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57 results for “deadwood”
Deadwood stocks and dynamics
<b>Description: </b><p>Survey of coarse woody debris</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/59"><b>Deadwood stocks and dynamics</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=70">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Deadwood_Data</b> (Worksheet Deadwood)</p><p>Dimensions: 4776 rows by 18 columns</p><p>Description: Details about all individual deadwood items</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE Project plot number (Field type: Location)</li><li><b>SurveyNum</b>: Deadwood survey number. Used for aggregating plot surveys together. (Field type: Replicate)</li><li><b>Date</b>: Date of plot survey (Field type: Date)</li><li><b>ZType</b>: Type of deadwood (Field type: Categorical)</li><li><b>Zarea</b>: Subplot location (Field type: Categorical)</li><li><b>Easting</b>: X-coordinate of the deawood item within the vegetation plot (Field type: Numeric)</li><li><b>Northing</b>: Y-coordinate of the deawood item within the vegetation plot (Field type: Numeric)</li><li><b>TrunkLength</b>: Length of the deadwood item (Field type: Numeric)</li><li><b>TrunkHeight</b>: Height of the deadwood item (Field type: Numeric)</li><li><b>Diameter1</b>: Diameter at one end of the deadwood item (Field type: Numeric)</li><li><b>Diameter2</b>: Diameter at the other end of the deadwood item (Field type: Numeric)</li><li><b>DecompStatus</b>: Score indicating the level of decomposition of the deadwood item (Field type: Ordered Categorical)</li><li><b>DirectionFallen</b>: Compass bearing from base-to-apex showing direction in which the item has fallen (Field type: Numeric)</li><li><b>NumTreesInvolved</b>: Number of trees involved in a single treefall event (Field type: Numeric)</li><li><b>CollectedOrObservedByText</b>: Names of field team collecting the plot data (Field type: Comments)</li><li><b>Comment</b>: Field observations about the deadwood item (Field type: Comments)</li><li><b>deathcause</b>: Cause of death (Field type: Comments)</li></ul><br></li></ol><p><b>Date range: </b>2010-07-02 to 2011-07-31</p><p><b>Latitudinal extent: </b>4.6353 to 4.7714</p><p><b>Longitudinal extent: </b>116.9477 to 117.7028</p>
Deadwood carbon stocks under logging and fragmentation impacts
<b>Description: </b><p>Wood density estimates of deadwood across decomposition classes</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/23"><b>Deadwood carbon stocks under logging and fragmentation impacts</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=79">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Wood density estimates</b> (Worksheet WoodDensity)</p><p>Dimensions: 418 rows by 23 columns</p><p>Description: Estimates of wood density for deadwood samples</p><p>Fields: </p><ul><li><b>Site</b>: Specific locations where deadwood samples were collected (Field type: Location)</li><li><b>Location</b>: SAFE Project sample block where deadwood samples were collected from (Field type: Location)</li><li><b>Block</b>: SAFE Project sample block where deadwood samples were collected from (Field type: ID)</li><li><b>Samples</b>: Unique identifier for each deadwood item sampled from in the field (Field type: Replicate)</li><li><b>DecayClass</b>: Decomposition level of the deadwood sample (Field type: Ordered Categorical)</li><li><b>SubS</b>: Replicate number. Multiple samples were analysed from a single 'Samples' (Field type: Replicate)</li><li><b>DeadPiece</b>: Type of the deadwood item in the field that the sample for analysis was extracted from (Field type: Categorical)</li><li><b>SpeciesID</b>: Identity of the deadwood item in the field that the sample for analysis was extracted from (Field type: Taxa)</li><li><b>D_upper_cm</b>: Diameter at one end of the deadwood item in the field that the sample for analysis was extracted from (Field type: Numeric)</li><li><b>D_lower_cm</b>: Diameter at other end of the deadwood item in the field that the sample for analysis was extracted from (Field type: Numeric)</li><li><b>Length_cm</b>: Length of the deadwood item in the field that the sample for analysis was extracted from (Field type: Numeric)</li><li><b>Type</b>: Shape of the deadwood sample being analysed (Field type: Categorical)</li><li><b>MassFresh _ g</b>: Wet weight of the deadwood sample (Field type: Numeric)</li><li><b>L1_mm</b>: Dimension measurement (Field type: Numeric)</li><li><b>L2_mm</b>: Dimension measurement (Field type: Numeric)</li><li><b>L3_mm</b>: Dimension measurement (Field type: Numeric)</li><li><b>V_fresh_ml</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>MassDry_g</b>: Dry weight of the deadwood sample (Field type: Numeric)</li><li><b>V_dry_ml</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>V_Piece_cm3</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>V_Piece_cm3_set5</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>Density</b>: Wood density (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2012-01-01 to 2013-12-31</p><p><b>Latitudinal extent: </b>4.6880 to 4.7436</p><p><b>Longitudinal extent: </b>117.5346 to 117.6290</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Plantae<br> - [Kayu malam]<br> - [Laran]<br> - [Sadaman egari]<br> - [Sedaman]<br> - Tracheophyta<br> -  - Magnoliopsida<br> -  -  - Malpighiales<br> -  -  -  - Euphorbiaceae<br> -  -  -  -  - <i>Macaranga</i><br></div><p></p>
