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94 results for “dieback”
Ash dieback mortality and damage at the Botanic Garden Meise, Belgium
<p>Four 10m × 10m plots were laid out in the naturally regenerating woodland at the Botanic Garden Meise (WGS84: 50° 55ʹ 37ʺ N, 4° 19ʹ 18ʺ E; 50° 55ʹ 37ʺ N, 4° 19ʹ 17ʺ E; 50° 55' 38.6" N 4° 19ʹ 21ʺ E; 50° 55ʹ 39ʺ N, 4° 19ʹ 29ʺ E). They were selected because the areas contained a large number of ash saplings. Within these plots all ash seedlings greater than 40 cm tall were labelled with a small (2 cm × 4 cm) plastic tag attached with stretchable plant tie. Each tag was engraved with a unique number so that the tree could be identified. These plots were not intended to be replicates but just a convenient method of refinding the tagged trees. In the first year either the height or the girth of the tree was measured with a tape measure, depending upon whether the tree was small enough to measure the height. In the first year and each subsequent year each tree was scored for the apparent damage caused by ash dieback (<em>Hymenoscyphus pseudoalbidus</em>). The same scoring scheme was used as that by Pliūra et al. (2011). This is a 5 point system where 1 is a dead tree; 5 is an undamaged tree and 2–4 are progressively less damaged trees. The plots were laid out on 14 April 2013. In 2014 plots 1 and 2 were scored on 14th April and plots 3 and 4 on 21st April. In 2015 plots 1 and 2 were scored on 6th May and plots 3 and 4 on 30th April.</p>
Annotation of the the assembled genome of Fusarium oxysporum f. sp. albedinis strain 133, the causal agent of date palm dieback.
<p>Annotation of the the assembled genome of <em>Fusarium oxysporum f. sp. albedinis</em> strain 133 (Khayi et al., 2020). Gene prediction and annotation were carried out using funnotate pipeline v1.8.1 (Stajich, 2020), which includes masking, ab initio gene-prediction training, using Augustus and Genmark, with the EST dataset reported to the Ganoderma mycocosm repository, gene prediction, and the assignment of functional annotation to protein-coding gene models.</p>
Models simulating abrupt changes in the Chilika lagoon fishery, the Easter Island community, forest dieback and lake water quality
<p>This deposit is in support of Willcock et al "Earlier collapse of Anthropocene ecosystems driven by multiple faster and noisier drivers". It covers the following items: (i) A list of the files contained within this data deposit; (ii) How to access and download the specialist software required to view and simulate the system dynamics models (STELLA ‘isee Player’); (iii) How to run isee Player to simulate the models; (iv) How to access and download the standard statistical software ‘R’ to run the R scripts; (v) How to load ‘R’ and modify the standard R script to analyse a subset of the model runs. This file will also details the ‘required content’ (e.g. software versions), as specified in the ‘nr-software-policy.pdf’ document.</p> <p>The full descriptions of each of the four system dynamics models used in this manuscript can be read in the following papers:</p> <ol> <li>Lake Chilika – Cooper, G. S. & Dearing, J. A. Modelling future safe and just operating spaces in regional social-ecological systems. <em>Sci. Total Environ.</em> <strong>651</strong>, 2105–2117 (2019), <a href="https://doi.org/10.1016/j.scitotenv.2018.10.118">https://doi.org/10.1016/j.scitotenv.2018.10.118</a></li> <li>Easter Island – Brandt, G. & Merico, A. The slow demise of Easter Island: Insights from a modeling investigation. <em>Front. Ecol. Evol.</em> <strong>3</strong>, 13 (2015), <a href="https://www.frontiersin.org/article/10.3389/fevo.2015.00013">https://www.frontiersin.org/article/10.3389/fevo.2015.00013</a></li> <li>Lake phosphorus – Wang, R. <em>et al.</em> Flickering gives early warning signals of a critical transition to a eutrophic lake state. <em>Nature</em> <strong>492</strong>, 419–22 (2012), <a href="http://dx.doi.org/10.1038/nature11655">http://dx.doi.org/10.1038/nature11655</a></li> <li>TRIFFID - Ritchie, P. D. L., Clarke, J. J., Cox, P. M. & Huntingford, C. Overshooting tipping point thresholds in a changing climate. <em>Nat. 2021 5927855</em> <strong>592</strong>, 517–523 (2021), <a href="http://dx.doi.org/10.1038/nature11655">http://dx.doi.org/10.1038/nature11655</a></li> </ol>
