@misc{SchurrPagelSarmentoetal.2012,
  author    = {Schurr, Frank Martin and Pagel, J{\"o}rn and Sarmento, Juliano Sarmento and Groeneveld, Juergen and Bykova, Olga and O'Hara, Robert B. and Hartig, Florian and Kissling, W. Daniel and Linder, H. Peter and Midgley, Guy F. and Schr{\"o}der-Esselbach, Boris and Singer, Alexander and Zimmermann, Niklaus E.},
  title     = {How to understand species' niches and range dynamics: a demographic research agenda for biogeography},
  series = {Journal of biogeography},
  volume    = {39},
  journal   = {Journal of biogeography},
  number    = {12},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0305-0270},
  doi       = {10.1111/j.1365-2699.2012.02737.x},
  pages     = {2146 -- 2162},
  year      = {2012},
  abstract  = {Range dynamics causes mismatches between a species geographical distribution and the set of suitable environments in which population growth is positive (the Hutchinsonian niche). This is because sourcesink population dynamics cause species to occupy unsuitable environments, and because environmental change creates non-equilibrium situations in which species may be absent from suitable environments (due to migration limitation) or present in unsuitable environments that were previously suitable (due to time-delayed extinction). Because correlative species distribution models do not account for these processes, they are likely to produce biased niche estimates and biased forecasts of future range dynamics. Recently developed dynamic range models (DRMs) overcome this problem: they statistically estimate both range dynamics and the underlying environmental response of demographic rates from species distribution data. This process-based statistical approach qualitatively advances biogeographical analyses. Yet, the application of DRMs to a broad range of species and study systems requires substantial research efforts in statistical modelling, empirical data collection and ecological theory. Here we review current and potential contributions of these fields to a demographic understanding of niches and range dynamics. Our review serves to formulate a demographic research agenda that entails: (1) advances in incorporating process-based models of demographic responses and range dynamics into a statistical framework, (2) systematic collection of data on temporal changes in distribution and abundance and on the response of demographic rates to environmental variation, and (3) improved theoretical understanding of the scaling of demographic rates and the dynamics of spatially coupled populations. This demographic research agenda is challenging but necessary for improved comprehension and quantification of niches and range dynamics. It also forms the basis for understanding how niches and range dynamics are shaped by evolutionary dynamics and biotic interactions. Ultimately, the demographic research agenda should lead to deeper integration of biogeography with empirical and theoretical ecology.},
  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}
}