IUCN / SSC Cat Specialist Group - Digital Cat Library
   

 

View printer friendly
Kramer-Schadt, S.; Niedballa, J.; Pilgrim, J.D.; Schr”der, B.; Lindenborn, J.; Reinfelder, V.; Stillfried, M.; Heckmann, I.; Scharf, A.K.; Augeri, D.M.; Cheyne, S.M.; Hearn, A.J.; Ross, J.; MacDonald, D.W.; Mathai, J.; Eaton, J.; Marshall, A.J.; Semiadi, G.; Rustam, R.; Bernard, H.; Alfred, R.; Samejima, H.; Duckworth, J.W.; Breitenmoser-Wrsten, C.; Belant, J.L.; Hofer, H.; Wilting, A.
The importance of correcting for sampling bias in MaxEnt species distribution models
2013  Diversity and Distributions (19): 1366-1379

Aim Advancement 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. Location Borneo, Southeast Asia. Methods We 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. Results Spatial 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 Conclusions We 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.

PDF files are only accessible to Friends of the Cat Group. Joining Friends of the Cat Group gives you unlimited access and downloads in the Cat SG Library for one year, and allows you to receive our newsletter Cat News (2 regular issues per year plus special issues). More information how to join here

 

(c) IUCN/SSC Cat Specialist Group ( IUCN - The World Conservation Union)