Leaf traits plus growth and dieback data for cloud forest epiphytes in a cloud exclusion treatment at Wayqecha Biological Station, Peru
Morphological traits [specific leaf area, stomatal length and density, leaf thickness, cuticle thickness and hydrenchymal layer thickness] and physiological traits [stomatal conductance, minimum leaf conductance and foliar water uptake capacity] along with pressure volume curves were measured at the Wayqecha Biological Station, Peru. Vascular epiphytes were sampled from a control plot and fog reduction treatment plot constructed from two 30 m tall aluminum towers, 40 m apart with panels of polyethylene mesh strung between the towers. Measurements of leaf morphological traits were taken in June-July 2022 in the control and treatment plots to assess plasticity to fog reductionin the fifth year of the experiment. Samples of branches and leaves for each individual approximately 1-2 meters off the ground were collected in the plot, close enough to the curtain to maximize any effects of fog interception. Growth and dieback data were also collected species of vascular and non-vascular epiphytes attached to wooden transplant boards in the control and treatment plot in 2017. Rates of growth and dieback were recorded for vascular epiphytes from 2017-2019 and for non-vascular epiphytes from 2018-2019. The 2017 data collection took place in November while data for 2018 and 2019 were collected in June.
Ash dieback mortality and damage at the Botanic Garden Meise, Belgium, 2016
<p>These data add to the data already published for previous years and are available at Groom, Quentin J. (2015). Ash dieback mortality and damage at the Botanic Garden Meise, Belgium. Zenodo. 10.5281/zenodo.17640.</p> <p>Four 10m × 10m plots were laid out in the naturally regenerating woodland at the Botanic Garden Meise (WGS84: 50° 55ʹ 37ʺ N, 4° 19ʹ 18ʺ E; 50° 55ʹ 37ʺ N, 4° 19ʹ 17ʺ E; 50° 55' 38.6" N 4° 19ʹ 21ʺ E; 50° 55ʹ 39ʺ N, 4° 19ʹ 29ʺ E). They were selected because the areas contained a large number of ash saplings. Within these plots all ash seedlings greater than 40 cm tall were labelled with a small (2 cm × 4 cm) plastic tag attached with stretchable plant tie. Each tag was engraved with a unique number so that the tree could be identified. These plots were not intended to be replicates but just a convenient method of refinding the tagged trees. In the first year either the height or the girth of the tree was measured with a tape measure, depending upon whether the tree was small enough to measure the height. In 2013 and each subsequent year each tree was scored for the apparent damage caused by ash dieback (<em>Hymenoscyphus pseudoalbidus</em>). The same scoring scheme was used as that by Pliūra et al. (2011). This is a 5 point system where 1 is a dead tree; 5 is an undamaged tree and 2–4 are progressively less damaged trees. In 2016 the plots were scored on 4<sup>th</sup> May.</p>
50000 SNPs for genomic prediction of ash dieback susceptibility in European Ash
<p>This file is <a href="https://www.nature.com/articles/s41559-019-1036-6">Stocks et al (2019)</a> Supplementary Table 7j with major allele (MAA) and minor allele (MIA) identities added.<br> Estimated effect sizes (EES) from genomic prediction model trained on the pool-seq data using the top 50000 SNPs from the pool-seq GWAS<br> Contig = Contig in BATG0.5 assembly <br> Pos = SNP location in contig <br> EES.MIA = Estimated effect size of minor allele <br> EES.MIA.SE = Standard Error of Estimated effect size of minor allele<br> EES.MAA = Estimated effect size of major allele<br> EES.MAA.SE = Standard Error of Estimated effect size of major allele <br> MIA = identity of minor allele <br> MAA = identity of major allele</p>
The ash dieback invasion of Europe was founded by two genetically divergent individuals (https://doi.org/10.1038/s41559-018-0548-9)
<p>Hymenoscyphus fraxineus, genome, annotation and genetic diversity data used in analysis of the ash dieback invasion of Europe (McMullan et al., 2018; see README.txt)</p> <p>McMullan M, Rafiqi M, Kaithakottil G, Clavijo BJ, Bilham L, Orton E, et al. The ash dieback invasion of Europe was founded by two genetically divergent individuals. Nat Ecol Evol. 2018; Available from: https://doi.org/10.1038/s41559-018-0548-9</p>
Gas exchange, dieback, leaf water potential and chlorophyll content during a greenhouse drought experiment: An evolutionary perspective on functional diversity in co-occuring willow(salix) species
