Publication Details
Category Text Publication
Reference Category Journals
DOI 10.1111/2041-210X.12224
Title (Primary) Inferring model-based probability of occurrence from preferentially sampled data with uncertain absences using expert knowledge
Author Manceur, A.M.; Kühn, I. ORCID logo
Source Titel Methods in Ecology and Evolution
Year 2014
Department CLE; BZF; iDiv
Volume 5
Issue 8
Page From 739
Page To 750
Language englisch
Keywords expert knowledge; Bayesian statistics; restoring species distribution maps; species distribution modelling; observer bias; non-detection
UFZ wide themes RU1;
Abstract
  1. For several data bases in species distribution modelling (SDM), presences are known but absences are uncertain and can be non-detections. The environment influences both the non-detections and the species occurrences (preferential sampling), affecting model predictions.
  2. The objective is to estimate model-based probability of occurrence when absences are uncertain and sampling is preferential. Our Bayesian image restoration (BIR) model has an environmental, spatial and non-detection component. The latter is based on the number of ‘control species’ (selected by expert knowledge to have wide geographic range and occupy several habitats) observed. Control species are assumed present in all grid cells so an observed absence of the control species is a non-detection.
  3. The observed absence of a focal species is likely a non-detection when the environment is conducive, the focal species was observed in the neighbourhood, and the number of control species is small.
  4. Simulating an artificial species (true prevalence 0·31) and preferential sampling (observed prevalence 0·18), the BIR modelled prevalence was 0·32 (0·30–0·34). The estimation was robust to failure of assumptions with a mis-specified environmental model. The restored map was close to the true map, and the true realized niche of the species was inferred (with uncertainty). The link between the non-detection and control species is crucial, and a sensitivity analysis is recommended.
  5. BIR restored the simulated atlas map more accurately than a covariate adjustment method. When applied to the vascular plant Asarum europaeum L., BIR changed the interpretation. The uncertainty maps assisted decision-making. A BIR implementation new to the SDM field is presented.
  6. BIR using control species is most fruitfully applied at coarse scale and intermediate extent, with limited environmental extremes and sufficient expert knowledge (e.g. many Central European countries).
  7. BIR is a framework, and we studied one implementation. Any proxy (road density, distance to city, direct expert knowledge, detectability traits, etc.) associated with non-detection can be used so BIR can be applied in diverse settings. Including non-detections reduces model indeterminacy but often requires expert knowledge. However, the benefits of using a priori knowledge and Bayesian estimation are expected to outweigh the risks of model-based inference.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=15307
Manceur, A.M., Kühn, I. (2014):
Inferring model-based probability of occurrence from preferentially sampled data with uncertain absences using expert knowledge
Methods Ecol. Evol. 5 (8), 739 - 750 10.1111/2041-210X.12224