Data for "Effects of forest dieback on deadwood patterns: large scale trends from a cross-analysis of European databases"
<p><strong><span>Aims</span></strong></p> <p><span>We carried out an opportunistic correlative study between past crown conditions and current deadwood volumes.</span></p> <p><span>Our aim was to mobilise available data on site factors and long-term monitoring of crown vitality indicators in Europe to investigate the influence of current and recent local defoliation levels on plot-level deadwood volume.</span></p> <p><span>For a subset of level I, 16*16-km monitoring plots located throughout Europe, we benefitted from data on both (i) deadwood measurements carried out within the framework of the Forest Focus Biosoil Project </span><span>(Galluzzi et al., 2019)</span><span>, pre-processed into a consistent and harmonized deadwood dataset by </span><span>Puletti et al. (2019)</span><span>, and (ii) defoliation assessments provided yearly since 1989 by the International Co-operative Program on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests), the most comprehensive European monitoring network for the large-scale assessment of forest ecosystem health </span><span>(Vitale et al., 2014)</span><span>. </span></p> <p><span>Biosoil data on deadwood and ICP data on defoliation have never been crossed before.</span></p> <p><span>We used defoliation level as a proxy for the severity of stand dieback. Deadwood patterns can be addressed through deadwood profiles, which subdivide local deadwood stocks into classes based on size, position and decay stage.</span></p> <p><a name="_Toc175840512"></a><a name="_Toc116027761"></a><span><strong><span>ICP database and defoliation protocol</span></strong></span></p> <p><span>The International Cooperative Program to assess and monitor air pollution effects on the forest (ICP Forests) is responsible for an extensive level I monitoring system of forest sites </span><span>(Hauβmann & Fischer, 2004)</span><span>, which has been in operation since 1986. This large-scale level I network is made up of dense, spatially representative sampling points placed throughout European forests on a 16 × 16 km virtual grid, and is dedicated to monitoring forest conditions. The sampling points cover most European forested areas and encompasses ca. 6000 monitoring plots in 42 countries. In each plot, a visual evaluation of defoliation and discoloration of tree crowns is performed annually to survey forest health status (<a href="http://icp-forests.net/page/largescale-forest-condition">http://icp-forests.net/page/largescale-forest-condition</a>). Data management is presently carried out at the Programme Co-ordinating Centre (PCC) of ICP Forests in Eberswalde, Germany, and all data are available upon request. Since 1989, a standardized procedure for “annual surveys of crown condition’’ has been applied to 24 selected dominant and co-dominant trees with a minimum height of 60 cm and showing no significant mechanical damage. The defoliation and discoloration level of each tree crown is visually assessed on a sliding scale of 5% increments as the percentage of needle/leaf loss in the assessable crown as compared to a reference tree with full foliage. Mean defoliation at the plot scale was defined as the proportion of “damaged” trees i.e., with a defoliation rate of more than 25%, and used as a proxy for plot decline level. In the ICP database, the factors associated with observed defoliation related to natural disturbances or management (i.e., vertebrate or insect herbivory, fungal or fire damage, drought impacts, signs of removal of coarse woody debris, past landscape) were not recorded in a sufficiently standardized way to be used as covariates in our models. Similarly, plot-level living tree density and above-ground biomass for standing living trees (expressed in kg.ha<sup>−1</sup>), presumably surveyed in subplot 2, were not available.