Thirteen willow (Salix) species occur in southeastern Minnesota and often co-occur within the same wetlands. This high local diversity is challenging to explain since closely related species are often functionally similar and density-dependent interactions such as competition and susceptibility to pests and pathogens should limit their co-occurrence. However, if willow species are partitioning resources, or if they are phylogenetically structured so that closely related species rarely co-occur, then the impact of these density-dependent processes could be reduced. In this study, I examined the role of niche partitioning in maintaining local willow diversity by comparing species physiology in a greenhouse.
Landscape epidemiology of ash dieback
<ol> <li>Ash dieback is induced by <em>Hymenoscyphus</em> <em>fraxineus</em>, an invasive pathogenic fungus. It is causing severe damage to European ash populations. However, the local environment, such as climate or site conditions, is known to affects ash dieback.</li> <li>We studied the landscape epidemiology of the disease on a 22 km² area in north-eastern France at two stages of the invasion process using Bayesian spatio-temporal models fitted with integrated nested Laplace approximation (INLA). Several features characterizing disease severity, crown dieback, frequency of collar canker, and density of infected leaf debris in the litter were determined on a regular grid over a 3.5 x 6.5 km area. We first analysed the effect of landscape features on the disease establishment stage in 2012, two years after the first report of the disease in the area, and then on further disease development, in 2016–2018.</li> <li>Landscape features had little impact on the disease at the establishment stage but strongly determined its further development. Local fragmentation of tree cover was the most important factor, with trees that are isolated or in hedges far less affected than trees in a forest environment. We showed that they were subjected to different microclimates, with higher crown temperatures unfavourable to pathogen development. Low host density strongly reduced disease development. The presence of large ash populations in the vicinity affected local disease severity up to several hundred meters.</li> <li> <em>Synthesis</em>. We showed that the landscape characteristics strongly affect the development and spread of ash dieback. The disease is far less severe in forest conditions when ash density is low or in open canopies such as hedges and isolated trees. Ash trees are often in these types of landscapes, which should strongly limit the overall impact of Ash dieback.</li> </ol>
Data and code for the publication "Dieback of Calluna vulgaris in a coastal heathland after the 2018 extreme drought"
<p>Original data and code for the publication "Dieback of Calluna vulgaris in a coastal heathland after the 2018 extreme drought"</p>
Mistletoes could moderate drought impacts on woodland birds, but are themselves susceptible to drought-induced dieback
<p>Mistletoes are hemiparasitic plants and keystone species in many ecosystems globally. Given predicted increases in drought frequency and intensity, mistletoes may be crucial for moderating drought impacts on community structure. Dependent on host vascular flows, mistletoes can succumb to stress when water availability falls, making them susceptible to mortality during drought. We counted mistletoe across >350,000 km<sup>2</sup> of south-eastern Australia and conducted standardised bird surveys between 2016 and 2021, spanning a major drought event in 2018-19. We aimed to identify predictors of mistletoe abundance and mortality and determine whether mistletoes might moderate drought impacts on woodland birds. Live mistletoe abundance varied with tree species composition, land use and presence of mistletoebirds. Mistletoe mortality was widespread, consistent with high 2018/19 summer temperatures, low 2019/20 summer rainfall and the interaction between summer temperatures and rainfall in 2019/20. The positive association between surviving mistletoes and woodland birds was greatest in the peak drought breeding seasons of 2018/19 and 2019/20, particularly for small residents and insectivores. Paradoxically, mistletoes could moderate drought impacts on birds, but are themselves vulnerable to drought-induced mortality. An improved understanding of the drivers and dynamics of mistletoe mortality is needed to address potential cascading trophic impacts associated with mistletoe die-off.</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>
