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4 results for “Ring-Widths”
Standardized ring-width chronologies of thinleaf alder growth along the Tanana River floodplains spanning 1968-2006.
This dataset contains landscape level and site level standardized ring-width chronologies (1968-2006) of thinleaf alder growth along the Tanana River floodplains. The raw tree ring-width series from each alder disk was individually detrended to remove age-related growth trends. The chronologies represent the high-frequency variation in ring-width to be analyzed at the inter-annual time scale.
Standardized ring-width chronologies for white and black spruce at three sites in the southern Brooks Range, Alaska
Although black spruce is the dominant treeline species in the eastern boreal forest, its distribution stops several kilometers short of treeline in the Brooks Range in Alaska, and white spruce is the dominant treeline species. The explanation for this distribution is not known, but two hypotheses are plausible. First, black spruce may be less tolerant of climatic conditions near treeline than white spruce. Second, black spruce may be unable to regenerate successfully near treeline due to long intervals between fires. We are establishing permanently marked study plots along a transect from the Yukon River basin, where black spruce is the dominant species, to the foothills of the Brooks Range, where it reaches its distributional limit. We are reconstructing recruitment history of both black and white spruce at our study sites, and are reconstructing recent fire history from analysis of fire scars and stand age structures. These data are being used to parameterize matrix population models, with which we are describing patterns of population stability.
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.
Data from: Towards a common methodology for developing logistic tree mortality models based on ring-width data
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