</span></p> <p><a name="_Toc175840513"></a><a name="_Toc116027762"></a><span><strong><span>Biosoil database and deadwood protocol</span></strong></span></p> <p><a name="_Toc116027763"></a><span>In the framework of the large collaborative European Forest Focus BioSoil-Biodiversity project</span><span>, a system of circular concentric subplots was built around certain ICP level I plots to collect additional data on stand structure and biodiversity between 2005 and 2008 (Figure 1). </span><span><span>The individual countries were responsible for selecting the ICP level I plots to be included in the BioSoil project </span></span><span><span>(Galluzzi et al., 2019)</span></span><span><span>. Overall, a total of 3243 geocoded Level I plots were considered in 19 European countries </span></span><span><span>(Puletti et al., 2017)</span></span><span><span>: Austria, Belgium (Flanders only), Cyprus, the Czech Republic, Denmark, Finland, France, Germany (eight federal states only), Hungary, Ireland, Italy, Latvia, Lithuania, Poland, Slovakia, Slovenia, Spain, Sweden and the United Kingdom (Figure 1). BioSoil project results are recorded in the multi-dimensional LI-BioDiv geodatabase that contains raw data on forest structure and vegetation records used to calculate simple plot-level structural and compositional forest variables (i.e., biomass, deadwood volume, plant alpha-diversity; </span></span><span><span>Bastrup-Birk et al. 2007; Hiederer & Durant 2010)</span></span><span><span>. At each plot, deadwood was quantified on an area of 400 m<sup>2</sup> (BioSoil subplots 1 and 2, radius of 11.28 m; </span></span><span><span>Puletti et al., 2017)</span></span><span><span>. The deadwood survey included coarse woody debris (including lying dead trees), snags (including standing dead trees) and stumps more than 10 cm in diameter. Only snags and stumps more than 130 cm in height were considered. Diameter, length or height, tree species and decay stage (5 classes) were recorded for each deadwood piece. The raw ICP deadwood data were processed by </span></span><span><span>Puletti et al. (2017, 2019)</span></span><span><span> into a consistent and harmonized pan-European deadwood dataset, which we used in this study. The dataset provides total deadwood volume and the volume of several deadwood types for each plot. Further details can be found in the ICP Forests manual (</span></span><a href="http://icp-forests.net/page/icp-forests-manual"><span><span>http://icp-forests.net/page/icp-forests-manual</span></span></a><span><span>), </span></span><span><span>Puletti et al. (2019)</span></span><span><span> and </span></span><span><span>Augustynczik et al. (2024)</span></span><span><span>.</span></span></p> <p><span><span>In our study, we considered the following response variables</span></span><span>: (i) total deadwood volume, (ii) </span><span>standing deadwood (snags) volume, (iii) volume of ground-lying deadwood, (iv) </span><span>fresh deadwood volume </span><span>(= Vm3_dec1_Biosoil + Vm3_dec2_Biosoil), and (v) decayed deadwood volume = (= Vm3_dec4_Biosoil + Vm3_dec5_Biosoil).</span></p> <p><span>A few environmental covariates were collected from the Biosoil data: (i) management intensity (grouped into two classes: recently harvested, i.e., with management evidence within the last 10 years; and not recently harvested, i.e., unmanaged (no management evidence) or managed a long time ago (management evidence but more than 10 years previously), (ii) average stand age (separated into 3 classes: mature [>100 yrs], mid-aged [41-100 yrs], young [1-40 yrs]), (iii) elevation (above sea level, a.s.l.), a continuous quantitative variable, (iv) dominant tree genus, and (v) forest type, depending on the dominant tree species: coniferous, deciduous or mixed.</span></p> <p><a name="_Toc175840514"></a><a name="_Toc116027764"></a><span><strong><span>Database joint</span></strong></span><span><strong><span>: <a name="_Toc116027765"></a>plot matching in time series</span></strong></span></p> <p><span>After harmonizing plot names and coordinates in the two datasets (ICP-defoliation and Biosoil-deadwood), only plots with matched data in both datasets were selected. Plots with a maximum of one year’s discontinuity in the data were retained, and the missing values were reconstructed from the average values in contiguous years. Plots with discontinuities in defoliation measurements of more than 2 years were deleted. We matched defoliation measurements for the Biosoil-ICP datasets from 1989 to 2007 and finally obtained 2,070 five-year, 1,804 ten-year and 1,399 fifteen-year time series. This approach made it possible to define three 10-year time series [1995-2005, 1996-2006, 1997-2007] with plots in 17 countries, from five plots in Ireland and nine in the United Kingdom, to 337 plots in Finland and 461 in France.</span></p> <p><a name="_Toc175840515"></a><a name="_Toc116027766"></a><span><strong><span>Calculation of global defoliation metrics</span></strong></span></p> <p><span>We calculated 16 univariate metrics to summarize changes in defoliation throughout the 10-year period prior to the Biosoil deadwood measurements. Some of the selected parameters describe the immediate possible effects of defoliation severity in the recent past on a given year: (i) defoliation level of the previous year (n-1), (ii) defoliation level of the year before the previous year (n-2), (iii) defoliation level of the year two years before the previous year (n-3). Other defoliation metrics relate to the cumulative effects of defoliation levels in the near or the distant