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76 results for “Tree mortality”
Data from: Negative density dependence in the mortality and growth of tropical tree seedlings is strong, and primarily caused by fungal pathogens
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Relative tree growth and mortality responses to the 2012 midwestern US drought in the central hardwood ecoregion
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Data from: Woodland resilience to regional drought: Dominant controls on tree regeneration following overstorey mortality
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Exploring the role of genetic diversity and relatedness in tree seedling growth and mortality: a multi‐species study in a Bornean rain forest
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Data from: Disturbance detection in Landsat time series is influenced by tree mortality agent and severity, not by prior disturbance
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Data from: Three decades of annual growth, mortality, physical condition, and microsite for ten tropical rainforest tree species
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Decomposition of bark beetle-attacked trees after mortality varies across forests
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Tropical tree species differ in damage and mortality from lightning
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Climate‐driven tree growth and mortality in the Black Forest, Germany: Long‐term observations
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Dead tree removal after drought mortality increases understory plant diversity in a mixed conifer forest
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Data from: Linking physiological drought resistance traits to growth and mortality of three northeastern tree species
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Data from: Drought and heat-induced mortality of conifer trees is explained by leaf and growth legacies
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Dead Again: Predictions of repeat tree die-off under hotter droughts confirm mortality thresholds for a dryland conifer species
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Effects of niche marginality on hotter-drought tree mortality in angiosperms and gymnosperms
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Data from: Positive effects of tree species diversity on productivity switch to negative after severe drought mortality in a temperate forest experiment
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Data from: Betting on rains that do not come: Monsoon failure and leaf area overshoot relate to increased tree mortality from drought
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Data from: Light availability predicts mortality probability of conifer saplings in Swiss mountain forests better than radial growth and tree size
<p>Radial and height growth rates are suitable indicators of impending tree mortality risk of adult trees, but their applicability to saplings remains unknown. We compared radial growth of living and dead saplings of different heights and quantified the effects of light availability, growth and tree size on mortality. We sampled an equal number of living and dead saplings of four coniferous tree species (<i>Pinus cembra</i>, <i>Larix decidua</i>, <i>Picea abies</i>, <i>Abies alba</i>) in nine forests along an elevational gradient of the Swiss Alps. Based on tree-ring widths reconstructed from stem disks at multiple tree heights, we calculated radial growth rates. We observed a divergent pattern in radial growth of living and dead saplings, with reduced growth of dead saplings starting several years prior to death. By matching living and dead saplings of similar ages, we tested whether mortality probabilities of saplings were influenced by light availability, recent growth rates and diameter. Mortality of coniferous saplings in mountain forests was mainly influenced by light availability, with changing effects along the elevational gradient. Recent radial growth rate and tree size were only weakly associated with sapling mortality. Our study establishes the importance of long-term predisposing factors for the mortality probability of conifer saplings in mountain forests, thus extending well-established findings from the adult stage to saplings, which represent a critical stage of forest dynamics.</p>
Predicting tropical tree mortality with leaf spectroscopy
