@article{MusterRileyRothetal.2019,
  author    = {Muster, Sina and Riley, William J. and Roth, Kurt and Langer, Moritz and Aleina, Fabio Cresto and Koven, Charles D. and Lange, Stephan and Bartsch, Annett and Grosse, Guido and Wilson, Cathy J. and Jones, Benjamin M. and Boike, Julia},
  title     = {Size distributions of arctic waterbodies reveal consistent relations in their statistical moments in space and time},
  series = {Frontiers in Earth Science},
  volume    = {7},
  journal   = {Frontiers in Earth Science},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {2296-6463},
  doi       = {10.3389/feart.2019.00005},
  pages     = {15},
  year      = {2019},
  abstract  = {Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (<5 m) circum-Arctic Permafrost Region Pond and Lake (PeRL) database recently becoming available. We have used this database to make the first consistent, high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0.0001 km(2) (100 m(2)) to 1 km(2). We found that the size distributions varied greatly across the thirty study regions investigated and that there was no single universal size distribution function (including power-law distribution functions) appropriate across all of the study regions. We did, however, find close relationships between the statistical moments (mean, variance, and skewness) of the waterbody size distributions from different study regions. Specifically, we found that the spatial variance increased linearly with mean waterbody size (R-2 = 0.97, p < 2.2e-16) and that the skewness decreased approximately hyperbolically. We have demonstrated that these relationships (1) hold across the 30 Arctic study regions covering a variety of (bio)climatic and permafrost zones, (2) hold over time in two of these study regions for which multi-decadal satellite imagery is available, and (3) can be reproduced by simulating rising water levels in a high-resolution digital elevation model. The consistent spatial and temporal relationships between the statistical moments of the waterbody size distributions underscore the dominance of topographic controls in lowland permafrost areas. These results provide motivation for further analyses of the factors involved in waterbody development and spatial distribution and for investigations into the possibility of using statistical moments to predict future hydrologic dynamics in the Arctic.},
  language  = {en}
}
@article{MoustakasGuentherWiegandetal.2006,
  author    = {Moustakas, Aristides and G{\"u}nther, Matthias and Wiegand, Kerstin and M{\"u}ller, Karl-Heinz and Ward, David and Meyer, Katrin M. and Jeltsch, Florian},
  title     = {Long-term mortality patterns of the deep-rooted Acacia erioloba},
  series = {Journal of vegetation science},
  volume    = {17},
  journal   = {Journal of vegetation science},
  publisher = {Blackwell},
  address   = {Malden},
  issn      = {1100-9233},
  doi       = {10.1111/j.1654-1103.2006.tb02468.x},
  pages     = {473 -- 480},
  year      = {2006},
  abstract  = {Question: Is there a relationship between size and death in the Iona-lived, deep-rooted tree, Acacia erioloba, in a semi-arid savanna? What is the size-class distribution of A. erioloba mortality? Does the mortality distribution differ from total tree size distribution? Does A. erioloba mortality distribution match the mortality distributions recorded thus far in other environments? Location: Dronfield Ranch, near Kimberley, Kalahari, South Africa. Methods: A combination of aerial photographs and a satellite image covering 61 year was used to provide long-term spatial data on mortality. We used aerial photographs of the study area from 1940, 1964, 1984, 1993 and a satellite image from 2001 to follow three plots covering 510 ha. We were able to identify and individually follow ca. 3000 individual trees from 1940 till 2001. Results: The total number of trees increased over time. No relationship between total number of trees and mean tree size was detected. There were no trends over time in total number of deaths per plot or in size distributions of dead trees. Kolmogorov-Smirnov tests showed no differences in size class distributions for living trees through time. The size distribution of dead trees was significantly different from the size distribution of all trees present on the plots. Overall, the number of dead trees was low in small size classes, reached a peak value when canopy area was 20 - 30 m(2), and declined in lamer size-classes. Mortality as a ratio of dead vs. total trees peaked at intermediate canopy sizes too. Conclusion: A. erioloba mortality was size-dependent, peaking at intermediate sizes. The mortality distribution differs from all other tree mortality distributions recorded thus far. We suggest that a possible mechanism for this unusual mortality distribution is intraspecific competition for water in this semi-arid environment.},
  language  = {en}
}