past: (i) average defoliation level over the last two years, (ii) average defoliation level over the last three years, (iii) average defoliation level over the last five years, (iv) average defoliation level over the first five years of the 10-year time series, and (v) time elapsed since last peak defoliation. Several other parameters depict general trends in the level of defoliation over the 10-year time series: for cumulative metrics: (i) arithmetic mean of annual defoliation level; (ii) geometric mean of annual defoliation level; (iii) Area Under the defoliation time Curve (AUC), i.e., the cumulative sum of defoliation levels; and for the overall trend: (iv) the estimated slope of the linear regression line for defoliation level over time. Finally, some of the metrics reflect defoliation severity and repetition along the 10-year time series, and their potentially time-lagged effects: (i) maximum defoliation level; (ii) total number of years elapsed after the dieback peak level, whether successive or not; (iii) the number of peaks, consecutive or discontinuous, i.e., the number of severe defoliation events and defoliation frequency; and (iv) duration of the longest peak, i.e., the longest continuous time during which the level of defoliation was greater than the relative threshold.</span></p> <p><span>A peak in defoliation was defined as a year in which the level of defoliation exceeded a relative threshold, i.e., the third quartile value. In our 10-year time series, the peak value was 25% and above. <span><span> </span></span></span></p>
Dataset for mangrove deadwood in Kenya
<p>Nine mangrove species are found in Kenya following a zonation pattern described in the National Mangrove Ecosystem Management Plan for Kenya (GoK 2017) and the "Mangroves of the Western Indian Ocean: Management and Status" (Bosire et al 2016). The dataset presents standing dead wood in the mangroves of Lamu, Mombasa and Tana River Counties in Kenya. It was collected between 2015 and 2020 following the "Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests", by Kauffman and Donato (2012). The data provides information on:</p> <ol> <li>collection date</li> <li>details of location</li> <li>Presence of social pressures</li> <li>Variables for the standing dead wood</li> </ol> <p> </p> <p> </p>
Data for "Trait-based response of deadwood and tree-related microhabitats to decline in temperate lowland and montane forests"
<p><strong>Sampling design and case studies</strong></p> <p>The study was conducted in two French regions, the Loire valley and the French Pyrenees, and one German region, the Bavarian mountains. In the Loire valley, we studied two lowland sites in oak-dominated (both <em>Quercus petraea</em> (Matt.) Liebl. and <em>Quercus robur</em> L.) forests, one in the Orleans State Forest (107-174 m a.s.l.) and one in the Vierzon State Forest (120-190 m a.s.l.). The main secondary species in these forests were hornbeam (<em>Carpinus betulus</em> L.) and Scots pine (<em>Pinus sylvestris</em> L.). In 2020, we selected nine plots to represent a decline gradient in each of these forests. While the Orleans Forest was healthy overall, the Vierzon Forest had undergone several decline events due to successive droughts aggravated by edaphic factors. In the Pyrenees, we studied two sites in montane forests dominated by silver fir (<em>Abies alba</em> Mill.), whose decline is mainly the result of successive droughts occurring since the 1980’s, and with Norway spruce (<em>Picea abies</em> (L.) H. Karst) and European beech (<em>Fagus sylvatica</em> L.) as secondary species. In 2017, we selected 43 plots: (i) 21 plots in the Aure Valley (854-1570 m a.s.l.) and (ii) 22 plots on the Sault Plateau (705-1557 m a.s.l.). The severe summer drought of 2003 had significant effects on tree mortality in oak and fir forests (Cours and others, 2022). Finally, we studied 19 plots of montane forest in the Bavarian Forest National Park, dominated by Norway spruce (<em>Picea abies</em> (L.) H. Karst) with European beech and silver fir as the main secondary species (Bässler and others, 2009). The dieback results from several cycles of windstorms followed by bark beetle (<em>Ips typographus</em> (L.)) outbreaks (Müller and others, 2010), the dominant drivers of forest dynamics in Norway spruce forests in temperate Europe (Zemlerová and others, 2023). This dieback phenomenon was more severe than either of the aforementioned drought-induced declines, and resulted in greater tree mortality (Cours and others, 2021). In the fir and oak forests in France, our plots were set up in managed forests, and the surrounding forest was also predominantly managed. On the other hand, in the German spruce forest, our plots were set up both within the core area of the Bavarian Forest National Park, and in the surrounding zone (BIOKLIM project), with little or no human intervention (Müller and others, 2010).