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  -