Hydraulic prediction of drought-induced plant dieback and top-kill depends on leaf habit and growth form
<p>Hydraulic failure caused by severe drought contributes to aboveground dieback and whole-plant death. The extent to which dieback or whole-plant death can be predicted by plant hydraulic traits has rarely been tested among species with different leaf habits and/or growth forms. We investigated 19 hydraulic traits in 40 woody species in a tropical savanna and their potential correlations with drought response during an extreme drought event during the El Niño–Southern Oscillation in 2015. Plant hydraulic trait variation was partitioned substantially by leaf habit but not growth form along a trade-off axis between traits that support drought tolerance versus avoidance. Semi-deciduous species and shrubs had the highest branch dieback and top-kill (complete aboveground death) among the leaf habits or growth forms. Dieback and top-kill were well explained by combining hydraulic traits with leaf habit and growth form, suggesting integrating life history traits with hydraulic traits will yield better predictions.</p>
Ability of the ash dieback pathogen to reproduce and to induce damage on its host are controlled by different environmental parameters
<p>Ash dieback, induced by an invasive ascomycete, <em>Hymenoscyphus</em> <em>fraxineus</em>, has emerged in the late 1990s as a severe disease threatening ash populations in Europe. Future prospects for ash are improved by the existence of individuals with natural genetic resistance or tolerance to the disease and by limited disease impact in many environmental conditions where ash is common. Nevertheless, it was suggested that, even in those conditions, ash trees are infected and enable pathogen transmission. We studied the influence of climate and local environment on the ability of <em>H. fraxineus</em> to infect, be transmitted and cause damage on its host. We showed that healthy carriers, i.e. individuals showing no dieback but carrying <em>H. fraxineus</em>, exist and may play a significant role in ash dieback epidemiology. The environment strongly influenced <em>H. fraxineus</em> with different parameters being important depending on the life cycle stage. The ability of <em>H. fraxineus</em> to establish on ash leaves and to reproduce on the leaf debris in the litter (rachises) mainly depended on total precipitation in July-August and was not influenced by local tree cover. By contrast, damage to the host, and in particular shoot mortality was significantly reduced by high summer temperature in July-August and by high autumn average temperature. As a consequence, in many situations, ash trees are infected and enable H. fraxineus transmission while showing limited or even no damage. We also observed a decreasing trend of severity (leaf necrosis and shoot mortality probability) with the time of disease presence in a plot that could be significant for the future of ash dieback.</p>
Plant dieback in Mediterranean shrublands under 2014 extreme drought (Alicante, Spain)
<p>Field data from four Mediterranean shrublands monitored during an extreme drought in 2014 (hydrological year) in SE Spain. Data are plant dieback of the total plant community and main plant biotypes (shrubs, subshrubs and grasses), vegetation structure attributes (phytovolume, plant height, LAI, and sizes of patch and inter-patch areas), soil properties (soil depth and soil surface cover types) and water availability indicators (soil moisture and environmental variables).</p>
Neighbours matter and the weak succumb: chalara ash dieback infection is more severe in trees with fewer conspecific neighbours and lower prior growth rate