<p>Do tropical trees close to death have a distinct change to their leaf spectral signature? Tree mortality rates have been increasing in tropical forests globally, reducing the global carbon sink. Upcoming hyperspectral satellites could be used to predict regions close to experiencing extensive tree mortality during periods of stress, such as drought. Here we show, for a tropical rainforest in Borneo, how imminent tropical tree mortality impacts leaf physiological traits and reflectance. We measured leaf reflectance (400-2500 nm), light saturated photosynthesis (A<sub>sat</sub>), leaf dark respiration (R<sub>dark</sub>), leaf mass area (LMA) and % leaf water across five campaigns in a six-month period during which there were two causes of tree mortality: a major natural drought and a co-incident tree stem girdling treatment. We find that prior to mortality, there were significant (P<0.05) leaf spectral changes in the red (650-700 nm), the NIR (1000 -1400 nm) and SWIR bands (2000-2400 nm) and significant reductions in the potential carbon balance of the leaves (increased R<sub>dark</sub> and reduced A<sub>sat</sub>). We show that the partial least squares regression technique can predict mortality in tropical trees across different species and functional groups with medium precision but low accuracy (r<sup>2</sup> of 0.65 and RMSE/mean of 0.58). However, most tree death in our study was due to girdling, which is not a natural form of death. More research is needed to determine if this spectroscopy technique can be applied to tropical forests in general.</p>
Data from: Towards a common methodology for developing logistic tree mortality models based on ring-width data
Tree mortality is a key process shaping forest dynamics. Thus, there is a growing need for indicators of the likelihood of tree death. During the last decades, an increasing number of tree-ring based studies have aimed to derive growth–mortality functions, mostly using logistic models. The results of these studies, however, are difficult to compare and synthesize due to the diversity of approaches used for the sampling strategy (number and characteristics of alive and death observations), the type of explanatory growth variables included (level, trend, etc.), and the length of the time window (number of years preceding the alive/death observation) that maximized the discrimination ability of each growth variable. We assess the implications of key methodological decisions when developing tree-ring based growth–mortality relationships using logistic mixed-effects regression models. As examples, we use published tree-ring datasets from Abies alba (13 different sites), Nothofagus dombeyi (one site), and Quercus petraea (one site). Our approach is based on a constant sampling size and aims at (1) assessing the dependency of growth–mortality relationships on the statistical sampling scheme used, (2) determining the type of explanatory growth variables that should be considered, and (3) identifying the best length of the time window used to calculate them. The performance of tree-ring-based mortality models was reasonably high for all three species (area under the receiving operator characteristics curve, AUC > 0.7). Growth level variables were the most important predictors of mortality probability for two species (A. alba, N. dombeyi), while growth-trend variables need to be considered for Q. petraea. In addition, the length of the time window used to calculate each growth variable was highly uncertain and depended on the sampling scheme, as some growth–mortality relationships varied with tree age. The present study accounts for the main sampling-related biases to determine reliable species-specific growth–mortality relationships. Our results highlight the importance of using a sampling strategy that is consistent with the research question. Moving towards a common methodology for developing reliable growth–mortality relationships is an important step towards improving our understanding of tree mortality across species and its representation in dynamic vegetation models.
The magnitude of large-scale tree mortality caused by the invasive pathogen Phytophthora ramorum
<p>Forest pathogens are important drivers of tree mortality across the globe but it is exceptionally challenging to gather and build unbiased quantitative models of their impacts, which has resulted in few estimates matching the scale of disease. Here we harness the rare dataset matching the spatial scale of pathogen invasion, host, and disease heterogeneity to estimate infection and mortality for the four most susceptible host species of <em>Phytophthora ramorum</em>, an invasive pathogen that drives the most important biological cause of tree mortality in a broad geographic region of coastal California and southwest Oregon. As of 2012, the most current field survey year, we estimate 17.5 (± 4.6 95% CI) million tanoak (<em>Notholithocarpus densiflorus)</em> stems were pathogen-killed with an additional 71 (± 21.5) million infected. We estimated 9.0 million (± 2.2) coast live oak (<em>Quercus agrifolia)</em> and 1.7 million (± 0.5) California black oak (<em>Quercus kelloggii</em>) stems are disease impacted (mortality and infection combined). Lastly, our estimates showed 95.23 million (± 8.6) million infected California bay laurel (<em>Umbellularia californica</em>), which does not suffer mortality from infection and represents a critical source of continued spread. Using an extensive dataset on mortality rates of infected individuals we estimate that cumulative mortality likely increased from 20.8 to 42.8 million individual stems between 2012 and 2019 for all species. While these impacts are substantial, the majority of host populations occur in a yet to be invaded region of northern California indicating that the disease will intensify in the coming decades.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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.