</p> <p><strong>Field measurements</strong></p> <p>Plots were set up with a Bitterlich relascope with an opening angle corresponding to counting factor n° 1 (ratio 1/50), and mean plot area was about 0.3 ha. For each tree within the plot, we recorded its status (i.e. dead, living, snag, log), tree-species and diameter at breast height (DBH; minimum DBH recorded = 17.5 cm for living trees and logs, 7.5 cm for snags, 67.5 cm for very large trees). We took the proportion of dead trees in basal area (i.e. the ratio of the cumulative basal area of standing and lying dead trees to the basal area of all the trees in the plot), hereinafter referred to as “mortality rate”, as a proxy for the level of local stand decline. Note that this “mortality rate” does not reflect true overall mortality rate in managed oak forests, as foresters removed most valuable declining trees. We visually inventoried TreMs on living trees, logs and snags, and included the 47 types described by Larrieu et al. (2018).</p> <p>For each deadwood item (length > 1 m) in the plot, we measured its decay stage (from 1 = hard dead wood fully covered with bark to 4 = soft wood without bark), length, diameter at mid-length for logs and snags < 4 m long, and DBH for dead trees and snags > 4 m. Deadwood was classified in the following categories: ground-lying (logs and uprooted dead trees) vs standing (snags and standing dead trees); small and mid-size (less than 40 cm in diameter) vs large and very large (more than 40 cm in diameter); and fresh (decay class 1 and 2) vs decayed (decay stage 3 and 4). We calculated the total number of items per hectare by allocating a coefficient N<sub>d</sub> related to diameter (d) to each item observed in the relascope sampling: (N<sub>d</sub> = π 10<sup>8</sup> [ArcTan(1/50)/(π d)]<sup>2</sup>). We estimated TreM diversity and the number of deadwood types per plot.</p> <p>We compiled a list of eco-morphological traits for woody elements (i.e., life status (living, dead) and vertical position (downed, standing), decay stage and diameter) and for TreMs detected in the field (TreM nature, association with deadwood (saproxylic, epixylic, mould), type of bearing substrate (i.e., living tree, dead tree or snag, and log), position in the tree (i.e. base, trunk, crown), degree of wetness, life span or ontogenesis).</p>
Effective management for deadwood-dwelling lichen diversity requires landscape-scale habitat protection
<ol> <li>Habitat loss is considered a major threat for biodiversity. However, the scales on which its effects occur are still insufficiently understood, namely, is the amount of available habitat important for species richness on both local and landscape scales? We studied the effects of local and landscape-scale habitat amount on local-scale species density of deadwood-dependent lichens in Swedish boreal forests. Creation and retention of dead wood are common practices to benefit forest biodiversity, and recognizing the relevant scale is critical for them to be successful.</li> <li>We surveyed deadwood-dependent lichens in 90 unmanaged forest stands that differed in the local and landscape habitat amount. The local habitat amount was measured as the amount of dead wood in the sampled stands (m<sup>2</sup> dead wood/ha), while six alternative proxies were used to estimate the landscape habitat amount, i.e., the amount of dead wood in the landscapes surrounding the sampled forest stands. Lichen species density (number of species per standardized dead wood area of 3.7 m<sup>2</sup>) was modelled as a function of local habitat amount and landscape habitat amount at multiple scales (300 m – 5 km from the stands).</li> <li>We found that lichen species density increased with the landscape habitat amount. The proportion of old forests (> 100 years, including newly clear-cut stands that until recently were old forests) within 5 km from the studied stands explained species density better than the other proxies of landscape habitat amount. Local dead wood amount did not affect species density, and there was no interaction between the local and landscape habitat amount. </li> <li> <em>Synthesis and applications</em>: To promote the conservation of deadwood-dependent lichens, the amount of old forests in managed forest landscapes should be maintained or increased. A certain amount of dead wood hosted more lichen species when situated in a landscape with more old forest, while there was no effect of the local dead wood amount. This suggests that management aimed at increasing the local species density of deadwood-dwelling lichens should focus on creating and maintaining habitat in the surrounding landscape rather than on only adding deadwood to that local site. In other words, effective management for deadwood-dependent lichen diversity requires landscape-scale habitat protection.</li> </ol>
Coarse woody debris accelerates the decomposition of deadwood inputs across temperate forest
