TY - JOUR A1 - Kramer-Schadt, Stephanie A1 - Niedballa, Jürgen A1 - Pilgrim, John D. A1 - Schröder-Esselbach, Boris A1 - Lindenborn, Jana A1 - Reinfelder, Vanessa A1 - Stillfried, Milena A1 - Heckmann, Ilja A1 - Scharf, Anne K. A1 - Augeri, Dave M. A1 - Cheyne, Susan M. A1 - Hearn, Andrew J. A1 - Ross, Joanna A1 - Macdonald, David W. A1 - Mathai, John A1 - Eaton, James A1 - Marshall, Andrew J. A1 - Semiadi, Gono A1 - Rustam, Rustam A1 - Bernard, Henry A1 - Alfred, Raymond A1 - Samejima, Hiromitsu A1 - Duckworth, J. W. A1 - Breitenmoser-Wuersten, Christine A1 - Belant, Jerrold L. A1 - Hofer, Heribert A1 - Wilting, Andreas T1 - The importance of correcting for sampling bias in MaxEnt species distribution models JF - Diversity & distributions : a journal of biological invasions and biodiversity N2 - 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. KW - Borneo KW - carnivora KW - conservation planning KW - ecological niche modelling KW - maximum entropy (MaxEnt) KW - sampling bias KW - Southeast Asia KW - species distribution modelling KW - viverridae Y1 - 2013 U6 - https://doi.org/10.1111/ddi.12096 SN - 1366-9516 SN - 1472-4642 VL - 19 IS - 11 SP - 1366 EP - 1379 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Dellinger, Agnes S. A1 - Essl, Franz A1 - Hojsgaard, Diego A1 - Kirchheimer, Bernhard A1 - Klatt, Simone A1 - Dawson, Wayne A1 - Pergl, Jan A1 - Pysek, Petr A1 - van Kleunen, Mark A1 - Weber, Ewald A1 - Winter, Marten A1 - Hoerandl, Elvira A1 - Dullinger, Stefan T1 - Niche dynamics of alien species do not differ among sexual and apomictic flowering plants JF - New phytologist : international journal of plant science N2 - We compiled global occurrence data sets of 13 congeneric sexual and apomictic species pairs, and used principal components analysis (PCA) and kernel smoothers to compare changes in climatic niche optima, breadths and unfilling/expansion between native and alien ranges. Niche change metrics were compared between sexual and apomictic species. All 26 species showed changes in niche optima and/or breadth and 14 species significantly expanded their climatic niches. However, we found no effect of the reproductive system on niche dynamics. Instead, species with narrower native niches showed higher rates of niche expansion in the alien ranges. Our results suggest that niche shifts are frequent in plant invasions but evolutionary potential may not be of major importance for such shifts. Niche dynamics rather appear to be driven by changes of the realized niche without adaptive change of the fundamental climatic niche. KW - adaptation KW - asexual reproduction KW - niche shifts KW - plant invasion KW - reproductive system KW - species distribution modelling Y1 - 2016 U6 - https://doi.org/10.1111/nph.13694 SN - 0028-646X SN - 1469-8137 VL - 209 SP - 1313 EP - 1323 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Pagel, Jörn A1 - Schurr, Frank Martin T1 - Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics JF - Global ecology and biogeography : a journal of macroecology N2 - Aim The study and prediction of speciesenvironment relationships is currently mainly based on species distribution models. These purely correlative models neglect spatial population dynamics and assume that species distributions are in equilibrium with their environment. This causes biased estimates of species niches and handicaps forecasts of range dynamics under environmental change. Here we aim to develop an approach that statistically estimates process-based models of range dynamics from data on species distributions and permits a more comprehensive quantification of forecast uncertainties. Innovation We present an approach for the statistical estimation of process-based dynamic range models (DRMs) that integrate Hutchinson's niche concept with spatial population dynamics. In a hierarchical Bayesian framework the environmental response of demographic rates, local population dynamics and dispersal are estimated conditional upon each other while accounting for various sources of uncertainty. The method thus: (1) jointly infers species niches and spatiotemporal population dynamics from occurrence and abundance data, and (2) provides fully probabilistic forecasts of future range dynamics under environmental change. In a simulation study, we investigate the performance of DRMs for a variety of scenarios that differ in both ecological dynamics and the data used for model estimation. Main conclusions Our results demonstrate the importance of considering dynamic aspects in the collection and analysis of biodiversity data. In combination with informative data, the presented framework has the potential to markedly improve the quantification of ecological niches, the process-based understanding of range dynamics and the forecasting of species responses to environmental change. It thereby strengthens links between biogeography, population biology and theoretical and applied ecology. KW - Biogeography KW - ecological forecasts KW - global change KW - hierarchical Bayesian statistics KW - long-distance dispersal KW - niche theory KW - process-based model KW - range shifts KW - spatial demography KW - species distribution modelling Y1 - 2012 U6 - https://doi.org/10.1111/j.1466-8238.2011.00663.x SN - 1466-822X VL - 21 IS - 2 SP - 293 EP - 304 PB - Wiley-Blackwell CY - Malden ER -