within managed forests. The success and ecological implications of retention forestry are currently subject to intensive investigation and while some taxa like birds and insects have already been studied frequently, larger mammals have obtained less attention. Pine martens are one of the few larger mammals in central Europe preferring older forest and potentially profiting directly from deadwood retention as a consequence of implemented retention forestry. The goal of our study was to assess the response of European marten species to deadwood retention in montane mixed forests. Using marten detection rates from camera traps on 135 research plots we assessed the response of martens to deadwood on three different spatial scales using generalized linear mixed models. We found no effect of lying deadwood on marten detections on the plot scale (one-hectare) or in a ten-meter radius around the camera traps. However, we found a significant increase in marten detections if logs (>10 cm) were directly in front and in view of the camera trap. Our results show that deadwood retention as a measure of retention forestry does affect microhabitat use of martens, but not stand selection during the growing season. Logs directly in view of the camera trap increase marten detection rates as martens choose to move and forage along fallen trees when they are available. When using camera trapping to collect data on martens, trap positioning in front of logs can heavily bias trapping results when unaccounted for.</span></p>
Cross-kingdom interactions and functional patterns of active microbiota matter in governing deadwood decay
<p>Microbial community members are the primary microbial colonizers and active decomposers of deadwood. This study placed sterilized standardized beech and spruce sapwood specimens on the forest ground of 8 beech- and 8 spruce-dominated forest sites. After 370 days, specimens were assessed for mass loss, nitrogen (N) content and <sup><span>15</span></sup><span>N</span> isotopic signature, <span>hydrolytic and lignin-modifying enzyme activities. Each </span>specimen <span>was </span>incubated with bromodeoxyuridine (BrdU) to label <span>metabolically active fungal and bacterial community members, which were assessed using an amplicon sequencing. Fungal saprotrophs colonized the deadwood accompanied by a distinct bacterial community that was capable of cellulose degradation, aromatic depolymerisation, and N<sub>2</sub> fixation. The latter were governed by the genus <em>Sphingomonas</em>, which was co-present with the majority of saprotrophic fungi regardless of whether beech or spruce specimens were decayed. Moreover, the richness of the diazotrophic </span><span><em>Allorhizobium</em>-<em>Neorhizobium</em>-<em>Pararhizobium</em>-<em>Rhizobium</em></span><span> group were significantly correlated with mass loss, N content and <sup>15</sup>N isotopic signature. In contrast, presence of obligate predator <em>Bdellovibrio</em> spp. shifted bacterial community composition and were linked to decreased beech deadwood decay rates. Our study provides the first account of the composition and function of metabolically active wood-colonising bacterial and fungal communities, highlighting cross-kingdom interactions during the early and intermediate stages of wood decay.</span></p>
Fungi and deadwood diversity: A test of the area-heterogeneity trade-off hypothesis
<p>Environmental heterogeneity is one of the most fundamental drivers of species diversity. For decades, ecologists have suggested that heterogeneity-diversity relationships are generally positive. But today, a greater variety of heterogeneity-diversity relationships is discussed. In this study, we contrasted two hypotheses for wood-inhabiting fungi: The classical heterogeneity-diversity hypothesis, that predicts positive relationships due to an increase in niche dimensionality with increasing heterogeneity. And the more recently stated area-heterogeneity trade-off hypothesis, that predicts a unimodal pattern due to an inherent trade-off between the number of occupied niches and the effective area per species. It allows positive and negative relationships only as special cases.</p> <p>We sampled 3,715 deadwood objects on 135 plots along a forest structure gradient in the Black Forest, Germany, and recorded 284 wood-inhabiting fungal species. To assess heterogeneity of deadwood structures, we calculated two multidimensional structural diversity indices: Structural richness was used as a measure of available niche space, and structural divergence as a measure of multivariate variance within that niche space. Those indices were then related to species richness estimates for rare, common, and dominant species, using the framework of Hill numbers.</p> <p>We found a linear, positive effect of structural richness and a unimodal effect for structural divergence on estimated species diversity. Structural richness, but not structural divergence was strongly correlated with the number of sampled deadwood objects. No clear differences between the responses of rare, common, and dominant species to the two heterogeneity gradients were found. We also estimated the mean abundance as proxy for mean population size, which decreased significantly with structural richness, but was non-significantly related to structural divergence.</p> <p><em>Synthesis: </em>In general, the results of this study suggest a unimodal heterogeneity-diversity relationship for deadwood-inhabiting fungi, and are thereby in line with the area-heterogeneity trade-off hypothesis. Thus, the negative effect of heterogeneity should lead to lower species richness and higher risk of stochastic extinctions at high levels of heterogeneity. However, as deadwood amount and deadwood diversity are often strongly correlated, we argue that the positive effect of resource availability on species richness may mask the negative effect of structural heterogeneity in some cases.</p>
FIG. 1 in New and noteworthy records of deadwood dwelling bryophyte species for Turkey and Southwest Asia
FIG. 1. — Position of Turkey and the investigated area (cornered).
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