<p class="MsoNormal">Wood decomposition is regulated by multiple controls, including climate and wood traits, that vary at local to regional scales. Yet decomposition rates differ dramatically when these controls do not. Fungal community dynamics are often invoked to explain these differences, suggesting that knowledge of ecosystem properties that influence fungal communities will improve understanding and projection of wood decomposition. We hypothesize that deadwood inputs decompose faster in forests with higher stocks of downed coarse woody material (CWM) because CWM is a resource from which lignocellulolytic fungi rapidly colonize new inputs. To test this hypothesis, we measure decomposition of 1,116 pieces of fine woody material (FWM) of five species, incubated for 13 to 49 months at five locations spanning 10°-latitude in eastern U.S. forest. We place FWM pieces near and far from CWM across observational transects and experimental common gardens. Soil temperature positively affects location-level mean decomposition rates, but these among-location differences are smaller than within-location variation in decomposition. Some of this variability is caused by CWM, where FWM pieces next to CWM decompose more rapidly. These effects are greater with time of incubation and lower initial wood density of FWM. The effect size of CWM is of the same relative magnitude as for the known controls of temperature, deadwood density and diameter. Abundance data for CWM is available for many forests and hence may be an ecosystem variable amenable for inclusion in decomposition models. Our findings suggest that conservation efforts to rebuild depleted CWM stocks in temperate forests may accelerate decomposition of fresh deadwood inputs.</p>
Data for: Diversity and specialization responses to climate and land use differ between deadwood fungi and bacteria
<p>This dataset contains data from a field study conducted in 2019 and described in the manuscript "Diversity and specialization responses to climate and land use differ between deadwood fungi and bacteria".</p> <p>To test the effects of land use and climate on diversity and community specialization (H2') of deadwood-inhabiting microbes, we exposed branches of four different tree species at 179 study sites, distributed over a spatial extent of 300 km x 300 km and 1000 m in elevation. Study sites were established in four local land-use types: forests, grasslands, arable sites, and settlements, embedded in near-natural, agricultural, or urban landscapes.</p> <p>We used negative-binomial generalized linear models for diversity and calculated community specialization on host trees by using the standardized two-dimensional Shannon entropy (H2') and beta-regression models.</p> <p>Our results show that host identity and hence specialization strongly exceed the effects of climate and land use for fungal but not bacterial communities. This suggests contrasting responses between microbial taxa to climate change and land-use intensification with further consequences on deadwood diversity interactions and hence decomposition processes.</p>
Fungi and deadwood diversity: A test of the area-heterogeneity trade-off hypothesis
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Coarse woody debris accelerates the decomposition of deadwood inputs across temperate forest
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Effective management for deadwood-dwelling lichen diversity requires landscape-scale habitat protection
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Data from: The wood economics spectrum modulates the positive effects of termite foraging intensity on deadwood invertebrate diversity
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Data from: Plant functional traits affect invertebrate predator diversity via bottom-up effects in a deadwood-based food web
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Cross-kingdom interactions and functional patterns of active microbiota matter in governing deadwood decay
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Data and code from: Decadal recovery of fungal but not termite deadwood decay in tropical rainforest
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Contrasting effects of deadwood and gaps on the trophic structure of forest soil microarthropods
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Retention forestry can maintain epiphytic lichens on living pine trees, but provides impoverished habitat for deadwood-associated lichens
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Data from: Optimizing enrichment of deadwood for biodiversity by varying sun exposure and tree species: an experimental approach
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Data for: Diversity and specialization responses to climate and land use differ between deadwood fungi and bacteria
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Data from: Deadwood supports carnivores in leaf litter communities in a bark beetle-attacked deadwood simulation experiment
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.