@article{GeyerKieferKreftetal.2011,
  author    = {Geyer, Juliane and Kiefer, Iris and Kreft, Stefan and Chavez, Veronica and Salafsky, Nick and Jeltsch, Florian and Ibisch, Pierre L.},
  title     = {Classification of climate-change-induced stresses on biological diversity},
  series = {Conservation biology : the journal of the Society for Conservation Biology},
  volume    = {25},
  journal   = {Conservation biology : the journal of the Society for Conservation Biology},
  number    = {4},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {0888-8892},
  doi       = {10.1111/j.1523-1739.2011.01676.x},
  pages     = {708 -- 715},
  year      = {2011},
  abstract  = {Conservation actions need to account for and be adapted to address changes that will occur under global climate change. The identification of stresses on biological diversity (as defined in the Convention on Biological Diversity) is key in the process of adaptive conservation management. We considered any impact of climate change on biological diversity a stress because such an effect represents a change (negative or positive) in key ecological attributes of an ecosystem or parts of it. We applied a systemic approach and a hierarchical framework in a comprehensive classification of stresses to biological diversity that are caused directly by global climate change. Through analyses of 20 conservation sites in 7 countries and a review of the literature, we identified climate-change-induced stresses. We grouped the identified stresses according to 3 levels of biological diversity: stresses that affect individuals and populations, stresses that affect biological communities, and stresses that affect ecosystem structure and function. For each stress category, we differentiated 3 hierarchical levels of stress: stress class (thematic grouping with the coarsest resolution, 8); general stresses (thematic groups of specific stresses, 21); and specific stresses (most detailed definition of stresses, 90). We also compiled an overview of effects of climate change on ecosystem services using the categories of the Millennium Ecosystem Assessment and 2 additional categories. Our classification may be used to identify key climate-change-related stresses to biological diversity and may assist in the development of appropriate conservation strategies. The classification is in list format, but it accounts for relations among climate-change-induced stresses.},
  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}
}