@article{HuaCookFohlmeisteretal.2017,
  author    = {Hua, Quan and Cook, Duncan and Fohlmeister, Jens Bernd and Penny, Dan and Bishop, Paul and Buckman, Solomon},
  title     = {Radiocarbon Dating of a Speleothem Record of Paleoclimate for Angkor, Cambodia},
  series = {Radiocarbon : an international journal of cosmogenic isotope research},
  volume    = {59},
  journal   = {Radiocarbon : an international journal of cosmogenic isotope research},
  number    = {Special Issue 6 / 2},
  publisher = {The University of Arizona, Department of Geosciences},
  address   = {Tucson, Ariz.},
  issn      = {0033-8222},
  doi       = {10.1017/RDC.2017.115},
  pages     = {1873 -- 1890},
  year      = {2017},
  abstract  = {We report the chronological construction for the top portion of a speleothem, PC1, from southern Cambodia with the aim of reconstructing a continuous high-resolution climate record covering the fluorescence and decline of the medieval Khmer kingdom and its capital at Angkor (similar to 9th-15th centuries AD). Earlier attempts to date PC1 by the standard U-Th method proved unsuccessful. We have therefore dated this speleothem using radiocarbon. Fifty carbonate samples along the growth axis of PC1 were collected for accelerator mass spectrometry (AMS) analysis. Chronological reconstruction for PC1 was achieved using two different approaches described by Hua et al. (2012a) and Lechleitner et al. (2016a). Excellent concordance between the two age-depth models indicates that the top similar to 47 mm of PC1 grew during the last millennium with a growth hiatus during similar to 1250-1650 AD, resulting from a large change in measured C-14 values at 34.4-35.2 mm depth. The timing of the growth hiatus covers the period of decades-long droughts during the 14th-16th centuries AD indicated in regional climate records.},
  language  = {en}
}
@article{KramerSchadtNiedballaPilgrimetal.2013,
  author    = {Kramer-Schadt, Stephanie and Niedballa, J{\"u}rgen and Pilgrim, John D. and Schr{\"o}der-Esselbach, Boris and Lindenborn, Jana and Reinfelder, Vanessa and Stillfried, Milena and Heckmann, Ilja and Scharf, Anne K. and Augeri, Dave M. and Cheyne, Susan M. and Hearn, Andrew J. and Ross, Joanna and Macdonald, David W. and Mathai, John and Eaton, James and Marshall, Andrew J. and Semiadi, Gono and Rustam, Rustam and Bernard, Henry and Alfred, Raymond and Samejima, Hiromitsu and Duckworth, J. W. and Breitenmoser-Wuersten, Christine and Belant, Jerrold L. and Hofer, Heribert and Wilting, Andreas},
  title     = {The importance of correcting for sampling bias in MaxEnt species distribution models},
  series = {Diversity \& distributions : a journal of biological invasions and biodiversity},
  volume    = {19},
  journal   = {Diversity \& distributions : a journal of biological invasions and biodiversity},
  number    = {11},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {1366-9516},
  doi       = {10.1111/ddi.12096},
  pages     = {1366 -- 1379},
  year      = {2013},
  abstract  = {AimAdvancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better-surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo. LocationBorneo, Southeast Asia. MethodsWe collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range-restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north-eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas. ResultsSpatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased. Main ConclusionsWe conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.},
  language  = {en}
}