<p>The epidemiology and severity of ash dieback (ADB), the disease caused by the ascomycete fungus, <em>Hymenoscyphus</em> <em>fraxineus</em>, has been linked to a variety of site conditions, however, there has been a lack of analysis at an individual-tree scale. <br> <br>Symptoms of ADB were scored on ca. 400 trees of <em>Fraxinus</em> <em>excelsior</em> (ash) in permanent sample plots during two successive years in a UK natural woodland reserve. Using comprehensive plot records maintained since 1945, and detailed spatial records updated since 1977, we assembled an array of potential explanatory variables, including site environment factors, ash tree density, previous and present tree condition and near neighbourhood summary statistics (NNSS), such as species mingling and size dominance. Their impact on the severity of ADB of focal ash trees was tested with generalised linear mixed effects models (GLMM).<br> <br>The severity of ADB was much greater in the lower slope parts of the site with moister soils and least in a managed area subject to tree thinning in the previous 35 years. Severity of ADB had a negative association with focal ash tree prior relative growth rate over a period of a decade immediately before the disease was detected at the site. Greater ADB severity was also significantly associated with smaller diameter-at-breast-height of ash trees. Additionally, ADB was significantly positively associated with a greater proportion of heterospecific trees amongst the six nearest neighbours of the focal tree. <br> <br><em>Synthesis</em>. The relationship of the severity of ADB disease with site environment, tree condition and neighbourhood is complex but nevertheless important in the progression of the disease. The findings suggest some silvicultural interventions, such as thinning to increase the vigour of retained ash trees, might reduce the impact of ADB.</p>
Dataset for: Population decline in a Pleistocene refugium: stepwise, drought-related dieback of a South Australian eucalypt
<p><span>Refugia can facilitate the persistence of species through long-term environmental change, but it is not clear if Pleistocene refugia will remain functional under anthropogenic climate change. Dieback of species within refugia therefore raises concerns about their long-term persistence. Using repeat field surveys, we investigate dieback patterns of an isolated population of <em>Eucalyptus</em> <em>macrorhyncha</em> during two droughts and discuss prospects for its continued persistence in a Pleistocene refugium. We first confirm that the Clare Valley in South Australia has constituted a long-term refugium for the species, with the population being genetically highly distinct from other conspecific populations. However, the population lost >40% of individuals and biomass through the two droughts, with mortality being just below 20% after the Millennium Drought (2000–2009) and almost 25% after the Big Dry (2017-2019). The best predictors of mortality differed after each drought. While the north-facing aspect of a sampling location was a significant positive predictor after both droughts, biomass density and slope were significant negative predictors only after the Millennium Drought, and distance to the north-west corner of the park, which intercepts hot, dry winds, was significant after the Big Dry only. This suggests that more marginal sites with low biomass and located on exposed, flat plateaus were more vulnerable initially, but that heat-stress was an important driver of dieback during the Big Dry. Therefore, the causative drivers of dieback may change during population decline. Regeneration occurred predominantly on southern and eastern aspects, which would receive the least solar radiation. Occurrence in a refugium did not protect this population from dieback. However, gullies with lower solar radiation are continuing to support relatively healthy, regenerating stands of red stringybark, providing hope for persistence in small pockets. Monitoring and managing these pockets during future droughts will be essential to ensure the persistence of this isolated and genetically unique population.</span></p>
Hydraulic prediction of drought-induced plant dieback and top-kill depends on leaf habit and growth form
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Mistletoes could moderate drought impacts on woodland birds, but are themselves susceptible to drought-induced dieback
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Dataset for: Population decline in a Pleistocene refugium: stepwise, drought-related dieback of a South Australian eucalypt
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Allen Brain Atlas
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DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.